Reviewerly/exHarmony huggingface dataset:
Multi-Object Recognition and Tracking with Automated Image Annotation for Big Data Based Video Surveillance

Presently, the scope and application of Big Data Analytics in video surveillance makes it possible in different domains. In the area of intelligent visual surveillance, the procedure of tracking is described as finding a path or trajectory of an object of a given video sequence. Multi-Object tracking (MOT) mechanism become more familiar because of its applicability in numerous ways. Generally, MOT is employed to predict the position of various specified objects across multiple consequent frames with the offered ground truth position of the target in the beginning frame. In this paper, we have introduced an improved region based scalable convolution neural network (IRS-CNN) based MOT model. The presented IRS-CNN model enhances the existing RS-CNN by incorporating an automated image annotation (AIA) tool for increasing the detection rate as well as reducing the computation time. The interesting feature of AIA tool helps to rapidly annotate the training images in an automatic way. The novel IRS-CNN approach is tested against a benchmark UCSD anomaly detection dataset. A broad experimental result verified the optimal behavior of IRS-CNN model against a set of applied test images over the compared methods.
Research on trajectory tracking control of mechanical motion based on convolution neural network

In order to better improve the effect of mechanical motion trajectory tracking control and ensure the quality and safety of mechanical operation, a mechanical motion trajectory tracking control method based on convolution neural network is proposed. In the process of mechanical motion trajectory tracking control, due to the influence of small disturbance piecewise linear error, the mechanical system will produce multivariable nonlinear motion phenomenon in the operation process. Boundary layer tracking error is easy to occur. In order to solve the above problem, the convolution neural network is used to optimize the mechanical trajectory tracking control algorithm, and the simulation model of mechanical motion environment is established. The spatial coordinates of the trajectory are abstracted as the virtual world of genetic population, and the spatial grid structure characteristics are obtained. According to the matching conditions and parameters of convolution neural network, an error tracking integral term is designed and improved. By accurately tracking the mechanical trajectory, the accuracy of path planning and autonomous positioning is effectively improved. The experimental results show that the control method based on convolution neural network has the advantages of fast convergence speed, high accuracy and high calculation accuracy.
Research and Implementation of Point Multiplication Over Elliptic Curve GF(2m) Based on VLSI

To realize the elliptic curve cryptography (ECC) effectively, the design method of modular multiplication based on optimized binary finite filed algorithm was presented. By the study of the binary finite fields, paralleled modular multiplication algorithm and inversion algorithm which was based on Euclidean algorithm were presented. The two algorithms were optimized during the process and then realized the fast evaluation of point multiplication by adopting Montgomery algorithm. ECC hardware implementation design was proposed based on the algorithm, and converted to logic designs using Verilog RTL, and finally, it worked on the XC7A100T FPGA platform of Xilinx. By pre-simulation, synthetical verification, and analyzing the results of post-simulation, the clock frequency of the designed circuit could reach up to 110 MHz and the operating rate attained to 2.92 ms which demonstrated the feasibility and effectiveness of the project.
The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture

The integer linear programming approach to structural learning of decomposable graphical models led us earlier to the concept of a chordal graph polytope. An open mathematical question motivated by this research is what is the minimal set of linear inequalities defining this polytope, i.e. what are its facet-defining inequalities, and we came up in 2016 with a specific conjecture that it is the collection of so-called clutter inequalities. In this theoretical paper we give an implicit characterization of the minimal set of inequalities. Specifically, we introduce a dual polyhedron (to the chordal graph polytope) defined by trivial equality constraints, simple monotonicity inequalities and certain inequalities assigned to incomplete chordal graphs. Our main result is that the vertices of this polyhedron yield the facet-defining inequalities for the chordal graph polytope. We also show that the original conjecture is equivalent to the condition that all vertices of the dual polyhedron are zero-one vectors. Using that result we disprove the original conjecture: we find a vector in the dual polyhedron which is not in the convex hull of zero-one vectors from the dual polyhedron.
ICIX: A Semantic Information Extraction Architecture

Public and private organizations produce and store huge amounts of documents which contain information about their domains in non-structured formats. Although from the final user’s point of view we can rely on different retrieval tools to access such data, the progressive structuring of such documents has important benefits for daily operations. While there exist many approaches to extract information in open domains, we lack tools flexible enough to adapt themselves to the particularities of different domains.
Sparsely Ensembled Convolutional Neural Network Classifiers via Reinforcement Learning

We consider convolutional neural network (CNN) ensemble learning with the objective function inspired by the least action principle; it includes resource consumption component. We teach an agent to perceive images through the set of pre-trained classifiers and want the resulting dynamically configured system to unfold the computational graph with the trajectory that refers to the minimal number of operations and maximal expected accuracy. The proposed agent's architecture implicitly approximates the required classifier selection function with the help of reinforcement learning. Our experimental results prove, that if the agent exploits the dynamic (and context-dependent) structure of computations, it outperforms conventional ensemble learning.
Inventory Visibility Scenario to Reduce Safety Stock in Supply Chain Network Using Blockchain Hyperledger Composer

Limited inventory visibility in supply chain networks increases stock levels and leads to negative consequences of stock repetition, bullwhip-effect, inventory backlog, and out of stock situations. It is essential to increase the inventory visibility and reduce the safety stock level to exchanged inventory data between supply chain stakeholders to achieve inventory accuracy and efficiency accordingly. This paper presents a technical implementation of inventory visibility scenario to reduce safety stock level in Blockchain-based supply chain network. The proposed inventory visibility scenario provides access to accurate and up to date inventory levels and reduces the safety stock level accordingly. The scenario implemented using Blockchain Hyperledger Composer and tested in Blockchain Hyperledger Composer Bluemix. In the performance evaluation, the inventory visibility scenario compared to Cloud-based and traditional supply chain models in terms of reducing the safety stock level. Scenario results show improved inventory visibility and significant reduction in inventory safety stock compared to other models. This reduction prevents negative consequences of stock repetition, bullwhip-effect, inventory backlog, and out of stock situations which helps in efficient inventory management and hence improves supply chain performance.
One-to-One Mapping-Like Properties of DCN-Based Super-Resolution and its Applicability to Real-World Images

Although super-resolution techniques based on deep neural networks (SRDNN) have drawn significant interest and numerous algorithms have been proposed, they still have reliability problems and produce artefacts when applied to new datasets. In this paper, the working mechanisms of SRDNN techniques are analyzed in terms of data mapping. Since most SRDNN techniques can be viewed as dynamic linear projections, we analyzed a large number of projection vectors (over 70 million) and found that the SRDNN method performs one-to-one mapping-like operations and may be vulnerable to unknown data patterns. Then, we applied several SRDNN techniques to real-world images and analyzed the output images. The current SRDNN methods failed to distinguish the blurred edges/lines due to low resolutions from coding artefacts and enhanced both, even though the SRDNN methods were trained using compressed low-resolution (LR) images. These analyses and results indicate that current SRDNN methods may not be able to provide robust performance and new structures may be necessary for reliable super-resolution performance.
Actor-only Deterministic Policy Gradient via Zeroth-order Gradient Oracles in Action Space

Deterministic policies demonstrate substantial empirical success over their stochastic counterparts as they remove a level of randomness in Policy Gradient (PG) methods when applied to stochastic search problems involving Markov decision processes. However, current implementations require the use of state-action value ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -function) approximators, also known as critics, to obtain estimates of the associated policy-reward gradient. In this work, we propose the use of two-point stochastic evaluations to obtain gradient estimates of a smoothed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -function surrogate, constructed by evaluating pairs of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -function at low-dimensional, randomized initial action perturbations. This procedure lifts the dependence on a critic and restores true model-free policy learning, and with provable algorithmic stability. In fact, our finite complexity bounds improve upon existing results by up to 2 orders of magnitude in terms of iteration complexity, and by up to 3/2 orders of magnitude in terms of sample complexity. Simulation results on an agent navigation problem showcase the effectiveness of our proposed algorithm in a practical setting, as well.
Pedagogical Conditions for Practice-Oriented Teaching of Mathematics to Students of Natural Sciences

The article reveals the methodology for the implementation of practice-oriented activities of students in the study of mathematics at a university. It is noted that at present, it is relevant to introduce pedagogical technologies into the educational process that ensure the achievement of students' personal educational results in the course of specially organized educational and cognitive activities. In this aspect, the work emphasizes that the creation of a practice-oriented educational environment of the university, as a pedagogical condition for the implementation of the requirements of the Federal State Educational Standard, contributes to the introduction of new knowledge into the educational process that is significant for vocational training and the formation of practical experience of their use to solve problems not only in the professional field of activity.&#x0D; The relevance of practice-oriented learning lies in a significant increase in the effectiveness of training based on a system of selection of educational material that helps students assess the significance, practical relevance of acquired knowledge and skills.&#x0D; The demand for mathematical knowledge is beyond doubt, it is necessary in almost all areas related to the natural sciences, technical and engineering professions, and economics.&#x0D; The relevance of practice-oriented teaching of mathematics is increasing in the context of the dominance of the competence-based approach, which combines the intellectual and practical components of learning in education. Mathematics, by virtue of its specificity, the fundamental nature of the content, becomes the basis for the natural-mathematical training of future specialists. It is emphasized that in the digital era, which most vividly reflected the connection of mathematics with natural, technical and many other sciences, as well as growing high-tech processes, automation and robotization of production, it is important to ensure the advanced nature of the mathematical training of students at the university, including the natural sciences. In the context of the stated research topic, the problem field of research is determined, which consists in resolving the contradiction between the need for a scientifically grounded selection of the content of the mathematics course for students, identifying the methodological features of its teaching and the spontaneously developing practice of teaching mathematics to students of the natural sciences.
An Ownership Verification Mechanism Against Encrypted Forwarding Attacks in Data-Driven Social Computing

Data-driven deep learning has accelerated the spread of social computing applications. To develop a reliable social application, service providers need massive data on human behavior and interactions. As the data is highly relevant to users’ privacy, researchers have conducted extensive research on how to securely build a collaborative training model. Cryptography methods are an essential component of collaborative training which is used to protect privacy information in gradients. However, the encrypted gradient is semantically invisible, so it is difficult to detect malicious participants forwarding other’s gradient to profit unfairly. In this paper, we propose a data ownership verification mechanism based on Σ-protocol and Pedersen commitment, which can help prevent gradient stealing behavior. We deploy the Paillier algorithm on the encoded gradient to protect privacy information in collaborative training. In addition, we design a united commitment scheme to complete the verification process of commitments in batches, and reduce verification consumption for aggregators in large-scale social computing. The evaluation of the experiments demonstrates the effectiveness and efficiency of our proposed mechanism.
THE DEVELOPMENT OF FACE RECOGNITION MODEL IN INDONESIA PANDEMIC CONTEXT BASED ON DCNN AND ARCFACE LOSS FUNCTION

The advancement of technology opens opportunities for implementation that benefits the social and economic aspects of human life. Given the latest achievement in face recognition technology that surpasses human ability to identify a face, the research explores the application of this scientific discovery in the Indonesian context during the current pandemic situation. Toward the effort to achieve this goal, the study develops an Indonesia Labelled Face in the Wild (ILFW) that collects face images of famous Indonesian people from the Internet in various poses, expressions, lighting/illumination, and fashion attribute. In response to the recent COVID-19 pandemic situation, the study also augmented a face mask to a portion of collected face images. Using DCNN, RetinaFace as the face detection model, and Arcface loss function, and adopting CRISP DM, the research contributes by providing a method to develop a face dataset with 1,200 identities, and face recognition model with 92 percent accuracy and be able to recognize Indonesian people with a face mask. The researchers also recommend use cases for realtime face recognition in the business organization. It uses CCTV to perform automatic attendance, security surveillance, and employee location tracking and exhibits deployment consideration. Future research could increase the accuracy of face recognition model by adding more identities to the face dataset.
Divided Caption Model with Global Attention

Dense video captioning is a newly emerging task that aims at both locating and describing all events in a video. We identify and tackle two challenges on this task, namely, 1) the limitation of just attending local features; 2) the severely degraded description and increased training complexity caused by the redundant information. In this paper, we propose a new divided caption model, where two different attention mechanisms are introduced to rectify the captioning process in a unified framework. Firstly, we employ a global attention mechanism to encode video features in the proposal module, which can obtain a better temporal boundary. Second, we design bidirectional Long short-term memory (LSTM) with a common-attention mechanism to counterpoise 3d-convolutional neural network (c3d) features and global attention video content effectively in caption module to generate coherent natural language descriptions. Besides, we divide forward and backward video features in an event into segments to relieve the stress for degraded description and increased complexity. Extensive experiments demonstrate the competitive performance of the proposed Divided Caption Model with Global Attention (DCM-GA) over state-of-the-art methods on the ActivityNet Captions dataset.
Face Shows Your Intention: Visual Search Based on Full-face Gaze Estimation with Channel-spatial Attention

research-article Open Access Share on Face Shows Your Intention: Visual Search Based on Full-face Gaze Estimation with Channel-spatial Attention Authors: Song Liu Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, China Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, ChinaView Profile , Xiang-Dong Zhou Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, China Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, ChinaView Profile , Xingyu Jiang Chongqing University of Education, China Chongqing University of Education, ChinaView Profile , Haiyang Wu Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, China Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, ChinaView Profile , Yu Shi Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, China Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences, ChinaView Profile Authors Info & Claims ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial IntelligenceMarch 2021 Pages 76–81https://doi.org/10.1145/3461353.3461362Online:04 September 2021Publication History 0citation60DownloadsMetricsTotal Citations0Total Downloads60Last 12 Months50Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
On the (In)Security of ElGamal in OpenPGP

Roughly four decades ago, Taher ElGamal put forward what is today one of the most widely known and best understood public key encryption schemes. ElGamal encryption has been used in many different contexts, chiefly among them by the OpenPGP email encryption standard. Despite its simplicity, or perhaps because of it, in reality there is a large degree of ambiguity on several key aspects of the cipher. Each library in the OpenPGP ecosystem seems to have implemented a slightly different "flavor" of ElGamal encryption. While-taken in isolation-each implementation may be secure, we reveal that in the interoperable world of OpenPGP, unforeseen cross-configuration attacks become possible. Concretely, we propose different such attacks and show their practical efficacy by recovering plaintexts and even secret keys.
Ivan Kavaleridze`s dramaturgy on the Sumy stage

The characteristic features of I. Kavaleridze’s drama is considered in the article. It is noted that there are signs of the artist’s individuality, attraction to expressionist forms, artistic techniques characteristic for the art of sculpture: symbolism, monumentality, hyperbole. I. Kavaleridze was well versed in the drama laws, understood the specifics of the stage events construction, had a large arsenal of literary means, thanks to which the characters’ monologues and dialogues were extremely expressive and colorful. In his work, he implemented original solutions that were ahead of time. Therefore, many of the artist’s ideas and achievements received due recognition only after his death. I. Kavaleridze’s creative heritage covers a wide range of both purely artistic and general philosophical problems. Among them the formation of the era of modernism and its features in the Ukrainian art of the early XX century, the impact of revolutionary ideas on the work of the 1920s, the role of spiritual leaders of the Ukrainian people T. Shevchenko and G. Skovoroda in the formation of national consciousness, political and ideological pressure on figurative art language and the formation of a socialist-realist canon, etc. The analysis of the productions of I. Kavalerizde’s plays “The First Furrow” and “Gregory and Paraskeva” on the stage of the Mykhailo Shchepkin Sumy Theater of Drama and Musical Comedy in 1970-1972. The article notes that these plays were staged in Sumy for the first time in the history of Ukrainian theater. The premiere of “The First Furrow” (the play was called “Old Men”) took place on March 19, 1970. The figure of the national genius Hryhoriy Skov oroda was als o embodied for the first time on t he stage in Sumy in th e play “Hryhoriy and Paraskeva”. It premiered on October 21, 1972. I. Rybchynsky, Honored Artist of the USSR, performed the production. Creating generalized historical outlines of people’s life, features of life at that time, depicting psychological portraits of people in various, sometimes-dramatic collisions, in the productions of I. Kavaleridze’s plays on the Sumy stage the emphasis was on universal values such as virtue, love. The main character was the Ukrainian people, who nurtured such large-scale historical figures, gave them strength and wisdom for great achievements. Based on publications in periodicals of that time, memoirs of Ukrainian directors, the peculiarities of the director’s interpretation, stenographic and musical design of these plays on the Sumy stage are considered. Considerable attention is paid to the analysis of acting works in I. Kavaleridze’s plays. In particular, the peculiarities of the actor’s embodiment of the image of the national genius Hryhoriy Skovoroda on the stage are presented. It is noted that I. Kavaleridze’s plays, created in a difficult political, social and ideological context, are rightly considered to be highly artistic works of Ukrainian drama. Their staging was carried out on various theatrical stages, including Mykhailo Shchepkin Sumy Theater of Drama and Musical Comedy is an important page of national theatrical art.
The COVID-19 Pandemic One Year On: Finding Balance Between Privacy and Public Health

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