Reviewerly/exHarmony huggingface dataset:
Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models.
Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020-2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1-13, 1442 (July 11-23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people's impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.
Sampling rare conformational transitions with a quantum computer

Structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, Molecular Dynamics (MD) simulations enable us to investigate these thermally activated processes with an atomic level of resolution. In practice, an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods focus the computational power on sampling the rare transitions between states. One of their outstanding limitations is to efficiently generate paths that visit significantly different regions of the conformational space. To overcome this issue, we introduce a new algorithm for MD simulations that integrates machine learning and quantum computing. First, using functional integral methods, we derive a rigorous low-resolution spatially coarse-grained representation of the system's dynamics, based on a small set of molecular configurations explored with machine learning. Then, we use a quantum annealer to sample the transition paths of this low-resolution theory. We provide a proof-of-concept application by simulating a benchmark conformational transition with all-atom resolution on the D-Wave quantum computer. By exploiting the unique features of quantum annealing, we generate uncorrelated trajectories at every iteration, thus addressing one of the challenges of path sampling. Once larger quantum machines will be available, the interplay between quantum and classical resources may emerge as a new paradigm of high-performance scientific computing. In this work, we provide a platform to implement this integrated scheme in the field of molecular simulations.
Quantum capacity and codes for the bosonic loss-dephasing channel

Bosonic qubits encoded in continuous-variable systems provide a promising alternative to two-level qubits for quantum computation and communication. So far, photon loss has been the dominant source of errors in bosonic qubits, but the significant reduction of photon loss in recent bosonic qubit experiments suggests that dephasing errors should also be considered. However, a detailed understanding of the combined photon loss and dephasing channel is lacking. Here, we show that, unlike its constituent parts, the combined loss-dephasing channel is non-degradable, pointing towards a richer structure of this channel. We provide bounds for the capacity of the loss-dephasing channel and use numerical optimization to find optimal single-mode codes for a wide range of error rates.
Quaternion-based knowledge graph neural network for social recommendation

In recent years, the surge in the number of users in recommender systems has brought unprecedented opportunities and challenges to research on recommender systems. The development of the graph neural network makes social recommendation based on graph data more advantageous, but it also brings problems with parameterization. Although the recommender system contains a large amount of data, such as users, items, and user-item interactions, the social relationship data of users in social recommendation are still sparse. To solve the problem of parameterization, we propose a novel quaternion-based knowledge graph neural network for social recommendation (QSoR), which applies hypercomplex space to the social recommendation. Without affecting the recommendation quality, QSoR utilizes the expressibility of the quaternion and weight sharing mechanism of the Hamilton product to reduce the parameters during training. To solve the problem of the sparsity of users’ social relationship data, we propose a quaternion-based explicit and implicit social relationships integration algorithm, which obtains users’ implicit social relationships from the user-item graph as a supplement for users’ social information. We design a double-end attention mechanism when obtaining the user’s preferences and social relationships, which makes full use of the item information and the interaction information between the user and the neighbor nodes. In this way, we can obtain users’ personalized preferences, distinguish the importance of user relationships, and obtain neighbors with a high degree of influence on users, thus improving the accuracy of recommendation results. The social relationship graph and the user-item graph are effectively combined in the quaternion space to make personalized recommendations for users. Experiments on three public datasets, Yelp, Last-FM, and Epinions, demonstrate the validity of the proposed model QSoR.
TCURL: Exploring hybrid transformer and convolutional neural network on phishing URL detection

Phishing is a growing threat that involves cybercriminals creating counterfeit websites to lure victims and obtain their sensitive information, such as login credentials and credit card numbers. According to the Q4 2021 Phishing Trends Report by the Anti-Phishing Working Group, the number of phishing attacks has tripled from early 2020. Conventional blacklist method cannot protect users from attacks using new phishing URLs. Traditional machine learning methods require complex feature engineering and generally cannot meet the detection accuracy requirements. Deep learning methods based on fully convolutional networks and pure transformers only pay attention to local correlations or long-term dependencies. To address these issues, we propose a hybrid network architecture, called TCURL, which considers both local and global correlations among the characters of URLs. TCURL has two parallel branches, a convolution branch and a transformer branch, and a fusion block used to deal with messages from the two branches. The convolution branch provides sufficient positional information meaning that no extra positional encoding is needed. Through experiments, we explored various design choices to optimize the model. The proposed method achieves an accuracy of 96.92%, 99.77%, and 89.73% on three sampled datasets, which further outperforms other existing methods.
Anchor-based incomplete multi-view spectral clustering

In the past decade, multi-view clustering has become a research hot spot of machine learning. In traditional multi-view clustering methods, all views of the data points are assumed to be complete. However, in the practical applications, some views of data points may be missing and incomplete multi-view clustering methods are developed to handle these incomplete multi-view data. The existing incomplete multi-view clustering methods still have some defects such as insufficient use of missing information or neglecting the underlying relations among different views. To address these limitations, we propose an Anchor-based Incomplete Multi-view Spectral Clustering (AIMSC) approach. Specifically, AIMSC utilizes anchor points to connect all instances of each view and recover the missing information. Then, the similarities between all data points are derived from the similarities between data points and anchor points. Finally, anchor-based spectral clustering is executed to generate the clustering results. Experimental results on multiple benchmark datasets demonstrate the superiority of AIMSC.
Explain yourself! Effects of Explanations in Human-Robot Interaction

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction.
Rapid detection of copper ore grade based on visible-infrared spectroscopy and TSVD-IVTELM

• A method for rapid detection of copper ore grade is proposed. • The method is based on visible-infrared spectroscopy and machine learning. • Spectral data is non-linearly detected by graphical diagnosis and numerical calculation. • After comparative test, TSVD-IVTELM has the highest accuracy. The rapidity of ore grade identification is key to speeding up the beneficiation process in the mining process. Traditional ore grade detection mostly relies on chemical methods. Although these methods have high accuracy, they take a long time, and the cost of detection has always been high. Therefore, this paper proposes a detection method for ore grade using visible-infrared spectroscopy and an incremental two hidden layer extreme learning machine with variable hidden layer nodes based on the truncated singular value decomposition (TSVD-IVTELM) algorithm. Firstly, the spectral data of each sample are obtained by spectrometer. Then, Monte Carlo cross-validation is used to eliminate abnormal samples, and partial least squares regression is used to extract the latent variables of the spectral data to reduce the data dimension. Finally, TSVD-IVTELM is used for regression analysis. TSVD-IVTELM is proved to have the smallest root mean square error and best fitting performance after comparison experiments.
An improved pathfinder algorithm using opposition-based learning for tasks scheduling in cloud environment

Cloud computing (CC) has become a major topic of study in recent years, providing a cost-effective deployment framework for hosting and running workflows. It is considered as the leading model for distributed computing due to its elasticity, speed, and pay-per-use model. Running scientific applications in the cloud environment with a number of criteria remains a great challenge for researchers, because it requires a robust infrastructure with high computing performance, communication and storage capabilities. Most previous works designed for cloud environments examine the fixed number of resources and aims only to reduce runtime. This has forced scientists to develop the workflow scheduling (WFS) optimization algorithms that strike the right balance between the three main qualities of service (QoS) parameters: time, cost and resource utilization. Due to its substantial implications for the front end and back end of the cloud research business, resource utilization is undoubtedly an important factor that should be taken into consideration. When efficient resource usage is performed in the cloud, good load balancing is obtained. To address the preceding problems, we propose in this paper an innovative workflow scheduling technique in the cloud employing the Pathfinder algorithm (PFA) and the oppositional-based learning algorithm (OBL). The proposed scheduler focused on three objectives (i.e., total execution time, cost, and resource utilization). Simulation tests are carried out using the workflowsim toolkit to evaluate the effectiveness of the suggested approach. The results of the experiments proved that the proposed OBLPFA algorithm outperforms other algorithms called (Particle Swarm technique (PSO), dragonfly (DFA), the Arithmetic Optimization Technique (AOA), the Reptile Search technique (RSA), the Aquila Optimization method (AO), and Lion Optimization method (LOA)) in terms of total execution time, cost, and resource utilization. • We use the OBL algorithm to initialize the population of individuals. • We present the OBLPFA method for scheduling the workflows in the cloud. • We design a multi-objective workflows scheduling strategy for finding optimal scheduling depending on a number of existing objectives namely: makespan, execution cost and resource utilization. • We compare the results of an experimental evaluation of the suggested algorithm with several recent algorithms in the literature.
How far does the predictive decision impact the software project? The cost, service time, and failure analysis from a cross-project defect prediction model

Cross-project defect prediction (CPDP) models are being developed to optimise the testing resources. Proposing an ensemble classification framework for CPDP as many existing models are lacking with better performances and analysing the main objectives of CPDP from the outcomes of the proposed classification framework. For the classification task, we propose a bootstrap aggregation based hybrid-inducer ensemble learning (HIEL) technique that uses probabilistic weighted majority voting (PWMV) strategy. To know the impact of HIEL on the software project, we propose three project-specific performance measures such as percent of perfect cleans (PPC), percent of non-perfect cleans (PNPC), and false omission rate (FOR) from the predictions to calculate the amount of saved cost, remaining service time, and percent of the failures in the target project. On many target projects from PROMISE, NASA, and AEEEM repositories, the proposed model outperformed recent works such as TDS, TCA+, HYDRA, TPTL, and CODEP in terms of F-measure. In terms of AUC, the TCA+ and HYDRA models stand as strong competitors to the HIEL model. For better predictions, we recommend ensemble learning approaches for the CPDP models. And, to estimate the benefits from the SDP models, we recommend the above project-specific performance measures.
A systematic literature review of blockchain-based Internet of Things (IoT) forensic investigation process models

Digital forensic examiners and stakeholders face increasing challenges during the investigation of Internet of Things (IoT) environments due to the heterogeneous nature of the IoT infrastructure. These challenges include guaranteeing the integrity of forensic evidence collected and stored during the investigation process. Similarly, they also encounter challenges in ensuring the transparency of the investigation process which includes the chain-of-custody and evidence chain. In recent years, some blockchain-based secure evidence models have been proposed especially for IoT forensic investigations. These proof-of-concept models apply the inherent properties of blockchain to secure the evidence chain of custody, maintain privacy, integrity, provenance, traceability, and verification of evidence collected and stored during the investigation process. Although there have been few prototypes to demonstrate the practical implementation of some of these proposed models, there is a lack of descriptive review of these blockchain-based IoT forensic models. In this paper, we report a comprehensive Systematic Literature Review (SLR) of the latest blockchain-based IoT forensic investigation process models. Particularly, we systematically review how blockchain is being used to securely improve the forensic investigation process and discuss the efficiency of these proposed models. Finally, the paper highlights challenges, open issues, and future research directions of blockchain technology in the field of IoT forensic investigations.
Weakly-Supervised Part-Attention and Mentored Networks for Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve images with the same vehicle ID across different cameras. Current part-level feature learning methods typically detect vehicle parts via uniform division, outside tools, or attention modeling. However, such part features often require expensive additional annotations and cause sub-optimal performance in case of unreliable part mask predictions. In this paper, we propose a weakly-supervised Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel recalibration and cluster-based mask generation without vehicle part supervisory information. Secondly, PMNet leverages teacher-student guided learning to distill vehicle part-specific features from PANet and performs multi-scale global-part feature extraction. During inference, PMNet can adaptively extract discriminative part features without part localization by PANet, preventing unstable part mask predictions. We address this Re-ID issue as a multi-task problem and adopt Homoscedastic Uncertainty to learn the optimal weighing of ID losses. Experiments are conducted on two public benchmarks, showing that our approach outperforms recent methods, which require no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded vehicle Re-ID task and exhibits good generalization ability.
Tailored Blockchain Applications for the Natural Gas Industry: The Case Study of SOCAR

Blockchain technology has emerging areas of deployment in diverse sectors and use cases. In this study, several potential application areas of blockchain with promising benefits have been identified in the natural gas industry. There is no single solution that can address different challenges and meet disparate requirements. Therefore, it is important to understand the needs of the natural gas industry and propose appropriate blockchain solutions. Moreover, in the literature, there is a lack of detailed case studies involving industrial experts from the natural gas sector. Expert opinion can be useful for prioritizing the most needed or expected blockchain application areas among several options. By considering privacy, authentication, speed, security, energy consumption, and costs, suitable blockchain types and consensus mechanisms can be determined. This study presents one of the first detailed case studies for tailored applications of blockchain in the natural gas industry. Through a two-staged semi-structured interview with executives from SOCAR Azerbaijan, the most important blockchain application areas and operational requirements were identified. Furthermore, the most suitable blockchain solutions that can address application-specific conditions and needs were determined. This study both, develops a replicable and reliable methodology to conduct detailed blockchain implementation case studies in the natural gas industry and various other sectors, and provides detailed insights into the primary application areas, operational expectations–requirements, and implementation challenges specific to each application.
Rank List Sensitivity of Recommender Systems to Interaction Perturbations

Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users' recommendations are drastically altered by minor perturbations in other unrelated users' interactions. We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data. We develop a method, CASPER, which uses cascading effect to identify the minimal and systematical perturbation to induce higher instability in a recommender system. Experiments on four datasets show that recommender models are overly sensitive to minor perturbations introduced randomly or via CASPER - even perturbing one random interaction of one user drastically changes the recommendation lists of all users. Importantly, with CASPER perturbation, the models generate more unstable recommendations for low-accuracy users (i.e., those who receive low-quality recommendations) than high-accuracy ones.