Reviewerly/exHarmony huggingface dataset:
FLFHNN: An Efficient and Flexible Vertical Federated Learning Framework for Heterogeneous Neural Network

The emergence of vertical federated learning (VFL) solves the problem of joint modeling between participants sharing the same ID space and different feature spaces. Privacy-preserving (PP) VFL is challenging because complete sets of labels and features are not owned by the same entity, and more frequent and direct interactions are required between participants. The existing VFL PP schemes are often limited by the communication cost, the model types supported, and the number of participants. We propose FLFHNN, a novel PP framework for heterogeneous neural networks based on CKKS fully homomorphic encryption (FHE). Combining the advantages of FHE in supporting types of ciphertext calculation, FLFHNN eliminates the limitation that the algorithm only supports limited generalized linear models and realizes “short link” communication between participants, and adopts the training and inference of encrypted state to ensure confidentiality of the shared information while solving the problem of potential leakage from the aggregated values of federated learning. In addition, FLFHNN supports flexible expansion to multi-party scenarios, and its algorithm adapts according to the number of participants. Our analysis and experiments demonstrate that compared with Paillier based scheme, FLFHNN significantly reduces the communication cost of the system on the premise of retaining the accuracy of the model, and the required interactions and information transmission for training are reduced by almost 2/3 and more than 30% respectively, which is suited to large-scale internet of things scenarios.
Development of Machine Learning Based Fraudulent Website Detection Scheme

The development of mobile computing and e-commerce has greatly changed traditional transactions and grown online shopping. People are buying and selling goods on websites or social platforms. However, there are many malicious and counterfeit products on fraudulent websites to deceive consumers and make high improper profits. Due to the obvious increase in the number of such fraudulent websites, it is difficult to identify and detect these websites by manual inspection. In order to solve this problem, we propose an intelligent detection mechanism by using a machine learning approach to classify fraudulent websites. We use data set containing 300 legitimate websites, 300 fraudulent websites, and 15 features for training. In two machine learning algorithms, Random Forest and Deep Neural Networks, we divided the training set and the test set in a ratio of 8:2. Finally, the prediction is compared with the previous research results. The experimental results show that the RF accuracy of the random forest algorithm is 99.3% which is better than other deep neural networks algorithms.
Vision-based Human Detection by Fine-Tuned SSD Models

Human-robot interaction (HRI) and human-robot collaboration (HRC) has become more popular as the industries are taking initiative to idealize the era of automation and digitalization.Introduction of robots are often considered as a risk due to the fact that robots do not own the intelligent as human does.However, the literature that uses deep learning technologies as the base to improve HRI safety are limited, not to mention transfer learning approach.Hence, this study intended to empirically examine the efficacy of transfer learning approach in human detection task by fine-tuning the SSD models.A custom image dataset is developed by using the surveillance system in TT Vision Holdings Berhad and annotated accordingly.Thereafter, the dataset is partitioned into the train, validation, and test set by a ratio of 70:20:10.The learning behaviour of the models was monitored throughout the fine-tuning process via total loss graph.The result reveals that the SSD fine-tuned model with MobileNetV1 achieved 87.20% test AP, which is 6.1% higher than the SSD fine-tuned model with MobileNetV2.As a trade-off, the SSD fine-tuned model with MobileNetV1 attained 46.2 ms inference time on RTX 3070, which is 9.6 ms slower as compared to SSD fine-tuned model with MobileNetV2.Taking test AP as the key metric, SSD fine-tuned model with MobileNetV1 is considered as the best fine-tuned model in this study.In conclusion, it has shown that the transfer learning approach within the deep learning domain can help to protect human from the risk by detecting human at the first place.
Neural network compression with feedback magnitude pruning for automatic modulation classification

In the past few years, there have been numerous demonstrations of neural networks outperforming traditional signal processing methods in communications, notably for Automatic Modulation Classification (AMC). Despite the increase in accuracy, these algorithms are notoriously infeasible for integrating into edge computing applications. In this work, we propose an enhanced version of a simple neural network pruning technique, Iterative Magnitude Pruning (IMP), called Feedback Magnitude Pruning (FMP) and demonstrate its effectiveness for the "Lightning-Fast Modulation Classification with Hardware-Effficient Neural Network" 2021 AI for Good: Machine Learning in 5G Challenge hosted by the International Telecommunications Union (ITU) and Xilinx. IMP achieved a compression ratio of 9.313, while our proposed FMP achieved a compression ratio of 831 and normalized cost of 0.0419. Our FMP result was awarded second place, demonstrating the compression and classification accuracy benefits of pruning with feedback.
C3P: Cross-Domain Pose Prior Propagation for Weakly Supervised 3D Human Pose Estimation

This paper first proposes and solves weakly supervised 3D human pose estimation (HPE) problem in point cloud, via propagating the pose prior within unlabelled RGB-point cloud sequence to 3D domain. Our approach termed C3P does not require any labor-consuming 3D keypoint annotation for training. To this end, we propose to transfer 2D HPE annotation information within the existing large-scale RGB datasets (e.g., MS COCO) to 3D task, using unlabelled RGB-point cloud sequence easy to acquire for linking 2D and 3D domains. The self-supervised 3D HPE clues within point cloud sequence are also exploited, concerning spatial-temporal constraints on human body symmetry, skeleton length and joints’ motion. And, a refined point set network structure for weakly supervised 3D HPE is proposed in encoder-decoder manner. The experiments on CMU Panoptic and ITOP datasets demonstrate that, our method can achieve the comparable results to the 3D fully supervised state-of-the-art counterparts. When large-scale unlabelled data (e.g., NTU RGB+D 60) is used, our approach can even outperform them under the more challenging cross-setup test setting. The source code is released at https://github.com/wucunlin/C3P for research use only.
Digital Tools for Developing Oral English Competency in Trainee Teachers

There are four skills to be developed in language teaching: writing, listening, reading, and speaking. All of them are equally important; however, oral competency is the most difficult for students. Therefore, digital tools play a significant role in optimizing activity class hours for interaction and communication. Off-class hours allow students to practice at their rhythm and in available space. In this qualitative research, we analyzed two apps, English Speaking Practice and ELSA Speak, for their utility in developing oral competency among 14 sixth-semester pre-service teachers pursuing the Bachelors in Primary Education and training in the Luis Hidalgo Monroy Normal School in Veracruz, Mexico. Results indicated positive performance changes in four evaluated criteria: fluency, content and vocabulary, pronunciation, and communication skills.
Fundamental Physics

In the late 1800s, physics appeared too much as a field that had reached its limit. It was thought that given enough perseverance everything could be understood, based on established laws like electromagnetics, mechanics and hydrodynamics.
Pengembangan Buku Penghubung Berbasis Android

This study aims to explain how the feasibility, practicality, and development of an android-based link book application at SMAN 8 Tanjung Jabung Barat. This research is a type of R&amp;D (Research &amp; Development) research using the ADDIE development model which consists of 5 stages, consisting of analysis (analysis), design (design), development (development), implementation (implementation) and evaluation (evaluation). Data was collected by means of observation and interviews which were used to conduct needs analysis, while the questionnaire was used to obtain data on the feasibility of the product. The data analysis technique used descriptive statistical analysis to analyze quantitative data obtained from expert validation questionnaires and product trial questionnaires. To analyze the qualitative data, the technique of checking the validity of the data is triangulation of sources, triangulation of methods and triangulation of theory. Based on the results of data analysis, it is known that the display aspect gets an average rating score of 4.5 with a percentage of 90%. The ease of use aspect obtained an average score of 3.67 with a percentage of 73.33%. Then the linguistic aspect obtained an average score of 4 with a percentage of 80%. Furthermore, the implementation aspect obtained an average score of 4.33 with a percentage of 86.67%. Of the four aspects of the assessment, the highest score is in the display aspect with a percentage score of 90%. While the overall aspect of the assessment by media experts, obtained a score of 16.5 with a percentage of 82.50%, which is included in the very decent category. In conclusion, the android-based link book is very feasible and practical to use at SMAN 8 Tanjung Jabung Barat. This android-based link book can help make it easier for teachers to report student activities at school.&#x0D; &#x0D; Keywords: Android Application. Liaison Book, Development
Intelligent English Automatic Translation System Based on Multi-Feature Fusion

The current automatic recognition methods of machine English translation errors have poor semantic analysis ability, resulting in low accuracy of recognition results. In order to improve the accuracy of English translation and realize the intelligence of the English translation system, an intelligent method based on multi-feature fusion is proposed. Conception of English automatic translation system. The combined form of semantic feature analysis and phrase translation is used to improve the English bilingual translation algorithm; in addition, cross-editing and multi-threaded phrase translation loading measures are used to complete the intelligent design of the translation system. According to the directed graph of wrong translation results, an automatic recognition algorithm for machine English translation errors is constructed to automatically identify translation errors. The relevant experimental simulations show that the newly designed English phrase translation combined machine automatic translation system has high translation accuracy and recall rate, rarely occurs translation errors, and has strong intelligence.
Time in the Chinari Circle: Leonid Lipavsky

This article explicates the concept of time in the works of Leonid Lipavsky, a member of the Leningrad literary and philosophical group OBERIU who wrote books entitled Explanation of Time, Treatise on Water, Study of Horror and a collection of private discussions that he brought together as Conversations. The article maintains that Lipavsky abandons a traditional view of time as an objective ontological characteristic of things and suggests considering it as a phenomenon related to the peculiarities of human sensation and of consciousness and language. According to Lipavsky, it is man who brings time into the world, which left to itself would be timeless. That possibility is contingent upon the structural uniformity between time and consciousness. The very source of human time appears to be the ability of consciousness to differentiate. The nature of time is revealed by the experience of extremity (the experience of horror) and is most explicitly manifested through language. In order to understand this process, the article analyzes special techniques developed by Lipavsky and the Chinari group to capture the contradictory experience of time. Lipavsky’s concept of the hieroglyph is central to the article’s finding that Lipavsky and the Chinari aimed at creating a synthetic methodology which would remain within the boundaries of a “critical” position (i.e. within a human-scale, phenomenological description of the world) while also pursuing a radical “empirical” inquiry into alien forms of reality. Lipavsky claimed that it is possible to make a catalogue of hieroglyphs which would contain a fund of “extra-human” experiences and then transform and expand the limits of the classical encyclopedic format inherited from the Enlightenment. The article argues that, despite its unique features, Lipavsky’s theory of time should be seen as falling within the philosophical tradition of phenomenalism. It is also evident that this theory resonates with the culture of the modern era and its critique of intellectualism and urge to synthesize science and art.
Efficient High-Resolution Network for Human Pose Estimation

Convolution neural networks (CNNs) have achieved the best performance nowadays not just for 2D or 3D pose estimation but also for many machine vision applications (e.g., image classification, semantic segmentation, object detection and so on). Beside, The Attention Module also show their leader for improve the accuracy in neural network. Hence, the proposed research is focus on creating a suitable feed-forward AM for CNNs which can save the computational cost also improve the accuracy. First, input the tensor into the attention mechanism, which is divided into two main part: channel attention module and spatial attention module. After that, the tensor passing through a stage in the backbone network. The main mechanism then multiplies these two feature maps and sends them to the next stage of backbone. The network enhance the data in terms of long-distance dependencies (channels) and geographic data. Our proposed research would also reveal a distinction between the use of the attention mechanism and nowadays approaches. The proposed research got better result when compare with the baseline-HRNet by 1.3 points in terms of AP but maintain the number of parameter not change much. Our architecture was trained on the COCO 2017 dataset, which are now available as an open benchmark.
A STUDY OF ANXIETY EXPERIENCED BY EFL STUDENTS IN PRESENTING PROPOSAL SEMINARS AT ENGLISH STUDY PROGRAM MUHAMMADIYAH UNIVERSITY OF BENGKULU

This descriptive qualitative research aimed to find out the factors that cause anxiety in EFL students when presenting proposal seminars. A number of 20 students who had carried out proposal seminars participated as the subject of this research. Video and audio recording were conducted during the interviews to find out the factors of students’ anxiety during the presentation of the proposal seminars. The interviews revealed that, there were several factors that cause students’ anxiety in presenting proposal seminars, such as fear of failure, fear of negative criticism, lack of vocabulary, lack of grammar, poor pronunciation, fear of losing the thread of material, fear of being stopped, negative thinking, fear of making mistake, fear negative evaluation, and feel incompetent or appearing awkward in front of many people. It is suggested that EFL students who intend to conduct proposal seminars consider those factors to minimize anxiety. The finding of this research at the same time can be as a reference to create a better proposal seminar presentation. The last but not least, material mastery, presentation practices, watching some similar videos of seminars presentation, hard effort, and determination are truly essential factors contributing to the success of any proposal seminars presentation.&#x0D; &#x0D; Keywords: Anxiety, presentation, proposal seminar
What does Inter-Cluster Job Submission and Execution Behavior Reveal to Us?

Modern High Performing Computing (HPC) facil-ities have multiple computing clusters that serve different pur-poses. These include large-scale computing clusters and smaller data visualization and analysis clusters, which are meant to shift the load of data analytics jobs from the large-scale systems. We perform the first in-depth characterization of cross-cluster behavior of users and jobs and provide an analysis of three inter-related systems at the Argonne Leadership Computing Facility (ALCF). Our analysis reveals interesting trends related to the resource utilization and predictability of user and job behavior across different clusters.
Smart Adware and Product Recommendation using Object Detection

Object detection (OD) has been used in a number of different case studies. Many times, while viewing videos on a streaming platform, viewers may desire to learn more about a specific object or product. Many video streaming applications have been discovered to employ predefined tags or video titles to promote adverts to viewers, and these applications do not use OD to recommend products. Adding predetermined tags to videos can be time consuming and have an influence on the app’s usefulness. To address this problem, this research proposes a novel approach that employs machine learning-based OD to identify potential objects/products that the recommendation module with mAP of 51.3 which can be used to find appropriate internet links.
Training Quantum Boltzmann Machines with Coresets

Recent work has proposed and explored using coreset techniques for quantum algorithms that operate on classical data sets to accelerate the applicability of these algorithms on near-term quantum devices. We apply these ideas to Quantum Boltzmann Machines (QBM) where gradient-based steps which require Gibbs state sampling are the main computational bottle-neck during training. By using a coreset in place of the full data set, we try to minimize the number of steps needed and accelerate the overall training time. In a regime where computational time on quantum computers is a precious resource, we propose this might lead to substantial practical savings. We evaluate this approach on 6x6 binary images from an augmented bars and stripes data set using a QBM with 36 visible units and 8 hidden units. Using an Inception score inspired metric, we compare QBM training times with and without using coresets.
Covert Communications in Satellite Internet: A Survey

The broadcast nature of wireless channels and broad coverage brings significant challenges to the security of Satellite Internet. Recently, a new security paradigm named covert communication aims to enhance security by hiding the transmission process and has received great research attention. Various covert transmission schemes were proposed to achieve covertness for different network scenarios. Motivated by the importance of promising security techniques, this survey provides a comprehensive overview of the recent works on covert communication in Satellite Internet for the first time. We first introduce the basic architecture and characteristics of Satellite Internet, as well as its access security challenges. Then, an in-depth overview of covert communication technologies is provided with an emphasis, which is divided into two categories, i.e., traditional ones and information theory-based ones. Finally, several key challenges and future research directions on covert communication are presented.
Study of the Attitude of the Students of the Institute of Culture to Preservation and Transmission of the Ethno-Cultural Heritage

The article deals with the problems of the attitude of students of a creative higher educational institution to the need of preservation of the ethno-cultural heritage in a globalizing world. The article takes into account the results of the author's long-term research activity in the field of modeling, the formation of a multicultural society, problems of education in the field of culture and art, which made it possible to identify the main personality qualities of a student of a creative higher educational institution. The main approaches and optimal ways of coexistence of people of different ethnic groups in a multicultural society, based on universal values are highlighted. The practical significance of the research lies in the concretization of the foundations for the preservation and translation of ethno-cultural heritage, the experience of intercultural interaction in the context of globalization, aimed at preserving the stability of a multicultural society.
TOS: A Relative Metric Approach for Model Selection in Machine Learning Solutions

One of the major tasks for a machine learning practitioner is to determine a classifier that would perform well not only on the test data but also on the unseen data from the real world in the future. Metrics to evaluate machine learning algorithms play a significant role to adopt one model from a wide range of machine learning algorithms to achieve optimal performance. Popular metrics like accuracy, precision, recall, F1 measure are greatly affected by the proportion of the majority class, sometimes provide less distinctive and discriminating value and also neglect the impact of true-negative in the outcome result. Moreover, real-world scenarios are skewed. Depending on the environment, problems can be either Type-I or Type-II error case sensitive and a particular error case has to be minimized whether the other one increases. To address this, we proposed a novel relative metric, Trade Off Score(TOS) with informativeness property, good distinctive property considering the type of sensitivity the environment holds and also immune to an imbalanced dataset. TOS provides a score based on the trade-off between accuracy and error rate according to case sensitivity. The proposed metric also quantifies model efficiency by taking into account all the model performances thus forming a relative-metric. We also evaluated our metrics from three different perspectives to justify the performance along with other metrics.
Optimally Solving the Multiple Watchman Route Problem with Heuristic Search (Extended Abstract)

In the Watchman Route Problem (WRP), the task is to find a path for a watchman agent such that all locations in the given map will be visually seen by the watchman at least once during the path traversal. Recently, the problem has been optimally solved on a grid map using heuristic search. In this paper, we extend this work to the case of multiple agents. We call this problem the Multiple Watchman Route Problem (MWRP). In MWRP, the task is to find a path for each watchman such that each location on the map will be seen by at least one watchman. We optimally solve MWRP with heuristic search for two different objective functions with a number of A*-based variants, including an enhanced branching mechanism. We then provide an experimental study on these methods and on other attributes of this problem.