Reviewerly/exHarmony huggingface dataset:
Deep Learning Shared Bandpass Filters for Resource-Constrained Human Activity Recognition

Classification of human activities from wearable sensor data is challenged by inter-subject variance and resource-constrained platforms. We address these issues with SincEMG, a deep neural network that exploits digital signal processing concepts and transfer learning to reduce model size for activity recognition on raw sensor data. The model’s first layer decomposes signals into frequency bands using finite impulse response filters optimized directly from the data. The subsequent convolutional layers downsample across time and aggregate the first layer’s band data. Batch normalization and dropout help to regularize intermediate layer outputs. This approach reduces compute requirements by decreasing the number of learned parameters and eliminating any significant data pre-processing. In addition to these improvements, the model’s first layer learns a set of bandpass filters, which provide insight into predictive regions of the source spectrum. We evaluate SincEMG using two publicly available surface electromyography datasets. Our model uses far fewer parameters and achieves state-of-the-art results with 98.53% accuracy for 7-classes and 68.45% accuracy for 18-classes.
gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.
EVeREst: Bitrate Adaptation for Cloud VR

Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.
Blockchain Technology for Limiting the Impact of Pandemic

Blockchain technology was introduced to the world as the brain behind the invention of the digital currency (Bitcoin). According to the World Economic Forum which was held in 2016, it became clearly evident that information technology would play a major role in the fourth industrial revolution, going by the rapid development of interconnected systems as well as arising opportunities to improve already established systems and operational models. Technologies such as real-time information and data exchange are essential for developing a reliable vaccine in the shortest possible time. The practical implementation of a technology-based remote patient monitoring system is also essential to safeguard the frontline medical personnel engaged in treating asymptomatic Corona virus disease (COVID-19) patients. The subclasses generated for this study are COVID-19, blockchain applications in agriculture, business, energy, finance, health, manufacturing, music, security, and transportation. In prevention of virus spread, there are a lot of data visualization solutions currently being used to monitor real-time conditions.
Graph Embedding via Graph Summarization

Graph representation learning aims to represent the structural and semantic information of graph objects as dense real value vectors in low dimensional space by machine learning. It is widely used in node classification, link prediction, and recommendation systems. However, directly computing the embeddings for original graphs is prohibitively inefficient, especially for large-scale graphs. To address this issue, we present the GSE (Graph Summarization Embedding) model, a more efficient model that computes the nodes’ embeddings based on graph summarization. Specifically, the model first searches for the minimum information entropy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> groups to transform the original graph into a hypergraph with higher-order structural features. Next, the summarization graph’s connection probabilities are used to determine the biased random walks on the hypergraph, which then generates the sequences of the super-nodes. Finally, the node sequences are fed into the skip-gram to generate the vectors of these nodes. Our proposed model improves the efficiency of graph embedding on big data graphs and effectively alleviates the local optimal problem caused by the random walks. Experimental results demonstrate that GSE outperforms main existing clustering baselines, such as K_Means Clustering, Affinity Propagation Clustering, Canopy Clustering, and ACP Clustering. Moreover, our model can be coupled with the main graph embedding methods and improves the Macro-F1 scores and Micro-F1 scores for classification tasks on a variety of real-world graph data.
Community Learning Center Efforts to Improve Information Literacy in the Community for Cyber Crime Prevention during a Pandemic

This study explores the community learning centers' effort in improving the community's information literacy to prevent cybercrime during the pandemic era through an empirical examination of the phenomenon of pandemic COVID 19 social impacts in the specific context of Indonesia. The study used a simple research design approach with the case study method. The subject of this study consisted of five Community Learning Center’s managers from Cimahi District (CLC Nurul Islam Al Husna), Garut District (CLC Ngejah), East Jakarta District (CLC Faradika), and Karawang District (CLC Ash Sholahiyah and CLC Sanggar Juang), and two community education experts from Indonesia. Data were analyzed using triangulation, including extensive interviews from five community learning center's managers, interviews with community education experts, as well as secondary data. The study found that community learning center's efforts consist of conducting virtual socialization and also mentoring the community on information literacy, revitalizing parenting education on guiding children during their screen time, activating community reading center to create a lively discussion, and fostering community's critical thinking and information assessment. The contribution of this study to the literature on community education consists of presenting empirical evidence on the real contribution of Community Learning Center in improving the community's information literacy level while preventing the wide spreading of cyber-crime during the pandemic COVID 19 era.
Argumentation and Persistent Disagreement

Some disagreements seem to be persistent: they are, pretty much, immune to persuasive argumentation. If that is the case, how can they be overcome? Can argumentation help us? I propose that to overcome persistent disagreements through argumentation, we need a dynamic and pluralistic version of argumentation. Therefore, I propose that argumentation, more than a tool that uses persuasion to change the mind of the counterpart, is a toolbox that contains persuasion, deliberation, negotiation, and other dialogical strategies that can be used to reach an agreement.
Adaptively Robust Square-Root Cubature Kalman Filter Based on Amending

To solve the problem of decreased filtering accuracy and even filter divergence for the case that the model errors and measurement outliers exist simultaneously, an adaptively robust square-root cubature Kalman filter (SRCKF) based on amending is proposed in this paper. Based on the error analysis, the judgment criterion and the amending criterion related to the innovation are set. Then the filter could overcome the influence of the measurement outliers with the robust amendment of the measurement noise covariance matrix based on the principle of the innovation covariance matching. To further deal with model errors, the new method of the adaptive amendment of the predicted state based on the innovation is developed and combined. Finally, the proposed algorithm can balance the effect of the prior predicted value and the posterior feedback measurement value on the filtering process and reduce the state estimation error. The simulation results show that the proposed algorithm can effectively suppress the negative impact of the model errors and the measurement outliers and can obtain better estimation performance and obviously decreasing running time compared with the adaptively robust unscented Kalman filter (ARUKF) and the robust multiple fading factors cubature Kalman filter (RMCKF).
FAWA: Fast Adversarial Watermark Attack

Recently, adversarial attacks have shown to lead the state-of-the-art deep neural networks (DNNs) to misclassification. However, most adversarial attacks are generated according to whether they are perceptual to human visual system, measured by geometric metrics such as the l2-norm, which ignores the common watermarks in cyber-physical systems. In this paper, we propose a fast adversarial watermark attack (FAWA) method based on fast differential evolution technique, which optimally superimposes a watermark on an image to fool DNNs. We also attempt to explain the reason why the attack is successful and propose two hypotheses on the vulnerability of DNN classifiers and the influence of the watermark attack on higher-layer features extraction respectively. In addition, we propose two countermeasure methods against FAWA based on random rotation and median filtering respectively. Experimental results show that our method achieves 41.3% success rate in fooling VGG-16 and have good transferability. Our approach is also shown to be effective in deceiving deep learning as a service (DLaaS) systems as well as the physical world. The proposed FAWA, hypotheses, and the countermeasure methods, provide a timely help for DNN designers to gain some knowledge of model vulnerability while designing DNN classifiers and related DLaaS applications.
The Formation Of Research Competencies Of Future Primary School Teachers

This article discusses the role of educational technologies in the effective organization of the educational process. It would be useful to use the methods described in the article in the educational process.
Fuzzy‐based secure exchange of digital data using watermarking in <scp>NSCT‐RDWT‐SVD</scp> domain

Summary Due to the remarkable development of Internet technologies, a great deal of valuable digital data is now transmitted over public networks. To guarantee the security of this data during the transfer process, the authentication of its integrity is extremely important. This paper introduces a robust and secure dual‐watermarking‐based fusion of watermarking, optimization, and a compression method utilizing non‐sub‐sampled contourlet transform (NSCT), redundant discrete wavelet transform (RDWT), and singular value decomposition (SVD). In our method, we first apply the NSCT to a higher entropy sub‐band of the host image. Then, our method uses RDWT‐SVD on higher frequency coefficients of the NSCT image. A similar procedure is followed for both mark images. Finally, an appropriate scaling factor, as obtained by fuzzy inference system, is used to invisibly embed the singular values of both mark data into the host image. Here, any more important mark data are scrambled before the embedding process. The simulation tests reveal that the proposed technique is not only imperceptible and secure but also robust against common attacks. The suggested method has a superior ability to extract hidden information than previous conventional techniques.
Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

In many discourses, popular as well as scientific, it is suggested that the "massive" use of Artificial Intelligence (AI), including Machine Learning (ML), and reaching the point of "singularity" through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race.Speaking in terms of manufacturing systems, it would mean that the intelligence and total automation would be achieved (once the humans are excluded).The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, the ML algorithms will be not able to become totally autonomous and, consequently, the human role will be indispensable.In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and present an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will always be human dependent.In addition, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in Industry 4.0.The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning.The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning.Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, is proposed in order to evaluate way of use of ML functionality in the context of CPS.Similar to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology does not provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper.The paper is divided into two parts.In the first part a review of argumentation from literature in favour of and against the thesis on the human role in future was presented, as well as the concept of the manufacturing singularity was introduced.Furthermore, an intelligent machine architecture towards the manufacturing singularity was proposed, arguing that the intelligent machine will be always human dependent _____________
LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery

In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements—attention mechanism and semantic consistency loss—having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.
Bubbles in Ethereum

We apply the (Phillips et al., 2015a,b) methodology to date-stamp bubbles in the Ethereum blockchain. Our analysis of the drivers of fundamental value suggests that the explosive behavior documented in ether prices does not constitute speculative bubbles but reflects the abrupt rally of demand for the use of the Ethereum Virtual Machine tied to the development of the decentralized application (dApp) ecosystem. • We date-stamp several episodes of explosivity in the price of ether. • The fundamental drivers of Ethereum are investigated. • Mild explosivity in Ether are driven by a surge in demand of smart contracts.
5. The emergence of the dative alternation in Dutch: towards the establishment of a horizontal link

Present-day Dutch displays a productive argument structure alternation between a double object construction with bare NP Theme and Recipient objects and a prepositional-dative in which the Recipient is marked by the preposition aan. The latter construction is the younger of the two: it is a post-Middle Dutch innovation, which became a well-established part of the grammar of Dutch in 17th century language only. The present chapter investigates the emergence of an embryonic dative alternation in the immediately preceding period, through a detailed investigation of the presence of aan-datives in three nonbelletristic texts from the latter half of the 16th century. It will be argued that the degree of semantic overlap between the DOC and the “new” aan-pattern increased through the combined effect of chains of local extensions in various sub-regions of “dative” semantic space.
Multilabel CNN-Based Hybrid Learning Metric for Pedestrian Reidentification

Pedestrian reidentification has recently emerged as a hot topic that attains considerable attention since it can be applied to many potential applications in the surveillance system. However, high-accuracy pedestrian reidentification is a stimulating research problem because of variations in viewpoints, color, light, and other reasons. This work addresses the interferences and improves pedestrian reidentification accuracy by proposing two novel algorithms, pedestrian multilabel learning, and investigating hybrid learning metrics. First, unlike the existing models, we construct the identification framework using two subnetworks, namely, part detection subnetwork and feature extraction subnetwork, to obtain pedestrian attributes and low-level feature scores, respectively. Then, a hybrid learning metric that combines pedestrian attributes and low-level feature scores is proposed. Both low-level features and pedestrian attributes are utilized, thus enhancing the identification rate. Our simulation results on both datasets, i.e., CUHK03 and VIPeR, reveal that the identification rate is improved compared to the existing pedestrian reidentification methods.
Revisiting the VCCFinder approach for the identification of vulnerability-contributing commits

Abstract Detecting vulnerabilities in software is a constant race between development teams and potential attackers. While many static and dynamic approaches have focused on regularly analyzing the software in its entirety, a recent research direction has focused on the analysis of changes that are applied to the code. VCCFinder is a seminal approach in the literature that builds on machine learning to automatically detect whether an incoming commit will introduce some vulnerabilities. Given the influence of VCCFinder in the literature, we undertake an investigation into its performance as a state-of-the-art system. To that end, we propose to attempt a replication study on the VCCFinder supervised learning approach. The insights of our failure to replicate the results reported in the original publication informed the design of a new approach to identify vulnerability-contributing commits based on a semi-supervised learning technique with an alternate feature set. We provide all artefacts and a clear description of this approach as a new reproducible baseline for advancing research on machine learning-based identification of vulnerability-introducing commits.