Reviewerly/exHarmony huggingface dataset:
Application of Blockchain Technology in Intellectual Property Protection

The emergence of digital copyright in the internet era altered the traditional method of information dissemination and triggered a new copyright revolution, which benefited copyright owners by increasing revenue. However, as a result of the efficiency and convenience of technical capture, the data-based form of copyright faces new challenges and the emergence of a large number of infringement acts has become a barrier to the development of the digital copyright industry. Blockchain technology is decentralized, impenetrable, timestamped, traceable, and capable of smart contracts, among other characteristics. It was initially applied in the financial sector before being expanded to other sectors, resulting in a variety of blockchain + X models. The application of blockchain technology to digital copyright protection represents an attempt to protect copyright owners’ rights through the use of a novel technical method and protection concept, and it has a bright future. This study analyzes the current state of digital copyright protection in detail, discusses the benefits of applying blockchain technology in three areas: digital copyright registration and confirmation, transaction monitoring, and evidence maintenance, as well as the potential difficulties associated with the application of blockchain technology, and finally considers and proposes the system design for blockchain technology in the application of digital copyright protection. The study discovered that by utilizing blockchain technology to establish a unified blockchain digital copyright protection platform, it is possible to completely and efficiently record the entire process of copyright registration and confirmation, monitor data capture infringement, provide objective electronic evidence, reduce the cost of copyright owner rights protection, and increase the success rate of judicial remedies.
Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation

Turtles are one of the ancient marine animals that live today. However, the population is threatened with extinction, so its existence needs to be protected and preserved because turtles often eat plastic waste in the ocean whose shape, texture, and color are similar to jellyfish. The technology in the computer vision area can be used to find the solution related to the case of reducing plastics and bottles trash in the ocean by implementing robotics. The region-based Convolutional Neural Network (CNN) is the latest image segmentation and has good detection accuracy based on the Faster R-CNN algorithm. In this study, the training image was built based on two different objects, namely plastic bottles and plastic bags. The target is that the two objects can be recognized even if there are other objects in the vicinity, or the image quality will be affected by the color of the seawater. The results obtained are that plastic objects and bottles can be recognized correctly in the picture. Of the five-color hues tested, the results show that the object detection process is valid on the average color hue, sepia, bandicoot, and grayscale. In contrast, the object detection process is invalid in black-and-white tones. The test results shown in the table explain that the object detection that gets the highest results is an image with normal coloring, while the lowest value is on bandicoot. The average accuracy of all types of images tested is 96.50. However, the accuracy value still needs to be improved to apply feasibility permanently to hardware such as diving robots.
How Good is My HTAP System?

Hybrid Transactional and Analytical Processing (HTAP) systems have recently gained popularity as they combine OLAP and OLTP processing to reduce administrative and synchronization costs between dedicated systems. However, there is no precise characterization of the features that distinguish a good HTAP system from a poor one. In this paper, we seek to solve this problem from the perspectives of both performance and freshness. To simultaneously capture the performance of both transactional and analytical processing, we introduce a new concept called throughput frontier, which visualizes both transactional and analytical throughput in a single 2D graph. The throughput frontier can capture information regarding the performance of each engine, the interference between the two engines, and various system design decisions. To capture how well an HTAP system supports real-time analytics, we define a freshness metric which quantifies how recent is the snapshot of the data seen by each analytical query. We also develop a practical way to measure freshness in a real system. We design a new hybrid benchmark called HATtrick which incorporates both throughput frontier and freshness as metrics. Using the benchmark, we evaluate three representative HTAP systems under various data size and system configurations and demonstrate how the metrics reveal important system characteristics and performance information.
Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set

In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic regression. When using an explanatory method, and since interpretability lacks a rigorous definition, there is a need to evaluate and compare the quality and different explainers. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models. Experiments were performed using 100 physics equations with different interpretable and non-interpretable regression methods and popular explanation methods, evaluating the performance of the explainers performance with several explanation measures. In addition, we further analyzed four benchmarks from the GP community. The results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models that is capable of returning accurate models with appropriate explanations. Regarding the explainers, we observed that Partial Effects and SHAP were the most robust explanation models, with Integrated Gradients being unstable only with tree-based models. This benchmark is publicly available for further experiments.
Guiding Vector Fields for Following Occluded Paths

Accurately following a geometric desired path in a two-dimensional (2-D) space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this article, we develop a composite guiding vector field via the use of smooth bump functions and provide theoretical guarantees that the integral curves of the vector field can follow an arbitrary sufficiently smooth desired path and avoid collision with obstacles of arbitrary shapes. These two behaviors are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reactive</i> since path (re)planning and global map construction are not involved. To deal with the common deadlock problem, we introduce a switching vector field, and the Zeno behavior is excluded. Simulations are conducted to support the theoretical results.
A Study on the Factors Affecting Academic Achievement in the Non-Face-to-Face Class Environment Due to COVID-19: Focusing on Computer Liberal Arts Education Class

As a result of the COVID-19 pandemic, many universities have shifted to non-face-to-face classes resulting in numerous changes in the educational system. Since programming education includes a greater proportion of practice than theory-oriented courses, non-face-to-face classes have several constraints. As a result, to properly execute software education and enhance educational performance for non-major students, it is required to conduct research. Students’ psychological moods and activities collected in online classrooms were used to investigate factors impacting academic success as measured by scores and grades. Multiple regression analysis and logistic regression analysis were conducted by using data mining analytical approach. Attendance, effort expectancy, hedonic motivation, confidence, frequency of communication in mobile chat rooms, and Python programming intention factors were retrieved as an outcome of the performance. The relevance of the factors was confirmed, and it was revealed that hedonic motivation was crucial for students in Class A, while attendance had a significant impact on academic progress for students in the other grades. The goal of this research is to assist university organizations in making decisions by enhancing computer liberal arts education and offering implications for future non-face-to-face teaching environments such as during the COVID-19 pandemic.
Identification of Radioactive Mineralized Lithology and Mineral Prospectivity Mapping Based on Remote Sensing in High-Latitude Regions: A Case Study on the Narsaq Region of Greenland

The harsh environment of high-latitude areas with large amounts of snow and ice cover makes it difficult to carry out full geological field surveys. Uranium resources are abundant within the Ilimaussaq Complex in the Narsaq region of Greenland, where the uranium ore body is strictly controlled by the Lujavrite formation, which is the main ore-bearing rock in the complex rock mass. Further, large aggregations of radioactive minerals appear as thermal anomalies on remote sensing thermal infrared imagery, which is indicative of deposits of highly radioactive elements. Using a weight-of-evidence analysis method that combines machine-learned lithological classification information with information on surface temperature thermal anomalies, the prediction of radioactive element-bearing deposits at high latitudes was carried out. Through the use of Worldview-2 (WV-2) remote sensing images, support vector machine algorithms based on texture features and topographic features were used to identify Lujavrite. In addition, the distribution of thermal anomalies associated with radioactive elements was inverted using Landsat 8 TIRS thermal infrared data. From the results, it was found that the overall accuracy of the SVM algorithm-based lithology mapping was 89.57%. The surface temperature thermal anomaly had a Spearman correlation coefficient of 0.63 with the total airborne measured uranium gamma radiation. The lithological classification information was integrated with surface temperature thermal anomalies and other multi-source remote sensing mineralization elements to calculate mineralization-favorable areas through a weight-of-evidence model, with high-value mineralization probability areas being spatially consistent with known mineralization areas. In conclusion, a multifaceted remote sensing information finding method, focusing on surface temperature thermal anomalies in high-latitude areas, provides guidance and has reference value for the exploration of potential mineralization areas for deposits containing radioactive elements.
Levelling of multi-generational and spatially isolated geochemical surveys

Large-scale, low-density geochemical surveys, aimed at chemically characterising the near surface and providing a range of geochemical environmental data, have been undertaken around the world. In Australia, the National Geochemical Survey of Australia (NGSA) covers ~81% of the continent with a lower sample density than most other continents, reflecting the remoteness of a large proportion of the continent and the associated cost of such programs. These factors make it highly unlikely that a high-density national survey will ever occur. Fortunately, a large number of spatially constrained higher-density surface sediment geochemical surveys have been undertaken in Australia over the last 50 years. Merging these survey data with the NGSA baseline presents an opportunity not only to provide a more detailed understanding of the chemistry of the Australian continent, but also to investigate issues related to sample density, e.g. scale-related chemical variance and reliability of the national low-density data. Merging these surveys however represents a challenge into how best to level the data in order to create a seamless dataset. We have investigated the possibility of merging such disparate datasets and show that this is feasible as long as: 1) sample media and grain size fraction are equivalent, and, 2) individual surveys are levelled to eliminate inter-laboratory differences. Levelling of geochemical data between surveys is a vital step and here we document a number of approaches we have utilised to undertake this. The results show that a variety of levelling methods (re-analysis, single standards, and multiple standards) can be used, with varying degrees of effectiveness, to create a seamless dataset, from spatially isolated surveys. We demonstrate the impact that such levelling can have on data using examples from the southern Thomson Orogen in southern Queensland and northern New South Wales, and the much larger survey from Cape York in north-eastern Australia. The levelled results show a significant improvement in the data allowing for more value to be extracted.
More results on hulls of some primitive binary and ternary BCH codes

The (Euclidean) hull of a linear code is defined to be the intersection of the code and its Euclidean dual. It is clear that the hulls are self-orthogonal codes, which are an important type of linear codes due to their wide applications in communication and cryptography. Let C be an [n,k] cyclic code over Fq, where Fq is the finite field of order q. In this paper, we will employ the defining set of the code C to present a general characterization when its hull has dimension k−ℓ. Furthermore, we mainly focus on the primitive q-ary BCH codes C(q,n,δ,b) when b=0 and b=1 based on the general characterization. Especially for binary and ternary cases, we will present several sufficient and necessary conditions that the hulls of the codes C(q,n,δ,b) have dimensions k−2 and k−3 by giving lower and upper bounds on their designed distances, which extends the results of [17]. In addition, several classes of binary and ternary self-orthogonal codes are proposed via the hulls of BCH codes and their parameters are investigated in some special cases.
Aircraft Detection from Remote Sensing Images using YOLOV5 Architecture

Aircraft is a means of transport and weapons that plays an important role in the civil and military sector for detection from remote sensing images. Although many algorithms have been proposed to improve this work but it is not very easy due to non-availability of structured datasets and annotations. However, the suggested algorithms have a number of flaws; for example, when applied to a remote sensing image, the algorithms would overlook several small-scale aircrafts. The problem has two primary causes. One is because the aircrafts in the remote sensing image are often modest in size, making detection difficult. The other issue is that because the backdrop of a remote sensing image is frequently complicated, the algorithms used to solve the problem are easily influenced by it. At present, the target detection methods supported by convolutional neural networks (CNNs)&#x0026; YOLOV3 lack the sufficient extraction of remote sensing image information, lack of pre-processing steps and unfocused image augmentation which results in high missed detection rate and False alarms when facing small targets or object placed in the corners and boundaries. Aiming at the above questions, here is a proposed solution with the help of target detection model, using deep learning architecture that will be training on the custom-made model based on YOLOV4, YOLOV4 tiny, YOLOV5. In Proposed model YOLO series are chosen here because they have better detection techniques, more real-time applications present and the speed is relatively better than other series. The aim of the paper is to compare the model&#x2019;s performance and choose the best working model on CGI plane Dataset. Basically, these models are detecting aircraft, creating annotation &#x0026; bounding box to identify the target easily. These models have two stage Detectors which is used for achieving high accuracy in image recognition tasks. YOLOV4 have achieved the Mean Average Precision which is higher than YOLOV5 and YOLOV4 TINY on CGI Planes in Satellite Imagery in which firstly 400 images are present but after proper augmentation techniques there are 1000+ images present right now in which 800 images are in training set and rest 200+in test set. These models have less computational cost which reduces the execution time per time step during simulation as YOLOV5 takes 1.38 sec in 1 iteration which is way less than other working models.
An approach to the classification of educational chatbots

Nowadays, chatbots have become popular tools in such a way that they are used in different sectors like commercial, elderly care, tourism, and education. The COVID-19 pandemic has forced many students and teachers to suspend face-to-face classes. Therefore, schools and governments have found it necessary to continue education remotely, using the resources provided by the Internet. This fact has created a greater interest in educational chatbots, so several projects have been proposed to develop these academic tools, each following its way of implementation and addressing issues from different points of view. This paper presents a proposal for chatbot classification, following the Systematic Mapping Study and an iterative method to review and classify educational chatbots. We also discuss the resulting categories and their characteristics and limitations and possible uses by developers and researchers.
Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing

Strategy video games challenge AI agents with their combinatorial search space caused by complex game elements. State abstraction is a popular technique that reduces the state space complexity. However, current state abstraction methods for games depend on domain knowledge, making their application to new games expensive. State abstraction methods that require no domain knowledge are studied extensively in the planning domain. However, no evidence shows they scale well with the complexity of strategy games. In this paper, we propose Elastic MCTS, an algorithm that uses state abstraction to play strategy games. In Elastic MCTS, the nodes of the tree are clustered dynamically, first grouped together progressively by state abstraction, and then separated when an iteration threshold is reached. The elastic changes benefit from efficient searching brought by state abstraction but avoid the negative influence of using state abstraction for the whole search. To evaluate our method, we make use of the general strategy games platform Stratega to generate scenarios of varying complexity. Results show that Elastic MCTS outperforms MCTS baselines with a large margin, while reducing the tree size by a factor of 10. Code can be found at https://github.com/egg-west/Stratega
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems

Query scheduling is a crucial task for analytical database systems that can greatly affect the query latency. However, existing scheduling approaches are based on heuristics and not optimal. A recent trial proposed to use reinforcement learning for automatically learning end-to-end scheduling policies. However, such trial was not capable of considering the database-specific characteristics (e.g., operator types, pipelining), and hence becomes not efficient for analytical database systems. In this paper, we try to fill this gap and introduce LSched (Learned Scheduler), a fully learned workload-aware query scheduler for in-memory analytical database systems. LSched provides an efficient inter-query and intra-query scheduling for dynamic analytical workloads (i.e., different queries can arrive/depart at any time). We integrated LSched with an efficient in-memory analytical database system, and evaluated it with TPCH, SSB, and JOB benchmarks. Our evaluation shows that LSched improves over the performance of existing state-of-the-art query schedulers and heuristic-based ones by at least 35% and 50% in both streaming and batching query workloads.
Scanpath Comparison using ScanGraph for Education and Learning Purposes

research-article Share on Scanpath Comparison using ScanGraph for Education and Learning Purposes: Summary of previous educational studies performed with the use of ScanGraph Authors: Stanislav Popelka Department of Geoinformatics, Palacky University Olomouc, Czech Republic Department of Geoinformatics, Palacky University Olomouc, Czech RepublicView Profile , Marketa Beitlova Department of Geoinformatics, Palacky University Olomouc, Czech Republic and Science and Research Centre, Faculty of Health Sciences, Palacky University Olomouc, Czech Republic Department of Geoinformatics, Palacky University Olomouc, Czech Republic and Science and Research Centre, Faculty of Health Sciences, Palacky University Olomouc, Czech RepublicView Profile Authors Info & Claims ETRA '22: 2022 Symposium on Eye Tracking Research and ApplicationsJune 2022 Article No.: 51Pages 1–6https://doi.org/10.1145/3517031.3529243Online:08 June 2022Publication History 1citation45DownloadsMetricsTotal Citations1Total Downloads45Last 12 Months45Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Novel Consensus Protocol in Blockchain Network based on Proof of Activity Protocol and Game Theory

Blockchain networks are already extensively used in various applications because of their increased security. The unique characteristics of blockchain technology, such as decentralized, peer-to-peer, and invariable distributed ledger qualities, make it appealing to researchers, academics, and industry. The consensus protocol is a fundamental part of blockchain technology. PoW (Proof of Work) or fixed-validator consensus protocols comprise most of the existing consensus mechanisms. However, the tremendous computational effort required for PoW leads to excessive energy and computing resource usage. On the other hand, Fixed-validator protocols validate new blocks by a fixed, static set of validators, allowing attackers to execute multiple attacks against these validators. In this article, we proposed a novel consensus protocol base on the Proof of Activity protocol and game theory. Our consensus protocol is efficient in energy consumption and can deal with selfish mining and majority-attack.