Reviewerly/exHarmony huggingface dataset:
Maraudrone’s Map: An Interactive Web Application for Forensic Analysis and Visualization of DJI Drone Log Data

Unmanned Aerial Vehicles (also known as drones) are an increasingly important source of forensic evidence, especially for commercial drones offered by the market leader DJI. The forensic analysis of this type of evidence, however, is still in its infancy. We present the design and implementation of an open-source tool that supports the visualization and analysis of log data acquired in the DATv3 format, which is the standard log format used by DJI. In our evaluation we not only show the usefulness of the interactive visualization of our tool, but also give an empirical overview over the log message types and other artefacts that can be used for anomaly and manipulation detection, an extensible feature also offered by our tool.
Phishing and Intrusion Attacks: An Overview of Classification Mechanisms

The digital world is becoming increasingly interconnected and cyberattacks such as phishing are becoming more common. Fraudulent emails and bogus websites are used to obtain sensitive information from online users to obtain their personal information. Cyberattacks are becoming increasingly sophisticated, which makes detecting scam attacks more difficult. In order to detect phishing attacks accurately, a variety of approaches have been examined, including rules-based systems, lists-based systems, heuristic-based systems, and content-based systems, among others, with the most effective list-based systems and machine learning systems. Over the past couple of years, Deep Learning has proven to be one of the most effective algorithms for machine learning. Specifically, this paper explores and provides an overview of existing anti-phishing approaches, as well as how fraudulent URLs can be classified using machine learning and deep learning algorithms.
Few-Shot Detection Based on an Enhanced Prototype for Outdoor Small Forbidden Objects

In this paper, we propose an enhanced prototype based on a regional many-to-many attention mechanism for few-shot object detection of forbidden objects such as knives and sticks. Specifically, First, we use the original prototype to obtain the invariance of the image to better represent the invariant features of images. Then, we use the enhanced prototype to weight the support features of different query images of knives and sticks to avoid over-fitting. Finally, we use a joint regional consistency loss function to balance and maximize the consistency between the enhanced prototype and the original prototype, which facilitates online learning of invariant object features and improves the efficiency of object detection. The results of experiment show that the enhanced prototype can effectively detect knives and sticks, compared with state-of-art methods. Our method achieves significant improvements in both visual and quantitative evaluation metrics.
Bi-LSTM Model to Recognize Human Activities in UAV Videos using Inflated I3D-ConvNet

Human activity recognition in aerial videos is an emerging research area.In this paper, an Inflated I3D-ConvNet (Inflated I3D) and Bidirectional Long Short-Term Memory (Bi-LSTM) based human action recognition model in UAV videos have been proposed.The initial module was pre-trained using the Kinetics-400 video dataset, which consisted of 400 classes of human activities and around 400 video clips for each class culled from real-world and arduous YouTube videos.The proposed inflated I3D-ConvNet which was built on 2D-ConvNet inflation learns and extracts spatio-temporal features from aerial video while leveraging the architectural design of Inception-V1.The proposed model employs Bi-LSTM architecture for human action classification on the Drone-Action dataset which is a smaller benchmark UAV-captured video dataset.This model considerably improves the state-of-the-art results in activity classification using the SoftMax classifier and retains an accuracy of about 98.4%.
Emotional Semantic Neural Radiance Fields for Audio-Driven Talking Head

Generating audio-driven talking head videos is a challenging problem which receives considerable attention recently. However, the emotional expressions of the speaker are often ignored, although the emotion information is expressed in the audio signal. In this paper, we propose Emotional Semantic Neural Radiance Fields (ES-NeRF), an audio-driven method for generating high-quality and emotional talking head videos based on neural radiance fields. Our method extracts the content features and the emotion features of the audio as additional inputs to construct a dynamic neural radiance field, applies the semantic segmentation map to constrain the speaker’s expression, generates a dynamic three-dimensional emotional facial semantic representation, and then synthesizes the final high-quality video through the semantic translation network. Experiments show that our method can achieve high-quality results with corresponding expressions for audios containing different emotions that surpass the quality of state-of-the-art talking head methods.
Tail Risk Spillover Effects Among Four Major Cryptocurrencies: A Comparison Before and After the COVID-19 Epidemic

This paper compares and analyzes the tail risk spillover effects of four major cryptocurrencies (BTC, ETH, DOGE, and XRP) before and after the COVID-19 pandemic, empirical evidence based on the methodology of Diebold and Yilmaz (2012) and Baruník and Křehlík(2018) suggest three primary conclusions. First, the COVID-19 outbreak increased the tail risk of cryptocurrencies and slightly reduced the tail risk spillover in the cryptocurrency market, but caused the tail risk spillover to fluctuate back and forth between high and low spillover, which increased the uncertainty of the market. Second, the tail risk spillover in the cryptocurrency market has the characteristic of “cumulative slow release”, and we decompose it into frequencies and find that the tail risk spillover in the cryptocurrency market is mainly high-frequency spillover, while medium-frequency and low-frequency spillover are relatively stable. Third, for different cryptocurrencies, the outbreak of COVID-19 changed the risk positioning of the leading currencies(BTC and USDT) in the risk contagion network, while the role of the secondary currencies(XRP and ETH) is not clear, but the risk transmission and risk reception of both are tremendous. Therefore, these results provide an important reference for financial risk management and macroprudential design.
Pain-Illusion for Human-Machine Interface

In nature, pain is one of the most important signals to preserve life for humans and animals. The unpleasantness of pain is a signal for a dangerous situation that should urgently be resolved. While humans experience many degrees of pain, and even use it for interacting with their living environments in daily life, there are few studies that utilize pain as an interface between human and their surrounding environment including machines. In this study, we develop a device to generate illusion of pain by using the “thermal grill illusion” caused by temperature stimulation on human skin. The objective of this research is to develop a new modality for interaction between humans and machines using illusion of pain, and to test the interface for remote operation of robot. The proposed interface induces a temporary illusion of mild pain on human skin without inflicting any injuries. The proposed interface enables humans to interact with machines including remotely located robots that convey their unfavorable situations in the form of pain-illusions that is directly sensed by humans. In this paper, the hardware platform for the pain-illusion generating device will be elaborated. The efficacy of the proposed interface will be evaluated through remote operation of mobile robot.
Metacognitive Adaptation to Enhance Lifelong Language Learning

Lifelong language learning (LLL) aims at learning new tasks and retaining old tasks in the field of NLP. LAMOL is a recent LLL framework following data-free constraints. Previous works have been researched based on LAMOL with additional computing with more time costs or new parameters. However, they still have a gap between multi-task learning (MTL), which is regarded as the upper bound of LLL. In this paper, we propose Metacognitive Adaptation (Metac-Adapt) almost without adding additional time cost and computational resources to make the model generate better pseudo samples and then replay them. Experimental results demonstrate that Metac-Adapt is on par with MTL or better.
Neural Networks for Predicting Severity of Ovarian Carcinomas

Among gynecological cancers, ovarian cancer is the greatest cause of death. The lack of efficient early detection measures is linked to diagnosis at an advanced stage and a bad prognosis. Several genes have been shown to have significantly expressed in early and late stages of ovarian cancer. The use of a neural network can help infer meaning and find patterns from large data sets. The benefit of a neural network is that it is adaptive in nature, learning from the information it receives and adjusting its weights for a better prediction in instances when the outcome is unknown. Some studies have shown effective utilization of neural networks with miRNA data in ovarian cancers, others have used it in gastric cancers with survival data sets, and this study focuses on testing them on gene expression data in ovarian cancers. We found a robust RMSE values for prediction, a reasonable k-fold cross validation, and robust cross grade-stage predictions. This study defines a clear scope of utilizing neural networks in predicting grades and stages in ovarian cancers.
Solution Approach for Detection of Stock Price Manipulation by Market Operators

Nowadays, many so-called stock analyst people are now sending tips via SMS, e-mails, and social media giving targets to stocks that are of very bad quality. In market, these are called pump and dump schemes, where “operators” or “manipulators” increase the price of a stock by various strategies. The increase in price attracts retail investor to purchase that stock. When the stock price crosses the required targets, set by the manipulators who sell it out and public is left holding stock whose price gets decreased suddenly. In this paper, we present a solution approach which can be implemented to detect such manipulation of stock price and avoid such malicious activity. In our solution approach, we suggest ideas, criteria which may be used to build a model based on data analytics, machine learning which can return us the list of stocks that is expected to be manipulated by the operator. This paper also proposes the data analytic models, machine learning models which may be used while implementing the solution approach suggested in this paper.
Remote Entanglement via the Two-Photon Scheme

In Chap. 3 , we prepared the entangled states between the atomic ensembles and the photons, and in Chap. 4 , we coherently converted the photon frequency to the fibre telecommunication band. The next step is to transmit photons from the two atomic ensembles to an intermediate node and establish entanglement between the two atomic ensembles by photon interference.
Remote Entanglement via the Single-Photon Scheme

In Chap. 5 , we established a remote entanglement using the two-photon scheme. To improve the entangling rate and extend the entanglement distance, in this chapter, we try to long-range entanglement by using the single photon scheme.
Mental Disorder Detection in Social Networks Using SVM Classification: An Improvised Approach

Online social networking has caused profound changes within the manner folks communicate and move. These changes might have an effect on sure traditional aspects of human behavior and cause medical specialty disorders. Mental disease is quickly turning into one in every of the foremost current public unfitness around the world. Social media networks, wherever clients will categorical their emotions, feelings, and thoughts, area unit a worthy supply of knowledge for analyzing mental state, and procedures supported machine intelligence area unit more and more used for this purpose. It is difficult to sight social network mental disorder (SNMDs) as a result of the mental factors thought of in existing diagnostic criteria (questionnaire) cannot be determined from online group action logs. To mechanically sight SNMDs cases of OSN clients, taking out these constraints to evaluate user’s online psychological states is extremely difficult. As an instance, the range of isolation with the impact of less inhibition of OSN clients do not seem to be simply discernible. Abnormal activity connected keywords area unit generated and hold on server. Each user activity (tweets, post, comments, etc.) information area unit hold on in information that may be accustomed analyze folie. This can facilitate to observe user activities in social network. Projected work detects sever style of SNMDs with a binary SVM classification approach.
The Maze: Scarecrow’s Revenge, Agricultural Education and Outreach Through Game Development

Extended reality is known as a technical term encompassing augmented, virtual, and mixed reality. It is a niche technology domain that finds its way through virtually every sector, from academia to businesses and industries. Its success is due to the level of immersion it brings to the user. The COVID-19 pandemic, with its unprecedented consequences and lockdowns, amplified the advantages of immersive education and learning through extended reality technology. The technology extension to smartphones and Web pages has enabled its accessibility to a level expected to grow beyond the pandemic era. Due to the rural locations of farms, agriculture is a great candidate where extended reality technology can strengthen its reach to a more enormous population for education, promotion, and outreach. The current study, at the intersection of the agricultural sector and extended reality technology, presents the development of a three-dimensional interactive game to promote the Tennessee corn. The game is developed in Unity engine (version 2020.3.12). It is a single-player, first-person perspective game, where the arrow keys make a farmer character navigate turns and obstacles within a corn maze. Scarecrows will appear over the game course and force the player to answer a trivia question about corn production and usage. The player moves forward unchanged with correct answers to the trivia questions; however, the player will lose a life from their five-life total with an incorrect answer. The goal is to escape the maze by correctly answering trivia questions, navigating the maze, and finding bonus lives scattered throughout. The study discusses the design process of player movements, camera movements, objects interactions, and other game-play mechanics. With our target audience primarily set to middle school students in rural areas, the game was successfully developed and released as a Web-based game ( https://bar.rancsgroup.com/TheMaze/ ). Our current plan is to extend the game with optional difficulty levels as well as release smartphones applications through major platforms.
Flood Identification with Fuzzy Logic Based on Rainfall and Weather for Smart City Implementation

Flood is one of the problems that often occur in big cities, one of which is Surabaya. This arises due to various factors, including changes in land use, relatively high rainfall, and an inadequate drainage system. Floods in several places in Surabaya are basically caused by the unavailability of ditches and places to drain rainwater. This causes rainwater to fall directly onto the road and cause air impacts. One way to anticipate flooding is to know the conditions that trigger flooding, namely rainfall and air temperature. In this study, a classification system for the level of rainfall and air temperature is proposed which affects flooding. The method we propose is fuzzy logic with the Mamdani approach. Our data set is real temperature and rainfall data in the city of Surabaya from 2017 to 2020. The test results from our proposed method, it can be analyzed that fuzzy logic can also study the relationship and degree between temperature and rainfall so as to result in the condition of the city in that month to predict there will be flooding or not.
A study on the appropriateness of elementary English textbooks for school education from the perspective of subject-matter education

This study develops a textbook selection criteria from the perspective of quality excellence of content and subject-matter education which reflects school-oriented practical knowledge. The field experts reevaluate the current elementary English textbooks developed according to the 2015 revised English curriculum, using the textbook criteria. To evaluate their validity, the textbook selection criteria were examined by eight English education experts, all English teachers, and based on these criteria the elementary English textbooks were reevaluated for its appropriateness for school education. The result was that the textbook criteria had high validity as a textbook evaluation tool and that the current five textbooks in elementary curriculum did not prove to be sufficient to satisfy the selection criteria. Current textbooks are unidentified in terms of school educational environment and more various activities that stimulate creativity need to be reflected. Subject-matter education consists of ‘curricular knowledge’, ‘subject- matter knowledge’, ‘learner knowledge’, ‘pedagogical knowledge’, and ‘knowledge of educational contexts’. Subject-matter education relates with field. The result of study demonstrates that the current textbook authorization criteria need to be improved in terms of subject-matter education. It is expected to contribute not only to the development of future textbooks but also to the development of overall English education.
Pengolahan Citra Digital Untuk Identifikasi Jenis Penyakit Kulit Menggunakan Metode Convolutional Neural Network (CNN)

Skin disease is the most common disease and the fastest to infect the human body. This happens because the skin is the first organ to receive external stimuli in the form of touch, temperature and other stimuli. Skin diseases consist of several types that have almost the same color and texture with the naked eye. Thus, an approach is needed to identify the type of skin disease with the help of an image processing system, and an artificial neural network. The identification method used in this research is Convolutional Neural Network (CNN). The infected skin image is used as an input image for image processing. Before being identified, image preprocessing is done, namely resizing, grayscalling, using the Convolutional Neural Network method. The testing process in this study uses 70 types of skin disease images, validation data and 35 types of skin disease images for testing data. The results of this study are the Convolutional Neural Network method can recognize each type of skin disease image with an accuracy of 98% in the validation testing process and 85% in the testing process.&#x0D; Keywords : Skin disease, Convolutional Neural Network, Digital Image Processing
Perancangan Prototyping Sistem Informasi Penilaian Kinerja Dosen Perguruan Tinggi

Higher Education Governance is very important in achieving its Vision and Mission, one of which is in managing Human Resources (HR) in this case are Lecturers, Lecturers can carry out many tasks that are carried out both in the Tridarma of Higher Education and Structural, in this case many obstacles are faced faced in assessing lecturer performance, so far monitoring of work is still done manually still collecting hardcopy reports, and evaluating lecturer work takes a long time. In this research, a Prototyping Design of Higher Education Lecturer Performance Assessment Information System will be made that can be used to evaluate and evaluate the performance of Higher Education lecturers to support Accreditation where this system can be used as a reference in decision making. The next goal is to implement a lecturer performance appraisal system in tertiary institutions. Thus, higher education accreditation can be increased
DEVELOPMENT OF IOT-BASED PRESENCE SYSTEM FOR SCHOOL USING PROTOTYPE METHOD (CASE STUDY: YPPMNU AJIBARANG)

Today's rapid development of technology makes it easy to do everything from shopping to reading today's news to studying online and more. This technology has been widely used, for example in government agencies, schools, hospitals, clinics, and so on. Digital or computerized information technology can improve work efficiency. The technology present in the current era of globalization has developed, including attendance or attendance systems using fingerprints, irises, and RFID cards. Many large companies, SMEs, and schools take advantage of technological developments, therefore this research wants to implement IoT technology developments at YPPMNU Ajibarang because of the large number of employees, there are around 200 active employees. Moreover, the attendance itself is carried out every day, of course, the attendance data that they process is very large. The Ma`arif NU Education Organizing Foundation (YPPMNU) Ajibarang is a foundation engaged in education in the Ajibarang area. YPPMNU has four technical implementing units (UPT), Ma'arif NU 1 Ajibarang Vocational School, Ma'arif NU 2 Ajibarang Vocational School, Ibnu Sina Ajibarang STIKES, and Ibnu Sina Ajibarang Modern Islamic Boarding School, with an active number of around 200 active employees. YPPMNU already uses an information system called the YPPMNU Ajibarang Personnel Management Information System, which has functions such as employee capabilities. The pandemic has brought the work system from working directly from the office to working from home. Previously, YPPMNU had its own attendance system using a web-based application. This study aims to develop an IoT-based attendance system using the prototype method and evaluate an IoT-based attendance system using the UAT testing technique so that it can be well received by users. The research method used in this design is to use the prototype method. The results of this study are an evaluation of how the IoT-based attendance system uses the UAT method, and the IoT-based attendance system uses the prototype method.
Pengenalan dan Pelatihan Menulis Teks Bahasa Inggris: Get More Insight and Improve Your English Skill (Academic Writing Skill) di SMAN 1

This Community Service aims at helping students of the SMA 1 Karrosa to write an academic text in English. The presentation material consists of the concepts of writing comprehension to write description paragraphs in English in various themes in writing descriptions of people, objects, and places. The methods used are lecturing, discussion, assignment, training, evaluation, and feedback. The result of this training is that students of SMA 1 Karrosa have high enthusiasm and motivation in learning English particularly in writing paragraph in English. Moreover, this training can improve students' ability to write a paragraph in English, individually, in pairs, or in group. It also increases the competence of students in the field of writing text or other forms of academic writing and has a very positive impact on students in completing daily school or assignments and also as capital in preparing other assignments.It can be seen on the attendance of students during the three months training which was 86%, four out of seven groups of student are alsoto able to write academic text in English, and questionnaire results showed that 87% of students give positive response which 87% of them claimed to know Engish Academic Writing thorugh this training and 75% of the students answered they can now write English Academic Text through this training and activity.
A Questionnaire on the Photobook: Publishers

Questionnaire on the photobook conceived by José Bértolo and David Campany. Featured publishers: Akio Nagasawa | Atelier EXB | Blow Up Press | Bookshop M | Chose Commune | Éditions Loco | FW:Books | Libro Arte | Roma Publications | Skinnerboox | STANLEY/BARKER | The Eriskay Connection | Void | XYZ Books | Zen Foto