Reviewerly/exHarmony huggingface dataset:
On the features of civil liability for the illegal use of images of fictional characters as objects of copyright

Theoretical and practical issues of civil liability of business entities that use images of fictional characters, mainly from feature films, without having the appropriate permission from the copyright holders, have been studied. The author has established that the illegal use of images of characters, as a rule, is associated with the production of goods whose packaging or appearance embody well-known fictional images. Consumers are subconsciously guided by these images and form demand necessary to increase sales. The infringer, embodying the images of famous characters protected both by copyright and as part of trademarks in goods and on their packaging, significantly reduces their costs due to non-payment of a license fee to the right holder and thereby reduces prices for these goods. The actual damage may be the costs of the right holder, which they have incurred or will inevitably incur when restoring their violated right. These costs can be incurred when making test purchases of counterfeit goods on which cartoon characters are placed, upon receipt of information fixing relevant violations, information on sales volumes of counterfeit goods, etc. Losses caused by violation of the exclusive right to depict a fictional character may also be expressed in the form of lost profits. Such a loss may manifest itself either in the failure of the right holder to receive remuneration for its illegal use, or in the failure to receive income from the independent use of the character in the implementation of certain entrepreneurial activities. If it is proved that the illegal user received income in a certain amount as a result of such a violation, then the right holder has the right to demand compensation, along with other losses and lost profits, in an amount not less than the income received by the violator. From the analysis of the judicial practice widely presented in this article, it follows that the most common way to protect violated exclusive rights to images of fictional characters is to claim monetary compensation, which is a borrowing from the Anglo-American legal order with its institution of statutory damages. The article analyzes the judicial practice related to the application of this sanction, notes its main shortcomings and concludes that the explanations of the Supreme Court of the Russian Federation in this part are rather superficial, not presented systematically and contradict the doctrinal provisions of the science of civil law. The author concludes that, to eliminate this shortcoming and to implement the principles of justice, equality of participants in civil relations, it is necessary to exclude the lower threshold of compensation from these norms.
Shibboleth IdP for Single Sign-On with Kubernetes and Persistent Volume Longhorn

Many organizations do not use centralized user authorization with Single Sign-On (SSO) Management to seamlessly move from one system to another. The same thing also occurred at Universitas Islam Indonesia (UII). Students were having trouble login in from one web service to another. The Board of Information Systems of UII, or Badan Sistem Informasi (BSI), implements SSO to avoid this problem. However, after BSI implemented SSO on the virtual machine, it turned out that the server load became too high. A spiking number of user logins happened in a short period. The centralized system could not handle this. The research's solution is to use a clustered service using Shibboleth IdP. The Shibboleth IdP customization can be carried out to be deployed into the Kubernetes cluster infrastructure ecosystem to meet the needs of authentication login on the business processes at UII. The Shibboleth IdP itself will be equipped with a persistent storage longhorn to support and maintain the service and avoid a single point of failure. The Kubernetes and Persistent Volume Longhorn provide a redundancy function in an application and a more flexible replication process. Inside Kubernetes, there is containerization technology. It was used to optimize the server's resources instead of replicating the application using virtual machines. With the use of centralized login by Shibboleth IdP and persistent storage longhorn, the error because of server load could be minimized. The downtime of the downed services can also be reduced. The research also proves that using Kubernetes and Persistent Volume Longhorn could help the system by preventing a Single Point of Failure using its redundancy function.
&lt;p&gt;Strategic Paragraphing 2.0: Techniques for Enhancing Inter-Paragraph Coherence&lt;/p&gt;

Most of us pay attention to intra-paragraph coherence: the idea that the sentences within a paragraph should logically develop a single idea. But we forget, or we struggle to master inter-paragraph coherence: the idea that paragraphs should be arranged so that our argument develops logically. Proximity isn’t always enough to signal to readers how paragraphs are building on one another to create a compelling argument. Readers may still end up having to infer how one paragraph is linked to the next, and when they can’t make those inferences (or they make them incorrectly) coherence suffers. This Writer’s Craft offers techniques to enhance inter-paragraph coherence more consciously so that readers don’t fall through the cracks between our paragraphs.
PERANCANGAN APLIKASI GAME VIRTUAL REALITY CERITA RAKYAT RAMAYANA MENGGUNAKAN PLATFORM ANDROID

Indonesia is the largest archipelago country in the world that has a variety of folklore from allparts of Indonesia. One of the most famous folklore in Indonesia is the Ramayana story. In thismodern era, people's interest in folklore, especially the younger generation is very alarming. This isbecause the background of the story and the characters in most folklore are considered outdated andno longer relevant to today's life. Based on this problem, the virtual reality game application of theRamayana folklore was made to visualize the Ramayana folklore in a more interesting and dynamicway in order to increase the interest of the community to be more fond of Indonesian folklore.Application development starts from the concept, design, material collection, assembly, testing, anddistribution stages. This application was built using C # programming language, Google CardboardSDK, Blender and Unity software. The results obtained are an application that can be used as amedium of entertainment or learning about Ramayana folklore. In this Ramayana folklore gameapplication, users can play 3 stage games that are equipped with game narratives consisting of textand images. Based on the test results using the Black-Box method, the functional application hasbeen able to run as it should. Based on testing the System Usability Scale (SUS), this applicationobtained an average value of 20 respondents at 73.13 with C of SUS’s Grade Scale, which isAcceptable category.
Do people perceive male and female artificial intelligence (AI) conversational agents’ voices differently?

The potential for the design of artificial intelligence (AI) conversational agents to reinforce gender-based stereotypes was recently highlighted in a UNESCO (United Nations Educational, Scientific and Cultural Organization) report. Addressing this issue requires knowledge of how male and female conversational agents’ voices are perceived. Consequently, we investigated whether male and female conversational agents’ voices are perceived differently for a range of traits commonly investigated in studies of natural human stimuli (trustworthy, emotionally stable, responsible, sociable, caring, attractive, intelligent, confident, weird, unhappy, mean, aggressive, dominant, competent, old, masculine, feminine). Male voices were rated as significantly more aggressive, dominant, mean, and masculine than female voices and were also rated significantly older. Female voices were rated as significantly more feminine than male voices. These differences in how male and female conversational agents’ voices are perceived may be usefully applied to the design of conversational agents to reduce their effects on gender-based stereotypes. This research was supported by the EPSRC grant ‘Designing Conversational Assistants to Reduce Gender Bias’ (EP/T023783/1), awarded to Benedict Jones. For the purpose of Open Access, the authors have applied a Creative Commons Attribution (CC BY) to any Author Accepted Manuscript (AAM) version arising from this submission.
Dynamic Virtual Machine Allocation in Cloud Computing Using Elephant Herd Optimization Scheme

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods.
Content Structure

This chapter discusses the technical aspects of managing and maintaining a website's content. Content must be properly structured for robots to understand the information contained within that content. This requires using proper HTML, JavaScript, and CSS code to display and render the content—including images and videos—in a way that robots can crawl it. If there are any issues with the website's code that disrupt how content, images, or videos are rendered, that can prevent pages from ranking in search results. Even simple mistakes within HTML can disrupt how robots process a page. In addition to improper code, there are other content-related issues, like duplication or thin content, that can also hold back rankings and SEO performance.
The Mobile Apps Development of Simple Machine Content to Improve Learning Achievement of Junior High School Students

This research aims to develop the content for Mobile Apps for simple machine learning materials and to study the validity and the impact of its product on improving junior high school students learning achievement. This development research follows the stages of ADDIE (Analysis, Design, Development, Implementation, Evaluation). The instruments used in this study include Mobile Apps product validation sheets and 30 multiple-choice questions. The validation results of media experts, material experts, and language experts state that this Mobile Apps product is suitable to be used in the learning process. The results of the implementation involving 24 students of class VIII at a junior high school in Ternate city show that the use of Mobile Apps can improve students' learning achievement
Camera Attributes Control for Visual Odometry With Motion Blur Awareness

The practical application of visual odometry (VO) is challenging under High Dynamic Range (HDR) environments which has a significant impact on the quality of feature point tracking. There are two key attributes that affect the image quality of a camera, namely exposure time and gain. The goal of camera attributes control research is to design algorithms that can actively adjust camera acquisition attributes to obtain clear, information-rich images. We propose an efficient and stable camera attributes control method with distinct image quality metrics and a linear convergence search algorithm. Specifically, we propose to use the weighted sum of image gradient and entropy to represent the image quality. Its first order derivative is also computed for a line search algorithm to determine the optimal camera exposure. Then, the algorithm estimates scene change speed by optical flow and finds out the maximum exposure time without motion blur. Ultimately, the maximum exposure time and optimal camera exposure are used to determine the camera properties including exposure time and gain. Additionally, the proposed method speeds up the convergence of the algorithm with the help of simulated image generation which enables nearly optimal attributes to be obtained directly from an overexposed or underexposed image. To validate the proposed approach, we test it in challenging HDR environments with fast motion and compare it with built-in automatic exposure. It will be open source for the benefit of the community.
Recognition of Space Target Based on GNN under the Condition of Small Samples for ISAR Image

With the development of space science and technology and the increasing demand for space monitoring, high-precision recognition technology of space targets has become more and more important. Inverse Synthetic Aperture Radar (ISAR) is an effective means of space detection, with the advantages of all-weather and high resolution. In recent years, there have been more and more techniques for applying deep learning methods to ISAR image recognition. However, for non-cooperative space targets, the lateral resolution of ISAR images is difficult to determine, and due to factors such as speckle noise and interference fringes, the quality of ISAR images will decrease to varying degrees. Therefore, the number of effective ISAR images obtained for the target is very small. This paper uses graph neural network (GNN) to find the deep relationship between similar samples by calculating the adjacency matrix between graph data nodes to realize small-sample recognition. Experimental results show that under the same conditions, the recognition rate of this method is about 10% higher than that of the CNN method with 20 times the sample size, which proves that this method can improve the reliability of spatial target recognition and has practical value.
A Large-scale Friend Suggestion Architecture

Online social as an extension of traditional life plays an important role in our daily lives. Users often seek out new friends that have significant similarities such as interests and habits, motivating us to exploit such online information to suggest friends to users. In this work, we focus on friend suggestion in online game platforms because in-game social quality significantly correlates with player engagement, determining game experience. Unlike a typical recommendation system that depends on item-user interactions, in our setting, user-user interactions do not depend on each other. Meanwhile, user preferences change rapidly due to fast changing game environment. There has been little work on designing friend suggestion when facing these difficulties, and for the first time we aim to tackle this in large scale online games. Motivated by the fast changing online game environment, we formulate this problem as friend ranking by modeling the evolution of similarity among users, exploiting the long-term and short-term feature of users in games. Our experiments on large-scale game datasets with several million users demonstrate that our proposed model achieves superior performance over other competing baselines.
The Management of Blended Learning During Covid 19 Pandemic of Private Senior High School in Narmada

The impacts of the COVID-19 pandemic on learning in private secondary education causes learning to be carried out offline and online (blended).The implementation of offline learning during the pandemic is different from regular face-to-face learning where students have to follow health protocols and reduction of learning time in class, so that students feel stiff and uncomfortable with all the limitations compared to regular face-to-face learning before.The aim of this study is to describe the management blended learning from planning, organizing, implementing, and evaluating.The Qualitative method was used in this study and was carried out in private senior high schools in Narmada Sub District.The study subject was teachers, headmasters, and superintendent of schools.Data were collected by analyzing documents of school development plan, and lesson plan, and interviewing schools' teachers, headmasters, and questionnaire were given to school's teachers, headmasters.Data analyzed technique used in this qualitative study namely data collected and analyzes preparation, read all data, data code, text and theme code for the study report.The results show that the planning of blended learning at the school level has not shown a comprehensive plan, organizing has not been involve all school members.Implementation is also needs to be well organized so that all educators refer to standard related to blended learning.In addition, in the context of the evaluation carried out by the school, it has not been comprehensive so that it does not obtain comprehensive information as well and will have an impact on the formulation of the school's annual plan.The problems faced are related to all the resources needed in the implementation of blended learning.Evaluation of blended learning do not carry out all aspect due to planning point out target.The management of the blended learning still needs to be perfected from planning to evaluation, including the problems faced by school level, so that the quality of the blended learning from year to year is getting better and has more impact on the graduate's quality of students.
Multi-Object Tracking with Adaptive Trajectory PMBM under Limited Field of View Radars

This paper addressed the challenging problem of Multi-Object Tracking (MOT) without prior knowledge of clutter rate and detection probability under limited Field of View (FoV) radars, which will result in biased estimates. The robust filter is that of cardinality Probability Hypothesis Density (CPHD) filter applied to adaptively learn the clutter rate and detection profile. Moreover, the track initiation in the limited FoV is clearly inefficient due to the object birth intensity without covering the whole state space for multi-object. An adaptive trajectory Poisson multi-Bernoulli mixture (A-TPMBM) tracker is proposed to adaptively design the object birth intensity by the received measurements at multi-scan, which can proceed to improve performance of track initiation. The proposed measurement-driven birth intensity benchmarks the tracking accuracy of against three idea filters for MOT with radar network.
Positive-Unlabeled Domain Adaptation

Domain Adaptation methodologies have shown to effectively generalize from a labeled source domain to a label scarce target domain. Previous research has either focused on unlabeled domain adaptation without any target supervision or semi-supervised domain adaptation with few labeled target examples per class. On the other hand Positive-Unlabeled (PU-) Learning has attracted increasing interest in the weakly supervised learning literature since in quite some real world applications positive labels are much easier to obtain than negative ones. In this work we are the first to introduce the challenge of Positive-Unlabeled Domain Adaptation where we aim to generalise from a fully labeled source domain to a target domain where only positive and unlabeled data is available. We present a novel two-step learning approach to this problem by firstly identifying reliable positive and negative pseudo-labels in the target domain guided by source domain labels and a positive-unlabeled risk estimator. This enables us to use a standard classifier on the target domain in a second step. We validate our approach by running experiments on benchmark datasets for visual object recognition. Furthermore we propose real world examples for our setting and validate our superior performance on parking occupancy data.
Towards Efficient Discovery of Target High Utility Itemsets

Finding High Utility Itemsets (HUls) in databases is crucial for identifying items that are of high importance (like profit) for decision-making. However, current High Utility Itemset Mining (HUIM) algorithms often ignore the interest or target of users in favor of effectively identifying categories of HUls using various measures and constraints. As a result, these techniques usually return a set of HUls that are either too large or small to employ in decision making. Nevertheless, it is apparent that users are often interested in a select group of HUls which may be among the set of HUls reported using existing techniques. While some recent and early works have offered methods for discovering user-targeted HUls, these methods are neither memory- nor time- efficient as they depend on post-processing or pattern matching. Additionally, throughout the discovery process, these techniques are required to scan the database twice. To address these issues, this paper proposes an efficient Target High Utility Itemset Miner (TarHUIM). In contrast to current methods, TarHUIM uses the users' target list and a single database scan to significantly reduce the search space and amount of time needed to find the user-targeted HUls. Extensive experimental analysis show that TarHUIM is efficient and effective in discovering the set of targeted HUls.
Range-Velocity Tracking of Dynamic Finger Gestures Using 120GHz Radar With LFM-MFSK Waveform

Hand and finger gesture sensing with millimeter wave (MMW) radar is an emerging technology. In this paper, we propose a novel linear frequency modulation and multiple frequency shift key (LFM-MFSK) combined waveform to sense the finger gesture and a new 1D searching phase unwrapping algorithm for the unambiguous range estimation of the significant scatterers on the hand of interest. By utilizing the new waveform, the hand is resolved from other objects in the range domain, and the different fingers can be resolved in the Doppler domain with high Doppler resolution. Besides, the unambiguous high accurate range estimation of the fingers can be obtained from the phase differences of the returns via a 1D searching phase unwrapping algorithm based on the Chinese remainder theorem (CRT). The 120GHz millimeter wave radar with the LFM-MFSK waveform is designed, and the proposed method is verified with the returns of two common finger gestures.
Design of a Low-Cost Autonomous Mobile Robot for Outdoor Applications

Abstract In today’s world, many companies are looking for low-cost automated alternatives to industrial processes that can save time and money. Autonomous Mobile Robots (AMR’s) are an appealing new alternative to complete tasks with base chassis and interchangeable tops that allow for standardization along with multifunctionality. With improvements in motors, motor controllers, location technology, vision, and IR sensors and a variety of other sensors, many complex tasks are attainable and simpler to complete. AMR’s can also be adapted to the operating environment whether it is on a manufacturing floor or in the field exposed to different elements. Using this equipment and implementing an odometry positioning method, an AMR can be designed to travel to pre-planned coordinates and perform desired tasks at each target point. Calculations to select hardware that satisfies requirements for this application are discussed. Strategies for motion control and positioning are also shown along with their advantages and disadvantages. Other subsystems are designed and attached to the robot to allow the robot to operate with awareness of its environment. To test the AMR’s abilities, a series of experiments are designed to analyze the robot’s strengths and identify sources of error. With these tests, data is collected to compare the position calculation accuracy and analyze the speed and orientation of the robot. Based on these results, there will be suggested adjustments for future work of AMRs in this field.