Reviewerly/exHarmony huggingface dataset:
Compositional data analysis (CoDA) and geochemical signatures of the terminal complex aquifer in an arid zone (northeastern Algeria)

Authors:
raw_author_name: Ugochukwu Ewuzie
raw_affiliation_string: Department of Chemical and Biochemical Engineering, Missouri University of Science and Technology, Rolla, MO-65401, USA; Department of Pure and Industrial Chemistry, Abia State University, PMB 2000 Uturu, Nigeria

Abstract: The Biskra region, one of the arid regions in North-East Algeria, has known remarkable agricultural development, necessitating additional mobilization of groundwater resources, which are almost the only source of water in the region. In the long term, this heavy mobilization may threaten the quantity and quality of groundwater. This study was carried out to gain a better understanding of groundwater quality in the Terminal Complex (TC) aquifer of the Biskra region, to determine the hydrogeochemical processes controlling water mineralization, and further to investigate the suitability of the groundwater for consumption. For this purpose, multivariate statistical analysis, based on compositional data analysis (CoDA), including robust PCA, cluster analysis (HCA), ilr-ion diagram, and correlation analysis as well as graphical approaches, was carried out on the hydrochemical results of forty-four (44) groundwater samples analysed by standard methods. The results showed that most groundwater parameters exceeded WHO-recommended safe limits for drinking water. Robust CoDA revealed three distinct processes controlling groundwater hydrochemistry: the first “carbonate link” [NO3−, HCO3−, pH] at the northern boundary, which was progressively replaced by the second link [Ca2+, Mg2+, SO42−], and the third “mineralization link” [Na+, Cl−, K+, EC, TDS], located on the Outaya, Biskra, Sidi Okba, and El-Houche axes. In addition, around 93% of samples were within the geochemical zone [SO42− +Cl− >CO32-- HCO3−], while only 7% fell within the [CO32− - HCO3−> SO42− + Cl−] zone, with the following chemical facies dominating: Ca–SO4 (52.27%), Ca–Mg–SO4 (22.72%), Na–Cl (20.45%), and Ca–HCO3 (4.56%). The modified water quality index (WQI) revealed five quality classes: excellent (9.10%), good (11.36%), poor (65.90%), very poor (11.36%), and unsuitable quality (2.28%), with an apparent deterioration in water quality from north to south, following the same direction of groundwater flow. Finally, the use of CoDA with isometric log-ratio plotting, for the first time in the study area, has enabled a more thorough and reliable assessment of the processes controlling groundwater quality, which is valuable for rationalizing pumping and can help decision-makers to plan current and future water resource management practices.
Detecting multiple moving objects for unusual crowd activities detection in video surveillance system

Authors:
raw_author_name: S. Thanga Ramya
raw_affiliation_string: Department of Computer Science and Design, RMK Engineering College, Kavarapettai, Tiruvallur, Tamilnadu, India

Abstract: Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods.
SDGIN: Structure-aware dual-level graph interactive network with semantic roles for visual dialog

Authors:
raw_author_name: Kaili Sun
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan 430072, China
raw_author_name: Zhiwen Xie
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan 430072, China
raw_author_name: Chi Guo
raw_affiliation_string: Artificial Intelligence Institute, Wuhan University, Wuhan 430072, China
raw_author_name: Huyin Zhang
raw_affiliation_string: School of Computer Science, Wuhan University, Wuhan 430072, China
raw_author_name: Yuan Li
raw_affiliation_string: School of Computer Science, Central China Normal University, Wuhan 430079, China

Abstract: Visual Dialog aims to answer an appropriate response based on a multi-round dialog history and a given image. Existing methods either focus on semantic interaction, or implicitly capture coarse-grained structural interaction (e.g., pronoun co-references). The fine-grained and explicit structural interaction feature for dialog history is seldom explored, resulting in insufficient feature learning and difficulty in capturing precise context. To address these issues, we propose a structure-aware dual-level graph interactive network (SDGIN) that integrates verb-specific semantic roles and co-reference resolution to explicitly capture context structural features for discriminative and generative tasks in visual dialog. Specifically, we create a novel structural interaction graph that injects syntactic knowledge priors into dialog by introducing semantic role labeling that imply which words are sentence stems. Furthermore, considering the single perspective limitation of previous algorithms, we design a dual-perspective mechanism that learns fine-grained token-level context structure features and coarse-grained utterance-level interactions in parallel. It possess an elegant view to explore precise context interactions, realizing the mutual complementation and enhancement of different granularity features. Experimental results show the superiority of our proposed approach. Compared to other task-specific models, our SDGIN outperforms previous models and achieves a significant improvement on the benchmark dataset VisDial v1.0.
Sensor fault diagnosis and correction for data center cooling system using hybrid multi-label random Forest and Bayesian Inference

Authors:
raw_author_name: Zhaohui Qi
raw_affiliation_string: School of Energy Science and Engineering, Central South University, Changsha, Hunan, 410083, PR China
raw_author_name: Peng Wang
raw_affiliation_string: School of Civil Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, PR China

Abstract: The measurement biases in working sensors significantly hampers the effective operation and control of cooling systems in the data center. However, previous studies only focus on sensor fault diagnosis or sensor bias correction, neglecting the simultaneous detection, diagnosis and correction of sensor faults. This study proposed a novel method, combining Multi-Label Random Forest and Bayesian Inference (HMLRF-BI), to simultaneously detect, diagnose and correct sensor faults. Case studies were conducted involving eight single sensors and six multiple sensor fault cases in the Computer Room Air Handler (CRAH) to comprehensively evaluate the diagnostic and correction performance of the proposed method. Simulation results demonstrate that the method accurately diagnoses sensor fault types with a diagnostic accuracy of 97.42 % and an F1 score exceeding 97.90 %. Moreover, the proposed method also performs well in single/multiple sensor bias correction scenarios, with a correction accuracy exceeding 96.81 % and 97.10 %, respectively. Furthermore, a novel cycle mechanism is proposed, which utilized the MLRF fault diagnosis model to re-diagnose the corrected fault sensor to complete the closed-loop cycle of the HMLRF-BI method with an overall accuracy of 96.17 %. This study successfully filled the knowledge gap in the simultaneous detection, diagnosis and correction of sensor faults in data center CRAH, providing a comprehensive solution to restore sensor measurement performance, which is a promising application method.
Deep Contrastive Clustering via Hard positive sample Debiased

Authors:
raw_author_name: Xinyu Zhang
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
raw_author_name: Huiying Xu
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
raw_author_name: Xinzhong Zhu
raw_affiliation_string: AI Research Institute of Beijing Geekplus Technology Co., Ltd., Beijing, 100101, China; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
raw_author_name: Yuhang Chen
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China

Abstract: Deep graph clustering aims to reveal the underlying information of the graph and provide accurate embedding for the node clustering task, in which contrastive learning plays an important role. However, the commonly used contrastive loss function incorrectly classifies elements outside the diagonal of the cross view as negative samples, which contain a large number of positive sample pairs. In order to overcome the above problems, we propose a new deep graph contrastive clustering method, which combines hard positive sample debiasing and sample pair weighting, and improves the recognition ability of the network by removing the potential positive sample pairs and hard sample pair weighting in the negative sample pair of the loss function. More specifically, we developed a symmetric graph neural network to encode node representations. Using two sets of node representations, the correctness of negative cases is increased by clustering to generate high-confidence pseudo-label pairs for labels and confidence. The similarity distribution differences are weighted by adapting to different dataset samples to improve the sample recognition ability. To verify the efficacy of our DCHD, we compare it to existing state-of-the-art methods for node clustering tasks on six real-world datasets. Overall, the experimental results show that our proposed method outperforms current state-of-the-art graph clustering methods.
Fragility, robustness and antifragility in deep learning

Authors:
raw_author_name: Chandresh Pravin
raw_affiliation_string: University of Reading, United Kingdom
raw_author_name: Ivan Martino
raw_affiliation_string: KTH Royal Institute of Technology, Sweden
raw_author_name: Giuseppe Nicosia
raw_affiliation_string: University of Cambridge, United Kingdom; University of Catania, Italy
raw_author_name: Varun Ojha
raw_affiliation_string: Newcastle University, United Kingdom; University of Reading, United Kingdom

Abstract: We propose a systematic analysis of deep neural networks (DNNs) based on a signal processing technique for network parameter removal, in the form of synaptic filters that identifies the fragility, robustness and antifragility characteristics of DNN parameters. Our proposed analysis investigates if the DNN performance is impacted negatively, invariantly, or positively on both clean and adversarially perturbed test datasets when the DNN undergoes synaptic filtering. We define three filtering scores for quantifying the fragility, robustness and antifragility characteristics of DNN parameters based on the performances for (i) clean dataset, (ii) adversarial dataset, and (iii) the difference in performances of clean and adversarial datasets. We validate the proposed systematic analysis on ResNet-18, ResNet-50, SqueezeNet-v1.1 and ShuffleNet V2 x1.0 network architectures for MNIST, CIFAR10 and Tiny ImageNet datasets. The filtering scores, for a given network architecture, identify network parameters that are invariant in characteristics across different datasets over learning epochs. Vice-versa, for a given dataset, the filtering scores identify the parameters that are invariant in characteristics across different network architectures. We show that our synaptic filtering method improves the test accuracy of ResNet and ShuffleNet models on adversarial dataset when only the robust and antifragile parameters are selectively retrained at any given epoch, thus demonstrating applications of the proposed strategy in improving model robustness.
Texture and motion aware perception in-loop filter for AV1

Authors:
raw_author_name: Zhijun Lei
raw_affiliation_string: Meta, Menlo Park, CA 94025, USA
raw_author_name: Ruogu Fang
raw_affiliation_string: J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
raw_author_name: Dapeng Wu
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China

Abstract: Lossy compression introduces artifacts, and many conventional in-loop filters have been adopted in the AV1 standard to reduce these artifacts. Researchers have explored deep learning-based filters to remove artifacts in the compression loop. However, the high computational complexity of CNN-based filters remains a challenge. In this paper, a Texture- and Motion-Aware Perception (TMAP) in-loop filter is proposed to addresses this issue by selectively applying CNNs to texture-rich and high-motion regions, while utilizing non-learning methods to detect these regions. The proposed method introduces a new CNN structure, the Dense-Dual-Field Network (DDFN), which leverages a larger receptive field to enhance the quality of reconstructed frames by incorporating more contextual information. Furthermore, to improve perceptual quality, a novel loss function integrating wavelet-based perceptual information is presented. Experimental results demonstrate the superiority of our proposed models over other lightweight CNN models, and the effectiveness of the perceptual loss function is validated using the VMAF metric.
Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Authors:
raw_author_name: Anum Yasmin
raw_affiliation_string: Department of Computer and Software Engcineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
raw_author_name: Wasi Haider
raw_affiliation_string: Department of Computer and Software Engcineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
raw_author_name: Ali Daud
raw_affiliation_string: Abu Dhabi School of Management, Abu Dhabi, United Arab Emirates; Department of Information systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
raw_author_name: Ameen Banjar
raw_affiliation_string: Department of Information systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

Abstract: Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.
A content-based recommender system using stacked LSTM and an attention-based autoencoder

Authors:
raw_author_name: Kapil Saini
raw_affiliation_string: Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India
raw_author_name: Ajmer Singh
raw_affiliation_string: Department of Computer Science & Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India

Abstract: Recommender systems (RS) are popular in many areas, such as movies, music, news, books, research articles, search queries, and social tagging. Proprietary recommender systems are used by e-commerce websites like eBay, Amazon, and Alibaba to better match customers with products they are likely to purchase. This study suggests a recommender system that combines stacked long short-term memory (LSTM) and an attention-based autoencoder. This system would be used in a self-supervised learning paradigm, and the Amazon product datasets were used to run simulations. The results showed that the proposed method is more accurate, uses less computing power, and can be used on a large scale. No matter how big the data is, it can capture low-dimensional fixed latent representations and use the bare minimum of information already in the items, whether they are new or old. Several evaluation metrics show that the method works and that the cold start problem has been solved.
Dynamic ε-Multilevel Hierarchy Constraint Optimization With Adaptive Boundary Constraint Handling Technology

Authors:
raw_author_name: Jinze Liu
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
raw_author_name: Jian Feng
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
raw_author_name: Shengxiang Yang
raw_affiliation_string: School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, United Kingdom
raw_author_name: Huaguang Zhang
raw_affiliation_string: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning 110819, China
raw_author_name: Shaoning Liu
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China

Abstract: Real-world optimization problems are often difficult to solve because of the complexity of the objective function and the large number of constraints that accompany it. To solve such problems, we propose Adaptive Dynamic ε-Multilevel Hierarchy Constraint Optimization (εMHCO). Firstly, we propose the dynamic constraint tolerance factor ε which can change dynamically with the feasible ratio and the number of iterations in the current population. This ensures a reasonable proportion of virtual feasible solutions in the population. Secondly, we propose adaptive boundary constraint handling technology (ABCHT). It can reshape the current individual position adaptively according to the size of constraint violation and increase the diversity of the population. Finally, we propose multi-level hierarchy optimization, whose multiple population structure is beneficial to solve real-world constraint optimization problems (COPs). To validate and analyze the performance of εMHCO, numerical experiments are conducted on the latest real-world test suite CEC’2020, which contains a set of 57 real-world COPs, and compared with four state-of-the-art algorithms. The results show that εMHCO is significantly superior to, or at least comparable to the state-of-the-art algorithms in solving real-world COPs. Meanwhile, the effectiveness and feasibility of εMHCO are verified on the real-world problem of the pipeline inner detector speed control.
Trajectory Planning for Hopping Rover on Small Bodies Under Pre-Collision Attitude Adjustment Modulation

Authors:
raw_author_name: Haibin Shang
raw_affiliation_string: School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Zichen Zhao
raw_affiliation_string: School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Bingwei Wei
raw_affiliation_string: School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Chaoyong Li
raw_affiliation_string: College of Electrical Engineering, Zhejiang University, Hangzhou, China 310027

Abstract: A novel pre-collision attitude adjustment (PCAA) modulation based on angular velocity and quaternions control is developed to more controllable and reliably adjust the non-spherical hopping rover's trajectory on small bodies. This corresponds to a hopping trajectory planning (HTP) framework that addresses inherent technical challenges, including attitude-trajectory coupled features, hard-to-depicting near-truth gravity and continues dynamical contact models, difficult-to-predetermine collision times with the small bodies’ surface, and complex judgments regarding whether the period of attitude adjustment is sufficient in current hops. Two main contributions are proposed, focusing on modeling and planning. To achieve a more realistic modeling of the rover's motion, its attitude-trajectory coupling dynamic model is researched, including a ground-truth irregular gravity model and continuous dynamical contact model. Additionally, a collision detection method based on the local surface smoothing technique is proposed for rapid and exact switching between the ballistic and contact motions for the subsequent planning purpose. On the other hand, redundancy planning and binary event-trigger techniques are combined for handling unpredictable available dimensions of optimization variables of PCAA-based HTP, which arise from the indeterminate scale of collision times and potential deficiency in ballistic flight time required for completing attitude maneuvers. Specifically, the solution space is preset to guarantee the unchanging dimensions of the design variables, which are activated or deactivated by coupling binary event variables for each dimension. These newly introduced events are triggered by comparing the realistic rebound time of the current hop with the necessary time for attitude maneuver, which is accurately computed by designing a first-order sliding mode controller. This approach enables an equivalent and easily implementable version of the problem to be obtained. Logically and numerically, the application of PCAA modulation is found to significantly enlarge or contract the area of the reachable region of the hopping rover and contribute to improving the convergence and reducing the relative final position error of the hopping trajectory planning problem.