Reviewerly/exHarmony huggingface dataset:
Reward Function Considering Ambiguity Using Inverse Reinforcement Learning and Fuzzy Reasoning

A reward function estimated with inverse reinforcement learning has been used to determine a method for controlling a robot. The number of state transitions that can be observed using the action sequence given to inverse reinforcement learning decreases drastically as the complexity of the planning problem increases. Although a reward function can be designed even from partial state transition information, ambiguity in the obtained reward function exists. A reward function that can tolerate ambiguity is required when learning with a reward function that includes ambiguity. In this paper, we propose a method for quantifying the ambiguity of the reward function, which is designed with inverse reinforcement learning using fuzzy reasoning. Experimental results was suggested that proposed method can learn the action sequence while considering the degree of risk and safety.
Social Media Intelligence for Brand Analysis

With the rise of social media users, the amount of data being generated every day from the social media websites is increasing. Social media web sites like Instagram, Twitter, Snapchat, etc. are allowing the users to express their opinions on any subject and share their opinion with people sitting in any corner of the world. Numerous posts are also posted about various brands belonging to multiple domains like technology, sports, fashion, etc. These posts, when analysed, can provide valuable insights about the social media users' opinion and prove to be an untapped gold mine for the brands. These insights can in turn help the brands to make better business decisions based on the insights that are extracted from the posts. This paper aims to provide a real-time solution to stream the tweets from Twitter related to a specific brand and analyse them in real-time in order to extract insights from the tweet data that allows the brands to know what exactly are the social media users' perceptions about them. This solution consists of multiple stages where scripts are deployed on servers and run around the clock to process the raw streamed tweet data using artificial intelligence techniques in order to extract location, sentiments, named entities, date and time to further categorise the information based on numerous factors mentioned in the paper and present the data in the form of charts displayed on a webpage which will enable the end user to gain insights and uncover hidden patterns from the data.
A Survey of Adversarial Examples and Deep Learning Based Data Hiding

Nowadays, the emergence of deep learning technology has brought breakthroughs to many fields and has been widely used in many practical scenarios. At the same time, the concept of adversarial examples are gradually known. By adding tiny disturbances to the original samples, the accuracy of the original classification depth model is successfully reduced, and the purpose of confronting deep learning is achieved. In this paper, the survey of adversarial examples and deep learning based data hiding are presented, and then the idea and possibility of combining them well are then puts forward, for providing a novel concept of data hiding based adversarial examples. In addition, this paper introduces the generation of adversarial examples and defense against them. The future research is prospected by using watermark to generate adversarial examples. Making use of the imperceptibility of data hiding, we present a novel concept of adversarial examples adding meaningful watermarks to the original image and attacking the deep neural network model.
Global Overviews of Granular Test Coverage with Matrix Visualizations

Existing IDE-based tools that are available to developers make understanding software testing difficult for a software system, for both granular tasks (e.g., answering questions such as, "which test cases execute this method?") and global tasks (e.g., answering questions such as, "what is the proportion of unit tests to system tests?"). IDE-based tools typically support local, file-based views of a project’s test suite, and rarely offer a global overview. Global overviews can provide a larger context for a method’s execution by test cases; help identify other similar, or related methods; and even reveal similarity between individual tests. This work approaches such challenges with a novel, interactive, matrix-based visual interface that provides a global overview of a software project’s test suite, specifically in the context of the methods available in the project’s codebase. Through a series of interactive functions to sort, filter, query, and explore a test-matrix visualization, we demonstrate how developers can effectively answer questions about their project’s test suite, and the code executed by such tests. Our evaluations, performed on four real-world software systems, show that the interactive visualization assisted developers to answer questions about software tests and the code they execute. Further, the visualization consistently outperforms traditional development tools, both in accuracy and time taken to complete software-engineering tasks.
The Knowledge Development of State and Municipal Employees Using Electronic Educational Technologies

This article describes the state of the educational system in the field of developing the competencies of state and municipal employees and the features of using electronic educational technologies to improve the professionalism and competence of employees. The objectives of the study include: to define the concepts of “professional competencies”, “professional competencies of a civil servant” and “competence”; to determine the features of training with the use of electronic educational technologies and to justify the effectiveness of it; to determine the degree of introduction of electronic and distance educational technologies in the state and municipal sector; identify and describe opportunities and prospects for further development and research on this topic. The methods used during the research include: the study, processing and analysis of scientific sources on the research problem; the method of content analysis; comparative analysis of data on the implementation of educational programs and projects; methods based on a systematic approach with a description of the effectiveness of solutions used in domestic practice, etc. The results of the study: it is necessary to maximize the introduction of the latest innovative technologies, equipment and new methods of education; the advantage of electronic educational technologies is the ability to attract high-class specialists without taking into account the territorial component; universal introduction of broadband Internet in all government bodies to improve the efficiency of services provided and training of employees; personalization of additional education programs; monitoring the needs of state and municipal employees to develop the necessary competencies for professional activity.
On Affine Symbolic Regression Trees for the Solution of Functional Problems

Symbolic regression has emerged from the more general method of Genetic Programming (GP) as a means of solving functional problems in physics and engineering, where a functional problem is interpreted here as a search problem in a function space. A good example of a functional problem in structural dynamics would be to find an exact solution of a nonlinear equation of motion. Symbolic regression is usually implemented in terms of a tree representation of the functions of interest; however, this is known to produce search spaces of high dimension and complexity. The aim of this chapter is to introduce a new representation—the affine symbolic regression tree. The search space size for the new representation is derived, and the results are compared to those for a standard regression tree. The results are illustrated by the search for an exact solution to several benchmark problems.
A Statistical Classification of the Benign and Malignant Neoplasm using Ensemble Learning and Classification Algorithms

Detection of Breast Cancer is the introductory stage of a cancer diagnosis. Therefore, it is essential to identify whether the tumor is cancerous or non-cancerous. The goal of the paper is to recognize the benignancy and malignancy of the cancerous cell precisely. The paper examines two machine learning algorithms and proposes a novel ensemble learning approach using a blend of classification algorithms namely ℌk-nearest neighbors (k-NN) with Neighborhood components analysis, and Support-vector machine classifier (SVM)ℍ. The proposed unification was examined on the Wisconsin Diagnostic Breast Cancer dataset, provided on the DCI machine learning repository. The suggested approach exhibits a better performance of 99.29% (10- fold CV), 98.41 % (10- fold CV), and 98.85% (10-fold CV) for k-nearest neighbors (NCA), Support-vector machine, and Ensemble method respectively.
Development of a Pen-Type Device for SPIDAR-Tablet that Presents Force and Thermal Sensations

The quality of education can be improved by combining information and communication technology (ICT) education using personal computers (PCs) and tablets, which have gained considerable popularity over the last few years with virtual reality (VR) technology. Many students have difficulty in comprehending various physical and mathematical phenomena intuitively even if they can be expressed in mathematical formulae. Sensitization (visualization, auralization, and tactileization) could help enhance the understanding of these phenomena. In this study, to increase the understanding of thermodynamic phenomena via force and thermal sensations, we used a tablet-type thermodynamics learning support system. In a previous study, a piston was displayed in a VR space on a tablet, and the pressure changes were presented as force sensation using a force feedback device and a SPIDAR-tablet. Using a pen-type thermal feedback device, the temperature changes were presented as thermal sensation; however, the current SPIDAR-tablet presents two issues: i) the force control unit and thermal control unit are separated during the development stage, and ii) the presented force is weak because the motor power is small; therefore, it is difficult to feel force changes. In this study, to solve these limitations, we first integrated the force and temperature control circuit, and then enhanced the presented force by changing the motor and fabricating a new frame. Then, we assessed and confirmed the improvement of the force that was presented.
An Image Captioner for the Visually Challenged

Spontaneously detecting and delineating the components of images is a core issue in the field of Artificial Intelligence. Further, the requirement that the generated captions are grammatically accurate and well-formed adds to the challenge of building intelligent image captioning systems. These systems, however, could greatly benefit those who are visually challenged to gain a superior sense of their neighborhood. With widespread access to mobile phones that are capable of taking photos on the go, the visually challenged user will be able to take a photo of his environment. A caption for the photo would be generated and read aloud to the user. In this paper, the focus is on the development of a deep recurrent neural network architecture-based model which can generate descriptive sentences that serve as captions for images. The model extracts the features from the image fed to it via a Convolutional Neural Network, which in turn is advanced to a Long Short-Term Memory network, that is an artificial recurrent architecture which produces a highly descriptive caption for the image fed in natural language. Training of the model focuses on achieving maximum similarity to a target sentence which is specified during the training phase; hence the model aims to achieve fluency in language and accuracy in identifying context and content of an image exclusively from the target descriptions provided for images in the dataset and use that to caption other images fed to it. The proposed model was evaluated quantitatively using BLEU scores and qualitatively as well. The model presented achieves high standards of accuracy and is capable of producing accurate, descriptive captions for images. The model, therefore, has immense potential to contribute to bettering the lives of those with visual impairments.
Smart Item Recommendation System for Offline Cloth Shop

Recommendation System are widely designed for online e-commerce but the proposed recommendation system will be used by offline cloth shop. Offline shops can also use the recommendation system to increase their sales. Most of the shops recommend the same items to every customer irrespective of their interest either recommend items whenever some new stock arrives. Sometimes this may frustrate customer because customer may get some irrelevant recommendations or a customer may get recommendation very frequently. So the main aim of the proposed system is to control the product to be recommended and how frequently recommendation should be sent to the customer.
Image Semantic Segmentation Based on Joint Normalization

Image semantic segmentation is an important research direction in image processing, computer vision and deep learning. Semantic segmentation is to classify the image pixel by pixel, so that the original image is divided into semantic segmentation images with specific pixel marks, which is the most challenging in image processing. Based on DSC-JFP (depthwise separable convolution-joint feature pyramid) model, ASPP model and auxiliary network are removed to improve the real-time performance of semantic segmentation. Combined with batch normalization and instance normalization, parallel batch and instance normalization (PBIN) and cascaded batch and instance normalization (CBIN) methods are proposed to improve the effect of semantic segmentation. The experimental results also show that the proposed method improves the real-time performance of semantic segmentation while ensuring the effect of semantic segmentation.
Development of a forecasting agent based on a fuzzy neural Petri net for predicting abnormal situations in automation systems

This article examines the issues of modeling a forecasting agent based on extensions of Petri nets. For this, a new class of Petri nets, fuzzy-neural Petri nets, was developed. It made it possible to build a model for predicting a change in the situation from standard to boundary and abnormal. The implementation of this model in the CPN Tools software package is also considered.
Implementation of an Object Recognizer Through Image Processing and Backpropagation Learning Algorithm

Artificial neural networks in an effort to emulate the operation of the human brain from the point of view of learning and adaptation, have evolved in such a way that different statistical and mathematical models have inspired biological models, example you have to nerve cells or better known as neurons; the same ones that are composed of dendrites which are responsible for capturing the nerve impulses emitted by other neurons. The present study aims to analyze the Backpropagation model and the multilayer topology using an object recognizer through digital image processing such as object segmentation by detection and edge determination.
Point-to-point Motion Planning with Obstacle Avoidance for Hard Constrained Redundant Robotic Manipulators

The redundant robots have more degrees of freedom than the required for the assigned task. For point-to-point motion redundant robots might pass through singularities. This is due to their hard joint constraints or due to obstacles within the workspace. The goal of this paper is to propose an approach based on graph theory for point-to-point motion planning which considers static obstacles placed within the workspace of hard constrained redundant robot. The applicability of the approach is validated through real experiments.
Research on Dynamically Configurable Data Acquisition System in the Big Data Era

Based on big data mining and Java Web development platform, this paper uses Apache POI plug-in to design and develop a dynamically configurable data collection system. The system completes service-oriented dynamic configurable data search and collection on the basis of big data mining and big data processing. It selects dynamic configurable data based on time objective function and comprehensive objective function, and designs a configurable data collection system. The simulation experiment results show that the data collection precision of this system is higher, more accurate and more feasible.
A Novel Content-based Image Retrieval Scheme based on Textual Information

Content-based image retrieval (CBIR) is an active research field in the domain of information retrieval. This domain is widely studied in the last two decades and there are lots of approaches are introduced to handle this. Most of the existing CBIR schemes consider shape, color, or texture features of the objects in the image to find similar images for the retrieval purpose. In this manuscript, we introduce a CBIR scheme to retrieve the relevant images based on the textual information present in the images rather than other features. The proposed scheme will take an image as the input and retrieve the images which contain the common text information. From both the query image and from all the images in the image pool, the proposed scheme will identify the text by using the text detection approach. It might be noted that the text detection from natural image scenes is not always working perfectly. So to do the matching between the query image and the images in the image pool, we have used the length of the longest common sub-sequences as the criteria. The experimental study of the proposed scheme is carried and the results show that the proposed scheme works well.
Key Text Frame Selection from Video

Text frame classification is needed in many applications such as event identification, exact event boundary identification, navigation, and video surveillance in multimedia.
Encryption Traceability Scheme Based on SGX and Blockchain

Blockchain has the characteristics of decentralization, difficult to tamper with and traceability. The traceability system based on blockchain solves the problems of insufficient transparency in the process of information processing and non-traceability of processing information. However, excessive information transparency will cause data information leakage, and the limitations of smart contracts used to process transactions on the blockchain make the physical information under the blockchain inconsistent with the information on the blockchain. In view of the above two problems, this paper provides an encryption traceability method based on SGX (Software Guard Extensions) and blockchain, which executes complex contracts in the trusted execution environment under blockchain, and encrypts the contract output results. In this way, inconsistency between information on the chain and information off the chain and transparency of blockchain information can be solved. The analysis results show that this method has the characteristics of non-transparent ledger storage, processing information traceability and decentralization to a certain extent, and can solve the problems of data leakage of the traditional blockchain-based traceability system and the inconsistency between the information on the blockchain and the physical information.
Modeling Indian Road Traffic Using Concepts of Fluid Flow and Reynold’s Number for Anomaly Detection

At present, road traffic anomaly detection is becoming a necessity. However, there are no databases available for the Indian road traffic scenario and hence there are almost no significant works done for anomaly detection in Indian road traffic. This work attempts to take the first step toward solving this problem using a limited video surveillance database, captured locally by the authors. This paper draws an equivalence between fluid flow and traffic movement, and anomalies in road traffic and turbulent flow. Taking this path, the reported work further computes a measure of the state of the flow, equivalent to Reynold’s number. The visual correlation of the traffic scene in the video frames with the plots of the equivalent parameter calculated from the frames shows promising results. The proposed method, equivalence of various fluid motion concepts with the traffic motion and the experimentation process with the results thus obtained are described in this paper. The results obtained show the equivalence between fluid motion and road traffic flow.
Simple Distribution of Sensitive Values for Multiple Sensitive Attributes in Privacy Preserving Data Publishing to Achieve Anatomy

In anonymizing data, k-anonymity is a pioneer model and become popular. However, it still a drawback in information loss when the sensitive values are not distributed evenly. This study aims to distribute evenly sensitive values in microdata with multiple sensitive attributes by extending simple distribution of sensitive values (SDSV) method to anatomy. Previously, this method works well when it is conducted in k-anonymity. This method is used with a little modification in last step by exchange randomly record if privacy level is not satisfied and this method is run in anatomy. The result shows that in anatomy SDSV has better performance than systematic clustering as a base line. SDSV has diversity value 1.02 and systematic clustering has 0.33. From information loss aspect in anatomy model the result also shows that SDSV outperforms systematic clustering.
Design and Development of Catfish Supply Chain Management System "Panen-Panen" Using Rapid Application Development Method

The fisheries sector in Indonesia is one of the gross domestic products that has tremendous potential both for domestic and foreign (export). One of the most popular types of aquaculture sector is catfish cultivation. Catfish has an advantage over other animal products, namely that it contains high levels of leucine and lysine. This is an opportunity for the Indonesian people to take the opportunity to become catfish cultivators. Opportunity is also to become a seller of processed catfish products, or as a buyer of catfish. All these opportunities open the potential of a system that regulates catfish farming. One such system is called "Panen-Panen". "Panen-Panen" is an application where catfish sellers, support sellers, and buyers, even suppliers, gather. The application involves a lot of sociopreneurship mobilization between harvesters. Therefore, various problems arise ranging from standard costs, consistency of feeding, recording of daily catfish cultivation information, determination of raw materials and transportation costs, and feed suppliers. These various problems require the creation of a special supply chain management system for "Panen-Panen" catfish as a solution. The "Panen-Panen" catfish feed supply chain management system will be designed and built mobile-based rapid application development.
Thermodynamic restrictions on artificial intelligence based on quantum systems

We discuss the possibility of emerging conscious behaviour in open quantum systems. Our consideration is based on the definition of consciousness as the capability of a system to model the behaviour of its environment and act accordingly to the model. According to the laws of thermodynamics the total entropy of the learning system and its environment cannot decrease, unless a quantum measurement is performed on it by external consciousness. This prohibits the emergence of conscious behaviour in artificial systems. The consideration is illustrated by numerical examples.
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