Reviewerly/exHarmony huggingface dataset:
OPTIMIZING COURSE SCHEDULING FACULTY OF ENGINEERING UNSOED USING GENETIC ALGORITHMS

In carrying out an activity regularly and smoothly, it is necessary to make an activity schedule that can manage the time of one activity with another so that unwanted things do not happen such as the same time, the same place, and others. Making a schedule of activities is quite easy to do if there are not too many entities involved and if the entities are not tied to each other, but for larger cases, creating a schedule of activities manually will take quite a lot of time and can result in errors in the schedule or shortages. effectiveness in the resulting schedule. This is commonly experienced in making course schedules at universities because there are a lot of course data and lecturers can teach several courses at once and at different times, therefore in making course schedules can be done by applying genetic algorithms so that the time required needed in making the course schedule shorter and the results obtained can be more optimal than the results of making the course schedule manually. In this study, the optimal course schedule was obtained in the 31st generation using data on rooms, courses, study time, lecturers, and departments so that one chromosome has 154 gen, then the population length is made up to 9 individuals or chromosomes, the mutation rate is set at 0.1, and the method used in the individual selection stage is the tournament selection method where the tournament size is set at 3. The fitness value is taken so that a schedule is said to be optimal, i.e. if the fitness value is equal to 1 because then it shows that there are no errors or problems (such as time, lecturers, conflicting rooms) that occur in the schedule.
The nexus between blockchain distributed ledger technology and financial crimes

Abstract: Purpose: The study sought to uncover the effect of blockchain digital ledger technology (BCDLT) on financial crimes. The study was driven by the need to promote blockchain technology in a bid to enhance financial sanity through elimination of financial delinquency. Research methodology: The study followed a quantitative paradigm using an explanatory research design. The study targeted financial executives, senior staff members at the Zimbabwe stock exchange, bankers and officials from the financial regulators. Data was collected using a structured questionnaire. Results: The study found out that of the four independent BCDLT antecedents, manual audit costs were insignificant, whereas the other three had strong positive associations with financial crime reduction. Limitations: The study targeted a specific group of financiers; hence the results may not be universal to other excluded categories Contribution: The study significantly guides policy formulation and laws in line with the adoption of the blockchain technology in the global financial system to guard against the possibility of new forms of financial crimes that could emanate from the use of technology. Keywords: 1. Blockchain digital ledger technology 2. Financial crime 3. Financial performance
Entanglement quantification from collective measurements processed by machine learning

This paper investigates how to reduce the number of measurement configurations needed for sufficiently precise entanglement quantification. Instead of analytical formulae, we employ artificial neural networks to predict the amount of entanglement in a quantum state based on results of collective measurements (simultaneous measurements on multiple instances of the investigated state). We consider collective measurement limited to two copies of the investigated state. This approach allows us to explore the precision of entanglement quantification as a function of measurement configurations in a relevant scenario for practical quantum communications. For the purpose of our research, we consider general two-qubit states and their negativity as entanglement quantifier. We outline the benefits of this approach in future quantum communication networks.
An Automated SMT-based Security Framework for Supporting Migrations in Cloud Composite Services

The growing maturity of orchestration languages is contributing to the elaboration of cloud composite services, whose resources may be deployed over different distributed infrastructures. These composite services are subject to changes over time, that are typically required to support cloud properties, such as scalability and rapid elasticity. In particular, the migration of their elementary resources may be triggered by performance constraints. However, changes induced by this migration may introduce vulnerabilities that may compromise the resources, or even the whole cloud service. In that context, we propose an automated SMT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -based security framework for supporting the migration of resources in cloud composite services, and preventing the occurrence of new configuration vulnerabilities. We formalize the underlying security automation based on SMT solving, in order to assess the migrated resources and select adequate counter-measures, considering both endogenous and exogenous security mechanisms. We then evaluate its benefits and limits through large series of experiments based on a proof-of-concept prototype implemented over the CVC4 commonly-used open-source solver. These experiments show a minimal overhead with regular operating systems deployed in cloud environments.
A novel algorithm to analyze the dynamics of digital chaotic maps in finite-precision domain

Chaotic maps are widely used to design pseudo-random sequence generators, chaotic ciphers, and secure communication systems. Nevertheless, the dynamic characteristics of digital chaos in finite-precision domain must be degraded in varying degrees due to the limited calculation accuracy of hardware equipment. To assess the dynamic properties of digital chaos, we design a periodic cycle location algorithm (PCLA) from a new perspective to analyze the dynamic degradation of digital chaos. The PCLA can divide the state-mapping graph of digital chaos into several connected subgraphs for the purpose of locating all fixed points and periodic limit cycles contained in a digital chaotic map. To test the versatility and availability of our proposed algorithm, the periodic distribution and security of 1-D logistic maps and 2-D Baker maps are analyzed in detail. Moreover, this algorithm is helpful to the design of anti-degradation algorithms for digital chaotic dynamics. These related studies can promote the application of chaos in engineering practice.
Learning multimodal relationship interaction for visual relationship detection

Visual relationship detection aims to recognize visual relationships in scenes as triplets 〈subject-predicate-object〉. Previous works have shown remarkable progress by introducing multimodal features, external linguistics, scene context, etc. Due to the loss of informative multimodal hyper-relations (i.e. relations of relationships), the meaningful contexts of relationships are not fully captured yet, which limits the reasoning ability. In this work, we propose a Multimodal Similarity Guided Relationship Interaction Network (MSGRIN) to explicitly model the relations of relationships in graph neural network paradigm. In a visual scene, the MSGRIN takes the visual relationships as nodes to construct an adaptive graph and enhances deep message passing by introducing Entity Appearance Reconstruction, Entity Relevance Filtering and Multimodal Similarity Attention. We have conducted extensive experiments on two datasets: Visual Relationship Detection (VRD) and Visual Genome (VG). The evaluation results demonstrate that the proposed MSGRIN has empirically performed more effectively overall.
Multimodal Learning with Transformers: A Survey

Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and big data, Transformer-based multimodal learning has become a hot topic in AI research. This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data. The main contents of this survey include: (1) a background of multimodal learning, Transformer ecosystem, and the multimodal big data era, (2) a theoretical review of Vanilla Transformer, Vision Transformer, and multimodal Transformers, from a geometrically topological perspective, (3) a review of multimodal Transformer applications, via two important paradigms, i.e., for multimodal pretraining and for specific multimodal tasks, (4) a summary of the common challenges and designs shared by the multimodal Transformer models and applications, and (5) a discussion of open problems and potential research directions for the community.
Feature Refine Network for Salient Object Detection

Different feature learning strategies have enhanced performance in recent deep neural network-based salient object detection. Multi-scale strategy and residual learning strategies are two types of multi-scale learning strategies. However, there are still some problems, such as the inability to effectively utilize multi-scale feature information and the lack of fine object boundaries. We propose a feature refined network (FRNet) to overcome the problems mentioned, which includes a novel feature learning strategy that combines the multi-scale and residual learning strategies to generate the final saliency prediction. We introduce the spatial and channel 'squeeze and excitation' blocks (scSE) at the side outputs of the backbone. It allows the network to concentrate more on saliency regions at various scales. Then, we propose the adaptive feature fusion module (AFFM), which efficiently fuses multi-scale feature information in order to predict superior saliency maps. Finally, to supervise network learning of more information on object boundaries, we propose a hybrid loss that contains four fundamental losses and combines properties of diverse losses. Comprehensive experiments demonstrate the effectiveness of the FRNet on five datasets, with competitive results when compared to other relevant approaches.
Data-painting: Expressive free-form visualisation

Data visualization can be powerful in enabling us to make sense of complex data.Expressive data representation -where individuals have control over the nature of the output -is hard to incorporate into existing frameworks and techniques for visualization.The power of informal, rough, expressive sketches in working out ideas is well documented.This points to an opportunity to better understand how expressivity can exist in data visualization creation.We explore the expressive potential of Data Painting through a study aimed at improving our understanding of what people need and make use of in creating novel examples of data expression.Participants use exact measures of paint for data-mapping and then explore the expressive possibilities of free-form data representation.Our intentions are to improve our understanding of expressivity in data visualization; to raise questions as to the creation and use of non-traditional data visualizations; and to suggest directions for expressivity in visualization.
Is this alive? Towards a vocabulary for understanding and communicating living material experiences

Living materials are a nascent material class where living organisms are embedded and kept alive in the design outcome to achieve novel functionalities, expressions, and interactions.Experiential characterisation studies with potential endusers will provide insights for developing these novel materials for meaningful material applications.Nevertheless, the current literature lacks a vocabulary to communicate and discuss living materials in user studies.To bridge this gap, our paper presents the development of a "Living Materials Vocabulary" consisting of 45 descriptive items.Through a term frequency analysis of relevant literature and in-depth interviews with eight biodesigners, we identified a set of descriptions which we clustered under five themes: origin, making, agency and autonomy, temporality, and impact of living materials.We selected representative items from these themes to compile our final vocabulary.We discuss how our vocabulary can be operationalised in living material characterisation studies and further inspire future biodesign practice.
A Healthcare Quality Assessment Model Based on Outlier Detection Algorithm

With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that the proposed approach has good potential for detecting hospitals with financial fraud and poor-quality medical care.
Influence of Front-Desk Staff Service Quality on Students’ Affective Commitment, Trust, and Word-of-Mouth in Higher Education

Private universities are growing in the present era. The enrollment ratio is also getting high, and universities are putting efforts to attract more students. Hence, it is essential to provide students with the best quality services as it spreads positive word-of-mouth. Therefore, the present article is designed to study the impact of front-desk staff service quality on word-of-mouth through the mediating effect of students’ affective commitment and students’ trust. An online questionnaire survey was conducted in Karachi, Pakistan. This study used the partial least square method to run structural equation modeling analysis. The findings revealed that all paths show a positive and significant relationship except front-desk staff service quality and word-of-mouth. Moreover, students’ affective commitment and trust mediate the relationship between front-desk staff service quality and word-of-mouth. Results also illustrate that gender exerts a significant moderating role on front-desk staff service quality, students’ affective commitment, and word-of-mouth. The study provides practical implications for policymakers, private university management, and educational institutions. Firstly, it is crucial to provide effective communication training to the front-desk staff to learn the basic etiquette of greetings and humbleness. Secondly, there must be experienced personnel who can assist students promptly.
A novel image dataset for source camera identification and image based recognition systems

Abstract Multimodal emotion recognition has attracted a great deal of attention in recent years, with new interesting applications now being considered. One promising application is in the digital image forensics fields where, for example, it gives the possibility to automatically highlight subjects that are in pain, in digital images under examination, by analyzing their facial expressions. However, finding an image that represents a possible crime leaves the problem of identifying the device used to take the image open. Such a problem has been addressed by Source Camera Identification algorithms (SCI, for short). These algorithms analyze some features hidden in a target image to find traces left by the sensor that captured the image. A particularly challenging case is when the candidate source cameras for an image under investigation are of the same manufacturer and model. A fair and universal assessment of these algorithms is only possible if standard datasets are used for their benchmarking. However, our comprehensive analysis has shown that the majority of the datasets proposed so far contain a collection of images taken with different types of cameras, mostly smartphones. We fill this gap by presenting UNISA2020, a novel image dataset that contains a large collection of real-world images taken with multiple conventional digital cameras of the same type. The images in our dataset have been assembled so as to avoid artifacts that could negatively affect the identification process. To validate our dataset, we also performed a comparative experimental analysis to investigate the performance of an SCI reference algorithm when running on our dataset as well as on other SCI standard datasets.
Intelligent Integration Method of AI English Teaching Resource Information under Multi-Agent Collaboration

In order to improve the intelligent effect of English teaching in the era of intelligent education, this article conducts an intelligent integration system of AI English teaching resources and information on the basis of multi-agent collaboration. Moreover, this article analyzes the principles and properties of the three algorithms, ALO, GWO, and MFO, and integrates them into the D2D power allocation problem in the two communication modes. In addition, this article studies the effects of population size, variable dimension, and convergence on the overall energy efficiency and generation of D2D users through simulation and combines improved algorithms to construct an intelligent integration system of AI English teaching resources and information under multi-agent collaboration. The simulation experiment study shows that the intelligent integration system of AI English teaching resources and information under the multi-agent writing proposed in this article can play an important role in the integration of English teaching resources and information.
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation

Fast and reliable evaluation metrics are key to R&D progress.While traditional natural language generation metrics are fast, they are not very reliable.Conversely, new metrics based on large pretrained language models are much more reliable, but require significant computational resources.In this paper, we propose Fru-galScore, an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.Experiments with BERTScore and MoverScore on summarization and translation show that Fru-galScore is on par with the original metrics (and sometimes better), while having several orders of magnitude less parameters and running several times faster.On average over all learned metrics, tasks, and variants, FrugalScore retains 96.8% of the performance, runs 24 times faster, and has 35 times less parameters than the original metrics.We make our trained metrics publicly available 1 and easily accessible via Hugging Face, to benefit the entire NLP community and in particular researchers and practitioners with limited resources.
Impact of Economic Freedom on Youth Unemployment in the Gulf Cooperation Council Countries

This study aims to examine the impact of economic freedom on youth unemployment in the Gulf Cooperation Council countries of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates during 2005-2019.Theoretically, it is assumed that the improvement of economic freedom reduces the unemployment rate.In this paper, we seek to verify this hypothesis for youth population in the Gulf Cooperation Council countries.Specifically, we will determine which specific component of economic freedom has the strongest effect on youth unemployment.Using the Panel Ordinary Least Square approach with fixed effects, we find that the greater the economic freedom score, the lower the youth unemployment rate.In addition, the results suggest that only six of the ten components of economic freedom have a significant effect on the youth unemployment rate, namely, property rights, government spending, monetary, trade, investment and, financial freedom.These areas should be targeted by policymakers in the Gulf Cooperation Council countries to develop appropriate policies aimed at enhancement of youth employment.
Multilingual Name Entity Recognition and Intent Classification employing Deep Learning architectures

Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.
How equalitarian regulation of online hate speech turns authoritarian: a Chinese perspective

This article reveals how the heterogeneous legal approaches of balancing online hate speech against equality rights in liberal democracies have informed China in its manipulative speech regulation. In an authoritarian constitutional order, the regulation of hate speech is politically relevant only because the hateful topics are related to regime-oriented concerns. The article elaborates on the infrastructure of an emerging authoritarian regulatory patchwork of online hate speech in the global context and identifies China’s unique approach of restricting political contents under the aegis of protecting equality rights. Ultimately, both the regulation and dis-regulation of online hate speech form a statist approach that deviates from the paradigm protective of equality rights in liberal democracies and serves to fend off open criticism of government policies and public discussion of topics that potentially contravene the mainstream political ideologies.
ADBench: Anomaly Detection Benchmark

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.