Reviewerly/exHarmony huggingface dataset:
PSYCHOLOGICAL AND PEDAGOGICAL MEANS OF POSITIVE AND EFFECTIVE ATTITUDE FORMATION OF STUDENTS TO THEIR OWN HEALTH

The article is devoted to the peculiarities of the use of psychological and pedagogical means for realization of the most effective steps of positive and effective attitude formation of students to their own health. The purpose of the article was to carry out the theoretical analysis of the basic psychological and pedagogical means of prevention and correction students' addictive behavior. A set of theoretical (reflexive analysis and generalization of scientific literature) and empirical (mathematical statistics tools) methods were applied, the choice and combinations of which were determined by the purpose and tasks of the research. An important psychological and pedagogical condition for the students' addictive behavior prevention is the implementation of prophylactic monitoring, that is, the tracking of indicators of possible conditions and factors of tobacco and alcohologenic risk, which implies the creation of an appropriate scientific basis. The necessary conditions for solving the problems of forming an interest of one's own health in the system of students` perception of the world is the formation of a motivational component, a positive attitude to educational activity and psychological well-being. Mindset training as a means of reprogramming the model of behavior and activity management that humans already have, in this case adolescents and youth is of particular importance in the context of these issues. A step-by-step model of addictive behavior is used in the training program. The forms of social and psychological assistance in working with students are the prevention and correction of cognitive disorders using exercises and games based on a combination of cognitive and suggestive components, correction of emotional and abnormal personality etc. The possibilities of correction of students' addictive behavior during the period of study in higher education institutions by means of psychological training of integrative type remain insufficiently investigated. Therefore, the practical importance, namely, of this complex of psychotherapeutic techniques expands the idea of ways to correct addictions in adolescence through the use of psychological means of developing a positive and effective attitude to health in the system of basic personality relationships.
An Improved Moving Tracking Algorithm With Multiple Information Fusion Based on 3D Sensors

The continuously adaptive mean shift algorithm suffers from the tracking offset phenomenon while tracking targets with colors similar to that of the background. In this paper, to improve the performance of this algorithm, the depth information is combined with the back-projection color image and the information from the moving prediction algorithm. The target search window is predicted based on switching filtering algorithm with the Extended Kalman Filter (EKF) method. The moving tracking synthesis algorithm which used 3D sensors and combines color, depth and prediction information is used to solve the problems that the continuously adaptive mean shift algorithm encounters, namely disturbance and the tendency to enlarge the tracking window. The experimental results show that the proposed algorithm can accurately track a moving target in the presence of a complex background, and greatly improves the interference resistance and robustness of the system.
Is My Home Smart or Just Connected?

The smart home market will approach 40 billion USD (United States dollars) by 2020 and household penetration to reach 47.40% in 2023. Experts say that we will move from turning lights on and off by giving a voice assistant commands to a smart home that collects, analyzes and acts upon information, turning our smart devices into intelligent homes. As homes become more intelligent with the use of artificial intelligence (AI), will the inhabitants understand what that might mean and what makes a device or home smart? Between February and June of 2019, we conducted semi-structured interviews of 40 smart home consumers to understand their perceptions of smart homes. During the interview we asked them what the Internet of Things (IoT) meant, what makes a smart device, what makes a smart home. Overall, participants were unfamiliar with the term IoT. We coded the responses with major codes. Reasons that participants said made devices smart: they were connected to the internet, were programmable, and had some type of learning component (e.g., natural language processing, machine learning, learn over time). Participants said the following made homes smart: have smart devices, connected, made life easier, were programmable and they could control devices in the home. Only a few participants included learning into their descriptions of smart homes. As smart devices and smart homes mature into an intelligent home driven by artificial intelligence, it is critical for consumers to understand the implications of use.
A new extension theorem for ternary linear codes and its application

An [n,k,d]q code is a linear code of length n, dimension k and minimum weight d over the field of q elements. An [n,k,d]q code C is called extendable if C can be extended to an [n+1,k,d+1]q code. We give a new extension theorem for ternary linear codes. As an application, we prove the non-existence of [512,6,340]3 code, which is a new result.
An Invisible Hybrid 3D Video Watermarking Robust Against Malicious Attacks

Digital watermarking is known as the most suitable solution to protect 3D videos. Hence, proposing a watermarking technique robust against malicious attacks and especially against collusion becomes a crucial challenge for many researchers. In fact, collusion is usually not considered in the assessment of anaglyph 3D video watermarking schemes. In this paper, an invisible and robust watermarking method dedicated to anaglyph 3D videos is proposed. It embeds a signature in the selected mosaics generated from different groups of frames forming the original video. The choice of the mosaic as an embedding target guarantees robustness against collusion which considered as the most dangerous attack for any video watermarking technique. First, several mosaic images are generated from every set of 25 frames of the original video and they are compared using a similarity measure. In order to obtain a high level of invisibility, the signature will not be embedded in all mosaics but only in the ones which are not similar. Second, the signature is inserted on each selected mosaic image using a hybrid embedding scheme based on the least significant bit and the discrete wavelet transform. Finally, all the marked frames are restored from the marked mosaic images and they are recombined to the unmarked ones to reconstruct the marked anaglyph video. To prove the robustness of the suggested method, different manipulations are applied to various anaglyph videos and several metrics are calculated. Obtained results confirm its robustness against the image processing attacks, the enhancement attacks, and the frame-based attacks, in addition to dangerous video attacks such as MPEG-4 and H264-AVC compression and collusion attacks.
Code Mixed Question Answering Challenge using Deep Learning Methods

In social media, code-mixed language questions(combination of two distinct languages) is turning into the favored method of expression and communication. In twitter people can go over mixed language where individuals frequently utilize their mother tongue along with English. But question answering systems do not support mixed language. They only work on a single language such as English, French and German. This paper presents an online question answering framework for mixed languages. This system analyzes the user's mixed language query and gives the answer and also presents a contrivance that facilitates decode of English mixed with 3 different Indian languages: Hindi, Telugu and Tamil. The code mixed words will be translated into English to reduce data complexity. Later, featurization is performed on them. Deep learning algorithms such as Recurrent Neural Network (RNN), Hierarchical Attention Network (HAN) are used for question classification. Confusion matrix is used as evaluation measure of RNN, HAN. This system is facilitated on the web and in future, it can be used for collecting more code mixed questions and answers data for further improvement.
Application of Deep Learning Method in Facilitating the Detection of Breast Cancer

Abstract Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. Early diagnosis is crucial when it comes to reducing the mortality rate. In this study, deep neural network method is applied to facilitate the detection of breast cancer. The aim of this study is to implement deep neural network in breast cancer classification models that can produce high classification accuracy. Deep Neural Network (DNN) with multiple hidden layers was applied to learn deep features of the breast cancer data. Dataset used in this study was obtained from the UCI Machine Learning Repository which consists of Wisconsin Breast Cancer Dataset (WBCD) and used for the original and diagnostic dataset. The performance of the proposed DNN method was compared against previous machine learning classifier in terms of accuracy. From the results, the accuracy obtained for the original dataset was 97.14% and 97.66% for the diagnostic dataset, which is better than previous SVM method.
From Programming Agents to Educating Agents – A Jason-Based Framework for Integrating Learning in the Development of Cognitive Agents

Recent advances and successes of machine learning techniques are paving the way to what is referred as Software 2.0 era and cognitive computing, in which traditional programming and software development is meant to be replaced by such techniques for many applications. If we consider agent-oriented programming, we believe that such developments trigger new interesting scenarios blending cognitive architecture such as the BDI one and techniques like Reinforcement Learning (RL) even more deeply compared to what has been proposed so far in the literature. In that perspective, we aim at exploring the integration of cognitive agent-oriented programming based on BDI with learning techniques so as to systematically exploit them in the agent development stage. The approach should support the design of BDI agents in which some plans can be explicitly programmed and others instead can be learned by the agent during the development/engineering stage. In that view, the development of an agent is metaphorically similar to an education process, in which first an agent is created with a set of basic programmed plans and then grow up in order to learn plans to achieve the goals for which the agent is meant to be designed. This paper presents and discusses this medium-term view, introducing a first model for a BDI agent programming framework integrating RL, a first implementation based on Jason programming language/platform and sketching a roadmap for this research line.
Repairing Binary Images Through the 2D Diamond Grid

A 2D binary image is well-composed if it does not contain a $$2\times 2$$ configuration of two diagonal black and two diagonal white squares. We propose a simple repairing algorithm to construct two well-composed images $$I_4$$ and $$I_8$$ starting from an image I, and we prove that $$I_4$$ and $$I_8$$ are homotopy equivalent to I with 4- and 8-adjacency, respectively. This is achieved by passing from the original square grid to another one, rotated by $$\pi /4$$ , whose pixels correspond to the original pixels and to their vertices. The images $$I_4$$ and $$I_8$$ are double in size with respect to the image I. Experimental comparisons and applications are also shown.
A Study of Searchable and Auditable Attribute Based Encryption in Cloud

In cloud computing, the data is stored and retrieved through the internet. There are some common systems for cloud storage which includes the system for e-health records, the file stored on to the cloud server includes information which is private and sensitive, and the main focus should be that at the time when data gets shared, the content of the file should not be revealed. One of the ways to secure the file data is to simply encrypt the file, but on the other side, the authenticate user to which the data is shared will not be able to use it. User's time and memory are saved by Storing data in the cloud. The main issue is that the user loses total control over the once it is upload. This issue needs to be addressed while designing the system. In this paper the study of various mechanisms and techniques for data security stored over the cloud and hiding of the sensitive and private data. The paper also discusses the various issues faced while using or applying the techniques. Here, a system is proposed to use the encryption techniques, algorithms as well as secure cloud storage.
Towards Automatic Textual Summarization of Movies

With the rapidly increasing number of online video resources, the ability of automatically understanding those videos becomes more and more important, since it is almost impossible for people to watch all of the videos and provide textual descriptions. The duration of online videos varies in a extremely wide range, from several seconds to more than 5 h. In this paper, we focus on long videos, especially on full-length movies, and propose the first pipeline for automatically generating textual summaries of such movies. The proposed system takes an entire movie as input (including subtitles), splits it into scenes, generates a one-sentence description for each scene and summarizes those descriptions and subtitles into a final summary. In our initial experiment on a popular cinema movie (Forrest Gump), we utilize several existing algorithms and software tools for implementing the different components of our system. Most importantly, we use the S2VT (Sequence to Sequence—Video to Text) algorithm for scene description generation and MUSEEC (MUltilingual SEntence Extraction and Compression) for extractive text summarization. We present preliminary results from our prototype experimental framework. An evaluation of the resulting textual summaries for a movie made of 156 scenes demonstrates the feasibility of the approach—the summary contains the descriptions of three out of the four most important scenes/storylines in the movie. Although the summaries are far from satisfactory, we argue that the current results can be used to prove the merit of our approach.
The Potential of Gesture-Based Interaction

Gesture control has been a topic of research and development since the 1970s. As hands are humans’ native tool for interaction, it is natural to look at gesture control when talking about next-generation human-computer interaction. Throughout history many have tried to take advantage of the potential of gesture control, without huge success. Gesture-based interaction for consumer electronics have been widely explored using gesture to control TVs, mobile phones and drones, without making it a preferred interaction pattern. It is hard to argue that screen-based media is a preferred application for gesture-based interaction due to the lack of clear benefits of changing a known interaction pattern. Digital objects without screen interfaces show potential for use of gesture control but lacks the possibility of giving proper feedback to make it intuitive. Considering design principles as visibility, feedback and constraints, VR and AR stands out as the most promising medias to apply gesture-based interaction. The potential is not only determined by the medium, but also in what domain it is applied to. Domains like education, healthcare, robotics, heavy industry and space show clear benefits. When designing for the global market (B2C), social acceptance, cultural differences and timing are complications one has to consider. What goes for any case applying this technology; gesture-based interaction should not be used because it is possible, but because it is needed.
Incorporating the human facet of security in developing systems and services

Purpose The purpose of this paper is to present an integrative framework for handling the security and usability conflicts during the system development lifecycle. The framework has been formulated while considering key concerns raised after conducting a series of interviews with practitioners from the industry. The framework is aimed at assisting system designers and developers in making reasonably accurate choices when it comes to the trade-offs between security and usability. The outcomes of using the framework are documented as design patterns, which are disseminated among the community of system designers and developers for use in other but similar contexts. Design/methodology/approach A design science research approach was used to develop the integrative framework for usable security. Interviews were conducted for identification of the key concerns; however, the framework was validated during a workshop. Moreover, to validate the patterns’ template and the usable security pattern identified after instantiating the framework, a survey instrument was used. Findings It is important to consider the usability aspect in the development of security systems; otherwise, the systems, despite being secure against attacks, would be susceptible to user mistakes leading to compromises. It is worthwhile to handle usable security concerns right from the start of system development life cycle. Design patterns can help the developers in assessing the usability of their security options. Practical implications Practical implications The framework would assist the designers and developers in handling the security and usability conflicts right from the start of the system development life cycle. The patterns documented after using the framework would help not only the designers and developers working in the industry but also freelancers. Originality/value The authors present a novel framework to handle the security and usability conflicts during the system development life cycle. The development process of the framework was driven by the concerns raised after a series of interviews with the practitioners from industry. The framework presented in this paper was validated during a workshop in which it was exposed for review and comments by the participants from the industry. To demonstrate the use of patterns in general and the framework in particular, a case study featuring smart grids from the domain of cyber-physical systems is presented, which (to the best of the authors’ knowledge) features the first work relevant to usable security in the domain of cyber-physical systems.
Another Week at the Office (AWATO) – An Interactive Serious Game for Threat Modeling Human Factors

Another Week at the Office (AWATO) is serious game aimed to educate users about threat modeling to help them and/or security analysts identify human factor related threats. AWATO offers an interactive experience where players assume the role of a security analyst where they must observe characters within an office, monitor their emails and phone calls, and identify concerning behavior (e.g. writing passwords on post-it notes).
Text Classification of News Articles Using Machine Learning on Low-resourced Language: Tigrigna

Text categorization or Textual document is a method that becomes more significant in tagging a textual document to their most relevant label. However, not all languages have parallel textual growth; without free and absences of a dataset, text categorization becomes interesting for Tigrigna language, i.e., low-resourced language. Our aim to identify the given document to its categories based on its linguistic features. To achieve our goal, we have constructed a new dataset from different Tigrigna news sources. The dataset has six main categories: Agriculture, Sports, Health, Education, Religion, and Politics. Each collected is article preprocessed from Latin characters, punctuations, and stop words. We deployed a collection of different classical machine learning classifiers to investigate its effectiveness in our datasets. Namely, 7 popular classifiers were used, Logistic Regression, Nearest Centroid, Decision Tree (DT), Support Vector Machines (SVM), K-nearest neighbors (KNN), Random Forest Classifier, and Multi-Layer Perceptron (MLP). Ensemble models also implemented to get the best accuracy by combining the best classifiers based on their majority-voting classifiers. Our experimental results showed reliable performance with a minimum F1-score of 89.1% achieved by Nearest centroid and top performance of 96 % achieved by SVM. The experimental results presented in terms of precision, Recall, and F1-scores.
Feminist chatbots as part of the feminist toolbox

In late August 2017, the Brazilian non-profit laboratory for activism called Nossas Cidades (Our Cities) launched a feminist chatbot called Betânia, or Beta for short (See Figure 1). Beta is a text...
Implementation of Improved Inline Deduplication Scheme for Distributed Cloud Storage

The recovery and backup system in which the mechanism involves reproducing and recording information on various cloud servers to retrieve the kind of information a while after, an unforeseen occurrence, using that database. The justification for backup is to restore data and boost information from a past period after the misfortune. The backup structures effectively spread the fragments of each datum document through several servers and create information documents' privacy. Deduction compression techniques may be used, but these application frameworks must be strengthened on volume constraints and record preservation must be enhanced. The eradication code in this text can be detected with test inline deduction and report the restoration to see the backup system better. Different documents, from 1 KB to 100 MB, are checked to verify the system view. Test results suggest, with limited data capacities and recovery of information, that the new system succeeds in its current structure.
Virtual Reality Online Shopping (VROS) Platform

Since 1980, with the advent of the personal computer (PC), and the development of the Internet, the first "online shopping" appeared in the world. However, many limitations still exist in current online shopping. For instance, experience, environment, interface, or the screen size are entirely different from physical shopping stores. Here, we implemented a system prototype—Virtual Reality Online Shopping (VROS) Platform, which brought the physical shopping experience into the VR world by incorporating VR hardware—Samsung HMD Odyssey+ as the platform base. The VROS was created by the core concept of "home-hub", which included five different spaces: living room, kitchen, farm, home library and walk-in closets. Different spaces represented as the entrance of shopping categories were connected to the different online shopping stores. The users were able to organize the shopping goods (no matter the user had or still on the wishlist) in the VROS home. To evaluate the VROS, we recruited two subjects to do the two-step experiments by using protocol analysis. The VROS is current an off-line system but is able to apply to real online shopping stores in next version, such as Amazon and eBay.