Reviewerly/exHarmony huggingface dataset:
A Secure Computing System with Hardware-Efficient Lazy Bonsai Merkle Tree for FPGA-Attached Embedded Memory

With high-impact cyber-attacks on the rise, provisioning cybersecurity to the emerging Internet of Things (IoT) systems typically comprising of modern embedded computing platforms becomes significantly more challenging to achieve. Contemporary secure-memory computing stipulates both content encryption and integrity protection that can seriously impede the computing performance and consume excessive amount of hardware resources. In this paper, we focus on hardware-efficient verification of the memory integrity in the mission-critical computing tasks executing on an FPGA-based secure embedded system, effectively mitigating adversarial attacks such as memory buffer replay. We proposed an innovative partitioned parallel cache structure that leverages the unique reconfigurable capability of modern FPGA devices and successfully circumvents the hardware implementation challenges due to the recursiveness that inherently exists in Merkle tree updating schemes. We designed and implemented a new Bonsai Merkle tree (BMT) lazy update controller specifically designed for FPGA to efficiently exploit the parallelism offered by its reconfigurable fabric. Our experimental results for the new system show up to 95x and 149x latency overhead reduction respectively for write and read and up to 17% better throughput in standard benchmarks compared to software-based approach. Critical system performance is also improved with the lowering of average evictions by up to 8%.
COMPARISON OF SCIENCE LEARNING RESULTS USED E-LEARNING AND ZOOM APPLICATIONS IN DISTANCE LEARNING AT SMP N 9 AMBON

The development of information and communication technology at this time is very rapid. This can be seen from the increasingly sophisticated communication and information tools such as mobile phones and computers. Mobile phones that used to only feature telephones, now they are very developed to the point of 4G technology that can speed up the flow of delivery. In help students in the learning process of SMP N 9 AMBON, they use 2 applications, namely the E-Learning application and the Zoom application. This study uses quantitative and qualitative descriptive analysis techniques. Data about the observations were analyzed qualitatively. Student learning outcomes data were analyzed quantitatively by calculating the average score, standard deviation, median, highest score, and lowest score, this study also used inferential analysis techniques used to test research hypotheses. Based on the results of research and data analysis, for students who were given the treatment of learning models using the Zoom application, the results obtained were the average value of 47.5 out of a maximum score of 100. Based on the results of research and data analysis, for students who were given the treatment of learning models using the E-Learning application, the results obtained were the average value of 45.71 from a maximum score of 100.
PENGEMBANGAN MODUL PEMBELAJARAN IPA BERBASIS GROUP DISCOVERY LEARNING (GDL) UNTUK MENINGKATKAN NILAI KOGNITIF BAGI SISWA MADRASAH TSANAWIYAH

Learning science using Group Discovery Learning (GDL) will help the learning process because this learning can train students to learn actively and independently in a learning process. The results of interviews with subject teachers showed that there are no modules, only LKS and a limited number of Package Books. Many students learning outcomes have not reached the KKM standard value. From the results of the interviews, researchers will develop a GDL-based module as a learning resource to improve students' cognitive learning outcomes. This study aims to produce learning tools developed, namely GDL-based modules, to enhance students' cognitive values by using modules in class VII MTs Riyadlatul Ulum. This type of research is the development of a Science Learning Module based on Group Discovery Learning using the 4D development model, Define, Design, Develop and Disseminate, suggested by Thiagarajan and Sammel. The analysis results from the design expert test were 95%, the material expert test was 85%, and the student response test was 89%. The results of the discussion regarding the development of the module can be concluded that before the development of the completeness module, students were only 33%. Still, after using the module, the first meeting increased by 70% and to 87% at the second meeting. So the module development is feasible to be used and used as a science learning resource and can improve students' cognitive learning outcomes.
The Business Case for IAM

Businesses are under enormous pressure to deliver their products and services in ways that profit the company. Areas that do not directly bring in funding are often moved lower in priority, resulting in a competition for resources that can see internal projects in areas such as IAM struggle to succeed. Projects that move to the top of the priority pile in this competition are ones that provide a compelling business case. This article focuses on how to develop a positive business case for your IAM programs.p { line-height: 115%; text-align: left; orphans: 2; widows: 2; margin-bottom: 0.25cm; direction: ltr; background: transparent }a:link { color: #0000ff; text-decoration: underline }
Social media attacks against female Canadian journalists

I investigate in this brief empirical study the social media attacks against female Canadian journalists who have frequently been targeted with online abuse. I used purposive sampling to focus on three journalists: Rachel Gilmore (formerly with Global News), Erica Iffil (freelance with The Hill Times), and Saba Eitizaz (Toronto Star). I employed a mixed method approach to conduct this study by collecting all the available Twitter replies to these three journalists ( n = 402,821) posted by 84,962 unique users. The digital analysis results show that there are slight differences in the quantity of attacks on these journalists, but the qualitative assessment of images associated with tweets indicate the need to use manual approaches to better understand the nuances and quality of these disinformation and often racist attacks.
Online Course Recommender System using Singular Value Decomposition

The COVID-19 pandemic has triggered advancements in education through digital devices, which now allow education to be carried out anywhere without being limited to a place. This has revolutionized the world of education. Choosing the correct course, meanwhile, becomes difficult as the number of users and courses rises. Therefore, an online course recommender system is needed. One of the recommender system paradigms is Collaborative Filtering (CF). CF uses two different sorts of approaches, i.e., memory-based collaborative filtering and model-based collaborative filtering. In memory-based CF recommendations are built with a neighborhood method that focuses on the relationship between items called item-based and the relationship between users called user-based. While model-based CF recommendations are built based on user ratings. Matrix factorization is proven to be a model-based technique that is able to provide good performance for recommender systems and is able to overcome the problem of high-dimensional data compared to other collaborative filtering techniques. Therefore, we built a recommender system in the online course domain using a Singular Value Decomposition (SVD), which is one of many matrix factorization techniques. To evaluate the effectiveness of testing the SVD model, we apply the Root Mean Square Error (RMSE) evaluation metric. The evaluation results show that the SVD model outperforms the base model, as evidenced by the t-test results with a sample size of ten experiments.
Virtual Reality Interactive Training System of Prefabricated Building Based on Internet of Things Technology

This paper based on the digital twin concept, relying on the Unity 3D platform, comprehensively uses the Internet of Things technology, Internet technology, cloud services and other means to link the physical physical scene of Prefabricated building construction training with the three-dimensional digital virtual scene in real time, virtual and real interaction, real-time synchronous intelligent perception, detection and feedback of the training process, and integrate teaching, training and evaluation. The system enable trainees to conduct theoretical learning and practical training in both virtual and real scenarios, efficiently completing teaching, training, and evaluation objectives.
Using Distributed Temperature Sensing for Long-Term Monitoring of Pockmark Activity in the Gulf of Patras (Greece): Data Processing Hints and Preliminary Findings

Patras Gulf pockmark field (Western Greece) is a tectonically controlled field that has been activated at least twice by strong earthquakes (M5.4, 14 July 1993 and M6.4, 8 June 2008), and episodic gas seepages have been recorded in the past using geophysical means. A distributed temperature sensor (DTS) system was deployed inside a shallow pockmark and along an active fault at the northern end of the field. This ongoing experiment represents the first long-term monitoring ever conducted on gas-bearing pockmarks and active faults by the DTS system. For now, we have acquired and analyzed data regarding about 1.56 years. One of the primary objectives of this study is to establish methodological queues for data processing and analysis, including spectral analysis and incomplete data treatment techniques, to be standardized for use in further stages of the experiment. Spectral analysis was proven capable of separating the temperature footprint of background environmental components, such as sea-atmosphere heat flux, tides, and winds/waves, from high-frequency temperature residuals. Those residuals represent unusual events that might be correlated to seismicity. Monitoring the causal relationship between seismic activity and seabed water temperature changes in the field was thus attempted. No significant local earthquakes occurred during the monitoring period. Although the relation between seismicity and irregular seabed water temperature events was not systematic, we postulate that four thermal events have a causative link with the local seismicity. The DTS system constitutes a low-cost monitoring system, and the promising preliminary results of this experiment suggest that it is worth testing for a longer period.
Mapping the Landscape of Research on Knowledge Workers: A Bibliometric Review

Knowledge workers remain to be seen as one of the biggest assets for modern enterprises to maintain their competitiveness in the twenty-first century. This has led to a steady increase in academic interest in knowledge workers research, as evidenced by the rise in the number of related publications. However, there is a dearth of research on the growth and existing literary landscape of research focusing on knowledge workers, thus making it challenging for scholars to determine the current trends and future direction in this area. To address the issue, this study provided an overview of the evolution of academic research on knowledge workers over four decades (1981-2021) and identified the existing key trends. A bibliometric review of the data extracted from the Web of Science database using Biblioshiny and VOS viewer. The analysis of 1667 articles confirmed a steady growth in the literature on knowledge workers both in terms of the number of publications and citations. The study identified the evolution of knowledge research in three key dimensions: knowledge work, performance, and human capital. Moreover, “performance”, “management” and “model” have also become the most frequently occurring words. The research findings not only deepened our comprehension of the research landscape but also provided insights into potential implications.
Influence of different image acquisition sources on the performances of a CNN-based object detector

Mastering the content quality, diversity and context representativeness of a database is a key step to efficiently trained deep learning models. This project aims at controlling the relevant hyper-parameters of training datasets to be able to guarantee a mission performance and to contribute to the explainability of the model behavior. In this presentation, we show an approach to design DRI (Detection, Recognition and Identification) algorithms of military vehicles with different acquisition sources. Starting from a definition of a mission-agnostic image database, this study is focused on controlled image acquisition sources which automates the collection of few but relevant object signatures and their metadata e.g. bounding box, segmentation mask, view angles, object orientations, lighting conditions... By putting the accent on the acquisition of a reduced amount of images coupled with data augmentation technics, it is foreseen to demonstrate a dataset creation method that is fast, efficient, controlled and easily adaptable to new mission scenarios and contexts. This study compares three different sources: an optical acquisition bench of scaled vehicle model, the 3D scanning of scaled models and 3D graphic model of vehicles. The challenge is to make predictions on real situations with a neural network model only trained with the generated images. First results obtained with the datasets extracted from the 3D environment with graphic models and with scanned scaled ones are not yet reaching the previous performance levels obtained with 2D acquisition bench. Further investigations are needed to understand the influence of the numerous hyper-parameters introduced by the 3D environment simulation.
CAMP: A Unified Data Solution for Mandarin Speech Recognition Tasks

Speech recognition, the transformation of spoken language into written text, is becoming increasingly vital across a broad range of applications. Despite the advancements in end-to-end Neural Network (NN) based speech recognition systems, the requirement for large volumes of annotated audio data tailored to specific scenarios remains a significant challenge. To address this, we introduce a novel approach, the Character Audio Mix-up (CAMP), which synthesizes scenario-specific audio data for Mandarin at a significantly reduced cost and effort. This method concatenates the audio segments of each character’s Pinyin in the text, obtained through force alignment on an existing annotated dataset, to synthesize the audio. These synthesized audios are then used to train the Automatic Speech Recognition (ASR) models. Experiments conducted on the AISHELL-3, and AIDATATANG datasets validate the effectiveness of CAMP, with ASR models trained on CAMP synthesized data performing relatively well compared to those trained with actual data from these datasets. Further, our ablation study reveals that while synthesized audio data can significantly reduce the need for real annotated audio specific to each scenario, it cannot entirely replace real audio. Thus, the importance of real annotated audio data in specific application scenarios is emphasized.
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party

Federated learning (FL) is a machine learning setting which allows multiple participants collaboratively to train a model under the orchestration of a server without disclosing their local data. Vertical federated learning (VFL) is a special structure in FL. It handles the situation where participants have the same ID space but different feature spaces. In order to guarantee the security and privacy of the local data of each participant, homomorphic encryption (HE) is often used to transmit intermediate parameters or data during the training process. In most VFL frameworks, a trusted third-party server is necessary because the plaintexts of the parameters need to be revealed for the computation. However, it is hard to find such a credible entity in the real world. Existing methods for solving this problem are either communication-intensive or unsuitable for multi-party scenarios. By combining secret sharing (SS) and HE, we propose a novel VFL framework without any trusted third parties called EFMVFL. It allows intermediate parameters to be transmitted among multiple parties without revealing the plaintexts. EFMVFL is applicable to generalized linear models (GLMs) and supports flexible expansion to multiple participants. Extensive experiments under Logistic Regression and Poisson Regression show that our framework is outstanding in communication (reduced by 3.2×– 6.8×) and efficiency (accelerated by 1.6×– 3.1×).
DEVELOPING INK’S KIT MEDIA TO STUDY SPEAKING SKILLS IN DESCRIPTIVE TEXT FOR THE SEVENTH GRADE JUNIOR HIGH SCHOOL

English is one of the subjects taught to students since elementary school. In this study, researchers conducted research on seventh grade junior high school students. The problems found are low speaking skills and students need improvement in English learning media. Researchers conducted research focused on speaking skills in descriptive text material. Researchers use flashcards media that are processed and updated in use during learning, which are packaged in one ink's Kit media package. The purpose of this study was to develop flashcards to teach speaking skills to seventh grade junior high school students. The researcher used simple random sampling. The subjects of this study were students of SMPN 2 Doko Class VII D as many as 35 students and 2 teachers. The data analysis technique used was qualitative and quantitative. Qualitative data were taken from observations, interviews, and documentation. Quantitative data is taken from the perception test. From the results of the perception test, it was found that the product was feasible to be used for learning students' speaking skills. Based on the Likert Scale in the perception test, it was found that 88.75% of students' perceptions with information strongly agree on the use of the product as a medium for learning English in descriptive text material. The results of the teacher's perception of the use of the developed product strongly agree that is equal to 85.3%. The results of the material validation assessment on the product were found to be 88.2% with information very suitable for use in learning. The results of the media validation assessment were found to be 81.1% with information very suitable for use in learning. The result of the research that by playing INK’s Kit Media can be used to improve their speaking skill.
XV International Conference on Mathematics, Science and Technology Education

Abstract This paper introduces the Proceedings of the XV International Conference on Mathematics, Science and Technology Education (ICon-MaSTEd 2023), which took place at the Kryvyi Rih State Pedagogical University, Ukraine, from 17 to 19 May 2023. It provides background information and the organizational structure of the conference, as well as the structure of the proceedings. It also acknowledges the many people who contributed to the success of the conference.
Track fusion algorithm for OTHR network based on deep learning

This paper presents a deep learning based over the horizon radar (OTHR) track fusion method, which fuses the tracks of two OTHRs to improve the track accuracy of the OTHR fusion system. This method generates random track data of two OTHRs within a certain distance and azimuth range through simulation, forming data samples. Firstly, at the fusion center, preprocess the local estimation results of the two OTHRs through methods such as time alignment and coordinate transformation. Then divide the dataset of the track data of the two OTHRs into samples to obtain training and testing samples. Secondly, we constructed a deep neural network model and trained the deep neural network using training sample data. Finally, the deep neural network is tested using the test trajectory data, and the fused trajectory is outputted by the network. The proposed method fully utilizes the powerful learning ability of deep learning technology and does not require a single OTHR to provide covariance information, making the track fusion system easier to implement. Due to the fact that the deep track fusion method proposed in this article only requires one forward propagation in practical use, it has a smaller computational complexity. Experimental results show that the track fusion algorithm proposed in this paper has higher fusion accuracy and fusion efficiency than the traditional simple convex combination and covariance cross fusion methods, which verifies the effectiveness of the proposed method.
Design and Application of an Intelligent Remote Learning System Based on Network Communication

Online communication refers to the process of transmitting content or information to the public through modern communication methods such as the internet and mobile internet. Intelligent remote learning refers to the use of computer networks and other technologies to learn online and obtain certificates or degrees. Building an intelligent remote learning system requires comprehensive consideration of multiple factors, including technology, user experience, teaching effectiveness, etc. It requires a good technical background and project management ability to complete an excellent intelligent remote learning system. This article applied experimental testing and performance comparison to test the personalized recommendation function of an intelligent remote learning system, and found that the courses recommended by the system match the actual interests and abilities of users. The learning evaluation and feedback module had the highest recommendation accuracy of 0.98, followed by the learning path management module with an accuracy of 0.96. The intelligent remote learning system based on network propagation performed well. The system can effectively improve the efficiency and effectiveness of distance learning, while also reducing the work burden of teachers and promoting educational equity.
Construction of Multi-direction Intelligent Detection System for Moving Objects Based on Computer Technology

Moving target detection is an important part of intelligent system. In the process of moving object detection, computer technology is an important foundation. In this paper, the computer vision technology is used to establish a multi-directional intelligent detection system for moving objects, aiming at improving the system capability of intelligent object detection. The design idea, method and algorithm process of the motion target multi-directional intelligent detection system are described in detail. Including image or video acquisition, motion target detection, tracking, classification and identification, and analysis and statistics. We propose a new multi-directional intelligent detection system for motion targets, which fills the gap of existing research and provides a comprehensive and efficient solution. In this paper, the experimental method and data comparison method are mainly used to study the role of several proposed algorithms in moving target tracking, and the system running time under different schemes is also studied. The experimental data shows that under several schemes, the running time obtained by adding intelligent adjustment algorithm is less than 4000ms, which is the least time-consuming.