Reviewerly/exHarmony huggingface dataset:
An Analysis of Different Browser Attacks and Exploitation Techniques

Modern browsers are very complex pieces of software, having capabilities, and functionalities that have been compared to those of the operating systems. Although not quite the same, browsers do pack a lot of processing power and enough versatility to dictate a big part of our everyday internet-related activities by providing optimization and robust frameworks. A large range of functionalities and mechanisms are directly related to a bigger attack surface, complexity comes with hidden bugs and deeper analysis requirements. This paper aims to take a look and study some of the attack vectors and exploitation techniques applied for browser research security based on the Windows environment. Among the examples, we can note that some browser engines are so complex that they have their own classes of vulnerabilities while others are notorious for their flawed design. The paper dives into ways of evaluating a browser’s security level and conducting research based on multiple attack vectors by taking into consideration the latest protection mechanisms, exploitation techniques, and vulnerabilities discovered.
KAAS: A Keyword-Aware Attention Abstractive Summarization Model for Scientific Articles

In this work, we focus on abstractive summarization methods for assisting medical researchers in effectively managing information. Particularly, we introduce a COVID-19-related summarization dataset (COVID-SUM) and propose a novel Keyword-aware Attention Abstractive Summarization (KAAS) model. The KAAS model consists of two encoders and one decoder. As for the encoders, one is a standard article encoder built on transformer layers, while the other one is a hierarchical keyword encoder that first encodes the words in a keyword using BiLSTM, and then passes the keyword representations to a transformer layer to connect the keywords in an example. Additionally, a decoder with keyword-focused attention is utilized to further direct the decoding process to generate comprehensive summaries of the scientific articles. We benchmark several summarization methods on the new COVID-SUM dataset and release this dataset in the hope to promote advances to summarization in the COVID-19 medical area ( https://github.com/ccip-author/COVID-SUM/releases ). Furthermore, we evaluate the KAAS on COVID-SUM, ArXiv, and PubMed datasets. Experimental results demonstrate that KAAS outperforms several state-of-the-art models on these datasets.
Student Teachers’ Reflections on the Use of Drama in Education to Motivate Young Children’s Science Play

Student Teachers’ Reflections on the Use of Drama in Education to Motivate Young Children’s Science Play 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.
An Effective Approach to the Cross-Border Exchange of Digital Evidence Using Blockchain

Cybercrime has become a prevalent threat over the last decade. Despite the numerous efforts devoted by national and supranational institutions, disparities on regulations along with the decentralisation of information networks have increased the complexity for judiciary forces, and all actors involved in the investigation process, of fighting against cybercriminals. With the aim to increase the security level of the actors involved in judicial processes and assure the proper collection and integrity of digital evidence, in this article we discuss how blockchain technology could help to ensure the chain of custody throughout the flow of forensic analyses. Likewise, besides the successful instruments promoted by the EU aimed at facilitating cross-border exchange of data, we identify further actions to improve the exchange of digital evidence among all entities involved in the investigation. In this context, we highlight the approach presented by the EU project LOCARD that provides a collaborative and distributed platform to automate the management of digital evidence with blockchain technology guaranteeing, therefore, the integrity and transparency of the cross-jurisdictional chain of custody.
SEMS Positioning Solar Panels

Problem statement: to improve the efficiency of solar panels, automatic systems for positioning them relative to the sun are usually used. Purpose of research: however, existing solar panel positioning systems do not provide effective operation in diffuse and reflected light, and in this case, the maximum energy flow may not come from the source, but from reflective surfaces (clouds, buildings, snow, etc.), which reduces the efficiency of the installation. In addition, the search for the position of the solar battery, at which it is possible to obtain maximum power, using existing positioning systems is associated with a large energy consumption for moving a massive panel, which can be commensurate with the generated electricity. Results: to solve this problem, SEMS has been developed, which improves the efficiency of solar panels by automatically orienting the solar panel to a position where it is possible to obtain maximum power with diffused light. To determine the optimal position of the solar panel, SEMS contains a small-sized scanning device that scans and records the brightness of the sky before turning on the battery. After that, using SEMS, the angular coordinates of the area of ыpace with the maximum brightness are determined and the battery is moved to these coordinates.
A Single Qubit Quantum Perceptron for OR and XOR Logic

Quantum computing is gradually gaining attention due to the properties of parallelism and superposition which can massively speed up data processing. This study deals with the development of quantum logic gates, fundamental to a quantum computer and trained on a single-layer perceptron quantum neural network. We demonstrate the proposed model with its implementation on Xanadu’s Pennylane quantum simulator. Along with the proposed model, a classical single-layered perceptron has been implemented so as to perform a comparative study between their performance. The proposed model has been trained on three different optimizers, namely Adam optimizer, momentum optimizer and gradient descent in order to study each optimizer’s performance to train the proposed single qubit perceptron.
A Prognosis and Prediction of Breast Cancer using Machine Learning Techniques

Breast cancer is one of the major cause of cancers occurring in women compared to all other cancers. It is the most common type of all cancers and the main cause of women’s death worldwide. Early prediction of breast cancer plays an important role in successful treatment and saving lives of number of patient every year. Today, it has become a hot issue in cancer research to make precise prognostic prediction for breast cancer patients. Research and prevention on breast cancer have more attention of research in recent years. Classification and machine learning methods are an effective way to classify data. Particularly in medical field, where those methods are extensively used in diagnosis and analysis to make decisions. In this paper, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (J48), Naive Bayes (NB), K- Nearest Neighbor (KNN) and Logistic Regression on the Wisconsin Breast Cancer (original) datasets is conducted. The main intention is to calculate the correctness in classifying data with respect to efficiency of each algorithm in terms of accuracy, sensitivity, specificity, error rate and Time(seconds). Experimental results show that SVM gives the highest accuracy (97.59%) with lowest error rate. WEKA simulation tool used for all experiments.
Analyze of facial expression and hand gestures using deep learning

For the computer to interact with human users intelligently, computers should be able to recognize the emotions and gestures of humans, by analyzing the human's emotional state, body action, a moment of hand, and behavior. A Facial expression and Hand Gesture play a communicative role in interpersonal relations with humans and computers to act intelligently by analysis of the human affective state, cognitive activity, intention, and personality. Automatic recognition of facial expressions and hand gesture recognition can be an important component of natural human-machine interfaces in many fields and areas. It may also be used in behavioral science, user reviews, and clinical practice. The system can recognize facial expressions and hand gestures by analyzing feature extraction of both facial and hand gesture recognition. Then the system will able to classify their respective classes. The Project results show that our system could distinguish Facial Emotions and Hand Gestures. Although a Face Emotion and Hand Gesture Analysis module is typically designed to deal with single images.
Integrated Information System at Travel Samarinda Based on Macro Microsoft Office Excel

This study aims to find out how to design and create an integrated information system application using macro Microsoft office excel and supported by visual basic language.The data collection technique in this study using the method of observation and literature study.The result of this study is the application of an integrated information system that is useful for data processing on this research object to present information or report better, quickly, on time, relevant, and easy to access.The program can be developed in the future, considering that the facilities and capabilities of this program still need to be improved.
Impact of Human-Computer Interaction in the Global Sustainable Development Goals

Sustainability and sustainable development are extremely critical aspects in modern society. Sustainability is the term employed for the practice of ensuring that goods and services are produced in ways that do not use resources that cannot be replaced. This concept can be extended to any environmental, political, and socio-economical system, becoming a global concern that has been highlighted in several different re-search agendas. According to the general assembly of the United Nations defined in 2015 a universal call to action, defining 17 Sustainable Development Goals (SDGs) addressed not only to governments but also to businesses and civil society. The agenda goes to the heart of tackling a number of interrelated global issues such as poverty, inequality, hunger, and environmental degradation. In this scenario, the research area of Human-Computer Interaction (HCI) plays a key role into the design and development of software applications and practices aimed to achieve the SDGs. This paper depicts a vision about how HCI could be integrated in a better way through the inclusion of human values aspects in the design of interactive applications) lines before and after the abstract.
Methodology of decision support systems for digital public governance

[enter Abstract BodyThe research argues that digital operations centers (DOC) serve as the core digital infrastructure for data-driven decision-making in public governance. Digital public governance is a multilayer concept that includes the hardware/software, the management system, and the data processing. The key issue for digital public governance is the capacity to connect all layers and exchange data across to take data-driven decisions, which are adjusted for the needs of each particular citizen in order to make public governance fully effective and efficient, i.e. smart. The concept of digital data-driven decision making in public governance has been fundamentally elaborated by Forrester, Winter and Marcuse. Tailor-made governance decisions correspond to the concept of mass customization, which derives from the Fourth Industrial Revolution theory and practice. The hypothesis suggests that DOC serve as a platform that unites core digital technologies (such as AI and data mining) and the physical infrastructure in the interest of public governance thus becoming a genuine decision support system. The omni-channel data collection and processing create the transition of reactive management patterns to predictive analytics and smart decision- making in public governance. The research leads to identification of relevant Russian experience in implementing and developing DOC in public governance at the regional level, as well as studying international experience to provide a set of recommendations for practitioners. Methodology of the research includes general scientific methods of cognition of processes and phenomena: an analytical approach, qualitative and comparative analysis, andsynthesis.
Personal health records: Blockchain-based identity management and data ownership

Data privacy and security preservation are essential issues in the public sector and, particularly, in the health and medical sectors. Patients often lose their data and medical history if they must move between hospitals. In these cases, other health care providers must repeat some procedures, such as analyses and x-rays. An approach that focuses on securely managing and sharing patient health records is thus required. As traditional centralised systems in hospitals do not meet the requirements for such an approach, this calls for the implementation of a secure structure that is capable of protecting patient data. One potential solution is the use of decentralised personal health records in a system that would allow the patient to own, manage and access their own data. This can be achieved by using the type of blockchain technology that would permit patients to identify shareable information and grant or prevent providers and other entities from accessing their data. Utilising the methodology of design science, this research will propose a solution for personal health records management by identifying the necessary requirements, and advantages and disadvantages of such a system. The study will propose a solution that will allow the patients to own, control, and benefit from their data.
Measurement-device-independent quantum key distribution with classical Bob and no joint measurement

Measurement-device-independent quantum key distribution (MDI-QKD) provides a method for secret communication whose security does not rely on trusted measurement devices. In all existing MDI-QKD protocols, the participant Charlie has to perform the Bell state measurement or other joint measurements. Here we propose an MDI-QKD protocol which requires individual measurements only. Meanwhile, all operations of the receiver Bob are classical, without the need for preparing and measuring quantum systems. Thus the implementation of the protocol has a lower technical requirement on Bob and Charlie.
A Recursive Exact Algorithm for the Closest String Problem

In this paper we study the Closest String Problem (CSP). To solve the CSP involves finding a string that minimizes the maximum Hamming distance from a given set of strings with an arbitrary alphabet. Its decision version is NP-complete. The Hamming distance between two strings $a$ and $b$ of equal length is calculated by simply counting the character positions in which $a$ and $b$ differ. In this work we propose two approaches to solve the CSP. The first is a greedy heuristic algorithm (GREEDY), the second is a recursive exact method. The algorithms are compared with an integer programming formulation for CSP. Furthermore, computational experiments in comparison tables will show the effectiveness of the proposed algorithms.
Corruption and Foreign Direct Investment Inflows: Evidence from West Africa

Purpose: The study investigates the effect of corruption on FDI inflows to West Africa, and also establishes a threshold level of corruption for the sub-region. Design/Methodology/Approach: Using secondary data for the period 1999-2018, the study adopted a panel Autoregressive Distributed Lag (ARDL) model to carry out regression analysis. However, to ensure results accuracy and validity, a cross section dependence test, panel unit root test and panel cointegration test was carried out. Findings: The results indicated that in the long-run, corruption adversely affects the inflow of FDI to West Africa, thus lending support to the grabbing hand hypothesis. The study found the long-run threshold level of corruption for West Africa to be 6.3, indicating that below this level FDI inflow cannot be discouraged by corruption otherwise, FDI inflows could be discouraged. Research limitations/Implications: The findings from the study suggests that governments from West Africa should focus on mechanisms that will strongly discourage people from engaging in corruption, such as reducing the delays in business registration, strengthening and ensuring of effective monitoring of public institutions, as well as introducing the practice of penalty and exhortation in the public sector. Quality/Value: The present study contributes to the literature by investigating the effects of corruption on the inflow of FDI to West Africa using a more appropriate macro panel estimation technique, the panel Autoregressive Distributive Lag (ARDL) technique. Furthermore, it provides a threshold level for corruption on FDI inflows in West Africa.
Design of Asymptotically Perfect Linear Feedforward Photonic Circuits

We propose a new architecture for feedforward photonic circuits based on a 3-splitter MZI. This architecture is more error tolerant than the standard mesh, supports self-configuration, and yields asymptotically perfect circuits for large mesh sizes.
EXT-TAURUM P2T: An Extended Secure CAN-FD Architecture for Road Vehicles

The automobile industry is no longer relying on pure mechanical systems; instead, it benefits from advanced Electronic Control Units (ECUs) in order to provide new and complex functionalities in the effort to move toward fully connected cars. However, connected cars provide a dangerous playground for hackers. Vehicles are becoming increasingly vulnerable to cyber attacks as they come equipped with more connected features and control systems. This situation may expose strategic assets in the automotive value chain. In this scenario, the Controller Area Network (CAN) is the most widely used communication protocol in the automotive domain. However, this protocol lacks encryption and authentication. Consequently, any malicious/hijacked node can cause catastrophic accidents and financial loss. Starting from the analysis of the vulnerability connected to the CAN communication protocol in the automotive domain, this paper proposes EXT-TAURUM P2T a new low-cost secure CAN-FD architecture for the automotive domain implementing secure communication among ECUs, a novel key provisioning strategy, intelligent throughput management, and hardware signature mechanisms. The proposed architecture has been implemented, resorting to a commercial Multi-Protocol Vehicle Interface module, and the obtained results experimentally demonstrate the approach's feasibility.
Classification of Red Blood Cell Shapes Using a Sequential Learning Algorithm

This paper presents a method that uses a sequential representation to train Hidden Markov Models as an algorithm for the supervised morphological classification of erythrocytes in peripheral blood samples from patients with sickle cell anemia, considering three classes: circular, elongated and with others deformations. This sequential learning method provides the probability of belonging the object to the class and for the representation of the red cell contour, characteristics are not obtained, but the contour is analyzed as a sequence of curvatures. The experimentation carried out analyzes each group as a class and considers 3, 8, 9, 10 and 11 states, so that the method is capable of dealing with the local angular differences existing in this representation, with the aim of improving the performance of the classification obtained so far. To check the effectiveness of this method, we use samples with balanced classes formed by images of individual erythrocytes, in similar amounts for each of the three classes. Measurements of sensitivity, precision, specificity, F1 and classification accuracy were obtained. The best results were obtained for the representation considering 10 states.
Photonic Integrated Unitary Processor based on Multi-Plane Light Conversion

Recent progress of developing universal optical unitary processors (OUPs) based on the concept of multi-plane light conversion (MPLC) is reviewed. The inherent redundancy of MPLC provides unique scalability and excellent robustness against fabrication imperfectness, enabling large-scale OUPs integrated on silicon and InP platforms.
Blockchain Technology Based Healthcare Supply Chain Management

Blockchain technology is one of the most important emerging technologies and creative developments that play a vital role in real-world applications. It has a collection of unique features, such as decentralized distributed ledger, transaction storage mechanism, smart contract, consensus, and asymmetric encryption. Healthcare records are the backbone of every modern health care system, and traditional supply chain management in medicine or healthcare products can be untrusted, tempered, or cost-effective. This chapter aims to integrate blockchain technology in healthcare and healthcare supply chain management sectors. Blockchain will help keep medical records safe and secure by putting health records on the blockchain. Smart contracts run on a blockchain enhance healthcare, giving interoperable access to health data, pharmacy, physicians, labs, insurers, data storage and security, value-based payment mechanisms, and medical supply chain efficiency. Using blockchain supply chain management in medicine or health care products can be easily handled securely and can be used to verify the authenticity of health care products. This chapter also provides a brief description of the future direction in the field of the healthcare industry.
Human Action Recognition Using Attention Mechanism and Gaze Information

Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Recognizing human object interactions (HOI) is an important part of distinguishing the rich variety of human action in the visual world. Since the interaction of humans and objects is complex and no temporal information is available, it is not possible to detect the interaction accurately just by extracting the appearance features of the image. In this work, we use human gaze to identify informative regions of images for the interaction. Experimental results on V-COCO dataset clearly represents the efficacy of our proposed model and validates our hypotheses about extra cues being integrated into the base model.
A Novel Video Reconstruction of Randomized Frames Using ORB Descriptor

Image processing techniques are applied to study the dynamics of object, from gigantic to microscopic levels. While considering the protein dynamics, protein under test is destroyed after measurement because of the image sensing equipment. When all the static images of every sample are combined, the problem can be considered as the reconstruction of randomized frames. The proposed algorithm is built up by considering feature-based extraction and matching. Features are detected by image descriptors, Oriented FAST and Rotated BRIEF (ORB). Distance metrics is calculated to match the features, merge sort is used for sorting. To check the efficiency of algorithm applied, performance metrics are calculated. The robustness of the descriptors to the external disturbances are calculated by rotating the video frames of randomised video and reconstructed video frames are observed resistant to noises.