Reviewerly/exHarmony huggingface dataset:
Use of Virtual Reality in Psychology

Abstract The field of psychology is advancing with incorporation of immersive technologies and Virtual Environments (VE) in research, treatment, assessment and learning etc. Virtual Reality (VR) is one of the focused sub-domains of immersive technologies that is being used vastly in psychology. This also comes under the paradigm of cyberpsychology. The advancements reported in this area bring the need to understand the benefits provided by virtual reality with respect to human interaction and behavior etc. The published literature available discusses the development and evolution of cyber psychology specifically with respect to virtual reality. To the best of our knowledge the latest developments due to virtual reality in various sub fields of psychology have not been collectively documented so far. This paper provides highlights of the developments and transformations linked with the usage of virtual reality with recommendations about future studies and provides the reader a broader and informed view of this cross-disciplinary area. Moreover, the paper also provides limitations that exist, and considerations required while using VR for a specific treatment or experimentation.
A Real-Time Driver Assistance System Using Object Detection and Tracking

ADAS (Advanced Driver Assistance System) has become a vital part of the driving experience. In recent years, there have been several advancements in ADAS technology such as parking assistance and lane detection. The proposed work presents a real-time Driver assistance framework by implementing the state-of-the-art object detection algorithm YOLOv4. This paper provides a comparison between and other state-of-the-art object detectors. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three different datasets and one standard dataset. YOLOv4 proves to be faster and more accurate than the other object detection algorithms in the comparison. This framework is used to build an application which helps users make better decisions on the road. This application consists of a simple user interface that displays alerts and warnings.
Sentiment Analysis Using State of the Art Machine Learning Techniques

Abstract Sentiment analysis is one of the essential and challenging tasks in the Artificial Intelligence field due to the complexity of the languages. Models that use rule-based and machine learning-based techniques have become popular. However, existing models have been under-performing in classifying irony, sarcasm, and subjectivity in the text. In this paper, we aim to deploy and evaluate the performances of the State-of-the-Art machine learning sentiment analysis techniques on a public IMDB dataset. The dataset includes many samples of irony and sarcasm. Long-short term memory (LSTM), bag of tricks (BoT), convolutional neural networks (CNN), and transformer-based models are developed and evaluated. In addition, we have examined the effect of hyper-parameters on the accuracy of the models.
Comparative Performance of Machine Learning Algorithms in Detecting Offensive Speech in Malayalam-English Code-Mixed Data

Offensive speech identification in social media communication has risen to the top of the priority list for avoiding confrontations and curtailing unwanted behaviour. Hate speech identification becomes difficult in a context, where multilingual speakers fluctuate between various languages, making algorithms built for monolingual corpora inadequate. We intend to undertake a comparative analysis of hate speech in a code-mixed social media text as part of our research. We created a standard Malayalam-English code-mixed dataset that may be utilised to detect hate speech and abuse in this article. We used five machine learning algorithms: support vector machine, logistic regression, K-nearest neighbour, random forest and XGBoost in this study and tuned all the models with hyper-parameters. The study finishes by analysing these five models using different performance metrics and then calculating the various parameters to find the optimum model. XGBoost achieved good results with 80% accuracy and with high precision, recall and F1-score.
A Sentiment Analysis Approach to Discover Public Panic: Based on Weibo Covid-19 Data

Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts.Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic.Methods: This paper comes up with a sentiment analysis approach to discover public panic.First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic.Second, we break down those sentences into independent words and clean the data by removing stop words.Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post.Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic.Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.
Intergenerational Interaction with Avatars in VR: An Exploratory Study Towards an XR Research Framework

Abstract The dynamic development of solutions in the field of virtual and augmented reality poses challenges to designers. These challenges relate to both technical conditions, including hardware capabilities and software solutions, as well as psychophysical constructs conditioning the end users’ reception of the generated multimedia message. One of the key elements of the virtual and augmented reality experience is the interaction with the system through a virtual agent represented by an avatar, i.e. a reflection of the image of a participant in the virtual world, carrying on a conversation with the user. This paper presents a proposed software and hardware solution for conducting multifaceted research and comparative analysis of diverse interfaces and human-computer interaction in virtual and augmented reality. In the course of this research, statistically significant results were obtained indicating differences in perception between three types of virtual agents. Each of them represented by different avatars in a specially created research environment that allowed to conduct usability tests under reproducible conditions to study user interaction in virtual reality.
Analysis and Applications of Biogeography Based Optimization Techniques for Problem Solving

Computational intelligence helps in detecting erroneous decisions and fastens the whole process of decision making by applying various techniques. In this study, we will discuss the Biogeography Based Optimization (BBO) and further it will be divided into three parts, firstly, we will describe natural biogeography of BBO with the help of model and algorithm, secondly, we will compare BBO with other optimization methods. Thirdly, we will detect the best location for decomposing solid waste and we will also analyze the benchmark functions with BBO and some recently developed algorithms. At the end of the paper, we will also examine some applications and future possibility of Biogeography Based Optimization.
Acute Lymphoblastic Leukemia Disease Detection Using Image Processing and Machine Learning

Acute Lymphoblastic Leukemia (ALL) is a cancer type in which there is an increase of white blood cells (WBCs) in our body. This article presents a method that detects the presence of these abnormal cells in the bloodstream using machine learning and image processing algorithms. A methodology to identify ALL using machine learning classification techniques like Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Logistic Regression, and Support Vector Machine (SVM) using the existing dataset (ALL-IDB2) is discussed. The outcome of the paper is to analyze the ALL IDB2 dataset and predict the output as ALL infected or not. According to the experimental results, it is observed that the performance of CNN supersites other machine learning classifiers for the proposed classification in terms of accuracy.
Attention Is All You Need to Tell: Transformer-Based Image Captioning

Automatic Image Captioning is a task that involves two prominent areas of Deep Learning research, i.e., image processing and language generation. Over the years, we have achieved a lot of success in being able to generate syntactically and semantically meaningful descriptions using deep learning architectures. Recent studies have implemented an attention mechanism that lets the model attend to different regions of the image at different timestamps while generating the caption. In this paper, we present a Transformer architecture that generates captions by just enforcing the attention mechanism. To understand the effect of attention mechanism on model performance, we separately train two LSTM-based image captioning models for a comparative study with our architecture. The models are trained on the Flickr-8K dataset using the Cross-Entropy Loss Function. For evaluating the models we calculate CIDEr-R, BLEU, METEOR, and ROUGE-L metric scores for the captions generated by these models on test split. Results from our comparative study suggest that the Transformer Architecture is a better approach toward image captioning and meaningful descriptions can be generated even without the use of traditional recurrent neural networks as decoders.
Spatial relation learning in complementary scenarios with deep neural networks

A cognitive agent performing in the real world needs to learn relevant concepts about its environment (e.g., objects, color, and shapes) and react accordingly. In addition to learning the concepts, it needs to learn relations between the concepts, in particular spatial relations between objects. In this paper, we propose three approaches that allow a cognitive agent to learn spatial relations. First, using an embodied model, the agent learns to reach toward an object based on simple instructions involving left-right relations. Since the level of realism and its complexity does not permit large-scale and diverse experiences in this approach, we devise as a second approach a simple visual dataset for geometric feature learning and show that recent reasoning models can learn directional relations in different frames of reference. Yet, embodied and simple simulation approaches together still do not provide sufficient experiences. To close this gap, we thirdly propose utilizing knowledge bases for disembodied spatial relation reasoning. Since the three approaches (i.e., embodied learning, learning from simple visual data, and use of knowledge bases) are complementary, we conceptualize a cognitive architecture that combines these approaches in the context of spatial relation learning.
Sistem Rekomendasi Tempat Parkir di Kota Lama Semarang Menggunakan Collaborative Filtering

Utilization of information and communication technology has become an inseparable part of people's lives today. In the last few years, there have been many studies that have used information technology to solve the problems of everyday life in society. Utilizes Collaborative Filtering and Location Based Filtering methods to build a tourism recommendation system in the special area of Yogyakarta. Based on previous research, the researcher will build a parking recommendation system in the old city of Semarang using the Collaborative Filtering method. Collaborative filtering has two processes, namely the similarity calculation process and the prediction calculation. A similar calculation process is carried out to find the value between parking lots which will continue the prediction calculation process. While the estimation process is carried out to find predictions of parking spaces for visitors. The calculation process that has been carried out on Andi users gets a recommendation on parking lot I2 with the highest score of 0.565, while the lowest score is obtained by parking lot I5 with a score of -0.696.
Model Pembelajaran CIRC (Cooperative Integrated Reading And Composition) sebagai Salah Satu Strategi Pembelajaran Matematika di Sekolah Dasar

Student's activities in learning mathematics in elementary schools are suggested to be more in reading, writing, communicating, making pictures, tables, and so on, to encourage problem solving, reasoning, and mathematical connection skills. This activity can be stimulated by giving word problems as mathematical problems that must be solved by students. Giving word problems is the right way when mathematics learning is ready to be accepted by students at the application level in everyday life. To support the strategy of giving word problems to stimulate student learning activity, the CIRC learning model can be applied. CIRC stands for Cooperative Integrated Reading and Composition, is a cooperative learning model that encourages active students to read and write in small groups. Word problems become a support system in learning mathematics by applying the CIRC learning model. By applying the CIRC learning model, students do more reading, writing, communicating, and even arguing and debating in math class. So that students' understanding of mathematics material will be stronger, and students' learning activities and mathematics outcomes will be better.
Code-based Sequential Aggregate Signature Scheme

This paper proposes the first code-based quantum immune sequential aggregate signature (SAS) scheme and proves the security of the proposed scheme in the random oracle model.Aggregate signature (AS) schemes and sequential aggregate signature schemes allow a group of potential signers to sign different messages respectively, and all the signatures of those users on those messages can be aggregated into a single signature such that the size of the aggregate signature is much smaller than the total size of all individual signatures.Because of the aggregation of many signatures into a single short signature, AS and SAS schemes can reduce bandwidth and save storage; moreover, when a SAS is verified, not only the valid but also the order in which each signer signed can be verified.AS and SAS schemes can be applied to traffic control, banking transaction and military applications.Most of the existing AS and SAS schemes are based either on pairing or Rivest-Shamir-Adleman (RSA), and hence, can be broken by Shor's quantum algorithm for Integer Factoring Problem (IFP) and Discrete Logarithm Problem (DLP).There are no quantum algorithms to solve syndrome decoding problems.Hence, code-based cryptography is seen as one of the promising candidates for post-quantum cryptography.This paper shows how to construct quantum immune sequential aggregate signatures based on coding theory.Specifically, we construct our scheme with the first code based signature scheme proposed by Courtois, Finiasz and Sendrier (CFS).Compared to the CFS signature scheme without aggregation, the proposed sequential aggregate signature scheme can save about 90% storage when the number of signers is asymptotically large.
Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

Osteosarcoma is a type of malignant bone tumor that is reported across the globe.Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the detection and classification of malignancies in biomedical images.In this regard, the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning (BOIC-EHODTL) model.The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma.At the initial stage, Gabor Filter (GF) is applied as a pre-processing technique to get rid of the noise from images.In addition, Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors.Then, EHO algorithm is utilized along with Adaptive Neuro-Fuzzy Classifier (ANFC) model for recognition and categorization of osteosarcoma.EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results.The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study.In order to demonstrate the improved performance of BOIC-EHODTL model, a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.6444
Generalization of Advanced Encryption Standard Based on Field of Any Characteristic

Nowadays most communications are done by utilizing digital transmission mechanisms.The security of this digital information transmitted through different communication systems is quite important.The secrecy of digital data is one of the burning topics of the digitally developed world.There exist many traditional algorithms in the literature to provide methods for robust communication.The most important and recent modern block cipher named the advanced encryption standard (AES) is one of the extensively utilized encryption schemes with binary based.AES is a succession of four fundamental steps: round key, sub-byte, shift row, and mix column.In this work, we will provide an innovative methodology for extending the AES in a Galois field with any characteristic p.All four steps in the fundamental process with binary characteristics will be adjusted because of the new enhancement.By applying double affine transformations, we have enhanced the number of options in our suggested substitution boxes.The reconstruction of the nonlinear confusion component and encryption structure provides robustness in the generalized AES.The increase in the keyspace due to the Galois field generalization implies that we have improved additional confusion abilities and broadened the current notions.The implementation of the proposed structure of AES for image, audio, and video encryption will provide high security for secure communication.
Innovative Fungal Disease Diagnosis System Using Convolutional Neural燦etwork

Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections.The outer layer of your body is called the integumentary system.Your skin, hair, nails, and glands are all part of it.These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun.The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect.Heat, light, damage, and illness are all protected by it.Fungi-caused infections are found in almost every part of the natural world.When an invasive fungus takes over a body region and overwhelms the immune system, it causes fungal infections in people.Another primary goal of this study was to create a Convolutional Neural Network (CNN)-based technique for detecting and classifying various types of fungal diseases.There are numerous fungal illnesses, but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis (IFDD) system of Candidiasis and Tinea Infections.This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings.To identify and categorize fungal infections, deep machine learning techniques are utilized.A CNN architecture was created, and it produced a promising outcome to improve the proposed system accuracy.The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards.Our CNN-Based can detect fungal diseases through medical images; earmarked IFDD system has a predictive performance of 99.6% accuracy.
High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons.Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result.However, these approaches are not as effective when directly applied to the image segmentation in a video.This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models.In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame.It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold.Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames.However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates.The proposed AFE approach addresses the limitations of the NFE method.Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe.We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view.The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each.This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models.The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video.
Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

Team Formation (TF) is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm.It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO's optimization process, a logistic chaotic map is used.The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA).Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum. 5246