Reviewerly/exHarmony huggingface dataset:
Hierarchical Task-aware Multi-Head Attention Network

Neural Multi-task Learning is gaining popularity as a way to learn multiple tasks jointly within a single model. While related research continues to break new ground, two major limitations still remain, including (i) poor generalization to scenarios where tasks are loosely correlated; and (ii) under-investigation on global commonality and local characteristics of tasks. Our aim is to bridge these gaps by presenting a neural multi-task learning model coined Hierarchical Task-aware Multi-headed Attention Network (HTMN). HTMN explicitly distinguishes task-specific features from task-shared features to reduce the impact caused by weak correlation between tasks. The proposed method highlights two parts: Multi-level Task-aware Experts Network that identifies task-shared global features and task-specific local features, and Hierarchical Multi-Head Attention Network that hybridizes global and local features to profile more robust and adaptive representations for each task. Afterwards, each task tower receives its hybrid task-adaptive representation to perform task-specific predictions. Extensive experiments on two real datasets show that HTMN consistently outperforms the compared methods on a variety of prediction tasks.
To Extend or Not to Extend? Enriching a Corpus with Complementary and Related Documents

An agent in pursuit of a task may work with a corpus of documents with linked subjective content descriptions. Performing the task of document retrieval for a user or aiming to extend its own corpus, an agent so far relies on similarity measures to identify related documents. However, similarity may not be appropriate if looking for new information or different aspects of the same content. Therefore, this paper combines complementarity- and similarity-based identification of documents, specifically, contributing (i) a formal definition of complementarity using the available subjective content descriptions in the form of relational tuples as well as a taxonomy interrelating the concepts of the tuples, (ii) a technique for classifying complementary and related documents in one go, and (iii) a case study assessing the classification performance for complementary and related documents.
Evaluation of 1st cycle students’ Digital Competencies: a methodological proposal

This article presents the conceptual and methodological design geared towards the construction of a student’s digital competences educational device in the school context, developed as part of a master’s investigation, in the educational innovation field at University of Lisbon. The present investigation adopts an innovative methodological approach that involves the teachers in the construction of said device. We pretend to introduce the designed methodological proposal, in accordance with the principles of the Design-Based Research (DBR) and the objectives of a qualitative educational investigation. The methodological proposal values the terrain specificities and the interaction with basic education 1st cycle teachers at the Amadora municipality (Portugal), particularly in the refinement of the evaluation device. For the purposes of this paper, we focus on the methodological definition process, describing thoroughly the different stages of the construction of our device and presenting the scientific basis that justify the chosen options. The different stages are composed of distinct data collection methods and data analysis techniques, in adequacy with the different natures of our data inputs. It was possible to confirm previous studies results regarding the potentialities of DBR in the creation of pedagogical artifacts and to critically assess what are the main potentialities and obstacles according to the adopted procedures. In summary, we believe that this methodological proposal could serve as a basis for investigations that aim to develop similar student’s competence assessment tools in educational context.
Investigating Machine Learning Techniques for User Sentiment Analysis

Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.
Stochastic Analysis of Asset Price Returns for Capital Market Domain

The solution of differential equations and stochastic differential equations of time-varying investment returns is considered. Precise conditions are obtained which govern asset price return rate through multiplicative and multiplicative inverse trends series. From the analysis of the proposed model showed that multiplicative inverse trend series is efficient and reliable than multiplicative trend in both deterministic and stochastic systems. Furthermore, the analytical solutions were explicitly verified graphically and discussed accordingly.
An Approach to Cloud Computing

The IT industries are using cloud computing technology today. The most efficient structure for cloud computing is one that is entirely Internet-based. It is made up of a combination of networked, integrated, and software- and hardware-based components. It offers many advantages over both grid computing and other types of computing. In this essay, I've even provided a brief evaluation of cloud computing based on reading more than 30 cloud computing articles. The findings of this evaluation signalise the evolution of the IT sectors before and after cloud computing.
Differences in Empathy According to Nonverbal Expression Elements of Emojis: Focusing on the Humanoid Emojis of KakaoTalk

To identify the most effective type of emojis for inducing empathy, the nonverbal expression factors of emojis that generate empathy differences were categorized as body language types (the presence of movement and contextual information), emotion type (joy and sadness), and degree of bodily expression (upper body and whole body). After dividing the data into joyful and sad emotion groups, differences in empathy according to the body language types and degree of bodily expression of emojis were confirmed. As a result, in the sad emotions group, empathy was higher in the movement type and the type combining movement and contextual information than the static body language type and the contextual information type without movement. However, the difference in empathy according to the degree of body expression and the interaction effect between body language types and degree of body expression were not significant. On the other hand, in the joyful emotions group, neither the main effect nor the interaction effect was significant. These results indicate that the effective emoji types for inducing empathy are the upper body of the movement type and the upper body combined with movement and contextual information. These types are also considered to work more effectively when applied to emotions with low mirroring and emotion recognition rates, such as sad emotion.
An Adaptive Tilting Interface to Alleviate Motion Sickness for Passengers in Vehicles

With today’s busy smartphone-based lifestyles and with the increase in the number of autonomous vehicles, there is a need for passengers to be able to perform tasks during their commute. However, motion sickness is elevated when passengers look at screens while performing tasks such as reading, messaging, or navigating while in moving vehicles. To address this problem, we created an interface that adjusts its orientation in real-time to match the direction of the vehicle to help reduce passenger motion sickness while looking at digital device screens. We explain the concept of our design, system architecture, and the signal processing implementations for our prototype. We also gather key insights based on user testing that inform the design improvements made to the prototype.
Mercury in Soils and Air in Areas with Anthropogenic Dispersion Halos and High Natural Geochemical Background

A monitoring study of Hg distribution in soils and air (pedogenic and atmospheric) in residential settlements and surrounding areas with high natural geochemical Hg background and/or affected by man-caused impacts due to mining activities has been performed. Sites of the AltaiSayan mercury province have been studied: part of the Kurai mercury zone (Aktash settlement, Kurai campo, Chazan-Uzun settlement, Altai Republic) and part of the Ursk ore field (Ursk settlement and Ursk tailing dump, Kemerovo region). The third site is located in Kuznetsk Alatau (part of the Saralinsky gold ore cluster, settlement of Priiskovy and its tailing dump, the settlement of Ordzhonikidzevsky, Republic of Khakassia). Hg background concentrations in air and soils have been detected.
FORMATION OF RESEARCH COMPETENCE OF HIGH SCHOOL STUDENTS

The article considers the possibilities of forming the research competence of high school students. It is highlighted that research competence reveals the level of development of students’ thinking processes and research activity. It is determined that the essence of research competence is to highlight the personal traits of students, which are expressed in the conscious willingness and ability to independently master and acquire systems of new knowledge, based on previously acquired set of knowledge, skills, abilities and methods. It was found that research competence can be formed only in the process of research. The key concepts that determine the necessary conditions for the organization of such activities of students in school are search, independence, initiative, practical action, experiment, teamwork, the situation of ambiguity, contradictions, the presence of different points of view. It has been proven that involvement in research activities is related to the level of readiness for it, which can be characterized by motivational, cognitive, procedural, creative and reflective-evaluation component. It is determined that the effective means of realization of the professional orientation of teaching physics are the emphasis of students on the universality of physical methods; use of practical tasks that illustrate the need for physical knowledge in different situations; use of interdisciplinary links. It was found that for the formation of research competence a special place in the physics course of primary school should be given to educational and research tasks. The importance of the competence approach in the educational process is shown. The concepts of “research competence” and “research activity” are revealed and the connection between them is shown. The research assignments and tasks that can be used during the teaching of physics in primary school in order to form the research competence are proposed.
Research on the Judicial Expertise of Electronic Evidence Based on Blockchain

Give full play to the advantages of blockchain technology, guarantee the consensus of public credit institutions with the help of hierarchical nodes, and form a judicial alliance chain, so that the public can participate in the data verification activities of electronic evidence in the form of organizations, promoting the socialization and development of judicial activities. By using the credible time-stamp evidence, the judicial block chain presents the extremely formidable application superiority, the node verification based on the block chain consensus mechanism, is helpful to form the block chain judicial system which has the extremely application prospect. Through the analysis of the practical application and application prospect of the judicial block chain, the author makes a complete analysis of the idea of the electronic evidence judicial appraisal based on the block chain.
Locally Regularized Collaborative Representation and an Adaptive Low-Rank Constraint for Single Image Superresolution

Learning-based superresolution reconstruction is an efficient image processing technique that has become a popular topic in recent years. Since superresolution is an ill-conditioned problem, appropriate image priors or examples are the key factors for recovering high-quality images with rich details. However, in some current advanced superresolution methods, the mappings established using dictionaries of low- and high-resolution examples cannot effectively reflect the relationship between the low- and high-resolution spaces. Therefore, we introduce a local structure prior to the collaborative representation to constrain the projection matrix; this structure can better represent the nonlinear mapping between the low- and the high-resolution feature spaces. Then, based on the redundancy of similar image patches, a shape-adaptive low-rank constraint is utilized to explore the images’ nonlocal self-similarity, and the local and nonlocal priors complement each other to enhance the recovered image quality. Finally, an iterative optimization algorithm is adopted to solve our proposed superresolution model. Numerous experiments were performed to verify the proposed method, and the results demonstrate its superiority to some state-of-the-art methods both quantitatively and qualitatively.
Social Media Visual Framing towards Political Participation: An Analysis on BERSIH 2.0

The political atmosphere in Malaysia had witnessed a growing trend of social movement organisations and protests. The recent attention is on the phenomenal record usage of social media platforms as tools to spread political ideology. The multimodal nature of social media has made it an efficient tool where users can post textual information as well as visual materials to reach out to its audience. This article sets out to examine the visual strategy of one of the largest political social movements in Malaysia, the BERSIH movement, within a social media setting. Building on the existing work on visual framing, it asks: How were issues visually portrayed on the BERSIH social media account during the peak of the movement in Malaysia? In this context, Facebook was chosen as the social media platform analysed as it is the main social media used by the movement. Adapting on the level of visual framing, a content analysis was done to a sample of 59 (N=59) top liked photos posted on the Bersih 2.0 Official Facebook account which received more than 0.5% engagement rate (ER= &gt;0.5%). The findings showed that at the denotative level, the main forms of visuals used were photos of protesters, followed by posters and photos of important individuals. The findings also showed that at the connotative level, the main frame highlighted was ‘solidarity’ followed by ‘call for action’ in the form of direct and symbolic visuals. Keywords: Visual framing, framing, social movement, political communication, social media.
Enhanced video-on-demand security in cloud computing against insider and outsider threats

Many video-on-demand (VoD) providers leverage the benefits of cloud computing despite the stemming various security threats.For instance, if the hosted multimedia contents are not encrypted, then the VoD providers must fully trust that the cloud provider will not illegitimately access, distribute, or modify the multimedia contents.Nonetheless, the latest ENISA report showed that approximately 25% of security threats over the last six years have been based on internal misuse.In this paper, we introduce a simple yet effective scheme to protect contents from internal and external threats.Encrypt once -confidentiality, privacy, and integrity (EO-CPI) focuses on protecting data confidentiality, integrity, and users' privacy from untrusted cloud-based VoD systems.We compared EO-CPI with advance encryption standard -Galois counter mode (AES-GCM).Results demonstrated a significant reduction of 79% of the server-side computational overhead when using EO-CPI with a re-encryption degree of one time per five users while preserving the user-side performance.
Towards Latent Space Optimization of GANs Using Meta-Learning

The necessity to use very large datasets in order to train Generative Adversarial Networks (GANs) has limited their use in cases where the data at disposal are scarce or poorly labelled (e.g., in real life applications). Recently, meta-learning proved that it can help solving effectively few-shot classification problems, but its use in noise-to-image generation was only partially explored. In this paper, we took the first step into applying a meta-learning algorithm (Reptile), to the discriminator of a GAN and to a mapping network in order to optimize the random noise z to guide the generator network into producing images belonging to specific classes. By doing so, we prove that the latent space distribution is crucial for the generation of sharp samples when few training data are at disposal and also managed to generate samples of previously unseen classes just by optimizing the latent space without changing any parameter in the generator network. Finally, we show several experiments with two widely used datasets: MNIST and Omniglot.
Breast Ultrasound Image Classification Using EfficientNetV2 and Shallow Neural Network Architectures

Health is the foundation of life. Along with the development of science and technology, and modern medicine, there are many methods to help diagnose diseases quickly and accurately detect diseases early with ultrasound images are the most popular and effective technique. This study has investigated and explored methods of predicting breast cancer based on ultrasound images classified into three categories: normal, benign, and malignant, with three deep learning techniques such as Fully Connected neural Network (FCN), Shallow Convolutional Neural Networks (CNN), and EfficientNetV2. Such techniques are evaluated on both the original data set and the dataset are applied data augmentation techniques to compare the efficiency of each algorithm and evaluate the influence of increasing data on the training phase. Experimental results show that the recent emerging CNN architecture, EfficientNetV2 provides the highest classification performance when taking the lead in all three metrics of ACC, AUC, and MCC with 0.77, 0.804, and 0.619, respectively. The results also show that data augmentation techniques can improve the efficiency of the classification algorithms on the ultrasound image dataset and are expected to combine the recent emerging CNN architectures to provide efficient disease diagnosis on ultrasound images.
Explainable Artificial Intelligence (XAI): Towards Malicious SCADA Communications

Critical infrastructure Supervisory Control and Data Acquisition (SCADA) systems have been designed to operate on closed, proprietary networks where a malicious insider posed the greatest threat potential. There is an emergence of new threat scenarios of command and data injection. Multiple machine learning methods have been used to predict instances of command and data injection attack scenarios. The models often lack transparency. The black-box nature of these systems allows powerful predictions, but it cannot be directly explained. Understanding the reasons behind predictions is, however, quite important in assessing machine decisions, predictions and justify their reliability. More an attack is threatening the more explainable it should be. We need to explain attacks on both a local and global scope to get complete transparency against the attack. We will apply global interpretability methods like Partial Dependence Plots(PDP), Individual Conditional Expectations (ICE), Decision Trees to explain the whole logic of a model. For more menacing attacks we use local interpretability methods like Local Interpretable Model-Agnostic Explanation (LIME) to explain the reasons for specific decisions a model is taking.
Multidimensional Features Driven Phishing Detection Based on Deep Learning

Phishing is indeed a criminal activity using technological tools to obtain personal data from customers. This is actually a major hazard to the Cloud, so phishing-related casualties increasing rapidly. Throughout phishing site bar approaches, feature extraction is essential, however the quality of identification is highly dependent on basic information of features. Furthermore, while functions extracted from different measurements are much more detailed, the extraction process such features takes a very long time. That overcome this drawback, we recommend a multifaceted function phishing detection method based on a recent deep learning detection method. The first phase entails extracting number of characters attributes from the supplied Script before using them to quickly classify it using deep learning. Each step doesn't really include some third-party help nor background experience of phishing. Throughout the stage 2, we create multidisciplinary apps by combining URL data samples, homepage software features, website textual information, as well as the fast classification outcome of the deep learning technology. This method will shorten that time it takes to identify something and build a limit. That reliability hits a peak, as well as the error rate is small, when tested on a database contains thousands of phishing Attacks and genuine Hyperlinks. This research findings demonstrate that perhaps the embedding method could be increased through appropriately changing the limit.
Efficient Privacy-Preserving Forensic Method for Camera Model Identification

To address the camera origin identification problem of inquiry images, many forensic methods have been proposed. However, the heavy computational overhead and the potential threat of privacy leakage for inquiry images make many existing forensic methods less applicable. Only a few research works have proposed secure forensic methods to address the aforementioned issues; however, they did not give a detailed analysis for the statistical performance. In this paper, we propose an efficient privacy-preserving forensic method with analytical statistical performance to solve the camera model identification problem efficiently and securely. To preserve the privacy of inquiry images, we propose a hybrid privacy-preserving scheme consisting of two operations: <i>Position Scrambling Encryption</i> to preserve the privacy of image content and <i>Noise Linear-Mapping Processing</i> to preserve the privacy of camera model identity for inquiry images. In the encrypted domain where the proposed privacy-preserving scheme is employed, we first propose a novel statistical noise model, which can accurately characterize an encrypted JPEG inquiry image. Then, a noise model-based detector is designed to identify different camera models. Experimental results verify the feasibility of our proposed method from both privacy-preserving and forensic effectiveness and report that our method outperforms the state-of-the-art secure forensic methods, especially when sample images used to estimate camera fingerprints are insufficient, such as only 2 available images.