Reviewerly/exHarmony huggingface dataset:
Manik angkeran storytelling based on android mobile tap gameplay

Abstract This research objective was to structure a presentation of a Balinese popular folk tale as an educational strategy through a mobile game with easy-to-play gameplay. This study had 3 (three) main objectives: (1) building the Manik Angkeran story, which is the famous folk tale circulating in Balinese society, Indonesia; (2) realizing the Manik Angkeran game that highlights Balinese characteristics; (3) implementing easy-to-play gameplay. In developing the original story of Manik Angkeran, the strategies carried out were: (1) building a new story with the main character, Manik Angkeran; (2) highlighting Balinese characteristics in character design, game background, and background music in fighting scenes; (3) chosing tap-mode game considering the simplicity and efficiency. The result of this study was Manik Angkeran Game with Tap Game genre based on Android. The game requires players to tap on the screen to move Manik Angkeran, with a mission to defeat the enemy/monster to collect gold coins (Pis Bolong Mas). Later, these coins can be used to buy building materials, which will be used to build dormitories ( Pesraman ). The test results using the BlackBox Testing method showed that the functionality of the navigation buttons and the entire functions of the game were running well as desired. For the results of testing the game on several different screen resolutions, showed that the display of this game was able to adjust on different screen resolutions and the image display remains sharp. This game was also capable to run on a minimum Android device v.3.0 HoneyComb, until the latest version at this time is v.9.0 Pie. Research disclosure starts with the Introduction, which reveals the background and purpose of the study. Furthermore, the discussion swooped over to the research method used. The process of pre-production, production to post-production was he model or technique used in making games. The results revealed the evaluation of the black box to test the readiness of the system.
Stream Sediment Datasets and Geophysical Anomalies: A Recipe for Porphyry Copper Systems Identification—The Eastern Papuan Peninsula Experience

Airborne magnetic and radiometric datasets have, over the past few years, become powerful tools in the identification of porphyry systems which may host economic porphyry copper–gold–molybdenum ore bodies. Magnetisation contrasts with the unaltered host rocks, coupled with the elevated radiometric signature, compared to the host rock, makes identification of large-scale porphyry copper systems possible. Integrating these two different datasets with stream sediment data and other geochemical exploration methods results in a higher degree of confidence. Stream sediment data were analysed to see the distribution of copper and gold elements throughout the study area, located within the Eastern Papuan Peninsula of Papua New Guinea. Airborne geophysics data over the same area were also processed for magnetic and radiometric responses. The processing of the magnetic data revealed several magnetic anomalies related to concealed intrusive rock units, with associated radiometric signatures. The distribution of gold and copper anomalism was correlated with the geology and geophysical signatures. Results indicate varying degrees of correlation, with some areas showing a strong correlation between gold/copper occurrence and geophysical signatures, compared to other areas. Some factors that we believe impact the level of correlation may include tectonic history, volcanic cover, and weathering patterns. We recommend caution when applying multi-data exploration for porphyry copper systems.
The Effect of Body-Based Haptic Feedback on Player Experience During VR Gaming

As the interest in virtual reality (VR) technology as a game console has rapidly grown over the past few years, many new technologies are also being developed to further enhance the VR gaming experience. One of these technologies is haptic feedback vests, which are beginning to hit the market with claims of elevating the player experience (PX). Since the use of haptic vests during gameplay is still in its early stages, their influence on the PX during VR gaming is understudied. Accordingly, the current study investigated the effect of providing body-based haptic feedback on players' sense of presence and overall PX during VR gaming. Participants played a VR-based rhythm game both with and without a haptic feedback vest and provided self-reported ratings of their sense of presence and PX. Results revealed that there was no significant difference between playing with a haptic vest and without a haptic vest in terms of players' sense of presence and PX during the gameplay. As a whole, these results indicate that providing body-based haptic feedback using a haptic vest may not always increase sense of presence and PX levels when playing a rhythm game in VR.
Modular Safety Control for Mobile Robots Using Barrier Certificates and Modified Feedback

A modular control design is proposed for nonholonomic mobile robots where the trajectory tracking algorithm is equipped with dynamic contouring and obstacle avoidance. Barrier certificates are used to update the robot's angular velocity to avoid obstacles scattered in the operational environment. The safe range of the heading angle is calculated by enforcing an exponential decay rate on the barrier certificate. The proposed obstacle avoidance technique is compatible with a variety of trajectory tracking algorithms. Also, the dynamic contouring algorithm and feedback modification allow precision trajectory tracking using linear control components. The proposed control structure is numerically efficient, easy to tune, and independent of the number and location of obstacles. Thus, control scalability is dramatically improved, which facilitates control implementation and calibration. The numerical simulations verify the effectiveness of the proposed safety control.
Strengthening security of Mobile, IOT and Edge applications in Post-Quantum era using Quantum Random Number Generators

Mobile devices have become a gold mine for attackers because of the increased and rather unplanned boom in digital activity. Recently there is more demand for interconnected IOT and edge computing services as well. All this increased the value and amount of data in these devices. With the increase in sensitivity of data available in these devices, we see more and more powerful attacks every day for breaking the existing cryptographic systems. Advancements in Quantum Technology is another potential threat for existing cryptographic standards. Random Number Generators (RNG) are a primitive tool required for securing these devices and computations, starting from their role in generating good and unpredictable encryption keys. Today, the vast majority of Cryptographic implementations make use of Pseudo-Random Number Generators which are prone to existing classical and Quantum attacks. Careful choice of True Random Number Generators is the best way to go. In this paper, we did an in-depth investigation on the different key generation techniques used in existing cryptographic schemes and the impact of quantum computers on their security. Then, we give an overview on the Random Numbers and different types of Random Number Generators. Next, we justify that the Quantum Random Number Generators based on Quantum Mechanics possess the desirable True Randomness and why we need quantum-safe key generation that can be an immediate step in strengthening security against both classical and quantum attacks. Finally, we introduce different types of QRNG implementations along with their challenges, drawbacks and suitability for Mobile, IOT and Edge applications.
Interactive query expansion for professional search applications

Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expert knowledge to formulate accurate search strategies. Interactive features such as query expansion can play a key role in supporting these tasks. However, generating query suggestions within a professional search context requires that consideration be given to the specialist, structured nature of the search strategies they employ. In this paper, we investigate a variety of query expansion methods applied to a collection of Boolean search strategies used in a variety of real-world professional search tasks. The results demonstrate the utility of context-free distributional language models and the value of using linguistic cues to optimise the balance between precision and recall.
Towards a Domain-Agnostic Computable Policy Tool

Policies are often crucial for decision-making in a wide range of domains. Typically they are written in natural language, which leaves room for different individual interpretations. In contrast, computable policies offer standardization for the structures that encode information, which can help decrease ambiguity and variability of interpretations. Sadly, the majority of computable policy frameworks are domain-specific or require tailored customization, limiting potential applications of this technology. For this reason, we propose ADAPT, a domain-agnostic policy tool that leverages domain knowledge, expressed in knowledge graphs, and employs W3C standards in semantics and provenance to enable the construction, visualization, and management of computable policies that include domain knowledge to reduce terminology inconsistencies, and augment the policy evaluation process.
An Optimal Data Entry Method, Using Web Scraping and Text Recognition

Data entry is one of the most tedious jobs which consumes huge manpower in creating structured data from the given inputs. A large amount of data entered in the system can be contrasting to the original data causing confusions, especially when the data has to be gathered from image files. In this paper, we propose a text recognition system that can be employed to detect text from images automatically and update it to a target file. The proposed method accepts a web URL as the input and fetches the text or image using web scraping technique. The system extracts textual data from a user specified region. Further, the extracted text is classified using Support Vector Machine (SVM) and Naive Bayes Classifier. The output is saved it in the form of Google-Sheet, CSV, PDF, text, or Excel based on the user's choice. Contemporary models for text recognition such as PyTesseract, PyOCR, and TesserOCR are compared based on evaluation metrics such as accuracy, precision, execution speed. The experimental results exhibit that PyTesseract gives an accuracy of 83.45% and precision of 75.55%. The performance of the Support Vector Machine (SVM) and the Naive Bayes Classifier are compared. Naive Bayes Classifier with 92.08% precision, 90.148% recall, and 90.99% F-measure shows better performance than SVM.
Performance and Usability of Visual and Verbal Verification of Word-Based Key Fingerprints

The security of messaging applications against person-in-the-middle attacks relies on the authenticity of the exchanged keys. For users unable to meet in person, a manual key fingerprint verification is necessary to ascertain key authenticity. Such fingerprints can be exchanged visually or verbally, and it is not clear in which condition users perform best. This paper reports the results of a 62-participant study that investigated differences in performance and perceived usability of visual and verbal comparisons of word-based key fingerprints, and the influence of the individual's cognitive learning style. The results show visual comparisons to be more effective against non-security critical errors and are perceived to provide increased confidence, yet participants perceive verbal comparisons to be easier and require less mental effort. Besides, limited evidence was found on the influence of the individual's learning style on their performance.
Analysis of Modality-Based Presentation Skills Using Sequential Models

This paper presents an analysis of informative presentations using sequential multimodal modeling for automatic assessment of presentation performance. For this purpose, we transform a single video into multiple time-series segments that are provided as inputs to sequential models, such as Long Short-Term Memory (LSTM). This sequence modeling approach enables us to capture the time-series change of multimodal behaviors during the presentation. We proposed variants of sequential models that improve the accuracy of performance prediction over non-sequential models. Moreover, we performed segment analysis on the sequential models to analyze how relevant information from various segments can lead to better performance in sequential prediction models.
Data-Mining Based Novel Neural-Networks-Hierarchical Attention Structures for Obtaining an Optimal Efficiency

Big data and its classification have been the recent challenge in the evolving world. Data evolving needs to be classified in an effective way. For the classifying process, deep learning and machine learning models are evolved. Hierarchical Attention Network (HAN) is one of the most dominant neural network structures in classification. The major demerits which the HAN is facing are, high computation time and numerous layers. The drawback of HAN is vanquished by a new idea arrived from the mining methods that yield mixed attention network for android data classification. By this flow it could handle more complex request apart from the concept identified. The EHAN (Enhanced Hierarchical Attention Network) has two prototypes. The first one is the attention model to distinguish the features and the second one is the self-attention model to identify worldwide facts. By the demonstrated outcome, it is inferring that the partitioning of task is constructive and therefore the EHAN features shows a significant growth on the news database. In addition to this paper we could add other subnetworks subsequently to assist this ability further.
A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning

Nowadays, deep representations have been attracting much attention owing to the great performance in various tasks. However, the interpretability of deep representations poses a vast challenge on real-world applications. To alleviate the challenge, a deep matrix factorization method with non-negative constraints is proposed to learn deep part-based representations of interpretability for big data in this paper. Specifically, a deep architecture with a supervisor network suppressing noise in data and a student network learning deep representations of interpretability is designed, which is an end-to-end framework for pattern mining. Furthermore, to train the deep matrix factorization architecture, an interpretability loss is defined, including a symmetric loss, an apposition loss, and a non-negative constraint loss, which can ensure the knowledge transfer from the supervisor network to the student network, enhancing the robustness of deep representations. Finally, extensive experimental results on two benchmark datasets demonstrate the superiority of the deep matrix factorization method.
Elastic AI: system support for adaptive machine learning in pervasive computing systems

Abstract Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.
HaptMR: Smart Haptic Feedback for Mixed Reality Based on Computer Vision Semantic

This paper focuses on tactile feedback based on semantic analysis using deep learning algorithms on the mobile Mixed Reality (MR) device, called HaptMR. This way, we improve MR’s immersive experience and reach a better interaction between the user and real/virtual objects. Based on the Mixed Reality device HoloLens 2. generation (HL2), we achieve a haptic feedback system that utilizes the hand tracking system on HL2 and fine haptic modules on hands. Furthermore, we adapt the deep learning model – Inception V3 to recognize the rigidity of objects. According to the scenes’ semantic analysis, when users make gestures or actions, their hands can receive force feedback similar to the real haptic sense. We conduct a within-subject user study to test the feasibility and usability of HaptMR. In user study, we design two tasks, including hand tracking and spatial awareness, and then, evaluate the objective interaction experience (Interaction Accuracy, Algorithm Accuracy, Temporal Efficiency) and the subjective MR experience (Intuitiveness, Engagement, Satisfaction). After visualizing results and analyzing the user study, we conclude that the HaptMR system improves the immersive experience in MR. With HaptMR, we could fill users’ sense of inauthenticity produced by MR. HaptMR could build applications on industrial usage, spatial anchor, virtual barrier, 3D semantic interpretation, and as a foundation of other implementations.
Architecture of InnoChain, a Formally-Verified Distributed Ledger System

In this paper we consider the software architecture of InnoChain, a distributed ledger system (DLS) with 5 levels of formal verification, including a formally-verified underlying operating system (OS). The objective of this architecture is to achieve a higher level of DLS dependability compared to more traditional software architectures and quality assurance (QA) methods. The architecture of InnoChain includes (1) a programming language for smart contracts which is a domain-specific language with formal semantics embedded into CakeML, which is a functional language of the ML family; this allows us to carry out formal verification of smart contracts’ correctness properties using higher-order logic systems, such as HOL4; (2) trusted compilation of smart contracts into the machine code using the verified compiler available for CakeML, rather than relying on a virtual machine for execution of smart contracts; (3) using CakeML for implementation of InnoChain node functionality which allows for formal verification of code correctness and trusted compilation into the machine code; (4) formal verification of the consensus protocol used InnoChain, namely Chained HotStuff; (5) using seL4, a formally-verified microkernel, as the underlying OS for InnoChain instead of more traditional general-purpose OSes such as Linux. The proposed verified architecture will allow InnoChain to be used in mission-critical applications, such as the decentralized Aircraft Fuelling Control System which is currently under development for JSC Aeroflot, the Russian national air carrier.
Utilisation of EU Employment Data in Lecturing Data Mining Course

This article describes the utilisation of Eurostat employment data in the Data Mining course. The course is the obligatory course for a master degree Geoinformatics and Cartograhy study program at Palacký University in Olomouc. The article shows an example of the implementation of several methods like correlation, principal components analysis, k-means and hierarchical clustering on the same dataset in the course’s teaching. The processing data in the Orange software and following interpretation of results gained by these methods are explained to students. Moreover, students create the MS PowerBI dashboard based on the same data. Teacher final finding is that the use of the current European data is for students more illustrative and increases their awareness of the status of employment in European countries within economic activities categorised by NACE. Practical processing of real data brings a deeper understanding of the lectured topics. Presented outputs, such as clustering, discover similar countries according to the same sector of employment.
MSVD-Turkish: a comprehensive multimodal video dataset for integrated vision and language research in Turkish

Automatic generation of video descriptions in natural language, also called video captioning, aims to understand the visual content of the video and produce a natural language sentence depicting the objects and actions in the scene. This challenging integrated vision and language problem, however, has been predominantly addressed for English. The lack of data and the linguistic properties of other languages limit the success of existing approaches for such languages. In this paper we target Turkish, a morphologically rich and agglutinative language that has very different properties compared to English. To do so, we create the first large-scale video captioning dataset for this language by carefully translating the English descriptions of the videos in the MSVD (Microsoft Research Video Description Corpus) dataset into Turkish. In addition to enabling research in video captioning in Turkish, the parallel English–Turkish descriptions also enable the study of the role of video context in (multimodal) machine translation. In our experiments, we build models for both video captioning and multimodal machine translation and investigate the effect of different word segmentation approaches and different neural architectures to better address the properties of Turkish. We hope that the MSVD-Turkish dataset and the results reported in this work will lead to better video captioning and multimodal machine translation models for Turkish and other morphology rich and agglutinative languages.
Application of VR instruments in preprofessional education in the area of mechatronics and robotics in a nuclear research university

In the area of computer sciences there is a rapidly developing subject of mechatronics and robotics since its relevance to the fourth industrial revolution, especially in preprofessional education – schools etc. On the other side, technologies of virtual reality (VR technologies) have been spreading in education so far, including computer sciences and mechatronics and robotics education in Russian leading research universities. In National Research Nuclear University MEPhI we have a broad experience of application of hi-tech in education. In this paper we present the results of our practical research of application of VR instruments in preprofessional education in the area of mechatronics and robotics. One of the features of our research is that we have developed the educational program for obtaining practical skills in the area of VR digital technologies in compliance with the CDIO standards.