Reviewerly/exHarmony huggingface dataset:
Exploring Biofoam as a Material for Tangible Interaction

Each new material developed opens a broader pallet of aesthetic and functional possibilities for designers. This paper introduces DIS to biofoam, a material that is water-soluble, biodegradable, and can be made conductive. We describe the material in detail: the process of making the material from scratch, the material’s fabrication into forms with hand-craft techniques, and present two HCI specific applications of the biofoam. The biofoam can be cooked, molded, layered, extruded, dissolved or recooked opening up possibilities to consider the entire life cycle of the material in the design process. We contribute design considerations to allow designers to “tune” the biofoam to the desired quality, as well as a characterization of many aspects of the biofoam such as compression, spring back time, water permeability, and electrical conductivity. Finally, we discuss the unique opportunities this material and its life cycle bring to the design and HCI communities.
Towards DeepFake video forensics based on facial textural disparities in multi-color channels

With the development of deep learning, AI-synthesized techniques, such as DeepFake, are widely spread on the Internet. Although many state-of-the-art detection methods have been able to obtain a good detection performance, most neural network models based on data-driven training lack interpretability during feature extraction and analysis. In this study, we propose an interpretable DeepFake video detection method based on facial textural disparities in multi-color channels. We observe that the face region from the DeepFake video appears to be smoother than that of the real one. First, we analyze the statistical disparities between the real and fake frame in each color channel. Next, it is proposed to use the co-occurrence matrix to construct a low-dimensional set of features to distinguish the real video from the DeepFake video. Meanwhile, we evaluate the video-level and frame-level detection performance on the benchmark, where the method can achieve AUC value of 0.996 on FaceForensics++, and 0.718 on Celeb-DF. Our proposed method performs remarkably better than the traditional machine learning based detectors, and comparably to some current deep learning based detectors. More importantly, our proposed method is robust in the face of compression attacks, and more time-efficient compared to existing methods based on deep learning.
Empirical analysis of the tool support for software product lines

Abstract For the last ten years, software product line (SPL) tool developers have been facing the implementation of different variability requirements and the support of SPL engineering activities demanded by emergent domains. Despite systematic literature reviews identifying the main characteristics of existing tools and the SPL activities they support, these reviews do not always help to understand if such tools provide what complex variability projects demand. This paper presents an empirical research in which we evaluate the degree of maturity of existing SPL tools focusing on their support of variability modeling characteristics and SPL engineering activities required by current application domains. We first identify the characteristics and activities that are essential for the development of SPLs by analyzing a selected sample of case studies chosen from application domains with high variability. Second, we conduct an exploratory study to analyze whether the existing tools support those characteristics and activities. We conclude that, with the current tool support, it is possible to develop a basic SPL approach. But we have also found out that these tools present several limitations when dealing with complex variability requirements demanded by emergent application domains, such as non-Boolean features or large configuration spaces. Additionally, we identify the necessity for an integrated approach with appropriate tool support to completely cover all the activities and phases of SPL engineering. To mitigate this problem, we propose different road map using the existing tools to partially or entirely support SPL engineering activities, from variability modeling to product derivation.
Detecting fake reviews through topic modelling

Against the uncertainty caused by the information overload in the online world, consumers can benefit greatly by reading online product reviews before making their online purchases. However, some of the reviews are written deceptively to manipulate purchasing decisions. The purpose of present study is to determine which feature combination is most effective in fake review detection among the features of sentiment scores, topic distributions, cluster distributions and bag of words. In this study, additional feature combinations to a sentiment analysis are searched to examine the critical problem of fake reviews made to influence the decision-making process using review from amazon.com dataset. Results of the study points that behavior-related features play an important role in fake review classifications when jointly used with text-related features. Verified purchase is the only behavior related feature used comparatively with other text-related features.
Graph non-negative matrix factorization with alternative smoothed $$L_0$$ regularizations

Graph non-negative matrix factorization (GNMF) can discover the data's intrinsic low-dimensional structure embedded in the high-dimensional space. So, it has superior performance for data representation and clustering. Unfortunately, it is sensitive to noise and outliers. In this paper, to improve the robustness of GNMF, $$l_0$$ norm is introduced to enhance the sparsity of factorized matrices. As the discontinuity of $$l_0$$ norm and minimizing it is a NP-hard problem, five functions approximating $$l_0$$ norm are used to transform the problem of the sparse graph non-negative matrix factorization (SGNMF) to a global optimization problem. Finally, the multiplicative updating rules (MUR) are designed to solve the problem and the convergence of algorithm is proven. In the experiment, the accuracy and normalized mutual information of clustering results show the superior performance of SGNMF on five public datasets.
Calibration of deterministic NWP forecasts and its impact on verification

Deterministic forecasts (as opposed to ensemble or probabilistic forecasts) issued by numerical weather prediction (NWP) models require post-processing. Such corrective procedure can be viewed as a form of calibration. It is well known that, based on different objective functions, e.g., minimizing the mean square error or the mean absolute error, the calibrated forecasts have different impacts on verification. In this regard, this paper investigates how a calibration directive can affect various aspects of forecast quality outlined in the Murphy–Winkler distribution-oriented verification framework. It is argued that the correlation coefficient is the best measure for the potential performance of NWP forecast verification when linear calibration is involved, because (1) it is not affected by the directive of linear calibration, (2) it can be used to compute the skill score of the linearly calibrated forecasts, and (3) it can avoid the potential deficiency of using squared error to rank forecasts. Since no single error metric can fully represent all aspects of forecast quality, forecasters need to understand the trade-offs between different calibration strategies. To echo the increasing need to bridge atmospheric sciences, renewable energy engineering, and power system engineering, as to move toward the grand goal of carbon neutrality, this paper first provides a brief introduction to solar forecasting, and then revolves its discussion around a solar forecasting case study, such that the readers of this journal can gain further understanding on the subject and thus potentially contribute to it.
What Can Cognitive Science Do for People?

The critical question for cognitive scientists is what does cognitive science do, if anything, for people? Cognitive science is primarily concerned with human cognition but has fallen short in continuously and critically assessing the who in human cognition. This complacency in a world where white supremacist and patriarchal structures leave cognitive science in the unfortunate position of potentially supporting those structures. We take it that many cognitive scientists operate on the assumption that the study of human cognition is both interesting and important. We want to invoke that importance to note that cognitive scientists must continue to work to show how the field is useful to all of humanity and reflects a humanity that is not white by default. We wonder how much the field has done, and can do, to show that it is useful not only in the sense that we might make connections with researchers in other fields, win grants and write papers, even of the highest quality, but useful in some material way to the billions of non-cognitive scientists across the globe.
The influences of a virtual instructor's voice and appearance on learning from video lectures

Abstract Background Video lectures which include the instructor's presence are becoming increasingly popular. Presenting a real human does, however, entail higher financial and time costs in making videos, and one innovative approach to reduce costs has been to generate a virtual speaking instructor. Objectives The current study examined whether the use of a virtual instructor in video lectures would facilitate learning as well as a human instructor, and whether manipulating the virtual instructor's characteristics (i.e., voice and appearance) might optimize the effectiveness of the virtual instructor. Methods Our study set four conditions. In the control condition, students watched a human instructor. In the experiment conditions, students watched one of (a) a virtual instructor which used the human instructor's voice and an AI image, (b) a virtual instructor which spoke in an AI voice with an AI image made to speak using text‐to‐speech and lip synthesis techniques, or (c) a virtual instructor with used an AI voice and an AI likable‐image of an instructor. Results and Conclusions The AI likable instructor condition had a significant positive effect on students' learning performance and motivation, without decreasing the attention students paid to the learning materials. Implications Our findings suggest that instructional video designers can make use of AI voices and AI images of likable humans as instructors to motivate students and enhance their learning performance.
A three-stage transfer learning framework for multi-source cross-project software defect prediction

Transfer learning techniques have been proved to be effective in the field of Cross-project defect prediction (CPDP). However, some questions still remain. First, the conditional distribution difference between source and target projects has not been considered. Second, facing multiple source projects, most studies only rarely consider the issues of source selection and multi-source data utilization; instead, they use all available projects and merge multi-source data together to obtain one final dataset. To address these issues, in this paper, we propose a three-stage weighting framework for multi-source transfer learning (3SW-MSTL) in CPDP. In stage 1, a source selection strategy is needed to select a suitable number of source projects from all available projects. In stage 2, a transfer technique is applied to minimize marginal differences. In stage 3, a multi-source data utilization scheme that uses conditional distribution information is needed to help guide researchers in the use of multi-source transferred data. First, we have designed five source selection strategies and four multi-source utilization schemes and chosen the best one to be used in stage 1 and 3 in 3SW-MSTL by comparing their influences on prediction performance. Second, to validate the performance of 3SW-MSTL, we compared it with four multi-source and six single-source CPDP methods, a baseline within-project defect prediction (WPDP) method, and two unsupervised methods on the data from 30 widely used open-source projects. Through experiments, bellwether and weighted vote are separately chosen as a source selection strategy and a multi-source utilization scheme used in 3SW-MSTL. And, our results indicate that 3SW-MSTL outperforms four multi-source, six single-source CPDP methods and two unsupervised methods. And, 3SW-MSTL is comparable to the WPDP method. The proposed 3SW-MSTL model is more effective for considering the two issues mentioned before.
A survey and comparative evaluation of actor‐critic methods in process control

Abstract Actor‐critic (AC) methods have emerged as an important class of reinforcement learning (RL) paradigm that enables model‐free control by acting on a process and learning from the consequence. To that end, these methods utilize artificial neural networks, which are synergized for action evaluation and optimal action prediction. This feature is highly desirable for process control, especially when the knowledge about a process is limited or when it is susceptible to uncertainties. In this work, we summarize important concepts of AC methods and survey their process control applications. This treatment is followed by a comparative evaluation of the set‐point tracking and robustness of controllers based on five prominent AC methods, namely, DDPG, TD3, SAC, PPO, and TRPO, in five case studies of varying process nonlinearity. The training demands and control performances indicate the superiority of DDPG and TD3 methods, which rely on off‐policy, deterministic search for optimal action policies. Overall, the knowledge base and results of this work are expected to serve practitioners in their efforts toward further development of autonomous process control strategies.
Adaptive Bitrate with User-level QoE Preference for Video Streaming

Recent years have witnessed tremendous growth of video streaming applications. To describe users’ expectations of videos, QoE was proposed, which is critical for content providers. Current video delivery systems optimize QoE with ABR algorithms. However, ABR is usually designed for an abstract "average user" without considering that QoE varies with users. In this paper, to investigate the difference in user preferences, we conduct a user study with 90 subjects and find that the average user can not represent all users. This observation inspires us to propose Ruyi, a video streaming system that incorporates preference awareness into the QoE model and the ABR algorithm. Ruyi profiles QoE preference of users and introduces preference-aware weights over different quality metrics into the QoE model. Based on this QoE model, Ruyi’s ABR is designed to directly predict the influence on metrics after taking different actions. With these predicted metrics, Ruyi chooses the bitrate that maximizes user-specific QoE once the preference is given. Consequently, Ruyi is scalable to different user preferences without re-training the learned models for each user. Simulation results show that Ruyi increases QoE for all users with up to 65.22% improvement. Testbed experimental results show that Ruyi has the highest ratings from subjects.
Efficient dual attention SlowFast networks for video action recognition

Video data mainly differ in temporal dimension compared with static image data. Various video action recognition networks choose two-stream models to learn spatial and temporal information separately and fuse them to further improve performance. We proposed a cross-modality dual attention fusion module named CMDA to explicitly exchange spatial–temporal information between two pathways in two-stream SlowFast networks. Besides, considering the computational complexity of these heavy models and the low accuracy of existing lightweight models, we proposed several two-stream efficient SlowFast networks based on well-designed efficient 2D networks, such as GhostNet, ShuffleNetV2 and so on. Experiments demonstrate that our proposed fusion model CMDA improves the performance of SlowFast, and our efficient two-stream models achieve a consistent increase in accuracy with a little overhead in FLOPs. Our code and pre-trained models will be made available at https://github.com/weidafeng/Efficient-SlowFast.
BFS: A blockchain-based financing scheme for logistics company in supply chain finance

Against the backdrop of the booming supply chain finance and logistics industry, Logistics 4.0 has emerged. However, the financing capacity of today's logistics companies is still unable to respond to the needs of the rapid development of the supply chain. On the one hand, the problem of logistics companies’ lack of existing high-value collateral in supply chain finance has led to a clutter of their credit data and difficulty in verifying their creditworthiness. On the other hand, the massive access to logistics companies’ business information can also lead to privacy leaks. At the same time, the transparency feature of blockchain is used to solve the financing dilemma of many industries in supply chain finance. On this basis, we propose a blockchain-based financing scheme (BFS) for logistics company. BFS utilises more efficient and interpretative smart contract technology, as well as improved privacy information query and invocation algorithms, to realise automatic control of entity node privacy information flow and significantly simplifying the steps and lowering the thresholds to financing for logistics companies, while protecting the privacy of their data. After extensive simulation testing, BFS can run supply chain blocks at a stable transaction throughput of around 280 RPS, with data transfers that meet the financing needs of logistics companies, providing a higher and more stable performance than the native Hyperledger Fabric.
Reliable Semantic Communication System Enabled by Knowledge Graph

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.
A Deep Learning Modified Neural Network(DLMNN) based proficient sentiment analysis technique on Twitter data

The rapid enhancement in social media over the internet generates massive information in real-time scenarios, which has a striking impact on big data analysis. It resulted in the elevated usage of emotions and sentiments in social media. This paper proffers a proficient sentiment analysis technique in Twitter data. The Twitter database is preprocessed includes, stemming, tokenisation, number removal and stop word removal, etc. The preprocessed words are then passed into the HDFS (Hadoop Distributed File System) to reduce the repeated words and are eliminated using the MapReduce technique. The emoticons and the non-emoticons are extorted as features. The resulted features are ranked with their intended meaning. Then, the classification is performed utilising the DLMNN (Deep Learning Modified Neural Network). The experimental results were examined by using the output parameter such as Accuracy, Recall, Precision, F-Score and Execution Time with other conventional techniques such as ANN, SVM, K-Means and DCNN to show the greatest outcome of the proposed model. Evaluation result shows that DLMNN achieved the greatest performance in terms of precision (95.78%), Recall (95.84%), F-Score (95.87%) and Accuracy (91.65%) when compared with the existing models.
Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience

Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience are becoming progressively more relevant. We present a physiologically adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of non-player characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal regarding the task complexity. Our findings suggest that physiologically adaptive virtual reality systems can improve users’ experience in a wide range of scenarios.
Challenges of Using Blockchain in the Education Sector: A Literature Review

Blockchain is one of the latest innovations that is increasingly attracting the attention of various stakeholders in different fields, including the education sector. This is primarily due to its attractive features, such as decentralization, transparency, traceability, security, and reliability. Despite its advantages, blockchain still faces several challenges, and the acceptance rate of this technology is still low. Thus, the purpose of this study was to conduct a review of published articles that have discussed the challenges of adopting blockchain in the education sector. The review contained scientific papers published from 2017 to 2022 and, from the screened records, 32 articles were analyzed in full-text form. In this review, 14 challenges were reported and classified, based on the technology-organization-environment (TOE) framework. In addition, this review showed that organizational and environmental barriers received little attention in the literature, compared to technological barriers.
Review on Facial-Recognition-Based Applications in Disease Diagnosis

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.