Reviewerly/exHarmony huggingface dataset:
Hand Gesture Recognition Using Machine Learning for Bionic Applications: Forearm Case Study

The ubiquitous use of accelerometers and gyroscopes can provide meaningful knowledge about the recognition of human limb movements and offers significant potential for the design of human-machine interaction and the control of bionic systems. Our research puts an emphasis on linking acceleration data produced by hand movements to surface electromyograms (sEMG) collected in the arm in order to actively participate in robotic prosthesis control and the development of digital healthcare systems. The goal of this work is to demonstrate how to model a flexible system of hand gesture recognition using a dual-sensor accelerometer and gyroscope as a first stage, with the ability to evolve and improve its efficiency by incorporating other signals in subsequent stages of feature extraction and processing. Our research is primarily focused on identifying the triaxial signal generated via the accelerometer and gyroscope that appears when the hand is performing six gestures. Based on a massive gesture library collected over a long period of time by nine individuals belonging to three age categories and different genders, a forearm gesture classification model has been successfully developed, implementing a novel set of measurement criteria and statistical characteristics by virtue of five reputed machine-learning classifiers with an overall accuracy of 97.9% for training and 88.0% for testing, which are employed to differentiate and learn the forearm direction classes and interpret the performances.
Digitalizing Teaching and Learning in Light of Sustainability in Times of the Post-Covid-19 Period: Challenges, Issues, and Opportunities

Most educational institutions were caught unprepared by the COVID-19 pandemic, which forced millions of academicians and schools to dramatically change their methods of instruction. For roughly 1.5 years, online learning was essential, and during this time, digital education played a fundamental function in allowing instructors to instruct pupils remotely utilizing the variety of digital platforms and technologies. The objectives of this present paper are to explore the challenges, opportunities and issues that come with digital education after the COVID-19 outbreak. The present paper comes to a conclusion that the integration of digital education into the educational system, the use of m-learning, more adaptable and digital education methods, the review and redesign of national policies and curricula, the improvement of institutional infrastructure, the development of educational resources, and the improvement of students’ and teachers’ digital technology (and online pedagogy) skills are all possibilities to explore. Some suggestions are eventually addressed with reference to taking the crises’ lessons into account and moving on in the post-COVID normal.
Image Encryption Algorithm Based on Improved Hill Cipher Using the 2D Logistic Map

This paper proposes a novel image encryption method, which is based on an improvement of the classic HILL technique using the 2D logistic map. First vectorize the original image and subdivide it into blocks of three pixels. Then several dynamic vectors are created from the 2D logistics map. These vectors are used with pseudo-random translation vectors, for the implementation of several affine transformations to overcome the problem of the linearity of the classical method of Hill. After that, chaining functions will be used to boost the avalanche effect's impact and defend the new system against any differential attack. Simulations on a set of images demonstrate the good performance of the algorithm in terms of image encryption and different attacks.
A Side Channel Attack Methodology Applied to Code-Based Post Quantum Cryptography

The present work proposes a Side Channel Attack that targets the multiplier of a code-based Post Quantum Cryptography primitive. The Secret Key has been recovered with the Correlation Power Analysis obtained with the use of a methodology that simulates the power consumption profile of a design and then validates the method with the real device. The methodology is proposed as a useful tool to study weaknesses of designs during their design phase.
Material and Methods

The data used in Ilyin et al. (Country-scale assessment of heavy metal pollution: a case study for Germany. Meteorological Synthesizing Centre-East, Moscow, Russia, pp. 1–12, 2020) include measurements of air concentrations (Cd, Hg, Pb; 2014, 2015, 2016) and wet depositions (Cd, Pb; 2014, 2015, 2016) from the 6 EMEP stations of the UBA monitoring network and up to 196 other stations in Germany whose measurements were submitted to UBA by the federal states.
Hate Speech Within the Electoral Political Processes in Zimbabwe

In Zimbabwe, it is becoming apparent that there are no rules for winning elections. The existing electoral guidelines are theoretical because politicians use all sorts of shenanigans to gain support from the electorate. As Zimbabwe draws closer to the 2023 harmonized elections, political hate speech has become more rampant. The availability and access to social media have heightened political name callings, derogatory speeches and sarcasm. The current political landscape is a conducive breeding ground for hate speech, decorated with multiple political rivals, party conflicts, schisms, coalitions and re-coalitions. This chapter explores the factors that perpetuate toxic political hate speech and hate infuriated political violence in Zimbabwe from a historical perspective. It taps into the Hate Speech Theory propounded by John Stewart Mill in the mid-nineteenth century, which argues that hate speech is an unavoidable part of free speech. Qualitatively, this paper uses grey literature to gather data.
A Blind Modulation Classification Method Based on Decision Tree and High Order Cumulants

The classification of modulation schemes will be widely used in future communication systems, among which the classification accuracy and speed of modulation classification methods have always been the focus of the research. Our goal is to simplify the complexity of the classification model and improve the modulation recognition accuracy. In this paper, we propose the blind modulation classification method based on the decision tree and high order cumulants. During the experiments, the results are visualized to observe the effect. By comparing the probability of accurate classification at different signal-to-noise ratios at each stage, it is used as an overall measure of the performance of the entire classifier. Moreover, experiments show that the performance of the independent block level and the whole system has excellent accuracy and stability under the optimal threshold, signal-to-noise ratio, and the number of samples.
A Recursive CEIEC Technique for Image Enhancement Employing Sharpening Filter

Contrast Enhancement (CE) changes the relative magnificence and obscurity of an image, enhancing information content and visual quality. Numerous researchers have proposed several CE techniques with the aim to improve contrast. One such recent proposal in this direction is Image Contrast Enhancement Based on Intensity Expansion Compression (CEIEC) [22]. Though this global CE technique outperforms others, its performance can be further enhanced by using it recursively on the whole image so as to improve the local areas. In addition, to improve Entropy, Gradient and Contrast, a sharpening filter is used for pre-processing. The proposed CE technique along with others is implemented in MATLAB 2015. The result shows that the proposed technique surpasses its counterparts and other popular techniques in the literature in terms of various performance metrics such as visual quality, Entropy, Gradient and Contrast, while the results are comparable in terms of Quality-aware Relative Contrast Measure (QRCM), Structural Similarity (SSIM) and Contrast Improvement Index (CII).
Research on Voiceprint Recognition Based on MFCC-PCA-LSTM

Voiceprint recognition is mainly to mine the personality factors of the speaker s physiological characteristics to identify his identity. It has the characteristics of non-contact, easy data acquisition and stable signal characteristics.This paper mainly uses deep learning network to realize text-independent voiceprint recognition. Firstly, the sound perception feature Mel frequency cepstrum coefficient (MFCC) and its first-order differential feature component ($\Delta$MFCC) are extracted and combined with feature vectors. Through principal component analysis (PCA) and data normalization method, the extracted voiceprint feature matrix is compressed and input into long short-term memory network (LSTM) and bidirectional LSTM network model for voiceprint target classification. Through the experimental verification of the four algorithm models, the results show that the (MFCC+MFCC)-PCA-LSTM model has the highest accuracy of voiceprint recognition in the THCHS-30 data set, and its accuracy can reach 93%.
Multi Terrain Motion Control Vehicle

The integration of an increasing number of functions within the Human–Machine Interface (HMI) of cars will increase the problem of tool coping with device handling operations. The current work focuses on the concept of using a car robot that can navigate wirelessly using an Arduino UNO. Robots are now playing a significant role in human lives. The present-day scenario to govern those vehicles are through the use of a keypad, controller or pre-planned commands. This work aims to introduce a new technique to interact with a robot through gestures. The task is to control a robotic vehicle from a distance through hand gestures. The idea has two major factors, one is the vehicle and the other is the controlling station. The controlling station has a gesture recognition system that will adhere to the instructions and send it to the vehicle. The control station is the microcontroller board Arduino UNO. With the help of the accelerometer, data from hand motions is supplied into the Encoder HT 12E IC via the Arduino UNO. After that, Tx 434 is used to convey the encoded data. The encoded data is received by Rx 434 in the receiver portion, where they are decoded by a Decoder HT 12D IC and transmitted to the L293D part that operates the motor. Thus, vehicle driving motors can be controlled with the instructions received from the vehicle driving motors.
DMRA: Model Usability Detection Scheme Against Model-Reuse Attacks in the Internet of Things

Internet of Things (IoT) devices can utilize deep learning (DL) to boost their intelligence, but also suffer from the long model training process. IoT devices thus may reuse public pretrained models to expedite the training through transfer learning. However, pretrained models may be subject to model-reuse attacks initiated by malicious DL servers, causing models to misclassify targeted data, which poses a threat to the security of IoT devices. In this work, we propose a new model usability detection scheme, the defense against model-reuse attacks (DMRAs), suitable for IoT scenarios. DMRA employs a variant of Lagrange’s mean value theorem to reverse-check the model, which is computationally efficient, thus, suitable for resource-constrained devices. Experimental evaluations on different data sets first validate that model-reuse attacks can attack models in federated learning. And, then demonstrate that DMRA detects such insidious attacks with up to 80% success rate at a lightweight computational cost.
Anomaly Recognition Method for Massive Data of Power Internet of Things Based on Bayesian Belief Network

At this stage, the massive data of the power Internet of Things (IoT) is collected and stored in a centralized way, this leads to the inefficient use and analysis of a large amount of information in the power IoT. The anomaly identification of massive data in the power IoT is the guarantee to effectively judge the operation status and security status of the power IoT. Therefore, this project takes the power grid IoT as the research object, and based on Bayesian theory, studies a new data anomaly identification method. First, the data conversion method is used to standardize the massive data of the power IoT, and the discrete wavelet transform method is used to preprocess the massive data of the power IoT. Secondly, the clustering algorithm is used to determine the feature density index of the massive data of the power IoT, and the high-order statistics method is used to extract the abnormal features of the massive data of the power IoT. Finally, based on Bayesian belief network, on this basis, establish and train the big data anomaly identification model of the power grid IoT to realize the big data anomaly identification of the power grid. The experimental results show that the proposed method can effectively improve the accuracy of anomaly recognition of the massive data of the power IoT.
Adversarial Multi-dimensional Distribution Alignment for Cross-domain Sentiment Analysis

Cross-domain emotion analysis is the application of transfer learning in emotion analysis task. It solves the problem of lack of labeled training data set in a certain field through knowledge transfer between domains. This paper analyzes the existing methods of cross-domain sentiment analysis and improves them from two aspects: extracting shared features between domains and shielding the differences in emotion expression between domains. The main contributions of this paper are as follows: (1) Aiming at the problem of insufficient emotion perception, we propose a Sentiment-aware Feature Extraction algorithm integrating external knowledge. (2) Aiming at the problem of obvious interference from the difference of feature distribution between domains, we propose a multi-dimensional domain difference measurement algorithm. Experiments show that the accuracy of AMDA proposed in this paper is significantly improved compared with the baseline algorithm: 1.83% higher in the English Amazon data set and 4.68% higher in the Chinese data set.
Promoting a Common Testbed for Natural Deduction Tutoring Systems

This paper makes the case for common testbeds for evaluating educational systems, such as cognitive tutors, and fully develops a language for specifying such testbeds in a particularly challenging case, that of cognitive tutors for logic and natural deduction. As the teaching of natural deduction spans several disciplines, and notation varies widely, it is impossible for a single specification to apply across all systems, so the key contribution of this paper is defining a "super-language" that we call the gold standard which can capture the capabilities of all currently-used systems, as well as other possible future directions. Translators both to and from individual logic languages allow this to serve as both a specification language for developing standard testbeds, and as an intermediate language to enable simpler system-to-system translation. The development described here can be used as an exemplar for developing common testbeds for other educational systems.
Evaluating the usage of Text-To-Speech in K12 education

With increased interest in the use of virtual avatars for educational purposes, there is a growing need for high-quality text-to-speech solutions. However, the effects of using synthesized speech in educational applications for younger listeners are still unclear as past findings have been inconsistent and most of them have been obtained in a lab setting with adult assessors. Next to that, it is unclear how much training material is needed for high quality speech synthesis. Particularly for low resource languages, the assumption that good quality synthesized speech requires substantial amounts of vocal recordings to train may be hindering the development of TTS-based solutions. In this study, we created four Dutch text-to-speech (TTS) models from different amounts of training material and evaluated the models in terms of voice perception and recall with K12 students in a classroom environment. Results showed that while the original human voice outperformed the synthesized voices in terms of the listening experience and knowledge test score, more hours of training material did not necessarily result in better outcomes suggesting that 10-15 hours of speech material might be sufficient for training a Dutch TTS. A weak positive correlation was found between listening experience and knowledge test performance, with the low listening effort being the most important factor. This outcome suggests that comprehensibility is likely the most important TTS feature for educational applications.
Integrating Stand-Alone New Media Technologies Such as Games and Virtual and Augmented Reality Software into Learning Management Systems

Both learning management systems (LMSs) and new media technologies (NMTs, e.g., Augmented/Virtual Reality software or Serious Games) can enhance teaching and learning, however, the integration of both technologies within an E-Learning system is challenging. NMTs are often developed as bulky stand-alone applications, for example, using game engines, which make them difficult to integrate into the LMS courses. Thus, connecting middleware technologies are needed, however, various technologies exist. In this paper, we introduce three techniques (SCORM-Data, xAPI-Injection, and JSON-REST) that integrate stand-alone NMT learning software into LMSs based on existing technologies and standards from the E-Learning domain. We discuss the techniques and their underlying processes based on a prototype implementation and the results of an expert interview and point out the advantages and disadvantages of each technique to give practitioners differentiated advice on which technique to use. Although SCORM-Data has fewer technical requirements, we conclude that our JSON-REST technique was favored through applicability, simplicity, ease of integration, and efficiency.
CloudPES: Cloud Portal of Educational Services for Higher Education Institutions in Nigeria

Globally, access to adequate funding by the Management of HEIs tops the list of challenges hampering the deployment of IT infrastructure and services that can efficiently drive various educational services of HEIs. This in turn affects the quality of both academic and administrative service delivery and the nation's economy. Therefore, the need to adopt a cloud portal of educational services (CloudPES), a cut-edge and low cost emerging IT service model is inevitable. This paper introduces a conceptual framework of CloudPES that describes a multi-tenant cloud portal where various educational services can be fully automated and integrated. With the use of explorative research method and object oriented analysis and design, fourteen different independent cloud applications as SaaS on deployment model of community cloud are presented for individual HEIs to subscribe. These applications are to automate educational services that can be rendered to/by staff; students; parents; management; and other stakeholders. CloudPES prototypes of the database design with its GUI implementation, checklist criteria for subscription of cloud applications and adoption benefits to HEIs are provided. The benefits of CloudPES to HEIs will include among others cost of software ownership reduction, increased return on investment, improved efficiency and productivity, high quality of educational service delivery, scalability, provision of secured source of big data analytics for research and decision making. As an example of software as a service (SaaS) that can run on community cloud of HEIs in Nigeria, subscription of CloudPES by HEIs in Nigeria is capable of revolutionizing and transforming HEIs into smart institutions.
The Sciences through Pixels: Virtual Reality as an innovation in the learning outcome in higher education in northwestern Mexico

In the last decade, there has been a reemergence of innovation practices in the education field, some of them has been the use of virtual reality (VR). In Mexico, the use of these technologies has been restricted to institutions that possess a specific capital and to academics that have the knowledge to use and adopt them in their learning outcomes. The emergence of remote learning due to the Covid-19 pandemic has generated a digital learner that requires different environments. This study (1) analyzes the use of VR among university and High School students to identify if they perceived that they improved their learning outcomes and if (2) the use of VR is considered as an innovation in their experiences. The study compares students' submissions from a private University and High School (ITESM, Sonora Norte Campus) in the State of Sonora, Mexico. The northwestern region of Mexico is known for its technological research and collaborations with North American institutions. We conducted a semi structured survey and analyzed their answers about VR use and innovation in their studies. We analyzed their interactions with the software and the literature that aided us in understanding innovation. Our study showed that most participants perceived that VR enhanced their learning outcomes. We conclude that innovation and technology must be added to the conversation when policymakers, education and industry agents come together to generate new outcomes for the new reality, the new learner and the future global market as well.
Design and Implementation of System of Recognition of Students’ Learning Behavior in Classroom Teaching Videos

Student learning behavior not only shows student participation in the teaching process, but also affects the quality of teaching directly. However, manual observation and coding of student behaviors exist with high workload and are susceptible to subjective factors of analysts. Combining artificial intelligence and other technologies to achieve recognition and analysis of classroom student behavior is also a prevailing research trend. The current student behavior recognition system has problems such as failing to pay attention to the interference of teacher activates for student behavior recognition, fewer behavioral categories of students that can be recognized through visual information alone. Therefore, this paper develops a system of recognition of student learning behaviors in classroom teaching videos by a method on recognition of student learning behaviors based on audio-visual information to identify student learning behaviors in teaching videos. First, this paper designs a pre-annotation module in which the user marks the teacher's image, voice, and silent clips for providing accurate identification of subsequent student behaviors. Second, the student behavior recognition module provides for the recognition of eight categories of student behaviors in real classroom teaching videos and visualizes the results so that users can understand the percentage of student learning behaviors in the current teaching clip and the temporal changes of individual behavior categories. The purpose of system is to assist teachers to be able to count student learning behaviors in the classroom quickly and scientifically and grasp student learning dynamics for data-enabled classroom teaching analysis.