Reviewerly/exHarmony huggingface dataset:
EDA based Deep Neural Network Parameter Optimization

Deep neural network has been applied in kinds of areas due to the excellent performance. The capacity of deep neural network relies on the parameter training algorithm which is always based on the gradient information. However, for deep neural network, it is harder and harder for training due to depth of the neural network and a large number of parameters. Therefore, kinds of techniques are proposed to cope with this problem. However, the training algorithm is almost based on the gradient information with inherent defect. Evolutionary algorithm has the advantage of global optimization capability, independent of gradient information etc. Estimation of distribution algorithm (EDA) is a typical evolutionary algorithm which relies on the probability model of population. Therefore, the EDA is adopted to train the weight and bias of deep neural network in his paper. However, the large scale optimization capability of EDA is limited. Thereby, an improved strategy is proposed to enhance the large scale optimization capability of EDA. In the improved scheme, a random selection strategy is carried out to select partial variables for probability modeling instead of all of the variables, in order to reduce the computing time and the probability of combination explosion. A simulation is carried out to exhibit the validity of the improved algorithm.
Applying OWA Operator in the Semantic Processing for Automatic Keyphrase Extraction

The automatic keyphrases extraction from texts is a useful task for many computational systems in the natural language processing and text mining fields. Although several solutions to this problem have been developed, the semantic analysis has been one of the linguistic features less exploited in the most reported proposal, causing that the obtained results still show low accuracy and performance rates. This paper presents an unsupervised method for keyphrase extraction, which is based on the use of lexical-syntactic patterns for extracting information from texts and a fuzzy modelling of topics. An OWA operator which combines several semantics measures has been applied in the topic modelling process. This new approach was evaluated with Inspec and 500N-KPCrowd datasets and compared with other reported systems, obtaining promising results.
Investigation of Imperfections Formed at the Ultrasonic Welding of Copper Sheets

Abstract Ultrasonic welding is a very useful and simple welding process. It is suitable for establishing a joint between thin sheets and dissimilar metals with short preparation and finish work time [1]. Some welding defects were detected due to less than optimal ultrasonic welding parameters. These defects were ruptures, surface colour change and unacceptable deformations. This article aims to identify these failures and their causes.
Towards a Higher Accuracy of Optical Character Recognition of Chinese Rare Books in Making Use of Text Model

The legibility of the text of rare books is often subject to precarious conditions: natural decay or erosion, and ink bleed caused by flawed printing methods centuries ago often make such texts difficult to recognize. This difficulty hardens the challenge for optical character recognition (OCR), whose task is to convert images of printed text into machine-encoded text when the rare book has been digitized.
R and exploratory data analysis

Abstract R is a powerful, free software package for performing statistical tasks. It will be used to simulate data, analyze data, and make data displays. More details about R are given in Appendix B.
PROJECT-BASED TASK AS A MEANS TO FORM COMMUNICATIVE UNIVERSAL EDUCATIONAL ACTIVITIES AT THE LESSONS OF UDMURT AS A FOREIGN LANGUAGE

The paper focuses on analysing the formation of communicative universal educational activities of Russian-speaking pupils at the lessons of Udmurt as a foreign language. The author reveals the content of the notions “project”, “project-based task”, “communicative universal educational activities”, introduces an educational-methodological complex for junior pupils “Ӟеч-а, бур-а, удмурт кыл!” . The article provides examples of project-based tasks on the discipline “The Udmurt Language” aimed to form junior pupils’ communicative universal educational activities. The efficiency of using the project-based task as a method of teaching Udmurt to Russian-speaking pupils is emphasized.
Reference data preparation for complex satellite image segmentation

The high spatial resolution satellite images covering multiple objects of the urban area are visually complex in nature. This visual complexity causes ambiguity during segmentation of such images when the targets are unknown. In such case, reference data are required to assess the segmentation methods. Due to excellence of humans in visual analysis, the presented work has attempted for a psycho-visual approach to prepare the reference segmented complex HRS images. The reference data have has prepared through the correlation of the quantified eye-tracking data (metrics) and corresponding concurrent think-aloud (CTA) data for each of the created segments. Segments get updated based on Gestalt principles and Gibson's theory. Those segments having the best correlation between metrics and CTA data have been opted as the final output. The results suggest that the functional grouping of objects while preferring perceptual grouping for segments drawing conforms the most to the participants' verbal response. The final results have been compared with the existing notion of full segmentation used for complex images. The comparison also proffers the superiority of the proposed segmentation over full segmentation to be used as reference. In the future, a large number of images may be used to prepare better reference data.
Identifying Non-pulsar Radiation and Predicting Chess Endgame Result Using ARSkNN

We are currently living in a data age. DueAgarwal, Yash to the expansion of Internet of Things platform, there has been an upsurge in the number of devices connected to the Internet. Every device, ranging from smart sensors and smart phones to systems installedKumar, Ashish in manufacturing units, hospitals and vehicles is generating data. Such developments have not only escalated the generation of data but also created a need for analysis of raw dataBhatnagar, Roheet to identify patterns. Thus, data mining techniques are being deployed extensively to extract information. The accuracy and effectiveness of data mining techniques in providing better outcomes and cost-effective methods in various domains has already been established. Usually, in supervised learning, distance estimation is used by instance-based learning classifiersSrivastava, Sumit like kNN. In this analysis, the regular kNN classifier has been compared with ARSkNN which instead of following the conventional procedure of distance estimation uses the mass estimation approach. ARSkNN has been proved to be commensurate (or superior) to kNN in accuracy and has been found to reduce the computation time drastically on datasets chosen for this analysis.
STUDENT ASSESSMENT OF TEACHERS IN THE CONTEXT OF RAISING EDUCATION QUALITY

The article examines the problem of student assessment of higher school teachers as a means to raise the teachers’ motivation for professional improvement, which certainly helps to raise the quality of higher education. Analysing the results and procedure of a student survey conducted at the Far Eastern State Transport University, the authors consider the problem of choosing optimal criteria to evaluate the quality of teaching. The paper provides recommendations to conduct a student survey and offers a sample questionnaire.
Ensemble smoothers for inference of hidden states and parameters in combinatorial regulatory model

In time-series analysis, state space models (SSMs) have been widely used to estimate the conditional probability distributions of hidden variables and to clarify statistical models when predicting data. Linear SSMs have often been used because they can be efficiently solved; however, the simulated data is often not consistent with the observed data because of the simplicity. Thus, an alternative nonlinear model, termed the combinatorial regulatory model, have been used as a simple extension, and ensemble-based filters have been applied to estimate their non-Gaussian probability distributions and parameter values in exchange for higher computational cost. In this paper, we extend such methodologies to Rauch–Tung–Striebel (RTS) smoother to simplify the computation. Through a large amount of computational experiments using synthetic data, we compare the computational cost, estimation accuracy of the parameter values, and model selection capability of the ensemble Kalman filter (EnKF), ensemble particle filter (EnPF), unscented Kalman filter, and their RTS smoothers and show the performance and the limitations of these methodologies according to the amount of data and the number of particles. Consequently, we show that EnKF-RTS is efficient and stable if there exists a lot of observation points, and EnPF and EnPF-RTS can be a good choice for estimating accurate parameter values and better classifying statistical models if there are many particles and not many observation points.
Background Modeling Through Spatiotemporal Edge Feature and Color

In this paper, we propose a new spatiotemporal edge feature for background modeling that can extract spatial and temporal (motion) features by considering the background model and current information. Previous work on background modeling considers mainly the spatial domain, which misses key temporal information. In our proposal, we create spatiotemporal edge features by using current and past background information by identifying the amount of change from past to present. By finding these differences, we can accurately detect the movement of objects that is more robust to noise and illumination variations. Moreover, our proposed background-modeling technique adapts to background changes that occur over time through a dynamic model update strategy. Additionally, we are enhancing the spatiotemporal edge features with color to maintain the characteristics of each other. Finally, we evaluated our proposed method on the publicly available CDNET 2012 dataset and compared with state-of-the-art methods. Our extensive evaluation and analysis show that our method outperforms previous methods on this dataset.
A Framework for e-Governance Using Federation of Cloud

Government is proactive toward providing the best and secure services to citizens. So the government always plans by adopting a new technology to interact with consumers, improve their services, and provide fast and efficient service. Hence, in order to accelerate the e-Governance services, cloud computing technology is the best option. It being a new architecture and secure technology accelerates the e-Governance services with lesser hassles, greater efficiency, secure, efficient, and optimize services with proper utilization of resources. The paper enlightens the utilization the cloud computing for e-Governance services and proposes a framework to get the optimal solution for e-Governance services using federation of clouds.
Linear statistical shape models and landmark location

This chapter describes linear models of shape variation which can compactly encode the mean shape of an object in a group and the ways in which the shape can vary across the group. Objects can be represented by sets of landmark points whose relative positions summarize the shape. By aligning examples from a training set and applying principal component analysis a statistical model can be constructed. Such models can be used to study shape variations and to help locate equivalent landmarks in new images.
Multi-domain Aspect Extraction Based on Deep and Lifelong Learning

Opinions concerning features or aspects of people, entities, products or services are some of the most important textual information. Several methods try to solve the aspect extraction task needed in sentiment analysis by using Deep Learning techniques in specific domains. However, catastrophic forgetting appears when these methods are used to learn aspects of multi-domains. In this paper, we propose a new approach to achieve aspect extraction in multi-domains based on Deep and Lifelong Learning techniques. Our proposal reduces catastrophic forgetting and improves one of the principal state-of-the-art results.
Toward a Theory of Vulnerability Disclosure Policy: A Hacker’s Game

A game between software vendors, heterogeneous software users, and a hacker is introduced in which software vendors attempt to protect software users by releasing updates, i.e. disclosing a vulnerability, and the hacker is attempting to exploit vulnerabilities in the software package to attack the software users. The software users must determine whether the protection offered by the update outweighs the cost of installing the update. Following the model is a description of why the disclosure of vulnerabilities can only be an optimal policy when the cost to the hacker of searching for a Zero-Day vulnerability is small. The model is also extended to discuss Microsoft’s new “extended support” disclosure policy.
PEF Framework for day to day Client Acceptance Testing

CAT is essentially the strategy of approving and agreed arrangement the whole thing for the quality client. It includes the end Client, quality affirmation group, designers and analyzers. CAT appearances issues, a number of troubles, for example,1) CAT is done by the preceding phase of the programming testing development so postponement in somewhat of the porous testing stages prompts weight and brief period for Client Acceptance Testing, 2) Fine-structured format incorporates a lot of prerequisites and the CAT for apiece is lost, 3) The jobs & duties of the clients are not strong. In this concept, suggest the structure of response these issues. The structure furnishes the analyzers with simple strides for Client Acceptance Testing and legitimately in consummation of each case there ought to be a CAT done in it and the testing is done according to daily basis.
Personalizing Student Learning and Enhancing Peer-to-Peer Networks Through the Pen-Based MyEduDecks Application

MyEduDecks is a pen-based flashcard application that has been in development at South Fayette High School since 2013. The project initially began as a South Fayette student project under the supervision of computer science professor Ananda Gunawardena of Carnegie Mellon University. The MyEduDecks team has extended the first iteration of the application to meet the user needs at South Fayette. This application intends to personalize student’s learning and build collaborative networks in the classroom by leveraging pen-based technologies and their benefits. The application encourages the user to use a stylus to input questions and answers in an electronic flashcard format. MyEduDecks uses the Microsoft Ink API to integrate digital ink into the app. This research chapter highlights our application, its continued development, impact on a K-12 learning institution, and both qualitative and quantitative data analyzing the impact of an application reliant on pen and touch technology.
Application &amp; Comparison of Several Sentiment Analysis Platforms with Open Source Review Dataset

Natural (Language Processing is a popular direction of research nowadays, and its sub field, sentiment analysis become one of the most active research area. With the development of sentiment analysis, lots of related softwares are emerging. For users' requirement, it is essential to figure out advantages and defaults of these softwares to improve their performance. Commercially available sentiment analysis software can be mainly divided into three categories. In this paper, we select three common and representative softwares in each of these three categories and test them through experiments on six real-word datasets. We design standardized steps and obtain experimental results from several angles. Through these results, we summary the characteristics and application scope of these softwares. They have problems of overfitting, semantic recognition error and defects in models. We point out the development direction of commercially available sentiment analysis software.
On System Thinking and Information Security

Security problems we have to deal with today regarding Internet are created by ourselves. Internet, initially created to handle US Government data traffic, evolved to become communication between d ...
Trajectory optimization for underactuated systems using reinforcement learning: cart-pole problem

In this article, trajectory optimization of an inverted pendulum on a cart with machine learning tools is analyzed. Reinforcement learning and Q-learning algorithm are used for searching and optimization of trajectories. Results show that combination of Monte Carlo update method with TD(0) outperforms each of them.
Noise removal from medical image using fusion technique based on DWT coefficient

Abstract This paper introduces new manner for removing noise from medical images using wavelet transform based on fusion technique. The main goal of this research is restaurant the medical image based on peak signal to noise ratio value. This paper contains of three key stages are discrete wavelet transform, denoising wavelet and fusion technique. The new idea is compared each sub band of wavelet with other sub band depend on PSNR value and is selected the higher value of measure. First, perform 2level of discreet wavelet transform on the noisy medical images. Then, perform denoising wavelet techniques (soft and hard thresholding) that achieved by threshold value for detail coefficient and compared it with wavelet coefficients for detail sub band. Second, select sub band that contains the higher peak signal to noise ratio value that has less amount of noise from each image and it is merged to form the fused image. Finally, perform inverse discrete wavelet transform to convert the output image (fused image) from frequency domain to spatial domain. The outcomes of the work showed that soft thresholding is better than hard thresholding based on higher PSNR value. The fusion process gives better results than wavelet denoising technique.
Probabilistic graphical models

Abstract Statistical machine learning and statistical DSP are built on the foundations of probability theory and random variables. Different techniques encode different dependency structure between these variables. This structure leads to specific algorithms for inference and estimation. Many common dependency structures emerge naturally in this way, as a result, there are many common patterns of inference and estimation that suggest general algorithms for this purpose. So, it becomes important to formalize these algorithms; this is the purpose of this chapter. These general algorithms can often lead to substantial computational savings over more brute-force approaches, another benefit that comes from studying the structure of these models in the abstract.
FUZZY LINEAR PROGRAMMING PROBLEMS IN MOTIVATED SYSTEMS

The paper presents an approach to solving the problem of agent selection model, where the agent endogenously forms the goals of its behavior. The approach involves the development of decision-making methods, taking into account the relationship of motivation with the desire of the agent to implement subjectively understood interests and the state of the environment. The environment exists only in the mind of the agent in the form of a symbolic model based on the information perceived from the sensors. It serves as a basis for the agent's understanding of the current situation and subsequent choice of the method of action. Desire of the agent to strengthen the confidence in feasibility of the method of action and possibility of achieving the desired state requires to use the procedures for forming a model-representation based on the measured values of the state of the environment. There are considered the cases of applying the methods of the possibility theory for modeling reality with an artificial or natural entity based on the perception of facts, existing knowledge, memory, judgments and hypotheses.
Integrating BIM and product manufacturer data using the semantic web technologies.

Building Information Modeling (BIM) technologies enable the AEC-FM industry to create a shared virtual model of a project. BIM not only includes 3D geometry information but also information assigned as parameters to BIM elements. However, the information stored in BIM model elements is not comprehensive. For example, a chiller manufacturer provides a large volume of product data in pdf file format that is not included in the BIM model. It is possible to attach PDF files to BIM elements; the problem with an attached PDF is that its content cannot be interpreted by a computer. Automated retrieval and integration of distributed sources of data requires data in a machine-processable format. One way to solve this problem is to manually add additional data as element properties to a BIM model. However, manually inputting data to BIM platforms (e.g. Autodesk Revit) is a very time consuming and error-prone task. In this article, the authors present the Semantic Web technology as a new solution to integrating building product manufacturers’ data with BIM information. The authors convert both BIM data and building product data to RDF/OWL format. Data stored in RDF/OWL format can be easily processed by machines and integrated with RDF/OWL formatted data from other sources over the Internet. The authors use Semantic Web Services to handle data communication between a BIM knowledge base and the product manufacturers’ knowledge bases. The authors compare manual entry of manufacturer data to a BIM model to the semantics-based data integration presented in the paper. Results show that the authors’ approach improves productivity.
Mobile Cloud Cloudlet Online Gaming Transmission System (MCCGT)

Cloudlet is introduced to decrease the distance between Main Cloud and mobile users, which directly helps to decrease the latency during game video transmission from the Cloud to mobile users. In this proposed work, a Pre-Stored Cloudlet is introduced. Pre-stored Cloudlet is where the Cloudlet is pre-stored with encoded game videos in it transmitted from the Main Cloud. The main drawbacks of the existing Mobile Cloud Gaming (MCG) are long distances between the Cloud and mobile users, strongly dependent on network, it consumes high bandwidth and causes latency. We proposed a system architecture to overcome the existing problem in MCG by introducing Pre-Stored Cloudlet and Ad Hoc mobile network into the architecture of MCG. Ad Hoc mobile network is formed within the range of Pre-Stored Cloudlets to enable the users to get the pre-rendered game videos from the nearby users. The proposed system has THREE main layers; Cloud, Cloudlet and mobile users and the video transmission is expected to happen between these layers aided with WiMax and WiFi connection. The architecture is implemented through simulation and it is simulated to find the Response Delay which consist of Network Delay, Processing Delay, and Playout Delay. Based on the simulation results obtained, it shows that the introduction of Pre-Stored Cloudlet and Ad Hoc environment has helped to decrease the latency in game video transmission.
Scholastic Talent Indicator Matrix

The compatibilities and discrepancies of various school subjects regarding indicators for talented students are taking as a starting point to combine and categorize general indicators. The scholastic talent indicator matrix combines talent indicators from various school subjects into general talent indicators.