Reviewerly/exHarmony huggingface dataset:
Hate Speech Detection in Twitter Using Different Models

Twitter’s primary objective is to facilitate free expression and the exchange of ideas, allowing individuals to share their thoughts, opinions, and information with others without any limitations or constraints. It helps a human being to perceive different scopes and points of view. It is used to serve the public discussion and it should not be used to undermine individuals based on their race, nationality, public standing, rank, sexual orientation, age, disability, or health conditions. So, using hate speech is not appropriate and removal of hate speech is necessary for achieving the goal. This paper aims to utilize machine learning algorithms such as Logistic Regression, Support Vector Machine, Random Forest, CNN-LSTM, and Fuzzy method to compare and evaluate their accuracy in detecting hate speech. The objective is to determine the best model for hate speech detection.
Discovery of Contrast Itemset with Statistical Background Between Two Continuous Variables

We previously defined ItemSB as an extension of the concept of frequent itemsets and a new interpretation of the association rule expression, which has statistical properties in the background. We also proposed a method for its discovery by applying an evolutionary computation called GNMiner. ItemSB has the potential to become a new baseline method for data analysis that bridges the gap between conventional data analysis using frequent itemsets and statistical analyses. In this study, we examine the statistical properties of ItemSB, focusing on the setting between two continuous variables, including their correlation coefficients, and how to apply ItemSB to data analysis. As an extension of the discovery method, we define ItemSB that focuses on the existence of differences between two datasets as contrast ItemSB. We further report the results of evaluation experiments conducted on the properties of ItemSB from the perspective of reproducibility and reliability using contrast ItemSB.
RODD: Robust Outlier Detection in Data Cubes

Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cubes has not yet been treated extensively. In this work, we provide the first framework to evaluate robust outlier detection methods in data cubes (RODD). We introduce a novel random forest-based outlier detection approach (RODD-RF) and compare it with more traditional methods based on robust location estimators. We propose a general type of test data and examine all methods in a simulation study. Moreover, we apply ROOD-RF to real-world data. The results show that RODD-RF leads to improved outlier detection.
Accounting for Imputation Uncertainty During Neural Network Training

In this paper we are interested in dealing with missing values in a machine learning context, and more especially when training a neural network. We focus on improving neural network training by reducing the potential biases that can occur during the training phase on artificially imputed datasets. We do so by taking into account the between-variance that can be observed between multiple imputations. We propose two new imputation frameworks, S-HOT and M-HOT, that can be used to train neural networks on completed data in a less biased way, leading to models able of more generalization, and so, to better inference results. We perform extensive comparative experiments and statistically assess the results on both benchmark and real-world datasets. We show that our frameworks compete against and even outperform existing imputation frameworks, while being both useful in different settings. We make our entire code publicly accessible to facilitate reproduction of our experimental results.
Challenges of Autonomous Driving Systems

Designing autonomous driving systems is particularly challenging for several reasons. These systems must always make the “right” decisions to avoid accidents, so computationally intensive machine learning, computer vision and robotic processing algorithms are used to provide the required high precision. Although computationally intensive for this purpose, it is crucial for such a mission-critical system to be able to react to hazardous situations in real time. This means that signal processing always needs to be done within the strict time limitation. In addition, the autonomous driving system needs to perform the necessary calculations within a certain power budget to avoid negatively affecting the vehicle’s range and fuel thermal efficiency. To address these challenges, the following key issues need to be addressed: In this chapter, we mainly provide a brief discussion about challenges while designing autonomous driving systems from the perspectives of functional safety, real-time computational constraints, storage limitations and power/thermal constraints. Then, we also briefly discussed several key components to design one autonomous driving system, such as the perception system, decision making and vehicle control. In this chapter, we also described several available computing resources for satisfying the autonomous driving system, including the DSP/GPU and FPGA. Finally, in this chapter, the contents of the books are summarized here to give the reader a brief overview of this book.
EXOS: Explaining Outliers in Data Streams

Real-time outlier detection is important in many data stream applications. To help analysts understand the detected outliers better, the outliers should be presented with their explanations. One type of explanations for an outlier is its set of outlying attributes which is a subset of features responsible for the outlier's abnormality. There exist techniques that generate outlying attributes in data streams; however, none simultaneously considers the cross-correlation among data streams, the unbounded volume of data, and concept drift. To fill this gap, we propose EXOS, a framework that generates outlying attributes in multi-dimensional data streams. For each outlier, it incrementally finds a local context to determine the decision boundary that separates the outlier from the normal data while handling both the unbounded volume of data and concept drift. It considers the potential data correlation within a data stream and across data streams to estimate the local context. The experiments using three real and two synthetic datasets show that, on average, EXOS achieves up to 49% higher F1 score and 29.6 times lower explanation time than existing algorithms.
Analysis of Blood Cell Detection Using Radial Symmetry and Ellipse Fitting Approaches

This paper addresses essential hematology that is automatically segmenting blood cells. We consider a unique hybrid ellipse fitting (EF) based blood-cell segmentation technique that combines noniterative-geometric and algebraic approaches to overcome current concerns with inaccurate seed-point identification, noise, and over-segmentation. We propose a hybrid EF approach based on least-squares (LS) for improved segmentation performance. To enhance segmentation while retaining a reasonable EF, we advise utilizing the specified value for the residue offset and computing the minor and major axes using the residue and offset residue variables. Different evaluation matrices such as precision, and F1 score are used where our approach performs better than existing EF algorithms. This approach is computationally effective and might be used in cybernetics and medicine.
UniCausal: Unified Benchmark and Repository for Causal Text Mining

Current causal text mining datasets vary in objectives, data coverage, and annotation schemes. These inconsistent efforts prevent modeling capabilities and fair comparisons of model performance. Furthermore, few datasets include cause-effect span annotations, which are needed for end-to-end causal relation extraction. To address these issues, we propose UniCausal, a unified benchmark for causal text mining across three tasks: (I) Causal Sequence Classification, (II) Cause-Effect Span Detection and (III) Causal Pair Classification. We consolidated and aligned annotations of six high quality, mainly human-annotated, corpora, resulting in a total of 58,720, 12,144 and 69,165 examples for each task respectively. Since the definition of causality can be subjective, our framework was designed to allow researchers to work on some or all datasets and tasks. To create an initial benchmark, we fine-tuned BERT pre-trained language models to each task, achieving 70.10% Binary F1, 52.42% Macro F1, and 84.68% Binary F1 scores respectively.
Human Activities Detection using DeepLearning Technique- YOLOv8

Using a mask during the pandemic has occasionally been crucial and difficult. The use of universal masks can greatly lower and possibly even stop the spread of viruses within communities. So, mask detection has become a very critical task for security agencies in all the buildings, Government offices &amp; other places. With the advent of GPUs, high computing machines, and Deep Convolution Neural Networks (DCCN), automatic Face &amp; Mask Detection is possible by considering the image processing feature of extracting, 3-dimensional shapes from 2- dimensional images. This paper discuss about the YOLOv8 model to confirm its overall applicability, on two datasets namely FDDB &amp; MASK. This helps to examine the behavior of the feature from the Mask dataset, which is intended for COVID-19 Mask Detection alone. Mask is the main dataset in this experiment. Above this, the ImageNet dataset is utilized for pretraining and FDDB (Face Detection Dataset &amp; Benchmarks) datasets for recognizing face of a human being. The precision of models on FDDB is 58.9 % &amp; on MASK dataset is 66.5%.
Bitwise Vertical Mining of Minimal Rare Patterns

Rare patterns are essential forms of patterns in many real-world applications such as interpretation of biological data, mining of rare association rules between diseases and their causes, detection of anomalies. However, discovering rare patterns can be challenging. In this paper, we present an efficient algorithm for mining minimal rare patterns from sparse and weakly correlated data. The algorithm non-trivially integrates and adapts vertical frequent pattern algorithm VIPER to discover minimal rare patterns in an efficient manner. Evaluation results on our algorithm RP-VIPER show its superiority over existing horizontal rare pattern mining algorithms. Results also highlight the performance improvements brought by our optimized strategies.
Imperceptible graph injection attack on graph neural networks

Abstract In recent years, Graph Neural Networks (GNNs) have achieved excellent applications in classification or prediction tasks. Recent studies have demonstrated that GNNs are vulnerable to adversarial attacks. Graph Modification Attack (GMA) and Graph Injection Attack (GIA) are commonly attack strategies. Most graph adversarial attack methods are based on GMA, which has a clear drawback: the attacker needs high privileges to modify the original graph, making it difficult to execute in practice. GIA can perform attacks without modifying the original graph. However, many GIA models fail to take care of attack invisibility, i.e., fake nodes can be easily distinguished from the original nodes. To solve the above issue, we propose an imperceptible graph injection attack, named IMGIA. Specifically, IMGIA uses the normal distribution sampling and mask learning to generate fake node features and links respectively, and then uses the homophily unnoticeability constraint to improve the camouflage of the attack. Our extensive experiments on three benchmark datasets demonstrate that IMGIA performs better than the existing state-of-the-art GIA methods. As an example, IMGIA shows an improvement in performance with an average increase in effectiveness of 2%.
Imitation camouflage synthesis based on shallow neural network

Deep learning technology has been widely used in the military field, which have achieved great success. The traditional method for painting camouflage either using the background information or the artificial pattern. None of the traditional methods can both consider the background information and camouflage rules. In this paper, a new automatic camouflage generation framework is proposed. A method for generating camouflage pattern is designed. The imitation camouflage pattern is synthesized from the features of both background and artificial pattern. In our method, the texture feature of both background and traditional pattern patches are extracted from the feature maps of shallow neural network (SNN). Based on the feature maps, statistic information of second order differential and mean subtracted contrast normalized coefficients for texture and color is extracted. By iterating to optimize the imitation camouflage to be generated, the statistical information of the imitation camouflage can approximate the characteristic statistical information of the background and pattern. The new generated camouflage pattern can contain the color and texture information of background; besides, it can maintain the traditional patch camouflage criteria. Our approach makes camouflage painting more flexible and allows the target to better infuse into the background. And our method is designed for the preparation of painting camouflage.
UV R-CNN: Stable and efficient dense human pose estimation

As a dense prediction task aimed at instance-level human analysis, dense-pose estimation seeks to accurately map 2D pixels onto the 3D surface of the human body. Despite significant progress has been made, two major challenges continue to confront the research community: the first is training instability caused by a large number of surface points to be regressed; the second is the significant amount of time and computational resources to manually adjust multi-task loss weights. To overcome these challenges, we present a novel dense pose estimator, named UV R-CNN, which is based on a detailed analysis of the loss formulation used in existing algorithms. The proposed UV R-CNN first introduces a novel surface point regression loss, which serves to constrain the immense loss and stable the training progress, named Dense Points Loss (DP-Loss). Additionally, we incorporates a Balanced Weighting Strategy (BWS) that allows for the automatic adaptation of loss weights. Remarkably, without auxiliary supervision and external knowledge from other tasks, UV R-CNN can be trained with larger learning rate, achieving 65.0% APgps and 66.1% $AP_{gps^{m}}$ on the DensePose-COCO validation subset with ResNet-50-FPN as backbone, competitive to the state-of-the-art methods.
MEASURING UX USING USABILITY AND HEURISTIC METHODS IN JKN MOBILE APPLICATION

The JKN Mobile Application is an application to provide services for BPJS Health participants that can access anywhere and anytime. Systems that many people use need to be evaluated. User satisfaction is one factor that can determine a system's success. This study measures the usability level of the JKN Mobile Application using the usability evaluation and Heuristic Evaluation methods. Usability can be identified based on four parameters: learning ability, efficiency, memory, and satisfaction. The Heuristic Evaluation method by Nielsen has ten principles: Visibility of system status, Match between the system and the real world, User control and freedom, Consistency and standards, Error prevention, Recognition than recall, Flexibility and Efficiency of Use, Aesthetic and Minimalist Design, Help user Recognize, Diagnose and Recover from Errors, Help, and Documentation. This study aims to evaluate the application design and determine which parts need improvement. The results of the usability test obtained a percentage of 62.25%, which means that this application is still feasible to use. However, in the heuristic testing, it was found that there were several inefficient tasks, and there needed to be more help &amp; documentation.
Metric networks for enhanced perception of non-local semantic information

Metric learning, as a fundamental research direction in the field of computer vision, has played a crucial role in image matching. Traditional metric learning methods aim at constructing two-branch siamese neural networks to address the challenge of image matching, but they often overlook to cross-source and cross-view scenarios.In this article, a multi-branch metric learning model is proposed to address these limitations. The main contributions of this work are as follows: Firstly, we design a multi-branch siamese network model that enhances measurement reliability through information compensation among data points. Secondly, we construct a non-local information perception and fusion model, which accurately distinguishes positive and negative samples by fusing information at different scales. Thirdly, we enhance the model by integrating semantic information and establish an information consistency mapping between multiple branches, thereby improving the robustness in cross-source and cross-view scenarios.Experimental tests which demonstrate the effectiveness of the proposed method are carried out under various conditions, including homologous, heterogeneous, multi-view, and crossview scenarios. Compared to the state-of-the-art comparison algorithms, our proposed algorithm achieves an improvement of ~1, 2, 1, and 1% in terms of similarity measurement Recall@10, respectively, under these four conditions.In addition, our work provides an idea for improving the crossscene application ability of UAV positioning and navigation algorithm.
Genetic Approach to Improve Cryptographic Properties of Balanced Boolean Functions Using Bent Functions

Recently, balanced Boolean functions with an even number n of variables achieving very good autocorrelation properties have been obtained for 12≤n≤26. These functions attain the maximum absolute value in the autocorrelation spectra (without considering the zero point) less than 2n2 and are found by using a heuristic search algorithm that is based on the design method of an infinite class of such functions for a higher number of variables. Here, we consider balanced Boolean functions that are closest to the bent functions in terms of the Hamming distance and perform a genetic algorithm efficiently aiming to optimize their cryptographic properties, which provides better absolute indicator values for all of those values of n for the first time. We also observe that among our results, the functions for 16≤n≤26 have nonlinearity greater than 2n−1−2n2. In the process, our search strategy produces balanced Boolean functions with the best-known nonlinearity for 8≤n≤16.
Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks

Although the spectrum sensing algorithms based on deep learning have achieved remarkable detection performance, the sensing performance is easily affected by adversarial attacks due to the fragility of neural networks. Even slight adversarial perturbations lead to a sharp deterioration of the model detection performance. To enhance the defense capability of the spectrum sensing model against such attacks, an unsupervised learning-based spectrum sensing algorithm with defending adversarial attacks (USDAA) is proposed, which is divided into two stages: adversarial pre-training and fine-tuning. In the adversarial pre-training stage, encoders are used to extract the features of adversarial samples and clean samples, respectively, and then decoders are used to reconstruct the samples, and comparison loss and reconstruction loss are designed to optimize the network parameters. It can reduce the dependence of model training on labeled samples and improve the robustness of the model to attack perturbations. In the fine-tuning stage, a small number of adversarial samples are used to fine-tune the pre-trained encoder and classification layer to obtain the spectrum sensing defense model. The experimental results show that the USDAA algorithm is better than the denoising autoencoder and distillation defense algorithm (DAED) against FGSM and PGD adversarial attacks. The number of labeled samples used in USDAA is only 11% of the DAED. When the false alarm probability is 0.1 and the SNR is −10 dB, the detection probability of the USDAA algorithm for the fast gradient sign method (FGSM) and the projected gradient descent (PGD) attack samples with random perturbations is above 88%, while the detection probability of the DAED algorithm for both attack samples is lower than 69%. Additionally, the USDAA algorithm has better robustness to attack with unknown perturbations.
The Power of Large Language Models: A ChatGPT-driven Textual Analysis of Fundamental Data

In this study, we examine the effect of stale, factual information on stock price formation, utilizing Shikiho, the Japan Company Handbook, as a pivotal data source. The breadth and depth of information offered by Shikiho, covering all listed Japanese companies, and compiled by independent journalists, are unmatched by other market data sources. Our innovative approach involves the use of large language models, specifically ChatGPT, to interpret the business implications within Shikiho's corporate descriptions and future projections. The interpreted data serves as a basis to classify firms into positive and negative outlook groups, which are further quantified into confidence scores. These scores inform the construction of an equity portfolio. Benchmarking the portfolio's performance against relevant risk factors suggests that the market may not fully absorb the factual information encapsulated in Shikiho's text. These observations prompt a deeper inquiry into market efficiency, the role of public information in price discovery.
Arabic Grammatical Error Detection Using Transformers-based Pretrained Language Models

This paper presents a new study to use pre-trained language models based on the transformers for Arabic grammatical error detection (GED). We proposed fine-tuned language models based on pre-trained language models called AraBERT and M-BERT to perform Arabic GED on two approaches, which are the token level and sentence level. Fine-tuning was done with different publicly available Arabic datasets. The proposed models outperform similar studies with F1 value of 0.87, recall of 0.90, precision of 0.83 at the token level, and F1 of 0.98, recall of 0.99, and precision of 0.97 at the sentence level. Whereas the other studies in the same field (i.e., GED) results less than the current study (e.g., F0.5 of 69.21). Moreover, the current study shows that the fine-tuned language models that were built on the monolingual pre-trained language models result in better performance than the multilingual pre-trained language models in Arabic.
Image Encryption using Convolutional Neural Network

The use of cryptography has become increasingly important in the transmission of multimedia, such as digital images, text, audio, and video, to ensure secrecy, integrity, confidentiality, and prevent unauthorized access to sensitive information. While Chaos-based cryptosystems are not yet standardized like AES, DES, RSA, they have emerged as an active area of research in recent years and can provide additional security when used with standard public key cryptosystems. This project aims to implement an effective image encryption approach using a Chaos-based cryptosystem to overcome differential attacks. The system involves dividing the original image into parts and repositioning them to form the first level of encryption. The encryption process starts with generating a one-dimensional sequence using a logistic map, which is then multiplied by the maximum pixel value and subjected to bit-by-bit operation. The result is used to encrypt the image, which can be decrypted using the same process in reverse.