Reviewerly/exHarmony huggingface dataset:
Software defect detection based on selected complexity metrics using fuzzy association rule mining and defective module oversampling

Software defect is a major problem in software development. The cost of software development will be minimized when the software defects are detected earlier. Complexity metric is a mathematic calculation to calculate code complexity. It could be used to consider software defect detection. But, not all of complexity metrics influent on the occurrence of software defect, therefore it needs feature selection to select the most influent complexity metrics. Correlation-based Feature Selection (CFS) is used for selecting the most influent complexity metrics. This study conducted experiments on NASA Metric Data Program (MDP) datasets. NASA MDP contains software defect history logs based on several complexity metrics. But, there is an imbalanced distribution of defective and not defective modules in NASA MDP. The distribution of defective modules is less than not defective modules. It can reduce software defect detection performance. The distribution of defective module need to be reproduced. In this study, Synthetic Minority Oversampling Technique (SMOTE) is used to balance the distribution between defective and not defective modules. Software defect detection using Fuzzy Association Rule Mining (FARM) which is combined with the selection of complexity metrics using CFS and dataset balancing using SMOTE has sensitivity 85.51% and accuracy 91.63% in detecting software defective modules on NASA MDP dataset.
iDropout: Leveraging Deep Taylor Decomposition for the Robustness of Deep Neural Networks

In this work, we present iDropout, a new method to adjust dropout, from purely randomly dropping inputs to dropping inputs based on a mix based on the relevance of the nodes and some randomness. We use Deep Taylor Decomposition to calculate the respective relevance of the inputs and based on this, we give input nodes with a higher relevance a higher probability to be included than input nodes that seem to have less of an impact. The proposed method does not only seem to increase the performance of a Neural Network, but it also seems to make the network more robust to missing data. We evaluated the approach on artificial data with various settings, e.g. noise in data, number of informative features and on real-world datasets from the UCI Machine Learning Repository.
Exploring how expert and novice evaluators perceive G4H to consolidate heuristic evaluation

Game for Heuristic Evaluation (G4H) is a gamification technique designed to increase the evaluators' motivation and engagement during the Heuristic Evaluation (HE) consolidation. The preliminary study conducted to validate G4H has shown promising results for improving evaluators' motivation and satisfaction, however, G4H needed further evaluation considering other subjects. In this paper, we present an exploratory study carried out to investigate the G4H effectiveness for expert and novice evaluators and identify the perceptions that they have on G4H use in HE consolidation. The study was subdivided into two parts: experts are subjects in Study A and novices are subjects in Study B. In each study we conducting a user study to investigate the G4H use by the participants, and then performing a focus group to collect the participant's perceptions regarding G4H use. Our findings indicate that G4H fulfill the intention to improve motivation, satisfaction, and collaborative work, but had a different impact on experts and novices. Experts used G4H to systematize the HE consolidation tasks, and novices perceive G4H as a ludic approach to consolidate HE.
Towards Generating Image Assets Through Deep Learning for Game Development

This paper outlines some preliminary research toward the viability of utilizing unsupervised generative deep learning networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to create image assets for game development. This work explores existing GAN research and the viability of using generative networks to generate both textures and human face assets within a game engine. This research and its preliminary results examines the viability and advantages/disadvantages of utilizing generative networks directly within game engines. It also examines the potential trade-offs of a generative approach versus more traditional approaches for image assets in video game development.
A Recommender System for User-Specific Vulnerability Scoring

With the inclusion of external software components in their software, vendors also need to identify and evaluate vulnerabilities in the components they use. A growing number of external components makes this process more time-consuming, as vendors need to evaluate the severity and applicability of published vulnerabilities. The CVSS score is used to rank the severity of a vulnerability, but in its simplest form, it fails to take user properties into account. The CVSS also defines an environmental metric, allowing organizations to manually define individual impact requirements. However, it is limited to explicitly defined user information and only a subset of vulnerability properties is used in the metric. In this paper we address these shortcomings by presenting a recommender system specifically targeting software vulnerabilities. The recommender considers both user history, explicit user properties, and domain based knowledge. It provides a utility metric for each vulnerability, targeting the specific organization’s requirements and needs. An initial evaluation with industry participants shows that the recommender can generate a metric closer to the users’ reference rankings, based on predictive and rank accuracy metrics, compared to using CVSS environmental score.
Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks

This paper describes the use of two different deep-learning approaches for object detection to recognize a toy soldier. We use recordings of toy soldiers in different poses under different scenarios to simulate appearance of persons on footage taken by drones. Recordings from a bird’s eye view are today widely used in the search for missing persons in non-urban areas, border control, animal movement control, and the like. We have compared the single-shot multi-box detector (SSD) with the MobileNet or Inception V2 as a backbone, SSDLite with MobileNet and Faster R-CNN combined with Inception V2 and ResNet50. The results show that Faster R-CNN detects small object such as toy soldiers more successfully than SSD, and the training time of Faster R-CNN is much shorter than that of SSD.
From information transfer to knowledge sharing in corporate governance: The challenging role of Blockchain technology

The increasing market turbulence is recalling the attention of both researchers and practitioners interested in business dynamics on the need for developing conceptual and interpretative frameworks able to face the emerging challenges related to the on-going market changes. As a prominent domain of interest in business studies, corporate governance should rethink itself to overcome consolidated transactional approaches with the aim of ensuring companies’ survival thanks to the collaboration among all the actors involved in social and economic paths. Accordingly, the paper proposes conceptual reflections about the ways in which corporate governance could overcome traditional issues related to information asymmetry and agency problems and shift from an information transfer-based view to a knowledge-sharing approach thanks to the contribution provided by the information technologies. The Blockchain technology is used as an example for building a conceptual model able to explain the potential advantages of the proposed change in perspective for corporate governance
Motion Detection Using Continuous Frame Difference and Contour Based Tracking

Video Analytics is one of fastest thriving and crucial area of research. Video Analytics is process of automatically analyzing video to detect various activities. It is one of the most important areas for study today. Sometimes it is also called as Video Content Analysis as it is capable to analyze the video and detect the irregular events. In earlier age, video surveillance system mainly used to capture and monitor the events. In this system person has to keep watch on the camera scenes. Using Video analytics one can make the system more effective. Not only one can watch the video but also person detect the abnormal activity happened in the area. It decreases the workload of management staff. It preserves human's time and efforts. Video analytics finds application in many areas such as no man area, military, sports, civil etc. Many organizations use video analytics to detect the motion and capture the motion when organization is being closed. In this paper we were discussed about object motion detection and track the object when motion is detected. It is done with the help of video processing and Python and alerts with the help of Raspberry. Frame differencing is used for detecting movement of object and contour tracking algorithm to follow the movement of object. The code is simulated in Python.
Development of P2P Educational Service in Russia

Education is an important foundation to build on in any area of the modern world. Only quality education allows students to take up the most promising jobs, and for employers to get better trained employees. The purpose of this study is to develop an approach to the development of a new educational peer-to-peer (P2P) platform that can overcome existing barriers to the use of such services and meet the needs of the entire educational ecosystem. To achieve this goal, the research includes a comparative analysis of existing educational services in Russia, identifying their weaknesses and current barriers to the end users. The approach is based on a broad analysis of the needs of all market players and stakeholders. The result: the current paper provides recommendations for the development of an innovative educational P2P service, which will be able to solve the existing problems of the education market.
A Maximum Entropy Deep Reinforcement Learning Neural Tracker

Tracking of anatomical structures has multiple applications in the field of biomedical imaging, including screening, diagnosing and monitoring the evolution of pathologies. Semi-automated tracking of elongated structures has been previously formulated as a problem suitable for deep reinforcement learning (DRL), but it remains a challenge. We introduce a maximum entropy continuous-action DRL neural tracker capable of training from scratch in a complex environment in the presence of high noise levels, Gaussian blurring and detractors. The trained model is evaluated on two-photon microscopy images of mouse cortex. At the expense of slightly worse robustness compared to a previously applied DRL tracker, we reach significantly higher accuracy, approaching the performance of the standard hand-engineered algorithm used for neuron tracing. The higher sample efficiency of our maximum entropy DRL tracker indicates its potential of being applied directly to small biomedical datasets.
Transfer of efficient HR management technologies in the educational system

The article presents the generalized results of many years of work to identify international trends in the staffing of the educational process. The analysis of state educational policy conducted by the authors allows to draw conclusions about the strengthening of managerial impact on the improvement of the qualification of the academic community. The study of the peculiarities of using HR management technologies in educational organizations of the Ministry of Internal Affairs of Russia revealed the prospects for their implementation in solving general and particular tasks of staffing this category of universities. The article reflects the priorities in the recruitment of professorships of universities of the Ministry of Internal Affairs of Russia over the past two to three decades. It is scientifically substantiated that educational organizations subordinate to the Ministry of Internal Affairs of Russia, in particular, implementing educational programs of higher education, in the staffing of the educational process encounter problems both common for the Russian university community and specific, generated by departmental features of the reproduction of scientific and pedagogical personnel, their generalized list is given. The factors that negatively affect the staffing of the educational process in the universities of the Ministry of Internal Affairs of Russia are investigated. Recommendations on the implementation of modern personnel management technologies in the educational environment are given, potential risks associated with the use of such innovations are analyzed.
Speculo: A Tool for Multiple Identities Exploration and Detection in Social Networks

This paper introduces Speculo. The primary purpose of Speculo is to support in the exploration and detection of social networks accounts that are managed by the same physical person: Multiple Identities Detection (MID). Speculo investigates MID within the same social network, demonstrated here using Twitter. This is achieved by deeply exploring users’ data from different perspectives and presenting it in an interactive and user-friendly interface. Speculo opens new perspectives to build a platform that automatically helps mining identities in large social networks. Implementation details of Speculo and two application scenarios are described in this paper.
Modeling of Generative Mechanism from Burden Surface to Burden Distribution Matrix

Burden distribution matrix is a key part of smooth operation for the blast furnace ironmaking process. It directly influences the burden surface of the blast furnace. Recently, many researchers establish the mathematical model from the burden distribution matrix to the burden surface. However, the research from the burden surface to the burden distribution matrix is rare. Hence, this paper establishes the generative mechanism from the burden surface to the burden distribution matrix. In this paper, our previously improved multi-layer extreme learning machine (named as EAPSO-ML-ELM) is used as the modelling algorithm of the generative mechanism. And the real blast furnace production data are used to verify the generative mechanism. Compared with the generative mechanism based on ML-ELM, the simulation results demonstrate that the generative mechanism based on EAPSO-ML-ELM has better accuracy and generalization performance.
Preliminary Study of Screen Extension for Smartphone Using External Display

There are some techniques to show the smartphone's content on an external display in large. However, since smartphones are designed for mobility, a seamless interaction is necessary to make the best use of external display by a smartphone. We are currently exploring the feasibility of another technique, which we call Screen Extension. Our technique seamlessly adds display spaces to a smartphone using an external display, allowing users to use displays available in many places. To test search performance with Screen Extension, we conducted a pilot study; which suggested that Screen Extension helps users to search content faster.
Imbalanced Data Using with-in Class Majority Under Sampling Approach

The existing data can be further processed by using data mining techniques for novel discovery of knowledge. Classification is one of the techniques used for knowledge discovery. One of the most popular models used for knowledge representation is decision trees due to its easy of interpretation. Class imbalance data sources are of the critical and most popular data sources for their real time availability. The existing classification techniques are bottle necked in the scenario for skewed distributed data. To address his short coming, we have proposed a novel algorithm With In class Majority Under Sampling (WIMUS) for improved learning. The results generated are encouraging to show that our proposed approach is effective on class imbalance data sources.
Glomerular Microscopic Image Segmentation Based on Convolutional Neural Network

Accurate glomerular microscopic medical image segmentation is important for renal pathology for obtaining reliable diseases diagnosis. In this study, we construct a pixel-level labeled glomerular microscopic medical image segmentation dataset and improve a classic algorithm, Mask R-CNN, for implementing automatic segmentation of glomerular microscopic medical images. The Improved Mask R-CNN algorithm consists of two parts: in the first part, in order to enhance the accuracy of model training, the anchors in region proposal network are scaled down. In the second part, we increase the number of deconvolution layers in the head mask branch to further improve the glomerular segmentation precision. The experimental results indicate that the algorithm we improve offers higher precision than the original Mask R-CNN algorithm and achieves state-of-the-art segmentation of the glomerular microscopic medical image dataset.
Knowledge-oriented Hierarchical Neural Network for Sentiment Classification

Abstract Sentiment classification aims to classify the sentimental polarities of given texts. Lexicon-based approaches utilize lexical resources to explore the opinions according to some specific rules, whose effectiveness strongly depends on the goodness of the lexical resources and the rules. Traditional machine-learning methods tightly rely on feature engineering and external NLP toolkits with unavoidable errors. Deep learning models strongly rely on a large amount of labelled data to train their numerous parameters, which often suffer from overfitting issue since it is difficult to obtain sufficient training data. To address the issues, we design a model that combines Knowledge-oriented Convolutional Neural Network (K-CNN) and bidirectional Gated Recurrent Neural Network (biGRU) in a hierarchical way for sentiment classification. Firstly K-CNN is used to capture the n-gram features in sentences. Sentiment word filters are constructed in the knowledge-oriented channel of K-CNN based on the linguistic knowledge from SentiWv ordNet, which can capture the sentiment lexicons and alleviate overfitting effectively. Then biGRU with attention mechanism is utilized to model the sequential relations between sentences and obtain the document-level representation based on the relevance of each sentence to the final sentiment classification. Experiments on two datasets show that our model outperforms other classical deep neural network models.
A Study on the use of State-of-the-Art CNNs with Fine Tuning for Spatial Stream Generation for Activity Recognition

Recurrent neural network (RNN) models have been proven successful in modeling the temporal dynamics in videos of which Long Short-Term Memory (LSTM) networks have been specifically successful as it does not suffer from the vanishing gradient problem. They along with Convolutional Neural Networks (CNN) for visual feature extraction are popularly referred as the Long-term Recurrent Convolutional Networks (LRCN) and have been widely accepted in the recent times for activities like video activity classification, video captioning and video description. The features for these models may be generated using single spatial stream or dual streams, both spatial and motion streams from the video frames. The paper is a study on how the State-of-the-Art networks like ResNet50, InceptionV3 and MobileNet perform with fine tuning for spatial feature extraction in the task of activity recognition in videos using LRCN with stacked LSTM. The fine-tuning approach and optimization settings for the extraction of the visual features from the State-of-the-Art pretrained networks is also discussed in this paper.
Beyond the data management plan: Expanding roles for librarians in data science and open science

ABSTRACT Support for research data management (RDM) has become a popular focus for library services, and many training opportunities in RDM exist for librarians. However, fewer opportunities exist for librarians to develop the more advanced skills and expertise necessary to support data science and open science. To better understand how to develop a library workforce prepared to develop services in the emerging areas of data science and open science, the National Library of Medicine convened a workshop of 15 librarians and information professionals with a range of expertise in data and open science. This session will present the workshop's recommendations and use participatory facilitation methods to engage attendees in conversation about how to prepare information professionals to meet the evolving challenges of data science and open science.
Collaborative online ranking algorithms for multitask learning

There are many applications in which it is desirable to rank or order instances that belong to several different but related problems or tasks. Although unique, the individual ranking problem often shares characteristics with other problems in the group. Conventional ranking methods treat each task independently without considering the latent commonalities. In this paper, we study the problem of learning to rank instances that belong to multiple related tasks from the multitask learning perspective. We consider a case in which the information that is learned for a task can be used to enhance the learning of other tasks and propose a collaborative multitask ranking method that learns several ranking models for each of the related tasks together. The proposed algorithms operate in rounds by learning models from a sequence of data instances one at a time. In each round, our algorithms receive an instance that belongs to a task and make a prediction using the task’s ranking model. The model is then updated by leveraging both the task-specific data and the information provided by other models in a collaborative way. The experimental results demonstrate that our algorithms can improve the overall performance of ranking multiple correlated tasks collaboratively. Furthermore, our algorithms can scale well to large amounts of data and are particularly suitable for real-world applications in which data arrive continuously.
A Fast Image/Video Dehazing Algorithm Based on Modified Atmospheric Veil

In this paper, we propose a fast image/video dehazing algorithm based on modified atmospheric veil. For a single image, the coarse atmospheric veil is quickly estimated by the minimum and maximum channels. We take the depth image estimated by the color attenuation prior as the guided image to refine the coarse atmospheric veil and use down sampling technique to speed up the process. In addition, a fast Fourier enhancement method is used to improve the high frequency details. Based on the image dehazing, we design an atmospheric light adjustment strategy and a transmission optimized framework for video dehazing, which can effectively reduce flickering artifacts. Experimental results demonstrate that the proposed algorithm can achieve a very good dehazing effect while keeping a comparable processing speed with state-of-the-art algorithms.