Reviewerly/exHarmony huggingface dataset:
HyperJOIE: Two-View Hyperbolic Knowledge Graph Embedding with Entities and Concepts Jointly

Knowledge graphs have two views: an entity graph in the instance view and a concept graph in the ontology view. Recent studies reveal that modeling the two graphs jointly can benefit the understanding to either one. However, the existing work has flaws on both modelling the hierarchical structures in the Euclidean space, and capturing the deep cross-view interaction between an entity and its corresponding concept. In this paper, we propose to explore hyperbolic space for two-view knowledge graph embedding, which provides more effective and efficient embedding learning mechanism, especially for hierarchical structured knowledge. We also propose to capture the deep cross-view interaction between an entity and its corresponding concept through modeling local structure information from intra-view neighbor nodes with hyperbolic attention mechanism. Finally, we propose to maintain the structural correspondence between the concept graph and the entity graph by first encoding two graphs with the same embedding model respectively and then aligning the two graphs with a hyperbolic transformation. Our empirical study conducted on two benchmark data collections proves that our model outperforms several state-of-the-art two-view knowledge graph embedding models.
INVESTIGATING THE IMPORTANCE OF CONTEXT IN THE ANALYSIS AND INTERPRETATION OF OGUNDIMU’S A SILLY SEASON

This paper investigates the importance of context in the analysis and interpretation of Ogundimu’sA Silly Season. It aims to describe, analyse and interpret linguistic context, situational context, and cultural context of the production of speech events in A Silly Season. Basing on a descriptive survey method relevant passages that require the clarification of contexts for their full understanding have been selected. It has been contended that the description of the linguistic context of the highlighted lexical items has helped determine who the speakers and hearers are in terms of the roles they have played either individually or collectively. In the same way, the description of the situational context clarifies the circumstances in which the speech events take root and even helps disambiguate ambiguous sentences. The description of the cultural context of words and expressions used has helped contend that the author values Yoruba culture and tradition.
The Place and Role of the Formation of Grammatical Competence in Foreign Language Teaching

Research relevance in this article examines the place and role of grammatical competence formation in foreign language teaching. Research purpose: providing information on cognitive, operational, motivational and reflexive criteria, which are indicators of the final completeness of grammatical competence. Research methods and materials: studies of the concepts of ‘competence’ and ‘competence’ are described on the basis of numerous opinions of scientists, educators and psychologists. Research results: with the acquisition of linguistic competence, the graduate acquires knowledge of the norms and rules that reflect the individual and general laws in the language. Conclusions: students majoring in philology or pedagogy, as future teachers, should be able to study the culture of other nations through a foreign language, as well as through analysis, synthesis, comparison, oral, written speech, dialogue, monologue.
Efficient Coverage Path Planning in Initially Unknown Environments Using Graph Representation

Coverage path planning is an important problem with numerous practical applications. Many solutions to this problem have been proposed over the past few years. However, most existing methods assume that the robot or the agent possesses an a priori map of the environment. This assumption is not viable in many real-world situations, thereby limiting the applicability of these methods. Inspired by the recent success of deep reinforcement learning (DRL) in different control tasks, in this work, we propose a novel CPP algorithm, Adaptive Deep BA* (AD-BA*), which combines deep reinforcement learning with the traveling salesman problem to identify an optimal coverage path in an initially unknown environment. The results of our simulation in various 2D mazes indicate that our approach learns the optimal coverage path in a sample efficient manner by minimizing the overall overlap extent while always maintaining significant coverage. Our experimental results indicate that, on average, AD-BA* achieves a) 31.67% and 7.29% lower overlap percentage in room-sized environments and b) 17.77% and 6.39% lower overlap percentage in larger environments when compared to the state-of-the-art algorithms, AD Path and BA* respectively.
Measuring the Effectiveness of a Proposed Educational Program in Teaching Pattern-Making of Children’s Clothing to Severely Hearing-Impaired Students Using E-Learning

Severely hearing-impaired students are among student groups that are most in need of e-learning, as they rely mainly on the sense of sight when receiving information.Therefore, teachers are advised to activate the use of new technologies to overcome the academic difficulties and problems they face.That said, this study aimed to prepare a proposed educational program using e-learning for the severely hearing-impaired students in the (fashion manufacturing) course at the Faculty of Human Sciences and Designs of King Abdulaziz University.The study also sought to measure the program effectiveness in imparting basic knowledge and skills on (pattern-making of children's clothing) to the research sample, using e-learning.The study applied the descriptive approach so as to address the research theoretical framework, and to determine the basic knowledge on methods that are utilized in preparing electronic curricula, in consistent with the cognitive, educational and emotional characteristics of severely hearing-impaired students, as well as to address the basic knowledge of the (children's clothing) curriculum.It also applied the experimental approach to measure the effectiveness of the proposed educational program in imparting basic knowledge of the pattern-making of children's clothing.The study sample included 21 severely hearing-impaired students, who are studying fashion manufacturing course within a bachelor's program.The study tools included an evaluation form to be filled by expert assessors in the field of fashion and textiles as well as specialists in hearing impairment, on the effectiveness of the proposed educational program in imparting basic knowledge and skills of pattern-making of children's cloth-How to cite this paper:
Privacy-Preserving ID3 Algorithms: A Comparison

Many real-life scenarios require the analysis of large amounts of data from multiple sources. Often, the data contain highly sensitive information and may be subject to privacy laws preventing its aggregation and sharing. Privacy-preserving data mining has emerged as a solution to this problem. It enables data scientists to analyze the distributed data without having to place it in a central location and while guaranteeing its privacy. Decision tree classification is a popular and widely studied machine learning technique for which many privacy-preserving versions exist. In this paper, we review recent privacy preserving implementations of the ID3 classification technique in a distributed environment and compare them in terms of efficiency and privacy. We consider cases where data is split horizontally over multiple parties.
Backgammon Is Hard

We study the computational complexity of the popular board game backgammon. We show that deciding whether a player can win from a given board configuration is NP-Hard, PSPACE-Hard, and EXPTIME-Hard under different settings of known and unknown opponents’ strategies and dice rolls. Our work answers an open question posed by Erik Demaine in 2001. In particular, for the real life setting where the opponent’s strategy and dice rolls are unknown, we prove that determining whether a player can win is EXPTIME-Hard. Interestingly, it is not clear what complexity class strictly contains each problem we consider because backgammon games can theoretically continue indefinitely as a result of the capture rule.
SMS Text Classification Model Based on Machine Learning

Text classification is an important problem in natural language processing. The main task is to divide the text into different categories according to the content of the text. This article preprocesses the text in the SMS data set used to a certain extent, using the Tf-Idf model. The frequency of the text unit is counted as the feature value of the corresponding vector of the text, so that the text is converted into a vector, and then these vectors are fitted and predicted by the support vector machine algorithm.
Pixel Voting Decoder: A novel decoder that regresses pixel relationships for segmentation

With the rapid development of the convolutional neural network, both instance segmentation and semantic segmentation have achieved remarkable performances. Recently, many efforts have been made to use a unified Encoder-Decoder architecture to solve these two segmentation tasks simultaneously. The encoder extracts high-level features from the input images for both tasks. However, existing decoders cannot meet the performance requirements of these two tasks: the semantic segmentation decoder is not flexible enough for instance segmentation, and the instance segmentation decoder lacks the precision of semantic segmentation. Therefore, we introduce a novel Pixel Voting Decoder to satisfy both precision and flexibility. The proposed decoder regresses the interlayer pixel relationships between the input and output feature maps across the convolutional layers. Then, the pixel relationships are regarded as the pixel votes for dynamically decoding the higher level information from the encoder. Finally, we propose the dynamic deconvolution to make full use of the votes for each pixel during the decoding process. Meanwhile, the matrix computation for the dynamic deconvolution is designed to boost the calculation. Experiments show that the proposed method can achieve better performance than the well-known methods on both instance segmentation on the COCO dataset and semantic segmentation on the Cityscapes dataset. The matrix implementation of the dynamic deconvolution also shows its high efficiency and feasibility.
Course Recommendation System in Social Learning Network (SLN) Using Hybrid Filtering

Each and every day, a lot of courses are published online by various mentors to help the interested people learn online. But the students who are interested may find it difficult to associate themselves with appropriate course they want as there are so many courses available, even under just a single topic. To help select the best courses for the respective candidate, here a course recommendation system is designed with the concept of hybrid recommender system. Recommendation systems such Content-Based filtering and Collaborative filtering have their own advantages and disadvantages. So, to compensate the shot comes of the respective filtering models, both the models are combined to realize a hybrid recommender system for the Course Recommendation in the Social Learning network. Here, the content-based filtering uses Lasso algorithm and collaborative filtering uses KNN algorithm, and to combine the result obtained by both the filtering methods, weighted average method is used to gain the result as a hybrid result from both the filtering models. This will prove to perform very well with great efficiency for the Course Recommendation in Social Learning Network.
Meal-Monitoring Systems Using Weight and Temperature Sensors for Elder Residents in Long-Term Care Facilities

This paper presents a few meal-monitoring systems for elder residents (especially patients) in LTCFs by using electronic weight and temperature sensors. These monitoring systems enable to convey the information of the amount of meal taken by the patients in real-time via wireless communication networks onto the mobile phones of their nurses in charge or families. Thereby, the nurses can easily spot the most patients who need immediate assistance, while the families can have relief in seeing the crucial information for the well-being of their parents at least three times a day. Meanwhile, the patients tend to suffer burns of their tongues because they can hardly recognize the temperature of hot meals served. This situation can be avoided by utilizing the meal temperature-monitoring system, which displays an alarm to the patients when the meal temperature is above the reference. These meal-monitoring systems can be easily implemented by utilizing low-cost sensor chips and Arduino NANO boards so that elder-care hospitals and nursing homes can afford to exploit them with no additional cost. Hence, we believe that the proposed monitoring systems would be a potential solution to provide a great help and relief for the professional nurses working in elder-care hospitals and nursing homes.
Categorizing and Retrieving of Data Through Specified Pattern Drawn from Attributes

Categorizing and retrieving are the sub component of clustering on huge data i.e. it is observed that, Dynamic enhancement in size of data causing new calls for new efforts for research in this section by previous decades which promote the enhancement in the way of clustering. This paper presents its effort in the direction of clustering enhancement with respect to data size enhancement. The proposed approach of this paper work on specified pattern drown through attributes to perform clustering, categorizing and retrieving on considered sample data.
Multi-scale Feature Fusion Network with Positional Normalization for Single Image Dehazing

In order to remove the haze from hazy images, we design an end-to-end trainable multi-scale feature fusion network with positional normalization named MSFFP-Net which does not rely on the physical atmosphere scattering model, and the backbone of the proposed network is a multi-scale network (MSNet). The MSNet uses a up-sampling and down-sampling block to connect different scales, so the information in the net can be exchanged efficiently. The basic convolution module of MSNet is a fusion-attention block named feature fusion group (FFG). The FFG fuses the channel attention in channel-wise and pixel attention in pixel-wise, and uses a positional normalization that normalizes exclusively across channels is used to learn more weight from important features. Lastly, the MSNet fuses the features from adjacent row stream and column stream to get the contributions from different scales. Experimental results in RESIDE dataset and real-word dataset indicate that the MSFFP-Net outperforms the state-of-the-art methods.
An Error Self-learning Semi-supervised Method for No-reference Image Quality Assessment

In recent years, deep learning has achieved significant progress in many respects. However, unlike other research fields with millions of labeled data such as image recognition, only several thousand labeled images are available in image quality assessment (IQA) field for deep learning, which heavily hinders the development and application for IQA. To tackle this problem, in this paper, we proposed an error self-learning semi-supervised method for no-reference (NR) IQA (ESSIQA), which is based on deep learning. We employed an advanced full reference (FR) IQA method to expand databases and supervise the training of network. In addition, the network outputs of expanding images were used as proxy labels replacing errors between subjective scores and objective scores to achieve error self-learning. Two weights of error back propagation were designed to reduce the impact of inaccurate outputs. The experimental results show that the proposed method yielded comparative effect.
Fast and Dense Denoising Convolutional Neural Network

Deep neural networks show us their superior image denoising capability due to the powerful fitting ability. However, they suffer from the following drawbacks: (i) too deep neural networks often imply a very large number of parameters and considerable computational overhead; (ii) neural networks that are too deep are difficult to converge by training and may lead to degradation. In this study, we propose a novel denoising network called the fast and dense denoising convolutional neural network(FDDCNN). In particular, the depthwise separable convolutions in the fast module and the homogeneous cascade structure in the dense module can efficiently solve the above problem. Extensive experiments with publicly available datasets have shown that this model can have the same excellent denoising power as existing methods with fewer parameters and less computational overhead.
Analysis of Computer Information Processing Technology in the Big Data Era

With the rapid progress of computer technology, the amount of information and data continues to increase, the world has developed from the data age to the era of big data, the impact and pressure on computer information processing technology (CIPT) is also growing. Therefore, the processing of information and data is facing some challenges. On this basis, the CIPT based on big data is deeply analyzed and studied. Starting from the application advantages of big data, this paper analyzes the advantages and challenges brought by CIPT, points out the shortcomings of CIPT in the era of big data, and puts forward some promotion strategies for reference. This paper takes the CIPT based on big data as the experimental group and the traditional CIPT as the control group, analyzes and compares the data exchange, data storage and resource utilization. The results show that big data can realize the storage and calculation of data, and the speed is fast, and the resource utilization rate is high. The era of big data provides a large number of exchangeable data for people, which can greatly optimize the efficiency of data information processing. However, it is also faced with great challenges of information security in the development process.
Occluded Face Recovery by Image Retrieval

In this paper, we propose an occluded face recovery framework to improve the face recognition rate for the occlusion case. The occluded facial image of an unseen testing subject is automatically recovered, the generated results are diverse and fit the personal characteristics of the observable regions, and thus can be used to increase the face recognition performance for occlusion cases.
Embedded Centralism

Abstract Drawing from ethnographic research conducted within the Silicon Valley cryptocurrency and blockchain industry, Chapter 7 provides an account of the situated frictions among varying stakeholders in high-technology culture. The clashing of libertarian anarchy and entrepreneurial profit-seeking are forced into a vision reminiscent of the Californian Ideology, contributing to tensions of a splintering community. Blockchain technology is symptomatic of this polarising worldview. As “radical” and “disruptive” start-up companies are absorbed into the embedded spatial ties of the surrounding economy, they become increasingly “normalised” by their investors at the same time as scaling to enrol more users within their platforms. This has the effect of funnelling financial practices on blockchains through proprietary software controlled by a small number of technocrats, who can be more easily regulated by nation-state jurisdictions. The entrepreneurial geographies of high-technology agglomeration industries thereby act as another spatial limitation to algorithmic decentralisation.
Evaluation of Cluster Management Quality Based on Consumer Opinion Sentiment Analysis

Abstract This article discusses the issue of assessing the quality of cluster management by utilizing Internet customer feedback about companies that are members of clusters. Due to the growing number of Internet users, companies pay greater attention to the opinions published about them. Consumers are also increasingly willing to share their opinions and thoughts about the products they use. As a result, it has become possible to analyze the quality of services and products provided by an enterprise based on Internet opinions. In this article, we analyze the quality of cluster management as reflected in the European Cluster Excellence Initiative (ECEI) label, as measured by sentiment analysis of Internet opinions. The paper proposes a method for the identification and evaluation of Internet sources used in the opinion sentiment analysis. Sentiment analysis of Internet opinions of cluster and in-cluster business customers was performed, and the results were compared with the level of the ECEI label, which was awarded to the analyzed clusters. The conducted research showed convergences between formalized systems of management quality assessment and the level of opinions expressed on the Internet. The results testify that sentiment analysis can complement the evaluation of cluster management quality.
The Complexity and Expressive Power of Limit Datalog

Motivated by applications in declarative data analysis, in this article, we study Datalog Z —an extension of Datalog with stratified negation and arithmetic functions over integers. This language is known to be undecidable, so we present the fragment of limit Datalog Z programs, which is powerful enough to naturally capture many important data analysis tasks. In limit Datalog Z , all intensional predicates with a numeric argument are limit predicates that keep maximal or minimal bounds on numeric values. We show that reasoning in limit Datalog Z is decidable if a linearity condition restricting the use of multiplication is satisfied. In particular, limit-linear Datalog Z is complete for Δ 2 EXP and captures Δ 2 P over ordered datasets in the sense of descriptive complexity. We also provide a comprehensive study of several fragments of limit-linear Datalog Z . We show that semi-positive limit-linear programs (i.e., programs where negation is allowed only in front of extensional atoms) capture coNP over ordered datasets; furthermore, reasoning becomes coNEXP-complete in combined and coNP-complete in data complexity, where the lower bounds hold already for negation-free programs. In order to satisfy the requirements of data-intensive applications, we also propose an additional stability requirement, which causes the complexity of reasoning to drop to EXP in combined and to P in data complexity, thus obtaining the same bounds as for usual Datalog. Finally, we compare our formalisms with the languages underpinning existing Datalog-based approaches for data analysis and show that core fragments of these languages can be encoded as limit programs; this allows us to transfer decidability and complexity upper bounds from limit programs to other formalisms. Therefore, our article provides a unified logical framework for declarative data analysis which can be used as a basis for understanding the impact on expressive power and computational complexity of the key constructs available in existing languages.
Unsupervised feature learning of Noisy Arabic Characters using wavelet packet transform based SRBM

The unsupervised feature learning is an important task in optical character recognition models. In neural network there are two useful unsupervised architectures; Restricted Boltzmann machine (RBM) and Auto-encoder. In this work, we focus to improve performance the of Stacked RBMs algorithm in order to learn features from a set of Arabic characters in noisy situations. Therefore, we need to update these parameters by the gradient descent algorithm, which minimize cost function without over-fitting. We propose to learn features of noisy Arabic character image using wavelet packet transform before stacking RBMs. This method provides an independent time-frequency information in each level, which helps to minimize overlap of noise in the useful parts. Afterwards, we apply the SRBM and WRBM algorithms on a corpus of Arabic character images, thus, we evaluate their convergence speed by Root Mean Square Error (RMSE) for each RBM algorithm.