Reviewerly/exHarmony huggingface dataset:
Community manager en centros educativos de infantil y primaria. Un estudio de caso

The consolidation of social media as a form of institutional communication makes it necessary to incorporate specialised workers in this area. Thus, the figure of the community manager and his or her professional skills become very important. The research is based on the hypothesis that primary schools need to have a community manager or a designated worker in place, to carry out their core functions, as better and faster communication between centres and families is becoming essential. This study is an analysis of the inclusion of this professional profile in Primary Schools in Extremadura (Spain). A survey was designed and sent to all the schools, and the social media profiles of the schools were analysed. After processing the data obtained, the results show that the incorporation of this professional profile in the centres is not very widespread in the region analysed. However, primary school management teams are understanding the need for proper management and communication on social media in order to offer the best possible service to their stakeholders.
What You Say Is What You Show: Visual Narration Detection in Instructional Videos

Narrated ''how-to'' videos have emerged as a promising data source for a wide range of learning problems, from learning visual representations to training robot policies. However, this data is extremely noisy, as the narrations do not always describe the actions demonstrated in the video. To address this problem we introduce the novel task of visual narration detection, which entails determining whether a narration is visually depicted by the actions in the video. We propose What You Say is What You Show (WYS^2), a method that leverages multi-modal cues and pseudo-labeling to learn to detect visual narrations with only weakly labeled data. Our model successfully detects visual narrations in in-the-wild videos, outperforming strong baselines, and we demonstrate its impact for state-of-the-art summarization and temporal alignment of instructional videos.
Sequentially Controlled Text Generation

While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.
Neural Network Adaptive Control with Long Short-Term Memory

In this study, we propose a novel adaptive control architecture, which provides dramatically better transient response performance compared to conventional adaptive control methods. What makes this architecture unique is the synergistic employment of a traditional, Adaptive Neural Network (ANN) controller and a Long Short-Term Memory (LSTM) network. LSTM structures, unlike the standard feed-forward neural networks, can take advantage of the dependencies in an input sequence, which can contain critical information that can help predict uncertainty. Through a novel training method we introduced, the LSTM network learns to compensate for the deficiencies of the ANN controller during sudden changes in plant dynamics. This substantially improves the transient response of the system and allows the controller to quickly react to unexpected events. Through careful simulation studies, we demonstrate that this architecture can improve the estimation accuracy on a diverse set of uncertainties for an indefinite time span. We also provide an analysis of the contributions of the ANN controller and LSTM network to the control input, identifying their individual roles in compensating low and high-frequency error dynamics. This analysis provides insight into why and how the LSTM augmentation improves the system's transient response. The stability of the overall system is also shown via a rigorous Lyapunov analysis.
TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction

Deep saliency prediction algorithms complement the object recognition features, they typically rely on additional information, such as scene context, semantic relationships, gaze direction, and object dissimilarity. However, none of these models consider the temporal nature of gaze shifts during image observation. We introduce a novel saliency prediction model that learns to output saliency maps in sequential time intervals by exploiting human temporal attention patterns. Our approach locally modulates the saliency predictions by combining the learned temporal maps. Our experiments show that our method outperforms the state-of-the-art models, including a multi-duration saliency model, on the SALICON benchmark. Our code will be publicly available on GitHub.
Exploring Efficient Few-shot Adaptation for Vision Transformers

The task of Few-shot Learning (FSL) aims to do the inference on novel categories containing only few labeled examples, with the help of knowledge learned from base categories containing abundant labeled training samples. While there are numerous works into FSL task, Vision Transformers (ViTs) have rarely been taken as the backbone to FSL with few trials focusing on naive finetuning of whole backbone or classification layer.} Essentially, despite ViTs have been shown to enjoy comparable or even better performance on other vision tasks, it is still very nontrivial to efficiently finetune the ViTs in real-world FSL scenarios. To this end, we propose a novel efficient Transformer Tuning (eTT) method that facilitates finetuning ViTs in the FSL tasks. The key novelties come from the newly presented Attentive Prefix Tuning (APT) and Domain Residual Adapter (DRA) for the task and backbone tuning, individually. Specifically, in APT, the prefix is projected to new key and value pairs that are attached to each self-attention layer to provide the model with task-specific information. Moreover, we design the DRA in the form of learnable offset vectors to handle the potential domain gaps between base and novel data. To ensure the APT would not deviate from the initial task-specific information much, we further propose a novel prototypical regularization, which maximizes the similarity between the projected distribution of prefix and initial prototypes, regularizing the update procedure. Our method receives outstanding performance on the challenging Meta-Dataset. We conduct extensive experiments to show the efficacy of our model.
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch

We consider the problem of computing differentially private approximate histograms and heavy hitters in a stream of elements. In the non-private setting, this is often done using the sketch of Misra and Gries [Science of Computer Programming, 1982]. Chan, Li, Shi, and Xu [PETS 2012] describe a differentially private version of the Misra-Gries sketch, but the amount of noise it adds can be large and scales linearly with the size of the sketch: the more accurate the sketch is, the more noise this approach has to add. We present a better mechanism for releasing Misra-Gries sketch under $(\varepsilon,\delta)$-differential privacy. It adds noise with magnitude independent of the size of the sketch size, in fact, the maximum error coming from the noise is the same as the best known in the private non-streaming setting, up to a constant factor. Our mechanism is simple and likely to be practical. We also give a simple post-processing step of the Misra-Gries sketch that does not increase the worst-case error guarantee. It is sufficient to add noise to this new sketch with less than twice the magnitude of the non-streaming setting. This improves on the previous result for $\varepsilon$-differential privacy where the noise scales linearly to the size of the sketch.
Quantum Multiple Access Wiretap Channel: On the One-Shot Achievable Secrecy Rate Regions

In this paper, we want to investigate classical-quantum multiple access wiretap channels (CQ-MA-WTC) under one-shot setting. In this regard, we analyze the CQ-MA-WTC using simultaneous position-based decoder for reliable decoding and using a newly introduced technique in order to decode securely. Also, for the sake of comparison, we analyze the CQ-MA-WTC using Sen's one-shot joint typicality lemma for reliable decoding. The simultaneous position-based decoder tends to a multiple hypothesis testing problem. Also, using convex splitting to analyze the privacy criteria in a simultaneous scenario becomes problematic. To overcome both problems, we first introduce a new channel that can be considered as a dual to the CQ-MA-WTC. This channel is called a point-to-point quantum wiretap channel with multiple messages (PP-QWTC). In the following, as a strategy to solve the problem, we also investigate and analyze quantum broadcast channels (QBCs) under the one-shot setting.
Blogging and MICROBLOGGING linguistics for the lay reader

This chapter offers a firsthand perspective on blogging and tweeting as a ‘public linguist’ in the hope that it might offer a leg up to those who need a productive outlet for their obsessions. This includes reflections on the benefits of blogging, some practical tips and some thoughts on the emotional side of blogging: protecting yourself while giving yourself.
STUDENT DATA MANAGEMENT SYSTEM USING BLOCKCHAIN

Blockchain technology has the potential to revolutionize the way student information is managed in the education sector. Our report presents the design and implementation of a student information system (SIS) using blockchain technology, which allows students to store and manage their personal and academic information on a decentralized platform. It also enables educators to easily access and verify student data, streamlining the process of enrolment and grading. Results from user studies and technical assessments demonstrate that the system provides a secure and efficient way to store and access student data, while also reducing the workload for educators. Overall, the report highlights the potential of using blockchain technology to improve the management of student education. Key Words: blockchain, management, data, security, smart contracts, student information system.
Business Interruption Insurance and COVID-19: A Critical Analysis of the Jurisprudence and the Response of the Spanish Insurance Sector

The closure of commercial establishments decided by Spanish authorities on several occasions between 2020 and 2021 as a measure to prevent the spread of the COVID-19 virus resulted in a flood of claims to insurers for damages resulting from business interruption. In Spain, the insurance covering those losses is a guarantee within a multi-risk insurance that also covers material damage to the premises and the right to compensation for loss of profits only arises if there is prior material damage. As a result, the insurers refused to pay and ended up being sued in court. This chapter analyses: (i) the Spanish rulings that have been handed down in relation to these claims (a clear change of trend can be observed here); (ii) whether policyholders who have received government aid due to the temporary closure of their businesses should have their insurance compensation reduced (if they are entitled to receive it); (iii) the measures that insurers are introducing in their contracts and wordings to avoid being exposed to paying compensation for business interruptions resulting from pandemics in the future; and (iv) the position of the insurance companies’ association (UNESPA) and the Spanish government in relation to the future coverage of these damages.
SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm

Sentiment analysis becomes an essential part of every social network, as it enables decision-makers to know more about users' opinions in almost all life aspects. Despite its importance, there are multiple issues it encounters like the sentiment of the sarcastic text which is one of the main challenges of sentiment analysis. This paper tackles this challenge by introducing a novel system (SAIDS) that predicts the sentiment, sarcasm and dialect of Arabic tweets. SAIDS uses its prediction of sarcasm and dialect as known information to predict the sentiment. It uses MARBERT as a language model to generate sentence embedding, then passes it to the sarcasm and dialect models, and then the outputs of the three models are concatenated and passed to the sentiment analysis model. Multiple system design setups were experimented with and reported. SAIDS was applied to the ArSarcasm-v2 dataset where it outperforms the state-of-the-art model for the sentiment analysis task. By training all tasks together, SAIDS achieves results of 75.98 FPN, 59.09 F1-score and 71.13 F1-score for sentiment analysis, sarcasm detection, and dialect identification respectively. The system design can be used to enhance the performance of any task which is dependent on other tasks.
Deep leakage from gradients

With the development of artificial intelligence technology, Federated Learning (FL) model has been widely used in many industries for its high efficiency and confidentiality. Some researchers have explored its confidentiality and designed some algorithms to attack training data sets, but these algorithms all have their own limitations. Therefore, most people still believe that local machine learning gradient information is safe and reliable. In this paper, an algorithm based on gradient features is designed to attack the federated learning model in order to attract more attention to the security of federated learning systems. In federated learning system, gradient contains little information compared with the original training data set, but this project intends to restore the original training image data through gradient information. Convolutional Neural Network (CNN) has excellent performance in image processing. Therefore, the federated learning model of this project is equipped with Convolutional Neural Network structure, and the model is trained by using image data sets. The algorithm calculates the virtual gradient by generating virtual image labels. Then the virtual gradient is matched with the real gradient to restore the original image. This attack algorithm is written in Python language, uses cat and dog classification Kaggle data sets, and gradually extends from the full connection layer to the convolution layer, thus improving the universality. At present, the average squared error between the data recovered by this algorithm and the original image information is approximately 5, and the vast majority of images can be completely restored according to the gradient information given, indicating that the gradient of federated learning system is not absolutely safe and reliable.
Implementation of Blended Learning Model to the Non-English Major Students in EFL Setting in the Russian Arctic

The article considers a blended teaching of English to non-English major students in the Russian Arctic.The relevance of this study is due to the fact that new technologies are being introduced into the education system in order to improve the quality of education.Blended learning is understood as a hybrid learning integrating online education and in person education.The article provides an overview of the research conducted on blended learning abroad in the context of English as a foreign language (EFL).It presents a blended learning model that consists of 3 blocks: methodology, learning process and assessment of learning outcomes.In general, this model consists of face-to-face instruction and digital platform Skyes.The pilot training was conducted to prove the effectiveness of the discussed blended learning model.100 non-English major bachelor students of the 1 st year students from the North-Eastern Federal University, the Arctic State Institute of Culture and Arts and the Arctic State Agrotechnological University took part in the pilot training.The students were divided into 2 experimental and 2 control groups of 25 students.The students of the control groups attended traditional classes with a distance learning format while the students of the experimental groups studied English in a blended learning format.The pilot training lasted for one semester.During the training 4 English skills were tested in students: 1) listening 2) vocabulary 3) grammar 4) reading.The results before and after the pilot training are presented.The results of pilot training prove the effectiveness of the implemented blended learning model in comparison with face-to-face instruction.
Self-Regulation and Formative Assessment Format Interrelation in Mining Engineering ESP Course

The paper considers the issues of the interrelation between obtaining the self-regulation skill and the format of the formative assessment which represents an effective tool of educational process involving comprehensive processing of the material and further feedback.The participants of the research were first and second-year engineering students of the mineral resources specializations in St. Petersburg Mining University.Paper-based and online assessment methods were used in control and experimental groups accordingly during the autumn term 2021 which entails five to seven formative tests, depending on the number of modules in the ESP course.The gathered data were manually put into the SPSS software and analyzed statistically with the output of ANOVA test, Cronbach's Alpha reliability test and Pearson correlation test with identification of p-value.The purpose of the study was to determine whether there exists a correlation between the formative assessment format and the acquisition of some non-technical skills in the ESP course such as self-regulation.The results of the formative assessment as well as the results of the selfregulation questionnaire proved to be statistically effective and revealed uneven distribution.The interpretation of the results showed that although the experimental group performed better during the online formative assessment, their self-regulation skill was not formed as profoundly as that of the control group students whose formative assessment was in pen-and-paper format.
Survey on Fully Homomorphic Encryption, Theory, and Applications

This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.
PELATIHAN GURU SEKOLAH DASAR DALAM PENGINTEGRASIAN GAMIFIKASI KE TAHAP-TAHAP PEMBELAJARAN MATEMATIKA

Gamification is one strategy that can be applied to develop students' interest in learning mathematics. The aim of this training is to introduce gamification to elementary school teachers in learning mathematics, to introduce teaching modules (MA) to elementary teachers who have not yet implemented the independent curriculum, and to train teachers to be able to integrate elements of gamification into MA. This training was held at SD N Penanggungan, Malang City. The training was held on November 20, 2022. The training participants were twenty teachers at SD N Penanggungan. The training was carried out using discussion and question and answer techniques. After that, the presenters accompany the teacher to practice the theory learned by making MA integrated with gamification. The result of this training is that teachers can develop MA that is integrated with gamification. In addition, the teacher is also able to recognize the characteristics of gamification and MA stages that are in accordance with the gamification.
Adaptive Hiding Algorithm Based on Mapping Database

Information hiding one of the important field of security which provide secure level for the information. Achieving multi levels of security system often researchers used cryptography side by side with steganography. Utilizing message digest algorithm to play the role of crypto which is extracted from secret created database. Message digest algorithm (MD5) used two times as one-way function to provide data integrity. The implemented system evaluated based on peak signal to noise ratio (PSNR) metric and the best value reaches 62.46. the proposed system works in adaptive behavior due to the different use of images as well as the selected point could be used to generate the hash code as well. The implemented system reaches up to sufficient level of security through using both steganography and cryptography.
Preface: Special Issue on Workshop on Advances in Parallel and Distributed Computational Models 2022

The 24th Workshop on Advances in Parallel and Distributed Computational Models (APDCM), which was held in conjunction with the International Parallel and Distributed Processing Symposium (IPDPS) on May 30 - June 3, 2022, aims to provide a timely forum for the exchange and dissemination of new ideas, techniques and research in the field of the parallel and distributed computational models. The APDCM workshop has a history of attracting participation from reputed researchers worldwide. The program committee has encouraged the authors of accepted papers to submit full-versions of their manuscripts to the International Journal of Networking and Computing (IJNC) after the workshop. After a thorough reviewing process, with extensive discussions, four articles on various topics have been selected for publication on the IJNC special issue on APDCM. On behalf of the APDCM workshop, we would like to express our appreciation for the large efforts of reviewers who reviewed papers submitted to the special issue. Likewise, we thank all the authors for submitting their excellent manuscripts to this special issue. We also express our sincere thanks to the editorial board of the International Journal of Networking and Computing, in particular, to the Editor-in-chief Professor Koji Nakano. This special issue would not have been possible without his support.
Layanan Informasi dengan Media Brosur untuk Meningkatkan Pemahaman Karier Siswa

This study aimed to determine the effectiveness of information services through brochure media to improve students' career understanding. It applied a quantitative approach with pre-experimental research method. Then, the researcher used a one-group pretest-posttest design. The research population was students of class XII IPA1 &amp; XII IPA2 of SMA Muhammadiyah Jayapura City, totaling 60 students from 2 classes. The research sample was the total sample because the number was less than 100. Based on the previous statistical data obtained from the pre-test and post-test data, there was a significant increase between the initial pre-test and the final post-test. In the pre-test data, it obtained an average of 74.79 which was included in the medium category. Then, after giving treatment of information services through brochure media, it obtained post-test data with an average of 84.48 which was included in the high category. Thus, the increase in students' career understanding from pre-test to post-test increased by 9.35. Based on the previous statistical test data, it obtained a (z) value of 6.684. Then, Asymp. Sig. (2-tailed) showed 0.000 which meant significant. This indicated that information service using brochure media was effective in increasing students' career understanding.