Reviewerly/exHarmony huggingface dataset:
VLSP 2021 - SV challenge: Vietnamese Speaker Verification in Noisy Environments

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; The VLSP 2021 is the eighth annual international workshop whose campaign was organized at the University of Information Technology, Vietnam National University, Ho Chi Minh City (UIT-VNU-HCM). This was the first time we organized the Speaker Verification shared task with two subtasks SV-T1 and SV-T2. SV-T1 focuses on the development of SV models with limited data, and SV-T2 focuses on testing the capability and the robustness of SV systems. With the aim to boost the development of robust models, we collected, processed, and published a speaker dataset in noisy environments containing 50 hours of speech and more than 1,300 speaker identities. A total of 39 teams registered to participate in this shared task, 15 teams received the dataset, and finally, 7 teams submitted final solutions. The best solution leveraged English pre-trained models and achieved 1.755% and 1.950% Equal Error Rate for SV-T1 and SV-T2 respectively.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Differential Privacy Techniques for Healthcare Data

This paper analyzes techniques to enable differential privacy by adding Laplace noise to healthcare data. First, as healthcare data contain natural constraints for data to take only integral values, we show that drawing only integral values does not provide differential privacy. In contrast, rounding randomly drawn values to the nearest integer provides differential privacy. Second, when a variable is constructed using two other variables, noise must be added to only one of them. Third, if the constructed variable is a fraction, then noise must be added to its constituent private variables, and not to the fraction directly. Fourth, the accuracy of analytics following noise addition increases with the privacy budget, $\epsilon$, and the variance of the independent variable. Finally, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual healthcare data, we provide evidence supporting the two predictions on the accuracy of data analytics. Crucially, to enable accuracy of data analytics with differential privacy, we derive a relationship to extract the slope parameter in the original dataset using the slope parameter in the noisy dataset.
Towards Building a Speech Recognition System for Quranic Recitations: A Pilot Study Involving Female Reciters.

This paper is the first step in an effort toward building automatic speech recognition (ASR) system for Quranic recitations that caters specifically to female reciters. To function properly, ASR systems require a huge amount of data for training. Surprisingly, the data readily available for Quranic recitations suffer from major limitations. Specifically, the currently available audio recordings of Quran recitations have massive volume, but they are mostly done by male reciters (who have dedicated most of their lives to perfecting their recitation skills) using professional and expensive equipment. Such proficiency in the training data (along with the fact that the reciters come from a specific demographic group; adult males) will most likely lead to some bias in the resulting model and limit their ability to process input from other groups, such as non-/semi-professionals, females or children. This work aims at empirically exploring this shortcoming. To do so, we create a first-of-its-kind (to the best of our knowledge) benchmark dataset called the Quran recitations by females and males (QRFAM) dataset. QRFAM is a relatively big dataset of audio recordings made by male and female reciters from different age groups and proficiency levels. After creating the dataset, we experiment on it by building ASR systems based on one of the most popular open-source ASR models, which is the celebrated DeepSpeech model from Mozilla. The speaker-independent end-to-end models, that we produce, are evaluated using word error rate (WER). Despite DeepSpeech’s known flexibility and prowess (which is shown when trained and tested on recitations from the same group), the models trained on the recitations of one group could not recognize most of the recitations done by the other groups in the testing phase. This shows that there is still a long way to go in order to produce an ASR system that can be used by anyone and the first step is to build and expand the resources needed for this such as QRFAM. Hopefully, our work will be the first step in this direction and it will inspire the community to take more interest in this problem.
Efficient HPC and Energy-Aware Proactive Dynamic VM Consolidation in Cloud Computing

The adoption of High-Performance Computing (HPC) applications has gained an extensive interest in the Cloud computing.Current cloud vendors utilize separate management tools for HPC and non-HPC applications, missing out on the consolidation benefits of virtualization.Non-HPC applications executed in the cloud may interfere with resource-hungry HPC applications, which is a key performance challenge.Furthermore, correlations between application major performance indicators, such as response time and throughput, with resource capacities reveal that conventional placement strategies are impacting virtual machine efficiency, resulting in poor resource optimization, increased operating expenses, and longer wait times.Since applications often underutilized the hardware, smart execution of HPC and Non-HPC applications on the same node can boost system and energy efficiency.This research incorporates proactive dynamic VM consolidation to enhance the resource usage and performance while maintaining energy efficiency.The proposed algorithm generates a workload-aware fine-grained classification by employing machine learning techniques to generate complimentary profiles that alleviate cross-application interference by intelligently co-locating non-HPC and HPC applications.The research used CloudSim to simulate real HPC workloads.The results verified that the proposed algorithm outperforms all heuristic methods with respect to the metrics in key areas.
Shrinking Neural Style Transfer Model with Knowledge Distillation

Neural style transfer is a technique to transfer a style of an artistic image to another photorealistic image. The early development of this technique is exploiting the intermediate output of a deep learning model and utilizing it to optimize a targeted image. However, this technique needs some time to finish the process. An approach called knowledge distillation is widely used to optimize deep learning models so the speed of the model can be increased. Commonly, this technique is applied in a supervised task by providing a soft label as a target label for the student model. In this paper, a method is proposed to enable knowledge distillation happened in style transfer tasks. It is found this method is effective enough to make the style transfer process faster and give good results. In this paper also some discussion has been elaborated regarding the condition that is adequate to produce a good student model for style transfer tasks using knowledge distillation.
Didattica immersiva all’università: obiettivi e azioni del progetto SEPA360

This chapter introduces the relevance of the digital technologies for a constant update of the teaching and learning practices in the Higher Education and the challenges to cope with for their effective development, especially in the pandemic and post-pandemic period. The Erasmus+ project SEPA360 aiming at introducing the emerging technology of video 360° as a tool for helping the students in enhancing their capability in learning is then presented together with the activities carried out in the SEPA360 Consortium.
Vehicle Detection and Classification System Based on Magnetic Sensors: A Review

In an Intelligent Transportation System (ITS), vehicle identification and categorization (VIC) from a moving platform is critical (ITS). VIC is input into traffic control and management systems. Due to a variety of circumstances such as different types of cars, vehicles with nearly identical qualities having unique classes, and so on, in congested traffic, vehicle identification and classification is still a challenge. When compared to different sensors, the magnetic sensor has great implementation potential in the vehicle detection sector Because of its high sensitivities and lightweight. This paper aims to discuss how the magnetic characteristics of Vehicles can be used to detect and classify vehicles. First, a brief operating principle and properties of the most commonly used magnetic sensors are reviewed. This paper also reviewed the most recent applications, implementations, and research in using the magnetic sensor to detect and identify vehicles, as well as classify and determine Vehicle speeds.
Query Reformulation for Indonesian Question Answering System Using Word Embedding of Word2Vec

Query reformulation is one of the tasks in Information Retrieval (IR), which automatically creates new queries based on previous queries. The main challenge of query reformulation is to create a new query whose meaning or context is similar to the old query. Query reformulation can improve the search for relevant documents for Open-domain Question Answering (OpenQA). The more queries are given to the search system, and the more documents will be generated. We propose a Word Predicted and Substituted (WPS) method for query reformulation using a word embedding word2vec. We tested this method on the Indonesian Question Answering System (IQAS). The test results obtained an E-1 value of 81% and an E-2 value of 274%. These results prove that the query reformulation method with WPS and word-embedding can improve the search for potential IQAS answers.
Critique of: “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From UC San Diego

Bayesian networks (BNs) have become popular in recent years to describe natural phenomena in situations where causal linkages are important to understand.In order to get around the inherent nontractability of learning BNs, Srivastava et al. propose a markov blanket discovery-based approach to learning in their paper titled "A Parallel Framework for Constraint-based Bayesian Network Learning via Markov Blanket Discovery."We are able to reproduce both the strong and weak scaling experiments from the paper up to 128 cores, and verify communication cost scaling for all three algorithms in the paper.We also introduce methodological improvements to weak scaling that show the paper's findings are unique to the methodology and not the datasets used.Slight variations in performance were observed due to differences in datasets, core count, and job scheduling.
Risk assessment and control selection for cyber-physical systems: a case study on supply chain tracking systems

Aim: The paper proposes a novel risk assessment method ology for complex cyber-physical systems: The proposed method ology may assist risk assessors to: (a) assess the risks deriving from cyber and physical interactions among cyber-physical components; and (b) prioritize the control selection process for mitigating these risks. Methods: To achieve this, we combine and modify appropriately two recent risk assessment method ologies targeted to cyber physical systems and interactions, as underlying building blocks. By applying the existing method ology, we enable the utilization of well-known software vulnerability taxonomies, to extract vulnerability and impact submetrics for all the interactions among the system components. These metrics are then fed to the risk analysis phase in order to assess the overall cyber-physical risks and to prioritize the list of potential mitigation controls. Results: To validate the applicability and efficiency of the proposed method ology, we apply it in a realistic scenario involving supply chain tracking systems. Conclusion: Our results show that the proposed method ology can be effectively applied to capture the risks deriving from cyber and physical interactions among system components in realistic application scenarios, while for large scale networks further testing should be carried out.
Detecting Auto-generated Texts with Language Model and Attacking the Detector

We propose a simple approach to the detection of automatically generated texts. A pre-trained language model, fine-tuned on the shared task’s dataset, achieved 3rd place on the binary task leaderboard with 82.6% accuracy. In the multi-task leaderboard, the language model achieved an F1 score of 64.5% after being fine-tuned with the same procedure. In order to investigate the weaknesses of this approach, we explore two possible attacks on the detector: selecting from language model outputs and directed beam search. These attacks reduce the likelihood of detecting the generated texts without significant loss in quality. Both attacks do not require retraining the generative model and are applied at inference time.
Protein Folding Model Using Quantum Computation

We have performed a series of quantum computations for folding of the PSVKMA peptide by using the blueqat AutoQML simulator by which a given problem can be converted from QUBO (quadratic unconstrained binary optimization) of quantum annealing to QAOA (quantum approximate optimization algorithm) of VQE (variational quantum eigensolver). The IonQ quantum system of ion-trap type was utilized as well. A three qubit problem was successful by both. However, the situation became difficult for a five qubit case, especially for the IonQ having vulnerability to noises.
Hierarchical Dual Attention-Based Recurrent Neural Networks for Individual and Group Activity Recognition in Games

We study the problem of simultaneously recognizing complex individual and group activities from spatiotemporal data in games. Recognizing complex player activities is particularly important to understand game dynamics and user behavior having a wide range of applications in game development. To do so, we propose a novel framework by developing a hierarchical dual attention RNN-based method that leverages feature and temporal attention mechanisms in a hierarchical setting for effective discovery of activities using interactions among individuals. We argue that certain activities have dependency on certain features as well as on temporal aspects of the data which can be leveraged by our dual-attention model for recognition. To the best of our knowledge, this work is the first to address activity recognition using spatiotemporal data in games. In addition, we propose using game data as a rich source of obtaining complex group interactions. In this paper, we present two contributions: (1) two annotated game datasets that consist of individual and group activities, (2) our proposed framework improves the state-of-the-art recognition algorithms for spatiotemporal data by experiments on these datasets.
DRL and Blockchain Empowered Federated Learning Framework for Genetic Data Engineering

With the development of genetic data engineering and edge intelligence, more and more intelligent applications and services are trained in the edge side. However, the centralized training mode has the problems of high transmission delay and user privacy disclosure, while federated learning (FL) can protect the privacy of users, and reduce data transmission costs by distributing the training work. Existing FL schemes often ignore the impact of low-quality training nodes and the security issues in the data transmission process. To improve the accuracy of the FL model, we design a node selection algorithm based on deep reinforcement learning (DRL). In addition, we use blockchain for model transmission to complete the global aggregation of FL to enhance the security and reliability of model parameters. We design a blockchain empowered FL framework and further propose a two-layer consensus algorithm based on PBFT to improve consensus efficiency, reduce consensus delay and reduce communication resource consumption. Simulation results show that the proposed node selection algorithm outperforms other compared algorithms, and can well improve the accuracy of the model and reduce the loss function. The proposed consensus algorithm can balance the consensus efficiency and communication resource consumption.
Research on National Music Teaching Mode Based on Computer Platform

National music is the quintessence of Chinese traditional art. The artistic concept and humanistic aesthetics are of great significance to improve students' artistic quality and optimize the talent training system. However, in the environment of wisdom education, the traditional national music teaching model has been difficult to inspire students to understand and learn related music materials. Therefore, it is necessary to optimize the teaching ecology of national music by constructing an intelligent and three-dimensional national vocal music teaching mode based on the computer advantage platform. Based on the present situation of national music teaching, this paper puts forward a national music teaching mode based on computer platform.
Assessment of Readability Level of Reading Materials in Indonesia EFL Textbooks

The research aimed to assess the readability of reading material in two different English textbooks published by different publishers. Many pieces of research have been conducted to examine the reading materials in English textbooks, and it had been discovered that the reading materials were insufficiently adequate for the levels of students. The research applied a descriptive content analysis method to analyze, interpret, and describe the data. The Flesch Reading Ease formula was as an instrument. The research’s data were derived from the English textbook “Pathway to English” by Erlangga grade X and the English textbook “Bahasa Inggris” by The Ministry of Education and Culture grade X. Based on the findings of the research, five reading texts out of 15 match with students’ level (fairly difficult level) of grade X in the first textbook, while three texts out of 13 equal with students’ level (fairly difficult level) of grade X in the second textbook. Furthermore, the first textbook’s average readability score indicates that the texts are ‘standard’, whereas the second textbook indicates that the texts are ‘fairly difficult’. Based on the study findings, some recommendations for teachers are to use the readability formulas to provide appropriate reading material for students, as well as for other researchers to conduct a larger context about readability.
Object-Model Transfer in the General Video Game Domain

A transfer learning approach is presented to address the challenge of training video game agents with limited data. The approach decomposes games into objects, learns object models, and transfers models from known games to unfamiliar games to guide learning. Experiments show that the approach improves prediction accuracy over a comparable control, leading to more efficient exploration. Training of game agents is thus accelerated by transferring object models from previously learned games.
Modeling Topics in User Dialog for Interactive Tablet Media

In this paper, we present a set of crowdsourcing and data processing techniques for annotating, segmenting and analyzing spoken dialog data to track topics of discussion between multiple users. Specifically, our system records the dialog between the parent and child as they interact with a reading game on a tablet, crowdsources the audio data to obtain transcribed text, and models topics of discussion from speech transcription using ConceptNet, a freely available commonsense knowledge base. We present preliminary results evaluating our technique using dialog collected using an interactive reading game for children 3-5 years of age. We successfully demonstrate the ability to form discussion topics by grouping words with similar meaning. The presented approach is entirely domain independent and in future work can be applied to a broad range of interactive entertainment applications, such as mobile devices, tablets and games.
The Efforts to Improve Student Learning Outcomes with an Online Material Solution in School Classes in Rural Indonesia

This article was an effort to improve student learning in schools in rural areas that can be made by various solutions, including utilizing materials available on the Internet. The authors believe that schools in remote areas did not necessarily have to surrender to their conditions, but they must make various efforts to improve the quality of learning. Therefore, we have carried out this study by utilizing many existing data and literature sources, found by searching the Internet. Next, we examined the phenomenological approach to find an answer from a large number of data at the highest level. The analysis involved coding the data, evaluating, and drawing conclusions that we believe can answer the main problem. Based on the analysis and discussion of the results, we can conclude that the learning problems in remote areas can be solved using the internet network, which has slowly penetrated remote areas. The result was also said that online materials were solution when teachers and resources were limited access, and these online materials barely distinguish between schools in cities and villages. Thus, the results of this study can add insights that were useful at the academic level of high schools and their benefits for policy courts in government and education practitioners in the field. Keywords: Students Learning Outcomes, Online Materials, Rural School
Refining Medical Image Steganography Scheme Based on Pixels Disparity Value and Huffman Coding

The steganography is a new and challenging method for transmitting confidential information over the internet utilizing a hidden cover object. Developing an efficient picture steganography system is currently fraught with difficulties because of the limited capacity, weak resilience, and imperceptibility of current steganography systems under development. This problem can only be solved by increasing the picture steganography's capability and security levels. Methods: Based on these variables, this work aims to present the state-of-the-art approach known as the Pixels Disparity Value (PDV) that hides patient information reports in the distinct medical photos for the second party. It's recommended that the procedure be broken down into three primary phases, each with its own set of instructions. Results: The statistical and economic evaluations of the suggested approach.
STUDY OF EDUCATIONAL RESULTS OF STUDENTS IN DISTANCE LEARNING

The article presents the results of an empirical study of educational outcomes achieved by students in distance learning. In the course of the study, a survey of students and their parents was conducted, including the diagnosis of indicators of cognitive and communicative universal educational activities. The analysis of the data obtained confirms that the digitalization of the educational process has a significant impact on the motivation for learning, the development of cognitive processes, communication skills and subject results of students.