Reviewerly/exHarmony huggingface dataset:
A horizontal partitioning‐based method for frequent pattern mining in transport timetable

Abstract Analysing transport timetables is an important task, as it brings the opportunity to discover which routes commonly lead to delays. Frequent pattern mining is a technique used to support such type of discovery. However, functional dependencies are intrinsic properties present in timetables, particularly related to attributes derived from the origin–destination matrix. Such functional dependencies compromise the search for patterns in timetables in both the number of association rules (ARs) generated and the computational cost. Several of these ARs refer to the same information. Redundancy removal techniques can reduce the number of ARs. However, these techniques are designed to be used after mining finishes, which increases the computational cost of finding useful ARs. This work presents timetable pattern mining (T‐mine), a novel method for frequent pattern mining that improves knowledge discovery in timetables. We evaluated T‐mine using Brazilian Flight Data and compared T‐mine with the direct application of frequent pattern mining approaches with and without functional dependencies. Our experiments indicate that T‐mine is about one order magnitude faster than other methods with functional dependencies.
DocTable: Table-Oriented Interactive Machine Learning for Text Corpora

People working today with text data in any domain must develop understanding based on more documents than they can directly read. Tools exist that take advantage of visual analytics for a variety of text data tasks, including for technical experts, particularly in certain domains like intelligence and law. There is a missed opportunity to apply human-in-the-loop (HIL) machine learning to assist a general audience with text analysis tasks over large corpora that are difficult with visualization alone. In this paper, we propose an alternate approach to efficient sensemaking over document corpora, designed for users without technical expertise or training. We use a table-based interface, where the primary means of providing feedback to the machine is interactions with a data table view. This format is familiar to many, and augments the ability to tag documents into user-defined categories with machine learning that automatically predicts categories for additional documents. Marking only a few documents enables the machine learner to suggest automatic labels for the rest (with uncertainty scores for the predictions), and to reorder the table to reflect an integrated mix of the category models. Finally, interactive, force-directed layouts of topics and documents based on the models assist in sensemaking for workflow that scales beyond the practical limits of a table. To validate the technique, we present a prototype human-in-the-loop machine learning system, DocTable. We evaluate this technique with machine learning experiments that demonstrate the quality of the backend’s response to expected user inputs, a usage scenario to demonstrate its use on real world data, and a case study of expert feedback from our journalist collaborators.
Calibration of Inverse Perspective Mapping from Different Road Surface Images

Inverse perspective mapping (IPM) is a geometrical image processing technique to remove the perspective distortion in an image and transform the image as if seen from the bird's eye view (BEV). IPM can be used as one of the key features in the advanced driver assistance system (ADAS) in vehicles. IPM requires camera calibration in order to extract intrinsic and extrinsic parameters of the camera. However, IPM assumes flat road surfaces. The accuracy of the IPM decreases if these parameters are constant when dealing with variety of road surfaces. This paper presents an implementation of camera calibration technique with incorporation of selected point features from different images containing different road surfaces. Results show that the generated IPM images calibrated with different road surfaces, such as straight, hilly, and arterials (bumpy and zebra crossing) roads, produces enhanced visual appearance, less blurry and less pixelated.
Рhenomenon of states psychological self-regulation of teacher’s life activities

Based on the structural and system analysis, the article considers the need for self-regulation for teachers, substantiates and provides statistics on the negative psychophysical states of educators, which they acquire as a result of professional activity. One hundred teachers were interviewed in order to define the mastering level of their self-regulation and the need to educate teachers in the theory and practice of these aspects. Based on the data obtained, it is obvious that educators need education on this issue, and the level of assimilation of this information and skills still needs to be better. The necessity of developming the motivational system of self-regulation mastering by teachers through the following components of this motivation is substantiated in the article: it can be made through a condition of psychophysical health; because of the culturological need of educators and because of the need not to expose others, in particular children, according to the negatives of their emotional deviations; due to the need for pedagogical competence, which has the competence of the psychological spectrum, which determines the professional status; because of the need for the teacher to be a proactive person. Since teachers are mostly motivated to learn self-regulation mainly because of health and the effectiveness of such motivation is limited, we conclude that there should be several motivational areas, where each specialist will find what he needs. The article analyzes and systematizes theoretical principles of self-regulation of mental states of the personality in order to bring this information in an adaptive form for teachers. In this context, the periodization is made considering the research on self-regulation by scientists from foreign and national physiological and psychological schools. The meaningful information of theoretical bases of regulation is structured and systematized. The necessity of a regular system of education on the theory and practice of self-regulation is substantiated and in this perspective the article gives the basics of such education. In our publication the authors offer the term, forms of work and its content. Conclusions are made on improving the awareness of educators of the practical acquisition of knowledge, skills and abilities on the problem. Key words: self-regulation of personality mental states; motivation, mastery of self-regulation of mental states by the teacher; systematization of theoretical principles of self-regulation; basics of the teacher education project on self-regulation issues.
Self-supervised Learning Aided Blind Stitched Panoramic Image Quality Assessment

For deep learning based stitched panoramic image quality assessment, it is costly to train a network model using a large-scale dataset with human annotation. Moreover, it is practical to evaluate image quality without reference images (i.e., Blind Stitched Image Quality Assessment, BSIQA). Selfsupervised learning (SSL) avoids the use of annotated large-scale training dataset while few BSIQA schemes of panoramic stitched images using SSL have been proposed. Thus this paper proposes a SSL aided BSIQA scheme for panoramas. The first training phase learns from a large-scale dataset, where SSL based image colorization is incorporated into supervised learning based classification for learning generalized visual representations. With transferred knowledge from the first phase, the second training phase learns from a small dataset with subjective scores for the task of BSIQA. Test results show that SSL indeed improves prediction accuracy of BSIQA of panoramas on ISIQA dataset.
ARSci: The Framework for building Augmented Reality in Scientific Learning

Augmented Reality (AR) has been widely used in science education to motivate students and improve their learning outcomes in recent years. Although many pieces of research have been conducted on the effectiveness of using AR technologies in science education, they still have limited analysis in embedding the learning theory. This will generate a gap between science educators and AR technologies on applying the appropriate AR in education. This paper proposes a framework, called ARSci, for guiding the design of AR technology for scientific learning by revising the educational theory and state-of-the-art AR technologies. We hope to enlighten educators and researchers on developing AR platforms and applying learning theory for science education with AR technology.
A 3D Space Violation Detection Method of Substations Based on the Deep Neural Network

At present, smart grid construction has entered a new stage of the comprehensive and rapid development. In the field of sub stations, according to the requirements of the development plan formulated by the China Southern Power Grid Company Limited, the application of smart substations is imperative when the operation mode of traditional substations cannot meet the current development needs. Aiming at the problem of low efficiencies in the detection of violations and great harms to substations, this paper proposes a substation violation detection method based on a deep neural network from three-dimensional space. Based on the characteristic information of different violations, the characteristic expression of violations is established. Furthermore, an intelligent detection scheme for violations is established, in which the deep neural network is utilized to train samples. Effectivities and accuracies of the proposed intelligent detection scheme are verified by simulation experiments, and the results show that the algorithm can achieve good violation detection precisions.
Raptor

Stream processing applications are becoming increasingly important in areas such as IoT, video analytics and social media. As a result, developers and operators must meet stringent time-to-market and scale requirements before bringing them to production. Unfortunately, testing a networked stream processing system is currently a cumbersome process that usually requires an expensive testbed and deep expertise on both networking and distributed systems. In this poster, we present Raptor, a tool for the fast prototyping of large-scale networked stream processing applications. Raptor builds on Mininet and Apache Kafka, two widely adopted platforms, to enable stakeholders to easily test their solutions under various operational conditions. Through a reasonably large setup (20 nodes) running on a single server, we show how unbalanced Kafka's leader selection algorithm can be and its implications on the overall system's throughput. We envision this work can help paving the way for more reproducible research in the stream processing domain, currently a first-class network application.
Deterministic Approach to Information Queries Parallel Batch Processing Modelling and Evaluating

A deterministic analytical approach to modeling a batch processing of similar information queries in a multithreaded server on a single processor is considered. It is shown that the proposed approach allows getting an idea of the behavior and characteristics of the software server when processing queries in parallel mode. The features of the application of the model in a multi-core environment are described.
Locality-aware Thread Block Design in Single and Multi-GPU Graph Processing

Graphics Processing Unit (GPU) has been adopted to process graphs effectively. Recently, multi-GPU systems are also exploited for greater performance boost. To process graphs on multiple GPUs in parallel, input graphs should be partitioned into parts using partitioning schemes. The partitioning schemes can impact the communication overhead, locality of memory accesses, and further improve the overall performance. We found that both intra-GPU data sharing and inter-GPU communication can be summarized as inter-TB communication. Based on this key idea, we propose a new graph partitioning scheme by redefining the input graph as a TB Graph with calculated vertex and edge weights, and then partition it to reduce intra &#x0026; inter-GPU communication overhead and improve the locality at the granularity of Thread Blocks (TB). We also propose to develop a partitioning and mapping scheme for heterogeneous architectures including physical links with different bandwidths. The experimental results on graph partitioning show that our scheme is effective to improve the overall performance of the Breadth First Search (BFS) by up to 33%.
SHIM: Semantic Hierarchical clustering with Interactive Machine learning

Clustering is widely used, and there are available techniques for using semantic interactions to incorporate human feedback. Hierarchical agglomerative clustering produces complete substructure, offering rich possibilities for user interactions. However, currently available human-in-the-loop techniques miss out on the advantages of hierarchical agglomerative clustering. In this paper, we present a technique, SHIM, for semantic, interactive hierarchical clustering that helps users understand their data through exploring and modifying clusters and cluster substructure with semantic interactions and automatic descriptive rules. The proposed solution includes a compact visualization of hierarchical clustering customized for this context and connected to a table view that shows data detail and descriptive rules simultaneously. We assess our design decision and usability with expert feedback, and use simulations to test whether the metric learning method will improve a model with limited interactions.
Fine-grained Kinship Detection for Facebook Users based on Wall Contents

This paper investigates whether it is possible to automatically detect fine-grained kinship (not to detect its existence but to detect the type of kinship like child, father, grandfather, and so on) between two Facebook users or not. To do so, we present and employ a lexicon-based approach that completely depends on the wall contents of users. To the best of our knowledge, this is the first study towards kinship detection for both the type of input data (i.e., free OSN text) and the language (i.e., Turkish). We perform our experiments on a crawled snapshot of public Facebook data collected from accounts of users in Turkey. Our results are promising and show that a content-based approach can be a good starting point for future works even though it has some challenges.
Research on Electronic Data Forensics Based on RAM

Electronic data forensics is the process of obtaining, preserving, analyzing and presenting evidence for computer invasion, destruction, fraud, attack and other criminal acts. Some key digital evidence of cybercrime exists in physical memory or stored in page exchange files, so memory forensics is an important part of electronic data forensics. This paper studies RAM-based electronic data forensics with the use of the memory forensics tool Volatility. By obtaining the memory data of real equipment, cloud computing, virtual machine or virtual devices, performing the extraction and analysis of process information, registry, network connection, strings, access records and other contents, and extracting the digital evidence related to network attack or network crime.
Research on Evaluation Method of UAV's Contribution Degree to Army Aviation Combat System Based on Simulation Deduction

In order to support the application and development of unmanned aerial vehicles(UAVs), it is necessary to evaluate the system contribution rate of cooperative UAVs in combat system. However, the dynamic confrontation process of system operations was always overlooked in traditional evaluation methods. In this paper, a method of evaluating UAV contribution rate in the combat mission based on simulation deduction is proposed, which can test the improvement degree of UAV's operational effectiveness to combat system under the practical condition of attack and defense. The software AnyLogic was used to build simulation environment and agent model under the specific case of helicopters attacking armored targets. After 50 rounds of simulation, the results can be concluded that: the battle damage ratio of scheme with UAVs was 11.933:1, which was 18% higher than that without UAVs. The simulation result is consistent with the actual situation, and the reference value and effectiveness of the method are verified.
Factors that Affect Data Gathered Using Interviews for Requirements Gathering

Building software does not rely heavily on the technical side, i.e., coding. There are initial steps that need to be done before constructing the software, that is requirements gathering. Requirements gathering is frequently called as requirements elicitation because the good requirements cannot just be collected from the stakeholders but must be discovered. Therefore, gathering requirements should be done using interviews. While it seems that interviews can be done easily, the interview process can massively impact the result of the software by discovering hidden requirements. This study would like to find out the factors that can affect the data gathered using the interview method by conducting a systematic literature review (SLR). Based on our study, we can conclude that the main factor in the success of an interview relies on the interviewer or the analyst. Since interviewing is a social act, their communication skills will play a big role. Not only that, but the technical standpoint on the interviewing is also considered, with choosing the right structure of the interview and basic knowledge on the problem at hand.
Low-light-level image super-resolution reconstruction via deep learning network

Wide ﬁeld-of-view (FOV) and high-resolution (HR) imaging systems have become indispensable information acquisition equipment in many applications, such as video surveillance, target detection and remotely sensed imagery. However, due to the constraints of spatial sampling and detector processing level, the ability of remote sensing to obtain high spatial resolution is limited, especially in the wide FOV imaging. To solve these problems, we propose a multi-scale feature extraction (MSFE) network to realize super-resolution imaging in a low-light-level (LLL) environment. In order to perform data fusion and information extraction for low resolution (LR) images, the network extracts high-frequency detail information from diﬀerent dimensions by combining the channel attention mechanism module and skip connection module. In this way, redundant low-frequency signals can pass through the network tail-ends, furthermore, the more important high-frequency components calculation can be focused. The qualitative and quantitative analysis results show that the proposed method achieves the most advanced performance compared with other state-of-the-art methods, which shows the superiority of the design framework and the eﬀectiveness of presenting modules.
Discharges Pattern Recognition on Uniformly Polluted Glass Surface Using an MLPNN Classifier

this paper propose an approach for electrical discharges recognition using an MLPNN based on an image-processing tool. In fact, our proposed algorithm aims to detect and monitor the apparition of electrical discharges, from the apparition of smallest discharge to the flashover arc, through analysis of colors on the recorded images. For this purpose, first, we perform the acquisition of images using a High Definition (HD) camera in laboratory. These images are collected on a plan glass insulator model, where we dispose the polluted solution uniformly (using a spray, then a sieve). Second, we proceed to encode these images in RGB system. Therefore, each image has a corresponding feature vector that informs about the quantity of six colors that are contained in each discharge image. Third, an MLPNN algorithm is elaborated to detect the evolution of the discharges of the flashover phenomenon. Our algorithm is using the feature vector of each image as input, while the output of this classification algorithm is a class (from 1 to 5) indicating the stage of discharges on the plan insulating surface.
Machine translation post-editing – Current situation and the future of translator training in Bulgaria

The current paper is an attempt to analyse the situation on the market for specialised translation services, and more precisely for Machine Translation in Bulgaria. It provides an overview of some of the generic MT systems and analyses the results coming from the translation of two types of text. The aim of the paper is to raise awareness about the results of Neural Machine Translation and to reveal the need for MT post-editing courses.
An overview on Multimedia Big Data Analytics

today, multimedia database management is the most important for the new generation and specifically in big data issues for instant saving, calling and modifying operations for many types of documents as text, video and audio. A huge number of previous researches have been done in the multimedia subject, such as index and storage of multimedia data. However, very few resources on this research work that how to manage a large size of data, as future work. In this paper, suggestion of new method that describes the general model of any kinds of the multimedia database management system, that's lead to a decrease of seeking time/ increase speed time when we have to find for particular data type such as portable data format (PDF). Finally, this study concludes with potential future work for applying the current model to improve the result of usability, management the accessibility and effectiveness of multimedia in big data
Packet loss rate monitoring model of IoT based on differential evolution algorithm

In order to overcome the problems of high energy consumption and low execution efficiency of traditional Internet of things (IoT) packet loss rate monitoring model, a new packet loss rate monitoring model based on differential evolution algorithm is proposed. The similarity between each data point in the data space of the Internet of things is set as the data gravity. On the basis of the data gravity, combined with the law of gravity in the data space, the gravity of different data is calculated. At the same time, the size of the data gravity is compared, and the data are classified. Through the classification results, the packet loss rate monitoring model of the Internet of things is established. Differential evolution algorithm is used to solve the model to obtain the best monitoring scheme to ensure the security of network data transmission. The experimental results show that the proposed model can effectively reduce the data acquisition overhead and energy consumption, and improve the execution efficiency of the model. The maximum monitoring efficiency is 99.74%.