Reviewerly/exHarmony huggingface dataset:
Distribution preserving-based deep semi-NMF for data representation

The semi-nonnegative matrix factorization (Semi-NMF) is a promising soft K-means clustering technique. Deep Semi-NMF, which stacks one-layer Semi-NMF into multi-layer, is able to learn the hierarchical projections and can obtain the deep hidden representations according to the unknown attributes of the given data. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass data. Then one hopes to learn the new deep hidden representations which can preserve the intrinsic structures embedded in the original data space perfectly. Here seamlessly integrating the benefits of the Deep Semi-NMF and the distribution preserving strategy, we propose a novel distribution preserving-based deep semi-nonnegative matrix factorization method (DPNMF) to achieve this goal. In DPNMF, by maintaining the consistency of two distributions that can approximate the manifold structures, we can seek the deep hidden features which reveal the original intrinsic structures. As a result, the manifold structures in the raw data are well preserved in the new feature space. We also devise an adaptive projected Barzilai-Borwein method to optimize the proposed constrained objective function efficiently. The experimental results on the several real-world datasets show that the proposed DPNMF can achieve advantageous clustering performance in terms of accuracy (ACC), normalized mutual information (NMI), and adjusted rand index (ARI).
On joint parameterizations of linear and nonlinear functionals in neural networks

The paper proposes a new class of nonlinear operators and a dual learning paradigm where optimization jointly concerns both linear convolutional weights and the parameters of these nonlinear operators. The nonlinear class proposed to perform a rich functional representation is composed by functions called rectified parametric sigmoid units. This class is constructed to benefit from the advantages of both sigmoid and rectified linear unit functions, while rejecting their respective drawbacks. Moreover, the analytic form of this new neural class involves scale, shift and shape parameters to obtain a wide range of activation shapes, including the standard rectified linear unit as a limit case. Parameters of this neural transfer class are considered as learnable for the sake of discovering the complex shapes that can contribute to solving machine learning issues. Performance achieved by the joint learning of convolutional and rectified parametric sigmoid learnable parameters are shown to be outstanding in both shallow and deep learning frameworks. This class opens new prospects with respect to machine learning in the sense that main learnable parameters are attached not only to linear transformations, but also to a wide range of nonlinear operators.
Siam Deep Feature KCF Method and Experimental Study for Pedestrian Tracking

The tracking of a particular pedestrian is an important issue in computer vision to guarantee societal safety. Due to the limited computing performances of unmanned aerial vehicle (UAV) systems, the Correlation Filter (CF) algorithm has been widely used to perform the task of tracking. However, it has a fixed template size and cannot effectively solve the occlusion problem. Thus, a tracking-by-detection framework was designed in the current research. A lightweight YOLOv3-based (You Only Look Once version 3) mode which had Efficient Channel Attention (ECA) was integrated into the CF algorithm to provide deep features. In addition, a lightweight Siamese CNN with Cross Stage Partial (CSP) provided the representations of features learned from massive face images, allowing the target similarity in data association to be guaranteed. As a result, a Deep Feature Kernelized Correlation Filters method coupled with Siamese-CSP(Siam-DFKCF) was established to increase the tracking robustness. From the experimental results, it can be concluded that the anti-occlusion and re-tracking performance of the proposed method was increased. The tracking accuracy Distance Precision (DP) and Overlap Precision (OP) had been increased to 0.934 and 0.909 respectively in our test data.
STACoRe: Spatio-temporal and action-based contrastive representations for reinforcement learning in Atari

With the development of deep learning technology, deep reinforcement learning (DRL) has successfully built intelligent agents in sequential decision-making problems through interaction with image-based environments. However, learning from unlimited interaction is impractical and sample inefficient because training an agent requires many trial and error and numerous samples. One response to this problem is sample-efficient DRL, a research area that encourages learning effective state representations in limited interactions with image-based environments. Previous methods could effectively surpass human performance by training an RL agent using self-supervised learning and data augmentation to learn good state representations from a given interaction. However, most of the existing methods only consider similarity of image observations so that they are hard to capture semantic representations. To address these challenges, we propose spatio-temporal and action-based contrastive representation (STACoRe) learning for sample-efficient DRL. STACoRe performs two contrastive learning to learn proper state representations. One uses the agent's actions as pseudo labels, and the other uses spatio-temporal information. In particular, when performing the action-based contrastive learning, we propose a method that automatically selects data augmentation techniques suitable for each environment for stable model training. We train the model by simultaneously optimizing an action-based contrastive loss function and spatio-temporal contrastive loss functions in an end-to-end manner. This leads to improving sample efficiency for DRL. We use 26 benchmark games in Atari 2600 whose environment interaction is limited to only 100k steps. The experimental results confirm that our method is more sample efficient than existing methods. The code is available at https://github.com/dudwojae/STACoRe.
Robust homography-based visual servo control for a quadrotor UAV tracking a moving target

In this study, a new robust homography-based visual tracking control approach for the quadrotor unmanned aerial vehicle (UAV) is developed. Specifically, employing the homography matrix as feedback, a hierarchical homography-based visual servoing (HBVS) scheme with a new command attitude extraction method to account for the underactuation of UAV is proposed. On this basis, a smooth hyperbolic tangent function is fulfilled as an augmented part of the backstepping control scheme, which guarantees the non-negative total thrust and avoid singularity. Additionally, a cascaded filter-based estimator and adaptive laws with integrable functions are embedded to counteract uncertainties including external perturbations, unknown acceleration of the moving target, and unknown image depth, and to facilitate the system's asymptotic stability simultaneously. The theoretical analysis testifies that the whole close-loop system is asymptotically stable. Simulations further verify that the proposed HBVS controller can realize the visual tracking with a superior performance.
Geochemical characterization of altered rocks and element dispersion around an abandoned mining site in an arid zone: A case study of Fatira gold mine area, Eastern Desert, Egypt

Environmental impacts related to mining are widespread due to poor waste management. Fatira abandoned gold mine area in Egypt was studied for the geochemical/mineralogical properties of alterations haloes, mine tailings, stream sediments under dry conditions and impacts to nearby environment. Results of this study indicated that stream sediments and soil-tailings mixture were derived from felsic sources, while tailings, weathered and old tailings were obtained from intermediate-felsic sources. The Pb load was much higher in weathered rocks, soil-tailings mixture and old tailings, while metals like As, Co, Cr, Cu, Ni, V, and Zn show elevated values in weathered rocks nearby Fatira mine area and in downstream sediments. The tailings have high average concentrations of heavy elements, Sr (439), Zn (389), Cu (310), and Cr (50) (in mg/kg), higher than the toxicological standard values for contaminants indicating adverse effects to the neighbouring environment, whereas, soil-tailings mixture have higher As (263), Pb (56) and Al2O3 (17 wt%) reflecting the metavolcanics and granite composition of country rocks. At lower depths of tailings, escalated concentrations of Al2O3, K2O, Na2O, and Fe2O3, and little CaO, MgO, As, Pb, Zn, Cu, and Ni are indicate variable input from intermediate rocks, grey granites, felsite dykes and pink granites. Chemically altered granites showed replacement of hornblende into actinolite, calcite, pitted ilmenite and in-situ formation of Fe-oxides/hydroxides. Low hydraulic conductivity of the rocks increased the intensity of weathering. Gypsum and goethite are recorded due to repetitive sulfides decomposition. The element enrichment that exceeds the standard toxicological values in tailings, soil-tailings mixture, weathered rocks and stream sediments have severe effects on health. Integrating the chemistry, mineralogy and microtextural analyses in the study area is required for accurate geoenvironmental forecasting that facilitate mine rehabilitation with an effective management plan.
Action density based frame sampling for human action recognition in videos

In the action recognition, a proper frame sampling method can not only reduce redundant video information, but also improve the accuracy of action recognition. In this paper, an action density based non-isometric frame sampling method, namely NFS, is proposed to discard the redundant video information and sample the rational frames in videos for neural networks to achieve great accuracy on human action recognition, in which action density is introduced in our method to indicate the intensity of actions in videos. Particularly, the action density determination mechanism, focused-clips division mechanism, and reinforcement learning based frame sampling (RLFS) mechanism are proposed in NFS method. Via the evaluations with various neural networks and datasets, our results show that the proposed NFS method can achieve great effectiveness in frame sampling and can assist in achieving better accuracy on action recognition in comparison with existing methods.
Industry-academia collaboration for realism in software engineering research: Insights and recommendations

Effective industry-academia collaboration may increase software engineering research relevance by increased realism, yet very challenging for reasons like confidentiality concerns, different objectives and priorities. We analyse industry-academia collaboration scenarios based on our own experiences as Ph.D. student and supervisor, and provide insights and recommendations to facilitate future collaborations with industry. We first present our industry-academia collaboration experiences that span over two and a half years with different companies. Then, we analyse both facilitators and problems from those scenarios and synthesize recommendations based on that. Five different scenarios are analysed, including both success and failure scenarios. Reflections and insights into these experiences as well as some general recommendations are presented. We believe such experiences and insights are helpful for academic researchers to pursue industry-academia collaboration. We plan to continuously report our experience and provide our suggestions for effective collaboration with industry.
The Effects of Physical Coherence Factors on Presence in Extended Reality (XR)

This research investigates the influence of physical coherence factors on presence during user-object interactions in XR environments. Physical coherence was defined through four factors (force, occlusion, lighting, and material) and was examined through a perceptual matching experiment, where participants selected factors they intended to improve in a given XR environment to reach an acceptable level of presence. Research outcomes indicate that presence is enhanced with the improvement of physical coherence across all four factors. Also, the transition probability distribution and improvement route derived from a Markov Chain analysis found the force factor to be the most contributing factor when inducing presence, with the occlusion and lighting factor holding the subsequent priority. While most participants chose the force and lighting factor initially, the force and occlusion factors were most likely to be enhanced to the highest for final acceptance. Furthermore, findings suggest that the force and occlusion factors affect interactivity, while lighting affects visual realism of an XR environment. Connecting these findings, it is interpreted that the interactivity has a higher contribution to presence than the visual fidelity of virtual objects and /or virtual environment. Thus, sufficiently realistic and predictable movements of a virtual object can potentially counteract its visual discrepancy from reality. Moreover, this research provides the insight that the degree of interactivity of an XR environment is of essence to generate a high level of presence.
A novel optimization approach towards improving separability of clusters

The objective functions in optimization models of the sum-of-squares clustering problem reflect intra-cluster similarity and inter-cluster dissimilarities and in general, optimal values of these functions can be considered as appropriate measures for compactness of clusters. However, the use of the objective function alone may not lead to the finding of separable clusters. To address this shortcoming in existing models for clustering, we develop a new optimization model where the objective function is represented as a sum of two terms reflecting the compactness and separability of clusters. Based on this model we develop a two-phase incremental clustering algorithm. In the first phase, the clustering function is minimized to find compact clusters and in the second phase, a new model is applied to improve the separability of clusters. The Davies–Bouldin cluster validity index is applied as an additional measure to compare the compactness of clusters and silhouette coefficients are used to estimate the separability of clusters. The performance of the proposed algorithm is demonstrated and compared with that of four other algorithms using synthetic and real-world data sets. Numerical results clearly show that in comparison with other algorithms the new algorithm is able to find clusters with better separability and similar compactness.
CED: A case-level explainable paramedical diagnosis via AdaGBDT

The rapid growth of medical data has greatly promoted the wide exploitation of machine learning for paramedical diagnosis. Inversely proportional to their performance, most machine learning models generally suffer from the lack of explainability, especially the local explainability of the model, that is, the case-specific explainability.In this paper, we proposed a GBDT (Gradient Boosting Decision Tree)-based explainable model for case-specific paramedical diagnostics, and mainly make the following contributions: (1) an adaptive gradient boosting decision tree (AdaGBDT) model is proposed to boost the path-mining for decision effectively; (2) to learn a case-specific feature importance embedding for a specific patient, the bi-side mutual information is applied to characterize the backtracking on the decision path; (3) through the collaborative decision-making by globally explainable AdaGBDT with case-based reasoning (CBR) in the case-specific metric space, some hard cases can be identified by the means of visualized interpretation. The performance of our model is evaluated on the Wisconsin diagnostic breast cancer dataset and the UCI heart disease dataset.Experiments conducted on two datasets show that our AdaGBDT achieves the best performance, with the F1-value of 0.9647 and 0.8405 respectively. Moreover, a series of experimental analyses and case studies further illustrate the excellent performance of feature importance embedding.The proposed case-specific explainable paramedical diagnosis via AdaGBDT has excellent predictive performance, with both promising case-level and consistent global explainability.
On Accountable and Distributed Audit of Outsourced Data

Strong accountability identifies the misbehaving entity's activities and protects the honest entity from false accusations. This paper proposes a strong, accountable, and distributed auditing scheme for outsourced data in public cloud setup. A proof-based solution with fine-grained read-write access is used to address strong accountability and auditing of data services. A comparative analysis with similar work is carried out.
Perspectives of Cryptocurrency Price Prediction

Cryptocurrency is a relatively young research field. Since its development, when Bitcoin was lanced (2008) (Satoshi, N.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008)), until today it become a highly volatile market with more than 9 thousand types of cryptocurrencies (CoinMarketCap: preţurile criptomonedelor de ast˘azî in funcţie de capitalizare de piaţă (2021)). In latest years several algorithms and techniques have been developed in order to predict the cryptocurrency price evolution. However, a general classification of the available methods has not been proposed yet. For these reasons, in this paper the main ideas underlying cryptocurrency price forecasting are presented and compared. In the first part of the paper is presented the general framework of cryptocurrency development, namely Blockchain Technology and e-Business social environment. In the second part several techniques for cryptocurrency price prediction are presented and their main characteristics are discussed. The last section concludes.
Using Twitter Data and Lexicon-Based Sentiment Analysis to Study the Attitude Towards Cryptocurrency Market and Blockchain Technology

The use of messages and posts in the online environment has become increasingly popular when it comes to identifying public opinion on various phenomena. A reliable way to explore these attitudes is to collect short messages shared on the Twitter platform, recognized as tweets. This article aims to conduct a sentiment analysis of the cryptocurrency market and Blockchain technology using a series of 5,000 such messages and a lexicon-based approach. A comparison of the sentiment extracted with the help of three of the most popular lexicons (NRC, Bing and Loughran) will be also exposed.
Blockchain and Its Idiosyncratic Effects on Energy Consumption and Conservation

Blockchain technology, which has affected almost every industry and the business model, takes no exemption of the vast energy sector. Energy businesses in the world have already started reconnoitering the use of blockchain technology in various applications. The applications range from P2P energy trading, asset management, and demand and supply chain tracking. Moreover, with precise reliability alongside security requirements, the application range of the energy sector is relatively narrow. The article aims to define and show the environment and methodology for applying blockchain principles for solving operational technology challenges at energy utilities and for a much closer analysis of all the possible integrations between the energy sector and blockchain, an efficient, conservative, and self-sufficient energy industry. Moreover, this paper analyzes and reviews two innovative application-specific use cases of blockchain in the energy sector and one highly competent solidity code, which could revolutionize the energy sector.
Metrics for Evaluating Classification Algorithms

One of the most important topics in machine learning is how to evaluate the models, which means measuring how accurately they predict the expected outcome. In addition to accuracy, there are a number of other metrics for evaluating classifier performance. Model evaluation is introduced in a prediction framework that is implemented using automated machine learning. The performance metrics are calculated for each classification model generated for our analysis. Unlabeled data gathered using a 360-degree evaluation form goes through a clustering process before being analyzed by classification. The most performant classifier is identified through model evaluation, and a detailed metrics analysis is performed.
User-Adapted Semantic Description Generation Using Natural Language Models

Nowadays, conversational agents are making their way into our lives in many fields. The agent’s speech is an important element in human-computer interaction, and to appear natural and friendly it should avoid predefined texts. With this premise, together with the significant growth in natural language generation models, this work explores the capabilities of natural language models to lead to a more fluent human-computer interaction and open up a range of new opportunities and applications. The present work proposes a system for the generation of descriptions tailored to the user’s profile from different random topics using Natural Language models. After implementing the different applications, integrating natural language generation in conversational agents has proven to be highly effective.
Application of the Method of Expert Assessments in Determining the Level of Criticality of the Information Security Vulnerability of Smart Contracts

The article focuses on the development of a methodology based on expert assessment to assess the level of vulnerability criticality for subsequent use in the analysis of smart contract code to make a decision on its placement in a blockchain system. The task includes a problem statement, basic approaches to threat and vulnerability analysis, a proposed methodology for determining the level of vulnerability criticality, its testing, and recommendations for future work. The issues of protecting smart contracts operating within the blockchain system from computer attacks and vulnerabilities are becoming increasingly important for both developers and participants in information interaction. One of the most important trends in this area is the development of methods and tools to detect, model, analyze and assess the initiation and execution of malicious attacks on the target component of the system. #COMESYSO1120.