Reviewerly/exHarmony huggingface dataset:
Semantic Graph Queries on Linked Data in Knowledge Graphs

Dörpinghaus, Jens Stefan, AndreasKnowledge graphs have been shown to play a central role in recent knowledge mining and discovery, big data integration, especially for connecting data from different domains. Bringing structured as well as unstructured data, e.g. from scientific literature and various data sources, into a structured, comparable format is one of the key assets. KGs are usually stored in graph databases. Although a lot of research has been done on the field of query optimization, query transformation and of course in storing and retrieving large scale knowledge graphs the field of algorithmic optimization is still a major challenge and a vital factor in using graph databases. Few researchers have addressed the problem of optimizing algorithms on large scale labeled property graphs. Here, we present two optimization approaches and compare them with a naive approach of directly querying the graph database. The aim of our work is to determine limiting factors of graph databases like Neo4j and we describe a novel solution to tackle these challenges. For this, we suggest a classification schema to differ between the complexity of a problem on a graph database. In addition, we propose several other applications for graph methods within the domain of digital humanities. Here, we show how the schema helps to understand the algorithmic challenges for semantic graph queries. We evaluate other optimization approaches on a test system containing a knowledge graph derived biomedical publication data enriched with text mining data. This dense graph has more than 71 M nodes and 850 M relationships. The results are very encouraging and—depending on the problem—we were able to show a speedup of a factor between 44 and 3839.
Actual Problems of the Formation of an Individual Methodological Style of Teacher Activity

In the light of changes in the goals of education at the present stage, there is a change in the requirements for the professional activity of the teacher, his personal and individual qualities. The article demonstrates the results of an empirical study of the individual methodological style of teacher activity. The diagnostic criteria of the methodical style are described. The interpretation of the results obtained during the analysis of teacher questionnaires is presented. Their obvious ambiguity indicates the presence of contradictions in the aspect of understanding and self-diagnosis by teachers of the individual methodological style of the teacher's activity. Examples of variable manifestations of the proposed answers are given, normative characteristics of this professional and personal indicator are given. The article is analytical in nature.
Enhancing the Data Regularization Effect with Randomly Combined Features for Object Detection

Deep convolutional neural networks (CNNs) have made significant performance improvements on object detection and several augmentation techniques have been introduced to further improve the detection performance. We investigate how applying multiple augmentation techniques simultaneously can affect the learning capability of existing techniques and how providing more abundant training backgrounds to an image can have an effect. Our experimental results demonstrate that the performance has improved by combining Random Perceptive and Random Erasing techniques to Mosaic techniques on PASCAL VOC dataset. Our results also show that the combination of the augmentation techniques is also effective in small-sized specific datasets.
A Survey on Low Latency Blockchain Architectures for Industrial Networks

Blockchain has emerged as a solution to the limitation of some centralized network problems. It has solved many of the limitation of the legacy architectures. However, Blockchain architectures has been notorious for their high latency which has made it unsuitable for low latency time critical systems which are common in the industries. In this paper we investigate the works of various authors that have tried reducing the latency of the blockchain network. This paper focuses on Blockchain in Industries. It provides a lucid insight into the recent paradigm blockchain latency enhancement. This paper also points out some of the open issues and research directions to achieve a true realtime blockchain architecture.
Movie Poster Genre Classification with Convolutional Neural Network

Not infrequently we do not have a clear plan of events to do, and chose to do an activity spontaneously. One of the activities that is usually done spontaneously is watching a movie in the cinema, and in this case, information about the movie we want to watch is most likely not known to the prospective viewer. Therefore, this paper is expected to help build a software to classify film genres based on image data input in the form of movie posters. By utilizing the intended software, potential viewers can make genre predictions to help choose the films they want to watch. The genre classification process is carried out using the Convolutional Neural Network. MobileNetv2 architecture was chosen due to the minimal computing cost of this architecture, and given computing power of hardware in form of a smart phone is relatively smaller than the computing power of hardware in the form of a computer. The final result produced by this model is the classification is in the form of generic film genres, which are divided into: romance, action, horror, fantasy and comedy.
State Estimation Algorithm of the Maneuvering Target with the Time-varying Angular Velocity

The time-varying angular velocity state estimation of a UAV turning from a large field of view to a small field of view is a maneuvering target estimation problem. Firstly, based on the curvilinear motion model of the target, two kinds of nonlinear target maneuvering models are established by the state amplification method according to two different velocity expressions, namely the Cartesian velocity constant turning maneuver (CVCT) model and the polar velocity constant turning (PVCT) model. Secondly, the CVVT model and PVVT model are re-established by assuming the time-varying angular velocity as a zero mean first order Markov (ZFOM) process. Finally, the simulation experiment is carried out in combination with UKF filtering algorithm. The experimental results show that the PVVT-UKF model has higher accuracy in angular velocity, position and velocity estimation when the angular velocity is the time-varying slope function.
Prediction Model of Automatic Sleep Staging Based on Ensemble Learning

A kind of sleep staging prediction model based on signal data preprocessing and ensemble learning method is constructed to solve the low accuracy rate of traditional automatic sleep staging. First, the preprocessing of 3,000 samples aims to eliminate the non-characteristic signal data and cement the distribution difference of data at different sleep stages; Then the soft-voting classifier prediction model is constructed by applying the ensemble learning thought that integrating the Adaboost and KNN model. The average classification accuracy rate of the method reaches 87.6% after the comparison and analysis of experimental results. Compared with other machine learning models and signal processing methods, accuracy rate of classification prediction improves greatly.
Comparison of Feature Selection for Naive Bayes Classification Method in A Case Study of The Corona virus Lockdown

Classification in sentiment analysis often involves less relevant features for the modeling process. This causes the accuracy obtained to be not optimal. Therefore, a feature selection method is needed to sort out features that have high relevance to the dataset. This research aims to compare the accuracy between three different methods. They are Naive Bayes Classification without using any feature selection, using Information Gain, and using Chi-Square feature selection. The datasets used are sentiments related to the lockdown as a policy for the Coronavirus pandemic from Twitter. Feature selection methods affected the accuracy by filtering features and sorting the most relevant features based on its algorithm. The results showed that the average accuracy of Naive Bayes without feature selection, using Information Gain, using Chi-Square based on both Indonesian and English datasets were 63.2 %, 64.2%, and 65%, respectively.
Saliency detection method for probabilistic hypergraph ranking based on local features

The saliency detection algorithm achieves object detection by acquiring salient regions in an image. We propose a saliency detection algorithm based on probabilistic hypergraph ranking of local features. Firstly, a probabilistic hypergraph structure based on spatial location is constructed based on the connectivity of background nodes, and the background saliency map is built using the ranking algorithm; Secondly, according to the clustering of foreground nodes, a probabilistic hypergraph based on feature clustering is constructed, and the foreground saliency map is realized using the ranking; Finally, the background and foreground saliency map are fused to obtain the saliency detection results. The simulation experiment results show that the detection accuracy of the algorithm we proposed is higher.
Single station positioning of identification friend or foe signal

This paper studies a method of detecting and positioning the signal of identification friend or foe(IFF) signal. On the shipboard reconnaissance platform, the azimuth and elevation angle of the target are obtained in real time through the vertical and horizontal double-sided direction-finding antenna array, and the target height is grasped through the decoding of the IFF response signal C-mode and S-mode. Finally the single-station positioning is completed through the positioning algorithm. Carrying out practical experiments, the results show that the method can effectively form the target trajectory, and the positioning accuracy can reach 3% of the target distance circle error range.
Measuring Software Development Project Performance

Adopting agile methodologies to software development processes helps software companies to sustain their growth through efficiency for long term. In the digital transformation era, Industry 4.0 as part of High-Tech Strategy 2020 for Germany involves agile principles and brings the latest technological trends in production process. The purpose of this chapter is to design a proper agile project management performance measurement model for start-up software companies. First, all key performance indicators related to agile development in the literature have been listed. Then KPIs that are provided from literature review with content analysis have been reviewed and categorized by expert opinions that were collected through in-depth interviews. Seven strategic KPIs and their data collection systems are defined and designed. Lastly, process and data collection improvements are recommended in order to sustain agile development measurement model.
A Cloud-Based Batch Processing System for Loosely-Coupled Applications

With the increased amount of data available for processing, and the increased need of processing this data, loosely-coupled batch applications have become very popular. Many batch applications require a high level of processing capacity, which leads us to the need of high performance computing infrastructures. This approach has been used for a long time, mainly for scientific purposes, and focused on the conventional environments for HPC, namely local clusters and supercomputers. The high-speed networks present in these systems are paramount for the execution of tightly-coupled scientific applications, but are wasted when executing loosely-coupled applications. Cloud infrastructures, on the other hand, provide a more appropriate infrastructure to support such loosely-coupled applications. Unfortunately, the user experience in cloud systems is completely different from that of conventional batch systems, mainly because the infrastructure needs to be deployed and subsequently released, to achieve the desired gains. In this paper we propose the architecture of a batch processing system that takes advantage of common features of cloud infrastructures to minimize cost and waiting time, while providing a user experience that is similar to conventional HPC systems.
A Structured Approach Towards Big Data Identification

Big data is a ”relative” concept. It is the combination of data, application, and platform properties. The term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">big data</i> has been used with almost every problem involving large size, real time, and heterogeneous data. However, these data attributes are not enough to identify big data by ignoring the application and platform properties for finding processing thresholds. The equivocated identification of big data can lead to an inefficient use of optimization techniques, resulting into global inefficiency, reduced system performance, increasing power consumption, requiring greater effort on the part of the programming team, and misallocation of the hardware resources required for the task. In this regard, a structured approach has been presented for identification of big data. The approach is based on three equations that categorize the Volume, Velocity, and Variety characteristics by relating data, application, and platform properties. The 3Vs identification is necessary for enabling the relevant optimization techniques. In addition to 3Vs identification, it is required to discriminate whether the big data is due to 1V, 2Vs or 3Vs, as the involvement of more Vs increases the problem complexity. In this regard, the classification of big data into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strong</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">moderate</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weak</i> level has been proposed . To evaluate the proposed methods, a set of well-known applications have been experimented and categorized, depicting a saving of up to 58% main memory and 44% disk reads, as well as prescribing lower clock rate, lesser cores, sequential programming, and non adaptive processing & storage formats. Moreover, four case studies reported as big data have been analyzed according to the proposed system. The proposed method is able to categorize two case studies as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weak low</i> big data presenting only volume, the third case is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weak medium</i> due to velocity, whereas in the fourth case no V is involved. Also, the proposed equations reduce the computation and human resources up to 75% of Spark cluster execution. In this manner, the proposed work can save the unnecessary investments by relevant prescriptions. Furthermore, the proposed equations can be integrated into different tools for assisting selective offloading of big data workloads to appropriate software and hardware solutions.
Implementation of an Algorithm for Automatic Segmentation of Texts based on Stylometric Analysis

Segmenting the text into larger or smaller parts is useful for natural language processing. Starting from the initial segmentation of the literary text into three segments, (made by the author or a supposed expert reader), we developed an algorithm that segments the document at the page level, (a page being composed by a number of phrases), while keeping the number of initial sections (as they appear in the printed document). In this paper we present a program / software application for stylometric analysis, including the computation of some distances (Euclidean, Cosine) and other measurements (Bray - Curtis Dissimilarity, Correlation), for the obtained segments, in order to find out which is the best segmentation of the text. The software we developed has the purpose of exemplifying the algorithm and it matters little in what language or form it is made.
Multi-view SAR image registration based on feature extraction

Aiming at the problem of SAR image interpretation of the same target from different angles, the performance of registration algorithms based on different feature operators on multi angle SAR images is compared. Firstly, this paper introduces two kinds of universal registration methods. Combined with the characteristics of multi view SAR images, feature-based matching methods are selected for research; Secondly, sift, brisk and surf features are used as descriptors for feature selection and extraction, and the feature points extracted from SAR images with different viewing angles are matched by Euclidean distance matching method and random sampling consistency algorithm (RANSAC); Finally, the affine transformation model is used to realize the image transformation, and the registration accuracy of the three operators is quantitatively analyzed by calculating the maximum mean square error (RMSE). The results show that the registration algorithm based on surf feature has higher tolerance to scene complexity and stronger robustness,; Brisk based feature is the second; The registration algorithm based on SIFT features is most easily affected by the scene, and the matching effect is the worst.
M-learning and the EFL classroom

Technology is definitely ubiquitous. It is what keeps us constantly connected to different people on a daily basis. Since the advent of smartphones, technology has also changed the way we perform many of our everyday tasks, including the ones at the school local context. It also reinvented the way teenagers learn and behave in the classroom, EFL institutions included. This article, then, aims to present and discuss the role of mobile learning in the teaching of English-speaking skills and the concept of Bring Your Own Device. Additionally, based on the concept of Partnering Pedagogy (PRENSKY, 2012), it suggests a lesson plan for regular school pre-teens to show how teachers can give the first step toward the implementation of mobile devices in their educational practices. The lesson, which can be applied either to online or face-to-face contexts, showed how teenage students become more willing to participate in speaking activities. Finally, the text brings to light some reflection on the integration of mobile technologies in EFL learning practices, emphasizing the essence of delivering high quality learning lessons rather than giving central importance to technology per se.
Vehicle Target Detection and Tracking in Complicated Scenes of Infrared Videos

Optical image automatic recognition is widely used in the fields of the disaster relief, public security maintenance, biological disaster prevention and medical treatment, etc. It is common to accomplish the recognition task with Hungarian Algorithm and Kalman Filter Method over objects detected by deep neural networks. However, this method has large errors under the conditions where there is camera movement, image rotation or target occlusion. This paper aims to detect the vehicle targets and obtain their coordinates from the infrared aerial video data with two stages: target detection and target tracking. In the target detection stage, YOLOv5 is used as the backbone network for model training. Then, in the target tracking stage, the following methods are designed and implemented based on the Hungarian Algorithm and Kalman Filter Method. First, data enhancement is designed to solve the problem of frame rotation and disturbance. Second, the appearance matching and variance verification methods are proposed to deal with the failure in prediction of target positions caused by camera movement. Third, the hidden frame temporary storage method is proposed to solve the occlusion problem, whose results are further confirmed by appearance comparison. Finally, in the output stage, the brightest point of five sample points is selected as the target point. With these methods, the missing detection and false alarm rates decrease, and the trace of the same target in the same video can be obtained even in complicated scenes. In the experiments, compared with the baseline algorithm, the missing detection rate of the proposed algorithm in the validation set is decreased by approximately 26%, and the track continuity score is improved by approximately 8.5%. Meanwhile, the size of the final prediction model is about 100MB while the results can be obtained within several seconds, which can meet the demands of real-time deployment of mobile platforms.
Method of Hierarchical Protection of Biometric Information

This paper contains analysis of methods of increasing the information protection from unauthorized access using a multifactor authentication algorithm; figuring out the best, most efficient and secure method of scanning biometric data; development of a method to store and compare a candidate’s and existisng system user’s information in steganographic space. The urgency of the work is confirmed by the need to increase information security of special infocommunication systems with the help of biometric information and protection of this information from intruders by means of steganographic transformation.
Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification for Lung Cancer Prediction

One of the major causes of cancer-related mortality worldwide is lung tumors. An earlier prediction of lung tumors is crucial since it may severely increase the death rates. For this reason, genomic profiles have been considered in many advanced microarray technology schemes. Amongst, an Improved Dragonfly optimization Algorithm (IDA) with Boosted Weighted Optimized Neural Network Ensemble Classification (BWONNEC) has been developed which extracts most suitable features and fine-tunes the weights related to the ensemble neural network classifiers. But, its major limitations are the number of learning factors in neural network and computational difficulty. Therefore in this article, a Boosted Weighted Optimized Convolutional Neural Network Ensemble Classification (BWOCNNEC) algorithm is proposed to lessen the number of learning factors and computation cost of neural network. In this algorithm, the boosting weights are combined into the CNN depending on the least square fitness value. Then, the novel weight values are assigned to the features extracted by the IDA. Moreover, these weight values and the chosen features are processed in different CNN structures within the boosted classifier. Further, the best CNN structure in each iteration i.e., CNNs having the least weighted loss is selected and ensemble to predict and diagnose the lung tumors effectively. Finally, the investigational outcomes exhibit that the IDA-BWOCNNEC achieves better prediction efficiency than the existing algorithms.
Iteratively Generated Adversarial Perturbation for Audio Stego Post-processing

Recent studies have shown that adversarial examples can easily deceive neural networks. But how to ensure the accuracy of extraction while introducing perturbations to steganography is a major difficulty. In this paper, we propose a method of iterative adversarial stego post-processing model called IA-SPP that can generate enhanced post-stego audio to resist steganalysis networks and the SPL of adversarial perturbations is restricted. The model decomposes the perturbation to the point level and updates point-wise perturbations iteratively by the large-absolute-gradient-first rule. The enhanced post-stego obtained by adding the stego and the adversarial perturbation has a high probability of being judged as a cover by the target network. In particular, We further considered how to simultaneously fight against multiple networks. The extensive experiments on the TIMIT show that the proposed model generalizes well across different steganography methods.