Reviewerly/exHarmony huggingface dataset:
Supporting feature-oriented evolution in industrial automation product lines

In the domain of industrial automation companies nowadays need to serve a mass market while at the same time customers demand highly customized solutions. To tackle this problem, companies frequently define software product lines (SPLs), which allow to automatically derive and further customize individual solutions based on a common platform. SPLs rely on defining common and variable platform features together with mappings, which define how the features are realized in implementation artifacts. In concurrent engineering such a feature-oriented process is challenged by the evolution of features, the complexity of feature-to-artifact mappings, and the diversity of the implementation artifacts. To address these challenges this paper introduces an approach supporting feature-oriented development and evolution in industrial SPLs. We outline the key elements and operations of our approach, including an implementation in a development environment. We report results of evaluating our approach regarding functional correctness, usefulness, and scalability based on a case study of a Pick-and-Place Unit (PPU) and an industrial case system.
A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems

Critiquing is a method for conversational recommendation that incrementally adapts recommendations in response to user preference feedback. Specifically, a user is iteratively provided with item recommendations and attribute descriptions for those items; the user may then either accept the recommendation or choose to critique an attribute to generate a new recommendation. A recent direction known as latent linear critiquing (LLC) takes a modern embedding-based approach that seeks to optimize the combination of user preference embeddings with embeddings of critiques based on subjective item descriptions (i.e., keyphrases from user reviews); LLC does so by exploiting the linear structure of the embeddings to efficiently optimize their weights in a linear programming (LP) formulation. In this paper, we revisit LLC and note that it's score-based optimization approach inherently encourages extreme weightings in order to maximize predicted score gaps between preferred and non-preferred items. Noting that the overall end task objective in critiquing is to re-rank rather than re-score, in this paper we take a ranking optimization approach that seeks to optimize embedding weights based on observed rank violations from earlier critiquing iterations. We evaluate the proposed framework on two recommendation datasets containing user reviews. Empirical results demonstrate that ranking-based LLC generally outperforms scoring-based LLC and other baselines across a variety of datasets, critiquing styles, and both satisfaction and session-length performance metrics.
On the Engineering of IoT-Intensive Digital Twin Software Systems

Digital Twins (DT) are software systems representing different aspects of a physical or conceptual counterpart---the real twin, which is instrumented with several sensors or computing devices that generate, consume and transfer data to its DT with different purposes. In other words, DT systems are, to a large extent, IoT-intensive systems. Indeed, by exploiting and managing IoT data, artificial intelligence, and big data and simulation capabilities, DTs have emerged as a promising approach to manage the virtual manifestation of real-world entities throughout their entire lifecycle. Their proliferation will contribute to realizing the long-craved convergence of virtual and physical spaces to augment things and human capabilities. In this context, despite the proposal of noteworthy contributions, we argue that DTs have not been sufficiently investigated from a software engineering perspective. To address this, in this paper we propose GEMINIS, an architectural reference model that adopts self-adaptation, control, and model-driven engineering techniques to specify the structural and behavioural aspects of DTs and enable the evolution of their internal models. Moreover, we introduce an approach for engineering IoT-intensive Digital Twin Software Systems (DTSS) using GEMINIS' capabilities to deal with uncertain conditions that are inherent to the nature of mirrored physical environments and that might compromise the fidelity of a DT. With GEMINIS and the proposed approach, we aim to advance the engineering of DTSS as well as IoT and cyber-physical systems by providing practitioners with guidelines to model and specify inherent structural and behavioural characteristics of DTs, addressing common design concerns.
Achieve Efficient and Privacy-Preserving Disease Risk Assessment Over Multi-Outsourced Vertical Datasets

It is believed that online disease risk assessment system has great potential to alleviate the medical treatment problems for the future smart city and communities, as it can excavate disease risk factors from a large number of patient features, provide diagnostic references for doctors, and save medical treatment time for patients. However, the flourish of online disease risk assessment service still faces severe challenges including information privacy and security. In this article, based on the naïve Bayesian classification, we propose an effi <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u> ient and priv <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> cy-prese <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> ving dis <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> ase <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> isk assessment scheme over multi-outsourced vertical datasets, named CARER. With CARER, the e-healthcare provider can securely train a disease risk predication model over vertically distributed medical data from multiple medical centers (i.e., hospitals), and provide privacy-preserving disease risk predication services for users (i.e., patients and doctors). During the model training and disease risk prediction phases, all sensitive data are operated over ciphertexts without decryption. As a result, the private information of medical centers, e-healthcare provider, and users can be well protected. Detailed security analysis shows that CARER can resist various known security threats. In addition, we evaluate the performance of CARER with real medical datasets, and the results demonstrate that CARER is efficient.
Application of the BERT Language Model for Sentiment Analysis of Social Network Posts

The paper proposes a new algorithm for formation of training datasets for a neural network that provides sentiment analysis of social network posts. This article also describes the use of a neural network to determine the sentiment values of a social network posts using the word2vec and BERT algorithms. Also conducted experiments confirming the effectiveness of the proposed approaches.
Fault Detection Architectures for Inverted Binary Ring-LWE Construction Benchmarked on FPGA

Ring learning with errors (RLWE) is an efficient lattice-based cryptographic scheme that has worst-case reduction to lattice problem, conjectured to be quantum-hard. Ring-BinLWE is an optimized variant of RLWE problem using binary error distribution, resulting in highly-efficient hardware implementation. Efficient and low-complexity architectures in hardware, thwarting natural and malicious faults, are essential for lattice-based post-quantum cryptography (PQC) algorithms. In this brief, we explore efficient fault detection approaches for implementing the Ring-BinLWE problem. This brief, for the first time, investigates fault detection schemes for all three stages of RLWE encryption. Utilizing the stuck-at fault model, we employ recomputing with encoded operands schemes to achieve high error coverage. We simulate and implement our schemes on a field-programmable gate array (FPGA) platform. Our schemes provide low hardware overhead (area overhead of 15.74%, delay overhead of 7.74%, and power consumption overhead of 4.06%), with high error coverage, which can be suitable for resource-constrained as well as high-performance usage models.
Developing and using open electronic educational resources in educational activities

Abstract The article looks into the effective use of open electronic educational resources. The concept of open electronic educational resources is exposed, their structure and key elements are presented, issues of implementation in educational practice are considered. Requirements for open electronic educational resources are defined, tools for designing and platforms for their support and dissemination are identified. The analysis of the existing open electronic educational resources and the experience of their use is conducted. The principles of open electronic resources design based on crowdsourcing are revealed. The stages of creating e-learning resources that meet open Creative Commons licenses on the examples of pre-service teachers’ training are explored. The essence of Creative Commons licenses is reflected. The theoretical and methodological principles of the use of open electronic educational resources in the educational activity of pre-service teachers are considered. There is a direct link between open educational resources and university education.
A-Day-Ahead Photovoltaic Power Prediction Based on Long Short Term Memory Algorithm

In recent years, Photovoltaic System (PV) have been installed in parking lots in order to provide the green energy to Electric vehicles (EVs). Energy Synchronizing between PV generations and EVs demand is a function of different variables, and it is very challenging. Having an accurate prediction of PV generation helps to ease the complexity of this problem. Although various Machine Learning (ML) techniques have been applied and resulted well, traditional ML approaches need years of history of PV generations to make an accurate prediction. In many cases, parking lots or the houses recently equipped by PV panels, and this information is not available. Therefore, the primary motivation of this work is to build a reliable deep learning forecasting model based on Long Short Term Memory (LSTM) architecture in order to make a short-term prediction based on the limited previous observation. The proposed model is applied to a month of the PV power generation data and resulted in the promising accuracy with the Mean Absolute Percentage Error (MAPE) value of 0.028.
Towards Friendly Mixed Initiative Procedural Content Generation: Three Pillars of Industry

While the games industry is moving towards procedural content generation (PCG) with tools available under popular platforms such as Unreal, Unity or Houdini, and video game titles like No Man’s Sky and Horizon Zero Dawn taking advantage of PCG, the gap between academia and industry is as wide as it has ever been, in terms of communication and sharing methods. The authors have worked on both sides of this gap and in an effort to shorten it and increase the synergy between the two sectors have identified three design pillars for PCG using mixed-initiative interfaces. The three pillars are respect designer control, respect the creative process and respect existing work processes. Respecting designer control is about creating a tool that gives enough control to bring out the designer’s vision. Respecting the creative process concerns itself with having a feedback loop that is short enough, that the creative process is not disturbed. Respecting existing work processes means that a PCG tool should plug in easily to existing asset pipelines. As academics and communicators, it is surprising that publications often do not describe ways for developers to use our work or lack considerations for how a piece of work might fit into existing content pipelines.
Towards an Efficient Segmentation Algorithm for Near-Infrared Eyes Images

Semantic segmentation has been widely used for several applications, including the detection of eye structures. This is used in tasks such as eye-tracking and gaze estimation, which are useful techniques for human-computer interfaces, salience detection, and Virtual reality (VR), amongst others. Most of the state of the art techniques achieve high accuracy but with a considerable number of parameters. This article explores alternatives to improve the efficiency of the state of the art method, namely DenseNet Tiramisu, when applied to NIR image segmentation. This task is not trivial; the reduction of block and layers also affects the number of feature maps. The growth rate (k) of the feature maps regulates how much new information each layer contributes to the global state, therefore the trade-off amongst grown rate (k), IOU, and the number of layers needs to be carefully studied. The main goal is to achieve a light-weight and efficient network with fewer parameters than traditional architectures in order to be used for mobile device applications. As a result, a DenseNet with only three blocks and ten layers is proposed (DenseNet10). Experiments show that this network achieved higher IOU rates when comparing with Encoder-Decoder, DensetNet56-67-103, MaskRCNN, and DeeplabV3+ models in the Facebook database. Furthermore, this method reached 8th place in The Facebook semantic segmentation challenge with 0.94293 mean IOU and 202.084 parameters with a final score of 0.97147. This score is only 0,001 lower than the first place in the competition. The sclera was identified as the more challenging structure to be segmented.
Investigating the criticality of user‐reported issues through their relations with app rating

Abstract App quality impacts user experience and satisfaction. As a consequence, both app ratings and user feedback reported in app reviews are directly influenced by the user‐perceived app quality. Through an empirical study involving 210,517 reviews related to 317 Android apps, in this paper, we experiment with the combined usage of app rating and user reviews analysis (i) to investigate the most important factors influencing the perceived app quality, (ii) focusing on the topics discussed in user review that most relate with app rating. Besides, we investigate whether specific code quality metrics could be monitored to prevent the rising of negative user feedback (i.e., types of user review comments), connected with low ratings. Our study demonstrates that user comments reporting bugs are negatively correlated with the rating, while reviews reportingfeature requests do not. Interestingly, depending on the app category, we observed that different kinds of issues have rather different relationships with the rating and the user‐perceived quality of the app. In particular, we observe that for specific app categories (e.g., communication), some code quality factors have significant relationships with the raising of certain types of feedback, which, in turn, are negatively connected with app ratings.
Projective Double Reconstructions Based Dictionary Learning Algorithm for Cross-Domain Recognition

Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data crossreconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l1 optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the stateof- the-art methods.
Data-Driven Game Development: Ethical Considerations

In recent years, the games industry has made a major move towards data-driven development, using data analytics and player modeling to inform design decisions. Data-driven techniques are beneficial as they allow for the study of player behavior at scale, making them very applicable to modern digital game development. However, with this move towards data driven decision-making comes a number of ethical concerns. Previous work in player modeling [45] as well as work in the fields of AI and machine learning [9, 53] have demonstrated several ways in which algorithmic decision-making can be flawed due to data or algorithmic bias or lack of data from specific groups. Further, black box algorithms create a trust problem due to lack of interpretability and transparency of the results or models developed based on the data, requiring blind faith in the results. In this position paper, we discuss several factors affecting the use of game data in the development cycle. In addition to issues raised by previous work, we also raise issues with algorithms marginalizing certain player groups and flaws in the resulting models due to their inability to reason about situational factors affecting players’ decisions. Further, we outline some work that seeks to address these problems and identify some open problems concerning ethics and game data science.
Deep Reinforcement Learning in Immersive Virtual Reality Exergame for Agent Movement Guidance

Immersive Virtual Reality applied to exercise games has a unique potential to both guide and motivate users in performing physical exercise. Advances in modern machine learning open up new opportunities for more significant intelligence in such games. To this end, we investigate the following research question: What if we could train a virtual robot arm to guide us through physical exercises, compete with us, and test out various double-jointed movements? This paper presents a new game mechanic driven by artificial intelligence to visually assist users in their movements through the Unity Game Engine, Unity MI-Agents, and the HTC Vive Head-Mounted Display. We discuss how deep reinforcement learning through Proximal Policy Optimization and Generative Adversarial Imitation Learning can be applied to complete physical exercises from the same immersive virtual reality game. We examine our mechanics with four users through protecting a virtual butterfly with an agent that visually helps users as a cooperative “ghost arm” and an independent competitor. Our results suggest that deep learning agents are effective at learning game exercises and may provide unique insights for users.
An Immersive Self-Report Tool for the Affective Appraisal of 360° VR Videos

Immersive 360º virtual reality (VR) movies can effectively evoke a wide range of different emotional experiences. To this end, they are increasingly deployed in entertainment, marketing and research. Because emotions influence decisions and behavior, it is important to assess the user’s affective appraisal of immersive 360º VR movies. Knowledge of this appraisal can serve to tune media content to achieve the desired emotional responses for a given purpose. To measure the affective appraisal of immersive VR movies, efficient immersive and validated instruments are required that minimally interfere with the VR experience itself. Here we investigated the convergent validity of a new efficient and intuitive graphical (emoji-based) affective self-report tool (the EmojiGrid) for the assessment of valence and arousal induced by videos representing 360º VEs (virtual environments). Thereto, 40 participants rated their emotional response (valence and arousal) to 62 videos from a validated public database of 360º VR movies using an EmojiGrid that was embedded in the VE, while we simultaneously assessed their autonomic physiological arousal through electrodermal activity. The mean affective ratings obtained with the EmojiGrid and those provided with the database (measured with an alternative and validated instrument) show excellent agreement for valence and good agreement for arousal. The mean arousal ratings obtained with the EmojiGrid also correlate strongly with autonomic physiological arousal. Thus, the EmojiGrid appears to be a valid and immersive affective self-report tool for measuring VE-induced emotions.
New Approaches in the Learning of Complex-Valued Neural Networks

We consider neural networks with complex weights and continuous activation functions. The complex generalization of the backpropagation learning algorithm is studied in the paper. We introduce new kinds of activation functions, namely, the complex modifications of the rational sigmoid and the ReLU activation function, the use of which has two main benefits. The first is that the application of these functions allows to avoid the splitting of transfer functions. The second is that they are fast to compute. In order to improve the performance and to increase the training speed we use the complex version of the modern optimizers instead of the classical techniques based on the application of gradient descent. The design of a complex-weighted neural networks for multiclass classification is also treated. The simulation results confirm the assumption that the combination of complex version of ReLU-like activation functions and Adam optimizer can considerably speed up the training of complex-valued neural networks.
Secure IoT Data Management in a Private Ethereum Blockchain

IoT has proven valuable in many industries such as Supply Chain, Shipping and Transportation providing updates on the status of shipments in real time. This has resulted in a large amount of data created by IoT devices that require real time processing. Blockchain has also emerged as a trusted common data storage among multiple participants in IoT systems. However, it is essential to verify the authenticity of data before they go in to the blockchain. Some IoT data must be also encrypted in a secure manner. In this paper, we propose a method to collect sensor data from IoT devices and use blockchain to store and retrieve the collected data in a secure and decentralized fashion. A testbed private blockchain system with Ethereum and Raspberry Pi has been developed to test the feasibility and performance of blockchain-based secure IoT system. We employ private PKI for key management and TPM for storing keys, signing, verifying and encrypting data in a trusted way. We describe the structure of the testbed, decentralized storage system based on Ethereum Swarm and IPFS, TPM configuration processes, smart contract codes, and detailed operation sequences. Performance has been measured with 5000 test transactions in 8 different configurations. The result shows that Ethereum blockchain with Swarm can offer about 9 transactions per second. Encryption and decryption is shown to reduce the performance slightly to about 8 transactions per second. The proposed system can provide a secure IoT Blockchain platform for small industrial applications at low cost.