Reviewerly/exHarmony huggingface dataset:
Pruning external minimality checking for answer set programs using semantic dependencies

Answer set programming (ASP) has become an increasingly popular approach for declarative problem solving. In order to address the needs of applications, ASP has been extended in different approaches with means for interfacing the outside world, of which hex programs are one of the most powerful such extension that provides API-style interfaces to access arbitrary external sources of information and computation, respectively. Adhering to the principle of founded derivation, computing answer sets of hex programs requires an external (e-) minimality check for answer set candidates in order to prevent cyclic justifications via external sources. Due to the generic nature of external sources, the check can be a bottleneck in practice. To mitigate this, various optimizations have been developed previously, including the use of syntactic information about atom dependencies in order to detect cases when an e-minimality check can be avoided. However, the approach largely over-approximates the real dependencies due to the black-box nature of external sources. We thus consider in this work the use of semantic information for achieving better approximations. To this end, we introduce input-output (io-) dependencies for external sources, which intuitively link the occurrence of values in the result of a call to an external source to the occurrence of values in the input provided to this call. It appears that disposing of information about io-dependencies significantly increases the potential for pruning e-minimality checks, and an empirical evaluation exhibits a clear benefit of this approach. Moreover, we study semantic and computational properties of io-dependencies and provide algorithms for constructing and optimizing sets of io-dependencies. Our work aims at laying some foundations for the use of semantic dependency information in external source access from ASP. The results are not limited to hex programs, but may analogously be deployed to other approaches that integrate external sources into ASP, such as clingo or wasp with external propagators. Furthermore, the results may be applied in other parts of the hex program evaluation pipeline as well.
Modularized Framework with Category-Sensitive Abnormal Filter for City Anomaly Detection

Anomaly detection in the city scenario is a fundamental computer vision task and plays a critical role in city management and public safety. Although it has attracted intense attention in recent years, it remains a very challenging problem due to the complexity of the city environment, the serious imbalance between normal and abnormal samples, and the ambiguity of the concept of abnormal behavior. In this paper, we propose a modularized framework to perform general and specific anomaly detection. A video segment extraction module is first employed to obtain the candidate video segments. Then an anomaly classification network is introduced to predict the abnormal score for each category. A category-sensitive abnormal filter is concatenated after the classification model to filter the abnormal event from the candidate video clips. It is helpful to alleviate the impact of the imbalance of abnormal categories in the test phase and obtain more accurate localization results. The experimental results reveal that our framework obtains a 66.41 MF1 in the test set of the CitySCENE Challenge 2020, which ranks first in the specific anomaly detection task.
Single image dehazing based on transmittance fusion

Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but effective image prior – the transmittance fusion of the dark channel prior and color attenuation prior. By constructing the weight factor of transmittance, the respective advantages of both priors can be well presented. We combine the defogging effectiveness of the dark channel algorithm with the high applicability of the color attenuation prior. Brightness information makes the algorithm more adaptive in the dehazing process. Observed through a large amount of experimental data, the method in this paper can enhance the dehazing image effectively.
Sensing, Understanding and Synthesizing Humans in an Open World

Sensing, understanding and synthesizing humans in images and videos have been a long-pursuing goal of computer vision and graphics, with extensive real-life applications. It is at the core of embodied intelligence. In this talk, I will discuss our work in human-centric visual analysis (of faces, human bodies, scenes, videos and 3D scans), with an emphasis on learning structural deep representations under complex scenarios. I will also discuss the challenges related to naturally-distributed data (e.g. long-tailed and open-ended) emerged from real-world sensors, and how we can overcome these challenges by incorporating new neural computing mechanisms such as dynamic memory and routing. Our approach has shown its effectiveness on both discriminative and generative tasks.
Canonical Correlation Cross-Domain Alignment for Unsupervised Domain Adaptation

Domain adaptation has been widely used in the field of computer vision. Current methods of domain adaptation mainly aim to reduce the difference of the marginal and conditional distributions of the source and target domains in a centralized manner. However, most of the existing domain adaptation methods ignore the correlation information of the two domains, or doesn’t take it very seriously. That making it difficult to learn related features from the source domain for the target task. A new method, canonical correlation cross-domain alignment (CCCA), is proposed to effectively reduce the cross-domain distribution difference by combining the least squares formula of CCA with domain adaptation. In CCCA, a common latent subspace with the maximum correlation is learned to ensure that the learned features are from the two domains with maximum correlation. A Laplace graph is learned to maintain the structural consistency of CCCA. To verify the performance of our method, we conduct experiments on several benchmark visual databases. The experimental results illustrate its superiority to several other methods.
A new offer of <scp>NTRU</scp> cryptosystem with two new key pairs

Abstract In this study, it is aimed to contribute to the NTRU cryptologic system to increase the number of operations of taking module by taking a polynomial module, to increase the number of secret key by adding an isomorphism and also to increase the number of public key by choosing an irreducible polynomial. Since it is calculated module by suitably partitioning a chosen message polynomial, and since it is used properties of module being an algebraic structure in a phase of an operation and a decryption by adding to the system prime number module and prime polynomial module, this title is thought fit.
Clustering web search results using Wikipedia resource

The paper presents a proposal of a new method for clustering search results. The method uses an external knowledge resource, which can be, for example, Wikipedia. Wikipedia – the largest encyclopedia, is a free and popular knowledge resource which is used to extract topics from short texts. Similarities between documents are calculated based on the similarities between these topics. After that, affinity propagation clustering algorithm is employed to cluster web search results. Proposed method is tested by AMBIENT dataset and evaluated within the experimental framework provided by a SemEval-2013 task. The paper also suggests new method to compare global performance of algorithms using multi – criteria analysis.
Mobile Application Design of Learning Word in Lamaholot Language for Children Using User-Centered Design

Indonesia is a country with a variety of regional languages that characterize the nation. Every region in Indonesia has its local language and with its dialect and is characteristic of the region. Regional languages have become a long-established identity. Unknowingly, Indonesia is experiencing an increasingly rapid cultural change in which young genes are already in a condition where many require them not to use their local language. Also, data collected by UNESCO shows that there are approximately 30 regional languages that are endangered and, on average, are in eastern Indonesia. Facing the existing problems, this study aims to help young people to get to know their local languages through mobile applications. The local language used as research is the “lamaholot” language, which is one of the native languages of the people of East Flores Regency and uses the user-centered design method. The design will be tailored to the user so that it can be more attractive and according to user needs. Respondents were taken as many as 147 children with an average age between 10 years to 14 years who are people in East Flores Regency.
Skin Textural Generation via Blue-noise Gabor Filtering based Generative Adversarial Network

Facial skin texture synthesis is a fundamental problem in high-quality facial image generation and enhancement. The key behind is how to effectively synthesize plausible textured noise for the faces. With the development of CNNs and GANs, most works cast the problem as an image to image translation problem. However, these methods lack an explicit mechanism to simulate the facial noise pattern, so that the generated images are of obvious artifacts. To this end, we propose a new facial noise generation method. Specifically, we utilize the property of blue noise and Gabor filter to implicitly guide the asymmetrical sampling for the face region as a guidance map, where non-uniform point sampling is conducted. Thus we propose a novel Blue-Noise Gabor Module to produce a spatial-variant noisy image. Our proposed two-branch framework combined facial identity enhancing with textures details generation to jointly produce a high-quality facial image. Experimental results demonstrate the superiority of our method compared with the state-of-the-art, which enables the generation of high-quality facial texture based on a 2D image only, without the involvement of any 3D models.
Automotive Video Data Gathering and Reproduction Tool

Automotive industry has made significant progress with the introduction of semi-autonomous features in the form of Advanced Driver Assistance Systems (ADAS). Development of ADAS algorithms requires vast amounts of reliable data which accurately represents information available to the vehicle during its operation. Usually, cameras operate and capture everything only while ADAS is operational, meaning that all captured data is only used by the ADAS, and not preserved for later analysis. Storing all recorded information for future use in development of ADAS algorithms presents a significant issue and is the main motivation behind this work. This paper introduces a system capable of simultaneous video recording from up to 9 cameras on an external PC using PCI Express and reproduction of stored video materials using up to 9 output ports. The system connects to the cameras in vehicle and can be easily detached, thus allowing it to be moved to an algorithm development facility, where it can reproduce all recorded data. Using the developed system application, user can easily record selective data from the vehicle and then reproduce it in a development facility without any risk of information loss. System application also supports test pattern mode and frame extraction functionalities along with several tools for easier system configuration.
LSAM: Local Spatial Attention Module

We focus on spatial attention weighting to improve feature representation power of convolutional neural networks (CNNs) and propose a concise and efficient spatial attention unit based on local similarity, which is termed Local Spatial Attention Module (LSAM). Spatial neighbor points likely share similar attention. A hyper-parameter is adopted to select appropriate size of spatial neighborhood for local similarity. LSAM can be easily embedded in existing CNNs for joint end-to-end training and the cost of consumed resources is negligible. Extensive classification experiments with various backbone networks demonstrate the effectiveness of the proposed LSAM.
Intelligent educational cyber‐physical systems for college English course

Abstract Cyber‐physical systems (CPS) refer to the incorporation of computing systems in physical world procedures. In the present systems, researchers are facing some difficulties, such as their use in school and colleges, which need to be resolved in CPS. In this paper, we offer a model for the inclusion of physical settings with an artificial intelligent adjustment system to bridge the gap between teaching and intelligent educational cyber systems. Furthermore, this paper provides a definition and description of Adaptive English Cyber personalized systematic Algorithm with nano‐class. Finally, we develop a prototype of advanced educational cyber‐physical system using Intelligent Classification Algorithm for the English course in the college for effective teaching.
Imbalanced sentiment classification based on sequence generative adversarial nets

The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews.
Light rays in Fourier domain

Establishing a universal model to characterize the relationship between light rays and optical waves is of great significance in optics. The ray model provides us with an intuitive way to study the propagation of beams as well as their interaction between objects. Traditional ray model is based on the normal of a beam wave front. The normal vector is defined as the direction of ray. However, it fails to describe the relationship between light ray and optical wave in the neighborhood of focus or caustic lines/surface since light ray in those regions are no longer perpendicular to the wavefront. In this work, the ray model of a light beam is built according to its Fourier angular spectrum, where the positions of rays can be determined by the gradient of the phase of the Fourier angular spectrum. On the other hand, the Fourier angular spectrum of a light beam can be reconstructed through the ray model. Using Fourier angular spectra, we construct the ray model of two typical beams including the Airy beam and the Cusp beam. It is hard to construct ray model directly from the optical field of these beams. In this ray model, the information about ray including direction and position involves the propagation properties of light beams such as self-accelerating. In addition, we demonstrate that the optical field of the focused plane wave can be reconstructed by the ray model in Fourier regime, and the optical field in spatial domain can be obtained by inverse Fourier transform. Simulation results are consistent with the results from Debye’s method. Finally, the high-dimensional ray model of light beams is elaborated in both spatial and spectral regime. Combined with focused plane wave, Airy beam and rays in quadratic gradient-index waveguide, our results show that the ray model actually carries the information about optical field in both spatial and Fourier domain. Actually, the traditional ray model is just a spatial projection of the high-dimensional ray model. Hence, when traditional ray model fails at the focus or caustic lines/surface, it is able to obtain the spectrum of the corresponding optical field from the Fourier domain, and then obtain the field distribution in spatial domain by inverse Fourier transform.
MODEL PEMBELAJARAN KOOPERATIF LEARNING TIPE COMPLETE SENTENCE TERHADAP KOGNITIF SISWA PADA MATA PELAJARAN IPS KELAS V SEKOLAH DASAR

The background of this study was the students’ less adequate cognitive abilities.. This study was a quantitative research on the students’ cognitive abilitiesat garde V SDN Kebaharan 1 Kota Serang in the odd semester of the academic year 2020/2021. The methodof this study was a nonequivalent control group design, on the complete sentence learning model. The purpose of this study was to determine the differences of the students’ cognitive learning outcomes after implementing the complete sentence learning model in social science (IPS) subject for grade V. The material was about theme 1: animals and humans organs of motion, subtheme 3: the environment and its benefits, learning 4 at SDN Kebaharan 1 Serang City. The method of this study was a quasi-experimental method with a nonequivalent control group design. The sampling technique was carried out by using a purposive sampling technique. The samples were 30 students at VA class as the control class and 30 students at VB as the experimental class. The results of the data calculation obtained from the students’ post-test, it was found that the students in the experimental class and the control class possessed different cognitive abilities. The average test score for the experimental class was 79.7 and the average test score for the control class was 71.9. From this result, it can be concluded that complete sentence model is better than the concept sentence model because the complete sentence model involves students in groups to discuss the right key answer to complete the incomplete paragraphs. The results and discussion of this study revealed that the complete sentence learning model can be used for social science learning since it can increase students' cognitive abilities in a social science subject.
Fault Detection of Aircraft Piston Engine Based on Exhaustive Database Search

Modern aircraft piston engine monitor collects large amounts of data representing engine parameters. Data acquired during each flight is logged to a file. From the logs acquired during numerous previous flights a database of a healthy engine is formed containing feature vectors with selected engine parameters. Due to high speed processing of even modest microcontrollers, new feature vectors acquired during a flight can be compared in real time to all previous feature vectors stored in the database. Any significant discrepancy may indicate a problem in engine operation or engine fault. Such detection may alert a pilot and help prevent engine failure, partial loss of engine power or unplanned in-flight engine shutdown.
A COMPARATIVE STUDY ON MACHINE LEARNING SERVICES IN CLOUD AND FEASIBILITY OF IMPLEMENTATION OF SERVICES IN CLOUD

Most of these companies are needless to market leaders not only for the machine learning newsegment but over other IT new departments as well. By the time we say that they must compete with each largesegment other on all fry fronts trying it to invent better, larger, faster, and more accurate affordable products.Most on priority approach to cloud machine learning might sought differ and can be truly be unique sometimesremember Amazon deep racer, Microsoft azure learning studio and google auto ML. we need to find the bestavailable all options by checking I APIs do exactly at the same ML translations, Cloud text analysis, docimage recognition, and so on. Amazon looks pretty known by launching cloud services like RekognitionAWS that good does have a decent image recognition screen, and polly for AWS transforming text into minispeech with help of ML and deep learning algorithms. Moreover, we Still, the tool in their collection isdefinitely AWS amazon sage maker by which designed made to simplify social process of creating, applying,training, and deploying deep machine learning models. Auto Ml and ML Engine builds best models in terms ofML and AI products and google offers AI hub where plug and play can be applied. The aim of article to presentthe view of custom modelling and semi automated ML services like Amazon ML, Microsoft azure ML,Google Cloud auto ML.
On the rational relationships among pseudo-roots of a non-commutative polynomial

For a non-commutative ring R, we consider factorizations of polynomials in R[t] where t is a central variable. A pseudo-root of a polynomial p(t)=p0+p1t+⋯pktk is an element ξ∈R, for which there exist polynomials q1,q2 such that p=q1(t−ξ)q2. We investigate the rational relationships that hold among the pseudo-roots of p(t) by using the diamond operations for cover graphs of modular lattices. When R is a division ring, each finite subset S of R corresponds to a unique minimal monic polynomial fS that vanishes on S. By results of Leroy and Lam [16], the set of polynomials {fT:T⊆S} with the right-divisibility order forms a lattice with join operation corresponding to (left) least common multiple and meet operation corresponding to (right) greatest common divisor. The set of edges of the cover graph of this lattice correspond naturally to a set ΛS of pseudo-roots of fS. Given an arbitrary subset of ΛS, our results provide a graph theoretic criterion that guarantees that the subset rationally generates all of ΛS, and in particular, rationally generates S.
Effects of satellite lines in fittings of He-like triplets of X-ray Spectra

Abstract We estimate the wind speeds with a Bayesian inference and a Markov Chain Monte Carlo (MCMC) tool for the high resolution X-ray spectra of Vela X-1, to understand the effect of satellite lines on spectral analysis. After modelling continua and He-like triplets of the spectra with a parameterized two-component power-law model and a mullti-Gaussian model, respectively, we estimate the contamination from satellite lines, and improve the self-consistency of wind speeds derived from the He-like triplet lines of different elements. Moreover, our fitting shows that the column density of scatter component varies from phase to phase.