Reviewerly/exHarmony huggingface dataset:
Designing Human-Computer Interaction for E-Learning using ISO 9241-210:2010 and Google Design Sprint

The development of information technology has impacted the education sector. The use of technology in learning is commonly called E-Learning. In the usage of E-Learning for learning, there <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> are usability problems encountered from the registration process which is quite difficult, material that does not fit the user's needs, the learning process that doesn't effective, and the difficulties of finding the searched class. This study aims to produce an interface and test the results of the interface interaction design for ITB E-Learning. The approach used is human-centered design with the ISO 9241-210: 2010 methodology and Google Design Sprint. The result of this study is a high-fidelity prototype created using Adobe XD. The tests have been conducted on ITB students with usability testing and SUS Testing methods. The prototype is successful if it meets the SUS Testing value of 62.5, meets usability goals that are easy to use and can be learned easily, and user experience goals to help and satisfy the users. Based on the results of the tests conducted, there were SUS Test results of 94.17 and 5 out of 6 users rated the prototype as easy to use, easy to learn, satisfying, and assisted users in conducting learning activities.
Ququats as superposition of coherent states and their application in quantum information processing

Superposition of optical coherent states (SCS) [Formula: see text], possessing opposite phases, plays an important role as qubits in quantum information processing tasks like quantum computation, teleportation, key distribution, etc. and are of fundamental importance in testing quantum mechanics. Passage of such SCS from a 50:50 beam splitter leads to generation of entangled coherent states. Recently, ququats and qutrits defined in four- and three-dimensional Hilbert space, respectively, have attracted much attention as they offer advantage in secure quantum communication. However, practical utilization of these advantages essentially requires physical realization of quantum optical ququats and qutrits. Here, we define four new multi-photonic states (MPS) with [Formula: see text] (here, [Formula: see text] or 3 and [Formula: see text]) numbers of photon and show how the SCS can be used to encode ququat using these MPS as basis vectors of a four-dimensional Hilbert space. When these MPS fall upon a 50:50 beam splitter, the resulting states are bipartite four-component entangled coherent states (BFECS) equivalently representing the entangled ququats. We briefly discuss the photon statistical properties of such MPS and BFECS. We show that these MPS and BFECS can be synthesized using even coherent states as input to an interferometer. We give a simple linear optical protocol for almost perfect teleportation of a ququat encoded in SCS with the aid of BFECS as quantum channel. We also describe how these ququats can be used for realization of higher-dimensional BB84 protocol to increase the security of quantum key distribution. Finally, we discuss the possible advantages of using SCS as ququats and BFECS as quantum channel in different quantum information processing tasks.
The Ownership of Artificial Intelligence (AI) Generated &amp;amp; Created Inventions

The rudimentary and overarching goal of the patent law is to establish a balance between public interest and the inventor interest on an equilibrium of reward, a limited exclusive right is granted to the inventor as a reward for his or her contribution to the technical advancement of the general public. This equilibrium of reward provides the fulcrum that serves as an incentive for creativity, innovation, research and development. With the fast pace of advancement in technological developments, overtime machines have developed the capability and capacity to invent new products, this trend of machine’s invention was not envisaged by the global patent system and has triggered discussions for Intellectual Property policies reform. Debates surrounding who owns an Artificial Intelligence generated or created invention took a different trend recently after the European Patent Office (EPO) rejected two patent applications on the basis that they did not meet the requirements for inventorship as prescribed by the European Patent Convention (EPC). The EPC states unequivocally that an inventor is a “natural person” and by giving a machine a name is immaterial because things have no rights, therefore the grounds for refusal by the European Patent Office was that machines have no “legal personality”. There’s a gray line between the concepts of Ownership and Inventorship of patentable inventions that require clarification, people often confuse the inventor and owner as the same person which is often not factual; this research seeks to provide a scholarly adventure into setting a dichotomy between the two concepts including authorship. Discussions on the path ahead from the multilateral and the national jurisdiction levels are hinged on the objective of reviewing IP policies, legislative amendments and regulations on Artificial Intelligence in general.This research paper examines the ownership of Artificial Intelligence generated and created inventions, within the existing IP framework and what protection are ascribed to such inventions under the Paris Convention, the World Intellectual Property Organization Copyright Treaty (WCT), and the Trade-related Aspects to Intellectual Property Rights (TRIPS) Agreement from a substantive law perspective coupled with what policy reforms are required for its adaptation and enforcement. To review IP policies, the Natural Rights Theory, laws, regulations that are in contrast to the concept of “machine ownership” and find a middle ground of convergence; to investigate jurisdictional issues pertaining to AI enforcement at the domestic fronts. Moreover, to look keenly at the concepts of ownership, inventorship and authorship in relations to policy prescriptions required to adapt AI inventorship, ownership and authorship in the field of IP. And conclude with the path ahead from the international and national perspectives.
Analysis of the Contents of Hanbok in the「 Home Life and Safety」 section of the High School Technical Family Textbook: Content Analysis and Text Mining Techniques are utilized

This study is not just a meaning of costume but a function of culture and includes addresses the associated emotions. As the interest of youths has increased recently, the importance of traditional costume education has been growing. Therefore, this study aims to analyze the contents of Hanbok in the 2015 revised high school technology and home textbooks using content analysis techniques and text mining techniques. As a result of the study, first, the symbolic meaning and characteristics of Hanbok and the beauty of Hanbok were practiced in daily life, and the value was found through the excellence of Hanbok and the modernization of Hanbok was dealt with Second, most of the illustrations related to traditional costumes were presented in various ways, but there were some regrets due to lack of quantity and quality. Third, the words used to explain traditional costumes were used in the form of culture, excellence, tradition, modernity, harmony, succession, etc. except for the types of clothing. Therefore, the results and discussions derived from this study are expected to help the textbooks to be efficiently selected and used in the field of the front line school along with the correct understanding of traditional culture in the process of selecting traditional culture contents and illustrations.
USING THE INTERNET SERVICE OF AUGMENTED REALITY BLIPPAR AS A MEANS OF FORMING THE READING COMPETENCE OF SECOND-GRADERS

The article is devoted to the analysis of methodical features of the use of augmented reality as a means of forming the reading competence of pupils of the second grade of primary school. The paper presents the analysis of recent research on the importance of involving schoolchildren in the reading process. It is determined that in recent years the interest of pupils in reading has significantly decreased, and this in turn has affected the overall comprehensive development of pupils. It has been scientifically proven that reading plays an important role in the formation of a personality. Thus, thanks to reading, a person's horizons expand, their vocabulary is enriched, and their speech activity improves. Recently, the latest computer and Internet technologies have had a significant impact on the lives of the younger generation. ICT has made great changes in the organization of the educational process of the school. Teachers actively use Internet resources to achieve educational goals in various subjects, including reading. A sensitive period in the formation of pupils' reading competence is the period of primary schooling. The article identifies the features of the formation of reading competence of primary school pupils on the basis of the latest computer tools, analyzes the types of augmented reality Internet services that should be used in the formation of reading competence of primary school children: SketchAR, Quiver, BlippAR and others. Criteria, indicators of formation of reading competence of second-graders are developed: perception of works of art of different genres, readability, ability to build and express the opinion, motivation to reading. Based on the certain criteria and indicators, the levels of formation of pupils' reading competence were characterized. The method of formation of reading competence of second-graders by means of augmented reality on the basis of the BlippAR Internet service is offered. The results of the study prove the effectiveness of using the Internet service of augmented reality BlippAr as a means of forming the reading competence of second-graders.
Generation of Pseudo-Random Binary Sequence Based on Cipher Feedback Chaotic System for Cryptographic Applications

Generation of random key sequence is a primary component in stream cipher cryptosystem. Since chaotic sequences are inherently random, its properties like highly sensitive to initial conditions lead its application to cryptographic field. But, when the chaotic real sequences are converted into binary sequences, there will be loss of randomness. To improve the randomness of the binary sequence, a nonlinear cipher feedback structure is introduced. The nonlinearity present in the encryption part of the cipher feedback system will increase randomness of the sequence. The sequences generated are subjected to randomness tests like NIST, FIPS, cross-correlation and auto-correlation, and the results show that all generated sequences pass these tests. As the sequences qualify the randomness tests, stream ciphers can use them as key sequences.
Integrating High Fidelity Eye, Head and World Tracking in a Wearable Device

A challenge in mobile eye tracking is balancing the quality of data collected with the ability for a subject to move freely and naturally through their environment. This challenge is exacerbated when an experiment necessitates multiple data streams recorded simultaneously and in high fidelity. Given these constraints, previous devices have had limited spatial and temporal resolution, as well as compression artifacts. To address this, we have designed a wearable device capable of recording a subject's body, head, and eye positions, simultaneously with RGB and depth data from the subject's visual environment, measured in high spatial and temporal resolution. The sensors include a binocular eye tracker, an RGB-D scene camera, a high-frame-rate scene camera, and two visual odometry sensors, which we synchronize and record from, with a total incoming data rate of over 700 MB/s. All sensors are operated by a mini-PC optimized for fast data collection, and powered by a small battery pack. The headset weighs only 1.4 kg, the remainder just 3.9kg, and can be comfortably worn by the subject in a small backpack, allowing full mobility.
Security and Privacy Issues in Health Care

In today’s world, because of technological advancement, it is extremely important to safeguard the data from the dark web and the cybercriminals. The healthcare data that is collected and analyzed online is in jeopardy. The task of the data handler is to ensure that the patient’s information given to any healthcare organization is kept confidential so that their data is kept safe and secure. The information security and privacy are involved in the different phases like collection, usage, storage and transmission of the data especially with respect to Personal Health Information. With the advent of new technologies and because most of the devices are connected over the Internet and the data stored in cloud-based platforms, data could be compromised by the cybercriminals. It is about how well the data is protected and the confidentiality of the data is maintained. In this chapter, the challenges, threats, and concerns with respect to healthcare data and the solutions to the problems are looked at. Here we try and understand how these issues are handled by the various technology industries by looking at the several case studies in this area of healthcare and how the integrity of the data is maintained.
Network Risk Assessment Based on Attack Graphs

The paper discusses the problem of computer networks’ security evaluation. It focuses on attack graph based approach. The proposed method is based on MulVAL reasoning engine that identifies possible attack paths leading from an attacker to pointed assets of the assessed IT network. These paths create an attack graph used for attack probability calculation. The method takes advantage of information from vulnerability scanners and topology snapshot. A typical enterprise network has been examined and attack graph based security evaluation- presented. The case study probability calculations have been provided including possible remediation. Benefits and limitations of proposed method have been discussed.
Emerging Trends using Blockchain and Big Data

The data science technologies like Blockchain, Big Data, Internet of Things (IoT), and Artificial Intelligence (AI) have disrupted significant industries worldwide and provided intelligent, efficient, and secure solutions to conduct business in public and private sectors. With the internet, social media, and IoT devices generating increasing data volumes, the need for safe storage, transmission, and predictive data analysis is of paramount importance. This paper presents the five emerging trends across industries that use data science, Big Data, and Blockchain technologies: smart healthcare, smart cities, smart transportation, smart grids, and algorithmic government. The emerging trends that directly affect big data, data science, and blockchain advancement offer tremendous potential for process efficiency, reduced costs, and cross-industry adaptation with changing technologies. Our research shows that both Blockchain and Big data technologies have promising adoption in private and public sectors, with Governments and Private participants launching innovative projects at an increasing rate. The paper finally presents a conclusion.
Analysis of Turkish Sentiment Expressions About Touristic Sites Using Machine Learning

Analyzing data by inferring from unstructured data about customers is one of the main purposes of the tourism and many other industries as well.However, performing unstructured data analysis using traditional methods is quite inconvenient and costly.This can be overcome by using sentiment analysis, an area of application of text mining.Since there is no proven methodology for sentiment analysis, researchers often perform their studies by trial and error.Many studies on sentiment analysis have focused on comparing the preprocessing or the performance of various machine learning algorithms.Both for these reasons and since research on sentiment analysis with Turkish content is limited, this study aimed to determine the effects of labeling, stemming, and negation on the success of sentiment analysis using Turkish touristic site analysis.From the data set prepared for this study, 12 different variations were created according to labeling, number of classes, stemming, and negation.These data sets were classified using the algorithms Naive Bayes (NB), Multinominal Naive Bayes (MNB), k-Nearest Neighbor, and Support Vector Machines (SVM), often used in sentiment analyses, and the findings were compared.
The Next Security Frontier: Taking the Mystery Out of the Supply Chain

The modern technology supply chain is highly complex. Throughout the various stages of design, manufacture, assembly, transport, and operation, a compute device is subject to tampering (malicious or otherwise). This exposes end users and consumers of compute devices to a variety of risks at varying levels of impact. Two potential technology solutions, which aim to provide transparency and insight into the systems in use, are proposed. The success and adoption of these choices, or any other solutions developed, is highly dependent on the participation of ecosystem partners such as foundries, original device manufacturers (ODMs), and original equipment manufacturers (OEMs).
The role of artificial intelligence recognition in metallogenic prediction

Abstract Artificial intelligence algorithm has developed rapidly in recent years, and deep learning has become the research hotspot of many scholars. Artificial intelligence algorithm is just developing in geology, especially in metallogenic prediction. In this work, the 1:250000 aeromagnetic data image of a certain area is used to predict the gold deposit, and good research results are obtained.
Applications of Machine Learning in Steganography for Data Protection and Privacy

Data security is one of the most important aspects in today's scenario. Whenever we send our data from source to destination, data protection is one of the prime components. With the help of data hiding and data extraction techniques, we are able to provide the solution of different types of problems whenever we transfer our data. Steganography is a process where we can hide our data and maintain the quality of the image. At the same time, we think about data alteration. With the help of stegtanalysis method, we reverse engineer and extract the original data. In this chapter, data hiding and data extraction techniques are explained in the combination of machine learning architecture. The combination of steganography and steganalysis along with machine learning is used to identify protected data using different techniques.
Dynamical consistency in networks of nonlinear oscillators

We investigate, numerically and experimentally, the phenomenon of dynamical consistency in a network of nonlinear oscillators. Dynamical consistency is defined as the ability of a dynamical system to respond in the same way when it is perturbed by the same external signal. This property, fulfilled by specific dynamical systems, has been mainly studied in single oscillators (e.g., lasers or electronic circuits), but just a few works have focused on dynamical systems connected through a non-regular network. Using an electronic array, we can connect a series of nonlinear (analog) electronic circuits to create complex networks, where nodes are coupled between them in a given structure, and an external signal can also be introduced into each particular circuit. Furthermore, the strength of the connections is re-configurable. Next, we analyze the consequences of varying the network’s coupling strength and investigate how synchronization and consistency of the whole ensemble arise. Variations in both coupling strengths, i.e. (i) between oscillators and (ii) with the external signal, show the existence of a region where electronic oscillators can respond in the same way to an external perturbation even though they are not completely synchronized with the external signal. We use the Pearson correlation coefficient to measure the consistency of the network, which is characterized by a high correlation between the network output signals and, at the same time, a low correlation with the input signal.
Sustainable Fitness

In the current urgency to reduce the environmental impact of human practices, many industry sectors have been re-designing their practices to increase their environmental sustainability. The introduction of energy-converting equipment that turns the human output on cardio exercise machines into a usable power source a possible way to reduce the carbon footprint of fitness facilities. This research explores persuasive design to implement a novel interface of energy-converting cardio equipment to motivate the energy conversion, while raising environmental awareness and engender a feeling of positive ecological impact during the training. Two interface prototypes were developed and tested using different types of persuasive strategies based on non-authoritative instruction styles, intrinsic and extrinsic motivation and cooperative social feedback. The interfaces differed along one main variable: one of the interfaces showed the individual performance with the energy converting equipment (Individual View), while the second displayed not only the converted energy generated by single individuals during their cardio training, but also the energy generated by the other people training in the gym simultaneously (Community View). Both the interfaces were tested in their pragmatic and hedonic dimensions, their attractiveness, and the effectiveness of the persuasive strategies in stimulating a responsible behaviour in increasing green awareness. The outcomes of the study reveal that persuasive design can provide means to stimulate awareness on sustainable behaviours. In particular, cooperative social eco-feedback seems to stimulate the individual motivation towards training aimed at energy conversion.
Solving the problem of analytical design of the controller for a stationary discrete system with a small step

Abstract In this paper, we study the problem of designing a linear controller for a stationary discrete system with a small step. By the method of integral manifolds, the discrete optimal control problem with a small step is subdivided into two subproblems, the solutions of which are found independently of each other. Algorithms for analytical solutions of subproblems are based on the second Lyapunov method. As a result, a linear controller was designed for a controlled stationary discrete system with a small step, consisting of two subcontrollers, which separately regulate the movements of a system of lower dimension. The results obtained can be used to design an optimal linear digital controller.
Generalized Convolution: Replacing the Classic Convolution Operation with the Sub-network

A convolution operation is a backbone of nowadays machine learning approaches in computer vision problems. It is responsible for extracting low-level features from input data. However, this operation has not been modified for decades. The main contribution of this work is presenting a new concept, where a more generalized version of the convolution operation is introduced. The goal is to replace the classic convolution operation with the sub-network, for example, a Feed-Forward Neural Network. The classic convolution is the specific case of the proposed generalized version where the sub-network is a single artificial neuron. Increasing the complexity (i.a., more layers or additional nonlinear functions) allows for more complex decision boundaries, and as a result, enhances the ability of the network to learn more accurate patterns. We discuss the implementation details of the Generalized Convolution - as we called this approach - using the PyTorch library. The first experiments allowed increasing the VGG model accuracy.