Reviewerly/exHarmony huggingface dataset:
Multi-camera Live Video Streaming over Wireless Network

Due to the development of wireless communication technology, more and more streamers are using cameras mounted on mobile devices for live streaming in a wireless LAN environment. Conventional live streaming systems, which employ multiple images captured by multiple cameras, require relay equipment for transmitting images to a computer for streaming and a network environment with sufficient bandwidth for live streaming. Therefore, the restrictions on using live streaming systems by streamers are severe. In this paper, we propose a multi-view live streaming system using multiple mobile devices in a wireless LAN environment. Our proposed system reduces the processing load on live streaming by using video streaming functions implemented on several mobile devices based on WebRTC. By sending and receiving data between mobile devices, the proposed system also reduces the bandwidth needed for delivering data. Furthermore, with a video switching function during live streaming, the system can dynamically change viewpoints. Performance evaluation results using the proposed system confirmed that the delay time between transmitting and delivering devices can be reduced compared to conventional systems that apply Motion-JPEG and RTMP.
Data Fusion Performance Prophecy: A Random Forest Revelation

Data fusion synthesizes results from diverse sources, but the performance impact remains mysterious. This research reveals the inner workings of fusion through machine prophecy. Constructing a random forest model using TREC dataset benchmarks, we accurately predicted the performance of two fusion algorithms. The model achieved near perfect R2 scores above 0.9 by exploiting meaningful statistical features. Compared to linear regression, the tree-based ensemble provides superior insight. The importance of newly identified drivers, like P@1000 metrics, is quantified. With this prescient view, researchers can refine fusion techniques to offer better search. By uncovering the secrets of fusion success, machine learning guides the path to retrieval excellence.
Anonymously Publishing Liveness Signals with Plausible Deniability

Sometimes entities have to prove to others that they are still alive at a certain point in time, but with the added requirements of anonymity and plausible deniability; examples for this are whistleblowers or persons in dangerous situations. We propose a system to achieve this via hash chains and publishing liveness signals on Tor onion services. Even if one participant is discovered and (made to) cooperate, others still enjoy plausible deniability. To support arbitrary numbers of provers on a potentially limited list of online storage services, an additional “key” distinguishes multiple provers. This key should neither be static nor predictable to third parties, and provide forward secrecy. We propose both a derivation from user-memorable passwords and an initial pairing step to transfer unique key material between prover and verifier. In addition to describing the protocol, we provide an open source App implementation and evaluate its performance.
Intercultural dimensions of literary education

We live in a world of interference between cultures. In order to understand other ways of thinking, feeling and acting, different strategies of spiritual assumption are needed, one of them being interculturality. Conceptually, the notion emerged as a result of the tendencies of universalism and globalization, but as a phenomenon interculturality has manifested itself periodically throughout history. The phrase intercultural education is found today in many discourses that are also related to education. The literary text has a major contribution to the formation of the skills and life skills needed in a world of multiculturalism. Or, the core of the intercultural education is the dialogue of cultures.
RANCANG BANGUN MEDIA PEMBELAJARAN MATEMATIKA MENGGUNAKAN METODE DEMONSTRASI UNTUK KELAS 1 SEKOLAH DASAR

During this primary school mathematics learning method is conventional, namely the teacher notes the material on the blackboard and gives lectures. This causes students to be less efficient in understanding the mathematics subject matter delivered because of the lack of visualization in mathematics. So that mathematics learning media is needed to help students in learning. This study aims to build a mathematics learning media for grade 1 elementary school. Methods of data collection using interviews, documentation, and literature study. Mathematics learning media for grade 1 SD is built with Contruct 2 and CorelDraw Graphics Suite X7. The application development phase includes analysis, system design, implementation, and testing. Mathematics learning media applications for grade 1 elementary school can be used as media by teachers and students in giving and receiving mathematics learning materials, so that it can make it easier for students to understand the material and make it easier to understand the material. teacher to deliver the material. The system test results show that this application is feasible and can be used as a learning aid for mathematics in grade 1 elementary school.
Mapping Knowledge Management in Disseminating Information to Students in the Midst of the Covid Pandemic (Case Study at Vocational University Brawijaya Malang)

This study aims to analyze the mapping of several supporting and inhibiting factors of the knowledge management process in disseminating information in universities, especially Universitas Brawijaya Vocational Schools in the midst of the covid-19 pandemic. The research approach used is qualitative with the type of case study research. The data retrieval technique used was telephone interview with student resource persons and parties from the Vocational School of Brawijaya University. The results showed that; 1) Establishment of a knowledge sharing culture; 2) Facilities and infrastructure to support the knowledge management process; 3) Policy in using knowledge. In the process of disseminating information based on the knowledge management process, Universitas Brawijaya Vocational School must fix some of the shortcomings that occur in the process, such as improving the process of disseminating information with one door and also adding Human Resources in the service sector that can be proactive.
SISTEM PENILAIAN RAPOR PESERTA DIDIK BERBASIS WEB SECARA MULTIUSER

SDN 1 Pardasuka is a public elementary school located in the Pringsewu area. The process of managing values at SDN 1 Pardasuka still has problems. The assessment process is carried out using written media on daily, mid-semester and semester assessment forms conducted by each teacher based on the subjects followed by students. At the end of each semester, data processing of all grades and student records is carried out to make an assessment report in the form of a student report card. The process of processing report cards by the homeroom teacher is still carried out repeatedly from teacher assessment notes to recap of student report cards, resulting in ineffective and efficient work in terms of time and effort. The solution to this problem is to implement a web-based report card value processing information system. The development of a web-based system provides convenience in managing student grades. The system development method used is extreme programming and produces an electronic student score management system with several features such as daily scores, knowledge scores, skill scores, social scores, spiritual scores and final exam scores.Â
Information Leakage from Data Updates in Machine Learning Models

In this paper we consider the setting where machine learning models are retrained on updated datasets in order to incorporate the most up-to-date information or reflect distribution shifts. We investigate whether one can infer information about these updates in the training data (e.g., changes to attribute values of records). Here, the adversary has access to snapshots of the machine learning model before and after the change in the dataset occurs. Contrary to the existing literature, we assume that an attribute of a single or multiple training data points are changed rather than entire data records are removed or added. We propose attacks based on the difference in the prediction confidence of the original model and the updated model. We evaluate our attack methods on two public datasets along with multi-layer perceptron and logistic regression models. We validate that two snapshots of the model can result in higher information leakage in comparison to having access to only the updated model. Moreover, we observe that data records with rare values are more vulnerable to attacks, which points to the disparate vulnerability of privacy attacks in the update setting. When multiple records with the same original attribute value are updated to the same new value (i.e., repeated changes), the attacker is more likely to correctly guess the updated values since repeated changes leave a larger footprint on the trained model. These observations point to vulnerability of machine learning models to attribute inference attacks in the update setting.
Prosedur Sistem Penataan Arsip di Program Studi Administrasi Bisnis Jurusan Administrasi Bisnis Politeknik Negeri Manado

The business administration study program is one part of the Business administration Department owned by the Vocational education institution at the Manado State Polytechnic, established in 1987 and aims to produce student graduates who are able to carry out functions as an administrative staff professionally in accordance with their field of expertise. As well as being able to complete administrative tasks in a relatively short time which is supported by the ability to use language, the latest technology and supported by high morals, responsibility and discipline. In carrying out the activity process has benchmarks that must be achieved in improving quality, one of which is by always producing good quality archives and at certain times it is needed for smooth activities in the Business administration study program. For this reason, the resulting archive must be managed according to the procedures used and then stored and can be found quickly and precisely when needed. The role of archives as a support for the organization in the smooth implementation of activities in accordance with its management procedures. In fact, it was found when the initial observation was made that there was a lack of complete documents manually or electronically, there was no special officer to handle the management of archives, so that when needed it was very difficult to find. A number of documents or archives available are not well organized due to the availability of equipment that is still minimal. This research was conducted with the aim that the availability of documents and a number of documents can be well organized and protected, archives can be well organized with the availability of equipment and special officers who handle it. This research will use qualitative research methods by collecting data through observation, interviews and documentation.
Technical Solutions

Abstract This chapter reviews the technological solutions that organizations leverage to ensure the ethical management and downstream use of collected data for building analytic and AI models. Survey respondents discussed solutions that ranged from privacy preserving data management strategies such as differential privacy, to the use of virtualization and data lake control systems for secure access. Survey respondents also keyed in on the clear and pressing need for data and algorithmic auditing technology and systems to support ethical data governance. With respect to how such data is used ethically, respondents identified the importance of algorithmic fairness as well as model transparency as essential to help identify and also mitigate risks associated with real world modeling failures.
Improving Viewer Training in Visual Assessment

Subjective assessment in the context of video compression performance evaluation measures the impairment or the quality of the observed video by the mean opinion score provided by naïve viewers. In a recent update of Recommendation ITU-R BT.500-14, this concept has been extended to expert viewers. Experts, who often have strong opinions on specific artifacts, provide wider confidence interval values than naïve viewers; this implies, therefore, a reduced ability to rank the results. This paper presents a new training procedure for viewers, aiming to reduce the spread of ratings either due to strong personal opinions (i.e., those most likely from expert viewers) or to hesitation in performing a new task (i.e., those most likely from naïve viewers). The new design of the training session includes suggesting of the expected range of scores, thereby helping the viewer to achieve a more consistent understanding of the scoring scale. In our experiment we apply the DSIS (Double Stimulus Impairment Scale) / DCR (Degradation Category Rating) test protocols. The same video clips were assessed by expert viewers both with and without the new training method, as well as by naïve viewers in a laboratory test. The analysis of the results shows a promising consistency in the results of the different tests.
Privacy and security concerns with passively collected location data for digital contact tracing among U.S. college students

People continue to use technology in new ways, and how governments harness digital information should consider privacy and security concerns. During COVID19, numerous countries deployed digital contact tracing that collect location data from user’s smartphones. However, these apps had low adoption rates and faced opposition. We launched an interdisciplinary study to evaluate smartphone location data concerns among college students in the US. Using interviews and a large survey, we find that college students have higher concerns regarding privacy, and place greater trust in local government with their location data. We discuss policy recommendations for implementing improved contact tracing efforts.
A Hybrid Deep Learning Emotion Classification System Using Multimodal Data

This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion classification is important in diverse fields, including tailored corporate services, AI advancement, and more. Additionally, most sentiment classification techniques in speaking situations are based on a single modality: voice, conversational text, vital signs, etc. However, analyzing these data presents challenges because of the variations in vocal intonation, text structures, and the impact of external stimuli on physiological signals. Korean poses challenges in natural language processing, including subject omission and spacing issues. To overcome these challenges and enhance emotion classification performance, this paper presents a case study using Korean multimodal data. The case study model involves retraining two pretrained models, LSTM and CNN, until their predictions on the entire dataset reach an agreement rate exceeding 0.75. Predictions are used to generate emotional sentences appended to script data, which are further processed using BERT for final emotion prediction. The research result is evaluated by using categorical cross-entropy (CCE) to measure the difference between the model’s predictions and actual labels, F1 score, and accuracy. According to the evaluation, the case model outperforms the existing KLUE/roBERTa model with improvements of 0.5 in CCE, 0.09 in accuracy, and 0.11 in F1 score. As a result, the HDECS is expected to perform well not only on Korean multimodal datasets but also on sentiment classification considering the speech characteristics of various languages and regions.
Breast Cancer Detection and Classification Using Hybrid Feature Selection and DenseXtNet Approach

Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morphological features were extracted, along with features from deep learning architecture VGG16. Then, we employed a hybrid optimization model, combining the Sparrow Search Algorithm (SSA) and Red Deer Algorithm (RDA), called Hybrid Red Deer with Sparrow optimization (HRDSO), to select the most informative subset of features. For detecting BC, we proposed a new DenseXtNet architecture by combining DenseNet and optimized ResNeXt, which is optimized using the hybrid optimization model HRDSO. The proposed model was evaluated using various performance metrics and compared with existing methods, demonstrating that its accuracy is 97.58% in BC detection. MATLAB was utilized for implementation and evaluation purposes.
Ship Detection Algorithm Based on YOLOv5 Network Improved with Lightweight Convolution and Attention Mechanism

Aiming at the problem of insufficient feature extraction, low precision, and recall in sea surface ship detection, a YOLOv5 algorithm based on lightweight convolution and attention mechanism is proposed. We combine the receptive field enhancement module (REF) with the spatial pyramid rapid pooling module to retain richer semantic information and expand the sensory field. The slim-neck module based on a lightweight convolution (GSConv) is added to the neck section, to achieve greater computational cost-effectiveness of the detector. And, to lift the model’s performance and focus on positional information, we added the coordinate attention mechanism. Finally, the loss function CIoU is replaced by SIoU. Experimental results using the seaShips dataset show that compared with the original YOLOv5 algorithm, the improved YOLOv5 algorithm has certain improvements in model evaluation indexes, while the number of parameters in the model does not increase significantly, and the detection speed also meets the requirements of sea surface ship detection.
Spherical Centralized Quantization for Fast Image Retrieval

Existing supervised quantization methods usually learn the quantizers from pair-wise, triplet, or anchor-based losses, which only capture their relationship locally without aligning them globally. This may cause an inadequate use of the entire space and a severe intersection among different semantics, leading to inferior retrieval performance. Furthermore, to enable quantizers to learn in an end-to-end way, current practices usually relax the non-differentiable quantization operation by substituting it with softmax, which unfortunately is biased, leading to an unsatisfying suboptimal solution. To address the above issues, we present Spherical Centralized Quantization (SCQ), which contains a Priori Knowledge based Feature (PKFA) module for the global alignment of feature vectors, and an Annealing Regulation Semantic Quantization (ARSQ) module for low-biased optimization. Specifically, the PKFA module first applies Semantic Center Allocation (SCA) to obtain semantic centers based on prior knowledge, and then adopts Centralized Feature Alignment (CFA) to gather feature vectors based on corresponding semantic centers. The SCA and CFA globally optimize the inter-class separability and intra-class compactness, respectively. After that, the ARSQ module performs a partial-soft relaxation to tackle biases, and an Annealing Regulation Quantization loss for further addressing the local optimal solution. Experimental results show that our SCQ outperforms state-of-the-art algorithms by a large margin (2.1%, 3.6%, 5.5% mAP respectively) on CIFAR-10, NUS-WIDE, and ImageNet with a code length of 8 bits. Codes are publicly available: https://github.com/zzb111/Spherical-Centralized-Quantization.
УЛОГА КУЛТУРЕ МИРА, ЕТНИЧКЕ ТОЛЕРАНЦИЈЕ И МУЛТИКУЛТУРАЛИЗАМА У ОБРАЗОВАЊУ И ВАСПИТАЊУ НОВИХ ГЕНЕРАЦИЈА

The paper discusses the role of the culture of peace, ethnic tolerance and multiculturalism in the context of contemporary social relations in the Balkans and the world. The focus of consideration is contemporary conflict research and peace studies, their importance for the education and upbringing of new generations, as well as the role of schools and universities in affirming the value of the culture of peace. The paper emphasizes the importance of the struggle for peace, as a basic prerequisite for the sustainable development of humanity and the survival of the planet. Key Words: Culture of peace, Tolerance, Multiculturalism, Education and upbringing, Sustainable development
Time-Series Deep Learning Anomaly Detection for Particle Accelerators

High energy particle accelerators rely on superconducting radio frequency cavities to transfer energy and accelerate the beam. Such particle accelerators are complex and expensive systems prone to failures which lead to downtime of the whole experimental facility: it is thus of primary importance to anticipate and prevent these faults to improve the uptime and cost-effectiveness of particle accelerators. Data-driven methods are especially fit for this task as they can leverage all the data recorded and archived by a typical control system. Previous works used classical machine learning (ML) models for anomaly detection to detect early signs of an upcoming fault. We propose here a different approach based on deep learning (DL) models, exploiting the temporal correlation of the raw data. Three different models are tested on data from the ALPI (Linear Accelerator for Ions) linear accelerator in INFN (National Institute for Nuclear Physics) Legnaro National Laboratories in Italy and they are compared with the classical ML approach.
THE EFFECTIVENESS OF CLASS DIVISION BASED ON STIFIN (INTELLIGENCE MACHINE) AT THE SUKMA MEDAN COLLEGE OF MANAGEMENT SCIENCES, ESPECIALLY IN KIP STUDENTS IN THE SECOND SEMESTER OF THE AFTERNOON CLASS

Stifin is a tool for detecting intelligence and a person's brain type, it is used to find out a person's character and to find out what learning methods are suitable for each student. This type of research is a quantitative descriptive study using a survey method conducted online. Primary data collection in this study was carried out by distributing online questionnaires to 34 respondents who were students of KIP STIM SUKMA MEDAN which included students studying in the second semester in afternoon classes with combined concentrations. Respondents from this study were students of right brain b and left brain b management study program which included 2nd semester students. In this study students who were respondents and were willing to fill out google forms. Based on the analysis above, it can be concluded that many students think that they are more familiar with character their personal after knowing their MK. but having the Constitutional Court and adjusting the class according to the Constitutional Court does not guarantee that all students are comfortable, because there is no difference in assignments or anything else between right brain class B and left brain B.