Reviewerly/exHarmony huggingface dataset:
An Efficient CCA-Secure Access Control Encryption for Any Policy

Access control encryption (ACE) is a useful concept introduced by Damgård, Haagh and Orlandi in TCC 2016, which not only protects the data privacy but also controls the information flow. However, their DDH-based scheme suffered from the ciphertext revealing attack (CRA) introduced by Badertscher, Matt and Maurer in Asiacrypt 2017 and just satisfied chosen plaintext attack (CPA) security. Badertscher, Matt and Maurer strengthened the security model to chosen ciphertext attack (CCA) security and constructed a CCA secure scheme under Naor-Yung paradigm. However, they did not indicate how to fix the DDH-based scheme proposed by Damgård, Haagh and Orlandi. Their CCA secure scheme is inefficient and complicated due to the non-interactive zero knowledge proofs (NIZKs) of a very complicated relation. And their scheme is constructed just for limited communication policies. In this paper, we generalize the DDH-based scheme proposed by Damgård, Haagh and Orlandi and fix its flaw in a very efficient way. Then, we construct a CCA secure ACE scheme, which is efficient, simple, constructed for any communication policy and can be instantiated from many kinds of standard assumptions including the lattice assumptions. Finally, we propose two instantiations respectively based on the lattice assumptions and the decisional bilinear Diffie-Hellman (DBDH) assumption.
3D Perception With Slanted Stixels on GPU

This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier.
Patients Data Extraction from DDO Files Extension using Rule-based Classification Algorithm

Abstract Arrangement patient’s report process of Elim Rantepao hospital is especially complicated because patient data was obtained from FCR Console Prima what saved as DDO file extension as Unicode string. This problem makes understanding and select data manually become difficult. This research tries to give a solution on how to arrange reports easily through application tools developing. This research uses a rule-based classification algorithm to extract Patient Name, Age, ID Number, Date, and time. Applying this algorithm, increase system performance up to 96,6%. This application is very accurate as long as the file DDO has not broken.
Resourceful Investigation of Malnutrition with Machine Learning Using CNN Procedure

This System used to refer to any condition in which the body does not obtain enough nutrients for proper function in this system Image processing is a technique to convert an image into digital form and perform some action on it, in order to get an enhanced image or to citation some useful information from it. There are some types of image that is Tagged Image Format, Joint Photographic Group, Graphic Interchange Format, Portable Network Graphics, Row Image File. Computer can easily classify more than 16 million color where the eye capability has limitation while identifying color and also same people face the problem such as colors carelessness. So the execution nail color Analysis through computer is a higher procedure as compared with human eyes. Human eyes have limited resolution, finding deviation in nearby pixel intensity is not possible for human eyes, but computer vision can detect every pixel appropriately. So, in this system we used the Convolutional Neural Networks (CNN) algorithm &amp; carried out the result. this system is very helpful for the patient, to find out the prediction or detection of diseases without doctor.
Effect of Dimensionality Reduction on Prediction Accuracy of Effort of Agile Projects Using Principal Component Analysis

Abstract Agile framework for software development has received a lot of recognition in software industry in previous years as it focuses on rapid incremental delivery, lower risk and customer satisfaction. At early stages of development, the effort must be predicted so that the project is completed successfully within the time and cost deadlines. In recent years, various researchers have done study in this area and it is observed that the prediction of effort faces a problem of large dimension of features. Hence the prediction accuracy may be increased by reducing the dimensions of the features. In this paper, PCA has been used for reduction of feature dimensions for effort estimation. PCA identifies the key attributes by reducing the dimensions of the attribute which are those having highest correlation with the effort. The methodology shows the effect of PCA on the original dataset and the results are observed by applying various machine learning techniques pre and post PCA. The comparison metrics used are Mean Magnitude relative Error (MMRE), Root Mean Square Error (RMSE), and Prediction Accuracy (PRED (25)). The decreased values of errors and increased value of accuracy shows the better model accuracy when PCA is applied on the dataset. All the computations and implementations in this paper are done using Python on Scikit-learn library.
Feature Fusion Based on Sparse Block for Image Super-resolution

Recently, deep neural networks have been widely used in the task of single image super-resolution. However, existing deep neural networks always take huge parameters to map low-resolution images to high-resolution ones. In addition, most of them only consider high-level features to reconstruct high-resolution images. These two methodologies not only cause the difficulty of practical applications but also the inefficiency of restoring image details. Therefore, in this work, the authors propose a novel sparse block to learn high-level features. Based on this block, a fusion method is proposed to fuse features from multiple levels. By incorporating these two approaches, a lightweight neural network, i.e. Sparse Block Fusion Network (SBFN), is proposed for end-to-end training. Through extensive experiments, it is demonstrated the proposed methods can achieve comparable performance with few parameters. By making comprehensive comparisons, effectiveness of SBFN is also verified in multiple benchmark datasets.
The Effect of Additional Subsea Floater on Mooring Line Tension and Motion of CBM (Conventional Buoy Mooring) Ø 4.5 m

Abstract Single point mooring of Conventional Buoy Mooring (CBM) is an offshore mooring structure where the mooring line attached from bow of ship to CBM. Lack buoyancy on CBM needed Additional subsea floaters to increase buoyancy of CBM. Time domain simulation on Moses software version 11 used to calculate the effect of the variation number of subsea floaters to chain tension and CBM Motion. The result is a single configuration subsea floater has higher tension and extreme motion than multiple configuration subsea floaters.
The Effect of Using Pick List Evaluate Activate Supply End (PLEASE) Strategy Towards Students Ability in Writing Analytical Exposition Text at the Second Year of Senio High School 1 Gunung Talang

The purpose of the research is to find out whether or not the PLEASE strategy is effective in the students’ ability in writing analytical exposition text at the second year of SMAN 1 Gunung Talang. This research uses a quantitative with quasi-experimental research with Pre-Test and Post-Test Control Group Design. The populations in this research were second year students’ of SMAN 1 Gunung Talang who were registered in the January-June period of the 2020/2021 school year. The research sample was 35 students of class XI IPA 1 as the experimental class and 35 students of class XI IPS 4 as the control class. The results of the analysis indicated that the mean of post-test score 85.37 was higher than the mean of pre-test score 67.23. Thus, in this research, the test scores were compared using of the t-test formula to determine the effectiveness of using PLEASE strategy ability in writing. Based on the data analysis and the results of the t-test, it was concluded that learning using PLEASE strategy was give the better effect to students ability in writing analytical exposition text at SMAN 1 Gunung Talang because t count < t table at a significant level of 0.05. So, it was concluded that the teaching of writing in the second year students of SMAN 1 Gunung Talang by using PLEASE strategy was better effect than without using PLEASE strategy. Keywords: PLEASE strategy, writing ability, analytical exposition
A complexity chasm for solving sparse polynomial equations over <i>p</i> -adic fields

The applications of solving systems of polynomial equations are legion: The real case permeates all of non-linear optimization as well as numerous problems in engineering. The p -adic case leads to many classical questions in number theory, and is close to many applications in cryptography, coding theory, and computational number theory. As such, it is important to understand the complexity of solving systems of polynomial equations over local fields. Furthermore, the complexity of solving structured systems --- such as those with a fixed number of monomial terms or invariance with respect to a group action --- arises naturally in many computational geometric applications and is closely related to a deeper understanding of circuit complexity (see, e.g., [8]). Clearly, if we are to fully understand the complexity of solving sparse polynomial systems, then we should at least be able to settle the univariate case, e.g., classify when it is possible to separate and approximate roots in deterministic time polynomial in the input size.
Quantifying Gaze-Based Strategic Patterns in Physics Vector Field Divergence

We instructed a group of 20 undergraduate physics students with an instruction to visually interpret divergence of vector field with integral and differential strategies. We designed two distinct sets of 10 tasks and recorded the students’ eye gaze while they completed the task. In this study, we first developed Attentive Region Clustering (ARC), a novel unsupervised approach to analyze and evaluate the fixations and saccadic movements of the participants. Secondly, a linear Support Vector Machine model was used to classify the two problem-solving strategies in the vector field domain. The results revealed the implication of vector flow orientation in the eye movement patterns. We achieved an accuracy 10-fold cross-validation, we achieved $$81.2\%$$ $$(11\%)$$ accuracy by evaluating a linear Support Vector Machine model to classify which strategy was applied by the student to comprehend the divergence of a vector field problem. The outcome of this work is useful to monitor the student visual performance on similar tasks. Besides, advances in Human-Computer Interaction empower students by getting objective feedback on their progress by visual clues in a vector field problems.
Noise filtering algorithm for control of motion systems

Abstract Measurement noise and external disruptions are problematic in real world applications and every control system design. These unwanted disturbances affect the measured output signal and cause the system to provide an inaccurate output. We need to eliminate these noises for stabilizing of the control system design. In this paper, we propose how to eliminate effectively the noises in motion control systems using the Kalman filter. We investigate practically the Kalman filtering algorithm based on the measurements generated by the encoder sensor fixed on DC motor. The Kalman algorithm is used for noise filtering because it is recursive algorithm and it can handle the noises that occur when the sensor is used to read data. The performance of KF method is enhanced by adjusting its parameters according to the innovation requirements. In the experimental results, the velocity estimation of the Finite-difference (FD) method is demonstrated to compare with the velocity estimation of the Kalman filtering (KF) method.
An Early 3D Human Action Recognition (3DHAR) System by Training Subactions of an Action using Deep Networks

The Human action (HA) comprises of a set continues sub-actions. In this way, our point is to foresee the HA's utilizing those arrangement of sub-actions before finishing an action. Usually, human action recognition (HAR) has been performed utilizing joints, RGB recordings and Depth in all these cases forecasting an incomplete HA became most difficult task. So as to beat this issue, we proposed a novel approach utilizing bidirectional LSTM system so as to characterize the an action from a fully trained convolution neural network (CNN) features. CNN features are acquired from the 3D skeleton information modelled to joint angular displacement maps (JADM). JADM is spatio temporal presenation of a 3D skeleton information and is taken from our past works. This proposed strategy 3DHAR-Net is equipped for dealing with enormous action sequences to predict the action classes by analysing series of sub-actions in time sequences. The entire experimentation is carried on our self-made 3D human activity datasets KLYoga3D and KLHA3D-102 action dataset and accomplished better outcomes and our method is further evaluated on the three benchmark datasets CMU, HDM05 and G3D.
2021 Editorial IEEE Open Journal of Intelligent Transportation Systems

What a year to start a new journal! 2020 was the inaugural year of the IEEE Open Journal of Intelligent Transportation Systems. We are delighted that already so many authors have submitted their work to “OJ-ITS,” it shows that there is a lot of interest in the ITS community for a Gold Open Access Journal.
Reflection of meanings and peculiarities of subjectivity of employees of internal affairs bodies in the environment of continuous education

Abstract. The research problem lies in the need to study the contradiction between the high values of life-meaning orientations and the reflexivity of employees of the internal affairs bodies. Purpose of the article: to investigate the indicated contradiction and reveal the peculiarities of the subjectivity of the personality of employees. The analysis and interpretation of the characteristics of the personal development of employees in lifelong education was carried out by the method of clustering the sample using the values of the tests "Life-meaning orientations" D. Krambo, L. Makholik, adaptation - D.А. Leontiev, reflexivity (A.V. Karpov), as well as the subjectivity of a professional's personality and focus on the personal development of an employee, including in lifelong education based on the author's methodology. The work used the correlation analysis of the studied features by means of nonparametric statistical criteria (Spearman's rank correlation coefficient), cluster analysis. As a result of the study, it was revealed that reflexivity, orientation towards development in professional activity and focus on continuing education in clusters are expressed unevenly. The hypothesis of a strict inverse relationship between manifestations of reflexivity and life-meaning orientations was not confirmed. At the same time, a feature of the subject-personal orientation of employees is an insufficient connection between reflexivity and high values of meaningful orientations, subordination of the personal principle to the "professional role and position" (according to the terms of V.A. Lefebvre) due to the subordination and discipline in the profession, as well as the substitution of the moral imperative with legal norms. Strategies with extremely high values of life-meaning orientations of adult employees of internal affairs bodies, which have a negative correlation with reflexivity, regardless of subjectivity or the severity of orientation towards development in the profession, are assessed by us as unfavorable for personal development.
Exploration of English Teaching Methods Based on Computer Corpus in Distance and Open Education Environment

At present, the traditional English teaching mode has gradually withdrawn from the historical stage, and multimedia has been widely used in the process of distance open English teaching and achieved good results. The important feature of modern distance open education is to carry out various teaching activities under the network conditions. In college English teaching, corpora are widely used in language research and teaching, which has played an important role in improving the quality of college English teaching. The use of interactive activities in English teaching in distance and open education can bring good teaching results to English teaching, which is a brand-new model based on the law of language learning and classroom teaching rules. The traditional English teaching mode can not meet the needs of development. How to cultivate new compound talents with certain professional skills is the basic requirement of open education for English teaching. This paper discusses the feasibility of corpus in English teaching and how to apply corpus to English Teaching in distance and open education environment.
Can't find that perfect gift? Blame the bots

Online fraud rings and retail scalping outfits are increasingly turning to automation and artificial intelligence (AI) for the same reasons that businesses in every industry have embraced these technologies. Simply put, deploying smart machines allows businesses to become more accurate, more efficient and more profitable. In the end, bad actors who work to take advantage of online brands and retailers are entrepreneurs. They embrace innovation and new ways of expanding their portfolios – and their success. Online fraud rings and retail scalping outfits are increasingly turning to automation and artificial intelligence (AI). As much as 70% of traffic to ecommerce checkout pages is generated by malicious bots. Retailers need to turn to machine learning and a powerful data platform, combining a robust fraud solution that can differentiate legitimate from fraudulent transactions with a flexible tool that can understand and monitor complex business policies, explains Gayathri Somanath of Signifyd.
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We investigate (0,1)-matrices that are convex, which means that the ones are consecutive in every row and column. These matrices occur in discrete tomography. The notion of ranked essential sets, known for permutation matrices, is extended to convex sets. We show a number of results for the class C(R,S) of convex matrices with given row and column sum vectors R and S. Also, it is shown that the ranked essential set uniquely determines a matrix in C(R,S).
Research on Application of Blockchain Technology in Departure Audit of Natural Resources Assets

Abstract Blockchain technology is the first underlying support technology applied to bitcoin. In recent years, it has formed a “blockchain +” technology mode, entered various fields of economic and social development, and promoted the further integration of blockchain and real economy. As early as 2015, General Office of the State Council issued the “Implementation Opinions on Implementing the Audit Full Coverage”. There are still many difficulties in auditing the natural resources assets of leading cadres, which represents the general trend of economic responsibility. This paper studies the related issues of the combination of blockchain and natural resource assets departure audit, and discusses the development prospect of blockchain audit technology, in order to comply with the historical change of full audit coverage.
Unsupervised Feature Value Selection Based on Explainability

The problem of feature selection has been an area of considerable research in machine learning. Feature selection is known to be particularly difficult in unsupervised learning because different subgroups of features can yield useful insights into the same dataset. In other words, many theoretically-right answers may exist for the same problem. Furthermore, designing algorithms for unsupervised feature selection is technically harder than designing algorithms for supervised feature selection because unsupervised feature selection algorithms cannot be guided by class labels. As a result, previous work attempts to discover intrinsic structures of data with heavy computation such as matrix decomposition, and require significant time to find even a single solution. This paper proposes a novel algorithm, named Explainability-based Unsupervised Feature Value Selection (EUFVS), which enables a paradigm shift in feature selection, and solves all of these problems. EUFVS requires only a few tens of milliseconds for datasets with thousands of features and instances, allowing the generation of a large number of possible solutions and select the solution with the best fit. Another important advantage of EUFVS is that it selects feature values instead of features, which can better explain phenomena in data than features. EUFVS enables a paradigm shift in feature selection. This paper explains its theoretical advantage, and also shows its applications in real experiments. In our experiments with labeled datasets, EUFVS found feature value sets that explain labels, and also detected useful relationships between feature value sets not detectable from given class labels.
Developer Recommendation for Bug Reports based on Ontological Aspects

In software maintenance, assigning appropriate developers to handle bug reports is crucial. Although many recommendation schemes have been proposed in the past by studying many features of bug reports, their feature analysis process does not have a systematic view. This paper proposes an ontology-based developer recommendation (ODR) scheme by designing two ontology frameworks, namely the developer ontology and the bug-report ontology, and calculating their impact indices. Since various features in two ontology frameworks are considered in ODR, genetic algorithms are used to optimize their weights. The empirical experiments have been conducted with three realistic software projects. The results show that the proposed ODR scheme can effectively improve the recommendation performance.
Quantum Internet

A quantum internet will provide arbitrary accessibility of distributed quantum systems, enabling secure communication, quantum computing, quantum sensing, etc. The quantum link between distant systems is established by photons flying through optical fibers and satellites. In this review, I present fundamental methods to send, manipulate and detect photonic quantum states to build a quantum internet; especially, technologies related to superconducting photon detectors.
Cyclic self dual codes of length <i>pq</i> over <i>Z</i><sub>4</sub>

Explicit expressions for primitive idempotent in the ring are obtained, where p and q are distinct odd primes with multiplicative order of 2 modulo p and modulo q being and ϕ(q) respectively. Hence the idempotent generators of cyclic self dual codes of length pq over Z4 are obtained. Further, it is observed that when p = 8k - 1 and q = 8m - 3, the extension of each of these self dual codes augmented with all-ones vector is a Type I code.