Reviewerly/exHarmony huggingface dataset:
An Early Multi-Criteria Risk Assessment Model

Accurate time and budget is an essential estimate for planning software projects correctly. Quite often, the software projects fall into unrealistic estimates and the core reason generally owes to problems with the requirement analysis. For investigating such problems, risk has to identified and assessed at the requirement engineering phase only so that defects do not seep down to other software development phases. This article proposes a multi-criteria risk assessment model to compute risk at a requirement level by computing cumulative risk score based on a weighted score assigned to each criterion. The result of comparison with other approaches and experimentation shows that using this model it is possible to predict the risk at the early phase of software development life cycle with high accuracy.
Variable selection for skew-normal mixture of joint location and scale models

Abstract Although there are many papers on variable selection methods based on mean model in the finite mixture of regression models, little work has been done on how to select significant explanatory variables in the modeling of the variance parameter. In this paper, we propose and study a novel class of models: a skew-normal mixture of joint location and scale models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The problem of variable selection for the proposed models is considered. In particular, a modified Expectation-Maximization(EM) algorithm for estimating the model parameters is developed. The consistency and the oracle property of the penalized estimators is established. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies. An example is illustrated by the proposed methodologies.
A Novel Approach for Hand Gesture Recognition

As we move further in twenty-first century, usage of artificial intelligence is becoming more critical and wide spread. Also related Hand Gesture Recognition is taking its shape to make technology closer to real world. Hand gestures are associated with lots of information and with rapid improvement on computer’s vision, hand gesture recognition is now widely used in robot control, intelligent furniture, gaming, and other aspects. Now with advanced PC vision calculations and progress in Human Computer Interface, Hand gesture recognition is becoming more significant. There is huge demand on such kind of interfaces which recognize movement of human body and relate it to some significant and useful information. Picture division calculation plays an important role in choosing motion shapes and direction. It is visual non-verbal hand gestures which communicates information. There are methods to recognize this information such as electronic based, glove based, marker based, and more. The paper presents a study of different hand gesture techniques—loading gesture image and perform contour detection and localization. It is also representing optimized feature vector using PCA and CNN. We get 73.49% accuracy with 75 ms computation time using Linear Kernel and 91.09% accuracy with 90 ms computation time using RBF Kernel and for Single Image matching.
Design of classification model on high-dimensional imbalance data of motor bearing fault

Abstract According to the characteristics of high-dimensional imbalance distribution of motor bearing fault data, a design scheme of classification model is proposed for the high-dimensional data reduction problem in the classification algorithm. For details: Combining standard particle swarm optimization algorithm and random forest algorithm, a new high-dimensional data reduction algorithm is proposed. Aiming at the imbalance problem of data categories in the classification algorithm, we proposes to use machine learning under the sum of squares of dynamic deviations criterion to divide the minority sample data set into mixed regions, high-purity minority sample regions and outlier regions, and then use smote algorithm to complete the data equalization processing, so as to make the sample data equalization processing more reasonable, Focusing on the task of motor bearing fault classification, a design scheme of using standard particle swarm optimization algorithm to improve the least squares support vector machine model is proposed.
Mining Software Repository for Security Smell Code Review

Security smells found in a source code could be a subtle sign of security vulnerability in the future. Developers could do security code review to find those signs, however it takes a lot of time for a large code base. To increase security code review efficiency, developers should focus on files that most likely have high-risk. In finding those high-risk files, mining software repository is a promising method as it has been used for bug localization in some prior researches. Our analysis in 34 CVE-related (smelly) Java files from 7 different projects found that ordinal LOC, ordinal commit count, and ordinal author count, have positive correlation with the likelihood of a file being smelly (related to known security vulnerability/ies). We then built a tool that return a list of files that are considered smelly, using ordinal LOC, ordinal commit count, and ordinal author count information. We applied this tool on 2 test projects and analyzed the result manually. The manual analysis found that we could find as many as 69 smelly files which contain identified security smell/s by only manually inspecting 185 files rather than all 4,383 files. This means we could reduce the time needed for security code review, which we found could reach 84.63% time reduction. We also found positive correlation between smelly files with known security vulnerability, which shows that using mining software repository for finding security smells has the potential to prevent security vulnerability in the future.
A Detailed Review on CBIR and Its Importance in Current Era

The use of digital images is exponentially increased due to the availability of various image capturing devices and the provision of transmitting them with less expense. Digital images are used for various purposes such as personal, entertainment, medical diagnosis, fashion design, forensic analysis, etc. The retrieval of relevant images from a huge volume of images is a tremendous task. Content-based image retrieval (CBIR) is a solution to retrieve relevant images from a pool of images by considering a query image. In a CBIR scheme, the user will give a query image and the system supposes to return a set of the image which are very similar to the query image. The CBIR schemes use classification models and the visual features of the images while ranking and retrieving the images from the image pool. This manuscript discusses the overview of a CBIR scheme, its application domains, the existing approaches in this domain and various datasets available for the research in this domain. The manuscript has also listed a set of challenges in this domain so that future works can be focused on this area.
Effects of Data Sampling Interval on Accuracy of Authentication Using Wi-Fi Information Captured by Smartphone

There is a lot of researches on personal authentication methods that use behavior information. The information can be easily collected through smartphones which most people usually carry their own. The important thing to consider when collecting the information is setting the data sampling interval. The shorter the interval is, the more data we can collect, and the more we can know human behavior from the data. On the other hand, the issues of battery consumption and privacy for smartphones tracking human behavior get serious when the tracking interval is short. Therefore, the tracking interval should be long in the behavior authentication system if the change of the interval does not affect the authentication accuracy. In this paper, we examined the effects of tracking interval on behavioral authentication accuracy where the interval is set to five minutes and one hour. Wi-Fi information captured by smartphones are used and the number of the subjects are 100 randomly selected from 16,027 in this experiment. As a result, we conclude the effects on the authentication accuracy are minor.
Unsupervised Deep Learning for Secure Internet of Things

This chapter elaborates on the potential of unsupervised deep learning solutions for assuring the security of IoT-based systems to give the reader an insightful discussion of how these solutions could satisfy the IoT security requirements necessary to realize a reliable and trustworthy IoT environment.
Virtual Assistant Prototype for Property Sales

Artificial Intelligence technology, especially digital language processing, can be used to develop a prototype of the Virtual Sales Assistant in Property Project&#x2019;s House Show Unit which aims to help improve the sales process. Google Dialogflow applied intelligence technology was used to build this Virtual Assistant with complete features and abundant information base processing capabilities. However, to speed up the Virtual Assistant development, the knowledge base of project could be intensively being used. The use of the information base is adjusted to the advanced parameters of the Conversation Agent development by creation of Persona, Information Availability, and Question Models. The Virtual Assistant outputs in the form of a text and voice conversation bot. The use of the information base results in relatively quick and easy prototyping with accurate recognition of text and voice conversations. The accuracy of speech recognition based on text and voice also depends on setting the dependency of the prototype on the information base, the completeness of the data from the information base, and the conversational language library. Virtual Assistant for House Show Unit in property sales can be created easily and quickly using the available information base and the use of artificial intelligence-based prototypes can be extended to commercial models and other fields.
An interpolation algorithm with dynamic-weighting for color image restoration

With the rapid development of digital imaging technology, the Bayer filter array represents color images. How to restore color images based on the Bayer filter array has become a problem. This paper proposes a novel interpolation algorithm, which leverages a dynamic-weighting mechanism to weight interpolation direction vectors. Experiments show that the improved algorithm has a significant improvement on color image restoration.
Using the Hough Transform in the Automatization of Contact Angle Calculation

In this paper an algorithm for automatic contact angle calculation is presented. This algorithm came forth because of the need for a procedure which would minimize the variability that comes with the previously mentioned calculations. With the use of the Hough Transform this has been achieved. In the process of edge extraction, Sobel operators in combination with mean thresholding and morphological operations were implemented. Afterwards, Hough transforms for line and circle were used. With the use of analytic geometry on these features, contact angle was calculated. The described algorithm is compared to the ImageJ plugin for contact angle measurement.
Binarized Neural Network Acceleration with Field Programmable Gate Array and Its Application in Robot Vision

Based on the vigorous development of deep learning, image classification nowadays is mainly accomplished by using the Convolutional Neural Networks whose main computation tasks are usually calculated by Graphics Processing Units. Although the computing speed of Graphics Processing Units is faster than Central Processing Units when considering the neural networks, the power consumption of Graphics Processing Units is significantly huge. In this paper, we investigate the hardware acceleration of neural networks by using the Field Programmable Gate Array via High-Level Synthesis. We study the binarized neural networks with binarized weights and activation functions, which is suitable to be implemented by the Field Programmable Gate Array. The proposed design has high computing speed and low power consumption. Our method is applied to a robot vision application to classify robots from human and others. Experiment results show that our design provides good performance for image classification.
Transformer-Based BiLSTM for Aspect-Level Sentiment Classification

In order to further improve the effect of sentiment classification of multi-sentiment sentences, a hybrid model based on BiLSTM and aspect Transformer is proposed. First, BiLSTM is used to extract sentence context features, and then the obtained features are trained as multi-aspect Transformer modules, each of which is independent of each other. During the training, the parameters of each Transformer module are constantly adjusted to accurately refining the sentimental polarity of the sentence. Experimental results on SemEval data set show that the proposed method can effectively improve the accuracy of sentiment classification and optimize the performance of sentiment classification.
FastHorovod: Expediting Parallel Message-Passing Schedule for Distributed DNN Training

Large-scale deep neural networks training have been widely deployed on dense-GPU public cloud clusters. Intensive communication and synchronization cost for gradients and parameters is becoming the bottleneck of distributed deep learning training. Horovod is one of the most popular distributed communication frameworks to address the scale-out issue of deep learning training on GPU clusters. Existing public-cloud GPU datacenters, such as Amazon EC2 and Alibaba GPU cloud, are usually equipped with commodity high-speed Ethernet and TCP networking. In current vanilla Horovod, however, we observe that one GPU device is merely associated with at most one proxy communication process. The proxy process is responsible for dealing with all the communication operations of parameter all-reduce for one or multiple GPUs. Such configuration makes communication interface based on TCP protocols suffer from limited network goodput and incur training performance penalties. In this paper, we make the first attempt to improve the message passing interface of Horovod and address the mismatching between the computation and communication capability when deploying Horovod in TCP-based public-cloud GPU clusters. We propose FastHorovod to exploit more cost-efficient auxiliary communication processes on CPU to expedite parallel message-passing schedule for GPU. We conduct extensive experiments against state-of-the-art Horovod. The experiment results show that our design can significantly accelerate the distributed training communication on TCP-based public-cloud GPU clusters, and FastHorovod improves the training speed of AlexNet and VGG16 models by 64.5% and 72.6% respectively.
Learning Similarity via Subjective Evaluations and Deep Features of Histopathology Images

Visual similarity estimation for histopathology images plays a key role in many medical imaging tasks, especially in image search and retrieval. All image similarity evaluation approaches employ distance-based metrics to quantify the degree of (dis) similarity. However, it has always been challenging to numerically estimate the similarity between two images, which is compatible with subjective assessment of the human operator, i.e., physicians such as radiologists and pathologists. Relying only on distance calculations through Euclidean, Manhattan, Hamming, and cosine distances does not provide us with the result that can be translated to human judgment in linguistic terms and/or in a normalized range. There is a need for a reliable image similarity measurement compatible with the human assessment with minimum possible conflict. This work proposes a new scheme that evaluates the similarity between a pair of histopathology images close to human reasoning using a fuzzy-logic approach. To this end, we developed a web application to interface with users and to collect descriptive image similarity data for training and testing purposes. We designed an adaptive neuro-fuzzy inference system (ANFIS) to model the vague and uncertain nature of user image assessment for the histopathology image comparison task. The experimental results show that the trained ANFIS can estimate the image similarity with acceptable accuracy and consistent with the user evaluations.
Mathematical Model on the Visualization of coaxiality Error

The fundamental theory of generating coaxiality error graph is studied. A mathematical model for visualization of coaxiality error is established in accordance with the definition of coaxiality error in national standard. The theory of three-dimensional wireframe models on computer graphics is applied to realize the display of extracted derived feature and associated derived feature on the measured multi-diameter axle. Axonometric projection transformation is used to attain the realization of basic geometric shapes. The spacial hidden method is adopted to solve the problem of geometrical graph hidden. According to the above theoretical research, the corresponding software is developed on the LabWindows/CVI software platform to realize the visualization of coaxiality error.
Single Image Dehazing Method Based on Gradient Matrix and Ternary Parameter Search

Hazy images typically suffer from low contrast and poor visibility, which influences the performance of many computer vision systems. In this paper, a single image dehazing method called DG T is proposed. First of all, by employing the dark channel prior, atmospheric light and rough transmission map can be obtained. In subsequence, via utilizing the gradient matrix, the object details in the images can be enhanced. Finally, built on a minimum information loss principle, a ternary search algorithm is employed to determine the related parameters, and thus effectively restore the hazy images. Different from the previous works, DGT only needs to determine one unknown parameter with the cost of logarithmic time complexity. Experimental results show the superiority of the proposed method over state-of-the-art dehazing methods.
Statistical Inference on Nonparametric Spline Models and its Applications

Abstract One of the components of the Human Development Index which is still a problem and concern in the world today is the Life Expectancy Rate (LER). United Nations Development Program (UNDP). United Nations Development Program (UNDP) uses the LER to measure community health status as well as a benchmark for development success. LER in Indonesia continues to increase almost throughout the year. That is, the hope of a newborn baby to be able to live longer is getting higher. LER data modelling with parametric regression is not necessarily suitable to be applied because the LER relationship pattern has a pattern that varies at certain age intervals. Spline regression is a regression method that can handle data whose pattern changes at certain intervals. Spline is one of the models in nonparametric regression that has a very special and very good visual statistical interpretation. In addition, splines are also able to handle data characters or functions that are smooth (smooth). This study aims to derive the form of the estimator and the shortest confidence interval for the quadratic spline model and model the LER data in Indonesia.
Features of a prospective teacher’s professional formation in the educational environment of a pedagogical university

The paper considers the concept of professional formation and its projection on the professional training and professional activity of the prospective teacher through the concepts of formation and development. As a result of the analysis of existing views on the phases, stages and periods of professional formation, the main ones were identified, which are due to the time frame the period before choosing the institution where professional training will be carried out, the actual professional education and the period of it. Having analyzed the set of competencies (subject, general pedagogical, professional-communicative, managerial, innovative, reflective) provided by the Federal State Educational Standard in relation to the teachers profession, as well as referring to the models of Hard-skills and Soft-skills, we identified indicators (skills) of the professional development of the prospective teacher and invited the 3rd-year students of the Institute of Russian Language and Literature of the Volgograd State Socio-Pedagogical University to evaluate these skills in the order of their significance. The assessment was carried out on a 5-point scale, where 1 point is an absolutely insignificant indicator, 2 rather insignificant, 3 rather significant, 4 significant, 5 very significant. As a result, we were able to build their gradation and divide them by areas, as well as allocate 3 levels in the process of professional development. Taking into account these levels, we considered how the educational environment can influence the professional development of future teachers through educational, extracurricular activities and through the environment.
Towards Group Decision Support in the Software Maintenance Process

Software maintenance is an element-key of the life cycle of software. However,the techniques of software maintenance do not consider the diversity and the complexity of decisions which do not stop increasing. So, there are at present a few tools, susceptible to insure the relevance and the efficiency of the decision-making in this phase. The work presented in this paper aims to eliminate or at least to reduce the effect to fall in an expensive change by reducing the time to find a compromise on the adequate change. The development of the decision support system for software maintenance is an answer to the problem. The developed tool allows:-to make a fast diagnosis on the software by using the coupling metrics;-to help the decision-makers of the maintenance, according to their preferences often conflicting, to adopt a change among several proposed. To answer this group decision where various points of view are considered, we propose a negotiation protocol. This protocol try to find a compromise that suits best all the decision-makers.
Cloud-based ATC Platform Architecture Design Using Kubernetes

The current study discusses using Kubernetes to design an AI-based shared traffic data cloud platform and how to use it. The existing ATC platform provided VM-based services using KVM, but the new method constructs Kubernetes on VM and provides services through the Container. It allows users to access both the VM-based services and the Container-based services and administrators to manage resources more efficiently.
Multi-task Model for Named Entity Recognition

Although the model based on deep learning significantly improves the Natural Language Processing (NLP) model, compared with the real conversation scene, Natural Language Understanding (NLU) task is still limited by training data and training tasks. In this paper, we propose a Bi-directional Long Short-Term Memory network model with multi-task learning (Multi-Bi-LSTM + CRF) of Classification and named entity recognition, which combines multiple task training to NLU. Multi-Bi-LSTM + CRF uses a large number of cross task data and benefits from the regularization effect so that that word vectors can be more commonly expressed and NLU tasks can be understood more deeply. Based on Bidirectional LSTM + CRF models (Bi-LSTM + CRF), we propose a new neural network model, which can simultaneously carry out multi-task learning of intention recognition and named entity recognition, focusing on named named entity recognition training. In addition, we use different optimizers to control and update task parameters to adapt to the loss. Compared with the baseline model, our proposed model achieves better results.