Reviewerly/exHarmony huggingface dataset:
A novel method to recognize object in Images using Convolution Neural Networks

Detection and recognition of object is challenging task nowadays in the computer vision domain. To solve this problem, we have many techniques and algorithms that help us to detect objects in natural environment. Prime components of object detection are classification and feature extraction. In this paper we have used a method called Convolution Neural Network (CNN) a technique for training images for object recognition. Machine learning techniques are applied on computer vision domain. This research aims to propose a new design and a methodology to support a system in order to recognize the object and give us the pop-up message in case the object is considered as a weapon. Image processing techniques are used to develop the system. This experiment is conducted to access the following qualities, say, to verify whether the system is able to detect the object and to measure the accuracy of the proposed system.
Problems of Adaptation of Technical University Graduates in the Formation Conditions of New Economic Reality

The chapter analyzes current problems faced by modern graduates of technical universities in the digital economy. The use of synergetic methodology made it possible to consider the modern digital economy as a dynamic self-developing system, the main source of innovative transformations of which is human capital. The features of modern human capital include: transformation of human capital into intellectual capital, based on heuristic knowledge, the appearance of new requirements for human (intellectual) capital on the part of modern employers. It is proved that the carriers of intellectual capital are young professionals—modern graduates. The nature of the requirements facing employees of modern technological production is revealed. The author reveals the essence of the process of adaptation of graduates of technical universities to new economic conditions, which involves the transformation of the nature of their knowledge, skills, motivations, and values. It is concluded that the main mechanism of professional adaptation of graduates of technical universities should be the educational system of the country, which itself needs a comprehensive transformation. Measures are proposed to create a new educational paradigm—the so-called digital education on the basis of a self-developing digital educational environment.
Assembly of classifiers to determine the academic profile of students

The assembly methods, or combination of models, arise with the purpose of improving the accuracy of predictions. An assembly contains a number of apprentices (base models) which, when of the same type are called homogeneous and if of different, heterogeneous. The characteristic is that these apprentices do not perform well. The assembly is generated using another algorithm that combines the apprentices, examples of which are the majority vote, the decision table and the neural networks [1]. This article proposes the use of an assembly of classifiers to determine the academic profile of the student, based on the student’s overall average and data related to educational factors.
Ad-hoc Protocol for Drones Coordination in Urban Environment

In the paper ad-hoc protocol for distributed object search by multi-agent robotic system in urban environment are described. For this purpose, exploration and mapping process by the muti-agent drone system has been discussed and appropriate algorithms has been analyzed. Based on this, communication model for the system was proposed and ad-hoc protocol for communication between drones has been developed.
Regional Model of Foreign Students’ Professional Socialization: Basic Characteristics

The paper aims to identify and justify the basic characteristics of a regional model of foreign students’ professional socialization. The process of foreign students’ professional socialization is considered as enculturation implemented at different levels, including the regional one. Methodological foundations of a regional model of foreign students’ professional socialization are justified, its basic characteristics are revealed. It is shown that the process of foreign students’ professional socialization at the regional level is influenced by different factors - material, social and domestic, cultural, educational, educational and professional.
Research on Deploying the Deeplearning Models with Embedded Devices

Deep convolution network has done many awesome tasks in computer vision, showing its ability to go beyond the human, also shining on lots of applications. But high precision always come along with high resource occupation, both of compute resource and memory resource. How to deploy models with consuming resource as less as possible and complete the inference time as short as possible, is becoming an important part of deep learning research. Base on a generic hardware Raspberry 3B+, this paper implements three different ways to deploy a detector on it. The methods are kernel optimization, quantization and the use of movidius VPU. After comparing the results and inference time of different methods, we gave our analysis and summaries.
Developer Recommendation for Stack Exchange Software Engineering Q&amp;A Website based on K-Means clustering and Developer Social Network Metric

Nowadays Online question answering website platforms are getting popular as it allows the users to get responses from varied experts beyond their reach. Businesses use these sites for their growth and exposure. Enormous volumes of question are posted on these sites. Finding an expert for answering large voluminous questions requires specialized techniques. This paper proposes hybrid approach for finding prominent expert for the posted questions. The proposed approach uses clustering and social network metrics. The approach clusters the database questions based on tag similarity using K Means. The new posted question is matched with the clusters and best suitable cluster is extracted. Developer Social Network is constructed for chosen cluster and Page Rank with time decay has been applied to find the prominent developer. The proposed approach attains 60% accuracy.
Robot Drivers: Learning to Drive by Trial &amp; Error

Autonomous cars have been in the making for over 15 years. Skepticism has taken the place of initial hype and enthusiasm. Current autonomous driving systems give no guarantee of 100% correctness and reliability, and users are not willing to take a chance on a car that is unable to cope with all the possible driving scenarios. Robotic drivers are expected to be perfect. Major players such as Tesla and Waymo rely on highly detailed maps and very large sensor data in a race to build the ultimate robotic driver to cope with all possible driving scenarios. This approach optimizes for safety but delays the dream of fully autonomous cars. In this paper we consider robot-drivers as teen-drivers eager to learn how to drive but prone to mistakes in the beginning. The question we are trying to investigate is "what if we allow autonomous cars to make mistakes like young human drives do?" In this paper, we explore reinforcement learning for small size autonomous vehicles fusing information from several sensors including a camera, color sensors, and sonar sensors. The robot-drivers have initially no information about the driving scenarios they learn with experience through a mechanism of rewards designed to quickly help our robot-teen to learn its driving skills.
Adaptive neuro-fuzzy model for traffic signs recognition

Traffic sign recognition is a very important component of Intelligent Transport Systems (ITS), which is largely based on the application of artificial intelligence today. The aim of this study is to explore the ability to recognize traffic signs on an image using an adaptive neuro-fuzzy inference system (ANFIS) model. To date, many studies with the area of concern related to the recognition of traffic signs have been published. However, the application of the ANFIS model as a possible solution has not been sufficiently explored. The methodology presented in this paper uses the geometric properties of symbols on a traffic sign as input ANFIS variables. It is proposed to develop five independent models that should categorize the sign presented. The final decision is made based on the majority of the outputs of the ANFIS model, and the method showed a high level of recognition accuracy and adaptability.
Area-Aware Reflection Detection and Removal for Single Image

Images taken through transparent glass are usually covered with unwanted reflective layers, which can affect the effects of many computer vision tasks, so eliminating these reflections has a wide range of applications in image processing studies. In fact, the areas in the reflective layer that have a great impact on the image are often those with high reflectivity. Therefore, in this paper, we propose a deep learning model that can remove the reflection from a single image. The model can automatically detect the reflection area and obtains a reflective-free background image. After extensive comparative experiments and validation, the model shows excellent performance.
Cloud Cover Profile using Cloud Detection Algorithms towards Energy Forecasting in Photovoltaic (PV) Systems

Satellite images were used to study cloud coverage in Peninsular Malaysia. These clouds will disturb the power generation of Photovoltaic (PV) when they cross over the solar panels. An increase in cloud cover will reduce the solar irradiance and power generated in PV. However, there is a lack of researches on cloud coverage in Malaysia. Therefore, a suitable method of cloud detection algorithms must be developed to calculate the percentage of cloud cover. From past researches, several methods of cloud detection proposed, but it was difficult to decide on which method was most suitable for this case study. Thus, the main objectives of this research work are to develop suitable methods for cloud detection and to study cloud coverage in Peninsular Malaysia. The methods that were applied in this work are Otsu's and Histogram. From the results obtained, Otsu's performs better for cloud detection compared to Histogram. From analysis using Otsu's method, it was found that the highest percentage of cloud coverage in September 2018 was week 1 while in October was week 2. The results obtained may be useful for solar operators to generate the required power during that week or month.
Extracting Users’ Explicit Preferences from Free-text using Second Order Co-occurrence PMI in Indian Matrimony

This paper is a corpus-based method for extracting users' explicit preferences from free-text part of the registered user profile in Indian matchmaking system using Second order Co-occurrence PMI (SOC-PMI).In online Indian matrimonial system, while registering, users are asked to provide information about themselves, family and the qualities he/she is looking for in a desired partner but there is no means to automatically prioritize the desired partner features (which features are more important to him/her and it varies from person to person) except for explicitly asking the user. Extraction of users' preferences from unconstrained attributes has not been explored yet. This motivates us to extract users' preferences from an unexplored area. The contribution of this paper is focused on the above gap found in the research. The methodology explained in this paper automatically prioritizes these features which can be used to design a Weighted Reciprocal Recommendation model to generate more efficient recommendations. Experimental results show the efficiency of the applied methodology.
Creative virtual composer assistant based on the eBICA framework

The topic addressed here is the role of emotions in music creation. The fact that musical fragments can be evaluated on a number of emotional scales allows one to create a semantic map of fragments and to use this map as a guidance in music creation. The work presents results of the study of a virtual composer assistant based on the semantic map and on the graph of similarity (computed using LibROSA) of selected 158 musical fragments (Apple Loops). The assistant was built based on the eBICA cognitive architecture framework (Samsonovich, 2013, 2018). Participants were 16 NRNU MEPhI Undergraduate Students. Fragments were combined into compositions by participants guided by the assistant, and then evaluated on a number of scales by other participants. Four experimental conditions were compared: with and without guidance by the map, with and without guidance by the similarity graph. Results indicate that the usage of similarity data by the assistant significantly improves the quality of resulting compositions. At the same time, the effect of usage of the semantic map was found significant only in the absence of hints based on the similarity data. Nevertheless, obtained results support the idea of the semantic map significance in composer assistant’s function. One of the next goals in this study will be to generate music automatically, following the dynamics of actual or expected human emotional states, e.g., during a videogame. In general, the advantage of this new technology is in its generality: it will have broad applications in many areas of artificial intelligence.
Concepts of Oeuvre and Algorithmization in Modern Engineering Education

The work is devoted to the study of the coordination of creative and technological components in the teaching of the discipline material. Previously investigated questions of the sequence in time of these components and the correction of their shares directly during the educational process on the basis results of intermediate certification. This study explores the contrasting method of teaching; pedagogical means that “sharpen” the creative and technological aspects to the level of oeuvre and algorithmic, respectively; this means that learning becomes contrasting, and in some cases even paradoxical, and thus attracts the attention of students and improves memorization. At the same time, the concept of oeuvre and algorithmization in modern engineering education allows for greater contrast in the application of educational technologies and creative methods. However, the results of the experiment show that when the bias in one direction or the other, the most capable students with either high creative potential or a high level of system-logical thinking suffer.
Digitisation will transform the future of pathology

Technology has become an increasingly vital aspect of healthcare, but its impact on pathology has been particularly profound. Gemma Harris discusses these developments and their potential to transform the nature of the field.
Necessary and Sufficient Polynomial Constraints on Compatible Triplets of Essential Matrices

The essential matrix incorporates relative rotation and translation parameters of two calibrated cameras. The well-known algebraic characterization of essential matrices, i.e. necessary and sufficient conditions under which an arbitrary matrix (of rank two) becomes essential, consists of a single matrix equation of degree three. Based on this equation, a number of efficient algorithmic solutions to different relative pose estimation problems have been proposed in the last two decades. In three views, a possible way to describe the geometry of three calibrated cameras comes from considering compatible triplets of essential matrices. The compatibility is meant the correspondence of a triplet to a certain configuration of calibrated cameras. The main goal of this paper is to give an algebraic characterization of compatible triplets of essential matrices. Specifically, we propose necessary and sufficient polynomial constraints on a triplet of real rank-two essential matrices that ensure its compatibility. The constraints are given in the form of six cubic matrix equations, one quartic and one sextic scalar equations. An important advantage of the proposed constraints is their sufficiency even in the case of cameras with collinear centers. The applications of the constraints may include relative camera pose estimation in three and more views, averaging of essential matrices for incremental structure from motion, multiview camera auto-calibration, etc.
Group-attention Based Neural Machine Translation

Abstract Machine translation is a classic problem in natural language process (NLP). Recent years, the encoder and decoder through an attention mechanism has become a trend. Google proposed a new simple network architecture, the Transformer using attention mechanisms only rather than CNN or RNN in 2017. However, it may lose some important information (e.g., grammatical component, etc) when using attention mechanism for whole sentence.We propose a new brand model based on transformer using Group attention layers and group position embedding. It absorbs the features of Group-CNN combines the algorithm in computer vision (CV) and NLP. The model not only pays more attention to the ingredients (e.g., subject, predicate and adverbial, etc), but also enhances the connection of phrase. It outperforms SofA Transformer in using more syntactic information.
Software defect prediction based on weighted extreme learning machine

The uncertainty of developers' activity can lead to engineering problems such as increased software defects during software development. Therefore, advanced approaches to discovering software defects are needed to improve software systems by software practitioners. This paper describes a novel fram ework named Weighted Supervised-And-Unsupervised Extreme Learning Machine (WSAU-ELM) including the construction of supervised weighted extreme learning machine for software defect prediction (WELM-SDP) and unsupervised weighted extreme learning machine with spectral clustering for software defect prediction (WELMSC-SDP) that can perform significantly better than the previous software prediction methods. The key advantages of this proposed work are: (i) both the two algorithms can reveal the better learning capability and computational efficiency; (ii) the supervised prediction algorithm is more precisely and faster to handle data sets than the common models, and save more time and resources for software companies; (iii) the unsupervised prediction algorithm can increase accuracy compared to the current method; (iv) the paper also discusses the software defect priority for the defective data, and provides the detailed priority levels that is not discussed before. Experimental results on the benchmark data sets show that the proposed framework is not only more effectively than the existing works, but also can extend the study by the priority analysis of software defects.
Vision Based Autonomous Tracking and Landing on an Arbitrary Object of Quadrotor

This paper addresses the problem of tracking and landing on an arbitrary object of a quadrotor. It is assumed that the vertical and lateral velocity controllers of a quadrotor can be designed independently. We have developed an image based visual servoing method that uses only a downward looking camera for tracking and landing from a quadrotor. Our visual detection algorithm allows the user to specify an arbitrary object to detect its movements in the image plane. In the horizontal plane, we use the lateral velocity controller to keep tracking with the object in the center of the camera image. In the vertical profile, we adopted a log polynomial velocity to guarantee zero closing velocity and acceleration at touchdown. During the tracking phase, the quadrotor tracks the object continuously while maintaining a fixed altitude from the object and keeps it in the center. Once the quadrotor satisfies the landing condition, it moves on to the landing phase at the planned vertical velocity until touchdown. Here, we use the ultrasound sensor running kalman filter to detect the landing condition. Our experiments show that the system is able to track an arbitrary object and land on it successfully.
Analysis of Knowledge Management System in Grabb Mobile Application Based On Usability

Abstract Grabb mobile application is a tool that is often used by people to travel. Each will travel then grabb will be ordered and pickup in front of the house and delivered to the destination location according to the address written on the application. Much knowledge can be disseminated through this Grabb application. Among them Grabbb car when going to go using a car, grabbike, service and others. The method in this study uses the adoption of a knowledge management system life cycle (KMSLC). Where the stages used are consists of Analysis of Existing Infrastructure Evaluation, Knowledge Capture, Implement the KM system, Evaluation. The steps taken are system analysis, knowledge mapping in the application, the results of implementation and evaluation using usability with 10 respondents through the adoption of Software Usability Measurement Inventory (SUMI). The results of SUMI’s Usability are effectiveness with a value of 90, efficiency 70 and satisfaction 70. By looking at these results, the mobile grabb application is included in the good category because the value of usabililty is above average. A post-publication change was made to this article on 20 Apr 2020 as the previously published article was a duplicate.
DSCTS: Dynamic Stochastic Cloud Task Scheduling

Utilization of resources is an important issue in cloud computing which can be achieved by load balancing over virtual machines. In this paper, a dynamic stochastic cloud task scheduling (DSCTS) method is proposed to balance the workload among virtual machines. This method selects a base task, and the machines that can do it are filtered. Then, based on probability theory, the most suitable virtual machine will be selected to perform the task. Finally, more similar machines are chosen for processing other tasks. The results show that the presented method in this paper improves the utilization of the cloud systems by balancing the distribution of workload among the cloud resources.
Abnormal Crowd Behaviour Recognition in Surveillance Videos

The paper presents an approach to crowd behaviour recognition in surveillance videos. The approach is based on a 4-stage pipelined multi-person tracker adapted to microscopic crowd level representation and crowd behaviour recognition by the evaluation of fuzzy logic functions. The multi-person tracker combines a CNN-based detector and an optical flow-based tracker. The following tracker features are used: optical flow and histogram of optical flow orientation at the macroscopic level, and the tracklets and trajectories of a person and/or group of people at the microscopic level. The human interpretation of video sequences (real and/or video sequences obtained by simulators of crowds) is mapped into fuzzy logic predicates and fuzzy functions. Fuzzy logic predicates specify crowd motion patterns at the microscopic level for a person and/or group of people. They are building blocks of fuzzy logic functions which describe different scenarios of characteristic crowd behaviour. The preliminary results of three experiments for a runaway scenario show that the approach supports efficient and robust crowd behaviour recognition in surveillance videos.