Reviewerly/exHarmony huggingface dataset:
Rotation Measurement Using Speckle Photography with LiNbO3 Crystal: Theoretical and Experimental Analysis

Photorefractive crystals present varied features charming presence, such as high resolution, and normal handling.Depending on the portability of erasing images, photorefractive crystals are convenient for read-write implementations and hence find potential use in speckle photography, speckle interferometry, image processing and holography.A two-beam coupling arrangement using a LiNbO 3 crystal as a recording medium for real-time rotation measurement using the coherent and low-power laser source is presented in this paper.A speckle photography technique is advanced for the measurement of a small rotation of a transmitted glass slide sample.New theoretical analysis is formulated for a general case of a slide rotation.Experimental studies are carried out to verify the outcome of the theoretical predictions and accuracy of measurement.Uncertainty of rotation measurement is studied and quantified.The proposed technique is a simple, attractive and alternative method for fringe analysis.The method promises a high degree of accuracy and increased range for rotation measurement in real-time.
Methods for Emotional Psychotyping of Cyberspace Actors based on Their Digital Footprints

The article proposes an original approach for emotional psychotyping of social network users (actors) based on their digital footprints. Applying the approach results in the assessment of the agentś belonging to 8 emotional classes according to the Heymans - Le Senne typology, which is the psychological basis of our research. We analyze the actorś activities in the format of text messages in cyberspace and propose methods to determine the key criteria such as emotionality, activity, and primacy or secondariness. We analyzed the data of two different groups of a popular Russian Facebook-like social network VK and determined the psychotypes of their active subscribers. We have revealed that in both groups the dominating majority of users (50-60 %) may be classified as “apathetic” and “amorphous The minor part of actors (about 30 %) determine the main activity and emotion dynamics in these communities being “nervous”, “phlegmatic”, and “sanguine”. The scientific novelty of the research lies in the originality of the methods proposed for solving a complex and currently poorly developed problem - determining the character of a person by analyzing their activities in the cyberspace.
FOLASP<i>: FO(·) as Input Language for Answer Set Solvers</i>

Abstract Technological progress in Answer Set Programming (ASP) has been stimulated by the use of common standards, such as the ASP-Core-2 language. While ASP has its roots in nonmonotonic reasoning, efforts have also been made to reconcile ASP with classical first-order (FO) logic. This has resulted in the development of FO(·), an expressive extension of FO, which allows ASP-like problem solving in a purely classical setting. This language may be more accessible to domain experts already familiar with FO and may be easier to combine with other formalisms that are based on classical logic. It is supported by the IDP inference system, which has successfully competed in a number of ASP competitions. Here, however, technological progress has been hampered by the limited number of systems that are available for FO(·). In this paper, we aim to address this gap by means of a translation tool that transforms an FO(·) specification into ASP-Core-2, thereby allowing ASP-Core-2 solvers to be used as solvers for FO(·) as well. We present experimental results to show that the resulting combination of our translation with an off-the-shelf ASP solver is competitive with the IDP system as a way of solving problems formulated in FO(·).
Feature Based Framework for Web Spam Filtering

Today widely used source of information is internet. Internet users use Search engines to find relevant information. Search engines worked based on page ranking algorithms. But, some web spam attempts to cheat this search algorithm by providing wrong metadata. We used decision tree for classifying whether particular page is spam or not. In this paper we compare results from 3 different data mining algorithms viz. Random Forest, J48 and LAD Tree. Experiments were carried out on standard data set WEBSPAM-UK2007. We added 5 new feature attributes in existing data set which were words found in spam pages and also implemented attribute reduction algorithm to remove redundant attributes from the data set. Reclassification using updated dataset improved precision by 2%. Moreover noticeable improvement of 20% was found in FP rate. Also attribute reduction helped in reducing build time by 8%.
Analysis on Influencing Factors of College Evaluation Results Based on Grey Correlation Model and the Algorithm Theory

Comprehensive evaluation is the premise of scientific decision-making, and the analysis of the index factors affecting the evaluation results is the core content of evaluation problems. In this paper, 207 higher vocational colleges participating in the “Double High Plan” selection, 2 primary indexes and 50 second grade comprehensive evaluation indexes are selected to construct the grey correlation model, and the algorithm theory is applied to the field of education evaluation. The empirical results show that the index of college-enterprise deep cooperation has a high correlation with the selected high-level colleges, and the lowest correlation with the proportion of double quality full-time teachers. Through the analysis of the evaluation results of colleges, this paper finds out the advantages and disadvantages of colleges, and puts forward the goal direction, so as to provide reference for colleges in the decision-making of education evaluation.
A method of computing winning probability for Texas Hold'em poker

Texas Hold'em poker is one of the most popular card games in computer games. Most of the times hand evaluation is necessary for computing hand strength which is also called winning probability. We need to traverse all combinations of cards to get the accurate hand strength in conventional approach. Usually Monte Carlo simulation or look-up table could reduce the computation. To improve efficiency, this paper propose an algorithm for poker hand classification and introduce linear regression to calculate the approximate effective hand strength. Firstly getting the classifying result of 5 cards or 6 cards, then generating feature vector through data preprocessing, at last the predictive value of the effective hand strength calculated by the linear regression equation. The experiment results show that the average absolute error between the predicted value and the label is 0.034. Compared with the traditional evaluation approach, our method is more effective to computing winning probability without lookup table.
From Trees to Loops

This modern text combines fundamental principles with advanced topics and recent techniques in a rigorous and self-contained treatment of quantum field theory.Beginning with a review of basic principles, starting with quantum mechanics and special relativity, students can refresh their knowledge of elementary aspects of quantum field theory and perturbative calculations in the Standard Model. Results and tools relevant to many applications are covered, including canonical quantization, path integrals, non-Abelian gauge theories, and the renormalization group. Advanced topics are explored, with detail given on effective field theories, quantum anomalies, stable extended field configurations, lattice field theory, and field theory at a finite temperature or in the strong field regime. Two chapters are dedicated to new methods for calculating scattering amplitudes (spinor-helicity, on-shell recursion, and generalized unitarity), equipping students with practical skills for research. Accessibly written, with numerous worked examples and end-of-chapter problems, this is an essential text for graduate students. The breadth of coverage makes it an equally excellent reference for researchers.
An Empirical Research on the Effect of SPOC-based Blended Learning Mode in College English Curriculum

Blended learning is a combination of face-to-face learning and online learning in a way that the one complements the other. In view of the defects of traditional college English curriculum, the author's university carried out a SPOC-based blended learning mode reform with the aim to improve college English learning outcome as well as promote learner autonomy. Through a whole academic year's research, it is indicated that SPOC-based blended learning mode indeed exerted a significant effect on learning outcome and meanwhile facilitated the development of students' autonomous learning ability.
Customer Data Management Information System And Web-Based Digital Product Sales In CV. IT Brain Indonesia

Customer Data Management Information System is a program or information system that functions to assist customer and sales data recording activities as well as product activation processes on CV. IT Brain Indonesia. This system is designed to record and input customer data from requests for demo software and customer consultations that come in daily via email marketing and also through whatsapp marketing before making a purchase and activation process. This system is shown for the marketing team on CV. IT Brain Indonesia is still using Microsoft Excel to record these data, which takes a long time to group customer needs on different sheets. In this case the author designed an Information System for Customer Data Management and Web-Based Digital Product Sales at CV. IT Brain Indonesia. The method used is the equivalence method and blackbox testing. The results of this study are in the form of a web-based information system intended for marketing and customer support that can group customer data according to their needs and record sales and product activation processes. This web-based information system was created using the CodeIgniter framework.
An Overview of AI

In the 1940s to 1950s, a group of scientists from different fields began to explore the possibility of making an artificial brain. At the 1956 Dartmouth Conference, AI was officially established as an academic discipline. Over a half-century of development, scientists have made various attempts to explore different directions. The field of AI has thus given birth to many schools. At present, the primary approaches in AI are behaviorism, symbolism, and connectionism.
The Overview of Training-oriented 3D Visual Presentation

research-article Share on The Overview of Training-oriented 3D Visual Presentation Authors: Hao Dai Shenzhen Power Supply Co. Ltd, China Shenzhen Power Supply Co. Ltd, ChinaView Profile , Chao Lin Shenzhen Power Supply Co. Ltd, China Shenzhen Power Supply Co. Ltd, ChinaView Profile , Zhiwen Cui Shenzhen Power Supply Co. Ltd, China Shenzhen Power Supply Co. Ltd, ChinaView Profile , Zhicheng Xue Shenzhen Power Supply Co. Ltd, China Shenzhen Power Supply Co. Ltd, ChinaView Profile , Guang Li Shenzhen Power Supply Co. Ltd, China Shenzhen Power Supply Co. Ltd, ChinaView Profile , Peng Yuan Shenzhen Power Supply Co. Ltd, China Shenzhen Power Supply Co. Ltd, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 1849–1852https://doi.org/10.1145/3482632.3484052Online:22 November 2021Publication History 0citation4DownloadsMetricsTotal Citations0Total Downloads4Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Shadow Detection and Removal Using Dynamic Thresholding Approach for Driverless Car

In the most emerging concept of autonomous vehicle system, i.e. Driverless car, Road detection is one of the important aspects to be considered. Shadows play a vital role in vision-based road detection. The process of road detection can be significantly affected by shadows since the boundaries in the shadows can be detected false. Most shadow detection and segmentation methods are based on image analysis. The existing methods use recursive thresholding algorithm which is too time consuming. The proposed model considers image processing techniques such as colour conversion and thresholding. This model uses a dynamic thresholding approach which is time efficient with high accuracy rate.
Video Compression for Screen Recorded Sequences Following Eye Movements

With the advent of smartphones and tablets, video traffic on the Internet has increased enormously. With this in mind, in 2013 the High Efficiency Video Coding (HEVC) standard was released with the aim of reducing the bit rate (at the same quality) by 50% with respect to its predecessor. However, new contents with greater resolutions and requirements appear every day, making it necessary to further reduce the bit rate. Perceptual video coding has recently been recognized as a promising approach to achieving high-performance video compression and eye tracking data can be used to create and verify these models. In this paper, we present a new algorithm for the bit rate reduction of screen recorded sequences based on the visual perception of videos. An eye tracking system is used during the recording to locate the fixation point of the viewer. Then, the area around that point is encoded with the base quantization parameter (QP) value, which increases when moving away from it. The results show that up to 31.3% of the bit rate may be saved when compared with the original HEVC-encoded sequence, without a significant impact on the perceived quality.
BAJML algorithm based on joint likelihood in data association

In the process of SLAM (Simultaneous Localization And Mapping), an essential link is data association. Data association provides the basis for state estimation to ensure that the robot can make full use of the obtained environmental information to realize positioning and mapping. Aiming at the problems of low association accuracy of current single target data association algorithm and difficult calculation of multi-target data association algorithm, a bat optimized multi-target data association algorithm based on joint maximum likelihood is proposed to ensure accurate data association processing results, and reduce the computational complexity of SLAM algorithm. It is based on joint maximum likelihood, The multi-objective data association problem is transformed into an optimization problem to solve the optimal combination, and the bat optimization algorithm is used to search the optimal data association. The slam process of the mobile robot experiments show that the proposed bat optimized data association algorithm improves the rapidity and accuracy of slam process of mobile robot.
Face super resolution with texture prior

Recently, Face super-resolution (SR) based on deep learning and face shape priors have achieved great success. For the prior information provided by face images, people pay more attention to the shape prior represented by the heat maps and parsing maps of face landmarks. However, the texture prior is also crucial for this task. In this paper, we propose a novel SR method with texture prior. In particular, we construct a prior network to extract the texture prior information of face images. At the same time, a fine SR network is used to recover a high-resolution image from low-resolution input. After that, the obtained texture priors are concatenated with the preliminary SR results and sent to the subsequent fusion network to obtain the final output images. The results of quantitative and qualitative experiments show that our method is superior to state-of-the-art face SR methods.
A case study on the perspectives of University of Santo Tomas pharmacy professors on the influence of online classes in pharmacy education

Objective: The COVID-19 pandemic has shifted the academic environment into distance learning. In the University of Santo Tomas (UST) Faculty of Pharmacy, an Enriched Virtual Mode of Learning was used to deliver the online teaching and learning process. This study aimed to determine the influences that online learning had on the standard of pharmacy education. Methods: A qualitative case study research design was utilised and gathered the perspectives of eight UST pharmacy professors. Results: During thematic analysis, several themes were identified which were rooted in the ten key components of online learning. Conclusions: The study concluded that online learning had more negative influences than positive influences on pharmacy education. Regarding pharmacy courses, the Quality Control, Microbiology, and Public Health clusters were identified as the most difficult to teach online while the Biochemistry and Pharmacognosy clusters were the least. Recommended adjustments to improve learning outcomes focused on enhanced review methods during the course audit.
Research on Practical Intelligent Mode of Digital Image Economy Based on Improved Genetic Multilayer Neural Network

In the context of economic globalization and digitization, the current financial field is in an unprecedented complex situation. The methods and means to deal with this complexity are developing towards image intelligence. This paper takes financial prediction as the starting point, selects the artificial neural network in the intelligent algorithm and optimizes the algorithm, forecasts through the improved multilayer neural network, and compares it with the traditional neural network. Through comparison, it is found that the prediction success rate of the improved genetic multilayer neural network increases with the increase of the dimension of the input image data. This shows that, by adding more technical indicators as the input of the combined network, the prediction efficiency of the improved genetic multilayer neural network can be further improved and the advantage of computing speed can be maintained.
A linear adjustment-based approach to posterior drift in transfer learning

Summary We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted for transfer learning problems in various domains including epidemiology, genetics and biomedicine. As concrete applications, we illustrate the power of our approach (i) through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data, and (ii) in overcoming a spurious correlation present in the source domain of the Waterbirds dataset.
SGX -Capsule: A Confidential Execution Engine for Unmodified Libraries on SGX Enclave

Hardware-assisted lifetime confidentiality of unmodified libraries is required to protect proprietary IP from potentially compromised privileged software. Previous research, however, has found that they do not provide it with smaller TCB size to unmodified binaries. This paper proposes SGX-Capsule, a confidential execution engine that executes a native library within an SGX enclave while the library remains confidential at rest and in transit. Reduced TCB size by accommodating the target library only and interfacing with others across the enclave boundary decreases possible vulnerabilities and performance overhead from memory overcommitment.
Perspectives on Regulatory Compliance in Software Engineering

Compliance reviews within a software organization are internal attempts to verify regulatory and security requirements during product development before its release. However, these reviews are not enough to adequately assess and address regulatory and security requirements throughout a software's development lifecycle. We believe requirements engineers can benefit from an improved understanding of how software practitioners treat and perceive compliance requirements. This paper describes an interview study seeking to understand how regulatory and security standard requirements are addressed, how burdensome they may be for businesses, and how our participants perceived them in the software development lifecycle. We interviewed 15 software practitioners from 13 organizations with different roles in the software development process and working in various industry domains, including big tech, healthcare, data analysis, finance, and small businesses. Our findings suggest that, for our participants, the software release process is the ultimate focus for regulatory and security compliance reviews. Also, most participants suggested that having a defined process for addressing compliance requirements was freeing rather than burdensome. Finally, participants generally saw compliance requirements as an investment for both employees and customers. These findings may be unintuitive, and we discuss seven lessons this work may hold for requirements engineering.
Adaptively Calibrated Critic Estimates for Deep Reinforcement Learning

Accurate value estimates are important for off-policy reinforcement learning. Algorithms based on temporal difference learning typically are prone to an over- or underestimation bias building up over time. In this letter, we propose a general method called Adaptively Calibrated Critics (ACC) that uses the most recent high variance but unbiased on-policy rollouts to alleviate the bias of the low variance temporal difference targets. We apply ACC to Truncated Quantile Critics [1], which is an algorithm for continuous control that allows regulation of the bias with a hyperparameter tuned per environment. The resulting algorithm adaptively adjusts the parameter during training rendering hyperparameter search unnecessary and sets a new state of the art on the OpenAI gym continuous control benchmark among all algorithms that do not tune hyperparameters for each environment. ACC further achieves improved results on different tasks from the Meta-World robot benchmark. Additionally, we demonstrate the generality of ACC by applying it to TD3 [2] and showing an improved performance also in this setting.
Pattern Devoid Cryptography

Pattern-loaded ciphers are at risk of being compromised by exploiting deeper patterns discovered first by the attacker. This reality offers a built-in advantage to prime cryptanalysis institutions. On the flip side, the risk of hidden math and faster computing undermines confidence in the prevailing cipher products. To avoid this risk one would resort to building security on the premise of lavish quantities of randomness. Gilbert S. Vernam did it in 1917. Using modern technology, the same idea of randomness-based security can be implemented without the inconvenience associated with the old Vernam cipher. These are Trans Vernam Ciphers that project security through a pattern-devoid cipher. Having no pattern to lean on, there is no pattern to crack. The attacker faces (i) a properly randomized shared cryptographic key combined with (ii) unilateral randomness, originated ad-hoc by the transmitter without pre-coordination with the recipient. The unlimited unilateral randomness together with the shared key randomness is set to project as much security as desired up to and including Vernam levels. Assorted Trans Vernam ciphers (TVC) are categorized and reviewed, presenting a cogent message in favor of a cryptographic pathway where transmitted secrets are credibly secured against attackers with faster computers and better mathematicians.