Reviewerly/exHarmony huggingface dataset:
On Similarity Measures for a Graph-Based Recommender System

Recommender systems are drawing increasing attention with several unresolved issues. These systems depend on personal user preferences on items via ratings and recommend items based on choices of similar users. A graph-based recommender system that has ratings of users on items can be shown as a bipartite graph in which vertices match users and items nodes, and edges correspond to ratings. Recommendation generation in a bipartite graph can be moderated as a sub-problem of link prediction. In the relevant literature, modified link prediction methods are employed to differentiate between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationships between users/items are often ignored. We propose a new model that utilizes user-user and item-item similarity values with relational dualities in order to improve coverage and hits rate by carefully incorporating similarities. We compare five similarity measures in terms of hits rate and coverage while providing top-N recommendations. We scrutinize how such similarity measures perform with top-N item recommendation processes over the standard MovieLens Hetrec and MovieLens datasets. The experimental results show that hits rate and coverage can be improved by about 7% and 4%, respectively, with Jaccard and Adjusted-Cosine similarity measures being the best performing similarity measures. Significant differences/improvements are observed over the previous CORLP approach.
Retrospective Review of Perfect Ternary Sequences and Their Generators

Introduction. Perfect polyphase unimodular sequences, i. e. sequences with ideal periodic autocorrelation and single amplitude of symbols are widely used in modern radio communications and radar. A special place among them is occupied by perfect ternary sequences (PTSs) with elements {–1, 0, 1}. In fact, these are binary sequences with the alphabet {–1, 1}, but with zero symbols in some positions. It is known that PTSs are quite numerous and their length in comparison with perfect binary sequences is not limited from above. The charge for this is a peak factor greater than one, which causes energy losses in the receiver. A large number of research papers and books are devoted to the design of PTSs and the study of their properties. In particular, the handbook on sequence design by Fan and Darnell (1996) which provides an overview of the then known PTS families has become widely famous. However, over the past two decades, numerous new PTS families were discovered, some theorems on their existence were obtained, and connections were established between them and circulant weighing matrices. Therefore, there is a need for a new review of existing PTSs. Objective. The article is devoted to a retrospective review of existing PTSs and their generation devices. Materials and methods. Considered and analyzed domestic and foreign sources of information (books, journal papers, conference proceedings, patents). Results. Along with solving an informational bibliographic problem, the review shows the relationship between PTSs obtained at different times, their connection with circulant weighing matrices, and also describes the block diagrams of generators of some PTS families. Conclusion. A brief retrospective review of PTSs for their almost 60 years history is presented and the generators of some PTS families are considered. The results of the study are relevant for use in modern radio communications and radar systems and in particular, in CW and LPI radars.
THE CONTENT AND STRUCTURE OF FUTURE ENGINEERS’ COMMUNICATIVE CULTURE

Purpose: The article deals with the problem of the development of the communicative culture of future engineers, which determines success in any field of activity.&#x0D; Methodology: This is analytical-review research whose data have been collected based on previous studies. These data are gathered in four general topics: studies on the theory of culture; studies on the communicative culture of the individual; theories of professional communication and personal development in the process of communication and studies on the methodology of higher professional education.&#x0D; Result: The oil industry in petroleum engineers with formed professional competencies reflected in the Federal State Standard of Higher Education in the direction of bachelor’s studies in Oil and Gas, involving the development of a communicative culture of graduates of technical universities, successfully working in a team, presenting themselves, building relationships with other people.&#x0D; Applications: This research can be used for petroleum engineers and graduates of technical universities.&#x0D; Novelty/Originality: In this research, the model of the content and structure of future engineers’ communicative culture is presented in a comprehensive and complete manner.
LFSR based versatile divider architectures for BCH and RS error correction encoders

Error correction codes play a major role in real world data communication systems. This paper proposes linear feedback shift register (LFSR) based flexible and vector serial/parallel dividers for Bose–Chaudhuri–Hocquenghem (BCH) and Reed Solomon (RS) error correction encoders (ECEs). These can be used in variable length error correction codecs. Also, this paper elaborates the versatile hardware implementations of Turbo product encoders using the proposed flexible/vector serial dividers based BCH/RS encoders. The proposed flexible architectures are used to perform the division operation with variable length generator polynomials in BCH and RS based ECEs. In these flexible designs, the same hardware circuit is used to achieve the versatility without much compromise in throughput by allowing the trade-offs in area and power-delay-product (PDP). The proposed vector architectures are used to perform multiple division operations in parallel to improve the throughput with the trade-offs in area and PDP. The proposed and existing designs are implemented using 45 nm CMOS technology. The synthesis results show that the proposed vector designs achieve significant improvement in throughput over the existing designs. For example, the LFSR based proposed vector serial dividers achieve 47.1% and 83.3% of improvement in throughput as compared with the conventional designs for BCH encoder with generator polynomial length 65 and RS encoder with generator polynomial length 17 respectively.
Compact Cluster-based Balanced Distribution Adaptation for Transfer Learning

Recently, Domain Adaptation has received great attention in addressing domain shift problem, where source domain and target domain follow different distribution. Existing methods often seek to minimize the marginal distribution and the conditional distribution across domains however treat them equally, which is not reasonable in real applications. Moreover, it is easy to misclassify on target data with large within-class scatter. In this paper, we propose a Compact Cluster-based Balanced Distribution Adaptation (CC-BDA) method for cross-domain classification. CC-BDA exploits the balanced domain adaptation by increasing the balance factor dynamically in each iteration until convergence. In addition, we construct compact clusters to reduce within-class scatter alongside the domain adaptation, which endows samples with the same label much closer from their means. Finally, we present analysis and performance evaluation over seven state-of-the-art baseline methods. The results show that CC-BDA significantly outperforms other adaptation methods in accuracy and execution time cost.
Relay Protection Hidden Fault Monitoring and Risk Analysis Based on the Power System

With the continuous expansion of the grid, the safe operation of the power grid is also becoming more and more important than before. Relay protection is one of the important protection devices to ensure the safe operation of the power system; its failure can cause great influence on the power system. Relay protection hidden fault is a kind of the relay protection fault, however, the phenomenon of power outages caused by power system fault is the result of relay protection hidden fault in many situations, which is one of the most important hidden areas to threaten the safety of power grid. Therefore, the hidden failure of relay protection has a great influence on the electric power system, so monitoring the hidden fault of the relay protection will become more and more important. This paper introduces the concept of relay protection of hidden faults, its characteristics, and then analyzes the detection, risk and the calculation method of the relay protection of hidden fault.
Dilated Convolutional Networks Incorporating Soft Entity Type Constraints for Distant Supervised Relation Extraction

Although distant supervised relation extraction has been widely used, its performance is weakened by the wrong labeling problem. During eliminating noise, many previous work failed to grasp the trade-off between long dependencies and computational complexity while using neural networks, and ignored the potential noise in external knowledge. In this paper, we propose a neural model incorporating dilated convolutional neural networks with soft entity type constraints to jointly tackle the above two issues. Rather than using traditional convolutional neural networks or recurrent neural networks, we propose dilated convolutional networks as the sentence encoder so as to capture long dependencies in large scale context and improve the robustness against local noise while keeping the efficiency of computation. Furthermore, we utilize entity types, which is considerd as external knowledge with noise, to denoise in relation classification by taking the constraints between entity types and relations into account and learning more precise entity types and attention weights simultaneously. Experimental results on the New York Times dataset show that, our model incorporating dilated convolutional networks with soft entity type constraints promotes distant supervised relation extraction and achieves state-of-art results compared with baselines.
Weighted Local Discriminant Preservation Projection Ensemble Algorithm With Embedded Micro-Noise

High-dimensional data often cause the “curse of dimensionality” in data processing. Dimensionality reduction can effectively solve the curse of dimensionality and has been widely used in high-dimensional data processing. However, the existing dimensionality reduction algorithms neglect the effect of noise injection, failing to account for the datasets of large variance within classes and not effectively considering the stability of dimensionality reduction. To solve the problems, this paper proposes a weighted local discriminant preservation projection algorithm based on an ensemble imbedded mechanism with micro-noise injection (n_w_LPPD). The proposed algorithm aims to overcome the problem of large variance within classes and introduces an ensemble projection matrix via Bayesian fusion mechanism with micro-noise to enhance the antijamming capability of the model. Ten public datasets were used to verify the proposed algorithm. The experimental results demonstrated that the proposed algorithm is significantly effective, especially for the case of small sample datasets with high intraclass variance. The classification accuracy is improved by at least 10% compared to the case without dimensionality reduction. Even compared with some representative dimensionality reduction algorithms, the proposed n_w_LPPD has significantly superior classification performance.
A Fast and Secure GPU Memory Allocator

Graphics Processing Units (GPUs) is widely used to perform general purpose computing in many areas such as scientific computing and deep learning. In order to offer more flexibility in GPU programming, dynamic memory allocation has been introduced in GPU programming frameworks such as CUDA. However, the dynamic memory allocator in CUDA is inefficient in highly concurrent environments. Thus, several dynamic memory allocators are recently proposed to enhance the performance of dynamic memory management. However, these allocators only focus on achieving higher performance but ignore the security issues. In this paper, we propose a fast and secure GPU memory allocator based on ScatterAlloc. In order to efficiently protect against memory attacks such as buffer overflows, our allocator consists of several key techniques including canary-based memory protection (two options such as detection-on-free and always-on-detection are provided), address compression, and over-provisioning. Experimental results show that the allocator can effectively detect buffer overflow errors while it is still approximately 100 times faster than the CUDA toolkit allocator.
Research on Multi-layer Classification Method of Network Traffic Based on Machine Learning

According to the network traffic characteristic group, this paper designs a kind of network traffic classification with coarse and fine two layers. The first layer based on the experience attribute selection and the fast multilayer clustering method, constructs the unsupervised classifier, which realizes the coarse classification of the network traffic dataset and forms the data subset; the second layer, based on the selected subset of data, constructs a strong supervised classifier, which realizes the fine classification of the network traffic data set, according to the ensemble learning method. Theoretical analysis and experiments show that this method can not only improve the accuracy of network traffic classification, but also reduce the time of classification greatly, which is important to improve the accuracy of small class classification of imbalanced network traffic data.
Parallel Hybrid Join Algorithm on GPU

In data analytics applications, join is a general and time consuming operation. Optimizing join algorithms can benefit the query processing significantly. The emerging of GPUs provides a massive parallelism solution for improving the performance of the join operation. The hash join (HJ) and sort merge join (SMJ), which are two widely used join algorithms, have been proved effective for efficient join processing on the GPUs. Both algorithms have their own advantages and drawbacks, offering the chance of combining the advantages of HJ and SMJ on GPUs. In processing join operation on GPUs, data need to be transmitted between the CPU and the GPU due to the discrete GPU memory design, which causes performance degradation because of the high PCIe data transfer overhead. As GPUs are becoming more powerful than before, the performance gap between data transmission and GPU execution increases, which makes it even harder to implement an efficient join on GPUs. In this paper, we focus on the optimization of join algorithms on GPUs. We propose the Parallel Hybrid Join algorithm on GPUs(PHYJ) to combine the advantages of HJ and SMJ, and overlap the data communication and GPU execution with a pipeline mechanism. In our evaluation, the PHYJ shows up to 1.72X and 1.55X speedup over the up-to-date HJ and SMJ algorithms respectively on a NVIDIA GTX 1080ti-Pascal GPU. On the TitanV-Volta GPU, up to 1.54X and 1.42X improvements can be achieved over the baseline HJ and SMJ algorithms respectively.
Efficient and Robust Convolutional Neural Networks via Channel Prioritization and Path Ensemble

With the growing recognition of both efficiency and security issues in machine learning models, we propose a novel convolutional neural networks (CNNs) training algorithm, called channel prioritization and path ensemble (CPPE), to not only allow dynamically trade-offs between different resource and performance requirements but also enable secure inference without any extra computational cost or memory overhead. Our approach not only prioritizes channels to prune the network in a structured way and ensemble multiple inference paths over different utilization conditions. We demonstrated the effectiveness of channel prioritization by the experiment of the VGG-16 network on various benchmark datasets. The experimental results show that, on the CIFAR-10 dataset, a 10× parameters reduction and a 4× FLOPs reduction can be achieved, with only a 0.2% accuracy drop. Furthermore, we allow CNNs to dynamically trade-offs between resource demand and accuracy with only 4% degradation in accuracy in exchange for 16× FLOPs reduction. By ensembling multiple inference paths, our model can improve robustness against various adversarial attacks without any additional computational cost and memory overhead. Finally, our method is simple and easily applied to any convolutioanl neural networks.
Rule Based Pattern Type of Verb Identification Algorithm for The Holy Qur’an

This paper proposes an algorithm for identification of pattern type of verb in classical Arabic. This topic was proposed because of the problem on Arabic Al-Qur’an annotation is an important task. It is important for the processing of the Holy Qur’an data, and provides convenience for those who want to learn Arabic, especially understanding arabic on morphology aspect. Understanding verb is the first step to understand the arabic morphology. The effort is to recognize how the rules regarding the pattern type of verb with the rule based approach read the pattern with prefix, the diacritics and the suffix if the verb. Briefly entered verb (Arabic or transliteration) and its output is an attribute of verb which is pattern type of verb, pronouns and verb pattern with the main rule that is identifying pattern, getting verb attributes and determining verb pattern. Experiments show that the proposed algorithm obtained accuracy at 96.48% with soft calculation method and 89.46% with harsh method calculation method.
Classification of Plasmodium-Infected Erythrocytes Through Digital Image Processing

The development of antimalarial drugs requires performing laboratory experiments that include the analysis of blood smears infected with Plasmodium. Analyzing visually the resulting microscopy images is usually a slow and tedious task prone to errors due to fatigue and subjectivity of the analysts. These facts have motivated the creation of digital image processing systems to automate this analysis. In this work a computer vision solution to process microscopy images of blood smears containing erythrocytes infected with Plasmodium is shown. This system performs tasks like illumination and color correction, image segmentation including splitting of clumped objects and extraction of color features. A set of different classifiers was tested and evaluated to find the best one in terms of indexes of effectiveness. A new feature named pixels fraction was introduced and used together with a number of other color-related features, from which a subset to classify cells into normal or infected was selected. The classifiers evaluated were: support vector machines (SVM), K-nearest neighbors (KNN), J48, Random Forest (RF), Naïve Bayes and linear discriminant analysis (LDA). All of them were evaluated in terms of correct classification rate, sensitivity, specificity, F-measure and area under Receiver Operating Characteristic (ROC) curve (AUC). The effectiveness of the pixels fraction as a new feature was demonstrated by the experimental results. In regard to classifiers, J48 and Random Forest showed the best results.
FastCFI: Real-Time Control Flow Integrity Using FPGA Without Code Instrumentation

Control Flow Integrity (CFI) is an effective defense technique against a variety of memory-based cyber attacks. CFI is usually enforced through software methods, which entail considerable performance overhead. Hardware-based CFI techniques can largely avoid performance overhead, but typically rely on code instrumentation, which forms a non-trivial hurdle to the application of CFI. We develop FastCFI, an FPGA based CFI system that can perform fine-grained and stateful checking without code instrumentation. We also propose an automated Verilog generation technique that facilitates fast deployment of FastCFI. Experiments on popular benchmarks confirm that FastCFI can detect fine-grained CFI violations over unmodified binaries. The measurement results show an average of 0.36% performance overhead on SPEC 2006 benchmarks.
Application of OpenLayers in marine information monitoring

Marine information monitoring plays an important role in solving marine environment and controlling marine ecology. OpenLayers, as an open source development tool for WebGIS client, has the characteristics of small size and powerful function compared with commercial WebGIS software. This paper discusses the application of OpenLayers in ocean information monitoring based on OpenLayers. Firstly, The paper gives a basic overview of OpenLayers, then summarizes its related research, constructs the system framework for ocean information monitoring system and discusses the related key technologies. Finally, it studies the application of ocean information monitoring. From the research point of view, open source OpenLayers performs well, meets the general needs of marine information monitoring, and can significantly reduce development costs.
Analyzing Software Architecture Evolvability Based on Multiple Architectural Attributes Measurements

When the erosion of software architecture occurs, there is an increase in software maintenance costs, a decrease in software quality, and degradation of software performance, etc. Therefore, it is particularly crucial to find a feasible way to evaluate software architecture to detect and avoid the erosion of software architecture in a timely manner. Through empirical study, we find that software architecture (SA) evolvability is one of the critical causes that leads to the erosion of software architecture. In this paper, we propose an approach to analyze SA evolvability based on multiple architectural attribute measurements and further solve the above problems in software architecture evolution. Our approach consists of the following steps: first, according to the evolutionary process, we propose four corresponding architectural attributes; second, these attributes are measured based on basic information and dependency information; third, SA evolvability is measured based on multiple architectural attribute measurements. Our experiments are conducted on thirteen Java open-source projects to verify the effectiveness of our approach. The experimental results show that our approach can effectively reflect the SA evolvability from the following two aspects: a single attribute can reflect a specific aspect of the SA evolvability; the composition of attributes can reflect the composite SA evolvability. Furthermore, we can locate the causes of the erosion of SA by combining the measurements and the evolutionary activities, and we further propose evolutionary proposals to improve the SA evolvability.
AI-Assisted Annotator Using Reinforcement Learning

Healthcare data suffers from both noise and lack of ground truth. The cost of data increases as it is cleaned and annotated in healthcare. Unlike other data sets, medical data annotation, which is critical to accurate ground truth, requires medical domain expertise for a better patient outcome. In this work, we report on the use of reinforcement learning to mimic the decision making process of annotators for medical events, to automate annotation and labelling. The reinforcement agent learns to annotate alarm data based on annotations done by an expert. Our method shows promising results on medical alarm data sets. We trained DQN and A2C agents using the data from monitoring devices annotated by an expert. Initial results from these RL agents learning the expert annotation behavior are promising. The A2C agent performs better in terms of learning the sparse events in a given state, thereby choosing more right actions compared to DQN agent. To the best of our knowledge, this is the first reinforcement learning application for the automation of medical events annotation, which has far-reaching practical use.