Reviewerly/exHarmony huggingface dataset:
Weighting and pruning based ensemble deep random vector functional link network for tabular data classification

In this paper, we first integrate normalization to the Ensemble Deep Random Vector Functional Link network (edRVFL). This re-normalization step can help the network avoid divergence of the hidden features. Then, we propose novel variants of the edRVFL network. Weighted edRVFL (WedRVFL) uses weighting methods to give training samples different weights in different layers according to how the samples were classified confidently in the previous layer thereby increasing the ensemble’s diversity and accuracy. Furthermore, a pruning-based edRVFL (PedRVFL) has also been proposed. We prune some inferior neurons based on their importance for classification before generating the next hidden layer. Through this method, we ensure that the randomly generated inferior features will not propagate to deeper layers. Subsequently, the combination of weighting and pruning, called Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network (WPedRVFL), is proposed. We compare their performances with other state-of-the-art classification methods on 24 tabular UCI classification datasets. The experimental results illustrate the superior performance of our proposed methods.
An energy and carbon-aware algorithm for renewable energy usage maximization in distributed cloud data centers

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms.
Graph-|<i>Q</i>⟩⟨<i>C</i>|, a Graph-Based Quantum/Classical Algorithm for Efficient Electronic Structure on Hybrid Quantum/Classical Hardware Systems: Improved Quantum Circuit Depth Performance

We present a procedure to reduce the depth of quantum circuits and improve the accuracy of results in computing post-Hartree-Fock electronic structure energies in large molecular systems. The method is based on molecular fragmentation where a molecular system is divided into overlapping fragments through a graph-theoretic procedure. This allows us to create a set of projection operators that decompose the unitary evolution of the full system into separate sets of processes, some of which can be treated on quantum hardware and others on classical hardware. Thus, we develop a procedure for an electronic structure that can be asynchronously spawned onto a potentially large ensemble of classical and quantum hardware systems. We demonstrate this method by computing Unitary Coupled Cluster Singles and Doubles (UCCSD) energies for a set of [H2]n clusters, with n ranging from 4 to 128. We implement our methodology using quantum circuits, and when these quantum circuits are processed on a quantum simulator, we obtain energies in agreement with the UCCSD energies in the milli-hartree energy range. We also show that our circuit decomposition approach yields up to 9 orders of magnitude reduction in the number of CNOT gates and quantum circuit depth for the large-sized clusters when compared to a standard quantum circuit implementation available on IBM's Quantum Information Science kit, known as Qiskit.
Institutional Strategies for Cybersecurity in Higher Education Institutions

Cybersecurity threats have grown exponentially, posing a heavy burden on organisations. Higher Education Institutions (HEIs) are particularly vulnerable, and their cybersecurity issues are receiving greater attention. However, existing research on cybersecurity has limited referencing value for HEI leaders and policy-makers because they are usually technology-focused. Publications that showcase best practices often lack system-wide perspectives towards cybersecurity in HEIs. Our paper, therefore, aims to bridge this literature gap and generate institutional cybersecurity strategies for HEI leaders and policy-makers from a system perspective. We first review how the cybersecurity landscape has evolved over the last few decades and its latest trends and projections for the next decade. By analysing these historical developments and new changes, we further illuminate the importance of strengthening HEI cybersecurity capacities. As we explore why HEIs face severe challenges to tackle the ever-escalating cyberattacks, we propose a system-wide approach to safeguard HEI cybersecurity and highlight the necessity to reassess prioritised areas. By taking an extensive literature review and desk research of methods that could respond to the cybersecurity vulnerabilities of the next decade, we synthesise our findings with a set of institutional strategies, with takeaways designed to equip HEIs better to address cybersecurity threats into the future. The strategies include: (1) Strengthening Institutional Governance for Cybersecurity; (2) Revisiting Cybersecurity KPIs; (3) Explicating Cybersecurity Policies, Guidelines and Mechanisms; (4) Training and Cybersecurity Awareness Campaigns to Build Cybersecurity Culture; (5) Responding to AI-based Cyber-threats and Harnessing AI to Enhance Cybersecurity; (6) Introduction of New and More Sophisticated Security Measures; (7) Paying Attention to Mobile Devices Use, Using Encryption as a Daily Practice; and (8) Risk Management. We believe that cybersecurity can be safeguarded throughout the new decade when these strategies are considered thoroughly and with the concerted effort of relevant HEI stakeholders.
Multimodal multi-objective evolutionary algorithm for multiple path planning

The multi-objective path planning problem has received much attention recently. Traditional solving methods try to find a single optimal path without considering the multiformity of the paths. In this study, we first analyze the situation that several different paths may have the same objective values, termed as multi-modal minimum path problems. To address these problems, we propose a novel solution-encoding method, which decreases the size of decision-space greatly. Then, to maintain the population diversity in the decision space, we propose an environmental selection strategy, in which the duplicate solutions are deleted first and then a second-selection method is adopted. Finally, an effective multi-objective evolutionary algorithm based on the special environmental selection is proposed, termed MMEA-SES. Through the experiments, the proposed method is proved effective and efficient compared to other state-of-the-art algorithms for multimodal multi-objective path planning.
A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architecture

Recognizing various abnormal human activities from video is very challenging. This problem is also greatly influenced by the lack of datasets containing various abnormal human activities. The available datasets contain various human activities, but only a few of them contain non-standard human behavior such as theft, harassment, etc. There are datasets such as KTH that focus on abnormal activities such as sudden behavioral changes, as well as on various changes in interpersonal interactions. The UCF-crime dataset contains categories such as fighting, abuse, explosions, robberies, etc. However, this dataset is very time consuming. The events in the videos occur in a few seconds. This may affect the overall results of the neural networks that are used to detect the incident. In this article, we create a dataset that deals with abnormal activities, containing categories such as Begging, Drunkenness, Fight, Harassment, Hijack, Knife Hazard, Normal Videos, Pollution, Property Damage, Robbery, and Terrorism. We use the created dataset for the training and testing of the ConvLSTM (convolutional long short-term memory) neural network, which we designed. However, we also test the created dataset using other architectures. We use ConvLSTM architectures and 3D Resnet50, 3D Resnet101, and 3D Resnet152. With the created dataset and the architecture we designed, we obtained an accuracy of classification of 96.19% and a precision of 96.50%.
Blockchain-based healthcare management system with two-side verifiability

The lack of data outsourcing in healthcare management systems slows down the intercommunication and information sharing between different entities. A standard solution is outsourcing the electronic health record (EHR) to a cloud service provider (CSP). The outsourcing of the EHR should be performed securely without compromising the CSP functionalities. Searchable encryption would be a viable approach to ensure the confidentiality of the data without compromising searchability and accessibility. However, most existing searchable encryption solutions use centralised architecture. These systems have trust issues as not all the CSPs are fully trusted or honest. To address these problems, we explore blockchain technology with smart contract applications to construct a decentralised system with auditable yet immutable data storage and access. First, we propose a blockchain-based searchable encryption scheme for EHR storage and updates in a decentralised fashion. The proposed scheme supports confidentiality of the outsourced EHR, keyword search functionalities, verifiability of the user and the server, storage immutability, and dynamic updates of EHRs. Next, we implement a prototype using JavaScript and Solidity on the Ethereum platform to demonstrate the practicality of the proposed solution. Finally, we compare the performance and security of the proposed scheme against existing solutions. The result indicates that the proposed scheme is practical while providing the desired security features and functional requirements.
Evaluation of Production of Digital Twins Based on Blockchain Technology

A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers’ judgments as well as the direct impact on the sustainability of DT production.
NLFFTNet: A non-local feature fusion transformer network for multi-scale object detection

Recent object detection studies attempt to implement multi-scale feature fusion through complicated hierarchical structures. However, the existing feature fusion methods only focus on the interaction between the same local positions and fail to describe the long-distance dependencies of features. In this study, a novel non-local feature fused transformer convolutional network is proposed for object detection. This model can focus on global semantic information by calculating the attention of different positions to capture the long-distance dependency. Meanwhile, a dynamic data augment method called configurable mix-splicing is introduced to solve the problem of data imbalance between different classes. The experimental results indicate that attributed to the feature fusion and data augment method, our model achieves better performance than state-of-the-art models on two authoritative public datasets.
Clustering based on local density peaks and graph cut

Clustering by fast search and find of density peaks (DPC) is a widely used and studied clustering algorithm. In this article, we notice that DPC can achieve highly accurate clustering results when restricted to local neighborhoods. Therefore, by investigating density information in local neighborhoods, we propose to capture latent structures in data with family trees, which can reflect density dominations among nearest neighbors of data. A data set will then be partitioned into multiple family trees. In order to obtain the final clustering result, instead of exploiting the error-prone allocation strategy of DPC, we first elaborately design a novel similarity measure for family trees, characterizing not only the distance between data points, but also the structure of trees. Then, we adapt graph cut for the corresponding connection graph to also take global structural information into account. Extensive experiments on both real-world and synthetic data sets show that the proposed algorithm can outperform several prominent clustering algorithms for most of the cases, including the DPC and spectral clustering algorithms and some of their latest variants. We also analyze the robustness of the proposed algorithm w.r.t. hyper-parameters and its time complexity, as well as the necessity of its components through ablation study.
A Survey on Different Computer Vision Based Human Activity Recognition for Surveillance Applications

Articulating an individual behavior analysis from a video stream is considered by most researchers. It has its own applications in the field of computer vision-based Human activity recognition (HAR). HAR is the most widely utilized service in many of the systems across surveillance systems, healthcare, online education, and many more areas. Nowadays equally there is also increased interest in the researchers across the HAR community for works to be considered when multiple faces have to be recognized and predict the activity. This paper details the contribution made by the researchers in this aspects recently and also paper gives a comprehensive analysis of the methods adopted and concludes by examining the accuracy of the various work contributed. At last, this paper additionally is given the future directions to deal with for this application.
Arabic Language Opinion Mining Based on Long Short-Term Memory (LSTM)

Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.
Research on mine vehicle tracking and detection technology based on YOLOv5

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Space-efficient binary optimization for variational quantum computing

Abstract In the era of Noisy Intermediate-Scale Quantum (NISQ) computers it is crucial to design quantum algorithms which do not require many qubits or deep circuits. Unfortunately, most of the well-known quantum algorithms are too demanding to be run on currently available quantum devices. Moreover, even the state-of-the-art algorithms developed for the NISQ era often suffer from high space complexity requirements for particular problem classes. In this paper, we show that it is possible to greatly reduce the number of qubits needed for the Travelling Salesman Problem (TSP), a paradigmatic optimization task, at the cost of having deeper variational circuits. While the focus is on this particular problem, we claim that the approach can be generalized for other problems where the standard bit-encoding is highly inefficient. Finally, we also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models. All the proposed encodings have the same volume up to polylogarithmic factors and remain efficient to implement within the Quantum Approximate Optimization Algorithm framework.
Unintended bias evaluation: An analysis of hate speech detection and gender bias mitigation on social media using ensemble learning

Hate speech on online social media platforms is now at a level that has been considered a serious concern by governments, media outlets, and scientists, especially because it is easily spread, promoting harm to individuals and society, and made it virtually impossible to tackle with using just human analysis. Automatic approaches using machine learning and natural language processing are helpful for detection. For such applications, amongst several different approaches, it is essential to investigate the systems’ robustness to deal with biases towards identity terms (gender, race, religion, for example). In this work, we analyse gender bias in different datasets and proposed a ensemble learning approach based on different feature spaces for hate speech detection with the aim that the model can learn from different abstractions of the problem, namely unintended bias evaluation metrics . We have used nine different feature spaces to train the pool of classifiers and evaluated our approach on a publicly available corpus, and our results demonstrate its effectiveness compared to state-of-the-art solutions. • We investigated unintended gender bias on online social media. • Proposal of a multiview ensemble for unintended gender bias mitigation. • Evaluation of four real-world datasets in the context of gender bias mitigation. • Effectiveness of the mitigation of unintended bias compared to the state-of-the-art.
Research Trends in Virtual Reality Locomotion Techniques

Virtual reality (VR) researchers have had a long-standing interest in studying locomotion for developing new techniques, improving upon prior ones, and analyzing their effects on users. To help organize prior work, several researchers have presented taxonomies for categorizing locomotion techniques in general. More recently, researchers have begun to conduct systematic reviews to better understand what locomotion techniques have been investigated. In this paper, we present our own systematic review of locomotion techniques based on a well-established taxonomy, and we use k-means clustering to identify to what extent locomotion techniques have been explored. Our results indicate that selection-based, walking-based, and steering-based locomotion techniques have been moderately to highly explored while manipulation-based and automated locomotion techniques have been less explored. We also present results on what types of metrics have been used to evaluate locomotion techniques. While usability, discomfort, and travel performance metrics have been moderately to highly explored, other metrics, such as biometrics, user experience, and emotions, have been less explored.
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation

Personalized dialogue systems explore the problem of generating responses that are consistent with the user's personality, which has raised much attention in recent years.Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation.Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user's personality.Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personalized response generation.In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information.Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator.With these multi-level refiners, we can sparsely extract the most valuable information (tokens) from the dialogue history and leverage other similar users' data to enhance personalization.Experimental results on two real-world datasets demonstrate the superiority of our model in generating more informative and personalized responses.