Reviewerly/exHarmony huggingface dataset:
Analysis of the Challenging Relationship Between Intellectual Property Rights (IPR) and the Contemporary Art in Businesses

The Intellectual Property Right is an effective means of safeguarding the creativity conducted in a business process. The intellectual property right is a branch of law that prioritizes the innovation and skill of an artist. It states that it is vital to recognize the artist regarding their art and secure them from being unauthorized utilized to reproduce work. Contemporary art refers to the artwork that is being produced in the 21st century. This includes performing business activities, the trade secrets and contracts that need to be protected from being copied unethically by a third party. Artistic work and innovation are the direct means of success and competitive advantage in the current business situations. It must be protected from being copied to ensure expansion and business growth. The research highlights the critical interconnection between contemporary art and saving the same through intellectual property law.
An Optimal Composition Plan Selection Using Multi Objective Particle Swarm Optimization

Domain-ontology based Particle Swarm Optimization (PSO)-inspired Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) and Improved Bipartite graph is an efficient web service composition approach. It composed a number of web services to achieve high customer satisfaction. It also improves the clustering quality and reduced the processing time of web service composition. The most verification problems are easily verified by using Petri Net in planning, verification and execution phase. In order to provide more efficient automatic web service composition, in this paper multiple Quality of Service (QoS) parameters like cost, accuracy, accessibility, robustness, scalability, modifiability and security are included in Petri net-based algebra approach. While adding a greater number of QoS parameters there will be multi-objective services composition optimization problem. It is solved by introducing a Multi-Objective Genetic Algorithm (MOGA) and Multi-Objective PSO (MOPSO) algorithm. It selects qualitative different services from multiple functionally identical web services which achieve the best reliability model. Thus, the automatic web service composition is performed for discovering the appropriate services by satisfying the customer requirements. Experimental results show that the proposed method performs better than the existing method.
Dynamic Inference with Neural Interpreters

Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization
Strong quantum nonlocality from hypercubes

A set of multipartite orthogonal product states is strongly nonlocal if it is locally irreducible in every bipartition. Most known constructions of strongly nonlocal orthogonal product set (OPS) are limited to tripartite systems, and they are lack of intuitive structures. In this work, based on the decomposition for the outermost layer of an $n$-dimensional hypercube for $n= 3,4,5$, we successfully construct strongly nonlocal OPSs in any possible three, four and five-partite systems, which answers an open question given by Halder et al. [Phys. Rev. Lett.122, 040403 (2019)] and Yuan et al. [Phys. Rev. A102, 042228 (2020)] for any possible three, four and five-partite systems. Our results build the connection between hypercubes and strongly nonlocal OPSs, and exhibit the phenomenon of strong quantum nonlocality without entanglement in multipartite systems.
PENGGUNAAN MEDIA INFORMASI SEBAGAI PENDUKUNG KINERJA STAF (Studi Deskriptif pada Dinas Pendidikan dan Kebudayaan Kota Pagaralam)

The objectives of this study are to: 1). Describe an overview of the use of information media in supporting the performance of staff at the Pagar Alam City Education and Culture Office. 2). Describe what office technologies have been used to support staff performance at the Pagar Alam City Education and Culture Office. 3). Describe the positive and negative impacts of the use of technology-based information media at the Pagar Alam City Education and Culture Office. The approach used in this study is a qualitative approach. The type of method used in this research is a case study, this research seeks to explain and try to describe and study the use of information media as a support for staff performance at the Pagar Alam City Education and Culture Office. Subjects / respondents in this study were all employees / staff at the Education and Culture Office of Pagar Alam City. The data collection techniques used were interviews, observation, Focus Group Discussion and Documentation. The data collected in this study is data in qualitative form, so it uses qualitative data analysis. The results of the study contain a description of the use of information media in the scope of computer-based information technology in supporting staff / employee performance. Some of the advantages of using information media in the Pagar Alam Disdikbud office are: saving costs, saving time and effort, facilitating control and accuracy, facilitating supervision, producing better and tidier work, more information loaded, reducing boredom with manual methods, reduce employee fatigue
Deep Active Learning by Leveraging Training Dynamics

Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learning models, is usually based on empirical criteria without solid theoretical justification, thus suffering from heavy doubts when some of those fail to provide benefits in real applications. In this paper, by exploring the connection between the generalization performance and the training dynamics, we propose a theory-driven deep active learning method (dynamicAL) which selects samples to maximize training dynamics. In particular, we prove that the convergence speed of training and the generalization performance are positively correlated under the ultra-wide condition and show that maximizing the training dynamics leads to better generalization performance. Furthermore, to scale up to large deep neural networks and data sets, we introduce two relaxations for the subset selection problem and reduce the time complexity from polynomial to constant. Empirical results show that dynamicAL not only outperforms the other baselines consistently but also scales well on large deep learning models. We hope our work would inspire more attempts on bridging the theoretical findings of deep networks and practical impacts of deep active learning in real applications.
Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity

Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more accurate estimate of its performance. Furthermore, in games with non-transitivities diversity allows a player to cover several winning strategies. However, despite the significance of strategic diversity, training agents that exhibit diverse behaviour remains a challenge. In this paper we study how to construct diverse populations of agents by carefully structuring how individuals within a population interact. Our approach is based on interaction graphs, which control the flow of information between agents during training and can encourage agents to specialise on different strategies, leading to improved overall performance. We provide evidence for the importance of diversity in multi-agent training and analyse the effect of applying different interaction graphs on the training trajectories, diversity and performance of populations in a range of games. This is an extended version of the long abstract published at AAMAS.
Custom Editor with UI Toolkit

UI Toolkit is Unity’s new retained-mode GUI solution to help with both editor GUI and runtime UI creation. At the time of writing, the UI Toolkit for runtime is still in preview and under active development. However, it is usable for editor GUI authoring and has many new features plus flexibility over IMGUI. In this chapter, I will walk you through the basic ways of creating an editor GUI with UI Toolkit by implementing a custom editor, PropertyDrawer, and EditorWindow with UI Toolkit via C# and UI Builder (the drag-and-drop UI builder for UI Toolkit).
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021

This technical report presents our approach "Knights" to solve the action recognition task on a small subset of Kinetics-400 i.e. Kinetics400ViPriors without using any extra-data. Our approach has 3 main components: state-of-the-art Temporal Contrastive self-supervised pretraining, video transformer models, and optical flow modality. Along with the use of standard test-time augmentation, our proposed solution achieves 73% on Kinetics400ViPriors test set, which is the best among all of the other entries Visual Inductive Priors for Data-Efficient Computer Vision's Action Recognition Challenge, ICCV 2021.
Enhancing Digital Publications with Alpheios

Once Alpheios is turned on, a toolbar for quick access to all of the Alpheios resources is added to the page, as well as an event handler which listens for double-clicks of the mouse on any word on the page. Identifying a word the user is interested in, and the language that word belongs to, is the primary interaction between the Alpheios tools and the webpages on which it is used. But, for text such as this, Alpheios makes it possible for publishers of digital publications and websites to provide instructions to Alpheios in their HTML markup via data attributes and alpheios-namespaced CSS. Despite the availability of these customizations and the general ease of use of the Alpheios browser extensions, there are situations when it is desirable to have the Alpheios functionality made directly available to users of a digital publication.
Improvement of Student Learning Process and Outcomes About Experience in School Through The Application of The Make A Match Learning Model

The aim of the research is to improve the process and learning outcomes of the school experience by applying the Make A Match learning model. This type of research uses Classroom Action Research (CAR). Data collection techniques are observation and test learning outcomes. The tools used in data collection were observation sheets and test questions which were then analyzed in a quantitative descriptive way. The results showed an increase in the average process and student learning outcomes. In the pre-cycle conditions the average learning process was in the poor category, then it became a good category in Cycle I, lastly in Cycle II the learning process was in the very good category. Knowledge learning outcomes in pre-cycle conditions were 65.83, then increased to 72.50 and finally in the second cycle to 80.00. The average learning outcomes of skills in pre-cycle conditions students are less skilled in presenting the results of identification of experiences in school.
Chapter 12. Publishing Linked Open Data

The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.
Job Crafting as a Mediating Variable in the Relationship Between Empowering Leadership and Teleworking Effectiveness During Corona Pandemic: Applied to Faculty Members Assistants in Egyptian Universities and Higher Institutes

This research has endeavored to test the supposed causal relationships between empowering leadership, job crafting, and teleworking effectiveness during the Corona pandemic, by applying to a sample of 384 teaching assistants from the faculty members in universities and higher institutes.The results showed a significant effect of empowering leadership on job crafting, There is also significant effect of empowering leadership on the effectiveness of teleworking, as well as a significant effect of job crafting on the effectiveness of teleworking, and finally, it has been found that job crafting mediates the relationship between empowering leadership and the effectiveness of teleworking, and accordingly we recommend the need to develop the structure of functional relationships among faculty members and their collaborators to keep pace with the requirements of teleworking during the current circumstances.
Validitas Media Pembelajaran Ispring Suite Berbasis Android Pada Pemahaman Shalat Jama’ dan Qashar

learning media iSpring Suite is an interactive learning media that combines audio, video and quiz maker. This learning media is the development of Microsoft PowerPoint which is already known by teachers and students. The purpose of this learning media is to facilitate understanding of Islamic Religious Education subjects, especially the material for praying jama' and qashar. The research subjects were seventh grade students of SMPN 2 Megaluh Jombang. The type of research is RnD with Borg and Gall model. 4D in question is the define (defining), design (design), develop (development), and disseminate (dissemination). Data collection techniques in the form of observation, interviews, and questionnaires. Observations were made on the learning process that took place in the classroom. Interviews were conducted with teachers to find out students' problems in understanding Islamic Religious Education lessons. Questionnaires to assess learning media were assessed by three experts, namely material experts, media experts, and linguists. The rating scale uses a Likert scale with 5 categories, namely very feasible, decent, fairly decent, less feasible, and very less feasible. The results of the assessment from material expert validators are 90.625% in the very appropriate category, the assessment from linguists is 90.63% in the very appropriate category, and the assessment from media experts is 80.88% in the appropriate category. The average value of the three experts is 87.26% with a very decent category. Thus, thelearning media is iSpring Suite Android-basedvery feasible to use.
Study of the Technology of Obtaining Thin Films in the Field of Microelectronics

This article gives information about the field of microelectronics, its historical formation, several complementary directions of microelectronics, integral electronics, vacuum integrated circuits, functional microelectronics and the production of thin films and the study of their properties.
HEDP: A Method for Early Forecasting Software Defects based on Human Error Mechanisms

As the primary cause of software defects, human error is the key to understanding, and perhaps to predicting and avoiding them. Little research has been done to predict defects on the basis of the cognitive errors that cause them. This paper proposes an approach to predicting software defects through knowledge about the cognitive mechanisms of human errors. Our theory is that the main process behind a software defect is that an error-prone scenario triggers human error modes, which psychologists have observed to recur across diverse activities. Software defects can then be predicted by identifying such scenarios, guided by this knowledge of typical error modes. The proposed idea emphasizes predicting the exact location and form of a possible defect. We conducted two case studies to demonstrate and validate this approach, with 55 programmers in a programming competition and 5 analysts serving as the users of the approach. We found it impressive that the approach was able to predict, at the requirement phase, the exact locations and forms of 7 out of the 22 (31.8%) specific types of defects that were found in the code. The defects predicted tended to be common defects: their occurrences constituted 75.7% of the total number of defects in the 55 developed programs; each of them was introduced by at least two persons. The fraction of the defects introduced by a programmer that were predicted was on average (over all programmers) 75%. Furthermore, these predicted defects were highly persistent through the debugging process. If the prediction had been used to successfully prevent these defects, this could have saved 46.2% of the debugging iterations. This excellent capability of forecasting the exact locations and forms of possible defects at the early phases of software development recommends the approach for substantial benefits to defect prevention and early detection.
Local Culture and EFL Vocabulary Learning

This paper attempts to investigate the influence of learners‘ culture on foreign language vocabulary. It seeks to demonstrate that there is a link between culture and lexico-semantic errors committed by learners of English as a foreign language among Dioula speaking students in Burkina Faso. A sample population of 157 of Dioula speaking students from two secondary schools was purposively selected, using the non-probability sampling technique. A language test composed of two written activities was used as the data collection instrument. These activities were translation activities from Dioula into English. A tape-recorder was used during a story-telling session as an oral activity to collect supplementary data. The data were analysed first qualitatively and then quantitatively. The SPSS software version 17.0 (2008) was used to process the quantitative data. The qualitative method was employed to determine the link between Dioula culture and lexico-semantic errors committed by learners of English as a foreign language. Similarly, the quantitative method was used to establish the seriousness of the influence of the Dioula culture on the learning of vocabulary as a foreign language. The results revealed that the Dioula speakers‘ English is influenced by their culture. These results have some pedagogical implications. They, therefore, suggest that we adapt the teaching of EFL vocabulary to learners‖ culture.
Ethic in student's life : communication ethics / Fahada Nur Mohamad Izhar... [et al.]

Communication is an inseparable feature of human life. According to a book (Communication Skills and Soft Skills: An Integrated Approach, 2011). It includes all the activities of a person from his birth to death. We cannot think of passing even a single day without any short of communication. The scope or uses of communication cannot be demarcated, its importance is indispensable for all. There are rules in communication that we need to adapt in our daily life to have a better and good communication. For instant, as a humble of human being, we need to have the ethics when we are having a conversation with everyone. It is really important because the good ethics will lead to a harmony and create good surrounding around them.
2nd Place Solution to Google Landmark Retrieval 2021

This paper presents the 2nd place solution to the Google Landmark Retrieval 2021 Competition on Kaggle. The solution is based on a baseline with training tricks from person re-identification, a continent-aware sampling strategy is presented to select training images according to their country tags and a Landmark-Country aware reranking is proposed for the retrieval task. With these contributions, we achieve 0.52995 mAP@100 on private leaderboard. Code available at https://github.com/WesleyZhang1991/Google_Landmark_Retrieval_2021_2nd_Place_Solution
Research Guides: Science, Technology, & Society: Searching Journals at the Library

Information resources available from the Samuel C. Williams Library in the subject areas of Science, Technology, & Society.
Capstone Project Case Study: Texas Data Repository (TDR) Dataverse Integration with R

As a part of the Open Research, Open Data, and other Open initiatives, the Texas Data Repository (TDR) is currently exploring the integration and use of R client for Dataverse repositories by way of the R dataverse package. This integration supports open science and reproducible research by enabling researchers to seamlessly discover, retrieve, and archive data and metadata using RStudio interface. In this poster we describe the process of TDR Dataverse integration, testing of use cases, and creating documentation. We share the learnings and roadblocks we’ve encountered and provide suggestions on how other institutions can take advantage of these tools.
Temporal Knowledge Graph Reasoning Triggered by Memories

Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually attracts the attention of researchers since no direct history facts for prediction. Previous works attempted to apply evolutionary representation learning to solve the extrapolation problem. However, these techniques do not explicitly leverage various time-aware attribute representations, i.e. the reasoning performance is significantly affected by the history length. To alleviate the time dependence when reasoning future missing facts, we propose a memory-triggered decision-making (MTDM) network, which incorporates transient memories, long-short-term memories, and deep memories. Specifically, the transient learning network considers transient memories as a static knowledge graph, and the time-aware recurrent evolution network learns representations through a sequence of recurrent evolution units from long-short-term memories. Each evolution unit consists of a structural encoder to aggregate edge information, a time encoder with a gating unit to update attribute representations of entities. MTDM utilizes the crafted residual multi-relational aggregator as the structural encoder to solve the multi-hop coverage problem. We also introduce the dissolution learning constraint for better understanding the event dissolution process. Extensive experiments demonstrate the MTDM alleviates the history dependence and achieves state-of-the-art prediction performance. Moreover, compared with the most advanced baseline, MTDM shows a faster convergence speed and training speed.