Reviewerly/exHarmony huggingface dataset:
User Profile Ontological Modelling by Wiki-Based Semantic Similarity

We consider the use of ontological knowledge from user profile (domain of interests, current tasks, experience and competences etc.) for semantic retrieval. Domain ontologies, Wiki resources and task thesauri used in general technological chain of user-oriented semantic retrieval can be generated independently by different applications and are integrated with the help of the Semantic Web standards. Open information environment is considered as an external data base with great volumes of heterogeneous and semi-structured information that can be transformed into ontologies. Semantic Wiki resources provide generation of ontology for selected set of Wiki pages that formalizes their knowledge structure and explicitly represents its main features. Semantic similarity evaluations and knowledge about typical information objects of resources are used for selection of Wiki pages pertinent to user task. Such Wiki-ontology elements as classes, property values of class instances and relations between them are used as parameters for the quantitative assessment of semantic similarity. Task thesaurus that represents current task is generated on base of domain ontology and task description. The semantic retrieval system based on ontological representation of user needs is described. The set of domain concepts that are semantically similar to currents Wiki page can be used as a base for task thesaurus.
Computational Geometry Data Structures in Logistics and Navigation Tasks

Computational geometry data structures have many applications, such as network optimisation, location tasks, but they can also be used in robot motion planning when a path of the shortest length must be found between the start and target positions that guarantee movements without the risk of collisions with obstacles. The paper deals with visibility graphs, strip and cell decompositions, Voronoi diagrams and compares their properties and efficiency in the investigated area. Since cell decomposition-based approaches give exponential complexity depending on the number of cells, and the knowledge base makes it possible to reduce it mostly only insignificantly, Voronoi diagrams with their polynomial complexity are more efficient for large instances. In addition, their generalized version allows them to generate smooth trajectories.
An examination of scientific data repositories, data reusability, and the incorporation of <scp>FAIR</scp>

Abstract Scientific data repositories (SDRs) provide a way for scientists to share data through data deposition and reuse of deposited data. Over the last twenty‐plus years, hundreds of scientific SDRs have become available. This research examines 132 SDRs. This study assesses if the information available in the SDRs aligned with what scientists need to determine data reusability and if the SDRs enforce FAIR principles.
A Novel Method for Graph Matching

Graph matching is a fundamental NP-hard problem in computer science. We propose an approximate graph matching method. To match the nodes of two graphs, our method first constructs an association graph. For each pair of nodes within the association graph, our method computes their mutual consistency on the basis of the distance information and angle information of the original graphs' nodes. The consistencies of all pairs of nodes form an affinity matrix. With the affinity matrix, our method then performs random walks on the association graph to achieve a stable quasi-stationary distribution. Discretizing the distribution on the basis of the Hungarian algorithm, our method finally obtains the matching between the nodes of the two original graphs. The experimental results demonstrate the effectiveness of our method on graph matching.
Face Recognition in Kindergarten Students using the Principal Component Analysis Algorithm

In kindergarten, it is important to know the activities of each student to evaluate how individual student learns and adapts to school environment. Manually tracking individual student activities during class is hard for kindergarten teachers. In this paper, we propose face recognition in the kindergarten students as a first step to trace and record individual student activities. A video of kindergarten students is converted into digital images. Faces are detected using Viola-Jones method. The extraction feature on the images is done using the Principle Component Analysis (PCA) method. We implement Euclidean Distance to recognize student's face. Our experiments use 70 images as data training. The data training consists of 5 different images from 14 students. The experiment results show 91.42% accuracy by testing 14 new images of the students.
Neural Network Data Processing Technology Based on Deep Belief Networks

The paper provides approach for building neural network data processing technology based on deep belief networks. A neural network architecture, focused on parallel data processing and an original training algorithm implementing the annealing method, is proposed. The approach effectiveness is demonstrated by solving the image compression problem as an example.
Automatic Question Paper Pattern Generation using GA Approach

This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation system. By using Genetic Algorithm (GA) and educational taxonomies, this paper analyses the initial population generation, does chromosome encoding, applies genetic manipulations and experimentally proves that the generated question paper templates are best suited for the dynamic examination paper generation system. This new approach outperforms traditional algorithms that randomly generate examination papers in terms of their topic coverage, learning domains and marks distribution.
IoT Security Using Deception – Measuring Improved Risk Posture

Deception technology is a useful approach to improve the security posture of IoT systems. The deployment of replication techniques as a deception tactic is presented with a summary of our research progress towards quantifying the defensive improvement as part of overall risk management considerations.
Adaptive Visual Tracking Based on Key Frame Selection and Reinforcement Learning

When the appearance of a target changes dramatically, the traditional tracking methods can no longer be used for target tracking task. This paper proposes an adaptive visual tracking algorithm based on key frame selection and reinforcement learning (RL) to solve this problem. First of all, the probability value of the RL network output is analyzed, and the predicted value of output is normalized. At the model update stage of each frame, the probability value corresponding to the optimal behavior is judged. If it conforms to the preset rules, the last frame of the current frame is set as the key frame, and the network model is fine-tuned by using the key frame. The proposed algorithm is only fine-tuned in key frames to obtain multiple fixed prediction models. The experiment is conducted on 100 video sequences of the Object Tracking Benchmark to verify the effectiveness of key frame selection strategy. Compared with the original reinforcement learning based tracking algorithm, the tracking accuracy and the success rate of the proposed algorithm are improved respectively.
Numerical Simulation Analysis of the Characteristics of Torpedo Anchor Penetration in Cohesive Soil

Yue, L.; Wang, Q.; Wang, W.; Wang, Y., and Lv, J., 2020. Numerical simulation analysis of the characteristics of torpedo anchor penetration in cohesive soil. In: Wan, Z. (ed.), Coordinated Sustainable Development in Coastal Areas: Environment and Economy. Journal of Coastal Research, Special Issue No. 109, pp. 139–144. Coconut Creek (Florida), ISSN 0749-0208.Invented by Petrobras in 1996, the torpedo anchor has been successfully applied in Floating Production Storage and Offloading (FPSO) in the Gulf of Mexico (Medeiros, 2002). With a torpedo-like hydrodynamic shape, the torpedo anchor obtains the kinetic energy required for penetration by free-falling through the water. It then uses that energy to penetrate into the seabed, thereby completing its installation. This paper discusses the large deformation finite element method and the CEL method, which are used to calculate the torpedo anchor penetration in a cohesive-soil seabed. The characteristics of the soil and the anchor at different moments during the torpedo anchor penetration are analyzed based on simulation results.
Rethinking Software Protection

Abstract The core of artificial intelligence (AI) applications is software of one sort or another. But while available data and computing power are important for the recent quantum leap in AI, there would not be any AI without computer programs or software. Therefore, the rise in importance of AI forces us to take—once again—a closer look at software protection through intellectual property (IP) rights, but it also offers us a chance to rethink this protection, and while perhaps not undoing the mistakes of the past, at least to adapt the protection so as not to increase the dysfunctionality that we have come to see in this area of law in recent decades. To be able to establish the best possible way to protect—or not to protect—the software in AI applications, this chapter starts with a short technical description of what AI is, with readers referred to other chapters in this book for a deeper analysis. It continues by identifying those parts of AI applications that constitute software to which legal software protection regimes may be applicable, before outlining those protection regimes, namely copyright and patents. The core part of the chapter analyses potential issues regarding software protection with respect to AI using specific examples from the fields of evolutionary algorithms and of machine learning. Finally, the chapter draws some conclusions regarding the future development of IP regimes with respect to AI.
Active Vision Impacts How We Move Our Eyes and What We Attend To: Evidence from Eye-tracking in Immersive, 360° Real-World Environments

Eye-tracking studies offer substantial insight into cognition, revealing which visual features viewers prioritize over others as they construct a sense of place in an environment. Yet, one key feature of real-world experience is overlooked by traditional eye-tracking paradigms. Everyday visual environments are actively explored: we gain rich information about a place by shifting our eyes, turning our heads, and moving our bodies. In this study, we sought to understand how active exploration impacts the way that humans encode the rich information available in a real-world scene. To test this, we exploited recent developments in immersive Virtual Reality (iVR) and custom in-headset eye-tracking to monitor participants’ (N=18) gaze while they naturally explored real-world, 360º photospheres via head turns and eye movements (Active Condition). In half of the trials, photospheres were passively displayed to participants while they were head-fixed (Passive Condition), thus enabling us to perform quantitative, in-depth comparisons of gaze behavior and attentional deployment as subjects encoded novel real-world environments during active exploration vs. passive viewing. We report that active viewing impacts all aspects of gaze behavior, including 1) low-level oculomotor movements and 2) how individuals allocate their attention. Relative to fixations made in the passive fixation, active fixations were shorter (p < 0.001), more frequent (p < 0.001), less centrally tending (p < 0.001), and more entropic (p < 0.001). Furthermore, gaze behavior was overwhelmingly more guided by semantic, rather than low-level visual features in active, as compared with passive viewing (ANOVA interaction: p < 0.001). Taken together, our results demonstrate that active viewing influences nearly every aspect of gaze behavior, from how we move our eyes to what we choose to attend to.
Sikap Siswa terhadap Video Pembelajaran Jarak Jauh Materi Statistika pada Media Sosial Youtube

This research aims to describe the attitude of grade XII students of SMK Negeri 2 Salatiga towards videos that are functioned as a means of distance learning statistical material uploaded on social media youtube. This research is qualitative research. Data is collected through: 1) the filling out of questionnaires about students' attitudes towards content, contextual nature, interactive nature, accessibility, and video benefits; and 2) interviews to complete the poll data. Poll data is descriptively analyzed to obtain student attitude categories and confirmed with interview data. The results of this study include: 1) students' attitude towards the use of statistical learning videos uploaded on social media youtube is in the category quite well; 2) students' attitudes towards video content are in a good category; 3) the student's attitude towards the contextual nature of the video is in the category quite well; 4) students' attitude towards the interactive nature of the video is in the category quite well; 5) students' attitude to video accessibility is in the category quite well;  6) students' attitude to the benefits of video is in the category quite well; 7) Students' attitude towards the look of the video is in the category quite well.
Neuro-Symbolic Integration of Hopfield Neural Network for Optimal Maximum Random kSatisfiability (Maxrksat) Representation

Boolean satisfiability logical representation is a programming paradigm that has its foundations in mathematical logic. It has been classified as an NP-complete problem that difficult practical combinatorial optimization and search problems can be easily converted into it. Random Maximum kSatisfiability (MAX-RkSAT) composed of the most consistent mapping in a Boolean formula that generates a maximum number of random satisfied clauses. Many optimization and search problems can be easily expressed by mapping the problem into a Hopfield neural network (HNN) to minimize the optimal configuration of the corresponding Lyapunov energy function. In this paper, a hybrid computational model hs been proposed that incorporates the Random Maximum kSatisfiability (MAX-RkSAT) into the Hopfield neural network (HNN) for optimal Random Maximum kSatisfiability representation (HNN-MAX-RkSAT). Hopfield neural network learning will be integrated with the random maximum satisfiability to enhance the correct neural state of the network model representation. The computer simulation using C++ has been used to demonstrate the ability of MAX-RkSAT to be embedded optimally in Hopfield neural network to serve as Neuro-symbolic integration. The performance of the proposed hybrid HNN-MAXRkSAT model has been explored and compared with the existing model. The proposed HNN-MAXRkSAT demonstrates good agreement with the existing models measured in terms of Global minimum Ratio (Gm), Hamming Distance (HD), Mean Absolute Error (MAE) and network computation Time CPU time). The proposed framework explored that MAX-RkSAT can be optimally represented in HNN and subsequently provides an additional platform for neural-symbolic integration, representing the various types of satisfiability logic.
The influence of gaming frequency and viewing perspective on a remote robot operation task

Frequent video game players (“gamers”) perform better on certain visuospatial tasks compared to non-gamers (Green & Bavelier, 2003). Furthermore, visuospatial ability has been associated with performance on teleoperation tasks, in which participants remotely operate robots or devices (Gomer & Pagano, 2006; Menchaca-Brandan, Liu, Oman, & Natapoff, 2007). The present study sought to further explore these associations by testing gamers’ and non-gamers’ performance in a teleoperation task where they drove a Beam robot through an obstacle course using two different input devices (Xbox controller and keyboard) under two different viewing perspectives (egocentric or allocentric). Participants (N = 60; 35 female, 25 male) were instructed to drive the robot through the course as fast as possible while hitting as few cones as possible. Each participant completed two laps in each of the four combinations of conditions (2 controllers X 2 perspectives), in a randomized order. Performance was operationalized by course completion time and number of cones hit. Two-way ANCOVAs found that gamers (reporting an average of 8hrs of weekly gameplay in a post-task survey) showed faster course completion times (F(1,57) = 5.65, p < .05, partial η2 = .09), but no difference in errors, compared to non-gamers (average of 1hr of weekly gameplay). Additionally, two-way ANCOVAs indicated an interaction between input device and viewing perspective (p < .05), such that when using the Xbox controller, participants were faster to complete the course under an egocentric perspective compared to an allocentric perspective. Moreover, male participants performed faster and made fewer errors than female participants, and only female participants showed an overall advantage of the egocentric perspective, consistent with previous research (Nowak, Murali, & Driscoll, 2015). These results add to a growing body of literature demonstrating an association between gaming and teleoperation skills, although the underpinnings of this association require further research.
Efficient Static Analysis of Marlowe Contracts

SMT solvers can verify properties automatically and efficiently, and they offer increasing flexibility on the ways those properties can be described. But it is hard to predict how those ways of describing the properties affect the computational cost of verifying them. In this paper, we discuss what we learned while implementing and optimising the static analysis for Marlowe, a domain specific language for self-enforcing financial smart-contracts that can be deployed on a blockchain.
Anti-seismic Performance Comparison of Response Spectrum Analysis and Time History Analysis Based on Computer Big Data

Abstract This paper introduces the methods and principles of response spectrum analysis and time history analysis of seismic performance of structures based on computer big data. In order to discuss the common ground and the consistency of the calculated results between the two methods, the response spectrum analysis, time history analysis based on seismic level 8 synthetic seismic wave and seismic test were carried out on an IDC cabinet structure. The results showed that both the response spectrum method and the time-history analysis method reflect the effects of ground motion characteristics and structural dynamic characteristics on the seismic response of the structure in the seismic performance calculation, and the simulation results of the two methods were consistent in the response maximum value and response frequency.
Using Reverse Interactive Audio Systems (RIAS) to Direct Attention in Virtual Reality Narrative Practices: A Case Study

Game audio is widely implemented as being reactive to player actions and environmental influences. However, with virtual reality narratives, there is a growing need to be able to direct audience attention. ArrivalVR is the first nationally co-created storytelling project presenting curated immigration/migration stories of Americans in virtual reality (VR). In this paper, we discuss how reverse interactive audio systems could direct audience attention using ArrivalVR as a case study.
An Approach to Identify and Classify State Machine Changes from Code Changes

During the software development process, various types of artifacts are created representing different views in different levels of abstraction. Among these artifacts, UML Behavioral State Machine (SM) is one of the most used models to represent the software behavior. Due to the changes that occur in the source code of the software, updates are also required in the entire set of the produced artifacts, demanding time and effort. In this work, we propose an approach to identify and classify changes on SMs according to the source code changes. This approach is composed of (i) a taxonomy for changes on SMs; (ii) a mapping from source code changes to SM changes; and (iii) an algorithm that automatically classifies SM changes caused by code changes. Two studies with real and open source projects were carried out: a survey to evaluate the taxonomy and an experiment to evaluate the algorithm effectiveness. The results have shown that source code changes can cause changes on SM and that it was possible to establish a mapping between both kind of changes. The experiment assessed a precision of 65, 60% and a recall of 50, 80% to the algorithm. With the proposed approach, we hope to reach a reduction of the effort and time required to keep SM updated and in compliance with the source code, as well as to provide a way to better comprehend the software evolution.
AN EFFICIENT CONFUSION-DIFFUSION STRUCTURE FOR IMAGE ENCRYPTION USING PLAIN IMAGE RELATED HENON MAP

Chaos-based image encryption has great significance as a branch of image security. So, a series of chaos-based cryptosystems protecting digital images are proposed in recent years. But, most of them have been broken as a result of poor encryption structure. This research paper suggests an effective image encryption structure to resist possible attacks. The proposed method employs plain image related Henon map (PIHM) for shuffling and diffusion processes in a connected way which is different from conventional chaotic based image encryption systems, since the initial conditions of diffusion process are established based on the initial conditions of shuffling process. The principle of confusion is achieved by shuffling the pixels over all the rows and columns. And the diffusion is ensured by using XOR operation of current shuffled pixel value with the previous value, and random pixel produced from PIHM map. The results of simulation and security analysis indicate that the proposed scheme has desirable encryption effects and is robust against different common attacks.