Reviewerly/exHarmony huggingface dataset:
DS2B: Dynamic and Secure Substitution Box for Efficient Speech Encryption Engine

This paper proposes an efficient encryption technique based on Dynamic and Secure Substitution Box (DS2B) design suitable for IoT and resource-constrained platforms. The DS2B has the advantages of simple structure and good encryption performance. A different number of strong S-boxes could be generated with minor variations in the DS2B parameters. Performance analyses of the DS2B, including differential/linear cryptanalysis, bijective, nonlinearity, strict avalanche criterion (SAC), and bit independence criterion (BIC) have been presented where high nonlinearity, and low differential uniformity are achieved. Besides, a comparison with recent S-boxes is introduced which shows the robustness of the DS2B. To verify the DS2B, a speech encryption engine was realized on FPGA. Various security analyses were evaluated including the National Institute of Standards & Technology (NIST) statistical test suite, number of samples changes rate NSCR, mean square error (MSE), correlation, histogram, and spectrogram. In this work, the silence periods are encrypted in a way that makes them much harder to detect using the DS2B. The proposed encryption scheme achieved a throughput of 9.87 G bit/s, which is higher than previous work. A Xilinx Nexys 4 FPGA evaluation board was used for implementing and verifying the design.
A Framework on Deep Learning-Based Indoor Child Exploitation Alert System

Child violence is one of the most heinous crimes prevailing in our society. Any form of abuse or violence to a child does matter and cannot be overlooked. It affects the mental health of a child so deeply that it influences his later life. So, taking proper measurements for saving every child from any sort of violence is a must. This paper proposes a modified deep learning-based violence detection framework that will be able to detect and alert child exploitation in real-time without any privacy breach. The proposed framework is designed to work indoor, so primarily it can be installed in homes and educational institutions. The mechanism of the system is as such firstly, the surveillance video streams will be optimized using a modified convolutional neural network (CNN). Secondly, a sequence of frames will be passed through CNN for feature extraction and transferred to the long short-term memory (LSTM), which will act as a classifier. A softmax layer also has been introduced for the probabilistic distribution. Finally, the age and activities of a specific person will be detected. If there is any violent activity, an alert will be sent to the guardian through the system. We intend to ensure our proposed framework will be implied for automatic violence detection in a quick and safe approach.
Analysis of Online Learning Satisfaction and Influencing Factors of College Students During Epidemic Period : —Based on Probit Model

Under the epidemic, various teaching activities have to be transferred from traditional classrooms to network platforms. Does online learning satisfy learners? What are the factors that affect their satisfaction? Based on 3218 undergraduate questionnaires from Tianjin University, 10 common factors were obtained by factor analysis. Then, an ordered Probit model was used to conduct an empirical analysis of the main influencing factors of students' online learning satisfaction. The results show that the characteristics of teachers' teaching behavior and the positive cognition of students' online learning have the greatest positive impact on online learning satisfaction, while students' negative involvement in autonomous learning has a significant negative impact on satisfaction.
Systematic Mapping on Software Reuse Teaching

Software reuse (SR) is a crucial software engineering discipline that seeks to create new components through preexisting ones. Using the concepts of this discipline correctly can bring several advantages, such as: reducing product cost, reducing the number of errors, and producing more efficient coding, among others. However, this approach is often proposed but fails; one of the possible causes is being a short time for teaching this discipline. To study software reuse teaching, this work performed a systematic mapping to identify what are the main difficulties, characteristics and importance of teaching this discipline. Through this mapping, it was possible to observe that SR is not a discipline commonly presented in academic curriculum of universities and that the approaches that have been used to teach this discipline are very similar despite the passing of years. The main problems found are the lack of practical training and the lack of student's engagement and motivation. Based on these problems, possible solutions were proposed to help in the discipline's teaching scenario.
English-Vietnamese Machine Translation Using Deep Learning

Recently, artificial intelligence-based machine translation has been much improved over the traditional methods. A machine translator is very useful for translating text or speech from one language to another. Machine translators have replaced the word mechanism in one language for words in another with verbatim translations. However, a good translation should be employed a both a sentence and a word that have complete meaning in accordance with the context of relevant sentence. In this paper, we studied on English – Vietnamese translation using deep learning methods including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Attention, and Transformer. The deep learning-based machine translators were compared based on the test accuracy of results translation. It was found that best deep learning-based machine translator model was the Attention mechanism, achieving 98.8% accuracy. The Transformer yielded second rank or 98.5% accuracy.
FPGA Implementation of a pipelined and pseudo-randomized TDES algorithm

In today's world where data breaching and misuse of confidential data are so common, an end user's utmost priority should be to keep one's data secure. This is where information security and cryptography comes into the picture and in this paper, a pipelined and pseudo randomized implementation of TDES encryption standard on FPGA using Verilog HDL is designed which can encrypt and decrypt a given user's messages with high throughput and the pseudo randomizer randomizes the key with the help of a Linear Feedback Shift Register which makes the encrypted output random thereby making it more secure than the traditional TDES systems. The target device used is xc7vx330t-3ffg1157 from Virtex-7 FPGA family offered by Xilinx on which a maximum frequency of 608.224 MHz and maximum throughput of 38.92 Gigabits per second was achieved. The proposed design is also faster when compared to previous works on their respective platforms. The proposed system was designed and analyzed thoroughly using the Xilinx ISE Design Suite v14.7.
Fast-robust book information extraction system for automated intelligence library

At present, in the large-scale book management scene, book sorting, daily maintenance and book retrieval are very common, but the book information is complicated and the efficiency of relying on manual management is extremely poor. Although there have been many self-service book systems based on optics or vision, they are mostly based on traditional computer vision algorithms such as boundary extraction. Due to the fact that there are more artificial experience thresholds, some shortcomings such as low detection accuracy, poor robustness, and inability to systematically deploy on a large scale, which lack of insufficient intelligence. Therefore, we proposed a book information extraction algorithm based on object detection and optical character recognition (OCR) that is suitable for multiple book information recognition, multiple book image angles and multiple book postures. It can be applied to scenes such as book sorting, bookshelf management and book retrieval. The system we designed includes the classification of book covers and back covers, the classification of books upright and inverted, the detection of book pages side and spine side, the recognition of book pricing. In terms of accuracy, the classification accuracy of the front cover and the back cover is 99.9%, the upright classification accuracy of book front covers is 98.8%, the back cover reaches 99.9%, the accuracy of book price recognition get 94.5%, and the book spine/page side detection mAP reaches 99.6%; in terms of detection speed, Yolov5 detection model was improved and the statistical-based pre-pruning strategy was adopted, support by our algorithm the system reaches 2.09 FPS in book price recognition, which improves the detection speed to meet actual needs.
The Role of Mobile Applications in Learning Arabic-Indonesian Translation

Abstract The current study seeks to find to what extent mobile applications were explored in learning translation of Arabic-Indonesian by students of Arabic department at UIN Sunan Ampel Surabaya, Indonesia. This research aimed to survey students’ perceptions and attitudes towards the use of mobile applications during their course in Translation. To investigate students’ perceptions and attitudes, a set of questionnaires was employed to capture their opinion. 45 students were voluntarily participated in answering the questionnaire. The participants of the study were students of Arabic department in their 3 rd year at the university. The study revealed that students demonstrated positive attitudes towardss the use of mobile applications in learning Translation. It was also found that mobile applications applied in Translation were beneficial and effective. The key benefits students’ experienced were the improvement of learners’ motivation to study, the increasing of learners’ learning autonomy and confidence, and the encouragement of learners’ opportunities to practices and self-learning.
Dynamic measurement of thickness distribution in a soap film by using a phase-modulated large lateral shearing interferometer

Accurate measurement of soap film thickness distribution is a difficult and challenging task. In this paper, dynamic thickness distributions of a soap film were measured and visualized by using a phase-modulated large lateral shearing interferometer. A spatial light modulator (SLM) was utilized to modulate phase shifts of the incident laser, and film phases wrapped between 0 and 2π were calculated from a least square method. The continuities of soap film thickness in space and time were adopted to obtain precise phase distributions, and then thickness distributions were retrieved. During the experiment, film thickness distributions were reconstructed at a rate of about 12 ms per frame, and the entire process from the initial stage of the soap film formation to rupture was recorded by a high-speed camera. In this process, the overall thickness of the soap film became thinner and the absolute values of thickness gradient decreased, which can be explained by the drainage effect caused by gravity.
Construction of Virtual Simulation Experiment Teaching Platform for Large and Sophisticated Equipment of Electromechanical Engineering

This paper proposes a multi-dimensional progressive mechanical and electrical engineering virtual simulation experimental teaching platform for large sophisticated equipment aimed to cultivate students' comprehensive design and creative ability. Combining with related industries experimental teaching requirements such as automobile and building material and integrating and optimizing the existing resources such as software resources, information equipment and experimental course resources, the platform incorporates virtual simulation, multimedia and other information technology and consists of virtual simulation experiment platform for assisting real experiment teaching, virtual simulation experiment platform for expanding real experiment teaching and virtual simulation experiment platform for supplementing real experiment teaching. The teaching practice proves that the platform improves the students' engineering practice ability, comprehensive design ability and innovation ability, which provides a good support for cultivating compound talents mastering the structure and application of large industrial equipment.
Research on Substation Project Prediction Method in Power Transmission and Transformation by Improved Neural Network Intelligent Model

Abstract On the basis of collecting a large number of practical engineering cases, this paper uses the improved neural network prediction model based on multiple linear regression to build an improved substation project cost prediction model based on the division of substation projects. Using the actual project sample data for empirical research, the results show that the prediction model has high accuracy for the prediction of substation engineering. This research work provides a reliable technical scheme for the cost prediction of power transmission project.
Development of Answer Validation System Using Responders’ Attributes and Crowd Ranking

Crowdsourcing has found a wide range of application in Community Question Answering (CQA).However, one of its biggest challenges is the need to address the quality of crowd answers contributions.Therefore, this work proposed a system that seeks to validate answers to questions provided by respondents using responders' attributes and crowd ranking technique.Weights were assigned to respondent answers based on their academic records, experience and understanding of the question to obtain valid answers.Thereafter, valid answers were ranked by the crowd using Borda Count algorithm.The proposed system was evaluated using Usability and User experience (UX) measurement.The result obtained demonstrated the effectiveness of the applied technique.
Vision-Based User-Friendly and Contactless Security for Home Appliance via Hand Gestures

In the current scenario where newer technologies have detonated, people urge to be safe and secured in every nook and cranny, which encompasses their home appliances too. Usually, people do not appreciate someone entering the house and handling the appliances ineptly, and the indisposition further augments if the dear ones at home reckon in elderly or differently abled, as it increases the risk of spreading contagious or infectious diseases. Henceforth, it is the need of this hour to secure the appliances and that too with a security technique that is user-friendly and pocket friendly. A security system built on vision-based hand gesture recognition is undoubtedly a good fix to all the problems mentioned. The uniqueness of this concept is the exemption from managing too many passwords. In this methodology, we have used dynamic hand gestures as the password to operate different home appliances. The use of region-based convolutional neural network (RCNN) in localization of hand movement has given robustness to the technique, against the challenges associated with hand gesture recognition (HGR). Another advantage of using region-based deep learning is that the technique does not require large data base for testing and training the architecture. The proposed technique in this chapter is simple, user-friendly, and cost-effective since it utilizes a simple camera and is invariant to hand shape. Above and beyond, it is designed with full consideration to senior citizens and differently abled people.
Self-Governing Public Decentralised Systems

The selection of members responsible for data replication is a challenge in decentralised record-keeping systems. In ‘permissioned’ systems, this crucial task is performed by a central authority or consortium. In ‘permissionless’ systems, however, the selection process is not trivial and comes with risks. Malicious actors, in a privileged position, can tamper with data, threatening the integrity of the system as a whole. Permissionless membership selection protocols, popularised with the dissemination of distributed ledger technology, have the objective of limiting the influence of a single entity on the wider network. They do so by approximating a participant’s legitimacy to participate in record maintenance. These approximations come with downsides, in terms of attackability, system performance, supported use-cases and resource requirements. In this paper, we propose a prototypical membership selection protocol that uses the measure of personhood as an approximation of legitimacy. Interpreting a decentralised system as a political system, we frame the membership selection problem as one of political representation. We propose a protocol that democratically attributes a personhood score to members, thus creating a self-governing public decentralised system. This work in progress lays out a roadmap for the formal evaluation of self-governing public decentralised systems and describes the anticipated challenges in their implementation. Our proposals provide a means to evolve the membership selection protocol when a closed, permissioned system evolves to an open, permissionless system, as several commercial platforms intend to do.
Experimental Vulnerability Analysis of a 4-D Hyperchaotic Random Number Generator

This paper presents a clone system that is deployed to shows the vulnerability of an FPGA based 4-D hyperchaotic random number generator (RNG) that is presented as "True" random number generator. The unknown parameter of the target hyperchaotic RNG is uncovered by using auto-synchronization method. Synchronization is achieved by monitoring only one of the state variable for a limited scalar time. Experimental results support the numerical analysis by predicting the next bit of the RNG.
Research on Digital Circuit Experiment System Based on Cloud Experiment

With the rapid development of Internet cloud technology, the concept of cloud laboratory has emerged. Compared with the traditional experimental teaching mode, cloud experiments are more flexible and free. Students can perform experimental operations anytime and anywhere without time and space constraints. The resources are shared, which can greatly improve the utilization rate of experimental resources. Students do not have to directly touch the experimental hardware equipment, which can effectively reduce the loss and maintenance cost of experimental equipment. Aiming at digital circuit experiment teaching, this paper studies a digital circuit experiment system based on cloud experiment to solve the problems in traditional experiment teaching and improve students' learning enthusiasm and initiative.
Development and Research of Neural Network Based Method for Reconstructing Audio Signals

In this paper we investigate the possibility of using neural networks to solve the problem of restoring audio signal. Based on the previously obtained results of the convolutional neural networks application for the extraction of a vocal part, we developed the concept of a convolutional neural network designed to correct distorted audio signal. The paper presents the initial concept of this neural network architecture which, unfortunately, showed unsatisfactory results. Nevertheless, based on the concept of this network, several new neural network architectures were developed specifically focused on recovering a distorted audio signal but the shortcomings of the basic architecture were taken into account. The paper contains descriptions of all these architectures and the results of their application to restore the drummer's part in the musical composition where it was removed. The obtained results show the high potential of convolutional neural networks application for solving such a complex problem as audio signal restoration.
Challenges and Opportunities for the Use of Indoor Drones in the Cultural and Creative Industries Sector

This work summarises The AiRT project’s results, achieved jointly by all partners between January 2017 and January 2019. Significant innovative progress has been made in this short period of time. The far-reaching effects on the drone manufacturing industry as a whole, as well as in the cultural and creative industry (CCI), will likely only be precisely quantified in the coming years. This is primarily due to the fact that the innovative integrated indoor positioning system not only brings advantages to the creative industries, but will certainly inspire other industries in the future that will adapt this innovative product to their specific needs. Overall, however, we hope that the AiRT project has drawn attention to the needs of the CCCIs, especially for SMEs, so that they will be able to strengthen their economic position within the EU through the solution we offer.
The #MeeToo Movement as an e-Discourse: Social and Legal Effects

In this article, the #MeToo movement, as a socio-political form of e-discourse (discourse enacted on social media platforms and other types of online channels), will be examined in terms of the effectiveness of its discursive forms and the kinds of effects these have had on social consciousness generally with regard to sexual misconduct in the workplace, and in terms of the cases it has made famous against individuals via “trial-by-social-media,” and their outcomes in people’s lives. The specific cases discussed in this paper are those concerning well-known Swedish and American media personalities, which are assessed within a broad discourse and legal framework. Overall, we conclude that, while movement has had a profound effect on social consciousness, so far it has not impugned the validity of legal systems in countries such as Sweden and the United States.&#x0D; Keywords:#MeToo, e-discourse, social media trials, legal systems, sexual misconduct, sexual assault, sexual abuse
Optimization Design and Application of Hybrid Simulation Skill Training Platform

Skill training platform is an important support for the training of skill talents. Both virtual simulation training platform and real equipment training platform have their own advantages, and hybrid simulation training platform takes into account the characteristics of both. It is combined with the actual post better to meet the demand of students' practical training. In this paper, the framework model of hybrid simulation skill training platform is established through its performance requirements. And taking the intelligent substation as an example, hybrid simulation skill training platform is built to provide the high immersion training conditions for substation operators, which obtains satisfactory results in the training process.
Online Percentile Estimation (OPE)

Abstract We describe a control loop over a sequential online algorithm. The control loop either computes or uses the sequential algorithm to estimate the temporal percentiles of any univariate input data sequence. It transforms an input sequence of numbers into output sequence of histograms. This is performed without storing or sorting all of the observations. The algorithm continuously tests whether the input data is stationary, and reacts to events that do not appear to be stationary. It also indicates how to interpret the histograms since their information content varies according to whether a histogram was computed or estimated. It works with parameter-defined, fixed-size small memory and limited CPU power. It can be used for a statistical reduction of a numerical data stream and, therefore, applied to various Internet of Things applications, edge analytics or fog computing. The algorithm has a built-in feasibility metric called applicability . The applicability metric indicates whether the use of the algorithm is justified for a data source: it works for an arbitrary univariate numerical input, but it is statistically feasible only under some requirements, which are explicitly stated here. We also show the results of a performance study, which was executed on the algorithm with synthetic data.