Reviewerly/exHarmony huggingface dataset:
Oculomotor Abnormalities during Reading in the Offspring of Late-Onset Alzheimer’s Disease

Eye movement patterns during reading are well defined and documented. Each eye movement ends up in a fixation point, which allows the brain to process the incoming information and program the following saccade. In this work, we investigated whether eye movement alterations during a reading task might be already present in middle-aged, cognitively normal offspring of late-onset Alzheimer's disease (O-LOAD).18 O-LOAD and 18 age-matched healthy individuals with no family history of LOAD participated in the study. Participants were seated in front of a 20-inch LCD monitor, and single sentences were presented on it. Eye movements were recorded with an eye tracker with a sampling rate of 1000 Hz.Analysis of eye movements during reading revealed that O-LOAD displayed more fixations, shorter saccades, and shorter fixation durations than controls.The present study shows that O-LOAD experienced alterations in their eye movements during reading. O-LOAD eye movement behavior could be considered an initial sign of oculomotor impairment. Hence, the evaluation of eye movement during reading might be a useful tool for monitoring well-defined cognitive resources.
Improved CNN Based Feature Extraction for Data Mining

Data mining extricates the required data from the cluster of information's. Based on the given concept the irrelevant data are mined from the featured information. The proposed method includes four phases such as preprocessing, oversampling, feature extraction and classification. Initially, the dataset is preprocessed in three steps namely cleaning, encoding and normalization. In this paper, Spam base, WaveformEW, Wisconsin Diagnostic Breast Cancer (WDBC), SonarEW and SPECTEW datasets are utilized but which are not properly balanced and creates class imbalance problem. Therefore, Adaptive Synthetic(ADASYN) technique is utilized to oversampling the minority class. Feature extraction is done by animproved Convolution Neural Network (CNN). CNN is improved by clipped leaky rectified linear unit, rectified linear unit and Rectified linear unit (ReLU)activation functions for feature extraction. After feature extraction, SVM classifier is applied to classify the features and then it rectifies the class imbalance issue. The proposed method is evaluated with five datasets fromUCI in terms of precision, accuracy, specificity, sensitivity, etc. Based on the analysis the WDBC dataset provide better results in CNN with Clipped ReLU method. To compare with ReLU and Leaky ReLU, the Clipped ReLU method achieves higher accuracy of 99.30%.
Visually aligned sound generation via sound-producing motion parsing

The task of generating natural sounds from videos is still challenging because the generated sounds should be highly temporal-wise aligned with visual motions. To reach this goal, the model needs to extract the discriminative visual motions correlated to the corresponding sound. In this paper, we propose a novel method of sound-producing motion parsing to explore the discriminative temporal visual embedding, which can distinguish the sound-producing motion from still to facilitate the mapping from visual motion to the corresponding sound. In the proposed method, we first extract the time-redundant visual embedding in a sliding temporal window through video frames to better distinguish the transient visual motion from complex background information. Subsequently, a series of temporal regulators are performed to capture the various tempo-scales of visual motions, and then a cross-modal temporal aligner is introduced to align the visual and sound embeddings in finer temporal scale. Finally, the visual embedding is affined and upsampled to ensure that the generated sound is consistent with real one in the resolution scale. Furthermore in order to make the model more interpretable, we leverage the gradient flowing into the bottleneck layer to produce a coarse temporal activation map, highlighting the important temporal cues in the visual embedding for the sound prediction. To evaluate the effectiveness of our approach, we conduct several experiments on the generated sounds. Extensive evaluation results demonstrate that the proposed method can improve the temporal-wise alignment significantly and obtain the competitive results in sound quality as well. Code will be released here.
The Influences of a Knowledge Representation Tool on Searchers with Varying Cognitive Abilities

While current systems are effective in helping searchers resolve simple information needs (e.g., fact-finding), they provide less support for searchers working on complex information-seeking tasks. Complex search tasks involve a wide range of (meta)cognitive activities, including goal-setting, organizing information, drawing inferences, monitoring progress, and revising mental models and search strategies. We report on a lab study ( N = 32) that investigated the influences of a knowledge representation tool called the OrgBox, developed to support searchers with complex tasks. The OrgBox tool was integrated into a custom-built search system and allowed study participants to drag-and-drop textual passages into the tool, organize passages into logical groupings called “boxes”, and make notes on passages and boxes. The OrgBox was compared to a baseline tool (called the Bookmark) that allowed participants to save textual passages, but not organize them nor make notes. Knowledge representation tools such as the OrgBox may provide special benefits for users with different cognitive profiles. In this article, we explore two cognitive abilities: (1) working memory (WM) capacity and (2) switching (SW) ability. Participants in the study were asked to gather information on a complex subject and produce an outline for a hypothetical research article. We investigate the influences of the tool (OrgBox vs. Bookmark) and the participant’s working memory capacity and switching ability on three types of outcomes: (RQ1) search behaviors, (RQ2) post-task perceptions, and (RQ3) the quality of outlines produces by participants.
Reframing Project Assessments: Including People as a Priority

With constrained resources, librarians must identify the catalog projects most deserving of staff time. Projects are unique from regular work, arising from internal analysis or brought to technical services within the library organization. Assessment, using data to make positive change, describes a project’s value. While assessment is seen as crucial to library work, recently there has been a movement in librarianship to address bias within some long-used standards, some of which are part of a traditional assessment. This study identifies a set of assessment areas that describe and compare three cataloging projects involving bibliographic records for non-English language resources. It compares cost, quality, public impact, support of library strategic plan, support of division and personnel, support of professional values and ethics, and a free writing section. This article discusses the strengths and weaknesses of these assessment areas and describes the benefits of shifting the focus away from production as a means of description.
Social and Movie Video Data Analysis for Representing Sentiments based on ML Approaches

Sentiment analysis (SA) is the process of defining and classifying articulated thoughts or emotions in source documents. As per requirements on the different sentiments of given applications such as Twitter, Movie review data, Amazon purchase reviews, Flipkart purchase/customer reviews have been reviewed and implemented using Machine learning Approaches. Presently, a mathematical solution analysis on identifying the positive, negative and neutral with pie-chart modelling and data representations using Tabulated charts has been proposed. This proposed model uses two different datasets from Kaggle websites such as Twitter and Movie data. Each data is clustered and segregated with different set of class for Twitter and Movie Reviews from the each column considerations on the dataset that chosen. This design improvises on the Data analyzation with XGBOOST algorithms for each movie reviews in which data representation is trained with other ensemble methods such as RFC, ADABOOST and ET. The supervised approach from the class objects considered for Positive, Negative scenarios based on Bayes algorithm that provide 94.53% accuracy and also for Decision Tree (Bagging), and ADABOOST, XG-BOOST algorithm has accuracy of 93.2 rest of Ensemble approaches will suffice with same values at 93%.
Survival of Blockchain Process and Block Chain Technology

Due of transparency, decentralisation, and security qualities, Block chain is a cultural revalution which can be having significant process at current environment. For the process of its very first implementation of Crypto currencies, such as Bitcoin, Block chain has received a lot of attention. Distributed ledger system is designed for evolve at a rapid pace, conduct, and exchange information in future. Educators', corporate executives and scholars have recently been started to examine various elements of Ethereum in modern innovation. Among previous Chain polls that focus on the technology's deployments, difficulties, features, and protection. Researchers further give a comparison of frameworks, a categorization for compromise methods, and an examination on potential threats and unique experiences employed in the Block chain to date. Ultimately, the study discusses critical new approaches, unique application scenarios, and areas for further research that researchers might investigate in addition to making additional progress in this area.
DL Based Automatic Robbery Detection using Video Surveillance in Residential Areas

Surveillance is an observation of a place, large areas, behavior, or a variety of activities to acquire information, influence, manage, or guide it. When people talk about surveillance solutions, the growing demand for large area monitoring becomes one of the key trends in the security industry. Surveillance video is used in real-time to watch known threats. Suspicious activities through surveillance video are a major topic in image processing and deep learning research. Residential area security is very much important to people nowadays. The proposed system is concerned with the development of a surveillance video framework in the residential area to detect any type of suspicious robbery activity. This system makes effective use of deep learning techniques of yolo, this includes techniques like object detection and eventually identifying the actions required to prevent robberies. Surveillance cameras are used here to remotely monitor a residential area or building by transmitting recorded images or videos to a control station to thwart suspicious activities. As a result, deep learning techniques are employed to achieve outstanding detection of suspicious actions that yielded positive results.
A Knowledge-based Adaptive Algorithm to Recommend Interactive Learning Assessments

The popularity of m-learning has created endless possibilities for improving education. Mobile based educational applications assist to enhance knowledge, provide personalized learning experience, support interactivity and accessibility to different learning content. Among many different features in m-learning applications, competency-based adaptive delivery of content is an area that is still under study. This study presents a rule-based algorithm to dynamically recommend competencybased interactive assessments to learners. In our approach, the competency levels are calculated using constraints concerning the learner and the assessments, which are then used to determine the difficulty level of the succeeding assessment. This adaptive algorithm obtained a success rate of 86.11% from the evaluation.
Analyze Performance and Market Share of Various Cryptocurrencies

Bitcoin is the popular forms of digital currencies that is rapidly gaining ground and trending widely. Bitcoin has introduced an influx of cryptocurrencies. It is a cryptocurrency based on a decentralized shared system. It&#x2019;s gotten the true standard for cryptocurrencies, moving an ever-developing army of devotees and side projects. This paper attempts to respond to your inquiries concerning digital currency. In this paper, we study and analyze the various Cryptocurrencies by comparing them on the basis of market cap, mining technique, mining algorithm, use, and transaction settlement time of each mining technique. Specially we focus on how does Cryptocurrency mine? And depicted a comparative study of various cryptocurrencies&#x2019; performance in terms of settling time and market cap.
Action Recognition Based on Person-Object Relationship Spatio-Temporal Graph

Human action recognition has a wide range of applications in real life. Aiming at the problem that the existing action recognition framework cannot describe the current object state of the behavior and the interaction between the object,this paper proposes an action recognition method based on the modeling of person-object relationship and spatio-temporal relationship. This method uses the representation of the person-object relationship spatio-temporal graph to describe the behavior. In the graph, the nodes of the graph represent the time-space state of the object participating in the behavior, and the edges of the graph represent the interaction relationships between the object participating in the behavior, including the person-object and time sequence relationship. Optimize the person-object relationship spatio-temporal graph through graph convolution, and use sparse coding to convert it into behavior indicators. The method was tested on HMDB51 and UCF101, and the experimental results of 77.6% and 96.5% were achieved respectively.
Stochastic simulation of orbital uncertainty of potentially hazardous asteroids observed in one appearance

The paper discusses some features in stochastic simulation of the orbital uncertainty of potentially hazardous asteroids observed in one appearance (opposition). Due to scant observational information, most of these asteroids have huge orbital uncertainties which can be difficult for stochastic simulation because of the strong nonlinearity of the inverse problem. In the paper we present different stochastic methods for simulation of orbital uncertainty and analyze their efficiency; using an original nonlinearity indicator, investigate the nonlinearity for all potentially hazardous asteroids observed in one opposition; consider the conditions that entail strong nonlinearity as well as the causes of falsely strong nonlinearity.
A Study on Automatic Individual Identification of Wild Horses

Wild horses called "Misaki-uma" are inhabited Cape Toi in the southern part of Miyazaki Prefecture in Japan. Although wild horse, it needs health care. It is a difficult task for aging management association members to monitor the vast habitat range of horses and to identify individual during routine management. In addition, the current methods of individual identification because of contact with horses or to requiring specialized knowledge, ordinary people cannot perform it. In this paper we propose a method for automatic individual identification of wild horses without contact using an RGB camera.
The Effectiveness of Policies on ICT Resources and Passwords in Sri Lankan Universities

All universities are bound by different policies to set guidelines as to how and in what capacity the universities need to operate. These policies could be introduced by the respective universities or by a higher governing body such as the University Grants Commission (UGC). This research was conducted with the expectation of evaluating two policies prepared by the UGC Standing Committee on Computing and introduced through the Commission Circular No.: 02/2013. These two policies, namely policy on passwords and policy on computing resources, have been prepared to regularise the passwords used by the university staff members and govern the use of computing resources in universities. The main objective of this research was to see to what extent the universities have implemented these policies, which were introduced in 2013. The study was conducted among staff members representing all national universities in Sri Lanka. The research consisted of two parts. The first stage evaluated the knowledge of university staff members and IT staff members regarding the above two policies. An online questionnaire was distributed among 5150 university academic and non-academic staff members of all local universities. Eight hundred fifty responses were received for this questionnaire which was beyond the expected sample size. The second questionnaire was sent to each university&#x2019;s IT directors and other IT officers in a similar capacity. These surveys revealed that many staff members were not aware of these policies. 53.53% of staff members mentioned that they were unsure whether their own university had an IT policy. As the second stage of this research, the policy on passwords was cross-referenced with NIST 2020 guidelines for passwords to see how well the UGC policies are on par with the new guidelines. This revealed that the UGC policy on passwords needs to be updated according to the evolved techniques. The research makes some recommendations to improve the effectiveness of these policies, such as conducting periodic reviews and awareness sessions, carrying out surveys to check how well the university employees have understood and adhered to the guidelines, incorporating these policies into the university&#x2019;s own IT policies, etc.
Conditionals Support in Binary Expression Tree Based Genetic Programming

Inspired by the genetic algorithm (GA), the genetic programming (GP) was proposed for searching a program that fits a certain behavior. There are many aspects that distinguish GP from GA a lot, though GP concepts were originating from GA. In this paper, we focus on the representation scheme for a GP program. A GP program contains both operators and operands. Without proper encoding, the GP crossover and mutation are likely to produce invalid programs. Based on our previous design experiences, we proposed an alternative approach. It is a binary expression tree based representation with conditional behavior of each node. Therefore, the scheme supports unary, binary, and ternary operators. It also reduce the probability of producing invalid programs. A feature of the scheme is that conditional operators are first-class member because each evaluation embeds conditional processing. A few image-processing experiments were conducted to show the effectiveness of the design. The experimental results are also discussed in this paper.
Document Image Analysis of Salary Payslips

This paper presents a methodology for automated data entry of salary payslips from document images. The challenging problems are 1) the payslips vary from one company to another, and 2) the appeared wording terms are different but similar meaning terms. The proposed methodology is the essential preprocess by using image processing and regular expression setting before an optical character recognition or OCR. The post-process for number validation must be considered by checking the financial formula.
Repgen Security Checking Tool for Startups

Cyber security is one of the foremost habitually examined themes these days. Your company cannot protect itself from cybercriminals without a cyber security plan. As the number of security-related threats increases, so will the number of security professionals available. Small and medium-sized startups, in general, do not invest in data protection, making it easier for cyber criminals to target them. The report provided by the programmer will give us an idea of how vulnerable the system is to bad guys in society, and if they are critical, we should seek the assistance of security specialists. The goal of this term paper is to develop an instrument that can be used by security personnel who are less skilled or experienced. Cyber security plays a basic parcel with inside the field of data advancement. Securing the data have gotten to be one of the foremost unmistakable challenges interior the show up day. When we think of cyber security, the first thing that springs to mind is ‘internet crimes,’ which are rapidly growing. Various governments and businesses are taking various steps to prepare for these cyber intrusions. Aside from numerous precautions, cyber security is still a huge issue for a lot of people. On a very basic level, this study focuses on the issues that cyber security faces in the initial few steps. It also focuses on the most recent developments in cyber security tactics, ethics, and initiatives to change the state of cyber security.
On Explainability of A Simple Classifier for AR(1) Source

The heuristic reasoning and experiments based design approach have been the pillars of studies on artificial neural networks. The explainable network performance is required for most applications. We focus on a simple classifier network for the two-class case of AR(1) data sources. We trace the input statistics through the network and quantify changes to explain relationship between accuracy performance, optimized parameters and activation function types employed for the given architecture. We present test accuracy results for various network configurations with different dimension and activation types. AR(1) source model for a two-class case is utilized to generate training and test data sets of the experiments due to its ease of use for analytical study. We quantify the relationships with well known metrics among signal (class) statistics, network architecture, activation function type and accuracy for several correlation coefficient pairs of the two AR(1) sources utilized in this paper. It is observed from the experiments that the analyses of data, input-output relationships of hidden and output layer nodes for the given architecture provide invaluable insights and guidance to judiciously design a neural network and to explain its performance based on characteristics of the building blocks.
Clock Design Methodology for Energy and Computation Efficient Bitcoin Mining Machines

Bitcoin mining machines become a new driving force to push the physical limitation of semiconductor process technology. Instead of peak performance, mining machines pursue energy and computation efficiency of implementing cryptographic hash functions. Therefore, the state-of-the-art ASIC design of mining machines adopts near-threshold computing, deep pipelines, and uni-directional data flow. According to these design properties, in this paper, we propose a novel clock reversing tree design methodology for bitcoin mining machines. In the clock reversing tree, the clock of global tree is fed from the last pipeline stage backward to the first one, and the clock latency difference between the local clock roots of two consecutive stages maintains a constant delay. The local tree of each stage is well balanced and keeps the same clock latency. The special clock topology naturally utilizes setup time slacks to gain hold time margins. Moreover, to alleviate the incurred on-chip variations due to near-threshold computing, we maximize the common clock path shared by flip-flops of each individual stage. Finally, we perform inverter pair swap to maintain duty cycle. Experimental results show that our methodology is promising for industrial bitcoin mining designs: Compared with two variation-aware clock network synthesis approaches widely used in modern ASIC designs, our approach can reduce up to 64% clock buffer/inverter usage, 12% clock power, decrease 99% hold time violating paths, and achieve 85% area saving for timing fixing. The proposed clock design methodology is general and applicable to blockchain and other ASICs with deep pipelines and strong data flow.