Reviewerly/exHarmony huggingface dataset:
A Classifier-Based Two-Stage Training Model for Few-Shot Segmentation

Over the past few years, deep learning-based semantic segmentation methods reached state-of-the-art performance. The segmentation task is time-consuming and requires a lot of pixel-level annotated data, which restricts the segmentation application. Benefiting from the general segmentation task, few-shot semantic segmentation also developed significantly. In this study, we propose a real-time training method based on feature transformation and a multi-stage classifier. The generalization ability of the model is enhanced through the strategy of real-time training. Aiming at the inconsistency of the feature domain of the support set and query set, we propose a feature transformation module, which uses the memory mechanism to map the query set features to the feature domain of the support set. Then, the query set features can better adapt to the classifier. The multi-stage classifier is used to retain the hierarchical information of different scales, and the attention mechanism is introduced to further explore information in different sizes and channels to prevent the abuse of advanced features effectively. We conducted experiments on the COCO-20i dataset, and our model can obtain good performance, i.e., 32.7% and 41.7% mIoU scores for 1-shot and 5-shot settings, respectively.
Collaborative Multi-head Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

An efficient method of representing and retrieving information is an essential component of open domain QA. There are question and answer models that allow for real-time responses with speed benefit and scalability. Nonetheless, due to the limitations of existing phrase models, their accuracy is low. In this paper, we improve the contextualized sparse representation to strengthen the connection between contextual information. We achieve better answer retrieval by enhancing the embedding quality of the model for phrase representation. Specifically, based on original contextualized sparse representations, we transform the single self-attention into collaborative multi-head attention so that attention heads can connect and pay attention to crucial information in different context locations. Compared with learning sparse vectors in n-gram vocabulary space by rectified self-attention, collaborative multi-head attention performs better on the SQuAD dataset. Due to the increased efficiency of critical information representation, the model improves to varying degrees on both of the two evaluation metrics.
MatricEs: Matrices Embeddings for Link Prediction in Knowledge Graphs

Knowledge graphs have been constructed to represent real-world knowledge. However, downstream tasks usually suffer from the incompleteness of the knowledge graphs. To predict the missing links, various models have been proposed by embedding the entities and relations into lower-dimensional spaces. Existing approaches usually ignore the fact that there are much fewer relations than entities and allow redundant parameters for relations. In this paper, we present MatricEs, a novel approach for link prediction, and propose its variations to reduce the dimension of relations space. In particular, MatricEs utilizes matrix embeddings and models the relation as a linear transformation from the head entity matrix to the tail entity matrix. MatricEs is universal as it subsumes many link prediction models. Recently, relation patterns draw much attention in building models with better ability of expression and interpretability. We formally define the relation patterns which are satisfied by MatricEs, including symmetry, antisymmetry, inversion, commutative compositions and non-commutative compositions, absorption and transitivity. Theoretical analysis shows that MatricEs is a valid, simple and universal model. Experiments show that MatricEs is effective as it outperforms most existing methods on link prediction datasets.
Becoming Us

This group article explores personal and political experiences of six UK based artists who came together in 2016 to create a performance project ‘About Us’ and have continued to support each other throughout the pandemic. The article is a collage of writings and images which convey the personal stories of each author and reflect on differences and alliances concerning gender, racial identity, age, sexuality and social class.Lansley introduces the context in which this diverse group came together. She identifies the research themes explored in ‘About Us’, which threw up fundamental questions concerning what it is to be both human and animal, and why we oppress and demonize other animal species. Green discusses these ideas from her perspective as a black woman, for whom the position that we are all animals is complex, raising issues concerning slavery and scientific racism. Huss discusses taking on the lease of an old miners’ institute in a quest to bring back cultural life to an impoverished area of Northumberland. She tracks historical and cultural links between the closure of the pits in the 80s and the present, identifying forms of protest which brought miners' unions and artists together in solidarity. Taylor draws on writings by the philosopher Jacques Rancière to explore finding solidarity across difference within both his teaching and performance work. He discusses how the group supported him to develop a language that expressed intimate thoughts and feelings about being a gay man. Early uses poetry and parable to question our relationship to the planet and all the species we share it with. He interrogates the Judeo-Christian notion of hierarchical ‘civilization’ and explores the intelligence of trees. Mackinnon reflects on her position as a black woman and mother in a world that strives for unity, but continues to divide. She discusses how the group draws on strengths as movement and dance makers to share differences in ways that enable growth and understanding.
Impact analysis of using ML techniques on imbalanced datasets for leveraging security of industrial IoT

Machine learning calculations have been demonstrated to be reasonable for getting stages for IT frameworks.Nonetheless, because of the basic contrasts between the industrial internet of things (IIoT) and normal IT organizations, a unique exhibition survey should be thought of.The weaknesses and security prerequisites of IIoT frameworks request various contemplations.In this paper, we study the reasons why machine learning should be coordinated into the security components of the IIoT, and where it right now misses the mark in having an agreeable exhibition.The difficulties and certifiable contemplations related with this matter are concentrated in our exploratory plan.In this paper, we advocate a novel mechanism to evaluate the various ML techniques, with the help of an IIoT testbed.
Heterogeneous Network Embedding: A Survey

Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks. We also summarize the application fields, benchmark datasets, open source tools, and performance evaluation in the HNE area. Finally, we discuss open issues and suggest promising future directions. We anticipate that this survey will provide deep insights into research in the field of HNE.
Spectral Analysis for Semantic Segmentation with Applications on Feature Truncation and Weak Annotation

It is well known that semantic segmentation neural networks produce dense segmentation maps to resolve the objects' boundaries while restrict the prediction on down-sampled grids to alleviate the computational cost. A striking balance between the accuracy and the training cost of the neural networks such as U-net and DeepLab etc exists. We propose a spectral analysis to investigate the correlations among the resolution of the down sampled grid, the loss function and the accuracy of the neural networks. By analyzing the network back-propagation process in frequency domain, we discover that the traditional loss function, cross-entropy, and the key features of CNN are mainly affected by the low-frequency components of segmentation labels. The results not only justify the reason why a segmentation neural network trained by using only weak annotations can still achieve high accuracy but also provides us quantitative results to determine the efficacy of down-sampled grid of segmentation maps.Two applications are proposed in this paper including the feature truncation method and the block-wise annotation in which the high-frequency components of the CNN features and annotation is limited, respectively. The test results agree with our analysis. Thus, the success of the existing work utilizing low-frequency information of segmentation maps can now be clarified from the spectral analysis point of view.
Analysis of the implementation of the HyFlex model

The main purpose of the research was to determine the relationship between the HyFlex model and the teaching-learning process at the INEI 34 Educational Institution, located in the district of Chancay, Peru. In terms of methodology, a quantitative approach was used, with a non-experimental, transectional and correlational design. The study had a population of 90 teachers and a participating sample of 73 teachers. Likewise, the information was compiled by means of a survey, using a questionnaire designed on a Likert scale as an instrument. The hypotheses were tested using Spearman's Rho coefficient. It was demonstrated with a positive and moderate correlation of 0.640 and an asymptotic significance (0.001) lower than the significance level (0.05), that the HyFlex model has a direct relationship with the teaching-learning process in the INEI 34 Educational Institution, Chancay. This allowed inferring that the HyFlex model contributes to the implementation of content and educational commitment, using technology, to provide flexibility in the educational experience of the students. In other words, this model contributes to improve the teaching-learning process in a hybrid environment, in which students benefit from learning and in which the teacher is an agent of support for them.
Verifiable attribute-based keyword search scheme over encrypted data for personal health records in cloud

Abstract Personal health record (PHR) is a medical model in which patients can upload their medical records and define the access control by themselves. Since the limited local storage and the development of cloud computing, many PHR services have been outsourced to the cloud. In order to ensure the privacy of electronic medical records, patients intend to encrypt their health records before uploading them. However, encrypted PHR can not be accessed directly and not be retrieved by legitimate users. To solve these issues, in this article we propose a new searchable encryption scheme with ciphertext-policy attributes, which achieves fine-grained access control and exact keyword search over encrypted PHRs. Moreover, in our proposed scheme, the receiver can verify the integrity of the search result that the cloud server returns. Finally, we simulate our scheme, and the experiments show that our scheme has high practicability for cloud-based healthcare systems and has high efficiency in aspects of keyword search and results verification.
Vrste odgovornosti za povredu žigom zaštićenih oznaka u virtuelnoj realnosti

Due to the increased development of digital technology, a new form of social and economic exchange in Virtual Reality (VR) has been created. The users can socially interact with each other through their avatars. Development of VR technology brings a host of legal questions surrounding both the intellectual property of the actual technology as well as unlawful activity within the VR space itself. The use of trademarks in VR is a very complex issue. Any use of trademarks in VR by third party does not constitute an infringement. Depending on the facts of the case, a trademark owner may have potential causes of action against two separate defendants: the user of the platform or the platform itself. If the alleged infringer is a user of the platform and recreates a registrant’s trademark within the VR space of his own violation, the registrant would likely want to sue the user. The second option for plaintiffs is to sue the platform company itself under the theory of either primary or secondary liability. Primary liability will arise where the company itself decided to use trademark in the VR without permission, whereas secondary liability will arise where the company allows VR platform users to use someone else’s trademark, also without permission.
Design of restricted normalizing flow towards arbitrary stochastic policy with computational efficiency

This paper proposes a new design method for a stochastic control policy using a normalizing flow (NF). In reinforcement learning (RL), the policy is usually modeled as a distribution model with trainable parameters. When this parameterization has less expressiveness, it would fail to acquiring the optimal policy. A mixture model has capability of a universal approximation, but it with too much redundancy increases the computational cost, which can become a bottleneck when considering the use of real-time robot control. As another approach, NF, which is with additional parameters for invertible transformation from a simple stochastic model as a base, is expected to exert high expressiveness and lower computational cost. However, NF cannot compute its mean analytically due to complexity of the invertible transformation, and it lacks reliability because it retains stochastic behaviors after deployment for robot controller. This paper therefore designs a restricted NF (RNF) that achieves an analytic mean by appropriately restricting the invertible transformation. In addition, the expressiveness impaired by this restriction is regained using bimodal student-t distribution as its base, so-called Bit-RNF. In RL benchmarks, Bit-RNF policy outperformed the previous models. Finally, a real robot experiment demonstrated the applicability of Bit-RNF policy to real world.
Faculty–librarian cooperation in collection development at the University of Namibia library, with special reference to electronic resources

Purpose The purpose of this study was to determine the extent to which teaching staff cooperated with librarians in collection development, specifically in relation to electronic resources, and to identify barriers they encountered while performing collection development activities. Design/methodology/approach A mixed methods approach was adopted for the study. Quantitative and qualitative techniques of data collection and analysis were used to obtain a comprehensive understanding of the research topic. Data were gathered through a self-administered questionnaire and interviews. A total of 149 faculty members completed the questionnaire, yielding a response rate of 51.2%, while 16 library staff members were interviewed to obtain qualitative data. Findings The majority of the teaching staff who participated in the study affirmed that they had cooperated with subject librarians in collection development. A high percentage (62.4%) of the faculty members had collaborated with subject librarians in collection development activities. Only 37.6% of the faculty members had not participated in collection development activities with subject librarians to acquire library electronic resources. According to faculty members, some of the main challenges affecting collection development at the University of Namibia were a lack of catalogues for electronic resources and a lack of lists of titles from vendors. Moreover, librarians were not always available to assist faculty members. It is recommended that faculty members be part of the process of selecting materials and that a good relationship be fostered between librarians and faculty members to bring value to collection development activities. Originality/value Collection development in respect of electronic resources is a complex process to be undertaken by a single entity and, therefore, requires the collaboration of all stakeholders involved. In the case of institutions of higher learning, these stakeholders include faculty, librarians and vendors. The emergence of a variety of e-resources demands a meticulous strategy on the part of libraries to ensure they can offer a wide range of up-to-date and accurate resources that meet the evolving needs of their users. To the best of the authors’ knowledge, studies that are similar to this one have not been conducted in Namibia before. This case study presents useful findings and lessons on faculty–librarian cooperation for effective collection development, not only at the University of Namibia library but also at other academic libraries in economies with similar characteristics.
A systematic literature review (SLR) of circulation methods in academic libraries: proposing QR codes for self-issue/return method

Purpose The purpose of this study is to systematically examine the existing literature published on the circulation methods used in academic libraries and to discuss a proposed model for the self-check-in and check-out methods using quick response (QR) codes. Design/methodology/approach A systematic literature review (SLR) provided a complete overview of circulation systems used in academic libraries for the last more than 140 years. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) method for SLR was used. Several databases such as ProQuest, Emerald, Library and Information Science Abstracts, EBSCO and Google Scholar were searched. Out of 277 papers retrieved in the search, 43 most relevant papers were taken up for the SLR. These were classified into four themes based on the systems of circulation used, namely, manual (5), mechanized (13), automated (5) and networked system (20). Based on the findings of the SLR, a model of circulation system in which QR code technology has been used. Findings The result of SLR identified 33 systems used in above mentioned four groups. Among this, a large number 48.48% of mechanized systems were found. Each system has limitations either because of devices used or of technology. The present study proposes a QR code-based model at the circulation desk, for which a prototype of Android app has been designed. This would help to initiate the new “self-services” facility to users at the circulation desk. Practical implications The proposed model, after its successful implementation, can be adopted by academic libraries. Guidelines and a graphical representation of this study can be used by any researcher for further experimentation. Originality/value To the best of the authors’ knowledge, this is the first kind of study in which a QR code-based Android app model has been proposed for library circulation records.
IMPLEMENTASI MEDIA PEMBELAJARAN SISILABAR TERHADAP HASIL BELAJAR SISWA KELAS VII DI SMP SUSILA KOTING

Learning media is very useful in the teaching and learning process so that the achievement of learning objectives. Sisilabar media si one of the learning media tjat helps students understand the operational material for addition and reducing the form of algebra. This study aims to find out how the implementation of sisilabar learning media on student the learning outcomes of class VII at Susila Koting junior high school. The typr of research used is descriptive qualitative. The technique thet is taken in data is tests and documentation. The result in this study show positive results after the implementation of sisilabar media. This can be proven by the result pretest and posttest of student. This average pretest student score is 46.3 while posttest average result are 81.7 and tha percentage of completeness of student pretest result reaches 12.5% while the posttest reaches 87.5%. So it can be concluded that the learning in the operation material for addition and reducing algebra.
Liability for Artificial Intelligence - Adaptation of the Bosnian Legal Framework to the Challenges of the 21st Century

The article addresses the issue of liability for artificial intelligence through the analysis of civil liability for autonomous entities in Bosnia and Herzegovina. First, it addresses the issue of applying vicarious liability in the law of Bosnia and Herzegovina, and it elaborates on its limitations. The example of the treatment of animals in case law indicates the possibility of creatively interpreting the existing legal framework in order to create a functional equivalent of situations that are not included in the scope of vicarious liability, but nonetheless treat liability for autonomous entities.
Factors of Students’ Perceived Satisfaction with Distance Learning during the Covid-19 Pandemic

Bearing in mind the specifics of the educational process in the online context and the factors of satisfaction with this type of teaching in a crisis, the paper presents the results of research aimed at examining student satisfaction with distance learning in the conditions of the coronavirus pandemic. The research was conducted on a convenient sample of 505 students in the second cycle of primary school education in the Republic of Serbia. A modified version of the Technology Acceptance Model (Davis 1989) instrument was used for the research. Using factor analysis, we identified two subscales (Attitudes towards distance learning and Motivated behaviour) for which we established criterion validity. The obtained results for the entire scale showed that the respondents moderately positively assessed the factors of satisfaction with distance learning, while female students and respondents from two-parent families rated satisfaction with this aspect at a higher level. The analysis of the differences in the answers obtained concerning the measured socio-demographic variables showed that the answers on the subscale Attitudes towards distance education of the child differ concerning the birth order in the family. On the subscale Motivated behaviour differences were determined concerning gender, school achievement and family structure. In addition to the research limitations highlighted in the paper, the results generally suggest that strengthening the sense of personal control and self-regulation is one of the more essential ways to improve satisfaction, motivation and intention to learn during distance learning.
Information and Communication Technologies in the Process of Developing the Digital Competencies of Teachers/Professors in Music Education

The dynamic development of modern information and communication technologies, which play a significant role in music education, indicates the need to strengthen and develop the digital competencies of music educators. Accordingly, research was conducted to examine the current situation at different levels of music education on the use of information and communication technologies and the experiences of teachers and students, in future music pedagogues. Their experiences, which were the subject of the research, can serve as initial guidelines for further improvement of the digital competencies of teachers in music education.