Reviewerly/exHarmony huggingface dataset:
The Non-Walking Triangle Optimization Representation: Enabling Monte Carlo Tree Search-like Methods for Real Parameter Optimization Problems

Real parameter estimation is typically performed by an algorithm that operates directly on vectors of real parameters. This study presents an extension of a representation for real parameter optimization that is discrete and based on the iterated partition of simplices, known as the Walking Triangle Representation (WTR), and pairs it with Monte Carlo Tree Search (MCTS)-like algorithms. The number of moves allowed to the WTR is reduced to only its centering move, where a vertex of the simplex is replaced by its center of mass. This representation converts a real parameter optimization to a discrete form, which can then be paired with MCTS-like algorithms. The tree structure of MCTS allows one to keep track of and exploit information from previous attempts (tree extensions) when choosing the next set of moves to try. Six real parameter optimization problems were used to test the algorithm. Four parameters in the algorithm were studied, including: minimum gene length, maximum gene length, number of tree extensions, and probability of exploration (chance). The algorithm regularly performed consistently well, even with a low number of fitness evaluations (typical number of fitness evaluations is up to 3750 per run). This paper focuses on the ability of the Non-Walking Triangle Representation to convert real parameter optimization problems into discrete representations. This concept is demonstrated through the evaluation of the Non-Walking Triangle Monte Carlo Tree Search (MCNon-Walk) algorithm's ability to find optima in a variety of real parameter optimization problems, using differential evolution as a baseline for comparison.
Relational Calculus to Support Analytics and Information Fusion

This chapter proposes a discussion on the usage of relational calculus when applied to the analytics and information fusion (AIF) core processes that support the generation of actionable knowledge. This chapter discusses how relations and its calculus make AIF processes more capable technologically speaking to support the processing chain of transforming data into actionable knowledge.
Maximum Entropy Reinforcement Learning in Two-Player Perfect Information Games

This paper studies maximum entropy reinforcement learning in two-player adversarial games. Reinforcement learning (RL) has been effective for mastering such games since Alp-haZero carefully integrates Monte-Carlo tree search and self-play. Maximum entropy RL is a promising enhancement that maximize the entropy of a policy (or strategy in games) in addition to cumulative rewards (or wins in games), yielding effective methods such as soft Q-learning or soft actor-critic. The entropy objective improves the robustness of learned strategies by keeping (reducing) alternative choices on our (the opponent's) side when applied to games. Moreover, we show that maximum entropy RL also improves the stability and efficiency of learning by reducing the variance in learning targets of neural networks, which are typically {1, 0, -1} for win, draw, or loss in AlphaZero. Being augmented with the entropy and integrated through a game tree, maximum entropy RL yields soft state values taking intermediate real values, especially near zero in positions near opening positions. We qualitatively show these benefits in terms of an analysis of converged values done by extended retrograde analysis for dobutsu shogi and empirical learning efficiency for RL agents in self-play.
Reference Context Guided Vector to Achieve Multimodal Machine Translation

Abstract Traditional machine translation mainly realizes the introduction of static images from other modal information to improve translation quality. In processing, a variety of methods are combined to improve the data and features, so that the translation result is close to the upper limit, and some even need to rely on the sensitivity of the sample distance algorithm to the data. At the same time, multi-modal MT will cause problems such as lack of semantic interaction in the attention mechanism in the same corpus, or excessive encoding of the same text image information and corpus irrelevant information, resulting in excessive noise. In order to solve these problems, this article proposes a new input port that adds visual image processing to the decoder. The core idea is to combine visual image information with traditional attention mechanisms at each time step specific to decoding. The dynamic router extracts the relevant visual features, integrates the multi-modal visual features into the decoder, and predicts the target word by introducing the visual image process. At the same time, experiments were carried out on more than 30K datasets translated in the United Kingdom, France and the Czech Republic, which proved the superiority of adding visual images to the decoder to extract features.
The Information Search Habits Investigated by an Eye Tracking Technique: How to Manage the Documentary Heritage of the National Library of Latvia

Abstract The aim of the large-scale research entitled “Exploring Documentary Heritage for Building Synergies between Research and Society” is to investigate previously unexplored aspects of the documentary heritage, including the part of the legacy that has previously been subject to censorship and has therefore not been studied in depth. In order for these layers and stories to reach the public, it should initially be informed of the existence of such information, which, in turn, could create awareness and encourage action. As part of the large-scale research, the present article, using an eye tracking tool, reveals how target groups search for information on the Internet. The usability test indicates that respondents do not go to a specific information resource to search for specific information, but search is primarily started using one of the Internet search engines. The main conclusion is that, in order to ensure the availability of reliable high-quality information to the public, which is one of the key conditions for sustainable development, databases of the National Library of Latvia (NLL) should be designed in such a way that Google search engine can easily index the information contained in them.
A new multiple instance algorithm using structural information

Multiple instance learning (MIL) is semisupervised learning that predicts the label of a bag with a wide diversity of instances. It has many applications and thus attracts increasingly more attention. In this paper, we propose a new MIL algorithm using the structural information of a bag to predict its label. In the proposed method, a bag is transformed into a graph, and spectral clustering is employed to divide the graph into several subgraphs. Then, the graph Fourier transform is utilized to extract the features of the subgraphs. Finally, an end-to-end neural network is used to predict the label of a bag with the extracted features. An empirical study with 25 datasets was conducted to validate the effectiveness of the proposed method. The experimental results show that the proposed method performs better than the 6 baseline methods on most datasets.
Blockchain-Based Two-Factor Authentication for Credit Card Validation

The widespread adoption of the e-commerce and web-based business has brought great increase in credit card utilization for online transactions which in turn resulted in sophisticated fraud attempts. Accurate fraud prevention and detection is a key concern in cashless economy. Multifactor authentication among others such as machine learning based behavioral analysis, data mining, black listing is one of the effective methods augmenting primary information checking. SMS messages are sent to registered phone in addition to credit card information as a second level protection. However, this information might be vulnerable to various attacks as some third party services are in the game. This paper proposes adoption of blockchain as a secure platform to store the second factor security information. User’s mobile device signature attested by the bank is stored in a permissioned blockchain. This information is accessed by the merchant through user-friendly QR-code reading interface in order to verify that the user has the registered device. We present system design along with potential threats and security analysis.
Exploring Tele-Assistance for Cyber-Physical Systems with MAUI

In this paper we present an improved version of MAUI [9] (MAUI - Maintenance Assistance User Interface), extending the user-study, giving detailed insight into the implementations and introducing a new User-Interface for mobile use. MAUI is a novel take on tele-assisted tasks on cyber-physical systems. In its core we do not only provide real-time communication between workers and experts, but also allow an expert to have full control over the worker’s user-interface. By precisely separating levels of our software-stack, we enable features like hot-patching and hot-loading of any content or web-based application. Our results show reduced error-rate on task performance once an expert takes over virtual task to relieve the worker. Conditions evaluated include the worker operating on his own and the worker being actively supported. While in operation gauges have to be read, switches to be press and virtual user-interfaces of machinery have to be controlled. Furthermore, we explore the creation of user-interfaces through a developer-study and use the feedback to create a mobile version of MAUI.
A Programming API Implementation for Secure Data Analytics Applications with Homomorphic Encryption on GPUs

As sensitive data is frequently stored and processed in environments that are either shared, untrusted or otherwise can be compromised, privacy is frequently a concern. To address this, a method that has been gaining popularity is to use Homomorphic Encryption (HE), which allows computation over encrypted data, i.e., without decrypting the data first. However, the overhead of such analytics (up to 4 orders of magnitude) is a detriment and while there have been a few previous efforts on reducing these overheads through the use of accelerators like GPUs, programmability is a concern. This paper addresses both performance and programmability concerns with the use of HE. We port the major pieces of Simple Encrypted Arithmetic Library (SEAL) from Microsoft to GPU using CUDA. Through these GPU-based functions, a new HE application can be developed easily. We demonstrate this by developing encrypted versions of three applications - CNN, k-means, and KNN. The speedups of execution time for CNN, k-means and KNN on a single GPU over CPU implementation achieve up to 81, 133 and 7 respectively.
Visual Chatbot for Knowledge Transfer

This paper proposes an approach to design a visual chatbot to enhance the virtual knowledge sharing process. Existing chatbots are either textual or vocal whose performance has not exceeded 60%. However, in various fields a textual description is no longer sufficient, and it is so essential for users to exchange images to better express their preferences. This prevents them from individually describing the image content and transmitting it in writing, which is not always obvious. This work developed a preliminary version of a visual chatbot called SIRSBot (Smart Information Retrieval System roBot). The objective of this paper is to make experiments to identify the main challenges which may face visual information identification. The role of the visual chatbot is (1) to understand the user request, (2) to extract the characteristics of each object in the image that ultimately represent the user's preferences and finally (3) to find a response that meets the user's needs.
Improvement in design and training of feature pyramid network for contour refinement

• A simplified feature pyramid architecture, called Edge Pyramid Network, applied for the first time to edge detection. • A recursive fine-tuning strategy introducing a new weighted negative loss to detect accurate and thin edges. • State-of-the-art results on the benchmark datasets for semantic and category-agnostic edge detection. Semantic edge detection is a challenging problem which aims to produce multiple edge maps corresponding to different object categories. The design and the training of the related network are the two most powerful levers to optimize its accuracy. To this end, we first refine existing semantic edge detection architectures with low and high level features inside our Edge Pyramid Network, which already outperforms state-of-the-art networks on popular benchmark datasets. Then, we apply an ingenious fine-tuning strategy, which incrementally improves edge detection accuracy through a few alternated training cycles, using various loss functions to perform thick and thin edge detection. We observe that applying separately any of these refinements outperforms such networks as SEAL and STEAL for category-aware edge detection. More remarkable is the fact that these two refinements provide accumulated benefits on the edge detection performance (+0.85% and +1.78% on MF-scores compared to STEAL on SBD and Cityscapes datasets respectively). This improvement is visible in the edge maps in which EPN architecture results in smoother edges and the fine-tuning strategy is responsible for thinner edges. Furthermore, we show that EPN architecture achieves competitive performance (ODS F-measure of 0.828) against the state-of-the-art category-agnostic edge detection networks on the BSDS500 dataset.
ONLINE WRITING INSTRUCTION IN EMERGENCY SETTING: EXPLORING THE INSTRUCTIONAL CAPACITY OF DIGITAL SUPPORT

Introduction. This paper reports on the results of the initial stage of the study targeted at carrying out a comprehensive comparative research into the educational capacity of two online tools that were used to teach academic writing to university students in the emergency online learning under the COVID-19 pandemic. Since this kind of online educational activity was meaningfully different from well-planned online experiences in terms of planning, preparation, and development, the authors were required to find the tools and online platform to match a range of criteria, such as: accessibility of the tool/platform; high task generation capacity; feedback generation capacity as well as the capacity to disable academic dishonesty. The objective of this paper is to consider the impact of technological support of online writing instruction on the quality of the students’ cause-effect essays as well as on building writing skills of the learners. Methodology. The research is designed as an experimental online training of the 3rd-year students of the Institute of Philology, Taras Shevchenko National University of Kyiv (n=42). Authors 1 and 2 acted as teachers in the process of online writing instruction with the involvement of ZenGengo and PaperRater platforms, which, presumably, adds limitations to this research. Results and discussion. The research results prove a positive impact of the indicated online platforms on improving the overall quality of students’ written products. However, the data obtained show an uneven impact of the platforms on both improving the quality of a written product and the development of students’ integrated writing skills. Language, rhetorical, and textual skills of students were more influenced as opposed to the ability to integrate information from reading/listening sources. The overall conclusion drawn from the research exploration is that only targeted choice of online tools can lead to significant improvement in targeted skills. For online learning to be effective, the tools used in it need to be user-friendly and have a strong potential for optimization in terms of time-consumption and labor-intensiveness. Further prospects for research lie in exploring the educational potential of other platforms as well as investigating the impact of using multimedia tools on other aspects of academic writing
Circuit modeling for neuromorphic photonics in Verilog-A as a scalable simulation platform

We demonstrate a Verilog-A based approach to perform electro-optical co-simulation for neuromorphic architectures. Verilog-A models of primary photonic devices are discussed along with comparing the simulation and experimental results.
Gradient-Free Deep Q-Networks Reinforcement learning: Benchmark and Evaluation

The principles of reinforcement learning present a normative account, firmly related to neuroscience and psychological viewpoints on how animals or humans survive and find their optimal state-action values of an environment. In order to use reinforcement learning successfully in conditions approaching real-world application complexity, however, agents are confronted with a challenging task: they need to obtain correct information of the environment from various kinds of sensors and manage these to generate optimal state-action values from past experience to new conditions and also for long-term survival. Several dedicated approaches have been developed to improve reinforcement learning performances-DQN, Double DQN, SARSA name a few. Since reinforcement learning tasks require maximizing a reward function for the long term, we consider them as challenging optimization problems. Therefore, we use deep Q-network (DQN) that can solve many challenging classic Atari games and robotics problem. However, DQN with BP usually spend a long time to train and difficult to get convergence in short time training. In this paper, we optimize DQN with Genetic Algorithms (GA) for several applications problems. We also provide comparison results between DQN with GA and traditional DQN to show how efficient this method compared to traditional method. The results demonstrate that DQN with GA has better results on each reinforcement learning problem.
Improving QoS through network isolation in PaaS

The lack of knowledge about the architecture and dynamics of the hosted applications represents a significant limit for the QoS management, both on IaaS- and PaaS-based clouds. The interference among virtual machines, the heterogeneity of cloud clusters, the different resource requirements of micro-services based applications, and the network asymmetry make it difficult to find an effective mapping between the applications and the clouds’ resources. This paper aims to show how to improve the estimation of an applications’ dynamics within the cloud, combining the exploitation of the inherent flexibility of the containerization architecture with the availability of information about the structure of workflows. The authors propose two parameters, isolation index and closeness, to measure the degree of mutual influence among the components of distributed applications. These parameters will be the base for a deployment strategy aimed at minimizing the mutual interference among the applications and making their performance more predictable. The problem will be formulated through a IQP-based algorithm and its effectiveness will be shown by empirical tests and numerical simulations.
Self-Configuring Complex Photonic Circuits

Silicon photonics allow complex optical circuits for communications, sensing, and classical and quantum information processing. Specific interferometric mesh architectures allow self-configuring and self-stabilizing circuits that can operate in real time with little or no calculation, opening new classes of sophisticated, highly functional optics.
Teaching Model Design of Computer Programming Courses for Digital Media Technology Students

Digital media technology major is aimed at improving students’ skill on technological aspects of multimedia and developing their artistic and creative thinking. Therefore, the fundamental programming courses, including C, C++, Java, and Python, should meet the demand of digital media technology. In this paper, we design the teaching model of programming courses, which consists of three parts: theoretical research on the deep integration of programming course cluster and digital media, teaching and learning method of programming courses led by “art-technology integration,” and teaching quality control and evaluation system of programming course cluster.
Chinese High School Students' Leadership Learning Needs and Strategy Choices

A survey of 1800 high school students in Shanghai was conducted to examine the Chinese high school students’ leadership learning needs and strategy choice. The results indicated that most of Chinese high school students have a strong interest in leadership learning. Gender, grade level, and school reputation affect the subjects' leadership learning needs and strategy choices to different degrees. To enhance youth leadership development, school education should expand student leadership development resources to promote active and efficient leadership learning among high school students. Youth education and other related institutions need to play a greater role in enriching the supply of youth leadership learning education.
2D transforms for VR with natural content

Chapter Contents: 8.1 The affine transform 8.2 The homography 8.3 Homography estimation 8.4 Feature points and RANSAC outliers for panoramic stitching 8.5 Homography and affine transform revisited 8.6 Pose estimation for AR References
Virtual Galleries and Museums in Search of Their Own Specific Identity

In the spring of 2021, with the spread of the covid pandemic, there was a strong acceleration of interest in the virtual worlds. In particular, many galleries and museums without a virtual version had to consider the hypothesis of creating a presence in the web. All this has raised a number of issues related to the identity of virtual spaces. It is necessary to understand, in fact, if they should be twins of real spaces or if they can have their own defined and independent physiognomy. Furthermore, we need to rethink the very identity of virtual space, which does not necessarily must have the features of a walkable and navigable space, but can also be an environment defined by multimedia content. Here, in addition to addressing various crucial issues, such as storytelling in virtual exhibition spaces, some suggestions for good practices within galleries and museums are considered.
Design and Implementation of the Path for a Battle Flag Game Based on Unity 3d

This study is about the use of unity 3d to make a round game that controls the movement of characters as chess pieces. In the process of designing the realization of path function, ray detection is used to judge whether there are chess pieces on the grid and attack enemy chess pieces, and flood filling algorithm is used to detect the movable range of chess pieces. Then A star routing algorithm is used to determine the appropriate path from a chess piece to the target grid to realize the routing algorithm.