Reviewerly/exHarmony huggingface dataset:
Cluster-Based Improved Isolation Forest

Outlier detection is an important research direction in the field of data mining. Aiming at the problem of unstable detection results and low efficiency caused by randomly dividing features of the data set in the Isolation Forest algorithm in outlier detection, an algorithm CIIF (Cluster-based Improved Isolation Forest) that combines clustering and Isolation Forest is proposed. CIIF first uses the k-means method to cluster the data set, selects a specific cluster to construct a selection matrix based on the results of the clustering, and implements the selection mechanism of the algorithm through the selection matrix; then builds multiple isolation trees. Finally, the outliers are calculated according to the average search length of each sample in different isolation trees, and the Top-n objects with the highest outlier scores are regarded as outliers. Through comparative experiments with six algorithms in eleven real data sets, the results show that the CIIF algorithm has better performance. Compared to the Isolation Forest algorithm, the average AUC (Area under the Curve of ROC) value of our proposed CIIF algorithm is improved by 7%.
Many-fermion simulation from the contracted quantum eigensolver without fermionic encoding of the wave function

Quantum computers potentially have an exponential advantage over classical computers for the quantum simulation of many-fermion quantum systems. Nonetheless, fermions are more expensive to simulate than bosons due to the fermionic encoding---a mapping by which the qubits are encoded with fermion statistics. Here we generalize the contracted quantum eigensolver (CQE) to avoid fermionic encoding of the wave function. In contrast to the variational quantum eigensolver, the CQE solves for a many-fermion stationary state by minimizing the contraction (projection) of the Schr\"odinger equation onto two fermions. We avoid fermionic encoding of the wave function by contracting the Schr\"odinger equation onto an unencoded pair of particles. Solution of the resulting contracted equation by a series of unencoded two-body exponential transformations generates an unencoded wave function from which the energy and two-fermion reduced density matrix (2-RDM) can be computed. We apply the unencoded and the encoded CQE algorithms to the hydrogen fluoride molecule, the dissociation of oxygen ${\mathrm{O}}_{2}$, and a series of hydrogen chains. Both algorithms show comparable convergence towards the exact ground-state energies and 2-RDMs, but the unencoded algorithm has computational advantages in terms of state preparation and tomography.
A Binary Logistic Regression Model for Support Decision Making in Criminal Justice

Abstract Research background: The economics of incarceration is having an increasing impact on the economies of the world due to the rapid growth in the number of prisoners in the world The search for effective solutions that can help reduce government spending on prisoners in penitentiaries and at the same time ensure the safety of society is becoming increasingly important. These studies used the method of binary logistic regression to predict the probability of convicted criminal recidivism in the future. Purpose: The aim of the paper is to build an effective forecasting model that, based on the statistical and dynamic data of convicts, will provide information for optimal post-trial decisions, such as the grounds for possible parole, probation or length of sentence. Research methodology: The data were collected on the basis of statistical data of 13,010 convicts serving sentences in penitentiary institutions in Ukraine. To predict the probability of convicts committing criminal offenses binary logistic regression and ROC-analysis (Receiver Operator Characteristic analysis) were used. Results: A qualitative binary logistic regression model has been constructed, with the help of which it is possible to predict the probability of criminal recidivism by each of the convicts on the basis of its individual values of the variables included in the model. Novelty: For the first time in Ukraine, a model has been developed to predict the probability of convicts committing repeated criminal offenses.
Trust Model Concept for IoT Blockchain Applications as Part of the Digital Transformation of Metrology

Trends for the digital transformation of metrology and regulation of metrology through IT have some keywords in common with the main properties of the blockchain, such as traceability, immutability, and machine-readable documents. The possible applicability of the blockchain as an innovative IT solution for metrology regulation is known in the scientific community. Still, blockchain implementation must consider the entire metrology pyramid-the technical aspects and the legal framework intrinsic to metrology. This is also valid for possible IoT blockchain applications. In resolving the issues, this paper applies a bottom-up approach, starting from IoT devices analyzed as oracles and building up to the sole definition of measurement units, thereby discussing technical aspects concerning relevant standardization documents. The resulting trust model concept encompasses the vertical and horizontal traceability of the measurement results (oracle data), where normative standards and legal requirements are crucial for building trust. Conclusively, for practical implementations, it will be necessary to analyze blockchain properties and applicability with a view to the standard requirements, as shown for WELMEC.
What Drives Construction Practitioners’ Acceptance of Intelligent Surveillance Systems? An Extended Technology Acceptance Model

With the advent of intelligent construction, the intelligent surveillance system using computer vision technology has emerged as a prominent tool to identify unsafe behaviors on construction sites. At the same time, it is still viewed with suspicion by the construction industry, and its penetration rate remains low. To promote the successful implementation of the intelligent surveillance system, this study applied the technology acceptance model approach and developed an intelligent surveillance system acceptance model (ISSTAM) containing 12 variables from individual, organizational, environmental, and technical perspectives. Questionnaires were distributed to construction industry practitioners, 220 of whom provided valid data. Moreover, a structural equation model (SEM) was established for hypothesis testing. The research results suggest that job relevance, government action, training, and technical support positively and indirectly influence the use intention. Meanwhile, perceived usefulness, perceived ease of use, and cost savings directly and positively affect use intention, while privacy risk is verified to have a negative impact upon use intention. This study can help the government, organizations, and technology developers better apply the intelligent surveillance system to improve safety management levels.
Evolving Fuzzy logic Systems for creative personalized Socially Assistive Robots

Socially Assistive Robots (SARs) are increasingly used in dementia and elderly care. In order to provide effective assistance, SARs need to be personalized to individual patients and account for stimulating their divergent thinking in creative ways. Rule-based fuzzy logic systems provide effective methods for automated decision-making of SARs. However, expanding and modifying the rules of fuzzy logic systems to account for the evolving needs, preferences, and medical conditions of patients can be tedious and costly. In this paper, we introduce EFS4SAR, a novel Evolving Fuzzy logic System for Socially Assistive Robots that supports autonomous evolution of the fuzzy rules that steer the behavior of the SAR. EFS4SAR combines traditional rule-based fuzzy logic systems with evolutionary algorithms, which model the process of evolution in nature and have shown to result in creative behaviors. We evaluate EFS4SAR via computer simulations on both synthetic and real-world data. The results show that the fuzzy rules evolved over time are not only personalized with respect to the personal preferences and therapeutic needs of the patients, but they also meet the following criteria for creativity of SARs: originality and effectiveness of the therapeutic tasks proposed to the patients. Compared to existing evolving fuzzy systems, EFS4SAR achieves similar effectiveness with higher degree of originality.
Asymptotically improved circuit for a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math> -ary Grover's algorithm with advanced decomposition of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -qudit Toffoli gate

The progress in building quantum computers to execute quantum algorithms has recently been remarkable. Grover's search algorithm in a binary quantum system provides considerable speed-up over classical paradigm. Further, Grover's algorithm can be extended to a $d$-ary (qudit) quantum system for utilizing the advantage of larger state space, which helps to reduce the run-time of the algorithm as compared to the traditional binary quantum systems. In a qudit quantum system, an $n$-qudit Toffoli gate plays a significant role in the accurate implementation of Grover's algorithm. In this article, a generalized $n$-qudit Toffoli gate has been realized using higher dimensional qudits to attain a logarithmic depth decomposition without ancilla qudit. The circuit for Grover's algorithm has then been designed for any $d$-ary quantum system, where $d \ge 2$, with the proposed $n$-qudit Toffoli gate to obtain optimized depth compared to earlier approaches. The technique for decomposing an $n$-qudit Toffoli gate requires access to two immediately higher energy levels, making the design susceptible to errors. Nevertheless, we show that the percentage decrease in the probability of error is significant as we have reduced both gate count and circuit depth as compared to that in state-of-the-art works.
Outcome Prediction in Critically-Ill Patients with Venous Thromboembolism and/or Cancer Using Machine Learning Algorithms: External Validation and Comparison with Scoring Systems

Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. This study focuses on two target groups, namely patients with thrombosis or cancer. The main goal is to develop and validate interpretable machine learning (ML) models to predict early and late mortality, while exploiting all available data stored in the medical record. To this end, retrospective data from two freely accessible databases, MIMIC-III and eICU, were used. Well-established ML algorithms were implemented utilizing automated and purposely built ML frameworks for addressing class imbalance. Prediction of early mortality showed excellent performance in both disease categories, in terms of the area under the receiver operating characteristic curve (AUC-ROC): VTE-MIMIC-III 0.93, eICU 0.87, cancer-MIMIC-III 0.94. On the other hand, late mortality prediction showed lower performance, i.e., AUC-ROC: VTE 0.82, cancer 0.74-0.88. The predictive model of early mortality developed from 1651 VTE patients (MIMIC-III) ended up with a signature of 35 features and was externally validated in 2659 patients from the eICU dataset. Our model outperformed traditional scoring systems in predicting early as well as late mortality. Novel biomarkers, such as red cell distribution width, were identified.
THE MERLIN PROJECT: MALAYSIAN STUDENTS’ ACCEPTANCE OF AN AI CHATBOT IN THEIR LEARNING PROCESS

The COVID-19 pandemic has accelerated the educational landscape of institutions of higher education, which were predominantly face-to-face. In Malaysia, many universities converted their courses into online courses to keep up with the challenges of working from home during the pandemic, using web technologies and blended learning approaches. However, there are challenges in ensuring students’ attention and retention rates are maintained and enough support and scaffolding are provided to them when they are learning online. As such, the MERLIN Project was undertaken to develop a virtual learning assistant that would provide online support to students outside of their online classes. The project used Artificial Intelligence (AI) technology with Natural Language Processing (NLP) features to develop a chatbot that simulated human-like conversation between chatbot and students. Content in the learning environment was media-rich and mapped to Mayer’s 12 Multimedia Learning Principles. Data from 102 students was collected to gauge their perceptions and feedback. Results showed that students found the chatbot to be helpful in their learning process and improved their understanding of the course material. A conceptual learning framework for the use of AI chatbots as learning assistants is proposed to improve students’ online learning experiences in the new normal.
Barru Literacy Community as the Alternative Literacy Movement: A Study on Cultivating Reading Literacy toward Society in Barru Regency, South Sulawesi Province

Currently, technological developments have significant impact on people's lives. This development must also be followed by the mentality and readiness of the community to deal with it. One form of effort to keep pace with technological developments is through literacy-based community empowerment. This research focuses on community empowerment through cultivating reading literacy as a step in facing the challenges of the times while creating quality community characters based on literacy skills. Aspects of discussion in this research are alternative literacy movement carried out by the Barru Membaca community in the process of cultivating reading literacy among the people of Barru District, Barru Regency, South Sulawesi Province and responds of the community to the presence of the Barru Membaca community. This research uses descriptive research with a qualitative approach by conducting interviews with informants. The data analysis technique used is data collection, reduction, data presentation and drawing conclusions. The results showed that the process of cultivating reading literacy carried out by the Barru Membaca community had been running systematically, efficiently and independently, starting from the planning process of reading literacy acculturation programs, to the program evaluation stage in improving human resources literacy skills.
Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.
DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation

Model generalization to the unseen scenes is crucial to real-world applications, such as autonomous driving, which requires robust vision systems. To enhance the model generalization, domain generalization through learning the domain-invariant representation has been widely studied. However, most existing works learn the shared feature space within multi-source domains but ignore the characteristic of the feature itself (e.g., the feature sensitivity to the domain-specific style). Therefore, we propose the Domain-invariant Representation Learning (DIRL) for domain generalization which utilizes the feature sensitivity as the feature prior to guide the enhancement of the model generalization capability. The guidance reflects in two folds: 1) Feature re-calibration that introduces the Prior Guided Attention Module (PGAM) to emphasize the insensitive features and suppress the sensitive features. 2): Feature whiting that proposes the Guided Feature Whiting (GFW) to remove the feature correlations which are sensitive to the domain-specific style. We construct the domain-invariant representation which suppresses the effect of the domain-specific style on the quality and correlation of the features. As a result, our method is simple yet effective, and can enhance the robustness of various backbone networks with little computational cost. Extensive experiments over multiple domains generalizable segmentation tasks show the superiority of our approach to other methods.
Security and Privacy Challenges using IoT-Blockchain Technology in a Smart City: Critical Analysis

A smart city is a comprehensive concept created by multiple digital industries. Smart city is a new generation of information technologies such as the Internet of Things, cloud computing, big data, and geospatial information to promote smart new ideas for urban planning, construction, management and services, power city operation and administrative management, industrial development, and public services in various fields. It is a modern high-end urban development form. A smart city is to establish a city center system by connecting terminals, applying information technology and network, and ultimately promoting the efficiency improvement and economic structure optimization in various fields. However, in the construction of smart cities, due to the sharing of a large amount of data, a lot of data leakage is caused, and data protection faces many challenges. The article first introduces the structure of smart cities, the challenges faced by smart cities in data protection and data privacy protection; then summarizes the characteristics, applications, and challenges of blockchain technology in the application of blockchain in smart cities, and makes reference to references. The corresponding comparisons are made, and the future challenges are finally summarized and proposed.
3rd International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters

Abstract This paper represents a preface to the Proceedings of the 3rd International Conference on Sustainable Futures: Environmental, Technological, Social, and Economic Matters (ICSF 2022) held at the Kryvyi Rih, Ukraine, 24–27 May 2022. Background information and the organizational structure of the meeting, program committee, and acknowledgments of the contributions of the many people who made the conference a success are presented.
A Systematic Analysis of the Capital One Data Breach: Critical Lessons Learned

The 2019 Capital One data breach was one of the largest data breaches impacting the privacy and security of personal information of over a 100 million individuals. In most reports about a cyberattack, you will often hear that it succeeded because a single employee clicked on a link in a phishing email or forgot to patch some software, making it seem like an isolated, one-off, trivial problem involving maybe one person, committing a mistake or being negligent. But that is usually not the complete story. By ignoring the related managerial and organizational failures, you are leaving in place the conditions for the next breach. Using our Cybersafety analysis methodology, we identified control failures spanning control levels, going from rather technical issues up to top management, the Board of Directors, and Government regulators. In this analysis, we reconstruct the Capital One hierarchical cyber safety control structure, identify what parts failed and why, and provide recommendations for improvements. This work demonstrates how to discover the true causes of security failures in complex information systems and derive systematic cybersecurity improvements that likely apply to many other organizations. It also provides an approach that individuals can use to evaluate and better secure their organizations.
Multilingual hope speech detection in English and Dravidian languages

Recent work on language technology has aimed to identify negative language such as hate speech and cyberbullying as well as improve offensive language detection to mediate social media platforms. Most of these systems rely on using machine learning models along with the labelled dataset. Such models have succeeded in identifying negativity and removing it from the platform deleting it. However, recently, more research has been conducted on the improvement of freedom of speech on social media. Instead of deleting supposedly offensive speech, we developed a multilingual dataset to identify hope speech in the comments and promote positivity. This paper presents a multilingual hope speech dataset that promotes equality, diversity and inclusion (EDI) in English, Tamil, Malayalam and Kannada. It was collected to promote positivity and ensure EDI in language technology. Our dataset is unique, as it contains data collected from the LGBTQIA+ community, persons with disabilities and women in science, engineering, technology and management (STEM). We also report our benchmark system results in various machine learning models. We experimented on the Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) using different state-of-the-art machine learning models and deep learning models to create benchmark systems.