Reviewerly/exHarmony huggingface dataset:
Anomalous Event Detection and Localization Using Stacked Autoencoder

Anomalous event detection and localization from the crowd is a challenging problem to the computer vision community. It is an important aspect of intelligent video surveillance. Surveillance cameras are set up to monitor anomalous or unusual events. But, the majority of video data, related to normal or usual events, is accessible. Thus, analysis and recognition of anomalous events from huge data are very difficult. In this work, an automated system is proposed to identify and localize anomalies at local level. The proposed work is divided into four steps, namely preprocessing, feature extraction, training of stacked autoencoder and anomaly detection and localization. Preprocessing step removes background from video frames. To capture the dynamic nature of foreground objects, magnitude of optical flow is computed. Deep feature representation is obtained over the raw magnitude of optical flow using stacked autoencoder. Autoencoder extracts high-level structural information from motion magnitudes to distinguish between normal and anomalous behaviors. The performance of proposed approach is experimentally evaluated on standard UCSD and UMN dataset developed for anomaly detection. Result of the proposed system demonstrate its usefulness in anomaly detection and localization compared to existing methods.
Denoising Images with Varying Noises Using Autoencoders

Image processing techniques are readily used in the field of sciences and computer vision for the enhancement of images and extraction of useful information from them. A key step used in image processing involves the removal of different kinds of noises from the images. Noises can arise in an image during the process of storing, transmitting or acquiring the images. A model qualifies as a satisfactory de-noising model if it satisfies image preservation along with noise removal. There can be various kind of noises in an image such as Gaussian, salt and pepper, Speckle etc. A model which can denoise a different kind of noises is considered to be superior to others. In this paper, we have designed a model using autoencoder which can remove several kinds of noises from images. We have performed a comparative study between the accuracy of each kind using PSNR, SSIM and RMSE values. An increase in the PSNR and SSIM values was seen from the original and noisy image to the original and reconstructed image while a decrease was seen in the value of RMSE.
An Edge-Based Image Steganography Method Using Modulus-3 Strategy and Comparative Analysis

Steganography or “Covered Writing” is a security tool for hiding secret information inside any media or object of interest. It provides secure communication between the sender and the receiver. The spatial domain image steganography techniques such as the Least Significant Bit (LSB) substitution and edge-based hiding schemes have been vastly exploited in the literature. An improved LSB method using modulus-3 strategy can be found in the literature, which has the advantages of high embedding capacity and resistance against Subtractive Pixel Adjacency Matrix (SPAM) steganalysis. However, one drawback is that it hides data sequentially. The edge-based hiding techniques have the advantage that it can tolerate significant variations in edge pixels and fulfill all the requirements of the Human Vision System (HVS), but it has limited in embedding capacity. The proposed work is a reversible data hiding scheme that takes the advantages of both the edge-based algorithm and the novel modulus-3 hiding scheme. At first, we converted the message into ternary data, and then we applied the novel modulus-3 strategy to hide two ternary bits in the edge pixel of the image thereby taking the advantages of both modulus-3 strategy and edge-based hiding approach. We have implemented four edge detection algorithms like Laplacian, Canny, Sobel, and Prewitt to find four different edge areas from the cover image. For each edge area, we applied the modulus-3 strategy to hide the entire ternary message, and then, we made a comparative analysis based on the performance of four edge detectors, and finally, we concluded.
Lifecycle of Temporary Facts in Decision Making Systems

Modern intelligent systems based on production model of knowledge including decision making systems accumulate large amounts of rules and facts. Ones of the typical decision making systems are situation centers created at large airports and assisting in the emergency situation elimination. Large knowledge bases yield serious problems of inference complexity. One of the possible ways to reduce the complexity of logical search is saving the results of search as new axioms for future use. To implement this approach, we propose to arrange facts in a paradigm ‘events-states’. Events are primary facts and states are secondary, or temporal facts. Instead of analyzing event chain one can just read the current state. This approach is productive for non-volatile knowledge bases. If data in the knowledge base change then using these new axioms lead to improper results. In this paper, we propose three methods of knowledge bases maintenance depending on intensiveness of data change. The first method is checking absence of new events each time when a temporal fact is called. The second, a periodical updating temporal facts by a schedule, and the third, updating all temporal facts when a new event occurs. Results of an experimental research shows limits of applicability of each methods depending on volatility of the knowledge base and intensity of queries.
A numerical filtering method for linear state‐space models with Markov switching

Summary A class of discrete‐time random processes arising in engineering and econometrics applications consists of a linear state‐space model whose parameters are modulated by the state of a finite‐state Markov chain. Typical filtering approaches are collapsing methods, which approximate filtered distributions by mixtures of Gaussians, each Gaussian corresponding to one possibility of the recent history of the Markov chain, and particle methods. This article presents an alternative approach to filtering these processes based on keeping track of the values of the underlying filtered density and its characteristic function on grids. We prove that it has favorable convergence properties under certain assumptions. On the other hand, as a grid method, it suffers from the curse of dimensionality, and so is only suitable for low‐dimensional systems. We compare our method to collapsing filters and a particle filter with examples, and find that it can outperform them on 1‐ and 2‐dimensional problems, but loses its speed advantage on 3‐dimensional systems. Meanwhile, our method has a proven theoretical convergence rate that is probably not achieved by collapsing and particle methods.
The Graphical Experience: User Interface Design Approach Based on User-Centered Design to Support Usability

This paper introduces a visual design approach, which supplies UI researchers with testing methods that help them in solving the visual problems through all interaction process, and also guide them to know which part of the interface they need to refine it or introduce a different design solution, to meet the users’ needs and goals.
Improving Seeded k-Means Clustering with Deviation- and Entropy-Based Term Weightings

The outcome of document clustering depends on the scheme used to assign a weight to each term in a document. While recent works have tried to use distributions related to class to enhance the discrimination ability. It is worth exploring whether a deviation approach or an entropy approach is more effective. This paper presents a comparison between deviation-based distribution and entropy-based distribution as constraints in term weighting. In addition, their potential combinations are investigated to find optimal solutions in guiding the clustering process. In the experiments, the seeded k-means method is used for clustering, and the performances of deviation-based, entropy-based, and hybrid approaches, are analyzed using two English and one Thai text datasets. The result showed that the deviation-based distribution outperformed the entropy-based distribution, and a suitable combination of these distributions increases the clustering accuracy by 10%.
A Local Search with a Surrogate Assisted Option for Instance Reduction

In data mining, instance reduction is a key data pre-processing step that simplifies and cleans raw data, by either selecting or creating new samples, before applying a learning algorithm. This usually yields to a complex large scale and computationally expensive optimisation problem which has been typically tackled by sophisticated population-based metaheuristics. Unlike the recent literature, in order to accomplish this target, this article proposes the use of a simple local search algorithm and its integration with an optional surrogate assisted model. This local search, in accordance with variable decomposition techniques for large scale problems, perturbs an n-dimensional vector along the directions identified by its design variables one by one. Empirical results in 40 small data sets show that, despite its simplicity, the proposed baseline local search on its own is competitive with more complex algorithms representing the state-of-the-art for instance reduction in classification problems. The use of the proposed local surrogate model enables a reduction of the computationally expensive objective function calls with accuracy test results overall comparable with respect to its baseline counterpart.
KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources

Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory settings, which we support with a visual representation of graph structures, helping users to better understand relationships. In addition, our approach keeps track of context – an important challenge in SQA – by allowing users to make their focus explicit via subgraph selection. Our results show that the interaction principle is either understood immediately or picked up very quickly – and that the possibility of exploring the information space iteratively is appreciated.
Relative Full Completeness for Bicategorical Cartesian Closed Structure

Abstract The glueing construction, defined as a certain comma category, is an important tool for reasoning about type theories, logics, and programming languages. Here we extend the construction to accommodate ‘2-dimensional theories’ of types, terms between types, and rewrites between terms. Taking bicategories as the semantic framework for such systems, we define the glueing bicategory and establish a bicategorical version of the well-known construction of cartesian closed structure on a glueing category. As an application, we show that free finite-product bicategories are fully complete relative to free cartesian closed bicategories, thereby establishing that the higher-order equational theory of rewriting in the simply-typed lambda calculus is a conservative extension of the algebraic equational theory of rewriting in the fragment with finite products only.
The Semiotics of Cyberculture: the Example of Artistic Discourse

In this text the author reflects on the semiotic existence of culture discourses in connection with the rapid development of digital technology. The author analyzes selected texts of digital art as examples of the transformation in how works of literature, sculpture, or film exist. The article covers how movable font, which changes in shape and color, participates in shaping literary meanings; the creation of semiotic and interactive figures; the textualization of the user’s actions and body; dematerialization; processuality; narrativization; the temporalizing of sculpture, which changes before the eyes of the recipient; the presence of alternative narratives in literature and film; multi-variant plots co-created by the recipient; and the artistic use of other discourses. The author proves that the structure and specificity of the digital sign lies at the base of the changes. The digital sign is immaterial, programmable, and hybrid, and combines aspects of expression, meaning, and action, which makes it efficient.
Mitigate compression artifacts for face in video recognition

Face in video recognition (FiVR) is widely used in video surveillance and video analytic. Various solutions have been proposed to improve the performance of face detection, frame selection and face recognition in FiVR systems. However, all these methods have a common inherent &#99;eiling", which is defined by the source video's quality. One key factor causing face image quality loss is video compression standards. To address this challenge, in this paper, first, we analysis and quantify the effects of video compression on the FiVR performance; secondly, we propose to use deep learning based model to mitigate artifacts in compressed input video. We apply the image based convolutional auto-encoder (CAE) to extract the features of input face images and restore them towards less artifacts. From the experimental results, our approach can mitigate artifacts on face images extracted from compressed videos and improve the overall face recognition (FR) performance by as much as 50% in TPR (True Positive Rate) at the same FPR (False Positive Rate) value.
Ontology driven software development for automatic detection and updation of software requirement specifications

Recent studies have shown that due to tight schedule or cost/resource constraint, development or maintenance team generally develops or updates the software system without maintaining the proper Software Requirement Specification documentation. In the above scenario, the situation becomes troublesome to maintain the software system once it goes into the maintenance phase. Therefore, there is a need to develop a mechanism that automatically detects and updates the Software Requirement Specification documentation immediately after any update takes place in the concerned software system. Hence, in this research article, we provide ontology driven software development approach for automatic detection and updation of Software Requirement Specifications. To implement and evaluate the proposed approach, we picked a case study, which clearly shows that it automatically detects the outdated requirements. Also, as a result of our work, we automatically find the requirements that have been added, deleted, or modified to the existing Software Requirements Specification. Moreover, to reflect the updated requirement specification documentation, we provide a mechanism that automatically generates it. Further, the generated documentation is available in the machine as well as in human-readable form along with free of inconsistencies.
The algorithm of nighttime pedestrian detection in intelligent surveillance for renewable energy power stations

Intelligent surveillance is an important management method for the construction and operation of power stations such as wind power and solar power. The identification and detection of equipment, facilities, personnel, and behaviors of personnel are the key technology for the ubiquitous electricity The Internet of Things. This paper proposes a video solution based on support vector machine and histogram of oriented gradient (HOG) methods for pedestrian safety problems that are common in night driving. First, a series of image preprocessing methods are used to optimize night images and detect lane lines. Second, an image is divided into intelligent regions to be adapted to different road environments. Finally, the HOG and support vector machine methods are used to optimize the pedestrian image on a Linux system, which reduces the number of false alarms in pedestrian detection and the workload of the pedestrian detection algorithm. The test results show that the system can successfully detect pedestrians at night. With image preprocessing optimization, the correct rate of nighttime pedestrian detection can be significantly improved, and the correct rate of detection can reach 92.4%. After the division area is optimized, the number of false alarms decreases significantly, and the average frame rate of the optimized video reaches 28 frames per second.
INTEGRATION OF EDUCATIONAL SUBSYSTEMS IN AN ALL-IN-ONE INFORMATION AND EDUCATION ENVIRONMENT (with open source software)

Creating an information and educational environment of a higher education institution is the process of introducing and setting up a large number of subsystems - educational, scientific, managerial and others. All these parts are largely integrated with each other. They must transfer some data to other subsystems and receive from them. In spite of the large number of works devoted to the research of creation of information and educational environments of higher educational establishments, the majority of them concern only pedagogical and organizational aspects and very few consider the issues of practical implementation with real examples. The purpose of the article is to demonstrate the technical capabilities to organizing information and educational environment through the integration of various software components. In doing so, there is a special emphasis on the use of open source elements and integration with popular cloud services. It is a difficult and expensive task to collect all the subsystems required in the educational environment of a higher education institution. The cost of complex solutions from well-known brands is measured in thousands of dollars, and their implementation requires the same integrated approach. For Ukrainian educational institutions, the solution may be to build their own information and educational environment using open source components. In most cases, such programs already have options for integrating with one another based on the same open standards. It is important to know and understand the possibilities of such integration. Its use will allow to create environment for own needs and organizational features of each institution. The possibility of gradually connecting the necessary components will make it easier to go through the process of introduction of information and educational environment and involvement of all departments of higher education in it. The examples and links in the article demonstrate the potential of open source software as an integral part of the educational environment.
Logic-based specification and verification of homogeneous dynamic multi-agent systems

Abstract We develop a logic-based framework for formal specification and algorithmic verification of homogeneous and dynamic concurrent multi-agent transition systems. Homogeneity means that all agents have the same available actions at any given state and the actions have the same effects regardless of which agents perform them. The state transitions are therefore determined only by the vector of numbers of agents performing each action and are specified symbolically, by means of conditions on these numbers definable in Presburger arithmetic. The agents are divided into controllable (by the system supervisor/controller) and uncontrollable , representing the environment or adversary. Dynamicity means that the numbers of controllable and uncontrollable agents may vary throughout the system evolution, possibly at every transition. As a language for formal specification we use a suitably extended version of Alternating-time Temporal Logic, where one can specify properties of the type “a coalition of (at least) n controllable agents can ensure against (at most) m uncontrollable agents that any possible evolution of the system satisfies a given objective $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>γ</mml:mi></mml:math> ″, where $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>γ</mml:mi></mml:math> is specified again as a formula of that language and each of n and m is either a fixed number or a variable that can be quantified over. We provide formal semantics to our logic $${\mathcal {L}}_{\textsc {hdmas}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mstyle><mml:mi>H</mml:mi><mml:mi>D</mml:mi><mml:mi>M</mml:mi><mml:mi>A</mml:mi><mml:mi>S</mml:mi></mml:mstyle></mml:msub></mml:math> and define normal form of its formulae. We then prove that every formula in $${\mathcal {L}}_{\textsc {hdmas}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>L</mml:mi><mml:mstyle><mml:mi>H</mml:mi><mml:mi>D</mml:mi><mml:mi>M</mml:mi><mml:mi>A</mml:mi><mml:mi>S</mml:mi></mml:mstyle></mml:msub></mml:math> is equivalent in the finite to one in a normal form and develop an algorithm for global model checking of formulae in normal form in finite HDMAS models, which invokes model checking truth of Presburger formulae. We establish worst case complexity estimates for the model checking algorithm and illustrate it on a running example.
Unsupervised Learning of KB Queries in Task-Oriented Dialogs

Abstract Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries—these annotations can be time consuming, and expensive. In response, we define the novel problems of predicting the KB query and training the dialog agent, without explicit KB query annotation. For query prediction, we propose a reinforcement learning (RL) baseline, which rewards the generation of those queries whose KB results cover the entities mentioned in subsequent dialog. Further analysis reveals that correlation among query attributes in KB can significantly confuse memory augmented policy optimization (MAPO), an existing state of the art RL agent. To address this, we improve the MAPO baseline with simple but important modifications suited to our task. To train the full TOD system for our setting, we propose a pipelined approach: it independently predicts when to make a KB query (query position predictor), then predicts a KB query at the predicted position (query predictor), and uses the results of predicted query in subsequent dialog (next response predictor). Overall, our work proposes first solutions to our novel problem, and our analysis highlights the research challenges in training TOD systems without query annotation.
A Distributed Coordinate Descent Algorithm for Learning Factorization Machine

Although much effort has been made to implement Factorization Machine (FM) on distributed frameworks, most of them achieve bad model performance or low efficiency. In this paper, we propose a new distributed block coordinate descent algorithm to learn FM. In addition, a distributed pre-computation mechanism incorporated with an optimized Parameter Server framework is designed to avoid the massive repetitive calculations and further reduce the communication cost. Systematically, we evaluate the proposed distributed algorithm on three different genres of datasets for prediction. The experimental results show that the proposed algorithm achieves significantly better performance (3.8%–6.0% RMSE) than the state-of-the-art baselines, and also achieves a 4.6–12.3\(\times \) speedup when reaching a comparable performance.