Reviewerly/exHarmony huggingface dataset:
A Survey on Monitoring and Detecting Cyber Bullying Activities using Machine Learning Algorithms

The shrinking of the planet by technology is causing new age difficulties in youth culture. Technology surely has a lot of benefits, but it also has risks. It is where cyberbullying first started. Thus, there are many different types of cyberbullying. It might not necessarily involve pretending to be someone else or breaking into their online accounts. It also includes criticizing someone or spreading lies about them in an effort to cast doubt on them. Social media is widely used, making it incredibly easy for anyone to misuse this access. Cyberbullying is a serious issue today. It includes actions that harass, mislead, or defame someone. These violent behaviors are incredibly hazardous and can harm anyone quickly and severely. They appear on open discussion forums, social media sites, and other internet chat boards. A cyberbully is not always an anonymous person; they could be someone you know. The detection of online cyberbullying has grown in societal significance, research interest, and accessibility of open data. Even so, despite the continued rise in processing power and resource affordability, access limitations to high quality data constrain the use of cutting-edge methodologies. As a result, many recent studies use limited, heterogeneous datasets without fully assessing their usefulness. This study discusses effective techniques used to detect online abusive and bullying messages by merging natural language processing and machine learning algorithms with distinct features to analyze the accuracy levels of the algorithms.
During the Covid 19 Pandemic at SMAN 10 Depok, the role of Teacher Interpersonal Communication in Fostering Student Learning was Examined

This study intends to identify issues with teacher interpersonal communication in promoting student learning at SMAN 10 Depok during the Covid 19 epidemic. The research methodology adopted is interpretive and qualitative. The participants in this study include 8 (eight) teachers, 4 (four) students, and 2 (two) students who are also students of the SMA Negeri 10 Depok. Data collection methods use archival research, observation, and observational studies. The data analysis model that was used in this study is an interactive data analysis of the Miles and Huberman models. This data analysis includes data collection, data redaction, data processing, and data verification activities. The results of the study show that the interpersonal communication skills of the teachers and students at SMAN 10 Depok are quite good. This is accomplished by distributing educational materials using educational media, such as email, Google Classroom or Google Meet, and private or group WhatsApp.
School Culture Educating People: Realization Path and Action Mechanism

Culture is the core of education and the proper meaning of education.Exploring the realization path and function mechanism of school culture education is a major task for educators at present and in the future.Then, the current academic research started late, had few achievements and was relatively broad and weak.The article believes that the mechanism of school culture educating people includes three fields: "cultural field", "cultivation field" and "education field".School teachers and students should be guided to make cultural choices, consciously transform school culture into personal culture, and achieve the purpose of school culture educating people.
Can Graph Neural Networks Go Deeper Without Over-Smoothing? Yes, With a Randomized Path Exploration!

Graph Neural Networks (GNNs) have emerged as one of the most powerful approaches for learning on graph-structured data, even though they are mostly restricted to being shallow in nature. This is because node features tend to become indistinguishable when multiple layers are stacked together. This phenomenon is known as over-smoothing. This paper identifies two core properties of the aggregation approaches that may act as primary causes for over-smoothing. These properties are namely recursiveness and aggregation from higher to lower-order neighborhoods. Thus, we attempt to address the over-smoothing issue by proposing a novel aggregation strategy that is orthogonal to the other existing approaches. In essence, the proposed aggregation strategy combines features from lower to higher-order neighborhoods in a non-recursive way by employing a randomized path exploration approach. The efficacy of our aggregation method is verified through an extensive comparative study on the benchmark datasets w.r.t. the state-of-the-art techniques on semi-supervised and fully-supervised learning tasks.
A Bidirectional Guided Filter Used for RGB-D Maps

The RGB-D cameras are widely used in the fields of robotic spray painting, packaging and tracking. However, the quality of the depth maps of RGB-D maps is relatively low. Guided filters utilizing the color image as a guide map is a common method to deal with these problems. However, the noise in guidance will make the filtering output contains unexpected noise that doesn’t contain before filtering. In this paper, based on the classical guided filter, a bidirectional guided filter is proposed to filter the filtering input and the guidance simultaneously. The basic equations are proposed and the filter kernel is derived. The properties of the proposed bidirectional guided filter such as edge-preserving filtering property, insensitivity to the noise of guidance and filtering effects on guidance is investigated. Four different groups of experiments are performed to further verify the effectiveness of the proposed method. The classical guided filter and the bidirectional guided filter are compared to verify that the proposed bidirectional guided filter can reduce the impact of the noise of the guidance; and the bidirectional guided filter and the box mean filter are compared to verify the filtering effect on guidance of the proposed bidirectional guided filter.
CoSS: leveraging statement semantics for code summarization

Automated code summarization tools allow generating descriptions for code snippets in natural language, which benefits software development and maintenance. Recent studies demonstrate that the quality of generated summaries can be improved by using additional code representations beyond token sequences. The majority of contemporary approaches mainly focus on extracting code syntactic and structural information from abstract syntax trees (ASTs). However, from the view of macro-structures, it is challenging to identify and capture semantically meaningful features due to fine-grained syntactic nodes involved in ASTs. To fill this gap, we investigate how to learn more code semantics and control flow features from the perspective of code statements. Accordingly, we propose a novel model entitled CoSS for code summarization. CoSS adopts a Transformer-based encoder and a graph attention network-based encoder to capture token-level and statement-level semantics from code token sequence and control flow graph, respectively. Then, after receiving two-level embeddings from encoders, a joint decoder with a multi-head attention mechanism predicts output sequences verbatim. Performance evaluations on Java, Python, and Solidity datasets validate that CoSS outperforms nine state-of-the-art (SOTA) neural code summarization models in effectiveness and is competitive in execution efficiency. Further, the ablation study reveals the contribution of each model component.
An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain

Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.
Mandarin Text-to-Speech Front-End with Lightweight Distilled Convolution Network

Mandarin text-to-speech (TTS) systems heavily depend on front-end processing, such as grapheme-to-phoneme conversion and prosodic boundary prediction, to produce expressive, human-like speech. Utilizing a pre-trained language model, such as the bidirectional encoder representations from Transformers (BERT), could significantly improve the TTS front-end's performance. However, the original BERT is too big for edge TTS applications with tight limits on memory costs and inference latency. Although a distilled BERT can alleviate this problem, a considerable efficiency barrier may still exist due to the self-attention module's quadratic complexity and the feed-forward module's enormous computation. To this end, we propose a lightweight distilled convolution network as an alternative to the distilled BERT. Unlike previous knowledge distillation methods, which commonly used the same self-attention network for the teacher and student models, we transfer knowledge from the self-attention network to a convolution network. Experiments on two major Mandarin TTS front-end tasks have shown that our distilled convolution model can achieve comparable results to various distilled BERT variants while drastically reducing the model size and inference latency.
Comparison of the Principles of Head-Up Display Technologies forthe Implementation of Augmented Reality in the Automotive Industry: AStudy

In this paper, we present the possibilities of implementing augmented reality in the automotive industry. We will show the directions of augmented reality applications and we focus primarily on head-up displays, specifically on augmented reality head-up displays. In the first part, the chapter presents the use of augmented reality in the automotive industry from research, development, assembly, disassembly to our chosen issue of head-up displays. Subsequently, we presented and compared the three basic principles of head-up displays. The first is a fully graphical head-up display projected on the windshield, the second is a head-up display projected on a retractable translucent screen, and the third type is a head-up display projected from a mobile phone using specific applications. Each of the evaluated technologies has its advantages and disadvantages. This chapter points out the required parameters and possibilities how to increase driving safety and shorten the time when the driver is not engaged in driving. In the end, the individual technical solutions and mobile applications are compared. In this chapter, we try to point out that projecting information about the vehicle and driving significantly simplifies the driving process and allows the driver to focus exclusively on the road.
Guidelines for Mitigating Cybersickness During Training in VR Environment Using Head-Mounted Displays

Based on this paper’s literature review, we provide guidelines for mitigating a common problem when using virtual reality (VR) applications – cybersickness. Cybersickness is a frequent side effect when immersed in the VR environment, especially when users participate in longer VR sessions (gaming, educational, training, or other kinds) using head-mounted display (HMD) systems. The root cause of cybersickness is the computer-generated environment, designed to envelop the user’s senses, which ultimately tricks the human sensory systems responsible for detecting a motion. The severity of the effect varies on an individual-to-individual basis. Still, in all cases, it causes discomfort to the VR users and negatively affects their quality of experience (QoE). Given that VR applications are increasingly being used for educational and training purposes in different industries, we will summarize previous studies’ experiences and results to derive a compact set of guidelines that could help the industry professionals mitigate the cybersickness risk and effects.
Post-Quantum Anonymous One-Sided Authenticated Key Exchange without Random Oracles

Authenticated Key Exchange (AKE) is a cryptographic protocol to share a common session key among multiple parties. Usually, PKI-based AKE schemes are designed to guarantee secrecy of the session key and mutual authentication. However, in practice, there are many cases where mutual authentication is undesirable such as in anonymous networks like Tor and Riffle, or difficult to achieve due to the certificate management at the user level such as the Internet. Goldberg et al. formulated a model of anonymous one-sided AKE which guarantees the anonymity of the client by allowing only the client to authenticate the server, and proposed a concrete scheme. However, existing anonymous one-sided AKE schemes are only known to be secure in the random oracle model. In this paper, we propose generic constructions of anonymous one-sided AKE in the random oracle model and in the standard model, respectively. Our constructions allow us to construct the first post-quantum anonymous one-sided AKE scheme from isogenies in the standard model.
Turkish Sign Language Recognition by Using Wearable MYO Armband

In this study, it is aimed to automatically recognize Turkish Sign Language based on EMG signals gathered by a wearable device namely MYO armband. EMG, acceleration, and gyroscope data were recorded and then they were subjected to preprocessing and feature extraction. The data set formed as a result of these processes was classified by using Support Vector Machine, k-Nearest Neighbor, and Random Forest algorithms. In addition, the effect of EMG and inertial measurement unit (IMU) sensors on overall performance were evaluated. Additionally, the effects of the window size and sliding interval variables used in the sliding window method were also evaluated. As a result, the highest performance was obtained by using the RF algorithm with a window size of 100 and a scroll size of 20, with 96% when EMG and IMU sensors used together. It can be concluded that the MYO armband can be used successfully in recognition of Turkish Sign Language.
Group Ordering of Multi-attribute Objects

This chapter introduces new ways for representation and comparison of objects with many numerical and/or verbal attributes, which are present in several distinguished versions. Specifying multi-attribute objects with multisets allows us to solve all types of tasks of individual and collective choice.
Pre-production Design of a Robotic Arm Mounted on an Unmanned Aerial Vehicle (UAV)

It has been demonstrated through many applications that unmanned aerial vehicles, which have been enhanced with various equipment and whose performance has been increased, can perform more effective tasks. Considering the results of these successful applications, the further development of unmanned aerial vehicles with new equipment has recently gained momentum. Among these development studies, the integration of robotic systems in particular is of critical importance. In this study, a robotic arm design is presented to be mounted on unmanned aerial vehicles and used in a wide variety of critical missions. The geometric design of the robotic arm, which is adjusted to be integrated under the fuselage of a rotary wing aircraft, was carried out considering the geometrical characteristics of the unmanned system. The weight-to-load ratio of the robotic arm was arranged as 1 and the motors were selected accordingly. The limits of the geometric design were determined based on the system requirements. Within the scope of the kinematic model of the robotic arm, the general transformation matrix is also obtained based on all its joints and links.
A Comparative Study on Subdural Brain Hemorrhage Segmentation

Brain hemorrhages are one of the most dangerous disease groups. If not detected early, it can lead to death or severe disability. The most common method used to detect bleeding is the evaluation of computed tomography (CT) images belonging to the bleeding area by specialist physicians. Considering the difficulty of access to neurosurgery specialists and the lack of expertise of other doctors in emergency intervention on the subject, there is a need for decision support mechanisms to assist physicians in the diagnosis and treatment process. Artificial intelligence-based systems to be used for this purpose can accelerate the diagnosis and treatment process while reducing the burden on physicians. In this study, the suitability of Mask Region-Based Convolutional Neural Network (Mask R-CNN), Cascade Region-Based Convolutional Neural Network (Cascade R-CNN), Mask Scoring Region-Based Convolutional Neural Network (MS R-CNN), Hybrid Task Cascade (HTC), You Only Look At Coefficients (YOLACT), Instances as Queries (QueryInst), and Sample Consistency Network (SCNet) methods, investigated for the problem of detection and segmentation of subdural brain hemorrhages. The performance of the methods was determined over the images in the CQ500 dataset. This is one of the few studies that perform segmentation of subdural cerebral hemorrhages using CT images from an open dataset. The results were evaluated according to Intersection Over Union (IoU) and Mean Average Precision (mAP) metrics. Experimental results showed that two methods could detect and segment subdural hemorrhages more accurately than the others.
Multi-agents Path Planning for a Mobile Robot in a Dynamic Warehouse Environment

Route planning in robotic systems is a critical and complex task in any environment. Robotic systems allow multiple robots to accomplish multiple goals simultaneously. Many mobile service robots are now used in warehouses to reduce operating and overhead costs. Large warehouses may have multiple robots to handle a large number of tasks. Route planning means finding the best route, i.e. the route without collisions. Optimizing both parameters can be a daunting task. By properly addressing the problem of route planning between robots, we can improve the efficiency of the operation of the entire warehouse. At the beginning, every robot will navigate to its desired goal by funding the optimal route without collisions with other robots. In this work, a relative study with the notable route plan was presented. The proposed intelligent approach was presented for a multi-robot system that finds the best collision-free path in the warehouse and processes the storage box. This paper proposes a sensible variety metric for multi-robotic structures to intelligently become aware of goals and take the best minimum paths to attain them without encountering collisions. Using an intelligent variety metric to discover the route that we want to reach our goal. The proposed planning path are similar to different works including A *, RNN, PRM and heuristics. Three exclusive times of the warehouse have been taken into consideration to carry out experiments with parameters including route length, common route, and elapsed time. Experiments with 800 pods and sixteen robots have said overall performance enhancements of as much as 2.3%, common route length, and elapsed time of 11%.
Group Classification of Multi-attribute Objects

This chapter describes original methods of group verbal analysis for classifying multi-attribute objects. These are techniques for group multicriteria classification of objects without teachers by feature proximity and with teachers by aggregated decision rules. We demonstrate examples how to solve model tasks of collective multicriteria choice with the developed methods.
Image Cryptography Using Fibonacci Bit-plane Decomposition and Quantum Chaotic Permutation

The use of image encryption to safeguard the privacy and security of images is a good idea. This study presents a unique image encryption technique that encrypts images using Fibonacci Bitplanes as security key images. The Fibonacci numbers are used in this research to present a new encryption technique. Furthermore, a unique bit-plane decomposition for Fibonacci weights is explored, which has cryptographic advantages. The novel lossless image encryption technique provided may encrypt an image for privacy protection utilizing this new decomposition approach. It also offers two layers of encryption, allowing it to handle both pay-preview apps and encrypted communications simultaneously. The algorithm's performance in image encryption is confirmed by modeling and analysis. Finally, the suggested algorithm's encryption performance is shown using results obtained and security assessments.
Power Line Detection from Aerial Vehicles using Hough Transform

Automatic inspection and maintenance of remotely located power stations and transmission lines through unmanned aerial vehicles captured infrared and/or visible band image analysis is an important task. In this paper, an effective method based on fine scale edge map and Hough Transform is presented for automatic detection of power lines from infrared and visible light images. Publicly available real infrared and visible light images were used to evaluate the method and reasonably high performance is obtained (94.00% for infrared and 83.40% for visible light images). Even when the power lines are not very distinct in the images, the method detects them to some extent. The number of false negatives is comparatively less than the number of false positives, which is undoubtedly better.
Phishing Domain Name Detection Based on Hierarchical Fusion of Multimodal Features

Phishing attack could trick a victim into revealing sensitive personal, corporate, or financial information through various technical means. As a typical kind of cybercrimes, phishing attack has become one of the major security risks on the Internet leading to tremendous losses to the whole community. Note that phishing attackers could evade detection by multiple ways, for instance, by simply replacing the text with some image and by using slightly rotated or deformed logo images. To address these above limitations, in this paper we proposed a new phishing detection procedure based on hierarchical fusion features from multiple aspects, including domain name, webpage text, webpage structure and webpage logo image. We validated the above procedure through experiments, which indicates that our solution could detect phishing domain names (or websites) more effectively than those traditional ways.
Next Generation Federated Learning for Edge Devices: An Overview

Federated learning (FL) is a popular distributed machine learning paradigm involving numerous edge devices with enhanced privacy protection. Recently, an extensive literature has been developing on the research which aims at promoting the innovations of FL. Motivated by the explosive growth in FL research, this paper studies the next generation of Federated Learning for edge devices. We identify two key challenges, system efficiency and data heterogeneity, which impede the development of FL. We introduce some representative works which contribute to these challenges. Besides, we anticipate the future directions of FL for edge devices and provide guidance for future FL research.