Reviewerly/exHarmony huggingface dataset:
Online Engagement and Satisfaction of Undergraduate Engineering Students During the Covid-19 Pandemic

The COVID-19 pandemic has significantly changed the teaching-learning-evaluation process with the transition of universities to emergency remote education.As universities have moved to a new way of learning, student engagement and satisfaction became an evolving concern among academics.The aim of study was to explore the student engagement and satisfaction with online learning experiences in engineering courses in a hybrid approach during the pandemic.A total of 135 undergraduate students from Technical University of Cluj-Napoca, Romania, responded to the online survey based on two previously developed surveys.According to the results, the online student engagement and satisfaction were at a medium level.Female student engagement was higher than male student engagement, and the 4th year students had higher satisfaction scores with online learning.The study found that student engagement was significantly correlated with satisfaction in online learning experiences.The results have implications for educational theory and practice in creating effective blended learning environments and for future research.
Analysis of Various Facial Expressions of Horses as a Welfare Indicator Using Deep Learning

This study aimed to prove that deep learning can be effectively used for identifying various equine facial expressions as welfare indicators. In this study, a total of 749 horses (healthy: 586 and experiencing pain: 163) were investigated. Moreover, a model for recognizing facial expressions based on images and their classification into four categories, i.e., resting horses (RH), horses with pain (HP), horses immediately after exercise (HE), and horseshoeing horses (HH), was developed. The normalization of equine facial posture revealed that the profile (99.45%) had higher accuracy than the front (97.59%). The eyes–nose–ears detection model achieved an accuracy of 98.75% in training, 81.44% in validation, and 88.1% in testing, with an average accuracy of 89.43%. Overall, the average classification accuracy was high; however, the accuracy of pain classification was low. These results imply that various facial expressions in addition to pain may exist in horses depending on the situation, degree of pain, and type of pain experienced by horses. Furthermore, automatic pain and stress recognition would greatly enhance the identification of pain and other emotional states, thereby improving the quality of equine welfare.
Towards an Understanding of Character Believability

The term 'believability', when referring to game characters, is used in two incompatible ways: realism, meaning human-like, and character believability as used in the character arts (the illusion of life). The same term being used for two different concepts leads to confusion and inconsistent use of language in research communities. This paper examines the different research areas that cluster under realism and character believability, and then performs a deeper exploration of character believability, focusing on characteristics, examples, and evaluation methodologies.
Physical-layer security of optical communication based on chaotic optical encryption without an additional driving signal

We propose and numerically demonstrate a scheme for physical-layer security based on chaotic phase encryption, where the transmitted carrier signal is used as the common injection for chaos synchronization, so there is no need for additional common driving. To ensure privacy, two identical optical scramblers consisting of a semiconductor laser and a dispersion component are used to observe the carrier signal. The results show that the responses of the optical scramblers are highly synchronized but are not synchronized with the injection. By properly setting the phase encryption index, the original message can be well encrypted and decrypted. Moreover, the legal decryption performance is sensitive to the parameter mismatch, since it can degrade the synchronization quality. A slight drop in synchronization induces an evident deterioration in decryption performance. Therefore, without perfectly reconstructing the optical scrambler, the original message cannot be decoded by an eavesdropper.
Pengenalan Penggunaan Helm Proyek Berstandar Pada Citra Foto Berdasarkan SIFT Dengan SVM

One of the causes of the high number of work accidents in Indonesia is not using personal protective equipment. The project helmet is a personal protective equipment that serves to protect he head. However, the level of awareness of workers using helmets in this project is still lacking. his study aims to determine the accuracy level of introduction to the use of standard project helmets. Scale Invariant Feature Transform (SIFT) and Support Vector Machine (SVM) are feature extraction and classification methods used in this study. The data used is in the form of 90 photos which are divided equally into 3 types of images. Research shows that there are 170 out of 180 upper bodies that have been successfully detected. The kernels used are linear, gaussian and polynomial. By sing 119 data as training data and 51 data as test data, the highest accuracy results are obtained the linear kernel with an overall accuracy rate of 68.63%.
DIGITALIZATION AS A PROSPECTIVE DIRECTION OF THE CONTEMPORARY EDUCATION SYSTEM

The issues of digitalization, digitalization of the society and educational environment arecovered. It is investigated that digitalization is aimed at ensuring the longlife learning process, as well asits individualization on the basis of advanced learning technologies. Digitization of education directlydepends on the level of mastery of educator’s digital awareness which is aimed at making educationalactivities up to date and more productive. The scheme of the digital education system is given. Themanagement of digitalization in the educational environment is carried out through digital marketing, aimedat organizing the interaction with teaching and support staff, research and teaching staff, graduates,students, applicants using a range of digital communication channels; monitoring changes in the formationof a positive image of the educational institution; stimulating the creation of new digital communities andinnovations; development of personalized marketing materials for target audiences. The factors influencingthe digital transformation of educational institutions are considered in the article. It is established that oneof the main elements of education digitalization is digital literacy, which is defined as a set of knowledgeand skills necessary for the safe and effective use of the digital technologies and Internet resources. Basedon the analysis of various sources, digital literacy is based on the digital competence of the educator and isdefined as the willingness and ability to use digital resources, computers, mobile devices and cloudtechnologies in the educational process, and to create and effectively use educational digital systemopportunities and all its components in the educational process. The fundamental difference between digitalliteracy and digital competence is the inclusion of a component of motivation and responsibility. The types of skills on which digital literacy and its components depend on are highlighted. The main components ofdigital competence of educators are listed. The levels of educator’s digital competence are considered andcharacterized at the following levels: basic, advanced and proficient.
Semantic-Aware Dynamic Generation Networks for Few-Shot Human–Object Interaction Recognition

Recognizing human–object interaction (HOI) aims at inferring various relationships between actions and objects. Although great progress in HOI has been made, the long-tail problem and combinatorial explosion problem are still practical challenges. To this end, we formulate HOI as a few-shot task to tackle both challenges and design a novel dynamic generation method to address this task. The proposed approach is called semantic-aware dynamic generation networks (SADG-Nets). Specifically, SADG-Net first assigns semantic-aware task representations for different batches of data, which further generates dynamic parameters. It obtains the features that highlight intercategory discriminability and intracategory commonality adaptively. In addition, we also design a dual semantic-aware encoder module (DSAE-Module), that is, verb-aware and noun-aware branches, to yield both action and object prototypes of HOI for each task space, which generalizes to novel combinations by transferring similarities among interactions. Extensive experimental results on two benchmark datasets, that is, humans interacting with common objects (HICO)-FS and trento universal HOI (TUHOI)-FS, illustrate that our SADG-Net achieves superior performance over state-of-the-art approaches, which proves its impressive effectiveness on few-shot HOI recognition.
Website-Aware Protocol Confusion Network for Emergent HTTP/3 Website Fingerprinting

Website fingerprinting is exploited to analyze encrypted traffic traces and infer the visited website. Existing website fingerprinting methods can achieve satisfying performance for the HTTP traffic visiting websites over TCP. Recently, a new protocol QUIC has been proposed, and HTTP-over-QUIC has been formalized as the next generation HTTP, named HTTP/3. Thus, it is necessary to classify HTTP/3 traces. However, since HTTP/3 is newly proposed and is being deployed, it is difficult to collect a large number of HTTP/3 traces. Intuitively, we can use sufficient TCP traces to improve the performance of the QUIC trace classifier. Unfortunately, the protocol discrepancy exists between TCP and QUIC traces, which undermines the generalization ability of the classifier. In this paper, for practical website fingerprinting of HTTP/3, we propose a Website-Aware Protocol Confusion Network (WAPCN), which exploits only a few QUIC traces to train a website classifier with the help of lots of available TCP traces. It consists of four main parts: a feature extractor, a website classifier, a protocol discriminator, and a website-aware adaptor. The feature extractor aims to extract trace representations from both TCP and QUIC traces. It cooperates with the website classifier to learn the discriminative representation for the website classification. The role of the protocol discriminator is to confuse protocols and guide the feature extractor to learn protocol-invariant representations. The website-aware adaptor can enhance protocol-invariant representations to be aware of the website classification boundary. Extensive experiments are conducted on various tasks to demonstrate the effectiveness of WAPCN.
HSCFC: High-dimensional streaming data clustering algorithm based on feedback control system

At present, few studies have considered the clustering analysis of high-dimensional data collected by many sensors in a real-time streaming environment. Existing clustering analysis algorithms for high-dimensional data are primarily based on batch processing models, and most of them cannot meet the requirements of incremental high-dimensional data streams, which are extremely common in practical applications. To address the aforementioned problems, this paper focuses on the study of high-dimensional data clustering based on stream processing, and proposes a high-dimensional data stream clustering algorithm based on a feedback control system, which comprises three stages: window principal component analysis, feedback stream clustering, and feedback controller. The classic exponentially weighted attenuation function is used in the window principal component analysis to avoid concept drift in the data stream, and incremental feature extraction executes through the sliding window to improve the iterative efficiency of the data in the window. To minimize the errors caused by variability in projection angles during dimensionality reduction, a feedback stream clustering stage is designed with alternating iterations of window clustering and cluster aggregation. Aiming at the problems caused by manually adjusting the hyperparameters used in high-dimensional data stream clustering, a feedback controller is developed to adjust the hyperparameters in the two other stages by analyzing the clustering results in real time and using a discriminant score to adopt corresponding feedback strategies. The experimental comparisons between the proposed and the traditional algorithms on multiple datasets demonstrate the effectiveness of the former.
Agro Analysis System for Precision Agriculture

Abstract: Huge amount of data is collected by the sensors from the end. Subsequently, this considerably big amount of data must be processed, analyzed and stored in a cost effective way. In this manner, an enormous pool of computing resources and storage must be provided to compute this vast amount of data. We focused on introducing the latest technologies such as sensors, WSN to radically revise approaches to agriculture by collecting the data about the various parameters of soil, analyzing the data and performing the computations, giving the best optimal solutions for the farming. The application of computing in the agricultural economy will open up a vast range of prospects, such as the vast storage of agriculture information, the cloud management of agricultural production process, the storage of agricultural economy information, early-warning and policymaking based on the agricultural products market, the tracing management of agricultural products quality.
Riflessioni sull'insegnamento linguistico del Greco antico e del Latino nella scuola italiana

Criticism of the teaching of ancient Greek and Latin languages in Italian classical high schools has remote origins, if one is aware that one of the most famous exponents of this current is even Pascoli; over the last twenty years, however, the extensive querelle that has arisen around classical studies has led a large number of intellectuals not only to question the usefulness and relevance of tradere and declensions, but also to reflect on the possibility of questioning the teaching methods, attempting to overcome the secular paradigm of the so-called “traditional” method by adopting innovative techniques and assumptions: this is the case, for example, of the “natura” method, devised by the Danish latinist Ørberg in the 1950s.&#x0D; The fervent reflections conducted on Latin and Greek have so far led to a polarisation of positions around “traditionalism”, among whose ranks one can count those who persist in teaching the classical languages by maintaining as a reference the antiquated modus docendi overflowing with philology and devoid of glottodidactics, and to “naturalism”, a trend born of the insights of Ørberg, and before him Berlitz, based on the paradoxical principle of teaching the ghlóssai in the same way as English, French, or any other living language.&#x0D; The present study is entrusted with the task of synthesising both factiones, highlighting the strengths of both methods, with the aim of providing a rationalised, objective and fruitful field of investigation for the continuation of the intellectual debate on Greek and Latin, in the hope of reaching a unanimous consensus on the indispensable importance of classical studies, particularly linguistic and literary studies, for the construction of a critical and aware democratic society.
User Interface Design for Arm Robot Controller Application using User-Centered Design Method as a Learning Media

Arm Robot Controller is a controller that can be use to controlled wirelessly using Bluetooth based on an android application. Arm Robot Controller is designed as a learning media to help users(student) learn and understand material related to industrial robotic. This arm robot controller born because there is a need to facilitate student that have a large anthusiasm in understand industrial robotic. For that purpose they need a learning media of a controller that they can build with material that available in their surroundings to make it easier to explain on how the principle of controller works. The design method that use in this product are the User-centered Design (UCD) method. The UCD method has 5 stages, namely Plan the Human Centered Process, Specify the Context of Use, Specify User and Organizational Requirements, Produce Design Solutions and Evaluate Design against User Requirements. The design of the User Interface (UI) design follows the UI design principles of User Familiarity, Consistency, Minimal Surprise, Recoverability, and User Guidance. User Interface design uses a web-based designer application, namely MIT App Inventor which is open source. MIT app inventor can make it easier for users because programming uses blocks that are connected to each other. Evaluation of the design results uses the Overall Relative Efficiency method to measure the level of efficiency and error, while the System Usability Scale method is used to measure the level of satisfaction. The evaluation results show an efficiency level of 77.98% with an error rate of 18.6%. While satisfaction score is 79 so that the application can be categorized as acceptable.
Time-delayed Multivariate Time Series Predictions

A major issue with real-time monitoring is to collect complete data. Hardware or software failures, network issues or, more frequently, time delays can disrupt such a collection. This results in having two versions of the same information: one in real-time but with potentially missing data, and the another, albeit complete, is delayed. Many works have studied how to handle missing data for classification and prediction. However, to the best of our knowledge, they do not consider how to leverage the delayed complete data to assist in learning the representation of real-time available data with missing values. This is despite the fact that the delayed complete data contain all the information (e.g., periodicities and trends). In this paper, we propose a framework to enhance the representation learning of the real-time available data by aligning the representation of past real-time but with missing data to that of past delayed but complete data. We test both a distance metric and contrastive learning to achieve this alignment. We implement our framework on a Transformer-based model and experiment it on three datasets. The efficiency of our solution is evaluated against seven baselines and considering four distinct patterns of missing data. Our experiments show that this proposal has a significant improvement in prediction accuracy (5.21% on average) over the baselines.Keywordsmultivariate time series predictiontime delayrepresentation alignmentcontrastive learning
Spatial and Temporal Guidance for Semi-supervised Video Object Segmentation

Semi-supervised video object segmentation aims to segment the object in the video when only the annotated mask of the first frame is given. Recently, memory-based methods have attracted increasing attention with significant performance improvements. However, these methods employ pixel-level matching according to the similarity without considering the trajectory and the feature of the object, which may result in mismatching between the object and non-object region in complex scenarios. To relieve this problem, we propose spatial and temporal guidance for semi-supervised video object segmentation. The proposed method takes into account the consistency of the object in spatiotemporal domain and employs global matching to conduct pixel-level matching. Moreover, we design the spatial guidance module (SGM) to track the trajectory of the object. And we design the temporal guidance module (TGM) to focus on long-term object-level feature from the first frame. The proposed spatial and temporal guidance effectively alleviates mismatching and makes the model more robust and efficient. Experiments on YouTube-VOS and DAVIS benchmarks show that our method outperforms previous state-of-the-art methods with a fast inference speed.
Adversarial Hard Negative Generation for Complementary Graph Contrastive Learning

Graph contrastive learning (GCL) has attracted rising research attention recently due to its effectiveness in self- supervised graph learning. A key step of GCL is to conduct data augmentation, based on which self-supervised learning is performed through the contrast between two augmented data views. Existing approaches generally generate the two data views from the original graph, which has been revealed to be less effective due to the lack of data diversity. Meanwhile, although the data augmentation methods and the contrastive modes have been extensively studied, the effect of hard negative samples (i.e.samples that are difficult to distinguish from an anchor node) on GCL is not fully explored. In this paper, we propose a novel complementary graph contrastive learning method boosted by adversarial hard negative sample generation. Specifically, we first construct a κNN graph as the complementary counterpart of the original graph in the semantic space. Then graph augmentation is conducted in both the semantic and topology spaces for the two complementary graphs to obtain two contrastive views with a larger data diversity. To facilitate the contrastive learning, an adversarial network named ADNet is also proposed to generate hard negative samples. The generated samples are more informative and challenging, and thus can further boost the learning performance. Extensive evaluations over the node classification task demonstrate that our proposal outperforms existing state-of-the-art GCL methods, and even exceeds supervised approaches. The code of this work is publicly available at https://github.com/sktsherlock/HNGCL-V1.
Selectively Increasing the Diversity of GAN-Generated Samples

Generative Adversarial Networks (GANs) are powerful models able to synthesize data samples closely resembling the distribution of real data, yet the diversity of those generated samples is limited due to the so-called mode collapse phenomenon observed in GANs. Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector and focus on the conditional information. Recent methods proposed to mitigate this limitation increase the diversity of generated samples, yet they reduce the performance of the models when similarity of samples is required. To address this shortcoming, we propose a novel method to selectively increase the diversity of GAN-generated samples. By adding a simple, yet effective regularization to the training loss function we encourage the generator to discover new data modes for inputs related to diverse outputs while generating consistent samples for the remaining ones. More precisely, we maximise the ratio of distances between generated images and input latent vectors scaling the effect according to the diversity of samples for a given conditional input. We show the superiority of our method in a synthetic benchmark as well as a real-life scenario of simulating data from the Zero Degree Calorimeter of ALICE experiment in LHC, CERN.