Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces

Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorious mode collapse issue due to the lack of labels in target domain. Naturally, one of the effective ways to mitigate this issue is to reliably estimate the pseudo labels for target domain, which itself is hard. To overcome this, we propose a novel UDA method named Progressive Adaptation of Subspaces approach (PAS) in which we utilize such an intuition that appears much reasonable to gradually obtain reliable pseudo labels. Speci fically, we progressively and steadily refine the shared subspaces as bridge of knowledge transfer by adaptively anchoring/selecting and leveraging those target samples with reliable pseudo labels. Subsequently, the refined subspaces can in turn provide more reliable pseudo-labels of the target domain, making the mode collapse highly mitigated. Our thorough evaluation demonstrates that PAS is not only effective for common UDA, but also outperforms the state-of-the arts for more challenging Partial Domain Adaptation (PDA) situation, where the source label set subsumes the target one.
Efficient Non-Local Contrastive Attention for Image Super-Resolution

Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However, NLA gives noisy information large weights and consumes quadratic computation resources with respect to the input size, limiting its performance and application. In this paper, we propose a novel Efficient Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling and leverage more relevant non-local features. Specifically, ENLCA consists of two parts, Efficient Non-Local Attention (ENLA) and Sparse Aggregation. ENLA adopts the kernel method to approximate exponential function and obtains linear computation complexity. For Sparse Aggregation, we multiply inputs by an amplification factor to focus on informative features, yet the variance of approximation increases exponentially. Therefore, contrastive learning is applied to further separate relevant and irrelevant features. To demonstrate the effectiveness of ENLCA, we build an architecture called Efficient Non-Local Contrastive Network (ENLCN) by adding a few of our modules in a simple backbone. Extensive experimental results show that ENLCN reaches superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
Blockchain Technology for e-Government

Abstract: Information and communication technologies are used by e-Government to deliver governmental services to people and companies instantly, effectively, and efficiently. The majority of current e-government systems are centrally located on redundant servers and databases that may have single points of failure, rendering the systems susceptible to cyber-attacks. Blockchain technology makes it possible to build a decentralised, incredibly secure system without the need for a central authority to monitor transactions. Blockchain technology, which encrypts data and distributes it over the network, improves information security and privacy and gives governments new chances to increase transparency, stop fraud, and foster citizen confidence. In this paper, we primarily cover the overview of blockchain technology, architecture of blockchain, consensus models and the blockchain use cases in e-Government
Forensic Face Sketch Artist System

Abstract: In this modern age, the general crime rate is increasing day-by-day and to cope up with this the enforcement departments too should find ways in which would speed up the overall process and help them in bringing one to justice. One such way can be using face recognition technology for identifying and verifying the criminal. The traditional approach here is to use the hand-drawn face sketches drawn by forensic sketch artist tospot the criminal, modernizing this is able to mean using the hand-drawn sketch and then matching them with the enforcement departments database to spot the criminal. Using this approach would result in the various limitations with latest technologies andeven would be time consuming as there are very few forensic sketch artists available when compared to the increasing crime ratio. Our project is aimed on decreasing the time span and speeding up this process by providing a standalone platform to the law enforcement department which would allow users to create accurate face sketch of the suspect without the help of forensic sketch artist and no special training or artistic skills. The sketch can be created using drag and drop feature in the application with variety of face elements and can automatically match the drawn composite face sketch with the law enforcement departments database much faster and efficiently using deep learning and cloud infrastructure.
Clustering Of Student Learning Styles in the industri 4.0 Using KMeans Algorithm

Clustering is a technique for grouping homogeneous data so that the points in each cluster are as similar as possible according to convenience measures such as Euclidean-based distance or correlation-based distance. In the industrial era 4.0, learning media, the environment, the way teachers teach will affect student learning styles. From research on learning styles, many researchers agree on the importance of identifying learning styles to accelerate their learning performance. The purpose of this study is to classify student learning styles in the industrial era 4.0 using the Kmeans algorithm and the elbow method. The research method used is a waterfall. The number of research subjects was 108 students. the results of the research on the number of clusters (K), namely 6, obtained cluster 1 as many as 27 students, cluster 2 as many as 24 students, cluster 3 as many as 21 students, cluster 4 as many as 17 students, cluster 5 as many as 11 students and cluster 6 as many as 8 students. The performance of the grouping results based on the silhouette coefficient is 0.302, which means the grouping structure is weak. In cluster 1, the highest number has auditory elements, followed by kinesthetic and visual elements. The development of ICT-based media is one of the factors of student learning styles in the industrial era 4.0
Transition to Online Learning: Implementing the TPACK Framework by English Teachers due to the COVID-19 Pandemic

The objectives of this research is to explore how the English teachers implement TPACK in online learning context and to know the challenges faced by the English teachers in implementing TPACK. There were several studies in the implementation of TPACK, but still less on the discussion about the challenges that the English teachers faced while implementing it, particularly in the online learning due to the COVID-19 pandemic. This study employed a descriptive design with a qualitative approach, which were analyzed descriptively through reducing inappropriate data, displaying and concluding data. The results of the data revealed that the English teachers of Bunga Bangsa Islamic Junior High School Samarinda had implemented TPACK in online learning context by using WhatsApp, Zoom meeting and Google Classroom as the technologies used to teaching, conducting demonstration and recitation method as the pedagogy to teach about “object pronoun” and “will and going to” as the content. There are some challenges faced by the English teachers when they implemented TPACK in online learning context such as limited quota, bad signal of internet, students’ behavior and adapt to the applications used such as WhatsApp, Zoom meeting and Google Classroom.
Blockchain Enabled Vehicle Anti-theft System

Intelligent transportation system envisages a large network of intelligent vehicles to satisfy one of the insatiable demands of the smart cities. However, increasing vehicle theft cases impose another challenge on the researchers working in cybersecurity and privacy domain. In this work, we have developed a robust vehicle anti-theft system utilizing Blockchain security. Our proposed system incorporates various sensors to protect from any physical theft activities. It also has an alert system (via text message and email) including image and location of the intruders. Further, a three layered blockchain protection has been provided to protect any breach in cybersecurity. We design a traceable, immutable storage system based on blockchain and Inter-Planetary File System (IPFS) for vehicle safety. Here it preserves the privacy of the vehicle user as well as provides cybersecurity in case of malicious attacks.
A Game Model for Proofs with Costs

We look at substructural calculi from a game semantic point of view, guided by certain intuitions about resource conscious and, more specifically, cost conscious reasoning. To this aim, we start with a game, where player I defends a claim corresponding to a (single-conclusion) sequent, while player II tries to refute that claim. Branching rules for additive connectives are modeled by choices of II, while branching for multiplicative connectives leads to splitting the game into parallel subgames, all of which have to be won by player I to succeed. The game comes into full swing by adding cost labels to assumptions, and a corresponding budget. Different proofs of the same end-sequent are interpreted as more or less expensive strategies for I to defend the corresponding claim. This leads to a new kind of labelled calculus, which can be seen as a fragment of SELL (subexponential linear logic). Finally, we generalize the concept of costs in proofs by using a semiring structure, illustrate our interpretation by examples and investigate some proof-theoretical properties.
Analysis of Blink Data Behaviour Between Male and Female Adult Readers of English Text

This study intended to clarify the ambiguity around gender differences during electronic English reading. Through determining a correlation amongst blink eye movement behaviour and gender. The study reviewed blink behaviour between 16 participants (n=11) male and (n=5) female participants during a silent electronic English reading task. Using a standardised white background and black font comprising three reading slides. Eye parameters measured were (i) blink count, (ii) blink duration and (iii) blink frequency. Despite previous literature published, it was found during this study that no significant results were reported amongst the two groups for the three parameters examined. This study contributes that gender does not correlate with blink behaviour during reading, therefore both groups responded the same when viewing the reading stimuli. Furthermore, it was also found that the format of material should be considered i.e., hardcopy vs electronic when conducting further research. As these, amongst other factors such as stress and fatigue are identified to influence blink behaviour in male and female readers. Further investigation into this area includes exploration into alternative cognitive eye movement parameters such as pupil diameter.
Virtual Hologram Learning As A Publishing Media Of Nusantara Culture, Study Case: Mabayang-Bayang Tradition In Sengkiding Village

Technology has developed and affect so many aspects of people’s lives. The presence of increasingly rapid technology could be an effective medium to be use to distributing information. Virtual Hologram Learning is a technology product that is formed from the combination of two coherent light rays in microscopic form. Virtual Hologram Learning can be used as a medium of information for the people of the archipelago in terms of culture and use to preserve the culture of the archipelago. Bali is an island known as an island that has abundant cultural diversity has a unique cultures called Mebayang-Bayang Tradition from Sengkiding Village, Klungkung. Which is held every Pengrupukan (a day before Nyepi) involves the villagers pulling the cow bones. The method used in this study is a qualitative method which aims to understand the factors that influence the research subject (Sengkiding villager) want to carry out the tradition. The type of data used is primary data sourced from the results of direct observations from the informants who has understanding about this tradition. Furthermore, the data will be published through Virtual Hologram Learning as a form of publishing Nusantara culture.&#x0D; Keyword: Virtual, Hologram, Publishing, Cult
Whither Section 230? – Law and disruptive technology

Fast-moving technological innovations with major social impact pose a challenge to the U.S. legal system, which often has difficulty fashioning a timely and appropriate response, especially if the innovations create opportunities for new forms of social mischief. Courts have a poorly understood but critical role in providing clarity to any response. This article examines the role of the courts in interpreting Section 230 of the Communications Decency Act, and the implications of that role for the current policy debate over Section 230 reform.
Pelatihan dan Pendampingan untuk Pembuatan dan Pemanfaatan Multimedia Interaktif Berbasis Saintifik dalam Pembelajaran Sejarah pada Mgmp Sejarah Kabupaten Rembang

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Feature Reuse and Fusion for Real-time Semantic segmentation

For real-time semantic segmentation, how to increase the speed while maintaining high resolution is a problem that has been discussed and solved. Backbone design and fusion design have always been two essential parts of real-time semantic segmentation. We hope to design a light-weight network based on previous design experience and reach the level of state-of-the-art real-time semantic segmentation without any pre-training. To achieve this goal, a encoder-decoder architectures are proposed to solve this problem by applying a decoder network onto a backbone model designed for real-time segmentation tasks and designed three different ways to fuse semantics and detailed information in the aggregation phase. We have conducted extensive experiments on two semantic segmentation benchmarks. Experiments on the Cityscapes and CamVid datasets show that the proposed FRFNet strikes a balance between speed calculation and accuracy. It achieves 72% Mean Intersection over Union (mIoU%) on the Cityscapes test dataset with the speed of 144 on a single RTX 1080Ti card. The Code is available at https://github.com/favoMJ/FRFNet.
LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition

In recent years, deep learning has achieved great success in many natural language processing tasks including named entity recognition. The shortcoming is that a large amount of manually-annotated data is usually required. Previous studies have demonstrated that active learning could elaborately reduce the cost of data annotation, but there is still plenty of room for improvement. In real applications we found existing uncertainty-based active learning strategies have two shortcomings. Firstly, these strategies prefer to choose long sequence explicitly or implicitly, which increase the annotation burden of annotators. Secondly, some strategies need to invade the model and modify to generate some additional information for sample selection, which will increase the workload of the developer and increase the training/prediction time of the model. In this paper, we first examine traditional active learning strategies in a specific case of BiLstm-CRF that has widely used in named entity recognition on several typical datasets. Then we propose an uncertainty-based active learning strategy called Lowest Token Probability (LTP) which combines the input and output of CRF to select informative instance. LTP is simple and powerful strategy that does not favor long sequences and does not need to invade the model. We test LTP on multiple datasets, and the experiments show that LTP performs slightly better than traditional strategies with obviously less annotation tokens on both sentence-level accuracy and entity-level F1-score. Related code have been release on https://github.com/HIT-ICES/AL-NER
Robust Event-Driven Interactions in Cooperative Multi-agent Learning

We present an approach to safely reduce the communication required between agents in a Multi-Agent Reinforcement Learning system by exploiting the inherent robustness of the underlying Markov Decision Process. We compute robustness certificate functions (off-line), that give agents a conservative indication of how far their state measurements can deviate before they need to update other agents in the system with new measurements. This results in fully distributed decision functions, enabling agents to decide when it is necessary to communicate state variables. We derive bounds on the optimality of the resulting systems in terms of the discounted sum of rewards obtained, and show these bounds are a function of the design parameters. Additionally, we extend the results for the case where the robustness surrogate functions are learned from data, and present experimental results demonstrating a significant reduction in communication events between agents.
Innovation projects at the University-Pharmaceutical Industry interface: challenges and opportunities

COVID-19 pandemic accelerated the digital transformation of pharmaceutical R&amp;D. This paper aimed to diagnose some key aspects of University-Pharmaceutical Industry (U-Pharma) partnerships in Brazil that should be addressed in digital platforms to enable the creation of innovative solutions to COVID-19. A qualitative research was applied through an online guiding questionnaire to identify the main points that are considered as opportunities or challenges by the parties involved in the University-Pharmaceutical Industry interface. The responses were processed using content analysis to raise common themes. Further, in-depth interviews were performed to evaluate the subjective perception of these themes. The results of the questionnaire showed the topics bureaucracy, partnership, and agility as the most relevant. The in-depth interviews showed the subjective perception of these themes. The joint analysis of the results showed that in addition to problems related to project management processes, issues that involve the human dimension, such as pro-executive behavior and communication skills, are also extremely relevant to the success of U-Pharma projects. The development of digital solutions for Knowledge Management of innovative projects of U-Pharma collaborations should take into consideration both the project and human dimensions. It is recommended that Project and People Management should be integrated into digital platforms.
Perancangan Sistem Informasi Pemasaran Kakao Berbasis Web UD. Harta Sari

Marketing information systems are designed to make it easier for companies to promote their products. This study purpose is to design MIS of Harta Sari cocoa company and to find out what key factors are needed in designing MIS. Research on the design of MIS uses tools such as CMS, MySQL to generate MIS. Web quality analysis is done using blackbox method to test web functionality and user acceptance tests used to determine the level of website acceptance generated. The results of testing the blackbox method showed the website is functioning normally as it should, the key factors in the designing MIS of Harta Sari cocoa company are design, content and usability. The results of the questionnaire assessment using the user acceptance test method showed the results for the design factor were very good with an average percentage of 80.44%, for the content factor it was quite complete with an average percentage of 77.07% and for the usability factor it was sufficient easy to operate with an average percentage of 71.87%.&#x0D; Keywords: marketing, management information system, cocoa company.
A Fuzzy Comprehensive Evaluation Method on the Construction of High-Level Universities Based on Data from Jiangsu Province

To respond to the call for the construction of a “double first-class university” in China and implement the “Focus on innovation, focus on enriching the people, and build a moderately prosperous society at a high level” development strategy. Jiangsu Province issued the first round of the “Jiangsu High-level University Construction Program” in 2016. Taking the deepening of the innovation-driven development strategy as the starting point, the main objective of promoting higher education is to strengthen its ability to support economic and social development. In this study, the fuzzy-ANP comprehensive evaluation model is used to create an index system for building high-level universities with Jiangsu Province features. Using this system, high-level university construction in Jiangsu Province is evaluated. The results of the study show that the first round of Jiangsu’s high-level university construction has reached an excellent level and is the most effective in improving the quality of research. In addition, the participating universities are better able to promote socioeconomic development. However, the resources and social reputation of the participating universities are close to the level of excellence. Furthermore, due to the problem of prioritizing research above teaching, the teaching and internationalization levels of the participating universities are rather low.
Artificial Intelligence Teaching in High School Information Technology Course

The article deals with the software tools for organizing and planning educational process using models, methods and algorithms of artificial intelligence.
An Analysis of the Effectiveness of Augmented Reality Technology in Helping Children Learn

The issue of the use of gadgets and technology in the field of early childhood education is often a matter of debate among experts in this field. Students' achievement in the subject of Mathematics is actually closely related to students' mastery of learning Early Mathematics while in preschool. The study's main purpose was to develop a pattern learning module for Early Mathematics using Augmented Reality (AR). The methodology used is the TUP Bednarik Consumer AssessmentModel.Overall, it was found that there was a gap in student mastery in the pattern title where students mastered the exercises in the form of simple pattern rules compared to incremental pattern rules.
Application of Information System in College Teaching Management

This study aimed to identify the impact of management information systems in the performance of employees and to identify the effects resulting from the use of these systems as well as to identify the most important problems that hinder the application of information systems in the Nile College (NC). To achieve the objectives of the study a questionnaire was developed and designed for data collection and distributed to the samples of (30) employees, the statistical package for social Sciences(SPSS) was used to analyze the data, and used statistical methods descriptive to know the perceptions of respondents to the dimensions of information systems and functionality, and analysis of multiple regression to test the validity of the study sample and the impact of the independent variables on the dependent variable, and analysis of variance.