Reviewerly/exHarmony huggingface dataset:
A Tool to Measure TDD Compliance: A Case Study with Professionals

Context: There are several studies related to Test Driven Development (TDD), but many with divergences of results due to the short time to perform the experiments. Moreover, the environment where they are carried out is generally academic. On the other hand, the environment requires tools not used by practitioners or imposes many technical and training requirements for their application. Goal: The goal of this paper is to provide a tool that supports the evaluation of the TDD process in the software industry and academia settings. The tool focuses on analyzing the effects of verification, validation and test (VV&T). In addition, the compliance of TDD usage in software development was evaluated. Method: This study made use of the Goal Question Metric (GQM) paradigm to characterize a set of objectives using metrics towards TDD effects on software quality. A case study was conducted with IT professionals to evaluate the tool developed. Results: Considering the existing tools that perform TDD compliance assessment, the Butterfly tool was developed to enable the evaluation of the TDD lifecycle as the developer performs the coding of the software. With this tool it is possible to analyze the compliance of TDD usage during software development. Conclusions: The tool allows to measure the effects of TDD when developing software, which will support in the characterization of TDD contributions and interventions applied to software quality in future works.
Sorting Decisions in Group Decision Making

In the group decision making, alternatives amongst which a decision must be made can range from a few to a few thousand; the decision makers need to narrow the possibilities down to a reasonable number, and sort alternatives. Even when this is not the case, decision support approaches such as ontology-based frameworks potentially offer these capabilities and can assist the decision-makers in presenting the alternatives in a form that facilitates the decision making. In this research an ontology based approach is developed to screen and sort alternatives to the evaluation step. The resulting alternatives screening and sorting tool is based on ontologies whilst the evaluation tool is mainly based on Analytic Hierarchy Process. The ontologies have been built using the Web Ontology Language (OWL) which facilitates the sharing and integration of decision-making information between multiple decision makers.
Convolutional Neural Networks with Interpretable Kernels

We are focused on the theoretical background of convolutional neural networks. In particular, we examine the problem whether semantic meaning can be assigned to convolutional kernels in the first layers and how this fact can simplify the learning procedure. In this respect, we prove the suitability and efficiency of the F-transform kernels. We describe various experiments that support our claim.
An Approach of Extending Net Disks’ Collaborative Document Editing Functionality

Net disks are playing an increasingly important role in our daily works. Currently, net disks have given us an ideal ecosystem to store and share files; however, they generally cannot support collaborative document editing. To overcome the shortage, after analyzing the model of cloud storage and net disks, this paper proposes a solution to extend net disks' collaborative editing functionality via API. Based on the proposed method, a net disk system coupled with collaborative document editing functionality is built. The experiment result shows that the extending approach is feasible and effective. This approach can not only make net disks support document collaborative editing, but also be easy to be implemented.
Semi-supervised statistical learning systems using a posterior external quality estimation

With the development of Artificial Neural Networks (ANNs), they are becoming key components in many computer vision systems. However, to train ANNs or other machine learning programs it is necessary to create large and representative datasets, which can be a costly, hard and sometimes even impossible task. Another important problem with such programs is the data drift: in real-world applications input data can change with time, and the quality of a machine learning system trained on the fixed dataset may deteriorate. To combat these problems, we propose a model of ANN-based machine learning classification system that can be trained during its exploitation. The system both classifies input examples and performs training on the data gathered during its operation. We assume that besides ANN there is an external module in the system that can estimate confidence of the answers given by ANN. In this paper we consider two examples of such external module: a separate, uncorrelated classifier and a module that estimates ANN output by searching recognized words in a dictionary. We conduct numerical experiments to study the properties of the proposed system and compare it to ANNs trained offline.
Development of an Electronic Shift Lever Control System Using Hand Pose Recognition

In this paper, we introduce an electronic shift lever control system based on hand pose recognition technology to provide a convenient and simple interface to drivers. The hand pose recognition algorithm consists of three processes. The first is hand localization using a simple approach based on deep learning. The second step is the prediction of the 3D hand joint location, and the final step is hand pose recognition. During this process, we define six hand poses and extract feature vectors obtained using the 3D offset from the center of the hand location to the hand joint location. The hand pose is then recognized using a simple KNN classifier.
Using augmented reality in a beginning drawing course for design students

Augmented reality was used to enhance learning in a drawing studio course. Examination of the use of augmented reality centered on representational drawings of interior architectural spaces. Aspects of perspective and examples of previous work were presented in the environment, allowing students to see how a space could successfully be rendered by hand. Specific instructions for the construction of perspective drawings were also developed. Results from the course will be presented. The authors wished to know whether or not the use of augmented reality increased the beginning design students’ ability – or stereognostic sense – in translating three-dimensional space into two-dimensional drawings.
Association of computer-aided detection results and breast cancer risk

Since conventional computer-aided detection (CAD) schemes of mammograms produce high false positive detection rates, radiologists often ignore CAD-cued suspicious regions, in particular, the mass-type regions, which reduces the application value of CAD in clinical practice. The objective of this study is to investigate a new hypothesis that CAD-generated detection results may be useful and have a positive association to the mammographic cases with a high risk of being positive for cancer. To test this hypothesis, a large and diverse image dataset including mammograms acquired from 2,349 women was retrospectively assembled. Among them, 882 are positive and 1,467 are negative. Each case involves 4 images of craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breasts. A CAD scheme was applied to process all mammograms. From CAD results, a number of bilateral difference features from the matched CC and MLO view images of right and left breasts were computed. We analyzed discriminatory power to predict the risk of cases being positive using the bilateral difference features and a multi-feature fusion based Logistic-Regression machine learning classifier. By using a leave-onecase- out cross-validation method, the area under the ROC curve of the classifier for the multi-feature fusion was AUC=0.660 &plusmn;0.012. By applying an operating threshold at 0.5, the overall prediction accuracy was 67% and the odds ratio was 4.794 with a statistically significant increasing trend (p&lt;0.01). Study results indicated that from CAD-generated false-positives, we enabled to generate a new quantitative imaging marker to predict higher risk cases being positive and cue a case-based warning sign.
An Intelligent Management System of Instruments and Equipment Based on Power Line Communication

Instruments and equipment are an important fixed asset of companies and public institutions nowadays, which do not only need to be preserved in value but are also relied on to create more value. However, at present, the management of instruments and equipment is mainly based on manual statistics, sometimes with the help of QR codes to improve the efficiency. With this kind of management method, the statistics collected are oftentimes incomplete and untimely, resulting in repeated investment and a waste of resources. The intelligent management system based on power line communication designed in this solution can collect the device data comprehensively in real-time and form a report, providing data support for the decision-making of the management. Power lines are used as the communication path, so that no extra wire are needed to be deployed and cost can be reduced effectively. The system can comprehensively solve the problems that currently exist in the management of instruments and equipment.
Web Service Optimization Selection Model based on QoS Attributes

The optimal choice of Web services based on quality of service (QoS) is a hot issue. Quality of Service (QoS) describes the ability of a service to meet consumer needs. And providing guaranteed QoS is key to the success of Web services in business applications. However, the prcessing of QoS parameter is too complicated currently.Aiming at the complex QoS parameter processing in Web service selection, a new Web service selection optimization model based on QoS attributes is proposed. The model classifies the QoS attributes, simplifies the processing of QoS parameters, and gives the implementation ideas of related algorithms..
A Natural Language Based Approach to Generate Document Stores

For using system, under the need of getting quickly access to store and retrieve information Document store type of NoSQL database becoming important elements in the large-scale storage system and for real-time interactive tasks. An automated approach is presented to generate document store from NLP. The presented approach works to get inputs a piece of English specification according to requirements and our presented approach is capable to transforms input text to NLP based generation of document store. Our proposed system can generate the unambiguous and consistent result according to user requirements on NLP based generation of document store.
Similar CT image retrieval method based on lesion nature and its three-dimensional distribution

In imaging diagnosis, radiologists refer to the CT images of the similar cases. However, it is a big burden for them to search such CT images from the huge numbers of CT images. To solve this problem, many retrieval methods of CT images have been developed. Most existing retrieval methods target cases in which lesions exist within a limited region of the lung. These methods retrieve similar cases by calculating the similarity to the region specified on a slice image of the query case, for example, solitary pulmonary nodules. However, radiologists diagnose not only such cases but also diffuse lung disease (DLD), where lesions exist throughout the lung. Radiologists diagnose DLDs by grasping the threedimensional (3D) distribution of lesion textures. However, the existing methods cannot retrieve similar DLDs. We propose a novel method that can retrieve morphologically similar cases based on the radiologist’s knowledge, how they diagnose DLDs. In the proposed method, we configure a 3D model for the central-peripheral region of a lung, represent the similarity for the 3D distribution of lesions as histograms, and then retrieve the cases of the similar histograms. We evaluate the average precision of the proposed method for DLD CT images. For the top 5 cases, the mean of the average precisions of the proposed method is 0.84 and is better than that of the method that only calculates the volume rate of the lesions in the lung (0.64). The proposed method retrieves similar DLDs based on 3D distribution of lesion textures and is expected to contribute to diagnosis support in clinical practice.
Bullet hole detection using series Faster-RCNN and video analysis

Detecting small objects is challenging because of its low resolution and noisy representation. This paper focus on localize the bullet holes on a 4m*4m target surface and determine the shot time and position of new bullet holes on the target surface based on surveillance videos of the target. Under such a condition, bullet holes are extremely small compared with the target surface. In this paper, an improved model based on Faster-RCNN is proposed to solve the problem using two networks in series. The first network is trained using original video frames and obtain coarse locations of bullet holes, the second network is trained using the candidate locations obtained by the first network to get accurate locations. Experiment result shows that the series Faster-RCNN algorithm improves the average precision by 20.3% over the original Faster-RCNN algorithm on our bullet-hole dataset. To determine the shot time and improve detection accuracy, several algorithms have also been proposed, using these algorithms, detection accuracy of shot times and new shot points reaches the same level as human.
Traffic light recognition for autonomous vehicles by admixing the traditional ML and DL

Traffic light recognition (TLR) is an integral component of an intelligent vehicle and advance driver assistance systems (ADAS). At present, most of TLR solutions use vision based system along with prior knowledge of traffic light position (map information and height) provided by supporting sensors like Global Positioning System (GPS) sensor, to obtained high accuracy. In this work, we present a method that performs a real time TLR using only vision sensor and achieve good results. Our TLR process is divided into three stages, viz., traffic light box (TLB) detection, extraction of the glowing area from traffic light box and classification. Here, traffic light box detection is carried out using state-ofthe-art real-time object detection method, You Only Look Once (YOLO). For extraction, we project traffic light box region of interest (ROI) to custom color space and perform the blob analysis. In order to elimination false positives, we introduce light weight efficient classifier model in custom color space. For traffic light states classification, we use support vector machine (SVM) with RGB histogram of the cropped ROI as a feature. Bosch Small Traffic Lights Dataset has been used for the empirical validation of our method and achieving F1 score of 0.94 as a performance benchmark.
TRIERS: traffic burst oriented adaptive resource provisioning in cloud

A holistic solution for Traffic Burst Oriented Adaptive Resource Provisioning in Cloud (TRIERS for short) has been proposed to tackle the problem of maintaining performance scalability and service agility in the presence of sudden bursts of workload traffic. The TRIERS framework consists of three core components: workload classification by characterization, traffic prediction in class, and dynamic resource configuration and scheduling. Extensive experiments have been conducted to validate its practicality in terms of guarantee ratio (25.7% improved), resource utilization (5.6% improved) and total energy consumption (17.3% saved), indicating the effectiveness of TRIERS for adaptive resource provisioning in workload traffic burst.
LESSON-RESEARCH “CONFIGURATING THE SPECIALIZED PERSONAL COMPUTER” USING THE GOOGLE CLOUD SERVICE

The article describes the experience of conducting a lesson in high school with the use of cloud technologies when studying the theme “Computer device” in the 10th grade.
A Noble Algorithm to Secure Online Data Transmission One Hundred Percent at Zero Cost

This noble algorithm to provide security to online data transfer is an excellent means by which security can be received in transferring data over the network, and it cannot be detected by any technique or tools available in the market with attacker, unwanted parties, and intruder. In this chapter, a noble algorithm to secure data in online transmission is proposed that provides one hundred percent security to online data. This process makes the communication one hundred percent secure.
Quantitative Assessment of the Complexity of the Electric and Magnetic Fields Study in Various Physics Textbooks

Improvement of textbooks and manuals assumes the creation and development of quantitative estimation methods of didactic characteristics of these or those learning material elements. Not small interest is represented by the determination problem of didactic complexity of the educational texts on electrodynamics connected with introduction of the concepts "electric field" and "magnetic field". The research aim is to develop an objective method of quantitative complexity assessment of the physics course issues and the use of this method to determine the complexity of the study of electric and magnetic fields in various textbooks on physics for schools and universities. At this the educational text is considered as a set of sentences, each of which carries a certain idea and consists of “ordinary” words and scientific terms. It is supposed that the sentence complexity is the sum of the complexity of understanding each words (terms). The term complexity can be characterized by the folding coefficient which equal to the number of words in the corresponding concept explanation. To find the text complexity it is necessary to multiply the folding coefficient by the average quantity of words in the sentence. We applied the special computer program which analyzed the file and counted number of the each term uses and summarized their complexities. Thus it addressed to the dictionary-thesaurus, — special file containing a list of terms and folding coefficients corresponding to them. To assess the complexity of the formulas included in the educational text, they are replaced by verbal descriptions, and the resulting file is also analyzed using the computer program. Results of the content analysis of seven paragraphs from various physics textbooks for school and higher education institution are presented in article. They allow us to claim that the developed methodology of the folding coefficient assessment for educational information and complexity of the text is effective and gives the correct results.
Intelligent visual enhancement system

Vision-based augmented reality is a new kind of visual application technology, which transfers synthetic sensory information into a user's perception of a real environment. It is perceived by human senses. However, the existing visual enhancement equipment has single function, limited processing capacity and poor interaction. To overcome these shortcomings, we designed a visual enhancement system that integrates cloud computing, AR technology and deep learning. First, small, remote and wireless cameras are used to obtain image data, which need to be uploaded to a cloud. Then, the method of deep learning and feature matching is adopted to carry out facial consistency analysis, which improves the robustness of target detection. And stable target tracking is achieved by time sequence state filtering. Finally, the information of image analysis and processing is transmitted back to the AR device, so the prompts of target text and voice are given for intelligent auxiliary decision-making in time.
A New Control System of Point Light Source

The spotlight source control system includes a single control mode spotlight source control system. A single controller controls 32 points through video signal to differential control to achieve single point control, and arbitrarily sets the white balance of a certain point through the menu; The main and auxiliary machine online mode spotlight source control system, the controller and controller are connected by DMX signals, which can realize 1000 controller cascades (that is, a total of 32,000 points of cascade control can be realized), The system comes with a variety of effect modes, you can freely change the effect mode, connection mode and so on. The system has a wide range of applications in the landscaping of urban night scenes.
Automatic detection of small areas of Gleason grade 5 in prostate tissue using CNN

There are several different approaches used to treat prostate cancer, depending on age and general health conditions of the patient but also how severe the cancer is. To determine the latter, Gleason grading is used. The grade is determined by a pathologist, based on structures in histology samples from prostate biopsies. To determine the diagnosis, both the most common Gleason grade but also the highest Gleason grade occurring is used. Since the tumours typically split up the more malignant they are, single cells of Gleason grade 5, the highest and most malignant Gleason grade, can occur intermingled with benign tissue. Therefore, it is of great importance to fid even very small areas of the highest grade. This is what we aim to automatically do in this work. We have trained a convolutional neural network, with a ResNet design, to classify small areas of tissue in high magnification as either Gleason 5 or non-Gleason 5. The dataset used is generated from whole slide images from Skåne University Hospital, and consists in total of 19680 small images with the size 128&times;128 pixels in 40X. We try to make the algorithm more robust to stain variations, which is a common issue for this type of data, by using colour augmentation. The best accuracy we achieve for classification of Gleason 5 versus non-Gleason 5 images is 92%.
Clear and Private Ad Hoc Retrieval Models on Web Data

During the 1980s, and despite its maturity, the search information (RI) was only intended for librarians and experts in the field of information. Such tendentious vision prevailed for many years. Since the mid-90s, the web has become an increasingly crucial source of information , which has a renewed interest in IR. In the last decade, the popularization of computers, the terrible explosion in the amount of unstructured data, internal documents, and corporate collections, and the huge and growing number of internet document sources have deeply shaken the relationship between man and information. Today, a great change has taken place, and the RI is often used by billions of people around the world. Simply, the need for automated methods for efficient access to this huge amount of digital information has become more important, and appears as a necessity.