Reviewerly/exHarmony huggingface dataset:
Estimation of Target Detectability for Maritime Target Tracking in the PDA Framework

The detection probability of maritime vessels may change over time, either slowly or more abruptly, due to effects such as sea state and varying aspect angle. If the varying detectability is not accounted for in the tracking system, tracks may be terminated or lost. The undetected targets may cause dangerous situations in applications such as maritime collision avoidance. In the following, we propose two methods for tracking targets with varying detection probability, both in the integrated probabilistic data association (IPDA) framework. One method uses the number of validated measurements to estimate the detectability of the target, while the other calculates the joint detectability and existence probabilities based on the measurement association likelihoods. Both methods show significant improvements over the conventional Markov chain 1 and 2 IPDAs.
Passive and Active Sampling for Piecewise-Smooth Graph Signals

In this work, we study the sampling of piecewise smooth- graph signals that exhibit an inhomogeneous level of smoothness over the graph and are characterized by have abrupt, localized discontinuities between smooth regions of the graph. We propose an extension to the graph trend filtering framework under the sampling setting and present an ADMM-algorithm to efficiently reconstruct piecewise-smooth graph signals. Further, to alleviate the limitations of passive sampling in this setting, we develop an active sampling strategy that incorporates feedback to focus the sampling procedure near the boundary or discontinuities. We then conduct experiments that exhibit the performance of our algorithm on large complex graphs and validate the efficacy of our sampling strategies.
Comparative Study of Breast Cancer Detection Techniques

Diagnosis of breast cancer always comes with some level of uncertainty. Depending on the method of diagnosis, various issues can create uncertainty in a patient's diagnosis. An undiagnosed cancer and a falsely diagnosed one, both are bad for the patient in their own way. To prevent these kind of situations we can use machine learning techniques to aid the doctors in providing a more confident diagnosis. Several techniques have been used so far, which we summarise in this paper.
Dynamic Routing with Online Traffic Estimation for Video Streaming over Software Defined Networks

The traffic generated by video streaming applications constitutes a large portion of the Internet traffic carried over today’s networks. Video streaming demands low latency and high bandwidth. In particular, the transmission of high-quality (high-resolution) streaming video may put the network under pressure. Therefore, high-quality video traffic requires network managers to implement smart and fast routing decisions. Software Defined Networking (SDN) provides a global view and centralized control for the whole network which gives opportunities to dynamically manage networks. In this paper, we use an OpenFlow-based SDN environment and propose a dynamic routing scheme with online traffic estimation to increase the quality of high-quality video streaming and the throughput of the network. The traffic is clustered using an unsupervised machine learning algorithm, high-quality video flows are identified and routed over less congested paths. The whole design is tested in the Mininet simulator. Simulation results show that the proposed scheme improves the link utilization and reduces the amount of dropped frames as a result of excessive delay.
An Efficient Image Retrieval Using Fuzzy Based Clustering

Image searching requires valuable information from image for identification. In an image retrieval process, the major information is obtained from the image intensity, color feature, and pattern recognition. But by using these methods, we cannot get clear object shape. The proposed method combines the three methods to obtain a high quality information. Clustering based on fuzzy technique is used to classify the images. This helps to identify the color variation accurately. Pattern information can be obtained by using the circle pattern. It helps to extract the pattern of the image. That also gives the graphical information about image. The fuzzy based cluster gives background information and the pattern gives foreground shape. The graphical information gives dimension of the shape. This is used to find the correct image and to improve the precision of the image retrieval process.
Visualization of Integrated Indicators of Information Risk in Decision Support Systems

Decision-making is one of the essential functions of any manager. The means of visualization play a crucial role in supporting the manager in the decision-making process. This paper discusses the visualisation of information risks based on integral indicators. It provides a description of a mathematical model of the method of dynamic meta-anamorphosis and an example of its practical implementation. The advantages of using a hybrid approach to the development of dynamic meta-anamorphosis are discussed.
Characterizing the Utility of Surveillance Video for Person Re-Identification

Surveillance videos have many applications which can now be accomplished with automated systems. However, the performance of an automated system under realistic circumstances usually does not match the performance produced with a benchmark dataset, due to quality degradations and content variations. In this paper, we discuss several quality and non-quality factors that impact algorithm performance, and design a metric that measures the utility of a video for performing a specific task. Having such measurement allows a user to compress and/or select videos prior to implementing video analytics, reducing both computing power and storage. Here, we choose to study one of the most common applications for video analytics-person re-identification-and we call the corresponding quality measurement “identifiability”.
Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel

In the deep learning tasks, we can design different network models to address different tasks (classification, detection, segmentation). But traditional deep learning networks simply increase the depth and breadth of the network. This leads to a higher complexity of the model. We propose Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel(SKENet). SKENet extract features from different kernels, then mixed those features by elementwise, lastly do sigmoid operator on channel features to get adaptive weightings. We did a simple classification test on the CIFAR10 amd CIFAR100 dataset. The results show that SKENet can achieve a better result in a shorter time. After that, we did an object detection experiment on the VOC dataset. The experimental results show that SKENet is far ahead of the SKNet[20] in terms of speed and accuracy.
Selecting the Most Probable Author of Asocial Posting in Online Media

The paper is focused on the field of the detection of antisocial behavior in online communities. Within this field, the problem of an assigning of an author to an unknown suspicious document was solved. This problem solving supposes that a set of short informal documents posts of a set of known authors of the troll type are at disposal. Presented method of authorship attribution is based on the recognition of special dictionary of suspicious author. The designed method was implemented. It can be used to select the most probable author of toxic posts. The paper describes also tests of the designed method effectivity on short documents and a comparison with related works.
Why Feature Selection in Data Mining Is Prominent? A Survey

Feature selection is employed to diminish the number of features in various applications where data has more than hundreds of attributes. Essential or relevant attribute recognition has converted a vital job to utilize data mining algorithms efficiently in today’s world situations. Current feature selection techniques primarily concentrate on obtaining relevant attributes. This paper presents the notions of feature relevance, redundancy, evaluation criteria, and literature survey on the feature selection approaches in the different areas by many researchers. This paper supports to choose feature selection techniques without identifying the knowledge of every algorithm.
Calibrated Web Personalization with Adaptive Recurrent Computing

In recent times, large-scale data transmission and processing have become possible along with an increase in the processing and memory capacities of computer systems. With the advent of smart device technology, computing environments are being developed to support "interaction and feedback" that is specific to each customer's individual behavior. By acquiring a user's known information and monitoring his interests by following his online behavior, it has become possible to use his changing interests as triggers to learn and make more appropriate recommendations. In an online trading or e-commerce setting, multiple items are often purchased at the same time, which makes it different from the problem of determining the degree of preference for a single item at a time, as in the case of a movie recommendation. This method adjusts recommendations dynamically over the course of browsing for other products by a user, taking into account how the degree of preference for one product may affect those for others, when trying to predict the degree of preference for the next item. In this paper, a product recommendation method is proposed that dynamically understands customer needs and considers the degree to which each product itself is preferred (degree of preference). Based on this evaluation, it decides whether or not to intervene in a customer's perception of their individual product preferences, resulting in a recommendation method that can adapt to the customer's needs to a high degree. Further, it is able to make such effective recommendations in the time period between a customer's search and his decision to purchase.
Gaussian Filter Based Data-Driven Cepstral Features for Robust Speaker Verification System

In different speech-based applications, the speech-signal-based frequency cepstral coefficient (SFCC) based feature extraction method has been used successfully. The conventional method for extraction of such feature uses triangular filter banks similar to mel frequency cepstral coefficient (MFCC) features. In this work, we first present Gaussian filter based speech-signal-based frequency cepstral coefficient (GSFCC) based feature extraction technique derived from the speech-signal-based scale. Next, we use spectral entropy modulated speech-signal-based scale along with Gaussian filter to derive new cepstral feature for ASV task. We find improved performance of proposed features over baseline method in both clean and noisy conditions. The experiments were carried on NIST SRE 2001 database with simulated noise. In addition, a more recent real-world, noisy database VoxCeleb1 is used in the study. Finally, we do score level fusion of auditory filter based cepstral feature with data-driven filter based cepstral features. The performance obtained by the proposed features gives up to 15.71% and 15.37% relative improvement in equal error rate (EER) over the baseline method in NIST SRE and VoxCeleb1 databases, respectively.
An Algorithm for Path Planning Based on Improved Q-Learning

The path planning is a basic research question, where an Agent is going to look for an optimal path from the beginning to the terminal point. The earliest research problem was obstacle avoidance, that is, the Agent could not collide with obstacles or fall into traps in the process of path optimization. With rapid development of the AI (artificial intelligence) technology, the path planning has become an important application field to test intelligent learning algorithms, where reinforcement learning as an active learning method has the obvious advantage in path planning compared with traditional supervised learning and unsupervised learning. As is known to all, Q-Learning is one of the most successful reinforcement learning algorithms, and it surely can help solve the path planning. Aiming at the problems of slower convergence of the existing Q-Learning algorithm, this paper introduces dynamic search factor technology and puts forward a novel ɛ-Q-Learning algorithm. Experiments show that compared with the existing Q Learning algorithm, the ɛ-Q-Learning algorithm can not only present a better optimal solution, but also significantly reduce the number of iterations to quickly complete the optimal path generation.
Mammogram Classification using Supervising Vector Machine and K-Nearest Neighbors for Diagnosis of Breast Cancer

Breast cancer attacks women in their early productive years of life which become a public health problem, but if detected earlier it will be cured out with limited resources, while retching the advanced stage treating disease is too expensive and often poor outcome. The aim of this research is to obtain a method to classify the breast into either normal or abnormal tissues. The proposed method which is produced in this paper, is incorporating the Local Contrast (LC) with the Contrast Limited Adaptive Histogram Equalization (CLAHE), that will increase the contrast enhancement and to improve the appearance of the image. Region growing technique is used to extract and crop the region of interest (ROI), that contains the tumor with the texture features of that region automatically, with the help of using the Gray Level Co-occurrence Matrix (GLCM) technique. These features are fed into the Fine Gaussian Supper Vector Machine (SVM) classifier. As observed from the performance evaluation the proposed method classifies the mammography images with 97 % accuracy, 95% specificity and 98 % sensitivity.
Challenges and Opportunities: Quantum Computing in Machine Learning

Many computing applications are being developed and applied in almost every aspect of life and in every discipline. With increasing number of problems and complexities, there is requirement for more computational power, faster speed and better results. To overcome these computational barriers, quantum computers, which are based on principles of quantum mechanics were introduced. Faster computation is the main reason behind the evolution of quantum computers which is achieved by using quantum bits instead of bits as quantum bits store both the values 1 and 0 together in superposition. The article focuses on basics of quantum computing in brief and the underlying phenomenon behind quantum computers. Also this article exposes recent trends and the problems that are being faced in this quantum technology. The major impact of quantum machine learning is also discussed. The quantum machine learning is providing better application in this modern field. This article analyses the different research gaps and possible solutions in quantum computing. Recent days quantum computing is implemented in different applications which is also described.
SPET: Swarm Probability Exploration Tracker for Stochastic Distributed Exploration System toward Wide Area Exploration

Robots such as exploration rovers are widely used in planetary missions and tasks on the earth. The missions of these robots are mainly resource discovery and sample return, and various other tasks can be considered. Because it is a large robot, it can have many mounted devices and functions. On the other hand, as the size of the system increases, the development cost becomes enormous, and a long development period is required. In addition, because the loss cost of the robot is high, the robot is run with safety as a priority, and it cannot be said that search efficiency is good. In order to counter these problems, we have proposed a distributed exploration system using multiple small robots. This exploration system is examining a jumping parent robot and a ground traveling child robot. In this paper, the ground running robot SPET which is a child robot is explained. The child robot acts based on the positional relationship with the parent rover. In addition, since it is possible to travel on uneven terrain, posture estimation is also necessary. So, a compact and lightweight attitude / position estimation system for a compact robot with limited mass resources is needed. SPET has been developed to verify the exploration algorithm of the exploration system. In recent years, robots on the earth are expected to perform similar tasks at disaster sites and construction sites. In the future, the airframe will be improved, and practical applications in ground environments such as construction sites are being considered.
Generalized Combinatorial Approach Using Single Filter Basis for Convolutional Sparse Modeling

Learning separable filters, by either approximating from nonseparable ones or estimating in native fashion, have demonstrated to be a powerful strategy in Convolutional Neural Network (CNN) and Convolutional Sparse Representation (CSR). Particularly, in the latter field, a combinatorial separable filter based approach has been proposed in order to improve both runtime and memory requirements. It exploits the redundancy in the filter banks by efficiently modeling 2D dictionaries from all possible combinations of vertical and horizontal separable filters instead of the standard form based on pairwise sets. In this paper, we explore a generalized case of the combinatorial approach which models 2D dictionaries from a single set of 1D basis filters that can be used to represent natural images akin to the vertical and horizontal filters based approach. We show that our proposed method reduces the number of filter combinations involved in the image reconstruction by a half, while preserving quality performance for denoising and inpainting tasks. Furthermore, it also provides an increase of speedup by a factor of 10% during the learning process.
A Comparative Study on the Classification Performance of Machine Learning Models for Academic Full Texts

[Objectives] The study aims to compare the classification performance of various machine learning models, explore the classification effects of traditional machine learning models and deep learning models, solve the problem of missing category information of chapter structure in academic literature, promote the retrieval of the content of the specified chapter structure in the academic literature, and automatically extract and customize the formation of specific text services. [Methodology] 31,888 academic articles in the journal “PLOS ONE” were selected. After data cleaning and segmentation, a text classification corpus containing 313,952 chapter structure category information was constructed. Based on traditional machine learning models NB, SVM, CRF, and the deep learning model RNN model group, Bi-LSTM model group, IDCNN model group, BERT model group, a total of 17 machine learning models were used to carry out chapter structure division experiment. [Results] Among the classification tasks, the BERT-Bi-LSTM-CRF model has the best classification performance, with an average F value of 71.18%, which is 0.51% and 3.31% higher than the second CRF and the third Bi-LSTM-CRF, respectively. For deep learning models, the use of BERT for text representation is better than word2vec. Adding the Attention mechanism and replacing the Softmax layer with the CRF layer can achieve better classification results. In addition, the online version of the Chapter Structure Recognition Presentation and Application Platform has been developed, which can visually display the overall situation of the research and the model training process, and can realize machine learning and deep learning models such as NB, SVM, CRF, Bi-LSTM, IDCNN. The models can perform online recognition application of chapter structure.
A Brief Survey on Blockchain Technology

Blockchain technology is based on decentralized architecture. It allows peers to perform transactions without relying on the centralized server or third party but still ensures the privacy and security of users, which is grabbing the attention of many researchers and academicians. It includes cryptographically secured hash functions, public ledgers, consensus algorithms to ensure reliability of transactions and mining algorithms for validating the transactions. This survey is mainly focused on overview of blockchain architecture, basic concepts, applications and Proof of Work, Proof of Stake consensus algorithms.
A Chinese word segment model for energy literature based on Neural Networks with Electricity User Dictionary

Traditional Chinese word segmentation (CWS) methods are based on supervised machine learning such as Condtional Random Fields(CRFs), Maximum Entropy(ME), whose features are mostly manual features. These manual features are often derived from local contexts. Currently, most state-of-art methods for Chinese word segmentation are based on neural networks. However these neural networks rarely introduct the user dictionary. We propose a LSTMbased Chinese word segmentation which can take advantage of the user dictionary. The experiments show that our model performs better than a popular segment tool in electricity domain. It is noticed that it achieves a better performance when transfered to a new domain using the user dictionary.
Intity, Reality, Expertise in System of Humanitarian Science Methodology

The article analyses such forms of obtaining and processing information in the system of scientific and non-scientific knowledge as expert evaluation and expertise. The context of concepts «knowledge» and «cognition» evolves restrictions to the fields of using an expert approach. The concept «expert evaluation» is structured in correlation with conceptual basics implemented in the institutional approach. For analyzing the concepts «expert evaluation» and «experstise», the methodology of psychological and philosophical siences is used: psychoanalytical analysis, analysis, synthesis, modelling in conceptual basics of the structural method. The article creates a logical consequence of methodological devices in the system of humanitarian sciences that allow to obtain new results. The ground of using of the mentioned methodological approaches is building an integral сultural and semantic space that allow to conceptually determine the institutional processes.
The Sampling Task Planning of On-board Manipulator Based on Planning Knowledge Base

Task planning is used extensively in robot fields to help it complete some complicate tasks in a certain situation. Traditional task planning use path planning algorithm to get a collision-free and optimal resource allocation path to guide the manipulator to execute the task safely and efficiently, which is unable to memorize and learn from the past planning result, and thus severely affected the planning efficiency. In this paper, a planning method based on knowledge base is put forward to improve efficiency in task planning. Firstly, the complex sampling task is divided into several atomic tasks that can be recognized and executed by manipulator directly. Then, the knowledge base is introduced into PRM to improve the planning efficiency in the process of repeating planning. Then a similarity matching method of picture based on pHash is raised to match planning graph in knowledge base. Finally, the simulation process performed by matlab proved the feasibility and correctness of this method.