Reviewerly/exHarmony huggingface dataset:
Slow Feature Subspace for Action Recognition

This paper proposes a framework for human action recognition using a combination of subspace-based methods and slow feature analysis (SFA). Subspace-based methods can compactly model the distribution of multiple images from a video by a low dimensional subspace even when few data is available. However, the temporal information of the video is lost after generating the subspace using principal component analysis (PCA). In contrast, PCA-SFA, which is a variant of SFA, can produce a valid video descriptor as a basis of a slow feature space from a given image sequence. In the proposed framework, we extract a valid video descriptor from an input video by conducting PCA-SFA, and then transform the descriptor into a subspace by using PCA. This new representation of slow feature subspace includes temporal dynamic information. Thus, we can compare two sequences and perform classification by simply calculating the similarity between their slow feature subspaces. The effectiveness of our framework is demonstrated through extensive experiments with two publicly available datasets, KTH action and the Chinese sign language dataset (isolated SLR500).
Analysis and Research of Blockchain Technology in Audit System

Abstract Blockchain technology has attracted widespread attention from all walks of life due to its decentralization, openness and transparency, and data that cannot be tampered with. At the same time, it has a good development prospect in the accounting and auditing industries. This article is based on the consensus algorithm research experiment. In the network environment, computers will be attacked in many situations, which may cause unexpected behaviors of the computer and the network. The consensus algorithm can deal with these unpredictable behaviors. The experiment uses a questionnaire survey method, and the survey objects rank the importance of accounting informatization, the necessity and feasibility of applying blockchain technology and other related issues, and different scores form different results. The experimental data shows that it is necessary to analyze and build a credibility guarantee mechanism for audit information systems based on blockchain technology. Each aspect includes its own specific content and details, and an average score is obtained for each category of options. Let’s compare the importance levels of the four factors. The experimental results show that, overall, the average scores of the four factors are between 3.9-4.4 points, with little difference in importance levels. Audit information quality issues scored the highest at 4.37 points, followed by management control issues and security issues at 4.24 and 4.21 points respectively. Although blockchain technology in the audit system is still in the research and development stage, the application of blockchain in the audit industry is a major trend. Researchers are working hard to solve the problems of blockchain storage and confidentiality.
Denoising of Partial Discharge Signal by Common Factor Method and Wavelet Thresholding

Partial Discharge (PD) will make insulation of electrical apparatus decrease and threaten the security and stability of power system. On the other hand, it is also an indicator of insulation status of electrical apparatus. Therefore, it is important to monitor PD online. However, inevitable white noise exists and will interfere PD measurement, thus distorting it and leading to misjudging of insulation status. Therefore, denoising of PD signal is very necessary. Wavelet thresholding denoising method is one of the most commonly used method, but it lacks translation in-variance, and the denoising effect is influenced. To overcome this disadvantage, common factor method is proposed to construct dual-tree complex wavelet combined with wavelet thresholding in this paper. Finally, numeric simulation experiments have been set to verify the feasibility and effectiveness of the proposed method.
Development and Application of Sports Video Analysis Platform in Sports Training

Abstract The promotion of the concept of “healthy China” has set off a nationwide fitness boom, and sports training have been widely valued. As a coach, it is necessary to explain the action essentials to the athletes through the intuitive and vivid sports video. However, there are many problems in many sports analysis platforms, which need to be optimized. This paper focuses on the development and application of sports video analysis platform in sports training.
VLO V1.1- A Vietnamese Lexicon Ontology for Universal Dependency Parsing

Universal Dependency, which is developed from Stanford typed dependency, is a representation showing all dependencies between pairs of word in a sentence. In order to develop a Universal Dependency parser, a large Universal Dependency graphbank is needed. This graphbank can be built automatically with a converter. However, the converter may produce wrong dependencies because it does not have any semantic constraints between words in a sentence. Therefore, Vietnamese Lexicon Ontology has been built to provide these semantic constraints. In this paper, we would like to introduce the Vietnamese Lexicon Ontology version 1.1 in which the object properties have been modified to capture many syntactic and semantic dependencies between words in a sentence. We also present the affect of Vietnamese Lexicon Ontology version 1.1 on Universal Dependency parse result in Vietnamese as the evaluation of this ontology.
Evaluating the effectiveness of a software-defined cloud data center with a distributed structure

It is proposed by combining autonomous data centers using a software-defined networking to create an infrastructure of a single distributed cloud data center with centralized management of distributed resources, the formation of such a system is carried out by successively solving the problems of building a K-connected network topology, creating and placing virtual resources and providing resources using the directed method random search, modeling shows the effectiveness of the proposed method.
RankFromSets: Scalable set recommendation with optimal recall

We study a variant of user‐item recommendation where each item has a set of attributes, such as tags on an image, user reactions to a post or foods in a meal. We focus on the latter example, with the goal of building a meal recommender for a diet tracking app. Meal recommendation is challenging: (i) each item (meal) is rarely logged by more than a handful of users, (ii) the database of attributes (foods) is large, and (iii) each item is tagged with only a handful of attributes. We propose RankFromSets (RFS), a flexible and scalable class of models for recommending items with attributes. RFS treats item attributes as set‐valued side information and learns embeddings to discriminate items a user will consume from items a user is unlikely to consume. We develop theory connecting the RFS objective to optimal recall and show that the learnable class of models for RFS is a superset of several previously proposed models. We then develop a stochastic optimization method for RFS that uses negative sampling to scale to massive problems like meal recommendation. In experiments on a real dataset of 55k users logging 16M meals, the new method outperforms competing approaches while learning embeddings that reveal interpretable structure in user behavior. Code is available on GitHub ( https://github.com/altosaar/rankfromsets ).
Subpixel-Level Edge Feature Matching for SAR and Optical Images Based on Zernike Moments

A sub-pixel edge feature matching method based on Zernike moment is proposed. By using PPB filtering method to preprocess SAR images, Zernike moments are used to extract sub-pixel edge feature maps of SAR images and optical images. It innovatively proposes the extraction of phase consistency feature points based on low-contrast nonsuppressed SAR-Harris multi-scale space. Based on the feature detection results, a HOG feature descriptor is constructed on the subpixel edge feature map, and finally feature matching is performed according to geometric constraints. Experimental results show that this method has obvious advantages in matching SAR images with optical images compared with SIFT, Schwind, Suri, and SAR-SIFT methods.
Deep Trident Decomposition Network for Single License Plate Image Glare Removal

Deep convolutional neural networks have achieved state-of-the-art performance for the removal of atmospheric obscuration. However, most relevant studies have focused on eliminating the effects of atmospheric obscuration but not on the glare in images caused by reflected sunlight. On the basis of a glare image formation model, we propose a deep trident decomposition network with a large-scale sun glare image dataset for glare removal from single images. Specifically, the proposed network is designed and implemented with a trident decomposition module for decomposing an input glare image into occlusion, foreground, and coarse glare-free images by exploring background features from spatial locations. Moreover, a residual refinement module is adopted to refine the coarse glare-free image into fine glare-free image by learning the residuals from features of multiscale receptive field. The experimental results indicated that the proposed network significantly outperforms state-of-the-art atmospheric obscuration removal networks on the built dataset.
Provable Security Evaluation of Block Ciphers Against Demirci-Selçuk’s Meet-in-the-Middle Attack

The Demirci-Selçuk's meet-in-the-middle attack is one of the most important methods among all the cryptanalytic vectors, which gives the best result against the round-reduced AES with respect to the rounds, and tradeoffs between data, time and memory. While we have already built provable security models against the differential cryptanalysis, linear cryptanalysis cryptanalysis, impossible differential and zero-correlation linear cryptanalysis, the provable security against the meet-in-the-middle attack is missing. In this paper, we propose the subset representation of function based on which we could give an algorithm to compute the exact number of parameters of the Demirci-Selçuk's distinguisher given the input and output, respectively. Experiments show that this algorithm can be more efficient than the automatical tool presented by Shi et al. at Asiacrypt 2018. We further extract a formula based on this algorithm and show an upper bound for the length of the Demirci-Selçuk's distinguisher of an iterative SPN cipher. We prove that for an SPN block cipher whose block size equals the key size, an effective Demirci-Selçuk-type meet-in-the-middle distinguisher covers at most twice the maximum of the primitive indexes of the linear layer and its inverse. As a result, we show that the known length of the Demirci-Selçuk's distinguisher of the AES-128 cannot be improved unless the details of the S-boxes are exploited, which demonstrates that the AES has a provable security against the Demirci-Selçuk's meet-in-the-middle attack.
Reasoning about recursive tree traversals

Traversals are commonly seen in tree data structures, and performance-enhancing transformations between tree traversals are critical for many applications. Existing approaches to reasoning about tree traversals and their transformations are ad hoc, with various limitations on the classes of traversals they can handle, the granularity of dependence analysis, and the types of possible transformations. We propose Retreet, a framework in which one can describe general recursive tree traversals, precisely represent iterations, schedules and dependences, and automatically check data-race-freeness and transformation correctness. The crux of the framework is a stack-based representation for iterations and an encoding to Monadic Second-Order (MSO) logic over trees. Experiments show that Retreet can automatically verify optimizations for complex traversals on real-world data structures, such as CSS and cycletrees, which are not possible before. Our framework is also integrated with other MSO-based analysis techniques to verify even more challenging program transformations.
Common‐specific feature learning for multi‐source domain adaptation

Multi-source domain adaptation (MDA) aims to leverage knowledge from multiple source domains to improve the classification performance on target domains. Different degrees of distribution discrepancies between every two domains pose a huge challenge to MDA tasks. Most works focus on extracting features shared by all domains, which is critical but not enough to reduce distribution discrepancies. In this paper, we propose a method named as common-specific feature learning (CSFL). Constituting a framework of feature learning, CSFL explores a subspace where the combination of common and specific features makes learned representations comprehensive. Based on this framework, we conduct a metric learning method for learning a discriminative feature representation. Considering redundant information caused by source domains is likely to hurt the performance, we impose an effective low-rank constraint to remove the redundant information. Further, we adopt structure consistent constraint to preserve the local structure in each domain. CSFL has obtained about 1–5% improvement of mean accuracy, compared to the state-of-the-art shallow methods. Further, compared with 90.2% and 89.4% of the best baseline deep method, CSFL achieves mean accuracy of 90.8% and 89.7% on the Office-31 and ImageCLEF-DA datasets respectively. The encouraging results validate the effectiveness of our method.
Roadmap Subsampling for Changing Environments

Precomputed roadmaps can enable effective multi-query motion planning: a roadmap can be built for a robot as if no obstacles were present, and then after edges invalidated by obstacles observed at query time are deleted, path search through the remaining roadmap returns a collision-free plan. However, large roadmaps are memory intensive to store, and can be too slow for practical use. We present an algorithm for compressing a large roadmap so that the collision detection phase fits into a computational budget, while retaining a high probability of finding high-quality paths. Our algorithm adapts work from graph theory and data mining by treating roadmaps as unreliable networks, where the probability of edge failure models the probability of a query-time obstacle causing a collision. We experimentally evaluate the quality of the resulting roadmaps in a suite of four motion planning benchmarks.
A comparison of the finite difference and multiresolution method for the elliptic equations with Dirichlet boundary conditions on irregular domains

We make a comparison of the finite difference and multiresolution method for solving the elliptic equations on irregular domains. The Dirichlet boundary condition is treated by the ghost fluid method (GFM) for the finite difference method and the Lagrange multiplier for the multiresolution method. Numerical results illustrate the improved convergence rate of errors and their gradients with the multiresolution method, up to eighth-order. Moreover, the wavelet-based multiresolution has an advantage of preconditioning of the linear system arising from the discretization of elliptic equation over the finite difference method.
Code review effectiveness: an empirical study on selected factors influence

Code review is a widely adopted means of improving software quality. There are numerous techniques of reviewing including tool-assisted and over-the-shoulder. Both require significant effort to perform. However, the impact the over-the-shoulder has on review effectiveness remains unexplored. The authors goal was to compare the techniques and the effect of review size to analyse how that affects the effectiveness. They performed three experiments within one company. Changes provided by developers were reviewed twice by different reviewers, each time using a different technique. Afterwards, participants rated in a questionnaire the influence of each technique to the knowledge transfer. They observed that the number of accepted comments reported with the tool-assisted technique is approximately twice as big. Also, they noticed a relationship between comments density and the review size: the correlations vary from − 0.42 to − 0.33. The knowledge transfer was typically evaluated as it supports knowledge sharing to a limited extent for the tool-assisted technique but as it is good at supporting knowledge sharing for the over-the-shoulder technique. The results did not give a simple answer whether one technique outperforms the other. The tool-assisted technique results in more comments. However, the teams evaluated the over-the-shoulder technique as better supporting knowledge transfer.
New variable-length data compression scheme for solution representation of meta-heuristics

Solution representation is an important aspect of the development of a meta-heuristic. However, most meta-heuristics focus on the algorithm aspect rather than the solution representation. As the scale of the optimization problem under study rises, the solution representation becomes ever more crucial because the computational performance tends to deteriorate for three reasons: (1) increased landscape complexity, (2) exponential growth of search space, and (3) costly function evaluation. In this study, data compression of solution representation is explored to improve the performance of meta-heuristics in terms of compression efficiency and solution quality. A proposed new data compression scheme – variable-length solution representation compression for meta-heuristics – is developed and tested against benchmarked problem instances from the quadratic assignment problem library (QAPLIB) and additional large problem instances. The analyzed results indicate that the compressed solution representation scheme under a meta-heuristic framework performs well against the QAPLIB and large problem instances with less space complexity and computation time.
Using artificial neural networks of the type extreme learning machine for the modelling and prediction of the temperature in the head the column. Case of a C6H11-CH3 distillation column

This study establishes a reliable model based on artificial neural networks of the ELM (extreme learning machine) type. The use of this model must reflect the true behavior of the process under its normal operating conditions and enable distinguishing a normal mode from an abnormal one. The installation in this study is a continuous distillation column for methylcyclohexane from a toluene/methylcyclohexane mixture, which mass composition has been defined as 23% methylcyclohexane. The ANN-ELM model was applied to a database of 1000 samples. All the relevant inputs of the model are defined by the inputs of the continuous distillation column during its normal operation, namely: the heating power, the preheating power, the reflux rate, the feed rate, the pressure drops and the preheating temperature. On the other hand, the model's output is defined by the output of the continuous distillation column during its normal operation, namely the temperature at the head of the column. The ELM-type neural architecture obtained during the learning phase was tested on a 30% of the database. The results showed very good forecast accuracy using the ELM model. The low RMSE value (RMSE = 0.0168) was recorded during the test phase when the number of neurons in the hidden layer becomes 30. The prediction of the temperature at the head of the column by the ANN-ELM model has achieved its most accurate performance when the sigmoid activation function was adopted with a number of neurons in the hidden layer of 30. Moreover, the correlation coefficients were very close to unity during the test phase (R = 0.9345). The ANN-ELM prediction model is best suited for modeling the normal mode with variable operating points of the automated continuous distillation column and can be used online for the detection and diagnosis of malfunctions in this type of operation installation.
Railway track tracer system for creature detection

Abstract Railway Track Tracer System for creature detection is a system for detecting creatures on the railway tracks. This system will help to avoid many accidents that occur on rails. This system mainly focuses on some areas where creatures are always seen on the railway tracks. In some areas, elephants are crossing the railway tracks frequently. This system frequently monitors the railway tracks using a camera, so that the presence of creatures can be easily identified and then necessary actions can be taken to prevent accidents. The system includes a camera, train status system and an application for alert. The camera captures the images on the track at each interval of time and then through image processing the captured image will be recognized. If an object is identified on the track, then immediately an alert message will be sent to the nearby control room and loco-pilot. This system will be helpful for the society to prevent accidents due to creatures on the railway tracks.
Attention-Based Multi-modal Emotion Recognition from Art

Emotions are very important in dealing with human decisions, interactions, and cognitive processes. Art is an imaginative human creation that should be appreciated, thought-provoking, and elicits an emotional response. The automatic recognition of emotions triggered by art is of considerable importance. It can be used to categorize artworks according to the emotions they evoke, recommend paintings that accentuate or balance a particular mood, and search for paintings of a particular style or genre that represent custom content in a custom state of impact. In this paper, we propose an attention-based multi-modal approach to emotion recognition that aims to use information from both the painting and title channels to achieve more accurate emotion recognition. Experimental results on the WikiArt emotion dataset showed the efficiency of the model we proposed and the usefulness of image and text modalities in emotion recognition.
Implementation of Natural Language Processing in Seller-bot for SMEs

Abstract SMEs as one of main income for Indonesia make an important part in Indonesia industry. With more than 64% contribution and bring 92% opportunity to labor, SMEs become vital in Indonesia. But in the other hand, SMEs with their limitation of resource made most of them concentrate in the production phase and give a tiny concern in marketing and sales. Bases on this issue, researcher will develop a seller-bot with natural language processing algorithm in it. It will embed in Facebook Messenger which found that seller-bot could help SMEs doing their business with automate their business especially in marketing process. In the other hand, SMEs still using eCommerce platform such as shoppee, bukalapak and other so it is more difficult to integrate that platform with seller bot in messenger. It will be easier if the customer has their own website and have full control of the application so in the process integration from the messenger to their apps, the seller-bot can break more limitation and fit with the SMEs needs.
Estimating Displaced Populations from Overhead

We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery. We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh in 2018 and 2019. Our proposed approach achieves 7.41% mean absolute percent error on sequestered camp imagery. We believe our experiments with real-world displacement camp data constitute an important step towards the development of tools that enable the humanitarian community to effectively and rapidly respond to the global displacement crisis.