Reviewerly/exHarmony huggingface dataset:
Complex Query Answering with Neural Link Predictors

Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number of domains, such as queries using logical conjunctions (∧), disjunctions (∨) and existential quantifiers (∃), while accounting for missing edges. In this work, we propose a framework for efficiently answering complex queries on incomplete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods --- black-box neural models trained on millions of generated queries --- without the need of training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across different knowledge graphs containing factual information. Finally, we demonstrate that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms.
Political Arabic Articles Orientation Using Rough Set Theory With Sentiment Lexicon

Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning.In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information.A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature.In this study, we propose a detection model for political orientation articles in the Arabic language.We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences.The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles.We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms.It is shown that the performance of the proposed approach significantly improves by adding discriminating features.To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.
A Multi-View Co-Training Clustering Algorithm Based on Global and Local Structure Preserving

Multi-view clustering which integrates the complementary information from different views for better clustering, is a fundamental and important topic in machine learning.In this paper, we present a multi-view co-training clustering algorithm based on global and local structure preserving.Here the global structure is referred to the integration of the within-cluster compactness and between-cluster separation; the local structure is referred to the neighborhood information.Our algorithm at first preserves both the global and local structure to the subspace in each view.And then, this algorithm obtains the clustering result in the subspace of each view, and utilizes the clustering labels of one view to guide the subspace clustering in another view.In this way, the differences and compatibilities among the multiple views are fused together to form the final cluster partition.Therefore, the clustering result takes full account of the global and local structure information of the multi-view data, which is helpful to improve the of clustering accuracy.Experimental results on the multi-view text datasets and image datasets demonstrate the effectiveness and correctness of the proposed algorithm.
An optimized CNN-based quality assessment model for screen content image

Most existing convolutional neural network (CNN) based models designed for natural image quality assessment (IQA) employ image patches as training samples for data augmentation, and obtain final quality score by averaging all predicted scores of image patches. This brings two problems when applying these methods for screen content image (SCI) quality assessment. Firstly, SCI contains more complex content compared to natural image. As a result, qualities of SCI patches are different, and the subjective differential mean opinion score (DMOS) is not appropriate as qualities of all image patches. Secondly, the average score of image patches does not represent the quality of entire SCI since the human visual system (HVS) is sensitive to image patches containing texture and edge information. In this paper, we propose a novel quadratic optimized model based on the deep convolutional neural network (QODCNN) for full-reference (FR) and no-reference (NR) SCI quality assessment to overcome these two problems. The contribution of our algorithm can be concluded as follows: 1) Considering the characteristics of SCIs, a valid network architecture is designed for both NR and FR visual quality evaluation of SCIs, which makes the networks learn the feature differences for FR-IQA; 2) with the consideration of correlation between local quality and DMOS, a training data selection method is proposed to fine-tune the pre-trained model with valid SCI patches; 3) an adaptive pooling approach is employed to fuse patch quality to obtain image quality, owns strong noise robust and effects on both FR and NR IQA. Experimental results verify that our model outperforms both current no-reference and full-reference image quality assessment methods on the benchmark screen content image quality assessment database (SIQAD). Cross-database evaluation shows high generalization ability and high effectiveness of our model.
Blockchain out of the Box – Where is the Blockchain in Blockchain-as-a-Service?

Blockchain-as-a-Service (BaaS) is increasingly discussed as a way for companies to get started with blockchain projects.Different BaaS offerings are available, but a systematic categorization of what BaaS comprises is missing.In this research, we analyze the service offerings of BaaS providers based on available online information and identify a number of common characteristics in the BaaS offerings related to the use of service types, distributed ledger technology (DLT) systems, consensus mechanisms, and pricing models.These characteristics are then further analyzed in the light of available literature on BaaS, as well as conducted expert interviews.The objective of this research is to provide an overview of the BaaS landscap,e as well as a taxonomy that provides guidance for researchers and practitioners alike interested in BaaS.
Towards automated analysis of research methods in library and information science

Abstract Previous studies of research methods in Library and Information Science (LIS) lack consensus in how to define or classify research methods, and there have been no studies on automated recognition of research methods in the scientific literature of this field. This work begins to fill these gaps by studying how the scope of “research methods” in LIS has evolved, and the challenges in automatically identifying the usage of research methods in LIS literature. We collected 2,599 research articles from three LIS journals. Using a combination of content analysis and text mining methods, a sample of this collection is coded into 29 different concepts of research methods and is then used to test a rule-based automated method for identifying research methods reported in the scientific literature. We show that the LIS field is characterized by the use of an increasingly diverse range of methods, many of which originate outside the conventional boundaries of LIS. This implies increasing complexity in research methodology and suggests the need for a new approach towards classifying LIS research methods to capture the complex structure and relationships between different aspects of methods. Our automated method is the first of its kind in LIS, and sets an important reference for future research.
Smartwatch-Based Open-Set Driver Identification by Using GMM-Based Behavior Modeling Approach

Driver identification must be studied because of the development of telematics and Internet of Things applications. Many application services require an accurate account of a driver's identity; for example, usage-based insurance may require a remote collection of data regarding driving. Recently, a Gaussian mixture model (GMM)-based behavioral modeling approach has been successfully developed for smartwatch-based driver authentication. This study extends the GMM-based behavioral modeling approach from driver authentication to open-set driver identification. Because the proposed approach can help for identifying illegal users, it is highly suitable for real-world conditions. According to a review of the relevant literature, this study proposed the first smartwatch-based driver identification system. This study proposed three open-set driver identification methods for different application domains. The result of this research provides a reference for designing driver identification systems. To demonstrate the feasibility of the proposed method, an experimental system that evaluates the performance of the driver identification method in simulated and real environments was proposed. The experimental results for the three proposed methods of driver identification illustrated an equal error rate (EER) of 11.19%, 10.65%, and 10.50% under a simulated environment and an EER of 17.95%, 17.07%, and 16.66% under a real environment.
Intelligent Video Data Security: A Survey and Open Challenges

A range of video contents and technology have provided convenience to humans, with real-time video applications-such as surveillance applications-able to contribute to increasing public safety by reducing physical crimes. The development of video technology has made it possible to achieve an improved quality of life. However, this technology can also be exploited and lead to security issues such as physical and digital crimes. Unfortunately, security breaches are increasing in complexity and frequency, making current countermeasures insufficient to prevent them. Given recent trends, we recognize the need for security technology to respond to advanced video crimes. Intelligent security is one of the methods that can be used to respond to these issues. Although research on video data security has been actively conducted, not enough studies have been published on video data security that also addresses intelligent security. Specifically, a classification system for research on video data security has not been provided, and no systematic analysis has been conducted for advanced research. Thus, the purpose of this is to fill in these gaps in existing research. This study offers a classification of research on video data security based on the collection and analysis of related works. Moreover, this study presents an analysis of research on video data security technologies combined with intelligent technologies based on SLR methodology.
Nonstationary multivariate Gaussian processes for electronic health records

Advances in the modeling and analysis of electronic health records (EHR) have the potential to improve patient risk stratification, leading to better patient outcomes. The modeling of complex temporal relations across the multiple clinical variables inherent in EHR data is largely unexplored. Existing approaches to modeling EHR data often lack the flexibility to handle time-varying correlations across multiple clinical variables, or they are too complex for clinical interpretation. Therefore, we propose a novel nonstationary multivariate Gaussian process model for EHR data to address the aforementioned drawbacks of existing methodologies. Our proposed model is able to capture time-varying scale, correlation and smoothness across multiple clinical variables. We also provide details on two inference approaches: Maximum a posteriori and Hamilton Monte Carlo. Our model is validated on synthetic data and then we demonstrate its effectiveness on EHR data from Kaiser Permanente Division of Research (KPDOR). Finally, we use the KPDOR EHR data to investigate the relationships between a clinical patient risk metric and the latent processes of our proposed model and demonstrate statistically significant correlations between these entities.
Mining Pool Selection under Block WithHolding Attack

In current Proof-of-Work (PoW) blockchain systems, miners usually form mining pools to compete with other pools/miners in the mining competition. Forming pools can give miners steady revenues but will introduce two critical issues. One is mining pool selection, where miners select the pools to join in order to maximize their revenues. The other is a Block WithHolding (BWH) attack, where pools can inject part of their hash/mining power into other pools to obtain additional revenues without contributing to the mining process of the attacked pools. Reasoning that the BWH attack will have significant impacts on the pool selection, we therefore investigate the mining pool selection issue in the presence of a BWH attack in this paper. In particular, we model the pool selection process of miners as an evolutionary game and find the Evolutionarily Stable States (ESSs) of the game (i.e., stable pool population states) as the solutions. Previous studies investigated this problem from the perspective of pool managers and neglected the revenues from attacked pools (attacking revenues), leading to less accurate and insightful findings. This paper, however, focuses on the payoffs of miners and carefully takes the attacking revenues into consideration. To demonstrate how the problem is solved, we consider the scenario with two mining pools and further investigate the case where one pool attacks the other and the case where the two pools attack each other. The results in this paper show that pools can attract more miners to join by launching a BWH attack and the attack power significantly affects the stable pool populations.
Machine Learning for 5G MIMO Modulation Detection

Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.
Designing and Manufacturing of Automatic Robotic Lawn Mower

This study is about the manufacturing of a personified automatic robotic lawn mower with image recognition. The system structure is that the platform above the crawler tracks is combined with the lawn mower, steering motor, slide rail, and webcam to achieve the purpose of personification. Crawler tracks with a strong grip and good ability to adapt to terrain are selected as a moving vehicle to simulate human feet. In addition, a lawn mower mechanism is designed to simulate the left and right swing of human mowing to promote efficiency and innovation, and then human eyes are replaced by Webcam to identify obstacles. A human-machine interface is added so that through the mobile phone remote operation, users can choose a slow mode, inching mode, and obstacle avoidance mode on the human-machine interface. When the length of both sides of the rectangular area is input to the program, the automatic robotic lawn mower will complete the instruction according to the specified path. The chip of a Digital Signal Processor (DSP) TMS320F2808 is used as the core controller, and Raspberry Pi is used as image recognition and human-machine interface design. This robot can reduce labor costs and improve the efficiency of mowing by remote control. In addition to the use as an automatic mower on farms, this study concept can also be used in the lawn maintenance of golf courses and school playgrounds.
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation

We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to 1031m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) across multiple rooms (up to 40) and generalizes to unseen targets. We show that when compared to several baselines our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, and (3) produces better solutions with shorter paths for long-range navigation tasks.
Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models

Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved by formulating language modeling of low-resource languages as a domain adaptation task using transformer-based language models pretrained on corpora of high-resource languages. Our novel cross-lingual post-training approach selectively reuses parameters of the language model trained on a high-resource language and post-trains them while learning language-specific parameters in the low-resource language. We also propose implicit translation layers that can learn linguistic differences between languages at a sequence level. To evaluate our method, we post-train a RoBERTa model pretrained in English and conduct a case study for the Korean language. Quantitative results from intrinsic and extrinsic evaluations show that our method outperforms several massively multilingual and monolingual pretrained language models in most settings and improves the data efficiency by a factor of up to 32 compared to monolingual training.
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding

While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes. We present PhUSION, a proximity-based unified framework for computing structural and positional node embeddings, which leverages well-established methods for calculating node proximity scores. Clarifying a point of contention in the literature, we show which step of PhUSION produces the different kinds of embeddings and what steps can be used by both. Moreover, by aggregating the PhUSION node embeddings, we obtain graph-level features that model information lost by previous graph feature learning and kernel methods. In a comprehensive empirical study with over 10 datasets, 4 tasks, and 35 methods, we systematically reveal successful design choices for node and graph-level machine learning with embeddings.
Impact of Mobile Applications for a Lima University in Pandemic

The current global pandemic situation has forced universities to opt for distance education, relying on digital tools that are currently available, such as course management platforms like Moodle, videoconferencing applications like Google Meet or Zoom, or instant messaging apps like WhatsApp.In this study it is detailed that these tools have made virtual education an effective alternative to provide education without having a physical space where teachers and students can concentrate.In addition, this document shows that in this form of teaching learning it is not necessary to have a computer, it is enough to have a cell phone to access this type of education in Peru, since most of the country's homes have a smartphone .Both students and teachers affirm that, although a little more time is invested than usual, this teaching method is satisfactory.The result obtained is that the use of mobile applications plays a very important role in virtual classes since the vast majority of students use the cell phone.In conclusion, teaching and learning in higher university education with the use of mobile applications, both teachers and students said that it was of great help due to the interaction through communication with WhatsApp, zoom, Google meet, among others.In addition, being in constant communication with the students through the applications strengthened the teaching.
A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

In this paper, a combined approach CAZWNLP (a combined approach of zero-watermarking and natural language processing) has been developed for the tampering detection of English text exchanged through the Internet. The third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this study. The third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the textual characteristics of the given contexts. Moreover, the extracted features have been utilized as watermark information and then validated with the attacked English text to detect any suspected tampering occurred on it. The embedding mechanism of CAZWNLP method will be achieved logically without effects or modifying the original text document to embed a watermark key. CAZWNLP has been implemented using VS code IDE with PHP. The experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion, reorder, and deletion attacks, e.g., Comparison results with baseline approaches also show the advantages of the proposed approach.