Reviewerly/exHarmony huggingface dataset:
Image Quality Assessment for Image Segmentation Algorithms: Qualitative and Quantitative Analyses

Image segmentation is part of the image processing and it is important as it has been used in various fields such as medical imaging, autonomous driving and object detection. The selection of technique that is suitable for application is crucial in order to get a good result. The problem occurs when one would like to choose the right method that will give a good segmented image. Therefore, this paper presents the image quality assessment for image segmentation algorithms in terms of qualitative and quantitative analyses. In this project, three techniques of image segmentation which are K-means clustering, threshold and watershed marker controlled are compared with each other and the evaluation result is based on the image quality assessment. Those three methods used to segment greyscale images are taken from the Internet. The objective is to find which technique gives the best result based on the image quality assessment. The parameters used to analyse the quality of the processed image are the mean square error (MSE), average difference (AD), mean absolute error (MAE), structural similarity index metric (SSIM), also the structural dissimilarity index metric (DSSIM). The algorithm of the image segmentation techniques is built using open CV-C++. Based on the assessment, the results, it shows that K-means clustering gives a better result qualitatively and quantitatively while the watershed marker-based shows a good qualitative result. This quality assessment technique hopefully could help in the selection of the image segmentation technique for the respective applications.
Exchangeable Processes: de Finetti’s Theorem Revisited

A sequence of random variables is exchangeable if the joint distribution of any finite subsequence is invariant to permutations. De Finetti’s representation theorem states that every exchangeable infinite sequence is a convex combination of independent and identically distributed processes. In this paper, we explore the relationship between exchangeability and frequency-dependent posteriors. We show that any stationary process is exchangeable if and only if its posteriors depend only on the empirical frequency of past events.
Pixel Prediction-Based Image Steganography by Support Vector Neural Network

Abstract Securing the privacy of the medical information through the image steganography process has gained more research interest nowadays to protect the privacy of the patient. In the existing works, least significant bit (LSB) replacement strategy was most popularly used to hide the sensitive contents. Here, every pixel was replaced for achieving higher privacy, but it increased the complexity. This work introduces a novel pixel prediction scheme-based image steganography to overcome the complexity issues prevailing in the existing works. In the proposed pixel prediction scheme, the support vector neural network (SVNN) classifier is utilized for the construction of a prediction map, which identifies the suitable pixels for the embedding process. Then, in the embedding phase, wavelet coefficients are extracted from the medical image based on discrete wavelet transform (DWT) and embedding strength, and the secret message is embedded into the HL wavelet band. Finally, the secret message is extracted from the medical image on applying the DWT. The experimentation of the proposed pixel prediction scheme is done by utilizing the medical images from the BRATS database. The proposed pixel prediction scheme has achieved high performance with the values of 48.558 dB, 0.50009 and 0.9879 for the peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation factor, respectively.
A Survey on Robotics Arm Showing Writing Skills by Speech Recognition

Robotic arms are programmed robot manipulator with functioning similar to a human arm. Speech recognition has been evolved as an interface for computer control where speech recognition enables a person to sidestep the traditional keyboard and activate a computer through speech control. This novel conjunction of robotic arm and speech recognition opened up many areas which can be benefitted from its application. With advent of this technology, many commercially viable speech recognition enabled robotic arms are widely utilized in logistics, office and medical field. This paper reviews different methods of a robot arm exhibiting writing skills by speech recognition.
Wrist MEMS Sensor for Movements Recognition in Ball Games

In ball games, the arm movements are diverse and complex. The accurate recognition of the arm movements is of great significance to the quality of training and sports guidance. Wrist sensor is one of the important means. Traditional wrist sensors recognize batting action by measuring the acceleration and angular velocity of the arm and have a good performance. But they measure few features, and it has a slightly larger size that has some effect on player performance. In this paper, we develop a motion recognition platform for Basketball, Ping-Pong, and badminton. It is accurate enough for recognizing these motions whether hits or miss the Ping-Pong ball. The system integrates a low-power nine-axis inertial sensor and a low-power environment sensor that can output eleven different characteristics such as triaxial acceleration, triaxial angular velocity, triaxial magnetic field strength, air pressure, and temperature. This system includes the power management part, attitude detection part, Bluetooth wireless communication part and mobile terminal monitoring part. The inertial sensor can identify the arm action, and then transmits posture data to the Microcontroller Unit (MCU) for analysis and processing. Bluetooth 5.0 is used between the MCU and the mobile monitor for information exchange, which makes data transmission faster and more stable. Experiments of the 3 athletes have demonstrated that the movements can be accurately identified. And the entire system has very low power consumption and very small size (23.05mm*9.25mm*5.5mm). It provides a multi-feature measurement platform for body movement posture research and can recognize a variety of ball games movement, which is of great significance for intelligent sports training and research.
A low power Artificial Intelligence Processor for Autonomous Mobile Robots

The robot which makes use of AI as a mode of processing is getting more popular day by day, starting from the autonomous room cleaning robot to Amazon Prime Air. This autonomous robot overtakes traditional robots in following aspects such as implementing effective decision making in order to reduce the computational overhead by reducing the overall power usage of the robot. In this report, we have designed a low power [1] AIP without compensating in performance. The AIP which we have designed is a 64 processing element that uses parallel processing architecture. A map with 8 different routes is created in Xilinx where it calculates the shortest path from the source to destination using conditional operators. A* algorithm is implemented in Matlab to calculate the shortest distance and Dijkstra’s algorithm is converted to VHDL using Vivado HLS coder. A neural network is also created using Matlab to detect and avoid real time obstacle. The overall power report of the processor is implemented in Cadence.
Lip Expressions Based Control via Image Processing

This paper presents an image processing technique to identify lip expressions in producing control instructions. The purpose is to assist patients suffering from Tetraplegia especially in their mobility by providing alternative ways of controlling devices. In this work, motorized wheelchairs are used as the studied mobility device. Some of the methods used in motorized wheelchair do give user difficulties as they need extra effort in controlling the device. Thus, this project aimed to study, design and implement an image processing technique to differentiate four different lips expressions. The classified four different lip expressions will then be used as instructions for forward, turn right, turn left and stop movements of motorized wheelchairs. A technique of morphological processing is applied to the image of the face in order to extract the lips region. The obtained region is then calculated for several parameters. The parameters classification is done using Artificial Neural Network (ANN) to determine the category of lips expressions. In the performance analysis, the developed technique is proven to successfully classify the lips expressions into the categories and produce appropriate instructions for the use in motorized wheelchair control.
High-Performance Techniques for Big Data Processing

Supercomputers deliver a hundred- to millionfold performance compared to regular desktop PCs. This allows to tackle problems which could not be solved otherwise in a reasonable amount of time. At the same time, the massive amount of data generated and collected through sensors, experiments, etc., is constantly increasing, requiring new and analogously evolving approaches at hardware and software level for data processing and analysis. In this chapter, we shed light on recent hardware trends, programming paradigms, and software in the scope of high-performance computing and Big Data processing. We provide an overview of compute architectures, interwoven with programming examples, and discuss storage and data management concepts for both (super-)computing and Big Data processing. We further discuss performance metrics and benchmarking, as well as performance modeling and analysis for computing and data handling. Finally, we share some views on high-performance data analytics as well as concluding remarks on high-performance computing, Big Data, and convergence of these fields.
Deep Reinforcement Learning Based Game Decision Algorithm for Digital Media Education

On the traditional digital media education platform, problems raised by users are usually solved by searching the optimal solution in the existing database, which lacks the ability of autonomous learning and long-term planning. This paper introduces a Deep Reinforcement Learning algorithm based on Deep Deterministic Policy Gradient to solve these issues. We applied a trained neural network with this algorithm to play "Klotski" - a sliding block puzzle where the aim is to move a specific block to some predefined location. In this article, we explain why the game is selected and how our method works. We also discuss possible ways forward within the field in the future.
On the Moore-Penrose Pseudo Inverse of the Incidence Matrix for Weighted Undirected Graph

The interaction of intelligent agents implies the existence of an environment to support it. The usual representations of this environment are graphs with certain properties. Throughput is one of the most important characteristics of such graphs. A traditional metric, such as the usual shortest paths, forms the basis of the traditional throughput index. In this case, a metric is used to synthesize the distribution of multi-coloured flows in graphs more complex than trees. To achieve better results than when using ordinary shortest paths, one can use the Euclidian metric. Working with weighted graphs requires a generalization of the explicit form of the Moore-Penrose pseudo inversed incidence matrix. The validity of the generalization is confirmed by verification of the Penrose conditions. An example of using the Euclidian metric for the distribution of computer network flows is given.
Video in Radiation Environments Denoise Using Wavelet and Kalman Filter

There are lots of noises in video which captured in radiation environments. The wavelet denoise performs advantages on images. In the present study, a coupled wavelet and kalman filter method is applied in neighbor frames processing, while the necessary transformations are derived. The compared experiments for the video denoise in radiation environment are implemented, and the structural similarity index method has been adopted to analyze results, suggesting a significant effectiveness for processing video with radiation-noise.
Trust-Aware Group Recommendation with Attention Mechanism in Social Network

Due to the fast development of the Internet technology, a variety of social applications have emerged. The users of these social applications are expanding, forming a large-scale social network. In complex and huge social networks, there are a lot of valuable information waiting to be utilized, so that service providers can provide better services for users. On the other hand, to solve the challenge of information overload brought with the communication technology, the recommendation system is becoming more and more important in many fields. In the context of the social network, a recommendation system can better discover the relationship between users and preferences, thus giving a more accurate recommendation. However, most of the traditional recommendation systems mainly focus on providing personalized recommendation service to a single user which are not suitable for group recommendation such as recommendation for a team or a family. In this paper, we proposed a new method based on trust relationship in social network and attention mechanism of deep learning to solve the group recommendation problem. Specifically, we use attention mechanism to capture different group members's preference weight and we consider trust relation among group members when learning group preference in order to capture more information. Through this method, the accuracy of the recommended results can be further improved. The experimental results on Film Trust dataset show that the proposed algorithm in this paper is able to improve the quality of recommendation compared with the existing group recommendation methods.
Cyber Risk Assessment of Networked Cyber Assets using Probabilistic Model Checking

Attack path analysis to assess the path from the external facing entities to the inner hosts and network elements is a much researched problem. However, to compute a summary risk value per device, based on vulnerabilities discovered on a daily basis, is a much demanded capability in the arsenal of any security administrator of an enterprise network. Further, higher management such as CISOs have to be convinced with numerical risk comparisons to allow the down time required to patch the systems as opposed to defer it till a much later date during a scheduled shutdown. It must be noted that each security administrator's problem is different due to the difference in the structure and composition of the network they administer. Therefore, no industry data source can help in getting these numbers, as the risk numbers are specific to each network and its components. In this paper, we present a methodology based in probabilistic model checking to compute these risk scores for each device in an enterprise network.
An Overview of Handwritten Character Recognition Systems for Historical Documents

AnalysingHandwritten HandwrittenHandwritten Documents is a challenging task. This particular area cannot always come up with general solutions, given that most handwritten manuscripts contain unique characteristics that describe how the document was written, which include different handwritings. These challenges in transcribing different handwritingDifferent handwriting styles are due to various scribes contributing to the transcription of the text and degradation of the script. In this chapter, an overview of different techniques used in handwritten text recognitionHandwritten text recognition systems is presented. The approaches and algorithms can be adopted for different document types irrespective of the state of the scanned documents. Moreover, two different general approaches to handwritten character recognitionCharacter recognition are shown. The first approach goes through a fairly standard process to normalise, segment and recognise characters. The other approach is a segmentation free approach that uses neural networks for both segmentation and recognition.
3-D Partitioning for Large-Scale Graph Processing

Disk I/O is the major performance bottleneck of existing out-of-core graph processing systems. We found that the total I/O amount can be reduced by loading more vertices into memory every time. Although task partitioning of a graph processing system is traditionally considered equivalent to the graph partition problem, this assumption is untrue for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a vector of data elements is defined as the property for each vertex/edge. By dividing each vertex into multiple sub-vertices, more vertices can be loaded into memory every time, leading to less amount of disk I/O. To explore this new opportunity, we propose a category of 3-D partitioning algorithm that considers the hidden dimension to partition the property vector. The 3-D partitioning algorithm provides a new tradeoff to reduce communication costs, which is adaptive to both distributed and out-of-core scenarios. Based on it, we build a distributed graph processing system CUBE and an out-of-core system SINGLECUBE. Since network traffic is significantly reduced, CUBE outperforms state-of-the-art graph-parallel system PowerLyra by up to 4.7X. By largely reducing the disk I/O amount, the performance of SINGLECUBE is significantly better than state-of-the-art out-of-core system GridGraph (up to 4.5X).
A Local Dynamic Path Planning Approach for WMRs Based on Fuzzy Dual CHOMP

Wheeled mobile robots (WMRs) are widely utilized in exploration tasks on different planets. The path planning technique is required to guide a WMR to a target point or region. A new path planning method in a dynamic environment is proposed. The method improves the original dual Covariant Hamiltonian Optimization for Motion Planning (CHOMP) method by a fuzzy reasoning module and Nonmonotone Gradient Projection Algorithm (NGPA). Thus, this method can achieve low cost paths while maintaining the accessibility of each path nodes. The method to establish local dynamic cost map based on a new velocity-based artificial potential field (VAPF) is proposed as the environment for path planning. Thus, the low cost paths can guide a WMR away from obstacles. Results of the simulations show that the path planning method can dynamically generate a smooth path for a WMR to avoid obstacles.
A Bilingual Chatbot Using Support Vector Classifier On an Automatic Corpus Engine Dataset

Brands are shifting to digital services to cater to their customers who have been spending more time online. The technology that exists today enhances customer experience and actualizes customer expectations through virtual service agents or “e-service agents” during real-time interactions. Brands in most countries have to deal with bilingual customers as globalization occurs. Business process outsourcing is among the Philippines' top foreign exchange earner aside from overseas workers' remittances. These Philippine companies offer customer service but are mostly left manned-needing constant supervision. As a solution, the researchers present a bilingual retail chatbot that could handle the two official languages of the Philippines, Filipino-based on Tagalog-and English, and their code-switching variant Taglish. The proposed bilingual retail chatbot uses k-fold grid search cross-validation on a dataset constructed by a bilingual automatic corpus engine and a combination of both (1) support vector classifier-for intent identification, and (2) hash set containment-for attribute identification.
An experience-based policy gradient method for smooth manipulation

Policy gradient methods have achieved remarkable success in continuous controlling tasks. However, in robotic control, original policy gradient algorithms depend on the first succeed experience which is usually a suboptimal solution. To improve the performance, we propose an experience-based policy gradient method(EBDDPG) which guides the robot to move in a smooth way. Besides, extra OU-noise is added to the action space to improve exploration. We tested our algorithm on Gazebo simulation environment with Baxter robot. The experimental results show our method guides the robot to manipulate more smoothly and improves success rate of grasping tasks.
University Relations: University–Industrial Relations as the Main Factor in the Development of Polytechnic Education

The chapter is devoted to the problems of development of university–production relations within the framework of the program "Digital economy of the Russian Federation." It is shown that against the background of all-Russian changes in the institutional environment, one of the most important issues is the formation of a stable, long-term partnership between universities and industry. New innovative challenges require the strengthening of interaction between scientific, educational, and industrial spheres. In this regard, it is necessary to make a significant emphasis on the variability of higher education, which contributes to its adaptation to the new conditions of academic and economic autonomy of educational institutions. Research and innovation activities should be focused on obtaining new knowledge in the main strategic directions, the purpose of which is to attract young people from the sphere of science and education, to train highly qualified specialists, and to develop a socially oriented and competitive system of higher professional education. An important factor is the creation of new structural subdivisions aimed at organizing interaction between higher education institutions, research institutes, and enterprises in priority areas of research and education. It is shown that the integration of universities is the result of a targeted policy of increasing the competitiveness of higher education and states that use this potential for the development of their economy and social sphere. Stimulation of mutually beneficial partnership, creation of new graduate departments at production; research and innovation activities should contribute to the commercialization of scientific results and provide the Russian economy with qualified specialists with the necessary set of competencies for the generation and practical use of knowledge and skills in the field of information and energy-saving technologies, engineering, automation, new materials, etc.
A New Hybrid Approach For Privacy Preserving Data Mining Using Matrix Decomposition Technique

With the increase of data size, data analysis is becoming very important for finding hidden knowledge. This data may contain sensitive information of individuals that could be revealed while accessing the data from sources. Most individuals or organizations do not want to reveal their personal information because of privacy leakage. In recent years, many models and methods have been designed for preserving privacy such as using matrix decomposition techniques. In this paper, Sparsfied Singular Value Decomposition (SSVD) and Non-negative Matrix Factorization (NMF) are used and a hybrid approach based on those methods has been proposed. The main reason is that the decomposition of matrix more than once removes more sensitive information. By removing the sensitive data, we can improve the privacy of the dataset and keep the data utility considerable. The approach provided here ensures better privacy while keeping good data utility.