Reviewerly/exHarmony huggingface dataset:
Classification of Personality Traits on Facebook Using Key Phrase Extraction, Language Models and Machine Learning

The study of automated personality trait detection has become increasingly prevalent in the field of affective computing and sentiment analysis. Psycholinguistic databases and linguistic styles are found to have correlations with personality traits. Natural language processing has improved thanks to transfer learning. With pre-trained language models as features, this paper presents a machine learning model that benefits from transfer learning. The proposed method using key phrase extraction and SentenceTransformer through decision tree classifier surpasses the XGBoost by 26.79 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in terms of accuracy and exhibits 97.79% on myPersonality dataset.
Motivation and Hurdles for the Student Adoption of Metaverse-based Classroom: A Qualitative Study

Metaverse is an emerging technology that combines the virtual world and the real world, resulting in an immersive user experience. It has many applications. In this study, we inves-tigated the users' perception of the Metaverse-based classroom in the UAE by qualitatively surveying 84 higher education students. After coding the users' responses, we generated a world cloud and analyzed the user responses. A little more than a third of the surveyed users do not believe that they will benefit from using Metaverse in higher education, and they would not like to use it. Users are mainly concerned about their health conditions, security, and privacy of their information, and the students' movement may result in students losing focus (distraction). On the other hand, the learner will be motivated by the interactive nature of the Metaverse-based classroom and the education's location and time flexibility. Different practical and theoretical implications have been identified and discussed in this paper.
A Requirement Gathering Framework for Electronic Document Management Systems

The practice of requirement engineering has been used for several years in software development. The process of requirements engineering can be broken down into four stages namely elicitation, analysis and negotiation, specification, and validation. Because requirements engineering varies from organization to organization, it may be difficult for project managers to adopt the correct usage of the various stages of a project. Moving towards digital transformation, many organizations are moving towards electronic document management system (EDMS). EDMS has become a substantial tool in many organizations, when it comes to integrated data gathering and sharing. Nevertheless, requirement engineering for implementing EDMS projects should be carefully done to avoid project failure or poor-quality software. This paper thus presents a framework for requirement gathering for EDMS to promote successful implementation using a hybrid approach. The framework has been evaluated in a well-known company in Mauritius and results have been presented in the paper.
iOS Digital Evidence Comparison of Instant Messaging Apps

The presence of IM (Instant Messaging) applications can have a negative impact, one of which is cybercrime. Its development is also accompanied by the development of operating systems on mobile devices used, one of which is iOS. The research conducted not only analyzed digital evidence but also compared the findings of digital evidence in WhatsApp, Telegram, and Messenger IM applications based on iOS version 13.3 on two conditions (jailed and jailbreak). The NIST 800-101 Revision 1 method is used as a guide in conducting the forensic process. Data acquisition was carried out using the Cellebrite UFED 4PC tool, data examination and data analysis were carried out using Oxygen Forensic Detective, FTK Imager and Autopsy. The results of the analysis obtained are then compared to the condition of the iPhone and the scenarios that have been executed. The results of this study indicate that the condition of the iPhone that has been jailbroken greatly affects the findings of digital evidence. WhatsApp digital evidence found on jailed iPhone devices has the same amount as the jailbroken iPhone condition with a digital evidence finding value of 71.42%. Telegram obtains more digital evidence with a digital evidence finding value of 71.42% on a jailbroken iPhone. The Messenger application shows its significance to the digital found in a jailbroken iPhone which has a percentage value of 85.71% digital discovery which is the largest value between WhatsApp and Telegram.
Digital Healthcare using Blockchain

To protect personal patient information and comply with regulations, data security is a necessary aspect of the healthcare business. Because patient data is written on paper and stored in filing cabinets, it is simple to protect and secure it. However, all data is now saved digitally, such as patient records, which are now stored on computers, servers, and storage devices. Poor wireless network security, data breaches, legacy system use, malware, viruses, and other hostile attacks are all risk considerations in health care. When compared to other businesses, one of the key reasons to safeguard health care data is that healthcare companies gather and provide comprehensive patient records, including the date of birth, address, and social security number. This paper aims to discuss the issues and solutions to achieving digital healthcare using blockchain technology.
A Comparison of Different Models for the Detection of Hate Speech

With the increasing usage of social media, we have seen an increase in inflammatory and hate speech along racial and ethnic lines on platforms such as Twitter, Instagram, and Facebook. This issue has become a serious setback for humanity, and in order to address it, we used machine learning to recognise hate speech messages across a variety of datasets. Despite the fact that machine learning has been successful at detecting offensive and hate speech in a variety of English contexts, there is yet no proper study to compare multiple machine learning algorithms that outperform a typical publicly available dataset, to the best of what we have known. With this in mind, we've developed a set of feature engineering approaches and machine learning algorithms to assess the performance of models on a large scale.
Explainable Artificial Intelligence in Human Resources: a Computational Study

The application of Machine Learning in Human Resources is becoming increasingly common. The Human Resources industry relies on Machine Learning to automate processes, improve efficiency, and enhance decision making. However, using Machine Learning in Human Resources is not without its challenges. One of these challenges is the lack of interpretability. Interpretability is essential in Human Resources because many of the decisions made in the field have a direct impact on people's lives. As such, these decisions must be made transparently and understandably. In this paper, we apply a model-agnostic technique for providing post-hoc explanations, known as Anchors, to a real-world dataset from the Human Resources industry. The dataset was used to create a predictive pipeline for employee attrition. The result is a Human Capital Management system capable of explaining the reasons behind employee attrition, allowing the Human Resource professionals to enact retention policies promptly. Moreover, the results suggest that Anchors can be used to create a prescriptive pipeline that can be used to explain the reasons behind every single decision to leave the company, as they are easily interpretable by a non-expert. This system has the advantage of allowing the Decision Maker to act in a prescriptive way and retain valuable resources.
Hunger Games Search: A Scheduler for Cloud Computing

Cloud computing provides a powerful and on-demand environment in the form of infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). There are numerous challenges in the field of cloud computing, including resource allocation, task scheduling, task migration, VM placement, data privacy, and security, to name a few. Being an NP-hard problem, various meta-heuristic algorithms have been proposed in the literature to address the task scheduling problem in Cloud Computing. In this paper, a Transfer Function induced Hunger Games Search algorithm-based task scheduling algorithm is proposed to execute the various IoT tasks on the virtual machines in the cloud environment. Eight transfer functions, consisting of S-shaped and V-Shaped transfer functions, have been used to evaluate the effectiveness of the proposed algorithm. The proposed algorithm has been evaluated in different scenarios. The simulation study confirms the effectiveness of the proposed algorithm.
Cognitive Radar for Target Tracking

The motivation for conventional radar is the echolocation used by bats and dolphins. The introduction of cognition to classical radar increases the overall performance of radar tracking. Cognitive radar works on the perception-action cycle, taking its motivation from the visual brain. Nested Cognitive Radar (NCR) includes three new components: memory, attention, and intelligence which differentiate it from Basic Cognitive Radar (BCR). Cubature Kalman Filter (CKF) is more suitable for nonlinear tracking at the receiver. Cubature Kalman Filter is modified to make it work for real-world issues related to high dimensions so that the new CKF is termed as Continuous-Discrete Cubature Kalman Filter (CD-CKF). The CD-CKF is suitable to use as the optimal Bayesian filter. The Gaussian assumption is taken for Optimal Bayesian filtering because the practical radar systems are continuous-discrete. At the transmitter, Bellman dynamic programming can also be used for waveform selection. Mean squared error (MSE) is used for the feedback link between the transmitter, and the receiver. Three applications of cognitive radar are (i) linear target tracking, (ii) tracking of Ballistic targets, and (iii) high dimensional continuous-discrete model target tracking.
Physically Realizable Adversarial Attacks On 3D Point Cloud

Previous work has shown that mitigating potential vehicle control safety risks is becoming increasingly important with the development of autonomous driving systems. Although the security risks of camera-based sensors have been extensively and thoroughly researched, the vulnerability of relevant deep learning models for LiDAR-based sensors is still in the initial research stage. In this paper, we propose an attack method called Physical Point Attack (PPA) for generating perceptually 3D adversarial objects in a physical environment, which can be placed in front of the vehicle to fool LiDAR detectors. In contrast to existing work, our attack produces printable adversarial point clouds and maintains the perceptibility of the adversarial point clouds while remaining valid in real physical environments. Our code is available at https://github.com/chenxiaoyupetter/Physically-Realizable-Adversarial-Attacks-On-3D-Point-Cloud
Transformer-based Adaptive Interactive Promotion Network for RGB-T Salient Object Detection

RGB-Thermal salient object detection (RGB-T SOD) aims to better segment the most salient objects with the cooperation of visual and thermal infrared images. The addition of thermal infrared images helps to improve the accuracy of robot decision-making when performing complex visual tasks. How to exploit the potential of multi-modal complementarity, tap the dominant modal information, and better complete object location is still a problem worthy of exploration. In this paper, we propose an adaptive interaction promotion network (AIPNet). In specific, we design a modal interaction module (MIM) with two parallel units to fuse the two modal features extracted by the encoders. The spatial interaction unit (SIU) is responsible for directly completing modal interaction and integration. The self-reinforcement unit (SRU) is responsible for enhancing two single-mode features and amplifying the role of dominant modal features. Besides, we use a query-location module (QLM) for high-level features to accurately confirm the location of salient objects. Finally, we adopt a re-calibration dual branch decoder (RCDB) to integrate the output features. Sufficient experiments conducted on RGB-T and RGB-D SOD datasets demonstrate that the proposed method performs favorably against the other 13 state-of-the-art methods.
Search-Based Path Planning Algorithm for Autonomous Parking: Multi-Heuristic Hybrid A*

This paper proposed a novel method for autonomous parking. Autonomous parking has received a lot of attention because of its convenience, but due to the complex environment and the non-holonomic constraints of vehicle, it is difficult to get a collision-free and feasible path in a short time. To solve this problem, this paper introduced a novel algorithm called Multi-Heuristic Hybrid A* (MHHA*) which incorporates the characteristic of Multi-Heuristic A* and Hybrid A*. So it could provide the guarantee for completeness, the avoidance of local minimum and sub-optimality, and generate a feasible path in a short time. And this paper also proposed a new collision check method based on coordinate transformation which could improve the computational efficiency. The performance of the proposed method was compared with Hybrid A* in simulation experiments and its superiority has been proved.
The Path Planning Study of Autonomous Patrol Robot based on Modified Astar Algorithm and Genetic Algorithm

A method combining modified Astar algorithm and genetic algorithm was proposed to search the shortest patrol path of autonomous patrol robot. A simplified method was used to transform GPS longitude and latitude coordinate into planimetric rectangular coordinate, which was converted to grid coordinate later. The shortest patrol path network between any two patrol points was constructed by the search results of Astar algorithm. The objective function of multipoint patrol path planning problem was established based on the shortest patrol path network, and the global optimal patrol path was obtained by genetic algorithm. The path searched by Astar algorithm was optimized by testing the visibility between two cells and removing the redundant nodes. Simulation results show that this method can effectively eliminate redundant nodes, shorten the path length, improve the smoothness of the path, and plan a better patrol path.
Privacy-Preserving Power Consumption Prediction Based on Federated Learning with Cross-Entity Data

Big data analytics have become vastly popular in the power system. However, power-related datasets are often owned by different parties, and a centralized data mining scheme in the form of direct data sharing might compromise party benefits, individual privacy and even national security. To this end, we implement a systematic privacy-preserving federated learning framework in the power system, which enables collaborative learning of power consumption patterns with cross-entity data. Horizontal federated learning is employed when data are scattered by samples; vertical federated learning, on the other hand, is designed for the case with data scattered by features. Further, we propose an alternative encryption scheme based on the Diffie-Hellman key exchange protocol to greatly boost computational efficiency. Case studies show that models trained from the proposed federated learning framework are lossless, privacy-preserving, efficient and effective.
Feedback‐based self‐learning in large‐scale conversational AI agents

Abstract Today, most of the large‐scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages customer/system interaction feedback signals to automate learning without any manual annotation. Users of these systems tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by either errors in ASR, NLU, ER, or the application. In some cases, users may not properly formulate their requests (e.g., providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self‐learning system automatically detects the errors, generates reformulations, and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns, and coupling it with a guardrail rewrite selection mechanism that reactively evaluates these fixes using feedback friction data. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa‐user errors by pooling anonymized data across millions of customers. The proposed self‐learning system achieves a win‐loss ratio of 11.8 and effectively reduces the defect rate by more than 30 percent on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self‐learning large‐scale conversational AI system in production.
Composite Material Selection for Structural Applications Using WPM Method

Genetically Enhanced Neural Network System (GONNS) Proposed as a selection-making tool. Many neural networks represent the traits of the machine after a training method and Genetic algorithms stumble on best operating situations. In many packages, together with material selection in engineering, specialists evaluate diverse alternatives, The records were calculated to decide the proportion of the 2 strategies in estimating the scores, from which strategies of accuracy and espresso blunders prices had been received. To discover the proper one for each technique, create a rating of trainees who’ve obtained an internship application in a particular phase of the company. This From the constraints, it was important to evaluate the services within the computer faculties in the Poly Digital Library, therefore making tips for service enhancements. Although the rules to follow aren’t usually nicely documented, their final desire is typically correct and their predictions are reasonable. In principle, the evaluator is the one who collects complete and accurate records about the coverage in a selected product / challenge / provider / evaluate from the evaluator, is the analytical process. Therefore, it could be used as an advice in decision making. Evaluation is statistics approximately gathering, reading and dispensing particular fabric. Computer studying manage Provides suitable capability although the optimization method is used to deliver the final result, the professional opinion will inevitably reduce the number of options, so that it will be examined very cautiously. The rank of PSF1, PSF 2, PSF 3, VSF 1, VSF 2, VSF 3. The PSF 1 is on the 1 rank, The VSF 3 is on the 2ndrank, The PSF 2 is on the 3rd rank, The VSF 1 is on 4th rank, The PSF 3 is on the 5th rank and also VSF 2 is on the last rank.
Application of OMO Teaching Mode in Music Education in Colleges

With the development of COVID-19, more and more courses begin to be transferred to online teaching. The OMO teaching mode that focuses on offline teaching within a certain period of time and completes online teaching interaction at a later stage has become a trend. Based on the current teaching practice of music education in Colleges and universities, the paper combines teaching with Internet new media technology under the current environmental situation, realizes the new mode of offline and online joint teaching, realizes student-centered unlimited learning, completes the informatization and real-time teaching, and improves the teaching results..
An efficiency-driven, correlation-based feature elimination strategy for small datasets

With big datasets and highly efficient algorithms becoming increasingly available for many problem sets, rapid advancements and recent breakthroughs achieved in the field of machine learning encourage more and more scientific fields to make use of such a computational data analysis. Still, for many research problems, the amount of data available for training a machine learning (ML) model is very limited. An important strategy to combat the problems arising from data sparsity is feature elimination—a method that aims at reducing the dimensionality of an input feature space. Most such strategies exclusively focus on analyzing pairwise correlations, or they eliminate features based on their relation to a selected output label or by optimizing performance measures of a certain ML model. However, those strategies do not necessarily remove redundant information from datasets and cannot be applied to certain situations, e.g., to unsupervised learning models. Neither of these limitations applies to the network-based, correlation-driven redundancy elimination (NETCORE) algorithm introduced here, where the size of a feature vector is reduced by considering both redundancy and elimination efficiency. The NETCORE algorithm is model-independent, does not require an output label, and is applicable to all kinds of correlation topographies within a dataset. Thus, this algorithm has the potential to be a highly beneficial preprocessing tool for various machine learning pipelines.
Artificial intelligence-based multi-class histopathologic classification of kidney neoplasms

Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.