Reviewerly/exHarmony huggingface dataset:
A Novel Document Generation Process for Topic Detection Based on Hierarchical Latent Tree Models

We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top. For each document, the generative process first samples values for the latent variables layer by layer via logic sampling, then draws relative frequencies for the words conditioned on the values of the latent variables, and finally generates words for the document using the relative word frequencies. The motivation for this work is to take word counts into consideration with HLTMs. In comparison with LDA-based hierarchical document generation processes, the new process achieves drastically better model fit with much fewer parameters. It also yields more meaningful topics and topic hierarchies. It is the new state-of-the-art for the hierarchical topic detection.
SSDPose: A Single Shot Deep Pose Estimation and Analysis

Human posture estimation is a fundamental challenge in computer vision research. This is a task that has received substantial interest due to the importance of evaluating the human performance in several disciplines. The ultimate goal for the vision-based pose estimation task is the markerless accurate prediction of necessary postural information. This paper proposes a single shot deep human posture detection and estimation network. The proposed SSDPose architecture increments standard object detection networks to feature posture estimation. SSDPose is an end-to-end trainable model that detects and estimates the body posture from a single image. Further, our network has been trained to predict joint angles which are essential information for several domains such as biomechanic and ergonomic posture analysis. The reference joint angles have been generated using motion capture sequences and a novel inverse kinematics method. Experimental results demonstrate that SSDPose effectively detects and estimates the posture by achieving person mean average precision (mAP) of 98.2%, an average joint angles MAE of 3.16 ± 1.23 deg and an RMSE of 4.22 ± 1.73 deg at up to 30 FPS.
Optimal Hybrid Path Planning Considering Dynamic and Static Characteristics of Intelligent Vehicles

Aiming at the global path planning of intelligent vehicles, an optimal hybrid path planning algorithm considering the dynamic and static characteristics of intelligent vehicles is proposed. On the grid map with known static information of the environment, the improved A* algorithm is used for global path planning, and the obstacles in the path are expanded according to the static characteristics of the intelligent vehicle itself. Combined with the dynamic characteristics of the intelligent vehicles, dynamic window approach is used to carry out the local obstacle avoidance and path planning of the vehicle according to the unknown and varied environmental information around the vehicle. On this basis, the key turning point in the global path is used as the sub-target point correction of Dynamic Window Approach (DWA). The simulation results show that the proposed method can be used to avoid dynamic and static obstacles by guiding the vehicle to the target ending. Additionally, the dynamic constraints of the vehicle are satisfied during the journey without collision with the road boundary, which ensures the stability and safety of the vehicle.
Risk Analysis of Dutch Healthcare Company Information System

Abstract The purpose of this research is to assure the performance and quality of healthcare information system security from the Dutch Company, which is located in Rotterdam, Netherland. The research applies COBIT 5 to audit the existing information security management system (ISMS) based on ISO 27001 and NEN 7510-2 2017 toolkit to audit the information system security for healthcare-specific issue. The audit is applied in order to identify any risk that might come during the ISMS implementation. On the other hand the result will be used as an improvement for the successor version of ISMS which compatible with the ISO 27001 and NEN 7510-2. The research was composed by applying qualitative method that consists of observation of the activity of the company and reviews the existing ISMS-related documents. As a result of this audit, the company ISMS has an achievement from NEN 7510-2:2017 audit result that has a score for 92.86 % on security management section. For the COBIT 5 result, the audit yield a result that consist of two “Established Process” level on APO13 and MEA02 and “Predictable Process” level on DSS 05. The result indicates a good result and encourages the company to improve their ISMS for next period.
Applying Machine Learning Classification to Determining Outliers in Effort for Embedded Software Development Projects

This paper discusses the effect of under-sampling to determine outliers in terms of the amount of effort, which is the number of people hours needed to complete a task, in embedded software development projects. Estimating the amount of effort for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the amount of effort. We have also attempted to detect outliers in the amount of effort by using an ANN and an SVM. However, the accuracy of the classifications was insufficient due to a small number of outliers. This problem is called data imbalance, and occurs in most of machine learning methods. In order to avoid this problem, we explores rebalancing methods with k-means cluster-based under-sampling. The purpose of the methods are to improve the proportion of outliers that are correctly identified, while keeping the other classification criteria high. Evaluation experiments are carried out to compare the prediction accuracy of the methods with k-means under-sampling, with random under-sampling and without under-sampling using 10-fold cross-validation. The results show that the methods with under-sampling have higher sensitivity and lower precision than these of the methods without under-sampling. The results show that the proposed methods can improve the accuracy to determine outliers; however they pick up excess samples as outliers.
Design of Multimedia Interactive Courseware Based on Flash

Abstract As an advanced computer-assisted instruction teaching methods, its flexibility, intuitive, real-time, three-dimensional advantages, more and more by the majority of teachers and students of all ages. To make good use of computer-assisted instruction in this modern teaching methods, the key is to design and manufacture to meet the teaching requirements of multimedia courseware. Multimedia courseware is used to assist teachers in teaching tools, creative people based on their creativity, starting with general information on the classification of the organization, then the text, images, animation, audio, video and other media clips in both time and space integration, so that they blend and give them the interactive features to produce a variety of colourful multimedia educational software.
Mining Integrated Sequential Patterns From Multiple Databases

Existing work on multiple databases (MDBs) sequential pattern mining cannot mine frequent sequences to answer exact and historical queries from MDBs having different table structures. This article proposes the transaction id frequent sequence pattern (TidFSeq) algorithm to handle the difficult problem of mining frequent sequences from diverse MDBs. The TidFSeq algorithm transforms candidate 1-sequences to get transaction subsequences where candidate 1-sequences occurred as (1-sequence, itssubsequenceidlist) tuple or (1-sequence, position id list). Subsequent frequent i-sequences are computed using the counts of the sequence ids in each candidate i-sequence position id list tuples. An extended version of the general sequential pattern (GSP)-like candidate generates and a frequency count approach is used for computing supports of itemset (I-step) and separate (S-step) sequences without repeated database scans but with transaction ids. Generated patterns answer complex queries from MDBs. The TidFSeq algorithm has a faster processing time than existing algorithms.
CNN-Based Erratic Cigarette Code Recognition

Cigarette code is a string printed on the wrapper of cigarette packet as a basis of distinguishing illegal sales for tobacco administration. In general, the code is excerpted and entered to administration system manually during on-site inspection, which is quite time-consuming and laborious. In this paper, we propose a new solution based on convolutional neural network for intelligent transcription. Our recognition method is composed of four components: detection, identification, alignment, and regularization. First of all, the detection component fine-tunes an end-to-end detection network to obtain the bounding box region of cigarette code. Then the identification component constructs an optimized CNN architecture to recognize each character in the region of cigarette code. Meanwhile the alignment component trains a CPM-based network to estimate the positions of all characters including some missing characters. Finally, the regularization component develops a matching algorithm to produce a regularized result with all characters. The experimental results demonstrate that our proposed method can perform a better, faster and more labor-saving cigarette code transcription process.
Photovoltaic Technical Potential in Republic of Moldova

Nowadays, in the world and especially in European Union, the main concern is sustainable development linked to the use of Renewable Energy Sources (RES) and climate change mitigation. At the same time, in many countries there are being raised the idea of transition to 100% RES. The purpose of this paper is to evaluate the technical potential of the photovoltaic renewable energy sources available in Republic of Moldova with scope to contribute to achievement the goals of the Energy Strategy until 2030, the main state document in the field of energy. The Republic of Moldova is an Associate Membership of the EU and a Membership of Energy Community. These facts determines the country’s development direction and policies of its energy sector. The goals of this Strategy include diversification of energy sources, inclusively RES, ensuring the share of annual electricity production from RES at least 15% (about 1,274 TWh) in 2030 and reducing greenhouse gases emissions by 25% in 2020. Republic of Moldova has an insignificant amount of fossil energy resources (coal, natural gas and oil). At the same time, the renewable electricity production is insignificant and the economy entirely depends on imports of fossil energy resources from neighbor countries. Thus, there is considerable delay in achieving Strategy’s goals.
Image Super-Resolution Improved by Edge Information

As well as in other knowledge domains, deep learning techniques have revolutionized the development of image super-resolution approaches. State-of-the-art algorithms for this problem have employed convolutional neural networks in residual architectures with a number of layers and generic loss functions, such as L1 and Peak Signal-to-Noise Ratio (PSNR). These frameworks (architectures + loss functions) are generic and do not address the main characteristics of an image for human visual perception (luminance, contrast, and structure) resulting in better images, however, with noise mainly at the edges. In this work, we present an edge enhanced super-resolution (EESR) method using a novel residual neural network with focus on image edges and a mix of loss functions that use PSNR, L1, Multiple-Scale Structural Similarity (MS-SSIM), and a new loss function based on the pencil sketch technique. As main contribution, the proposed framework aims to leverage the limits of image super-resolution and presents an improvement of the results in terms of the SSIM metric and achieving competitive results for the PSNR metric.
Multipath Based Correlation Filter for Visual Object Tracking

This paper presents a new correlation filter based visual object tracking method to improve the accuracy and robustness of trackers. Most of the current correlation filter based tracking methods often suffer in situations such as fast object motion, the presence of similar objects, partial or full occlusion. One of the reasons for that is that object localization is performed by selecting only a single location at each frame (greedy search technique). Instead of choosing a single position, the multipath based tracking method considers multiple locations in each frame to localize object position accurately. In this paper, the multipath based tracking method is applied to improve the performance of the efficient convolution operator with handcrafted features (ECO-HC), which is a top performing tracker in many visual tracking datasets. We have performed comprehensive experiments using our efficient convolution operator with multipath (ECO-MPT) tracker on UAV123@10fps and UAV20L datasets. We have shown that our tracker outperforms most of the state-of-art trackers in all those benchmark datasets.
On the Use of Commit Messages to Support the Creation of Datasets for Fault Prediction: An Empirical Assessment

When committing source code in a Version Control System (VCS) as a consequence of a bug fixing task, a good practice should consist in writing a message that shortly explains how the bug has been fixed. In this paper, we empirically assess a heuristic based on the presence of keywords in developers' commit messages, while uploading their changes to source code classes on GitHub. A class is fault-prone if it is involved in a commit, whose message contains keywords that are related to bug-fixing tasks. After having analyzed all the commits between two consecutive releases of a software project, a class is labeled with the number of how many bug-fixing commits in which it was involved. We assessed whether our heuristic can be used to support the human annotating activity, by comparing the number of bugs provided by the popularity dataset of the PROMISE research repository with the one estimated through such heuristic. We considered the identification of (i) faulty source code classes (i.e., a class is faulty if it contains at least one fault) and (ii) the number of faults in source code classes. The average accuracy (i.e., the number of classes correctly identified as either faulty or not over the total number of classes) of our approach is 71%. As for the number of faults in source code classes, the concordance (i.e., the number of classes on which the heuristic correctly identifies the number of faults over the total number of classes) is 62% on average. Our proposal might support, but not replace, annotating activities when building datasets for fault prediction.
The Effects of Virtual Learning Environment on High School Students’ English Learning Performance and Attitude

The purpose of this study is to explore the effect of virtual multimedia teaching on high school students’ English academic performance and learning attitude. This study especially focuses on using virtual learning environments to assist students to language learning. This study developed a virtual learning environment (VLE) to help foreign language learners learn English. The use of VLE provides learners with the opportunity to develop a sense of community, like a learning community. In this study, 120 freshmen from a high school in Kaohsiung city were studied. The 120 students were divided into two groups. One was Experimental class; the other is Control class. This course is based on the English proficiency of the students. The results show that VLE can help students improve their English learning performance and attitude. Students in this study considered VLE to be a meaningful learning method, which provided them with more opportunities to interact with their classmates in online group work.
Information Technology for Accounting Application

Abstract This study aims to determine the importance of accounting with accurate applications, namely as a tool to determine the policies of business people. The method used in writing scientific papers was also a descriptive qualitative method using data related to the topic. the results of this study find that all types of professions and fields which certainly have close relations with the world of accounting. With this application, we can exercise control or supervision related to company activities, including by making appropriate decisions from the application of this accounting. Accounting courses help in separating personal and business money. Have a clear payment system. From the results of the explanation above, it can be concluded that this accounting system will be able to assist businesses in determining the continuity of their business and suggest the application of accounting to help business success that is on the way because accounting has contributed greatly to advancing the company and easier by using an accurate application
A Multi-object Detection Method Based on Connected Vehicles

Nowadays, with internet of vehicle developing, more and more research institute begin to research intelligent transportation systems. Vehicle-road collaborative system is a prominent one of these systems. Its main function is to percept traffic situation. Using image recognition technology is one of the methods. The advantages of this method are low cost, high data-correcting rate, and small interference to traffic flow. Traditional image recognition algorithms always have problems with high-processing time and low accuracy, such as HOG and DPM. They are not suitable to monitor real-time traffic videos of numerous image frames. In this paper, the structure of convolutional neural network is improved, different from original YOLOv3 algorithms. Compared with original YOLOv3 algorithm, the algorithm in this paper can not only realize multi-object detection, but also consume time and recognize more accurate. Moreover, this paper constructs a special identification data set of common traffic participants (pedestrians, cars, buses, etc.) to monitor traffic flow, basing on INRIA and KITTI data sets. Moreover, this paper collects images of commonly-seen objects at traffic intersections to test the performance of convolutional neural network structure of this paper. At the end of this paper, the performance of the proposed algorithm is verified, based on the real-time monitoring video of traffic intersections. According to the results, for common traffic participants, the mean average precise is 13.2% higher than that of original YOLOv3 algorithm, and detection time is reduced by 7.8%.
Using spaced education to deliver clinical information to medical residents: A mixed methods pilot study

Spaced education (SE) is a learning strategy that can improve long-term knowledge retention. Inspired by the concept of SE, we conducted a mixed methods study of a smartphone application (app) as a platform of SE. Objectives were to: (phase 1 quantitative) estimate the extent to which weekly alerts on the app can stimulate medical residents to visit the app, (connection of phases) identify participants for a second qualitative phase, and (phase 2 qualitative) describe factors, from the resident perspective, which influence sustainable participation in SE, and describe strategies for improvement of the app. Methodology and methods: phase-1 design was pre-experimental, phase-2 design was qualitative descriptive (deductive-inductive thematic analysis). Results: We observed a stimulating effect of weekly alerts for the first two months of the one-year study. Per participant, alert visits varied from 0 to 34 (mean = 1.7, SD = 4.5) and total page visits varied from 1 to 442 (mean = 28.3, SD = 61.4). Barriers and facilitators to sustainable participation in SE fell into five dimensions: user-related factors, information content factors, mobile app design factors, alert system factors, and service factors. Four strategies for app improvement were described. In conclusion, we propose five dimensions pertaining to potential predictors of sustainable participation in SE to deliver clinical information.
Good Bye Vendor Lock-in

Clouds offer significant advantages over traditional cluster computing architectures including flexibility, high-availability, ease of deployment, and on-demand resource allocation -- all packed up in an attractive pay-as-you-go economic model for the users. However, cloud users are often forced into vendor lock-in due to the use of incompatible APIs, cloud-specific services, and complex pricing models of the cloud service providers (CSPs). Cloud management platforms (CMPs), supporting hybrid and multi-cloud deployment, offer an answer by providing a unified abstract interface to multiple cloud platforms. Nonetheless, modelling applications to use multi-clouds, automated resource selection based on user requirements from various available CSPs, cost optimization, security, and runtime adaptation of deployed applications still remain a challenge. In this tutorial, we provide a practical introduction to multi-cloud application modelling, configuration, deployment, and adaptation. We survey existing CMPs, compare their features and modelling methods. Finally, we provide a practical hands-on training for getting your applications ready for multi-cloud using selected tools. By the end of this tutorial, attendees should be able to understand the benefits of the multi-cloud approach, and prepared to deploy their first managed multi-cloud application.
Selection in a 3D microtomographic image the region with the highest quality

We present an algorithm for processing of X-ray microtomographic (micro-CT) images that allows automatic selection of a sub-volume having the best visual quality for further mathematical simulation, for example, flow simulation. Frequently, an investigated sample occupies only a part of a volumetric image or the sample can be into a holder; a part of the image can be cropped. For each 2D slice across the Z-axis of an image, the proposed method locates a region corresponding to the sample. We explored applications of several existing blind quality measures for an estimation of the visual quality of a micro-CT image slice. Some of these metrics can be applied to ranking the image regions according to their quality. Our method searches for a cubic area located inside regions belonging to the sample and providing the maximal sum of the quality measures of slices crossing the cube across the Z-axis. The proposed technique was tested on synthetic and real micro-CT images of rocks.
Techniques for Mutual Auditability in a Cloud Environment

Mutual auditability offers visibility between a cloud service provider (CSP) and cloud service customer (CSC), informing both of the risks posed by their association with the other. In this work, we develop and experiment with two systems designed to enable such auditability: a specialized network-based intrusion detection system (NIDS) implementation, traditional-based intrusion system (TBIS), that gives CSPs insight into the malicious activity by clients' virtual machines (VMs) without undermining the CSC's privacy, and a complementary system, hypervisor-based intrusion system (HBIS), that provides visibility into malicious activities of co-resident CSCs by detecting side channel attacks. In order to ensure that our design does not introduce new vulnerabilities into the cloud environment, we examine the potential of using these auditing tools as attack vectors themselves and potential mitigations if such vulnerabilities are found.