Reviewerly/exHarmony huggingface dataset:
Kernel Density Estimation and Correntropy based Background Modeling and Camera Model Parameter Estimation for Underwater Video Object Detection

Abstract Underwater video object detection is challenging because of the complex background and the movement of the camera. In order to address this, we propose a novel scheme of simultaneously estimating the camera model parameters and detecting the object. The object detection phase includes background modeling and its learning. Background is modeled by the proposed Spatial Kernel Density Estimation (SKDE) model and the model learning happens in the SKDE feature space. Background modeling and its learning is pixel based approach. The model histograms learn the new pixel through its histogram representation. Our learning and classification strategy is different from the Heikkila et al. [17] in the context of similarity measure. We have proposed the correntropy based similarity measure that is used for model learning and pixel classification. The camera model parameters are estimated by 2D optimization method where we have used the corner features of an object at subpixel accuracy level. These subpixel level features are used in the proposed pipelining framework for model parameters estimation. The estimated model parameters are used to transform the input frame, which in turn is used for model learning and classification. The proposed scheme has been tested with underwater video frames from six data sets. The efficacy of the proposed scheme is compared with seven existing schemes and it is found that the proposed scheme exhibits improved performance as compared to the existing methods.
Neural Camera Simulators

We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It also contains a noise module using the noise level function and an aperture module with adaptive attention to simulate the side effects on noise and defocus blur. To facilitate the learning of a simulator model, we collect a dataset of the 10,000 raw images of 450 scenes with different exposure settings. Quantitative experiments and qualitative comparisons show that our approach outperforms relevant baselines in raw data synthesize on multiple cameras. Furthermore, the camera simulator enables various applications, including large-aperture enhancement, HDR, auto exposure, and data augmentation for training local feature detectors. Our work represents the first attempt to simulate a camera sensor's behavior leveraging both the advantage of traditional raw sensor features and the power of data-driven deep learning.
Research on Uncertain Listeners

In Chap. 6, I used two scales to show that the brand segmentation is significant: (1) consumers’ information sensitivity and (2) consumers’ affective commitment to the relevant product.
The matrix of verb-verb sequences in Tamil

Verb-Verb sequences characterize many types of grammatical constructions in Tamil. A subset of these sequences, called V-V sequences, characterize a predicate construction which is known otherwise as a complex predicate or compound verbs or serial verbs, though these are not identical. This predicate construction is shared by most Indian languages and found also in languages outside India such as Japanese. One of the verbs in the V-V sequence does not have lexical status referentially, or often even structurally. The comparison of Tamil and Japanese shows that the grammatical functions and the structural features of the verb sequence differ from language to language. An interesting conclusion drawn from this comparison is that specific members of each type of the sequence may be distributed differently across a matrix of types. In other words, an instance of a V-V sequence of Type 1 in one language may belong to Type 3 in another language.
Improvement for ranking method-based selection methods of government support R & D project

Recognition that R&D (Research and Development) project is important for national competitiveness and sustainable economic growth has been extended all over the world, and the government financial support for R&D projects has increased. Therefore, government needs to make a decision about whether it invests in any project or not, and this decision-making complies with evaluation of the relevant experts. In this study, we improve project selection methods and procedures based on outranking methods which have mainly focused on general project environmental analysis in order to apply to the government support R&D project selection, and suggest R&D project selection methods and procedures that meet the purpose of government support. Therefore, it demonstrates that improved methods organize more profitable project groups to the purpose of the selecting government support R&D project than existing methods. Also, it illustrates that it is possible for decision makers to analyze variously by changing composition of selecting project group with variation of α.
Fourier and Zak transforms of multiplicative characters

In this paper we derive formulas for the N-point discrete Fourier transform and the R1 x R2 finite Zak transform of multiplicative characters on Z/N, where N is an odd integer, and R1 and R2 are co-prime factors of N. In one special case this permits computation of the discrete Fourier transform and the finite Zak transform of the Jacobi symbol, the modified Jacobi sequence, and the Golomb sequence. In other cases, not addressed here, this permits computation of the discrete Fourier transform and the finite Zak transform of certain complex-valued sequences. These results constitute, to our knowledge, the first unified treatment of key Fourier and Zak space properties of multiplicative characters. These results also provide a convenient framework for the design of new character-based sequences.
Deviantological Aspects of the Methodology of Pedagogical Correction of Children on the Materials of the Republic of Belarus

The research is based on the destructive tendencies of information threats' influence on the destabilization of society, disadaptation of children in the context of a wave of protest attempts to overturn the constitutional system and transfer of color revolutions to the Republic of Belarus.Trends of preservation of external and internal threats to social immunity as constants of transition to regularity determined the purpose: development of deviantological aspects of the methodology of pedagogical correction of children.Deviantological regularities of pedagogical correction of children, the potential of correctional and developmental technologies are revealed.Practical results of testing a scientifically based deviantological model of social and academic mobility of young people within the educational cluster "Gymnasium-University-Science" on the materials of the Republic of Belarus are formulated.The determinant factors of pedagogical correction as a principle and method of deviantology are determined.The following categories were introduced into scientific circulation: "three-factor triad of the general development curve", "elasticity of neoplasms of the personality structure", "family as transmission".
Note booker pro case study

The main idea of the software named, “Note booker Pro” is to make the task of preparing a data science notebook, super easy and fast. With this software, any person can quickly get insights about a data set displayed in front of him. This software also provides effective visualization facilities with 6 types of commonly used graphs. Anyone can also compare through 30 different Machine Learning models of both regression and classification types to choose the best model that can provide the highest accuracy for a data set of a given size and split ratio, (which can also be set by the user as per his/her requirements).
Analisis Sistem Informasi Akuntansi Penjualan dalam Upaya Meningkatkan Efektivitas Pengendalian Intern (Studi Pada Perum BULOG Sub Divre Kediri)

This research aims to acknowladge how the application of accounting information system sales and the implementation of accounting information system sales in effort to improve internal control effektiveness at Perum Bulog Sub Divre Kediri. The research is a case study. The analyzed method were using the methods of analysis deskriptif. The result of this reasearch ishow that the application of accounting information system sales and the implementation of accounting information system sales in effort to improve internal control effektiveness at Perum Bulog Sub Divre Kediri most have been applicated and implemented in accordance with the theory formulate, then it can be concluded that the application of accounting information system sales and the implementation of accounting information system sales in effort to improve internal control effektiveness at Perum Bulog Sub Divre Kediri is running well.
PACStack: an Authenticated Call Stack

A popular run-time attack technique is to compromise the control-flow integrity of a program by modifying function return addresses on the stack. So far, shadow stacks have proven to be essential for comprehensively preventing return address manipulation. Shadow stacks record return addresses in integrity-protected memory secured with hardware-assistance or software access control. Software shadow stacks incur high overheads or trade off security for efficiency. Hardware-assisted shadow stacks are efficient and secure, but require the deployment of special-purpose hardware. We present authenticated call stack (ACS), an approach that uses chained message authentication codes (MACs). Our prototype, PACStack, uses the ARM general purpose hardware mechanism for pointer authentication (PA) to implement ACS. Via a rigorous security analysis, we show that PACStack achieves security comparable to hardware-assisted shadow stacks without requiring dedicated hardware. We demonstrate that PACStack's performance overhead is small (~3%).
Extensive and Intensive Manifestations of Activity Universalism of the Сreative Personality: Renaissance, Baroque, Classicism

The proposed study is devoted to the universal creative personality. The author considered this phenomenon in the aspect of activity universalism. In this research, the typology of universal creative personality is analyzed on the material of the musical culture of the European tradition.The main purpose of the article is to identify and characterize the features of the activity universalism of the creative personality in the musical culture of the Renaissance, Baroque and Classicism.Research Methodology. О. Komenda created her own author's activity-structural method, in which different psychological and art approaches to the study of universal creative personality combined.The scientific novelty of this study is the development and testing of the activity universalism theory of the creative personality.Conclusions. The author defined the essence of the activity universalism phenomenon and traced its evolution. She revealed his peculiarities in structural, typological, functional and individual-creative aspects. O. Komenda revealed a turning function of the opera in the formation of the universalism of the New Time and determined the role of Je. F. Rameau as a prototype of the classic universal creative personality. The author specifies such concepts: the phenomenon of activity universalism, historical and activity types of universalism, universal creative personality, leading and auxiliary activities, the structure of activity universalism, the functions of a universal creative personality and types of activity universalism in the music-historical process. The development of the typological, functional and individual aspects of activity universalism determined the theoretical significance of this thesis. The application of the interdisciplinary approach provided the modernity and significance of its theoretical achievements.
SF-QA: Simple and Fair Evaluation Library for Open-domain Question Answering

Although open-domain question answering (QA) draws great attention in recent years, it requires large amounts of resources for building the full system and it is often difficult to reproduce previous results due to complex configurations.In this paper, we introduce SF-QA: simple and fair evaluation framework for opendomain QA.SF-QA framework modularizes the pipeline open-domain QA system, which makes the task itself easily accessible and reproducible to research groups without enough computing resources.The proposed evaluation framework is publicly available and anyone can contribute to the code and evaluations.
Enhancement of Underwater Images Using Hybrid Channel Prior

In this paper, an underwater image dehazing and enhancement algorithm has proposed. A hybrid channel prior concept is introduced. Proposed method mainly contain dehazing of underwater image and contrast enhancement of underwater image. Proposed method make use advantage of both dark as well as bright channel to dehaze the hazed image. In the first step dehazing is done by using hybrid channel prior. In the second step contrast enhancement is done by using histogram matching. Proposed technique is compared with the existing techniques in both quantitative and qualitative manner. Underwater image quality measure (UIQM) is used for evaluating the performance of the proposed technique quantitatively. Proposed method can effectively enhance the quality of underwater images.
Question and Answer Classification with Deep Contextualized Transformer

The latest work for Question and Answer problems is to use the Stanford Parse Tree. We build on prior work and develop a new method to handle the Question and Answer problem with the Deep Contextualized Transformer to manage some aberrant expressions. We also conduct extensive evaluations of the SQuAD and SwDA dataset and show significant improvement over QA problem classification of industry needs. We also investigate the impact of different models for the accuracy and efficiency of the problem answers. It shows that our new method is more effective for solving QA problems with higher accuracy
Secure and Efficient Data Retrieval of Data from Cloud with Effective Deduplication Scheme

Temporary keyword search on confidential data in a cloud environment is the main focus of this research. The cloud providers are not fully trusted. So, it is necessary to outsource data in the encrypted form. In the attribute-based keyword search (ABKS) schemes, the authorized users can generate searching time and send them to the cloud for running the search operation. These search tokens can be used to extract all the cipher texts which are produced at any time and contain the corresponding keyword with respective time. Since this may lead to some information leakage, it is more secure to propose a scheme in which the search tokens can only extract the cipher texts generated in a specified time interval. In addition to utilize cloud storage effectively deduplication of file should be identified accurately. To obtain this goal content based duplication should be implemented without affecting privacy of data. This could be achieved through ABE algorithm and results shows that our system achieves better result in both parameter like storage utilization and secure data retrieval in cloud.
The Smart Skeleton: an open-source, interactive tool for teaching muscle actions and joint movements

This paper describes the design, construction, and use of an open-source hardware and software tool intended to help Anatomy and Physiology students test their knowledge of muscle actions and joint movements. Orientation sensors are attached to a model skeleton to turn the skeleton into an interactive, physical model for teaching limb movements. A detailed description of the construction of the tool is provided, as well as the configuration and use of companion software.
Efficient Methodology for Deep Web Data Extraction

Deep Web substance are gotten to by inquiries submitted to Web information bases and the returned information records are enwrapped in progressively created Web pages (they will be called profound Web pages in this paper). Removing organized information from profound Web pages is a difficult issue because of the fundamental mind boggling structures of such pages. As of not long ago, an enormous number of strategies have been proposed to address this issue, however every one of them have characteristic impediments since they are Web-page-programming-language subordinate. As the mainstream two-dimensional media, the substance on Web pages are constantly shown routinely for clients to peruse. This inspires us to look for an alternate path for profound Web information extraction to beat the constraints of past works by using some fascinating normal visual highlights on the profound Web pages. In this paper, a novel vision-based methodology that is Visual Based Deep Web Data Extraction (VBDWDE) Algorithm is proposed. This methodology basically uses the visual highlights on the profound Web pages to execute profound Web information extraction, including information record extraction and information thing extraction. We additionally propose another assessment measure amendment to catch the measure of human exertion expected to create wonderful extraction. Our investigations on a huge arrangement of Web information bases show that the proposed vision-based methodology is exceptionally viable for profound Web information extraction.
A STUDY ON SOFTWARE DEFECT PREDICTION SYSTEM USING DATA MINING TECHNIQUES

Defects in software modules are a source of significant concern. Software reliability and software quality assurance ensure the high quality of applications. A software defect triggers software malfunction in an executable product. A number of methods for forecasting machine faults have been suggested, but none have proven to be sufficiently accurate. In the design of software error prediction models, the aim is to use metrics that can be obtained comparatively early in the life cycle of software production to provide fair initial quality estimates of an evolving software framework.Here are various data mining classification and forecasting techniques. Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) have been analyzed and compared for software defect prediction model development. For this paper, the DATATRIEVETM project developed by Digital Engineering, Italy was used to validate the algorithm. The findings revealed that the model was an exceptional statistical model using the NN classification methodology. The main challenges faced in the secure software development process are quality and reliability. There are major software cost violations when a software product with errors in its various components is used on the customer’s side. The software warehouse is commonly used as a record keeping repository, which is often needed when adding new features or fixing bugs. Software errors can lead to erroneous and different results. As a result, software programs run late, are canceled, or become unreliable after use. Different social and technical issues are associated with software failure and software defects are the main reasons for deteriorating product quality. In software engineering, the most active research in software domain is defect prediction.This study discusses the bug-fix time forecast model, pre-release release, post-release error and different measurements to predict failures. Predicted results help developers identify and fix potential vulnerabilities, thereby improving software stability and reliability.
Internet of Things Based High Security Border Surveillance Strategy

Wireless sensor nodes are aimed at working within several scenarios, including surveillance, target acquisition, situation awareness, and chemical,biological, radiological, and nuclear early warning. WSN’s are in the domain of object detection, recognition, and tracking and a very challenging task is designing distributed video systems within the tight power budget. To support these capabilities, it is now necessary to develop new architectures and design concepts that offer multimodal sensing without sacrificing the attractive low size, weight, and power capability offered by the conventional motes. The key advantage of WSNs is the ability to bridge the gap between physical and logical world by collecting and sending useful information to devices that have the computational resources to process it.
Improving the Energy-Based Dpa/Dema Attack Flow Preprocessing Performance

This paper proposes an improvement in the DPA/DEMA energy-based attack ﬂow to reduce the time to guess a secret cryptographic key from supposed secure systems. This was done through the recoding for C ++ language and parallelization of the algorithms. The results highlight a reduction of up to 78.53% in the execution time of preprocessing algorithms guaranteeing a good performance even in the majority of off-the-shelf processors available in the market.
Maintainability Evaluation for Object Oriented Software Metrics Using Tool Cohesion Inheritance (COIN)

In modern era, maintainability is an important part for software development that covers approx. 70-75% of development cost of the software system. It can allow the customer to adapt the software quickly and easily in an agile manner. Object oriented software metrics plays an important role for the designing of software development. Its features can be categorized into the object oriented metrics and the hierarchies of the class. In this paper, a tool named as COIN can help for evaluating the maintainability factors of object oriented software system using metrics like cohesion, coupling, inheritance and other object oriented metrics as well as through which we can analyzed the all metrics for the software system for evaluating the maintainability factors and testability also.
Lost in Text: A Cross-Genre Analysis of Linguistic Phenomena within Text

Moving from the assumption that formal, rather than content features, can be used to detect differences and similarities among textual genres and registers, this paper presents a new approach to linguistic profiling -a well-established methodological framework to study language variation -which is applied to detect significant variations within the internal structure of a text.We test this approach on the Italian language using a wide spectrum of linguistic features automatically extracted from parsed corpora representative of four main genres and two levels of complexity for each, and we show that it is possible to model the degree of stylistic variance within texts according to genre and language complexity.
Level of Awareness of Social Media Users on Cyber Security: Case Study among Students of University Tun Hussein Onn Malaysia

The vast development of modern technology has resulted in a great impact on the life of children today and extended to teenagers group. This research was conducted to identify the level of awareness of university students on cybercrime. For this research purpose, the respondents involved were among the students of Universiti Tun Hussein Onn Malaysia, Pagoh campus. The total number of respondents that was targetted for this research was around 100 people. The instrument used was in quantitative form using questionnaire given to the respondents. The research outcome found that the level of university students’ awareness towards cyber security is at moderate level. Therefore, many proactive steps need to be implemented by the stakeholders so that issues that are relevant to cybercrime can be reduced and later be rid of.