Reviewerly/exHarmony huggingface dataset:
Enhancement of Ultrasound Images (Removing of Speckle Noise Using Special An Isotropic Diffusion Filter)

Medical imaging techniques are predominantly used in medical diagnosis and therapy. The success of this technique depends largely on the quality of images. Due to various factors images do not have appropriate contrast and are often overridden by noise, making the interpretation of the images too difficult leading to incorrect diagnosis. This will be a very important and significant contribution to the medical professional. Removing noise from the original image is still a challenging research in image processing. Generally there is no common enhancement approach for noise reduction. Several approaches have been introduced and each has its own assumption, advantages and disadvantages. The speckle noise is commonly found in the ultrasound medical images. This paper presents different filtering techniques based on Statistical methods for the removal of speckle noise in ultrasound images. The quality of the enhanced images is measured by the Statistical quantity measures: Peak Signal-to Noise Ratio (PSNR), Mean Square Error (MSE) and Correlation coefficient (COC).
Effectiveness Comparison of Email Addresses Recovery from Gravatars

Internet was not designed for anonymity, but most users posting comments with unidentifiable pseudonyms hope to stay anonymous. On many websites, especially those powered by WordPress, the comments system is linked with the Gravatar service. That service uses email address, in obfuscated form (MD5 hash function), to provide users’ avatars. This approach allows to deanonymize real emails of users. Emails, which according to EU law, are considered as a personal information. This article explains all stages and results of the real attacks on a three types on websites: national ones, security oriented national ones and global one. We compare the effectiveness of three types of attacks: brute-force, dictionary and hybrid in relation to the type of attacked website.
Partitions and Generating Functions

Here we shall be concerned with manipulating objects built up from rows of squares as in Figure 2.1. Open image in new window Fig. 2.1 Rows of squares.
Sentiment Analysis of Indonesia’s National Economic Endurance using Fuzzy Ontology-Based Semantic Knowledge

Campaigns of election candidates often enliven the social media platforms that they chose. The total number of social media users in Indonesia has reached approximately 130 million users. Making use of this momentum where the social media is very active in the year of campaigns and elections, we attempt to mine the public sentiment in terms of national endurance using fuzzy ontology-based semantic knowledge. A regular ontology is usually considered rather ineffective in extracting information from tweets; thus, we use the concept of fuzzy ontology-based semantic knowledge. Fuzzy ontology-based semantic knowledge is one method of sentiment analysis using combined approach of lexicon-based, ontology-based, and fuzzy logic. This method gives results whether a tweet is categorized as strong negative, negative, neutral, positive, or strong positive. Moreover, a regular ontology is unable to classify a tweet into sentiment categories when that tweet has more than one SentiWord value. From the 2032 tweets with sentiments, we found 205 tweets having more than one SentiWord values. Therefore, the application of FuzzyDL is needed to solve this problem. Using this method, we obtain the accuracy score of 78%, precision score of 93%, recall score of 73%, and F-measure score of 82%. Keywords- Economic Spect; Fuzzy Logic; nationa; Endurance; Ontology; Sentiment Analysis; Sentiword: Twitter.
Research on the application of Excel genetic algorithm in mathematical modeling of College Students

Modern college students want students how to use Excel for mathematical modeling, in the operation process to use a variety of algorithms, including genetic algorithm. However, many college students have not yet mastered the specific application methods. Therefore, this paper will carry out research. Through the table function, function and mixed programming function of Excel, the dynamic changes of genetic algorithm optimization process in Excel are displayed, and simulation charts are generated. The visualization of genetic algorithm optimization process can be realized through the icon, which provides reference for college students. Through the research, college students will have a deeper understanding of the essence and optimization process of genetic algorithm, and can master the genetic algorithm in a short time. Then, the algorithm will be applied to the combination optimization, function optimization and other mathematical modeling optimization problems, and finally solve the problem.
Using Ontologies in Autonomous Robots Engineering

The construction and operation of autonomous robots is heavily based of systemic conceptualizations of the reality constituted by the robot, its controller and the environment where it performs. In this chapter we address the role that computer ontologies play in the whole life cycle—engineering and operation—of autonomous robots: from its conception and construction by human engineering teams to deployment and autonomous operation in dynamic and uncertain environments. This chapter summarizes the state of the art, gives some examples and establishes a roadmap for future activity in this domain to produce shareable ontologies that could streamline autonomous robot development and exploitation.
More Correlations Better Performance: Fully Associative Networks for Multi-label Image Classification

Recent researches demonstrate that correlation modeling plays a key role in high-performance multi-label classification methods. However, existing methods do not take full advantage of correlation information, especially correlations in feature and label spaces of each image, which limits the performance of correlation-based multi-label classification methods. With more correlations considered, in this study, a Fully Associative Network (FAN) is proposed for fully exploiting correlation information, which involves both visual feature and label correlations. Specifically, FAN introduces a robust covariance pooling to summarize convolution features as global image representation for capturing feature correlation in the multi-label task. Moreover, it constructs an effective label correlation matrix based on a re-weighted scheme, which is fed into a graph convolution network for capturing label correlation. Then, correlation between covariance representations (i.e., feature correlation) and the outputs of GCN (i.e., label correlation) are modeled for final prediction. Experimental results on two datasets illustrate the effectiveness and efficiency of our proposed FAN compared with state-of-the-art methods.
Speed up Cassandra read path by using Coordinator Cache

The fast increasing amount of massive and complex data in today's Internet, called Big Data, requires sophisticated, comprehensive and highly operational databases. NoSQL databases are designed to fulfill Big Data requirements. Choosing an appropriate NoSQL database among various solutions to cover and manage big volume of data in Big Data, both in quantity and quality, itself is a big challenge. Cassandra is one of the distributed NoSQL databases mastered for managing very large amounts of structured and unstructured data spread out across many commodity servers, while providing highly available services with no single point of failure. Cassandra system was designed to run on cheap commodity hardware and handle high write through-put while not sacrificing read efficiency. This Paper will first present an overview of NoSQL databases, Big Data and IoT data as a controversial and complicated source of data in Big Data. Then, focuses on Cassandra database read request issues in its read path and suggests a model to reduce the time of read request (read query) coming from client side to Cassandra database. In this model we added a cache called Coordinator cache in Cassandra controlling nodes. Using a real dataset, we perform an analysis of Cassandra existing read path with suggested read path model and then compare the time of a read query before and after this model. The result shows that using Coordinator cache together with key cache offered by Cassandra database speedup data read request. Coordinator cache requires no extra memory because Cassandra Coordinator node does not store anything when doing controlling tasks over replica nodes and its potential memory space can be used for the introduced Coordinator cache.
Analysis of PDG, Penalty-BFGS and KKT in Economic Dispatch Problems

For the past several decades, Economic Dispatch problem has been a hot topic in the field of mathematics. In general, Economic Dispatch problem can be regarded as a quadratic optimization problem in power system. This paper utilized projected gradient descent algorithm, penalty-BFGS algorithm and KKT conditions method, which can be used to minimize the cost function with equality constraints and inequality constraints, with an attempt to explain and address Economic Dispatch problem. Based on the three methods above, the corresponding results are presented and analyzed. The results demonstrated that the three algorithms have different performances. Each can solve the quadratic problem with efficiency to different degrees. Keywords- Economic dispatch; Non-linear optimization; PGD algorithm; BFGS algorithm; KKT conditions.
Mediating Everyday Data Encounters with Mundane Video Vignettes

Data in everyday life is represented in ways that are supposed to connect with our needs, tasks and situations, to inform and facilitate effectiveness and functionalism. And still, all we see are numbers, charts or graphics. We engage in sense-making all day through analytical interpretation, which is often not intuitive and causes friction whenever we encounter it in an everyday context. In our work, we aim for a different experience in everyday data-sensemaking that translates sensor data, time and data from external sources into lively, yet mundane video vignettes. The aim is to connect the experience to the phenomenon behind data, to deny data the stage and spotlight in our Everyday. This case study shows the design and implementation of a novel, interactive video installation that projects mundane data into an Everyday context, giving the data a new form and meaning. We work with video vignettes that are designed as short, neutral video fragments and show a person in different mundane situations, acting alone or interacting with props. The design process went through three iterations before the installation was exhibited as part of a group exhibition. We reflect on the most important design decisions, turning points in the process and how design and implementation are intertwined throughout the installation preparation. We conclude with a short overview of the contribution and an outlook to future work.
Exploiting VR and AR Technologies in Education and Training to Inclusive Robotics

Nowadays, robotics applications are no more limited to industrial rigid production cells or research laboratories and are spreading in several domains in which interaction with humans is required. In this context, educating professionals, workers, and students in the use and understanding of robotic systems is paramount to ensure the social acceptance and uptake of interactive robots. At the same time, the request for innovative training and teaching methodologies is growing, and virtual and augmented reality technologies as educational tools are gaining the attention of public institutions, companies, and healthcare facilities. In this paper, we focus on how VR/AR technologies can be exploited to teach robotics and to train in the use of specific robot platforms, providing a review of the available resources and discussing their advantages and disadvantages in the “inclusive robotics” perspective.
Formal Cognitive Modeling of Swarm Intelligence for Decision-Making Optimization Problems

Decision-making is one of the most important cognitive processes in psychology, with outstanding implications in a number of fields including systems neuroscience, cognitive neuroscience, brain and cognitive sciences, logic, computation, artificial intelligence, machine learning, and many others. Basically, it consists of the process of identification and selection of an option or course of action among several alternatives, based on values, criteria, beliefs, and any other information available to the decision maker. The final output of the decision-making cognitive process is a choice, which may (or not) prompt action. Swarm intelligence is one of the tools typically applied to the decision-making process in optimization problems. This chapter aims at discussing this issue from the standpoint of cognitive informatics, an emerging interdisciplinary domain of cognition and information sciences, encompassing the study of internal information processing mechanisms and cognitive processes of the human brain and mind, the underlying abstract intelligence theories and denotational mathematics, and their potential applications in fields such as cognitive computing or computational intelligence. In this chapter, we provide a formal model of the swarm intelligence paradigm for the cognitive processes typically involved in decision-making for optimization problems.
Project Research Based on Electronic Engineering Innovation Practice

Abstract In this paper, combined with the practical conditions of our university training center, puts forward the project, for traction, on the basis of the module, dominated by technology training, to cultivate students’ engineering consciousness and innovation ability as the core of electronic engineering training teaching system, to adapt to the “double top” and innovation personnel training in our university. The teaching system and content, through one year’s teaching practice, integrates the design of the project, the design and production of PCB, the testing of electronic components, the assembly and debugging, the writing and defense of the science and technology report, and the teaching effect is very significant and is well received by the students.
Text Filling Method Based on Prediction Network and Semantic Fusion Loss

Aiming at the problem of semantic loss caused by poor semantic coherence in the text filling process, a text filling method based on prediction network and semantic fusion loss is designed to reduce semantic loss through fusion of cross entropy and similarity. First, the missing text is input into the bidirectional long-short-term memory network code after the word embedding end to extract the context semantic features; Then, the decoder uses the attention mechanism to capture contextual semantic information to fill in the missing blanks one by one; Finally, a semantic similarity fusion loss method is proposed, which calculates the distance between the semantic vectors of coding layer and prediction layer, and adjusts the prediction network together with cross entropy loss function to iteratively reduce the semantic loss and make the filled text semantic more coherent and smooth. The experimental results show that using different missing rates and screening strategies for testing on Yelp, Grimm and NBA news datasets has greatly improved accuracy and fluency. Indicating that the proposed method is better than other methods in text filling task.
IMPACTS OF READING BOOKS IN IMPROVING WRITING PROFICIENCY

This article highlights the importance of reading not only in education but also in a person's whole life, how it helps to improve writing skills, and how to solve the difficulties encountered in writing a task by reading a book. This article also presents the results of questionnaire, which determine how much reading has helped to improve writing proficiency.
A Study on the False Alarm Reduction of LPMS Caused by Thermal Shock

A number of unnecessary false alarms generated from a loose part monitoring system during heating and cooling operation of a power plant increase the workload of the power plant operators and the analysts. Accordingly, there is a need to develop an algorithm capable of reducing the occurrence of unnecessary false alarms by effectively decreasing the alarms generated by thermal shocks. In this study, the algorithm is suggested to effectively decrease the alarms generated by thermal shocks, and the performance of algorithm is tested by both simulation scenarios and actual alarm signals collected from power plants. The suggested algorithm only uses the values already calculated in LPMS and does not require additional computational effort. From the result, the algorithm is capable of reducing 62.5% of false alarms occurred by thermal shocks. Through this, it is expected that the effort spent on unnecessary task can be reduced, and the workload of the power plant operators and analysts can be reduced.
Real-Time Detection of Sports Broadcasts Using Video Content Analysis

The classification of both static images and video is a rapidly developing field and has great potential for commercialisation. In this chapter, the problem of detecting game (sport) matches in a video stream is considered and a frame-level lightweight, real-time classification algorithm is described. Video processing technology is implemented that provides the required per-frame classification accuracy in real time. The proposed approach can easily be extended to similar problems of classification, for example, to recognition of the genre of a video (for films, news, cartoons, computer graphics, concerts, documentaries, etc.). Our algorithm is based on so-called visual keys, which are characterised by one-dimensional and two-dimensional distributions of colour and texture features. Visual key detectors form the root vertices of an oriented acyclic graph, and the other vertices of this graph are functions of these one-dimensional or two-dimensional threshold transformations, linear classifiers and functions of Boolean logic. The developed algorithm can be implemented using a small number of arithmetic and logical operations and uses a single pass to analyse each frame of the video stream. This allows for a quick and cheap hardware implementation and in particular makes it possible to realise this system within the hardware of a TV receiver. The proposed solution can be used as a preprocessing cascade in tasks related to high-precision video classification in offline mode. In the last section, a brief review of recently developed methods of machine learning and artificial intelligence for video classification and genre detection of sports is presented.
Modulation Recognition based on Incremental Deep Learning

Incremental learning has emerged to solve the problem of incrementally updating the classification model as the number of data classes grows. There are many challenges in incremental learning such as catastrophic forgetting and learning efficiency. In this paper, we present a method of the modulation recognition based on incremental learning, that allows learning continuously with a class-incremental way. The new classes can be added into the existing model progressively from a sequential data stream. We conduct experiments on the modulation signal dataset characterized by the constellation diagram, the experimental results prove the feasibility of our incremental learning system. Our method performs similarly in classification accuracy compared to common multi-task joint training, but performs better in training efficiency.
Applying game design approach to build web-based collaborative tool with better social presence

The classical online collaboration tools use text as the main medium for communication. This limitation leaves users in an isolation sensation that can affect their performance and commitment. Modern videoconference tools can be used to ensure a better Social Presence and limit the effects of isolation. However, these tools need additional pieces of equipment and software. To guarantee the advantages of both categories, it is possible to follow a game design-inspired approach to a) maintain a high social presence, b) build a system that runs entirely inside the web browser. This paper aims to propose an architecture to respects when designing and building low cost and rich web-based collaborative tools. The architecture applicability, extensibility, and flexibility are demonstrated by a prototype of a shared, easy to integrate, and interactive whiteboard.
Improving the Performance of an Associative Classifier in the Context of Class-Imbalanced Classification

Class imbalance remains an open problem in pattern recognition, machine learning, and related fields. Many of the state-of-the-art classification algorithms tend to classify all unbalanced dataset patterns by assigning them to a majority class, thus failing to correctly classify a minority class. Associative memories are models used for pattern recall; however, they can also be employed for pattern classification. In this paper, a novel method for improving the classification performance of a hybrid associative classifier with translation (better known by its acronym in Spanish, CHAT) is presented. The extreme center points (ECP) method modifies the CHAT algorithm by exploring alternative vectors in a hyperspace for translating the training data, which is an inherent step of the original algorithm. We demonstrate the importance of our proposal by applying it to imbalanced datasets and comparing the performance to well-known classifiers by means of the balanced accuracy. The proposed method not only enhances the performance of the original CHAT algorithm, but it also outperforms state-of-the-art classifiers in four of the twelve analyzed datasets, making it a suitable algorithm for classification in imbalanced class scenarios.
Histogram Based Resolution Enhancement of an Image by Using Artificial Neural Network

High resolution (HR) images have more information about images than low resolution (LR) images. HR image can be more easily recovered from multiple low resolution images than the single LR image. Several LR images hold diverse details and can be dubbed together to get a HR image. Reconstruction of HR image using single LR image with fewer details remain more challenging. This paper proposes a neural network based model to increase the resolution based on feature extraction from a single image. Each LR and HR image is divided into smaller blocks and the histogram as a feature is calculated for each block. Back propagation neural network [BPNN] is used for learning of histogram features of LR image. The simulation has been performed on a dataset of brain MRI images and results show that the obtained neural network model has improved the PSNR and RMSE.
Estimate of Efficiency of Class and Learning Activities based on Criterion-Oriented Testing

The research featured the views of the teaching staff of a military engineering university about didactic testing and diagnostics. It included a survey of students' learning motivation based on the achievement goal theory. The military students proved to be oriented towards improving their skills and getting new knowledge in order to achieve professional and personal growth. The article introduces a criterion-oriented estimate of efficiency methods that can be used to assess class activities and communicative interaction between the teacher and the student. The modeling process was illustrated using the mathematical formalization method. The authors believe that criterion-oriented testing can help to verify teachers' achievements. The article justifies the need to develop new criterion-oriented methodologies for identifying teachers' performance and offers a detailed algorithm that can be used in future research and verification of professional achievements.