Reviewerly/exHarmony huggingface dataset:
A Frame Decomposition of the Funk-Radon Transform

The Funk-Radon transform assigns to a function defined on the unit sphere its integrals along all great circles of the sphere. In this paper, we consider a frame decomposition of the Funk-Radon transform, which is a flexible alternative to the singular value decomposition. In particular, we construct a novel frame decomposition based on trigonometric polynomials and show its application for the inversion of the Funk-Radon transform. Our theoretical findings are verified by numerical experiments, which also incorporate a regularization scheme.
Graph Laplacian for Semi-supervised Learning

Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors. For semi-supervised problems, the common approach is to solve a constrained optimization problem, regularized by a Dirichlet energy, based on the graph-Laplacian. However, as supervision decreases, Dirichlet optimization becomes suboptimal. We therefore would like to obtain a smooth transition between unsupervised clustering and low-supervised graph-based classification. In this paper, we propose a new type of graph-Laplacian which is adapted for Semi-Supervised Learning (SSL) problems. It is based on both density and contrastive measures and allows the encoding of the labeled data directly in the operator. Thus, we can perform successfully semi-supervised learning using spectral clustering. The benefits of our approach are illustrated for several SSL problems.
A study of ensemble feature selection and adversarial training for malicious user detection

The continuously booming of information technology has shed light on developing a variety of communication networks, multimedia, social networks and Internet of Things applications. However, users inevitably suffer from the intrusion of malicious users. Some studies focus on static characteristics of malicious users, which is easy to be bypassed by camouflaged malicious users. In this paper, we present a malicious user detection method based on ensemble feature selection and adversarial training. Firstly, the feature selection alleviates the dimension disaster problem and achieves more accurate classification performance. Secondly, we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction preference. Thirdly, we use an effective ensemble learning which could avoid over-fitting and has good noise resistance. Finally, we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the characteristics. It simplifies the parameters, obtaining more robust interaction features and pattern features. We demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection, where the robustness issues are notable concerns.
Improving Fine-Grained Emotion Control and Transfer with Gated Emotion Representations in Speech Synthesis

Fine-grained emotion strength control and prediction is recently studied in text-to-speech to adjust local emotion intensity in an utterance. Due to the lack of fine-grained emotion strength labelling data, emotion or style strength extractor is usually learned at the whole utterance scale through a ranking function. However, such utterance-based extractor is then used to provide fine-grained emotion strength labels, conditioning on which a fine-grained emotional speech synthesis model is separately trained. To bridge the granularity gap between emotion strength extraction and emotional synthesis speech generation, a simple yet effective component called Emotion Gate is designed to learn fine-grained emotion strengths in an end-to-end way, which are then used to create scaled emotion representations that serve as a condition of emotional speech synthesis. Furthermore, beside predicting from a jointly trained emotion strength predictor, our proposed method also allows to manually assign and control the fine-grained emotion strengths during inference. In experiment part, the proposed method is evaluated in both non-transferred emotional speech synthesis and cross-speaker transferred scenarios. Both objective and subjective evaluations show the effectiveness and superiority of the proposed method over the state-of-the-art baseline systems. The audio samples in our experiments can found in the demo page: https://kingstorm.github.io/emotiongate/ .
Adversarial Training Based on Meta-Learning in Unseen Domains for Speaker Verification

In this paper, we propose adversarial training based on meta-learning (AML) for automatic speaker verification (ASV). Existing ASV systems usually suffer from poor performance when apply to unseen data with domain shift caused by the difference between training data and testing data such as scene noise and speaking style. To solve the above issues, the model we proposed includes a backbone and an extra domain attention module, which are optimized via meta-learning to improve the generalization of speaker embedding space. We adopt domain-level adversarial training to make the generated embedding reduce the domain differentiation. Furthermore, we also propose an improved episode-level balanced sampling to simulate the domain shift in the real-world, which is an essential factor for our model to get the improvement. In terms of the domain attention module, we use the multi-layer convolution with bi-linear attention. We experimentally evaluate the proposed method on CNCeleb and VoxCeleb, and the results show that the combination of adversarial training and meta-learning effectively improves the performance in unseen domains.
Progression Towards a Safer and Private Authorization for Business Entities: Fire OAuth

There is growing interest in alternatives to conventional techniques like username and password combinations due to the growing requirement for safe and effective authentication and authorization systems.Token-based solutions, like the OAuth framework, are one such possibility.But there are a number of problems with how OAuth is currently implemented, including poor performance, a lack of privacy, and vulnerability to security risks.These problems are addressed by the revolutionary token-based authorization method called Fire OAuth that we provide in this work.Our technology offers quicker, safer, and more private authentication and authorization by combining cryptographic methods with a decentralised design.
Blind Image Quality Assessment via Deep Response Feature Decomposition and Aggregation

Image quality is related to image content and distortion information. Most learning-based image quality assessment (IQA) methods extract quality-oriented features with auxiliary tasks like detecting the distortion type and level. However, the perceptual quality degradation caused by the same distortion type and level varies substantially for different content in an image. To deal with this problem, in this paper, we propose a blind IQA method based on Deep Response fEAture decoMposition and aggregation (DREAM), which considers two factors affecting the image quality simultaneously. First, we use a convolutional neural network (CNN) to extract the basic features from the input image. Second, several parallel fully connected (FC) layers are employed to decompose these basic features into response features related to the image content, distortion type, and distortion level. Third, the graph attention network (GAT) is leveraged to aggregate these response features corresponding to the visual quality. Finally, a regression network is used to predict the quality score. The success of our method lies in the feature decomposition to obtain the response features of different content to a specific distortion in the given distorted image and the quality-oriented features obtained by feature aggregation using the internal relation of these response features. Experimental results indicate that our proposed DREAM achieves state-of-the-art (SOTA) performance on both synthetic and authentic distortion IQA datasets.
A Robust Combinatorial Defensive Method Based on GCN

Graph Convolutional Neural Networks (GCNs) often demonstrate poor robustness when faced with adversarial attacks, which can be generated with malicious intent.Several heuristic defensive methods have been proposed to mitigate this issue, but they are often vulnerable to stronger adaptive attacks.Recently, researchers have shown that the non-robust aggregation functions used in GCNs are responsible for their vulnerability, and adversarial training in the popular space can enhance the model's accuracy and robustness.Building on this prior research, this paper analyzes the robustness of the winsorised mean function and the mean aggregation function from the perspective of model interpretability, based on the theory of breakdown points and influence function robustness.We propose an improved robust combinatorial defensive method, WLGCN, which replaces the mean aggregation function in the GCN operator with the more robust winsorised mean aggregation function, and incorporates a robust adversarial regularizer on the manifold space hidden layer H (1) of the GCN.Finally, we evaluate the robustness of the proposed model under different levels of adversarial perturbation cost, using accuracy and classification margin as evaluation metrics.The experimental results demonstrate that the proposed defensive approach can effectively enhance the model's robustness against adversarial attacks while maintaining model accuracy, when compared to other baselines.
Enhancing Face Recognition Accuracy Using the ED-FFP Extraction Method and Ensemble Learning for Forensics and Cyber Security

Currently, Face Recognition is the most used biometric to determine an individual’s identity due to its natural and unobtrusive nature. This study proposes face recognition and verification of two-dimensional(2D) images by a feature extraction algorithm which involves identifying anthropological facial feature points and calculating the Euclidean Distance (ED) between these points (ED-FFP) as these distances, if required can be used as an objective measure during trials in courts. These measurements are then used as inputs for various classification methods, including Logistic Regression Classifier (LR), Decision Tree (DT), Naive Bayes (NB), and two other classifiers using the Ensemble Learning Model. The method was tested on 2D face image databases (Caltech, Yale, and ORL) and found to be more efficient and accurate for face recognition than other methods, with a maximum accuracy of 85% for predicting distinct faces using the Decision Tree classifier model. The ensemble learning model also had an accuracy of 85%, which could potentially be improved by using more photos for comparison. In future work, the method could be applied to 3D images, which is currently an open challenge in the field.
Fall Detection in the Wild: An Intelligent Emergency Assistance System

Recent years have witnessed increasing attention to fall detection, powered by the strong demands of Intelligent EmergencyAssistance Systems (IEAS), as it is crucial to reduce incidents of serious injuries caused by falls. In this paper, we propose an IEAS, a framework that integrates deep learning-based fall detection and edge computing-based local processing. First, we introduce a novel challenging benchmark dataset, called WildFall, to better evaluate fall detection models in real-world environments. The WildFall consists of 2,000 various types of video clips, involving movies, TV series, and surveillance. Then, the conceptual design, modules, and practical implementation of IEAS are described in detail. Finally, we discuss and analyze the extensibility of IEAS and future work. We believe that both the proposed IEAS and the public availability of WildFall will attract more research attention, to provide timely emergency assistance for the injured.
Diffeomorphic Image Registration Based on Medical Image

Medical image registration plays an important role in image surgical guidance, image information fusion, disease diagnosis and other clinical tasks. It can correct images of different periods, different modes and different imaging principles through certain spatial transformations to help doctors better understand patients’ physical conditions. In the past, image registration methods based on deep learning often ignore the conversion between images and can not avoid the overlap of deformation fields, that is, the differential diffeomorphism can not be guaranteed. Therefore, this paper proposes a differential diffeomorphic image registration method based on medical images, which can ensure the topological preservation and reversibility of the transformation. We use brain MRI image data set for training and testing, experimental results show that our method has advanced registration accuracy, has good application value.
Cross-Modality Person Retrieval with Cross-Modality Loss Functions

Cross-modality person retrieval aims to search a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Many approaches utilize dual-stream models to extract the features of images from different modalities. Then, they use different types of loss functions to overcome the intra-class modality variations and the large cross-modality discrepancy. In this review, we introduce three types of loss functions used in cross-modality person retrieval including probability based, distance-based, and center-based loss functions. Afterwards, we combine the baseline network with different loss functions, and perform the experiments on a publicly available dataset, i.e., SYSU-MM01 to evaluate their impact on cross-modality person retrieval.
USE OF DIGITAL TECHNOLOGIES FOR CONTROL OF KNOWLEDGE AND SKILLS OF STUDENTS

The article examines the experience of using modern digital technologies to control and accurately assess the quality of students ' knowledge and skills. It is shown how digital technologies allow us to quickly implement high-quality pedagogical control and objectively assess with high accuracy the level of the student's knowledge, allowing us to conduct testing more efficiently and quickly, as they are able to automate the process of creating tests, evaluating them and reporting results. In addition, the positive side of the systematic control of knowledge based on the use of digital technologies is its objectivity, based on the realization of the possibilities of their informative functions, since they do not allow the teacher to influence the test results, and also allow to establish the accuracy of the answer. When performing this type of control, conditions are created under which the student is not afraid of the control procedure and does not try to invent new ways to increase the grade. Digital technologies allow the teacher to create individual training programs, which makes it possible to adjust testing to the needs of each student and create a personalized approach to the educational process. The teacher has the opportunity to objectively assess the real level of students' knowledge and their compliance with the declared competencies. Digital tests can be taken at any time and from any place, making the testing process convenient and accessible for students. The use of digital technologies to monitor knowledge can be motivating for students, as it allows them to participate more actively in the learning process and increase their achievements.&#x0D; The text of the article briefly describes the need for digital technologies in education, types and applications of control and diagnostic systems and online services, justified expediency and advantages of digital testing technologies, as well as examples of modern services and programs for creating tests. It is also noted that digital technologies make it possible to save test results and obtain statistics on student answers, which allows for a more objective analysis of the level of knowledge. The use of digital technologies also makes it possible to increase the efficiency of knowledge control and reduce time spent on preparing tests and evaluating results.
Ricordo di Mario Pozzi

Mario Pozzi (1939-2022), Italian Literature professor at the University of Turin and editor in chief of the Giornale Storico della Letteratura Italiana, was one of the foremost scholars on Renaissance language debates and treatises. He conducted research and edited works that were pivotal in the field of artistic literature and travel writings of the 16th century. He explored the developments in the field of Italian Studies over time and conducted various studies on Benedetto Croce and on post-World War II scholars of Italian literature (Dionisotti, Fubini and Bonora). His compassion and generosity enabled many young scholars to further their research endeavours.
Modeling Search Behavior Evolution on a Specialist Search Engine

Finding desired information can still be a complex task, which is particularly challenging on specialist search engines. We propose a methodology to model search behavior evolution to better understand the familiarization process. As a case study, we analyzed features derived from search queries as well as user interface interactions of 239 users for 20 months, and following clustering, we characterized users based on their search and exploration behaviors. We analyzed the transitions between clusters over time to depict how search behavior evolution manifests. Our method enabled us to identify individuals who exhibited significant changes in search behaviors throughout their search journeys. As the study was conducted in the wild, without controlling for the tasks, topics, or demographics, the methodology holds high ecological validity for search engines that have access to unconstrained user interaction data. Ultimately, our method informs user models to better support effective web-search interactions.
Reform and Innovation Methods of College Physical Education in the Era of We-Media

With the development of Chinese science and technology, education has entered a new era, we media information technology combined with the way to become one of the most popular teaching means.Physical education, as the most important part, has been used more and more widely in China.Physical education is rich in content, diverse in form, vivid and interesting, which is a comprehensive training of students' physical quality.But at the same time, there are many shortcomings.Therefore, in order to achieve innovation and rapid development, physical education must keep pace with The Times, innovate and improve the quality of teaching.Therefore, from the perspective of college teachers' work, the author makes an in-depth discussion and analysis of the existing problems in physical education in the We-Media era and put forward the improvement mode and strategy to meet the needs of social development.Finally, I hope this article can be helpful to teachers and students.
A Literature Review of Adult Education Research

This paper analyzes and summarizes the current situation of adult education research in recent years and hopes to play a certain reference role in exploring and solving the problems existing in the current domestic adult education, so as to make the domestic adult education develop towards a more healthy and sustainable direction.
Security Flaws in Wireless Sensor Networks and Authentication Procedures for Internet of Things

The Internet of Things (IoT) refers to a new paradigm in which everyday products and tools are networked and shared amongst people and other devices. As a result of the benefits it provides to homeowners in terms of convenience and efficiency, IoT technology is expected to become a critical component in the creation of smart homes. Integrity, authenticity, and confidentiality of the data sensed, gathered, and transferred by IoT items in smart homes provide additional security and privacy problems brought on by the application of the IoT model. Because of these issues, smart homes based on the Internet of Things are not secure and are susceptible to a wide variety of security threats. A comprehensive risk assessment should precede any security measures in order to identify and resolve any underlying concerns. Analytical representation based on assessing security issues shows that the suggested method reduces throughputs by 5–17%, latency by 0–0.08, and time by 1–3% compared to the FBC approach and the EESCFD method.
Multi-agent Adversarial Reinforcement Learning Algorithm Based on Reward Query Attention Mechanism

In the multi-agent adversarial scenario, there are problems such as partial observability, exponential increase in the state space and action space of agents, non-stationary environment, and credit allocation. In order to solve the above problems,this paper proposes a value decomposition deep reinforcement learning algorithm QMIX-NA based on the reward query attention mechanism.The algorithm introduces batch regularization and attention mechanism to reduce the complexity of the algorithm and improve the performance of the algorithm. Finally, simulation experiments are carried out in the StarCraft 2 micro-management environment SMAC. The results show that the performance of the QMIX-NA algorithm is better than the traditional value decomposition deep reinforcement learning algorithm.
REKOGNISI ASPEK-ASPEK PEMBELAJARAN BAHASA INDONESIA BAGI PENUTUR ASING (BIPA)

The interpretation of this study applies an analytic-did active approach in the study based on qualitative descriptive standards. The understanding of text and context studies in content uses observation, then data is detected based on textual techniques from data from past study documents. An appropriate curriculum is needed to translate the internalization of Indonesian for foreign speakers. Multi-method engineering in multi-variation based on the objectives and characteristics of language skills with local culture fundamentals has become the standard for efforts to develop Indonesian by increasing the richness of the vocabulary, the standardization of the language system, language tunings, and increasing the functioning of Indonesian as an international language. BIPA learners psychologically need an interaction model in the curriculum that can provide interest to accelerate their language repertoire.&#x0D;
TEXT SENTIMENT ANALYSIS USING LSTM

Sentiment analysis is the study of extracting opinion from expressed language.It comprehensively examines data stored on the Internet to identify and categorise the views expressed in a text.Frequently, people express their opinions or views on various events in society or the world by writing textual reviews.The current work focuses on evaluating the reviews in terms reviewer's opinion and categorising them as a positive, negative and neutral.To find the sentiment from the reviews, well known machine learning techniques Long Short Term Memory (LSTM) and recurrent neural Network (RNN) are used and their performance is also evaluated.For experimentation, reviews data from Kaggle is used and the LSTM model achieves an accuracy of around 78%.
Design of Intelligent Tracking Car Based on Arduino Mega 2560

In certain special circumstances, human beings cannot enter to carry out expeditions or rescue missions.As a result, a smart tracked adventure vehicle equipped with multiple sensors has been created.In this study, an Arduino Mega 2560 is used as the main control board to design an intelligent trajectory adventure cart with automatic trajectory, metal detection, sound and light alarm, and obstacle avoidance functions.An intelligent cart system based on 2560 as the core, sensor information detection and fusion, audible alarm and LED display, and PID control [1] is built.The design has a simple structure, a full set of functions, and a certain degree of intelligence.