Reviewerly/exHarmony huggingface dataset:
Autonomous Obstacle Detection and Avoidance in Drones

Drone technology has recently advanced, and object detection technology is already evolving. These technologies can be used to find illegal immigrants, locate missing people and items, and detect industrial and natural disasters. In this research, we investigate how to improve object detection performance in such cases. Photography was carried out in a setting where it was difficult to identify objects. The experimental data was based on images taken under various situations, such as changing the drone's altitude, shooting pictures in the dark, and so on. In this work, we recommend a way to make YOLO models more effective at detecting objects. To determine the key indicators, we will input the collected data into the CNN model and the YOLO model, respectively. Precision, recall, F-1 score, and mAP are the major metrics of evaluation. On the basis of the data comparing the CNN model with the YOLO model, an inference will then be drawn.
SPoiL: Sybil-Based Untargeted Data Poisoning Attacks in Federated Learning

Federated learning is widely used in mobile computing, the Internet of Things, and other scenarios due to its distributed and privacy-preserving nature. It allows mobile devices to train machine learning models collaboratively without sharing their local private data. However, during the model aggregation phase, federated learning is vulnerable to poisoning attacks carried out by malicious users. Furthermore, due to the heterogeneity of network status, communication conditions, hardware, and other factors, users are at high risk of offline, which allows attackers to fake virtual participants and increase the damage of poisoning. Unlike existing work, we focus on the more general case of untargeted poisoning attacks. In this paper, we propose novel sybil-based untargeted data poisoning attacks in federated learning (SPoiL), in which malicious users corrupt the performance of the global model by modifying the training data and increasing the probability of poisoning by virtualizing several sybil nodes. Finally, we validate the superiority of our attack approach through experiments across the commonly used datasets.
A Study on Listening Acquisition Strategies of English Majors under the “Second Language Acquisition Theory”

English as a language has an important position in university education, and English listening is an essential part of listening, speaking, reading and writing, which is very much valued by students. As a second language, English is a difficult language for many English majors to learn, and the listening skills are not sufficiently trained and difficult to improve. By elaborating Krashen's "input hypothesis", the author proposes a more appropriate listening acquisition strategy for English majors' current listening situation.
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention

Although face recognition has made impressive progress in recent years, we ignore the racial bias of the recognition system when we pursue a high level of accuracy. Previous work found that for different races, face recognition networks focus on different facial regions, and the sensitive regions of darker-skinned people are much smaller. Based on this discovery, we propose a new de-bias method based on gradient attention, called Gradient Attention Balance Network (GABN). Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning. This method mitigates the bias by making the network focus on similar facial regions. In addition, we also use masks to erase the Top-N sensitive facial regions, forcing the network to allocate its attention to a larger facial region. This method expands the sensitive region of darker-skinned people and further reduces the gap between GAM of darker-skinned people and GAM of Caucasians. Extensive experiments show that GABN successfully mitigates racial bias in face recognition and learns more balanced performance for people of different races.
Socio-cultural rehabilitation of children with special needs: resources of artistic and creative activity

Socio-cultural rehabilitation of children with mental retardation sets a number of tasks, including involving children and their families in socio-cultural activities, creating conditions for their rehabilitation, developing positive motivational activity for creativity, and helping to adapt in society. The content of socio-cultural rehabilitation is of great importance for people with special needs: it helps them to establish social contacts and connections, identify and develop creative abilities, satisfy aesthetic interests, etc. It is a set of measures aimed at helping to achieve and maintain the optimal degree of participation of persons with special needs in society and realize cultural interests and aspirations.
Towards corporate digital responsibility

Digital technologies are challenging liability regimes by giving rise to responsibility gaps. In this chapter, corporate digital responsibility (CDR) is discussed as a tool to overcome these challenges. With its focus on data and data processing, CDR can help to define what behaviour the law expects from corporate directors when they use digital technologies. For instance, directors must make efforts to implement oversight systems applicable to digital technologies. Their oversight duties are not limited to compliance with legal provisions but can also require compliance with ethical standards, for example with regard to the responsible use of AI. With these various regulatory layers, CDR is likely to affect corporate law across its entire breadth but also has tort law implications. Due to the rapid pace of digital transformation, CDR is much more than a buzzword. It has the potential to bridge the fundamental responsibility gap that is created by digital technologies.
VulPathsFinder: A Static Method for Finding Vulnerable Paths in PHP Applications Based on CPG

Today, as PHP application technology is becoming increasingly mature, the functions of modern multi-layer web applications are becoming more and more complete, and the complexity is also gradually increasing. While providing developers with various business functions and interfaces, multi-tier Web applications also successfully cover the details of application development. This type of web application adopts a unified entrance, many object-oriented codes are used, and features such as encapsulation, inheritance, and polymorphism bring challenges to vulnerability mining from the perspective of static analysis. A large amount of object-oriented code makes it impossible for a simple function name-matching method to build a complete call graph (CG), resulting in the inability to perform a comprehensive interprocedural analysis. At the same time, the encapsulation feature of the class makes the data hidden in the object attribute, and the vulnerability path cannot be obtained through the general data-flow analysis. In response to the above issues, we propose a vulnerability detection method that supports vulnerability detection for multi-layer web applications based on MVC (Model-View-Control) architecture. First, we improve the construction of the call graph and Code Property Graph (CPG). Then, based on the enhanced Code Property Graph, we propose a technique to support vulnerability detection for multi-layer web applications. Based on this approach, we implemented a prototype of VulPathsFinder, a security analysis tool extended from the PHP security analyzer Joern-PHP. Then, we select ten MVC based and ten non-MVC-based applications to form a test dataset and validate the effectiveness of VulPathsFinder based on this dataset. Experimental results show that, compared with currently available tools, VulPathsFinder can handle framework applications more effectively, build a complete code property map, and detect vulnerabilities in framework applications that existing tools cannot detect.
Smart Voting System

The Smart Voting System is highly secured and involves verification of voters at 2 levels.It illustrates the hardware and software aspects of the project.The voting machine will be able to take in finger prints and eye image as inputs and will recognize it using the trained model.The vote cast by recognized inputs becomes a valid vote and the unrecognized input becomes an invalid vote.Such a system of two step verification makes voting process transparent.Here we are considering 3 nominated candidates.4 to 50 people can vote.Candidates should get enrolled before Voting.
Deep Learning-Based Automatic Modulation Recognition in OTFS and OFDM systems

Automatic modulation recognition (AMR) is one of the most essential techniques in non-cooperative orthogonal time frequency space (OTFS) and orthogonal frequency division multiplexing (OFDM) communication systems. Since coexistence of OTFS and OFDM is a potential and practical solution in the future wireless communication scenarios, classification of the OTFS scheme and the OFDM scheme will be a challenging and meaningful task. In this paper, we propose a deep learning-based method, including multi-layer convolution neural networks (CNNs) and an attention-based residual Squeeze-and-Excitation Module (SE), to extract effective characteristics of OTFS and OFDM signals in multi-path Doppler spread fading channel. To obtain comparable and convincing results, the design of OTFS transmitters is on the basis of OFDM systems and contains six different sub-carrier modulation modes (BPSK, QPSK, 8PSK, 16QAM, 64QAM and 256QAM). Meanwhile, data structures of the signals are all well-deigned for fair comparisons. In addition, datasets include five modulation modes (OTFS, OFDM and other commonly-used modulation modes) and different Doppler spread values to verify our proposed method. The simulations show that our proposed SE-CNN model performs better than other baseline methods. Moreover, extensive experiment results demonstrate the robustness of our proposed method.
Rancang Bangun Double Ring Infiltrometer Untuk Pengukuran Penambahan Ketinggian Campuran Sabut Kelapa Dan Pasir

In Indonesia, currently the problem of water infiltration into the ground has become a major problem in various areas, especially urban areas. One of these problems is influenced by changes in land use due to growth and increased population activity. In general, in Indonesia the transfer of land use from green open spaces to buildings. As a result of the lack of a good water absorption rate can cause many flood disasters. To prevent flooding, knowledge about the rate of water infiltration in an area is important. To measure the infiltration rate, infiltrometer technology is needed. The design of the double ring infiltrometer has 3 stages, namely first using 304 stainless steel material, the next stage is the rolling process using a 6 × 1300 mm BB613RL rolling machine, and the last stage is the welding process using TIG welding. The effect of adding a mixture of coconut coir and sand on changes in the infiltration rate that occurs in both rings, in this change the slowing of the infiltration rate does occur because the mixture of coconut coir and sand blocks water infiltration directly into the soil. Thus it can be concluded that the addition of a mixture of coconut coir and sand as a ground cover does not have much impact on the rate of water absorption into the soil, especially in the direction of water absorption perpendicular to the soil.
Cell segmentation in fluorescence microscopy images based on multi-scale histogram thresholding

Cell segmentation from fluorescent microscopy images plays an important role in various applications, such as disease mechanism assessment and drug discovery research. Exiting segmentation methods often adopt image binarization as the first step, through which the foreground cell is separated from the background so that the subsequent processing steps can be greatly facilitated. To pursue this goal, a histogram thresholding can be performed on the input image, which first applies a Gaussian smoothing to suppress the jaggedness of the histogram curve and then exploits Rosin's method to determine a threshold for conducting image binarization. However, an inappropriate amount of smoothing could lead to the inaccurate segmentation of cells. To address this crucial problem, a multi-scale histogram thresholding (MHT) technique is proposed in the present paper, where the scale refers to the standard deviation of the Gaussian that determines the amount of smoothing. To be specific, the image histogram is smoothed at three chosen scales first, and then the smoothed histogram curves are fused to conduct image binarization via thresholding. To further improve the segmentation accuracy and overcome the difficulty of extracting overlapping cells, our proposed MHT technique is incorporated into a multi-scale cell segmentation framework, in which a region-based ellipse fitting technique is adopted to identify overlapping cells. Extensive experimental results obtained on benchmark datasets show that the new method can deliver superior performance compared to the current state-of-the-arts.
Unpacking P-Hacking and Publication Bias

We use unique data from journal submissions to identify and unpack publication bias and p-hacking. We find that initial submissions display significant bunching, suggesting the distribution among published statistics cannot be fully attributed to a publication bias in peer review. Desk-rejected manuscripts display greater heaping than those sent for review i.e. marginally significant results are more likely to be desk rejected. Reviewer recommendations, in contrast, are positively associated with statistical significance. Overall, the peer review process has little effect on the distribution of test statistics. Lastly, we track rejected papers and present evidence that the prevalence of publication biases is perhaps not as prominent as feared.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Study of Particle Swarm Optimization Parameter Tuning for Camera Calibration

This paper aims to study Particle Swarm Optimization (PSO) parameter tuning for estimating intrinsic camera parameters. The parameters of typical PSO, on which tuning is mainly focused, include inertia weight and acceleration coefficients. These parameters need to be addressed to how they highly affect the estimation error of PSO by extensively exploring them. The results of this study illustrate their effects so that the proper selection of these parameters can be performed to obtain the achievable best solution.
Smart Healthcare Monitoring System For Healthy Driving in Public Transportation

In an age where citizens are constantly moving between different places, transport demand is extremely high, and so, it is important to have sophisticated public transportation systems in place to ensure a sustainable development of urban areas and meet the needs of citizens. Public transport operators consequently need to provide reliable services in order to minimize disruption events that can affect the vehicles and their drivers, such as breakdowns, accidents or illnesses. The project here described focuses on the type of events and approaches related with the vehicle drivers and the identification of both their performance profiles and health condition while in operation. For that purpose, existing nonintrusive technologies present on the vehicle are leveraged, able to collect data related to physiological measurements taken in realtime. Such sensitive data will be processed, stored and shared in a secure manner, using blockchain-based technologies, so that only authenticated and authorized parties will be able to access the data, according to their clearance level, through an Application Programming Interface (API) designed for that purpose. The architecture of the system will be microservices-based, with components deployed at different infrastructure levels—from On Board Units (OBUs) in vehicles up to cloud-based subsystems.
DCDAN-Based Incipient Fault Diagnosis for Satellite ACS Under Variable Operating Conditions

This article proposes a new distributed–collaborative domain adversarial network (DCDAN)-based incipient fault diagnosis method for satellite attitude control system under variable operating conditions. The designed DCDAN contains a new distributed domain classifier and collaborative domain classifier to provide the features of incipient faults for fault classifier. In the distributed domain classifier, the designed relative importance weight between the global distribution of all data and the conditional distribution of different faults can be adaptively adjusted by the contributions of different distributions. For the collaborative domain classifier, the weight constraint factor is introduced to deal with the loss of incipient fault information in the process of network forward propagation as the increasing of network layers. The experiments in a ground semiphysical platform are carried out, and the results show that the solution achieves over 95% accuracy for incipient faults and over 98% accuracy for the total test samples.
Subtle adversarial image manipulations influence both human and machine perception

Although artificial neural networks (ANNs) were inspired by the brain, ANNs exhibit a brittleness not generally observed in human perception. One shortcoming of ANNs is their susceptibility to adversarial perturbations-subtle modulations of natural images that result in changes to classification decisions, such as confidently mislabelling an image of an elephant, initially classified correctly, as a clock. In contrast, a human observer might well dismiss the perturbations as an innocuous imaging artifact. This phenomenon may point to a fundamental difference between human and machine perception, but it drives one to ask whether human sensitivity to adversarial perturbations might be revealed with appropriate behavioral measures. Here, we find that adversarial perturbations that fool ANNs similarly bias human choice. We further show that the effect is more likely driven by higher-order statistics of natural images to which both humans and ANNs are sensitive, rather than by the detailed architecture of the ANN.
An Intelligent Mobile Application to Help Students with Problems Between Their Peers Using Machine Learning

Tutoring is not a commonly available service for middle and high school students who often have to learn the toughest subjects in the K-12 education system [1][2]. This can negatively impact their learning and cause losses in opportunities that could have been avoided. In response, we suggest a mobile application that is able to assist students in their learning as well as help them educate others so that all students can learn together [3]. This application uses the Flutter framework as well as Google’s Firebase service to store user-generated data [4]. An AI recommender system as well as a subject assignment system is hosted on a Python Flask backend server [5]. It will determine what questions someone should look at, as well as what kind of subject a question seems to be, respectively. These systems rely on an AI that uses natural language processing. To test the effectiveness of our application, we devise two experiments. In the first experiment, we determine the accuracy of our subject assigner by giving it several mock questions. In our second experiment, we compare and contrast different recommender systems. Ultimately, our subject assigner is at 50% accuracy and the recommenders we used were roughly the same in accuracy. Overall, this application is polished and with some more improvements to the backend and AI integration, it will be a useful tool for students who need an extra boost in school.
INTERPERSONAL INTELLIGENCE AMONG 4–5 YEAR-OLDS IN BANTEN PROVINCE: THE ROLE OF NUTRITION AND MENTAL HEALTH

The purpose of this article is to examine the relationship between children's diet and their social and emotional well-being in Banten Province, focusing on kids aged four to five. This definitive study employed a methodology known as causality research. Interpersonal intelligence (Y) served as the dependent variable, while dietary intake (X1) and psychological well-being (X2) served as the independent variables. The participants or subjects of this study are all PAUD teachers in Pandeglang Regency, Banten Province, whether they are employed in a formal or informal setting. include the 1,469 teachers working in preschools, daycares, and kindergartens. For this study, we used multistage cluster random sampling to collect data from the minimum number of participants required by the Slovin formula (n=94). According to the findings, children's nutritional status significantly affects their interpersonal intelligence development, mental health significantly influences children's interpersonal intelligence development, and both factors significantly affect children's interpersonal intelligence development. An adjusted R2 score of 0.808 indicates that the relationship between children's nutritional state and mental health and their interpersonal IQ is robust.
ON THE EXHIBITION OF THE PUBLICATIONS BY IRINA E. DANILOVA IN THE RESEARCH LIBRARY OF THE PUSHKIN STATE MUSEUM OF FINE ARTS

The article presents the characteristics of the books and publications by Professor Irina E. Danilova (Art History), that were exhibited in the reading room of the research library of the Pushkin State Museum of Fine Arts and were devoted to the 100-anniversary of the author. It also contains some episodes from I.E. Danilova’s biography, her role in the scientific activities of the Pushkin Museum from the late 1960’s to the early 2000’s and her pedagogical achievements at the Russian State University of Humanities.
Introduction

This accessible and practical textbook gives students the perfect guide to the use of regression models in testing and evaluating hypotheses dealing with social relationships. A range of statistical methods suited to a wide variety of dependent variables is explained, which will allow students to read, understand, and interpret complex statistical analyses of social data. Each chapter contains example applications using relevant statistical methods in both Stata and R, giving students direct experience of applying their knowledge. A full suite of online resources - including statistical command files, datasets and results files, homework assignments, class discussion topics, PowerPoint slides, and exam questions - supports the student to work independently with the data, and the instructor to deliver the most effective possible course. This is the ideal textbook for advanced undergraduate and beginning graduate students taking courses in applied social statistics.
A Dominant Color Extraction Method Based on Salient Object Detection

Dominant color extraction is essential for image color analysis in the field of image processing. However, traditional dominant color extraction methods are unable to accurately characterize the whole image because they only extract the colors that appear more frequently in an image and ignore small regions of prominent colors. In this paper, a novel dominant color extraction method is proposed. Firstly, the location information of the prominent colors in small regions of an image is obtained by using salient object detection technology. Secondly, the most representative colors in the whole image and the region are accurately extracted as the candidate dominant colors by using a clustering-based dominant color extraction method. Finally, the colors with small differences in the candidate dominant colors are eliminated by using a color similarity deduplication strategy to obtain the final dominant colors. Experiments show that our method not only extracts the colors that appear more frequently in an image but also captures the prominent colors in small regions, which can accurately characterize the colors of the whole image and improve the effectiveness of dominant color extraction.