Reviewerly/exHarmony huggingface dataset:
Weakly-Supervised Attribute Segmentation

Semantic segmentation is a fundamental vision problem which aims to divide an image into non-overlapped regions and then assign them with predefined object labels. Despite its latest development (especially deep neural network-based), semantic segmentation is still far from comprehensive understanding of the visual world around us. For example, the obtained object labels are not fine-grained enough for solving more complicated vision tasks such as fine-grained retrieval with multiple attribute-based queries (e.g. the query ‘a bird’ along with the attribute ‘has wing colour: grey’). In this paper, we thus choose to study a more challenging vision problem called attribute segmentation, which aims to localize attributes within the targeted object (e.g. bird) in a given image. Due to the high cost of collecting pixel-level attribute annotations, we focus on weakly-supervised attribute segmentation (WSAS) that utilizes only image-level attribute labels for model training. Note that this new WSAS problem degrades to the conventional weakly-supervised semantic segmentation (WSSS) problem when attribute labels are considered as object labels. To overcome the attribute label noise caused by image-level weak supervision, we propose a novel sparse learning method for solving the WSAS problem. Extensive experiments demonstrate that the proposed WSAS method significantly outperforms the state-of-the-art WSSS and attribute localization methods.
Organizational Citizenship Behaviour in the Jordanian Telecommunication Sector: A Concept Paper

Organizational Citizenship Behaviour (OCB) refers to the voluntary behaviour which is exhibited by the employees in the organization, as good citizens of the organization. This paper aims to provide a historical account of OCB with behavioural factors based on the existing literature. As the interest in OCB continues to grow, consistent integration of the concept becomes progressively more complex and necessary.The current study presents details of its development from the time it was first conceptualized up until the present wherein leadership style, motivation, and OCB have been used to indicate organizational effectiveness and performance. This review paper presents a framework for delineating the determinants of OCB for the benefit of practicing managers as well as discerning academics and researchers.
Correlation Filters with Pre-position by Reconstruction Error for Visual Tracking

Correlation filter based on deep neural network is a kind of mainstream method for real-time object tracking. It combines the high efficiency of correlation filtering and the great representation ability of convolutional neural network. However, this method inherits most shortcomings of correlation filter such as boundary effects. If an object is close to the boundary of a search area due to a large displacement, the useful information will be filtered out by cosine window and padding. In order to alleviate boundary effects, we propose a coarse positioning module to fine tune the search area before cosine window and padding. The core of the proposed module is saliency detection based on reconstruction error. This enables the improved trackers to retain more object information than the prototypes. Experimental results show that our method obviously promotes the baseline model, namely DCFNet, in the case of fast motion. Due to the low computational cost of our coarse positioning module, the improved trackers still have real-time rate.
Experimental Comparison of Oversampling Methods for Mixed Datasets

In supervised classification, the class imbalance problem causes a bias that results in poor classification for the minority class. To face this problem, particularly in supervised classification with mixed data, some oversampling methods for mixed data have been reported in the literature. However, there is no experimental study comparing and evaluating these methods in a common setting. Therefore, in this paper, we present an experimental comparison of state-of-the-art oversampling methods designed specifically for mixed datasets. Our study reports the best oversampling methods for mixed data in terms of oversampling quality, taking into account the imbalance ratio, and runtime.
Document Clustering with Evolved Single Word Search Queries

We present a novel, hybrid approach for clustering text databases. We use a genetic algorithm to generate and evolve a set of single word search queries in Apache Lucene format. Clusters are formed as the set of documents matching a search query. The queries are optimized to maximize the number of documents returned and to minimize the overlap between clusters (documents returned by more than one query in a set). Optionally, the number of clusters can be specified in advance, which will normally result in an improvement in performance. Not all documents in a collection are returned by any of the search queries in a set, so once the search query evolution is completed a second stage is performed whereby a KNN algorithm is applied to assign all unassigned documents to their nearest cluster. We describe the method and compare effectiveness with other well-known existing systems on 8 different text datasets. We note that search query format has the qualitative benefits of being interpretable and providing an explanation of cluster construction.
Overview of Knowledge Graph Completion Algorithms Based on Representation Learning

In recent years, the KG (Knowledge graph), as a new generation of knowledge engineering technical representatives, has been developed vigorously. KG is a key step for artificial intelligence to move towards intelligence. Knowledge graph completion (KGC) is a key technology in the construction of KG. The aim is to complement the missing knowledge in the KG. With the development of the Internet, the scale of data increases exponentially, and the scale of KG is increasing day by day. It is very inefficient to make up the missing knowledge based on manual, so many algorithms of KGC have emerged. In this paper, some typical KGC algorithms based on representation learning are summarized and combed, which are mainly divided into tensor decomposition, neural network and translation transformation. The development direction and prospect of KGC in KG are discussed and prospected.
Descriptor based consensus for blockchain transactions

Blockchain networks use consensus mechanisms so participants can exchange transactions without the need to rely on a trusted third party. Consensus mechanisms using Proof of Work burn significant energy to select a block miner and the delay limits performance. Other consensus mechanisms such as Proof of Stake or Practical Byzantine Fault Tolerance still designate a single validator to append a block to the chain, preventing blocks from being built and published in parallel. In this paper we introduce a new consensus mechanism, Proof of Descriptor, enabling clients to work together to publish blockchain transactions using a descriptor object which stores information on the cooperative parallel execution of transactions. Proof of Descriptor consensus allows commutative transactions to be mined individually. It does not require a leader to propose the next block, enabling clients to cooperate on completing transactions, assembling blocks and publishing them. We demonstrate that our approach is less prone to attack since it is not vulnerable to a malicious leader, while simulations show a potential 20x improvement over the fastest sequential blockchain, Solana.
Induction in Machine Learning

Given today's context of data saturation, whereby more data exist than anyone knows what to do with, too many who employ machine learning techniques treat machine learning algorithms as oracles to truths unreachable by the human mind. The decision boundaries these algorithms produce can achieve wonderful performance during testing, but are all too often accepted with an inadequate causal justification assuming a causal explanation is attempted at all. This paper challenges the non-causal approach to machine learning for good reason. Abandoning causal justifications and relying on useful mathematical models to make predictions has been attempted in astronomy over two-thousand years ago, and those models have led to fourteen centuries of stagnation in that domain of science and inexplicable prediction failures. This approach has also been attempted in other domains of science over the last two-thousand years and the result is always the same-stagnation and inexplicable prediction failures. This paper begins its challenge against the non-causal approach to machine learning at the root by analyzing the machine learning process to reveal an error in its inductive method. It is shown that machine learning algorithms that employ this inductive method cannot be relied upon to learn valid generalizations. In order to avoid this invalid inductive method, a necessary modification to the machine learning process is presented, which assumes a valid inductive method is available to form valid generalizations before machine learning algorithms are employed. A valid method of induction is then presented, which is shown to be governed by the law of causality. This paper then turns to defining which features are causally valid to use for classification and a proper approach to searching for these features leveraging machine learning algorithms. Finally, an example comparing and contrasting the traditional versus the modified machine learning process is presented to illustrate the power of a causal approach. As a result, practitioners of this modified machine learning process have conscious control over their own success in domains that have reached valid generalizations, and conscious control over their own progress in domains that have yet to reach valid generalizations.
Measuring and Comparing QoE of Hybrid VR Applications under Increased Network Load

The elements of presence and interaction have been connected with high-quality learning in online learning environments. This can be achieved by offering learning environments and material that can engage the learner throughout the learning experience, allowing them to connect with the content, find relevance, construct meaning and critical thinking. Hybrid VR applications offer great potential for engaging learners in environments that accurately capture the world, allowing them to make personal connections and find relevance. Coupled with a branching narrative, computer-generated imagery (CGI) and interactivity can lead to highly engaging learning experiences. Hybrid VR applications, though, are bandwidth-demanding that can impact the learning experience. This paper presents a study comparing the Quality of Experience (QoE) of a hybrid VR application streamed over the standard internet and POINT network under increased network load. The paper presents the project motivations, a pilot study and its output. It closes with conclusions about the effect of the QoE and presence in learning and future directions.
Criminal legal regulation of the blockchain functioning sphere in Russia: challenges and barriers

Research background. Despite the enormous attention of the scientific community, legislators, and law enforcement officials to the development and implementation of measures to combat cybercrime, the sphere of blockchain functioning and cryptocurrency circulation remains outside the scope of most criminal law research. This causes perplexity in the context of the desire of state bodies to introduce blockchain technology in many significant areas of society, as well as to introduce a regulatory framework dedicated to the issues of private and public legal regulation of digital financial assets. Concerns are also caused by the increase in the number of cybercrimes and the increasing involvement in them of the blockchain technology and virtual currencies, the circulation of which is carried out based on blockchain. The need to study the prospects for criminal law regulation of the blockchain functioning spheres in the territory of the Russian Federation becomes more and more obvious in such conditions. Study objective: to identify and to study the main challenges (problems) for modern criminal law regulation of the blockchain functioning sphere, possible barriers (obstacles) that reduce the effectiveness of such regulation, as well as potential directions for responding to these challenges and overcoming such barriers. Methods: formal legal and comparative legal research methods are widely used in conjunction with systemic, logical, and philological methods of interpreting the norms of law. The empirical part of the study is based on the investigation of judicial and other law enforcement practices, as well as transcripts of meetings of the State Duma of the Russian Federation and information from the media about criminal offenses that have become widespread in the sphere of blockchain functioning. The analysis of modern foreign and Russian scientific literature relevant to the selected research topic is carried out. Results and novelty: it is the first time that comprehensive analysis of the challenges of the current stage of the blockchain functioning sphere development, as well as legislative, law enforcement, doctrinal, and social barriers for creating a system of effective and comprehensive criminal law regulation of the named sphere, is carried out. The author’s concept of the directions of responding to the analyzed challenges and overcoming the corresponding barriers is presented.
Uncertainty Linguistic Summarizer to Evaluate the Performance of Investment Funds

This chapter proposes a methodology to implement the uncertain linguistic summarizer posed in Liu’s uncertain logic to measure the performance of investment funds in the Colombian capital market. The algorithm extracts a truth value for a set of linguistic summaries, written as propositions in predicate logic, where the terms for the quantifier, subject, and predicate are unsharp. The linguistic summarizer proves to be autonomous, successful, efficient, and close to human language. Furthermore, the implementation has a general scope and could become a data mining tool under uncertainty. The propositions found characterize with plenty of sense the investment funds data. Finally, a Corollary that allows accelerating the obtention of the summaries is presented.
Understanding the Impact of Work from Home Arrangement on Psychological Empowerment

Psychological empowerment can be understood as a sense of self control with respect to one’s work and active involvement with one’s work related role. It is manifested when employees feel that the work they do is personally meaningful to them, they have required competence to get the work done, they also have a degree of self-determination in taking work related decisions and their work has some impact on others and on the organizational as a whole. Psychological empowerment results in positive organizational and managerial outcomes. Today’s era is marked by tremendous changes in ways of work including the work from home arrangements. Because of the COVID-19 Pandemic situation, working from home arrangement is peremptory. The current study focuses on the working from home arrangements, personal life characteristics of individuals which might have an impact on psychological empowerment of employees. Gender differences, marital status, working hours, childcare responsibilities are expected to have impact on psychological empowerment of employees. T Test and Analysis of Variance (ANOVA) were used to test the hypothesis. The results of this study show that gender differences, marital status, childcare responsibility and working hours affect psychological empowerment of employees. The results also focus on need to reduce insecurity in the minds of remote working employees. The implications for the organizations and limitations of the study are also discussed.
비대면 환경에서의 비판적 사고와 토론교육

This research is a case study about “Critical Thinking and Debate Education” class which was done for freshmen at the engineering college of “S” Women’s University. Real time remote classes through LMS and ZOOM were the most effective tools under on-line circumstances, considering the fact that video lectures only cannot cultivate students’ capabilities of critical thinking and communication.Throughout the analysis on students’ self-reflection journals and lecture evaluations, this paper examined considerable future points and the pros and cons of “Critical Thinking and Debate Education” under online presentation and discussion situation. As research outputs, students told they could feel less nervousness and anxiety when they exercise and have a presentation because they could choose familiar space for them. In addition, students also told that they feel comfortable about both self-feedback and peer evaluation, repeatedly seeing the recorded video clip. However, on the contrary, sometimes students felt uncomfortable due to unstable internet connection through the online classes, and they also were regretful about the missing chances of interaction between a teacher and students and of intimate exchanges among students. They also told they had felt a kind of limit of enhancing their presentation skills just in front of the monitor. Considering these outcomes, this research paper points out that online education needs to be proceeded by strengthening multi layered feedback to students with the build-up of a non-face-to-face stable educational infrastructure, application of online instructional strategy, and utilization of YouTube platform and video contents. Through this research paper, I hope the new system of encompassing on/off line “Critical Thinking and Debate Education” and effective teaching and learning method can be developed soon by strengthening the strength of online education.
Semi-AttentionAE: An Integrated Model for Graph Representation Learning

Graph embedding learns low-dimensional vector representations which capture and preserve information in original graphs. Common shallow neural networks and deep autoencoder only use adjacency matrix as input, and usually ignore node attributes and features. Shallow graph neural networks cannot spread the node characteristic information on a large scale. Many deep models suffer the problem of over-smoothing. Therefore, these methods can't fully incorporate network information. In this paper, we propose a novel Semi-AttentionAE model to fully utilize node features, node labels, and network structure. More specifically, we integrate a supervised information extraction graph attention network to capture both node features and network structure, with an unsupervised feature extraction autoencoder to reduce dimension while preserving structure information. Finally, ensemble learning is introduced to jointly train the combined model to obtain final embedding. We conduct the node classification and visualization experiments on four real-world datasets, including two citation networks, one co-occurrence network, and one commodity network. The results suggest that the proposed Semi-AttentionAE model is capable of embedding both graph structure and node features. The integrated model has successfully exceeded or matched performance across four well-established baselines.
Weather Forecast using Learning Vector Quantization Methods

Weather forecasting is one of the important factors in daily life, as it can affect the activities carried out by the community. The study was conducted to optimize weather forecasts using artificial neural network methods. The artificial neural network used is a learning vector quantization (LVQ) method, in which artificial neural networks based on previous research are suitable for prediction. The research is modeling weather forecast optimization using the LVQ method. Models with the best accuracy can be used in terms of weather forecasts. Based on the results of the training that has been done in this study produces the best accuracy on the LVQ method which is to produce 72%.
Graph Attention Autoencoder for Collaborative Pair-wise Ranking

Recently, top-k recommendation system is getting more and more attention from researchers and unlike the rating prediction task, the purpose of top-k recommendation is to present the user with a list of items they are most interested in. Many rating prediction models do not produce good ranking results, so the top-k recommendation faces two challenges: the first is how to accurately obtain user and item latent vector from the user-item interaction rating matrix, and the second challenge is how to combine the ranking strategy with the recommendation model deeply. The booming deep learning technology, such as autoencoder and graph neural networks, brings us new solutions. So in this paper, we propose a novel model: Graph Attention Autoencoder for Collaborative Pair-wise Ranking. The autoencoder consists of graph attention encoder and collaborative neural decoder, which is used to generate user and item latent vector accurately. And then we use the pairwise ranking learning process to ensure the rating value accuracy and rating value pairwise ranking consistency. Finally, experimental results on three real-world datasets demonstrate the superiority of our model.
The Desiderata of Blockchain and IoT in Medical and Pharmaceutical Enterprises

The advent of IoT and blockchain had led to its subsequent proliferation in the medical industry. IoT and its devices are exclusively accompanied by its security threats such as its access to extremely sensitive data while establishing security and confidentiality as top concerns; this can be anonymized by incorporating blockchain. Owing to the trust-building features of blockchain, it may encourage the enhanced and broad adoption of IoT devices into the healthcare industry. The surrounding robust data protection and integrity are pivotal keys to help secure these devices, enabling the respective personnel to have a command over who uses this data and its purpose. The records in IoT devices are more susceptible to chances of identity thefts, financial data crimes, and spamming as patients don’t own any control over their records/data. The availability of acutely handy IoT devices had still not let patients collect, analyze, secure, and exchange healthcare information seamlessly. Adopting a blockchain and IoT clustered solution can help accomplish a smooth, transparent, economically efficient, and easily operable system combined with secure payment options.
A Novel Image Inpainting Framework Using Regression

In this article, a blockwise regression-based image inpainting framework is proposed. The core idea is to fill the unknown region in two stages: Extrapolate the edges to the unknown region and then fill the unknown pixels values in each sub-region demarcated by the extended edges. Canny edge detection and linear edge extension are used to respectively identify and extend edges to the unknown region followed by regression within each sub-region to predict the unknown pixel values. Two different regression models based on K-nearest neighbours and support vectors machine are used to predict the unknown pixel values. The proposed framework has the advantage of inpainting without requiring prior training on any image dataset. The extensive experiments on different images with contrasting distortions demonstrate the robustness of the proposed framework and a detailed comparative analysis shows that the proposed technique outperforms existing state-of-the-art image inpainting methods. Finally, the proposed techniques are applied to MRI images suffering from susceptibility artifacts to illustrate the practical usage of the proposed work.