Reviewerly/exHarmony huggingface dataset:
Aplikasi model berbasis masalah dalam membaca bahasa Prancis bagi mahasiswa

The problem’s discussion of this research is comparing the application of Problem Based Learning Model (PBL) with conventional teaching in learning achievement of French reading skill for students of first semester students of French education program of Faculty of Teacher Training and Education of Lampung University in academic year 2015/2016. The purpose of this research is to know or describe (1) level in reading ability of French, (2) difference of median score of French reading skill’s achievement of the students who are taught by problem based learning with conventional teaching. The method of this research is used an experimental method. By knowing the hypothesis, the researcher uses t-test formula. To get the data of French reading skill, the researcher is used a test measurement. The result of the data analysis is as follow: 1) the population comes from the students of the first semester. Sampling technique will be used lottery technique, from the population of normal distribution and has homogeneous variety, 2) proposed hypothesis “Median achievement score of French reading skill of the students who are taught by problem based learning model is higher than median achievement score of French reading skill achievement of students who are taught by conventional teaching”, 3) level of reading skill of French student’s achievement in French education program of Faculty of Teacher Training and Education of Lampung University in academic year 2015/2016 is considered to be good, with a median score 75 and 4) the result of this research with the population of Fench education program of Faculty of Teacher Training and Education , first semester in academic year 2015/2016. The hypothesis is accepted if sign more than α. In this case, the significance level is 0.05, meaning only 5% are error and 95% correct. Keywords : problem based learning, French reading skill.
Guiding Visual Question Answering with Attention Priors

The current success of modern visual reasoning systems is arguably attributed to cross-modality attention mechanisms. However, in deliberative reasoning such as in VQA, attention is unconstrained at each step, and thus may serve as a statistical pooling mechanism rather than a semantic operation intended to select information relevant to inference. This is because at training time, attention is only guided by a very sparse signal (i.e. the answer label) at the end of the inference chain. This causes the cross-modality attention weights to deviate from the desired visual-language bindings. To rectify this deviation, we propose to guide the attention mechanism using explicit linguistic-visual grounding. This grounding is derived by connecting structured linguistic concepts in the query to their referents among the visual objects. Here we learn the grounding from the pairing of questions and images alone, without the need for answer annotation or external grounding supervision. This grounding guides the attention mechanism inside VQA models through a duality of mechanisms: pre-training attention weight calculation and directly guiding the weights at inference time on a case-by-case basis. The resultant algorithm is capable of probing attention-based reasoning models, injecting relevant associative knowledge, and regulating the core reasoning process. This scalable enhancement improves the performance of VQA models, fortifies their robustness to limited access to supervised data, and increases interpretability.
An Optimization Framework of Adaptive Computing-plus-Communication for Multimedia Processing in Cloud: A Review

Abstract: Clear trend within the evolution of network-based services is that the ever-increasing amount of multimedia system data concerned. This trend towards big-data multimedia system process finds its natural placement at the side of the adoption of the cloud computing paradigm, that looks the most effective solution to the strain of a extremely fluctuating work that characterizes this sort of services. However, as cloud data centers become a lot of and a lot of powerful, energy consumption becomes a significant challenge each for environmental concerns and for economic reasons. An effective approach to improve energy efficiency in cloud data centers is to rely on traffic engineering techniques to dynamically adapt the number of active servers to the current workload. Towards this aim, we propose a joint computing-plus-communication improvement framework exploiting virtualization technologies. Our proposal specifically addresses the everyday situation of data processing processing with computationally intensive tasks and exchange of a giant volume of data. The proposed framework not only ensures users the Quality of Service, but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies Keywords: Energy efficiency, Multimedia data processing, Cloud resource management, Load balancing, Dynamic voltage and frequency scaling (DVFS), Traffic engineering
Mono-objective Evolutionary Model for Affective Algorithmic Composition

The Affective Music Composition study indicates that musical features can be associated to emotions. Thus, Affective Algorithmic Composition systems implements such features in order to generate melodies that can express or induce emotions. These systems can be applied to different fields, like health and entertainment. However, the composition of melodies with unlimited duration time and diversity is still an open question. This work aims to identify strategies to perform multiple affective transformations in melodies. Therefore, emotional models and the most implemented musical features in the literature are presented, as well as a evolutionary mono-objective model to an affective transformative system.
Image preprocessing techniques for facial expression classification

Facial expression classification is an essential aspect of human interaction, and its proper classification is considered fundamental for the future inclusion of multimedia psychology into human-computer interfaces. For this purpose, this article aims to identify a preprocessing pipeline capable of reduce the accuracy variance for facial expression classification. Thereunto, Extended Cohn-Kanade Dataset, Support Vector Machine, and Bag of Features were used in six pipelines. Compared to a pipeline with minimal preprocessing, the best results presented an accuracy variance reduction of 65.63%.
Proof of Activity and Stake

Abstract We proposed a decentralized cryptocurrency protocol named proof of activity and stake in which participants have no admittance threshold and communicate in peer-to-peer network. Our protocol adopts the idea of stakeholders in proof of stake and combined it with the propagation activity to replace the resource-costly proof of work. It is also proposed to solve the contradiction of security and throughput, which discouraged the promotion of blockchain applications. With analysis of security assurance and performance, proof of activity and stake is proved to be robust and economical against double spending attack.
Do we need Label Regularization to Fine-tune Pre-trained Language Models?

Knowledge Distillation (KD) is a prominent neural model compression technique that heavily relies on teacher network predictions to guide the training of a student model. Considering the ever-growing size of pre-trained language models (PLMs), KD is often adopted in many NLP tasks involving PLMs. However, it is evident that in KD, deploying the teacher network during training adds to the memory and computational requirements of training. In the computer vision literature, the necessity of the teacher network is put under scrutiny by showing that KD is a label regularization technique that can be replaced with lighter teacher-free variants such as the label-smoothing technique. However, to the best of our knowledge, this issue is not investigated in NLP. Therefore, this work concerns studying different label regularization techniques and whether we actually need them to improve the fine-tuning of smaller PLM networks on downstream tasks. In this regard, we did a comprehensive set of experiments on different PLMs such as BERT, RoBERTa, and GPT with more than 600 distinct trials and ran each configuration five times. This investigation led to a surprising observation that KD and other label regularization techniques do not play any meaningful role over regular fine-tuning when the student model is pre-trained. We further explore this phenomenon in different settings of NLP and computer vision tasks and demonstrate that pre-training itself acts as a kind of regularization, and additional label regularization is unnecessary.
Secure Cloud Storage Using Different Algorithms in Cryptography

Abstract: Cloud computing offers online services that allow visible servers, dynamic reminiscence swimming pools, and so for a smooth get right of entry. Because dispensed computing is based on the internet, security concerns include information protection, confidentiality, records protection, encryption, and authentication seem. Cloud records garage protection is a primary difficulty. In this examination, we aimed to explore a variety of information safety strategies. The use of cryptographic algorithms is used to solve information protection and privacy troubles in cloud storage. Inside the proposed Hybrid set of rules system RC4, DES &amp; AES Algorithms have been used to enhance data security and privacy. Proposed hybrid system algorithm shield upload and download of statistics from cloud garage. In this case, mystery keys are required for each encryption to do away with encryption. As a result, several parameters have calculated the usage of taking a look at features that encompass encryption time, memory utilization, privacy length, and output to illustrate the performance of the hybrid machine, facts simulations are to be supplied to JAVA, the use of the Eclipse IDE device. The proposed hybrid machine set of rules is used and tested the usage of various record formats such as textual content and photo data. The proposed set of rules is thought to paintings properly to provide additional information safety. Keywords: Cryptography, cloud computing, encryption/decryption Algorithm
HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations

We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs effectively capture essential code features at lexical, syntactic, and semantic levels by abstracting coarse-grained code elements and incorporating fine-grained program elements in a hierarchical structure. Our HierarchyNet method processes each layer of the HCR separately through a unique combination of the Heterogeneous Graph Transformer, a Tree-based CNN, and a Transformer Encoder. This approach preserves dependencies between code elements and captures relations through a novel Hierarchical-Aware Cross Attention layer. Our method surpasses current state-of-the-art techniques, such as PA-Former, CAST, and NeuralCodeSum.
TREATMENT OF CONCEPT “AESTHETIC CULTURE” IN THE PSYCHICAL-PEDAGOGICAL LITERATURE

The article “Treatment of concept “aesthetic culture ” in the psychical-pedagogical literature ” is devoted to the analysis of various treatments of concept “aesthetic culture ” that has allowed the author to make the generalization necessary for it for carrying out of the further research work.
Podcast: Media Pembelajaran IPS yang Menyenangkan bagi Peserta Didik Kelas V SDN Klepu Patuk di Masa Pandemi

The Covid-19 pandemic that has hit the world has paralyzed the activities of almost all life sectors, including the education. Face-to-face teaching and learning activities have been abolished for quite a long time so that it becomes a tough challenge for teachers. One of the media that can be used as an alternative to online learning is podcasts. The aims of this study are: (1) to explain the social studies learning conditions during the pandemic for fifth grade students at SDN Klepu Gunungkidul; (2) to explain the social studies learning during the pandemic using podcasts for fifth grade students at SDN Klepu Gunungkidul; (3) to know the reasons why podcasts is become fun media of social studies learning for fifth grade students at SDN Klepu Patuk during the pandemic. This research uses a qualitative design with a case study approach. The data sources are 8 students of fifth grade at SDN Klepu Gunungkidul. The data collection techniques are observing communication via WhatsApp, interviews and documentation. The results of observations and interviews are then reduced to several important notes that provide a more specific picture. Furthermore, the results of the reduction are organized and arranged in the form of data presentations so that the relationship patterns are easier to understand and then drawn into conclusions. The results of this study are: (1) learning during the pandemic has made it difficult for teachers to convey material in a fun way, especially when learning is done online. (2) The teacher designs a fun online learning process during the covid-19 pandemic through podcasts so that students are very enthusiastic about participating in lessons (3) The use of podcast media as a learning media makes social studies learning more fun because students can view and listen to the information simultaneously through podcasts. It makes students' interest in learning online become increasing.
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

Motivations for methods in explainable artificial intelligence (XAI) often include detecting, quantifying and mitigating bias, and contributing to making machine learning models fairer. However, exactly how an XAI method can help in combating biases is often left unspecified. In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.
Chordal Sparsity for SDP-based Neural Network Verification

Neural networks are central to many emerging technologies, but verifying their correctness remains a major challenge. It is known that network outputs can be sensitive and fragile to even small input perturbations, thereby increasing the risk of unpredictable and undesirable behavior. Fast and accurate verification of neural networks is therefore critical to their widespread adoption, and in recent years, various methods have been developed as a response to this problem. In this paper, we focus on improving semidefinite programming (SDP) based techniques for neural network verification. Such techniques offer the power of expressing complex geometric constraints while retaining a convex problem formulation, but scalability remains a major issue in practice. Our starting point is the DeepSDP framework proposed by Fazlyab et al., which uses quadratic constraints to abstract the verification problem into a large-scale SDP. However, solving this SDP quickly becomes intractable when the network grows. Our key observation is that by leveraging chordal sparsity, we can decompose the primary computational bottleneck of DeepSDP -- a large linear matrix inequality (LMI) -- into an equivalent collection of smaller LMIs. We call our chordally sparse optimization program Chordal-DeepSDP and prove that its construction is identically expressive as that of DeepSDP. Moreover, we show that additional analysis of Chordal-DeepSDP allows us to further rewrite its collection of LMIs in a second level of decomposition that we call Chordal-DeepSDP-2 -- which results in another significant computational gain. Finally, we provide numerical experiments on real networks of learned cart-pole dynamics, showcasing the computational advantage of Chordal-DeepSDP and Chordal-DeepSDP-2 over DeepSDP.
Successful evaluation of early term birth using hybrid ant colony

Machine learning (ML) involves data mining, which is part of the science of data solving more problems. Machine learning application predicts the results depending on available data set. There are many predictive strategies available. The important method is dividing the most powerful predictions. Some of them predict results satisfactorily and some are accurately predicted. This research has carried out a process called as bagging based hybrid ensemble process that collects the accuracy of weak algorithms by combining many class dividers for improvement. This evaluation helps us to see the integration process that improves the accuracy in the result of preterm birth. This is not only the evaluation of weak separation algorithms, but also the use of algorithms using medical data, which predicts prematurely. This evaluation proves to be an effective method of classification by combining to improve the prediction accuracy of 96.2%.
Cesare Beccaria e la filosofia politica normativa

The article ponders a number of key concepts in Cesare Beccaria’s political thought (contractualism, the relationship between legal obligation, justice, utility and rights) and links them to the theory of legal obligation based on the duty of fair play proposed by Herbert L. A. Hart. Highlighting the importance of Beccaria’s positions to contemporary political philosophy, the essay also maintains the usefulness, for normative political philosophers, of taking a historical approach to this author.
React: A detailed survey

&lt;span&gt;developments in technology and the ever-growing number of web applications, it has become a necessity to create fast and scalable applications to cater to the current market. There are a plethora of frameworks available for web development, and a developer must choose the most appropriate framework for their use. In this paper, a detailed analysis of the history, prominent features and advantages of React, an open-source JavaScript library is presented. A discussion on React Native, a framework for building native applications is also given. This paper has provided an insight into the reason React is the leading web development framework in the world.&lt;/span&gt;
Compromised account detection using authorship verification: a novel approach

Compromising legitimate accounts is a way of disseminating malicious content to a large user base in Online Social Networks (OSNs). Since the accounts cause lots of damages to the user and consequently to other users on OSNs, early detection is very important. This paper proposes a novel approach based on authorship verification to identify compromised twitter accounts. As the approach only uses the features extracted from the last user's post, it helps to early detection to control the damage. As a result, the malicious message without a user profile can be detected with satisfying accuracy. Experiments were constructed using a real-world dataset of compromised accounts on Twitter. The result showed that the model is suitable for detection due to achieving an accuracy of 89%.
Augmented Reality in the Field of Health, Travel &amp; Tourism During Covid-19 Pandemic

The outbreak of coronavirus disease (COVID-19) is causing havoc all across the globe with its impact spread across all spheres of life, be it health or society or economy. With self-centered expanded reality (AR) use expanding lately, the usage of this innovation has a potential intending to wellbeing correspondence and conduct intercessions challenges. Though AR innovation layers computerized content onto this present reality self-centered AR outwardly expands oneself, layering advanced substance onto oneself. One development engaging conceited AR is video channels, which superimpose PC made content onto a client using their web or cell telephone camera ( Snap chat Lenses and AR impacts on Instagram. For instance, Snapchat's Time Machine AR focal point transforms a client's appearance to show what the client may resemble at various ages. Savvy reflects, another self-centered AR innovation, join advanced screens (eg, a LCD [liquid-gem display] screen) with cloudy glass. A savvy reflect looks and capacities correspondingly to a conventional mirror yet with advanced substance showed in the forefront. Certifiable utilizations of savvy mirrors are in plain view in vehicles, changing areas, and home exercise centres. For instance, the MIRROR home recs centre presentations wellness guidance layered on top of the client's appearance. Both AR video channels and savvy mirrors give new freedoms to showing wellbeing conduct correspondence to the general population.
Research results of Samara State University of Social Sciences and Education in 2021

Research results of Samara State University of Social Sciences and Education in 2021
Relevant Reasoners in a Classical World

We develop a framework for epistemic logic that combines relevant modal logic with classical propositional logic. In our framework the agent is modeled as reasoning in accordance with a relevant modal logic while the propositional fragment of our logics is classical. In order to achieve this feature, we modify the relational semantics for relevant modal logics so that validity in a model is defined as satisfaction throughout a set of designated states that, as far as propositional connectives are concerned, behave like classical possible worlds. The main technical result of the paper is a modular completeness theorem parametrized by the relevant modal logic formalizing the agent's reasoning.
MODULE TEACHING IN FORMING OF CULTURE OF THOUGHT OF TEACHER OF CREATIVE DISCIPLINES

In clause the urgency offormation of culture of thinking of the teacher of a polyart structure is considered (examined), the variants of modular training are described on the basis of integration o f arts. The examples of control questions assessment of levels forming of culture of thinking of the student are offered.
Pengembangan Media Kartu Kuartet ASEAN pada Muatan Pembelajaran IPS

This study aims to explain the steps in preparing the design of the quartet card learning media device for social studies learning material for the ASEAN region to improve the learning outcomes of sixth grade elementary school students. The type of research is R&amp;D (Research &amp; Development) with five steps of ADDIE model adoption. The resulting product is a multidisciplinary social studies learning media in the form of an ASEAN quartet card. The subjects in this study were 117 elementary school grade VI students from five elementary schools. The research instrument is a questionnaire and a test of student learning outcomes. Analysis of the data by means of analyzing the level of product validity and testing the gave effectiveness of using the product to improve learning outcomes. Data analysis used qualitative and quantitative analysis.The results of the validity test of the ASEAN quartet card learning media meet the very valid criteria according to material experts with a score of 98%, media experts 98% and learning experts 94%. This indicates that the learning media The ASEAN quartet card that was applied an increase in student learning outcomes and was declared effective in the ASEAN Social Studies lesson content in grade VI Elementary School.