1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the »Big Data» scale in order to prevent the well-known »overfitting» problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction, denoising, etc. Furthermore, due to the simple and compact configuration of such adaptive 1D CNNs that perform only linear 1D convolutions (scalar multiplications and additions), a real-time and low-cost hardware implementation is feasible. This paper reviews the major signal processing applications of compact 1D CNNs with a brief theoretical background. We will present their state-of-the-art performances and conclude with focusing on some major properties. Keywords - 1-D CNNs, Biomedical Signal Processing, SHM.

Agile methods increase the speed and reduce the cost of software projects; however, they have been criticized for lack of documentation, traditional quality control, and, most importantly, lack of security assurance - mostly due to their informal and self-organizing approach to software development. This paper clarifies the requirements for security assurance by using an evaluation framework to analyze the compatibility of established agile security development methods: XP, Scrum and Kanban, combined with Microsoft SDL security framework, against Finland's established national security regulation (Vahti). We also analyze the selected methods based on their role definitions, and provide some avenues for future research.

This paper presents a novel application of convolutional neural networks (CNNs) for the task of acoustic scene classification (ASC). We here propose the use of a CNN trained to classify short sequences of audio, represented by their log-mel spectrogram. We also introduce a training method that can be used under particular circumstances in order to make full use of small datasets. The proposed system is tested and evaluated on three different ASC datasets and compared to other state-of-the-art systems which competed in the 'Detection and Classification of Acoustic Scenes and Events' (DCASE) challenges held in 2016¹ and 2013. The best accuracy scores obtained by our system on the DCASE 2016 datasets are 79.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9% improvements with respect to the baseline system. Finally, when tested on the DCASE 2013 evaluation dataset, the proposed system manages to reach a 77.0% accuracy, improving by 1% the challenge winner's score.

Deep learning models are capable of achieving state-of-the-art performance on a wide range of time series analysis tasks. However, their performance crucially depends on the employed normalization scheme, while they are usually unable to efficiently handle non-stationary features without first appropriately pre-processing them. These limitations impact the performance of deep learning models, especially when used for forecasting financial time series, due to their non-stationary and multimodal nature. In this paper we propose a data-driven adaptive normalization layer which is capable of learning the most appropriate normalization scheme that should be applied on the data. To this end, the proposed method first identifies the distribution from which the data were generated and then it dynamically shifts and scales them in order to facilitate the task at hand. The proposed nor-malization scheme is fully differentiable and it is trained in an end-to-end fashion along with the rest of the parameters of the model. The proposed method leads to significant performance improvements over several competitive normalization approaches, as demonstrated using a large-scale limit order book dataset.

One approach for stereoscopic video compression is to down sample the content prior to encoding and up sample it to the original spatial resolution after decoding. In this study it is shown that the ratio by which the content should be rescaled is sequence dependent. Hence, a frequency based method is introduced enabling fast and accurate estimation of the best down sampling ratio for different stereoscopic video clips. It is shown that exploiting this approach can bring 3.38% delta bitrate reduction over five camera-captured sequences.

The task of additional lossless compression of JPEG images is considered. We propose to decode JPEG image and recompress it using lossy BPG (Better Portable Graphics) codec based on a subset of the HEVC open video compression standard. Then the decompressed and smoothed BPG image is used for calculation and quantization of DCT coefficients in 8x8 image blocks using quantization tables of the source JPEG image. A difference between obtained quantized DCT coefficients and quantized DCT coefficients of the source JPEG image (prediction error) is calculated. The difference is lossless compressed by a proposed context modeling and arithmetical coding. In this way the source JPEG image is replaced by two files: compressed BPG image and the compressed difference which needed for lossless restoration of the source JPEG image. It is shown that the proposed approach provides compression ratios comparable with state of the art PAQ8, WinZip and STUFFIT file archivers. At the same time BPG images may be used for fast preview of compressed JPEG images.

Heterogeneous computing platforms with multicore central processing units (CPUs) and graphics processing units (GPUs) are of increasing interest to designers of embedded signal processing systems since they offer the potential for significant performance boost while maintaining the flexibility of software-based design flows. Developing optimized implementations for CPU-GPU platforms is challenging due to complex, inter-related design issues, including task scheduling, interprocessor communication, memory management, and modeling and exploitation of different forms of parallelism. In this paper, we present an automated, dataflow based, design framework called DIF-GPU for application mapping and software synthesis on heterogeneous CPU-GPU platforms. DIF-GPU is based on novel extensions to the dataflow interchange format (DIF) package, which is a software environment for developing and experimenting with dataflow-based design methods and synthesis techniques for embedded signal processing systems. DIF-GPU exploits multiple forms of parallelism by deeply incorporating efficient vectorization and scheduling techniques for synchronous dataflow specifications, and incorporating techniques for streamlining interprocessor communication. DIF-GPU also provides software synthesis capabilities to help accelerate the process of moving from high-level application models to optimized implementations.

Design and implementation of smart vision systems often involve the mapping of complex image processing algorithms into efficient, real-time implementations on multicore platforms. In this paper, we describe a novel design tool that is developed to address this important challenge. A key component of the tool is a new approach to hierarchical dataflow scheduling that integrates a global scheduler and multiple local schedulers. The local schedulers are lightweight modules that work independently. The global scheduler interacts with the local schedulers to optimize overall memory usage and execution time. The proposed design tool is demonstrated through a case study involving an image stitching application for large scale microscopy images.

This paper introduces the AIE-Studio (Audio Interfaces for Exploration), a modular dataflow patching library implemented with Pure Data. The AIE-Studio introduces new tools for procedural sound design through generative sonic and musical structures. Particular focus is on aesthetic experience. The designed modules allow versatile dataflow mapping through matrix routing system while also enabling the sound designer to influence generative processes of music creation. In particular, The AIE-Studio was used to create generative sonic and musical material in an embodied game-like application. In this paper we present key questions driving the research, theoretical background, research approach and the main development activities .

In this paper, we propose a joint framework for target localization and classification using a single generalized model for non-imaging based multi-modal sensor data. For target localization, we exploit both sensor data and estimated dynamics within a local neighborhood. We validate the capabilities of our framework by using a multi-modal dataset, which includes ground truth GPS information (e.g., time and position) and data from co-located seismic and acoustic sensors. Experimental results show that our framework achieves better classification accuracy compared to recent fusion algorithms using temporal accumulation and achieves more accurate target localizations than multilateration.

Multilabel ranking is an important machine learning task with many applications, such as content-based image retrieval (CBIR). However, when the number of labels is large, traditional algorithms are either infeasible or show poor performance. In this paper, we propose a simple yet effective multilabel ranking algorithm that is based on k-nearest neighbor paradigm. The proposed algorithm ranks labels according to the probabilities of the label association using the neighboring samples around a query sample. Different from traditional approaches, we take only positive samples into consideration and determine the model parameters by directly optimizing ranking loss measures. We evaluated the proposed algorithm using four popular multilabel datasets. The proposed algorithm achieves equivalent or better performance than other instance-based learning algorithms. When applied to a CBIR system with a dataset of 1 million samples and over 190 thousand labels, which is much larger than any other multilabel datasets used earlier, the proposed algorithm clearly outperforms the competing algorithms.

In this paper the lightweight many-to-many authentication protocol, that uses Near Field Communications as a carrier technology is proposed. The solution works without any user interaction and can be applied for almost any data storage device: NFC or RFID tag, USB-flash drive, etc. The major novelty of the system is real-time encryption key generation algorithm. This approach doesn't require any computation power on the tag, trusted third parties or secure link between tag and information system. So far, the mentioned features transforms to significant advantages of the proposed solution, while compared to existing analogues: OAuth, Opacity and LMAP. At the same time, the integrity of key sequences is not guarantied, that brings motivation for future research in the field.

Dataflow-based application specifications are widely used in model-based design methodologies for signal processing systems. In this paper, we develop a new model called the dataflow schedule graph (DSG) for representing a broad class of dataflow graph schedules. The DSG provides a graphical representation of schedules based on dataflow semantics. In conventional approaches, applications are represented using dataflow graphs, whereas schedules for the graphs are represented using specialized notations, such as various kinds of sequences or looping constructs. In contrast, the DSG approach employs dataflow graphs for representing both application models and schedules that are derived from them. Our DSG approach provides a precise, formal framework for unambiguously representing, analyzing, manipulating, and interchanging schedules. We develop detailed formulations of the DSG representation, and present examples and experimental results that demonstrate the utility of DSGs in the context of heterogeneous signal processing system design.

We have created a movable, limitedly volumetric "immaterial" display. Our prototype is the first mobile, hand-held fogscreen. It can show e.g., slices of volumetric objects when swept across mid-air. It is based on the patented FogScreen [Fogio 2013] technology. The previous FogScreen installations have been fixed set-ups, where the screen device and a projector are typically rigged up, leaving space for the viewers to walk through the mid-air display. Also mid-air virtual reality and mid-air user interfaces have been implemented [DiVerdi et al. 2006, Rakkolainen et al. 2009]. 2013 Copyright held by the Owner/Author.

The C2NET project aims to provide cloud-based platform for the supply chain interactions. The architecture of such platform includes a Data Collection Framework (DCF) for managing the collection of the company's data. The DCF collects, transforms and stores data from both Internet of Things (IoT) devices in the factory shopfloor and company enterprises data via two types of hub; Legacy system hub (LSH) and IoT hub. Since the C2NET, targets the Small and Medium-sized Enterprises (SMEs), the enterprise data, or legacy data as called in the C2NET project, can be provided via excel files. Thus, this research work highlights a technique for processing the excel files in the LSHs. This technique adopts the concept of Multi-Agent Systems for processing the data as table in the excel files in the LSH. The multi-agent approach allows the LSH to process any excel file regardless the complexity in the data structure or in the file table. Furthermore, the presented approach enhances the processing of the excel files in different aspects, such as the size of the excel file or the required processing power.

In this paper, we develop new multiclass classification algorithms for detecting people and vehicles by fusing data from a multimodal, unattended ground sensor node. The specific types of sensors that we apply in this work are acoustic and seismic sensors. We investigate two alternative approaches to multiclass classification in this context - the first is based on applying Dempster-Shafer Theory to perform score-level fusion, and the second involves the accumulation of local similarity evidences derived from a feature-level fusion model that combines both modalities. We experiment with the proposed algorithms using different datasets obtained from acoustic and seismic sensors in various outdoor environments, and evaluate the performance of the two algorithms in terms of receiver operating characteristic and classification accuracy. Our results demonstrate overall superiority of the proposed new feature-level fusion approach for multiclass discrimination among people, vehicles and noise.

Software maintenance has dramatically evolved in the last four decades, to cope with the continuously changing software development models, and programming languages and adopting increasingly advanced prediction models. In this work, we present the initial results of a Systematic Literature Review (SLR), highlighting the evolution of the metrics and models adopted in the last forty years.

Cloud-enabled tools developed in the Cloud Collaborative Manufacturing Networks (C2NET) project address the needs of small and medium enterprises with respect to information exchange and visibility across the collaboration partners in the supply network, coupled with automated and collaborative production planning and supply management. This paper analyses a case of an oil lubrication and hydraulic systems manufacturer and describes a pilot application of C2NET where the production schedule is optimized according to the priorities of the pilot company. In this case the goal is a highly adaptive just-in-time manufacturing schedule with guaranteed on time delivery.

Background: Open Source Software (OSS) is used by a continuously growing number of people, both end-users and developers. The quality of OSS is thus an issue of increasing interest. Specifically, OSS stakeholders need to trust OSS with respect to a number of qualities. Objective: This paper focuses on the level of trust that OSS stakeholders have in OSS reliability, one of the most important software qualities. The goal of the work reported here is to investigate to what extent the perception of reliability by users depends on objectively measurable characteristics of software. Method: We collected subjective user evaluations of the reliability of 22 Java OSS products, and then we measured their code characteristics that are generally believed to affect the quality of software. Finally, we carried out a correlational study to predict the perceived level of reliability of OSS based on the measured characteristics of the software code. Result: We obtained a set of statistically significant quantitative models, collectively called MOSST\REL, which account for the dependence of the perceived reliability of OSS on objectively observable qualities of Java code. Conclusions: The models we obtained can be used by: 1) endusers and developers that would like to reuse existing OSS products and components, to evaluate the perceived level of reliability of these OSS products that can be expected based on the characteristics of code; 2) the developers of OSS products, who can set code quality targets based on the level of perceived reliability they want to achieve.

The 'App economy' is a highly lucrative and competitive market for independent software vendors as it potentially offers an easy highway to reach millions of users. However, the mobile application landscape is scattered and an application developer has to publish the software for several different platforms to be able to serve a majority of smartphone users. Therefore, a bunch of cross-development tools have been offered to simplify this workload. In this paper, we present an evaluation framework for comparing different cross-development tools. We use this framework to evaluate Adobe PhoneGap tool against native development in Android and Windows Phone platforms. The results of a case study reveal that while the cross-platform technique was easy to use, the appearance and usability of the app was mediocre at its best. The business impacts of these are also discussed.

Although eye typing (typing on an on-screen keyboard via one's eyes as they are tracked by an eye tracker) has been studied for more than three decades now, we still know relatively little about it from the users' point of view. Standard metrics such as words per minute and keystrokes per character yield information only about the effectiveness of the technology and the interaction techniques developed for eye typing. We conducted an extensive study with almost five hours of eye typing per participant and report on extended qualitative and quantitative analysis of the relationship of dwell time, text entry rate, errors made, and workload experienced by the participants. The analysis method is comprehensive and stresses the need to consider different metrics in unison. The results highlight the importance of catering for individual differences and lead to suggestions for improvements in the interface.

In this paper, we describe Antroposeeni, a mixed reality game designed and developed for mobile devices. Antroposeeni utilizes location-based services, GPS for tracking users and augmented reality techniques for displaying captivating audiovisual content and creating rich experiences. Our demonstration will introduce a pilot version of the game, which encompasses narrative elements of the game mediated through developed media technologies. The goal for the demonstration is to give the conference visitors a chance to test the game in a specifically tailored route close to the conference site. After conducting the pilot we plan to organize a short review regarding the user experience.

Video game genre classification has long been a focusing perspective in game studies domain. Despite the commonly acknowledged usefulness of genre classification, scholars in the game studies domain are yet to reach consensus on the game genre classification. On the other hand, Steam, a popular video game distribution platform, adopts the user-generated tag feature enabling players to describe and annotate video games based on their own understanding of genres. Despite the concern of the quality, the user-generated tags (game tags) provide an opportunity towards an alternative way of understanding video game genres based on the players' collective intelligence. Hence, in this study, we construct a network of game tags based on the co-occurrence of tags in games on Steam platform and analyze the structure of the network via centrality analysis and community detection. Such analysis shall provide an intuitive presentation on the distribution and connections of the game tags, which furthermore suggests a potential way of understanding the important tags that are commonly adopted and the main genres of video games.

Microservices is an architectural style increasing in popularity. However, there is still a lack of understanding how to adopt a microservice-based architectural style. We aim at characterizing different microservice architectural style patterns and the principles that guide their definition. We conducted a systematic mapping study in order to identify reported usage of microservices and based on these use cases extract common patterns and principles. We present two key contributions. Firstly, we identified several agreed microservice architecture patterns that seem widely adopted and reported in the case studies identified. Secondly, we presented these as a catalogue in a common template format including a summary of the advantages, disadvantages, and lessons learned for each pattern from the case studies. We can conclude that different architecture patterns emerge for different migration, orchestration, storage and deployment settings for a set of agreed principles.

The popularity of tools for analyzing Technical Debt, and particularly the popularity of SonarQube, is increasing rapidly. SonarQube proposes a set of coding rules, which represent something wrong in the code that will soon be reflected in a fault or will increase maintenance effort. However, our local companies were not confident in the usefulness of the rules proposed by SonarQube and contracted us to investigate the fault-proneness of these rules. In this work we aim at understanding which SonarQube rules are actually fault-prone and to understand which machine learning models can be adopted to accurately identify fault-prone rules. We designed and conducted an empirical study on 21 well-known mature open-source projects. We applied the SZZ algorithm to label the fault-inducing commits. We analyzed the fault-proneness by comparing the classification power of seven machine learning models. Among the 202 rules defined for Java by SonarQube, only 25 can be considered to have relatively low fault-proneness. Moreover, violations considered as 'bugs' by SonarQube were generally not fault-prone and, consequently, the fault-prediction power of the model proposed by SonarQube is extremely low. The rules applied by SonarQube for calculating technical debt should be thoroughly investigated and their harmfulness needs to be further confirmed. Therefore, companies should carefully consider which rules they really need to apply, especially if their goal is to reduce fault-proneness.

Diversification and obfuscation methods are promising approaches used tosecuresoftware and prevent malware from functioning. Diversification makes each software instance unique so that malware attacks cannot rely on the knowledge of the program's execution environment and/or internal structure anymore. We present a systematic literature review on the state of-the-art of diversification and obfuscation research aiming to improve software security between 1993 and 2014. As the result of systematic search, in the final phase, 209 related papers were included in this study. In this study we focus on two specific research questions: what are the aims of diversification and obfuscation techniques and what are the environments they are applied to. The former question includes the languages and the execution environments that can benefit from these two techniques, while the second question presents the goals of the techniques and also the type of attacks they mitigate. is held by the owner/author(s). Publication rights licensed to ACM.

This paper, as a generalization of our previous works, presents a unified time-optimal path-following controller for Wheeled Mobile Robots (WMRs). Unlike other path-following controllers, we solve the path-following problem for all common categories of WMRs such as car-like, differential, omnidirectional, all wheels steerable and others. We show that the insertion of our path-following controller into the kinematic and non-holonomic constraints of the wheels, simplifies the otherwise impenetrable constraints, resulting in explicit monotonic functions between the velocity of the base and that of the wheels. Based on this foundation, we present a closed-form solution that keeps all the wheels' steering and driving velocities within their corresponding pre-specified bounds. Simulation data and experimental results from executing the controller in a real-time environment demonstrate the efficacy of the method.

Positive face-to-face social encounters between strangers can strengthen the sense of community in modern urban environments. However, it is not always easy to initiate friendly encounters due to various inhibiting social norms. We present three inspirational design patterns for reducing inhibitions to interact with unfamiliar others. These abstractions are based on a broad design space review of concepts, encompassing examples across a range of scales, fields, media and forms. Each inspirational pattern is formulated as a response to a different challenge to initiating social interaction but all share an underlying similarity in offering varieties of barriers and filters that paradoxically also separate people. The patterns are "Closer Through Not Seeing"; "Closer Through Not Touching"; and "Minimize Encounter Duration". We believe these patterns can support designers, in understanding, articulating, and generating approaches to creating embodied interventions and systems that enable unacquainted people to interact.

The performance of many cryptographic primitives is reliant on efficient algorithms and implementation techniques for arithmetic in binary fields. While dedicated hardware support for said arithmetic is an emerging trend, the study of software-only implementation techniques remains important for legacy or non-equipped processors. One such technique is that of software-based bit-slicing. In the context of binary fields, this is an interesting option since there is extensive previous work on bit-oriented designs for arithmetic in hardware, such designs are intuitively well suited to bit-slicing in software. In this paper we harness previous work, using it to investigate bit-sliced, software-only implementation arithmetic for binary fields, over a range of practical field sizes and using a normal basis representation. We apply our results to demonstrate significant performance improvements for a stream cipher, and over the frequently employed Ning-Yin approach to normal basis implementation in software.

In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.

A satellite navigation receiver traditionally searches for positioning signals using an acquisition procedure. In situations, in which the required information is only a binary decision whether at least one positioning signal is present or absent, the procedure represents an unnecessarily complex solution. This paper presents a different approach for the binary detection problem with significantly reduced computational complexity. The approach is based on a novel decision metric which is utilized to design two binary detectors. The first detector operates under the theoretical assumption of additive white Gaussian noise and is evaluated by means of Receiver Operating Characteristics. The second one considers also additional interferences and is suitable to operate in a real environment. Its performance is verified using a signal captured by a receiver front-end.

We introduce a content-Adaptive approach to image denoising where the filter design is based on mean opinion scores (MOSs) from preliminary experiments with volunteers who evaluated the quality of denoised image fragments. This allows to tune the filter parameters so to improve the perceptual quality of the output image, implicitly accounting for the peculiarities of the human visual system (HVS). A modification of the BM3D image denoising filter (Dabov et al., IEEE TIP, 2007), namely BM3DHVS, is proposed based on this framework. We show that it yields a higher visual quality than the conventional BM3D. Further, we have also analyzed the MOSs against popular full-reference visual quality metrics such as SSIM (Wang et al., IEEE TIP, 2004), its extension FSIM (Zhang et al., IEEE TIP, 2011), and the noreference IL-NIQE (Zhang et al., IEEE TIP, 2015) over each image fragment. Both the Spearman and the Kendall rank order correlation show that these metrics do not correspond well to the human perception. This calls for new visual quality metrics tailored for the benchmarking and optimization of image denoising methods.

We present a study of using embodied health information system for developing regions focusing on users not familiar with technology. We designed and developed a health information system with two gesture-based selection techniques: pointing to a screen and touching one's own body part. We evaluated the prototype in user study with 37 semi-literate and literate participants. Our results indicate a clear preference (76%) for touching in the healthcare domain. Based on our observations and user feedback, we present four design guidelines for developing embodied systems for the developing world: designing bodycentric interaction to overcome literacy and technological proficiency barriers, addressing the misconceptions of system behaviors with users not familiar with technology, understanding effects of cultural constraints on interaction, and utilizing interactive virtual avatars to connect with the users.

Mobile handheld devices are an increasing part of everyday fieldwork of news professionals. Mobile assignments delivered to mobile journalists' smartphones are one potential future development step. We present findings on using mobile assignments from two exploratory user studies in which smartphones were used as news reporting tools. Mobile assignments were perceived as handy for fast reporting situations and simple stories but challenging in case of more complex tasks. Structured information content of assignments, process phase based information and supporting situation and activity awareness would support the work of both editorial staff and mobile journalists. The locationing of reporters for sending location-based assignments was found acceptable for coordinating the work although some privacy concerns were expressed. The findings provide new information on using mobile assignments in work where carrying out tasks involves creativity and the tasks may be complex, not strictly limited or they may not have clear completion criteria. © 2012 ACM.

Today many organizations have come to value knowledge as a production factor. Thus, there is a constant need for getting the information in and sorted. Business intelligence (BI) is a process for systematic acquiring, analyzing, and disseminating data and information from various sources to gain understanding about the business's environment. This is required for supporting decisions for achieving organization's business objectives. Literature has introduced models for planning and executing BI. However, as business environments and technologies evolve in a rapid pace, are the models still applicable? Not all recent issues are taken into consideration in the previous models. BI is considered to be integrated into business processes, so the similar evolution is expected to take place. There are two studies investigating BI instigating this study, but there are still questions to be answered. Literature on different models and findings of these studies were combined to form a vision to better match reality. Various issues like users' active involvement, real-time analysis and presentation, and social media resources were brought up. Practitioners can use the approach to assess their current state of BI activities or planning the organization of BI program.

In this paper, we explore how to better integrate virtual reality viewing to a smartphone. We present novel designs for casual (short-term) immersive viewing of spatial and 3D content, such as augmented and virtual reality, with smartphones. Our goal is to create a simple and low-cost casual-viewing design which could be retrofitted and eventually be embedded into smartphones, instead of using larger spatial viewing accessories. We explore different designs and implemented several prototypes. One prototype uses thin and light near-to-eye optics with a smartphone display, thus providing the user with the functionality of a large, high-resolution virtual display. Our designs also enable 3D user interfaces. Easy interaction through various gestures and other modalities is possible by using the inertial and other sensors and camera of the smartphone. Our preliminary concepts are a starting point for exploring useful constructions and designs for such usage.

Understanding data-based value creation helps organizations to enhance its decision-making and to renew their business operations. However, organizations aiming to use modern data analytics face several severe challenges that are not usually so evident or visible beforehand. In this paper we study a Finnish manufacturing company's data empowerment and information and knowledge management practices in order to identify the potential challenges related to modern data-based value creation within industrial context. The empirical data is consisted of group discussions, relevant data sets acquired from the case company's information systems, and lastly, 12 thematic interviews of the key actors in the company in relation to service development. The study provides valuable insights for managing service development and decision-making and creates understanding on data-based value creation. Achieved understanding provides meaningful knowledge for organizations utilizing or having plans to utilize, for example, data analytic methods in their businesses.

A visual data flow language (VDFL) allows graphical presentation of a computer program in the form of a directed graph, where data tokens travel through the arcs of the graph, and the vertices present e.g. the input token streams, calculations, comparisons, and conditionals. Amongst their benefits, VDFLs allow parallel computing and they are presumed to improve the quality of programming due to their intuitive readability. Thus, they are also suitable for computing education. However, the token-based computational model allowing parallel processing may make the programs more complicated than what they look. We propose a method for checking properties of VDFL programs using finite state processes (FSPs) using a commonly available labelled transition system analyser (LTSA) tool. The method can also be used to study different VDFL programming constructs for development or re-design of VDFLs. For our method, we have implemented a compiler that compiles a textual representation of a VDFL into FSPs.

Although video sharing is common among youth, schools are only beginning to apply digital videos and digital storytelling to formal learning. This paper presents pedagogical models, examples, best practices, and outcomes that illustrate how teachers and students design and use digital stories in knowledge creation in cross-cultural settings. The results are based on the empirical data and findings from several international pilot studies. On the one hand, working with digital video stories drove engagement. However, on the other hand, technical issues significantly lowered engagement. In addition, the video inquiry pedagogy supported inquiry learning. Students began to pose scientifically oriented questions and seek answers together.

In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during the growing process. These abnormalities need to be recognized and the problem returns to image classification. We have a collection of 80 iPSC colony images where each one of the images is prelabeled by an expert to class bad, good or semigood. We use intensity histograms as features for classification and we evaluate histograms from the whole image and the colony area only having two datasets. We perform two feature reduction procedures for both datasets. In classification we examine how different hierarchical constructions effect the classification. We perform thorough evaluation and the best accuracy was around 54% obtained with the linear kernel function. Between different hierarchical structures, in many cases there are no significant changes in results. As a result, intensity histograms are a good baseline for the classification of iPSC colony images but more sophisticated feature extraction and reduction methods together with other classification methods need to be researched in future.

We present a binary graph classifier (BGC) which allows to classify large, unweighted, undirected graphs. This classifier is based on a local decomposition of the graph for each node in generalized trees. The obtained trees, forming the tree set of the graph, are then pairwise compared by a generalized tree-similarity-algorithm (GTSA) and the resulting similarity scores determine a characteristic similarity distribution of the graph. Classification in this context is defined as mutual consistency for all pure and mixed tree sets and their resulting similarity distributions in a graph class. We demonstrate the application of this method to an artificially generated data set and for data from microarray experiments of cervical cancer.

In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis method that can be trained by exploiting fast linear system approaches, like the Cholesky decomposition. We verify our analysis in publicly available verification problems designed for human action recognition.

Smart city development relies heavily on creation of digital services that are available for the citizens and for the city authorities. At best, these services are co-created by the authorities, citizens and the digital solution supplier companies. Digital service co-creation is, however, a complex process and includes several contradictions due to presence of several stakeholders. In this paper, we present a case study of smart city initiated digital service co-creation process through the analytical lenses of activity theory.

Programming has become an essential subject for today's education curriculum and as a result, the importance of creating the right environments to teach is increasing. For such environments, featuring tangible tools enhances creativity and collaboration. However, due to their high prices, current tangible tools are not reachable by most of the students. We developed Code Notes as a low-cost, attainable and tangible tool aimed to motivate children to support programming education. Code Notes is comprised of an Android app and code-cardboards to teach the basic concepts in programming. We continue to develop the platform with insights gained from children. This paper shares the design phases of Code Notes and observations from our two-month programming project. We also presented some future concepts of Code Notes that offer an active and embodied interaction with the teaching material.

Enterprise software development is a complex effort that may last years. Enterprise software is often developed by a systems integrator that makes modifications to a pre-made package or builds tailored software for the specific purpose. The development may include many developer organizations, the user organization, and their different departments and sub-units. Their collaboration evolves through project incidents, phases and even crises. The practices of project management, communication, contracts, and ultimately personal relationships change intentionally or unintentionally. These changes may cause uncertainties and discontinuities for the development. This study observes changes during enterprise software development and their influence on collaboration practices in different situations. During twenty years of development both internal and external crises and changes in the business environment triggered changes in collaboration. The collaboration practices are classified with four modes of collaboration (contract, cooperation, personified, and process) that illustrate emphasis in collaboration in different circumstances.

Future home networks are expected to become extremely sophisticated, yet only the most technically adept persons are equipped with skills to manage them. In this paper, we provide a novel solution as to how complex smart home networks can be collaboratively managed with the assistance of operators and third party experts. Our solution rests in separating the management and control functionalities of the home access points and routers, away from the actual connectivity, traffic forwarding and routing operations within the home network. By so doing, we present a novel REST-based architecture in which the management of the home network can be hosted in an entirely separate, external cloud-based infrastructure, which models the network within the home as a resource graph.

Groups of mutually similar image blocks are the key element in nonlocal image processing. In this work, the spatial coordinates of grouped blocks are leveraged in two distinct parts of the transform-domain collaborative filtering within the BM3D algorithm. First, we introduce an adaptive 1-D transform for 3-D collaborative filtering based on sampling 2-D smooth functions at the positions of grouped blocks. This adaptive transform is applied for improved decorrelation of the 2-D spectra of the grouped blocks. Second, we propose a directional sharpening procedure whose strength varies adaptively according to the relative orientation of the transform basis functions with respect to the group coordinates. Experiments confirm the efficacy of the proposed adaptations, for denoising as well as for sharpening of noisy images.

There exists a large base of evidence for gender differences in human navigation. However, there is not much research on gender differences in collaborative aspects of navigation, including the interaction of individuals during collaborative wayfinding tasks in virtual environments. In light of this, we present a study of a collaborative virtual environment, Berlin Kompass. The goal of this study was to find out the main differences between genders in collaborative wayfinding. The application was evaluated in the context of foreign language learning in schools with over 200 students, where the users navigated through cityscapes while interacting verbally with each other. We collected and analyzed interaction logs, questionnaire data and audio and video recordings to gain insights into gender-related differences in wayfinding in virtual worlds. Our findings suggest that several differences that are evident in single user systems are not present when the collaborative aspect is added. Male users were more immersed during the task than females. One of the explaining factors for this might be video game experience. Genders also communicated differently - males spoke in longer utterances whereas females had more, shorter utterances. Males referred more to relative directions and dynamic landmarks such as cars and pedestrians while navigating. Males with more video game experience also provided more positive subjective user experience feedback on the application.

Software development methods are shifting towards faster deployments and closer to the end users. Their ever tighter engagement of end-users also requires new technologies for gathering feedback from those users. At the same time, widespread Internet connectivity of different application environments is enabling the collection of this post-deployment data also from sources other than traditional web and mobile software. However, the sheer number of different alternatives of collecting technologies makes the selection a complicated process in itself. In this paper, we describe the process of data-driven software development and study the challenges organizations face when they want to start guiding their development towards it. From these challenges, we extract evaluation criteria for technological approaches to usage data collecting. We list such approaches and evaluate them using the extracted criteria. Using a design science approach, we refine the evaluation criteria to a selection framework that can help practitioners in finding a suitable technological approach for automated collecting of usage data.

Social robots are entering our workplaces, homes, medical and educational systems in assistive and collaborative roles. In our research, we have investigated the use of a social robot Pepper as an interactive icebreaker host to create a positive atmosphere at events. This paper presents two user studies (total n=43) in which we evaluated two interactive prototypes of playful applications on Pepper, with the overall aim of providing a personal and entertaining service for event attendees. Data about users' experiences and attitudes were collected with semi-structured interviews, surveys, and observations. The results of the studies suggest that the majority of the participants had pleasurable and positive experiences with the robot and its applications. Moreover, their positive encounters led them to accept social robots as icebreaker hosts to connect with strangers. Based on our findings, we present a list of design implications to help the future design of social robots used to facilitate social connectedness, and to aid in the development of social robots as intelligent agents performing tasks as integrated parts of smart spaces.