Reviewerly/exHarmony huggingface dataset:
Efficient Implicit Fourier Compression Based Convolutional Features for Visual Tracking

Correlation filter has been popular in tracking due to the efficiency and robustness. Recently, tracking performance of correlation filter based trackers is significantly improved by using convolutional neural network features. However, the high dimensional feature maps lead to slow tracking speed and over-fitting. In this paper, we introduce an efficient tracking algorithm using the sparse features produced by Fourier compression to alleviate these issues. We show that Fourier compression can be implicitly conducted by discarding a large number of subproblems in our tracking framework, which is quite simple and efficient. The compression ratio of the proposed method reaches approximately 20% while maintaining the tracking accuracy. The sparse features can significantly reduce the computational complexity and over-fitting. Extensive experiments are performed in recent tracking benchmarks OTB50 and OTB100 to evaluate the proposed method. Results demonstrate that the proposed method achieves state-of-the-art tracking performance in terms of efficiency and accuracy in these benchmarks.
A General Technique to Combine Off-Policy Reinforcement Learning Algorithms with Satellite Attitude Control

Reinforcement learning method has great potential in constructing next generation of intelligent attitude control for satellite. However, designing reward function when using reinforcement learning algorithm to achieve specific task is a hard problem, which limits reinforcement learning algorithm used in satellite attitude control. For avoiding complicated reward engineering, we present a technique which allows the off-policy reinforcement learning algorithm can be easily migrated to construct satellite attitude control method. A satellite simulation environment is constructed. In this environment, we train an attitude control agent with the technique and validate the technique’s effectiveness.
Learning Depth from Monocular Sequence with Convolutional LSTM Network

Resolving depth from monocular RGB image has been a long-standing task in computer vision and robotics. Recently, deep learning based methods has become a popular algorithm on depth estimation. Most existing learning based methods take image-pair as input and utilize feature matching across frames to resolve depth. However, two-frame methods require sufficient and static camera motion to reach optimal performance, while camera motion is usually uncontrollable in most application scenarios. In this paper we propose a recurrent neural network based depth estimation network. With the ability of taking multiple images as input, recurrent neural network will decide by itself which image to reference during estimation. We train a u-net like network architecture which utilizes convolutional LSTM in the encoder. We demonstrate our proposed method with the TUM RGB-D dataset, where our proposed method shows the ability of estimating depth with various sequence lengths as input.
Proposal of Research Information Collection System

In recent years, information about research progress has become more obtainable by virtue of internet technology and the promotion of open science. Nevertheless, many steps are necessary to collect such information adequately. Research summaries alone require much time and effort. For this study, we propose a system that collects information about individual laboratories, and which then analyzes and provides information as a single site automatically. As a prototype system, we developed important functions such as research keyword extraction, identifying laboratories that conduct similar research, and web site analysis of research abstract text. This report describes the experiment and evaluation of the proposed system.
Cluster-Based Anonymization of Assortative Networks

Privacy preservation is a serious concern of today’s scenario. Anonymity is a way to preserve privacy of different types of data publications like structural and descriptive. In this paper, we discuss the preservation of both types of data. We use anatomy for descriptive data and sequential clustering for structural data anonymization. The proposed approach is based on assortativity called Anatomy Based Modified Sequential Clustering (ABMSC). We propose a new total info. loss measure for cluster optimization. The performance of the proposed approach is measured by information loss and number of node movements takes place to meet the optimization. The experimental results show that our proposed algorithm outperforms the two state-of-the-art algorithms.
Design of Smart Car based on Arduino and Wireless Router Technology

Aiming at the demand of wireless surveying robots in various industries such as industry and agriculture, a wireless smart car based on Arduino and sensor technology was developed. The car consists of an open source electronic platform based on Arduino and a QT-based upper computer control platform. Wireless communication is realized between the car and the host computer through a wireless router. The smart car running platform takes Arduino as the core, mainly realizes automatic obstacle avoidance, remotely monitors and accepts the command of the host computer through the wireless network. The test results show that the wireless smart car communicates normally, runs stably, and better realizes the required functions.
A study and implementation of large-scale log-determinant computation to cloud

Logarithm of a square matrix is a factor that clearly affects the quality or result of something. Logarithms are used to maintain exactitude in many numerical computations than the determinant. At present cloud computing is an emerging topic to perform this kind of operations. Cloud computing qualifies resource-awkward clients to solve their immense computational workloads through a cloud server that provides heavy computational power and it is also less expensive. This rising computing paradigm undoubtedly introduces new challenges and security concerns such as security, correctness, competency and result verifiability. Matrix log-determinant is a quite simple scientific, engineering and machine learning application. It inspired us to plan a protocol that satisfies all the concerns for the outsourcing of LDC to a cloud which is malicious in this paper. The primary concept to save privacy is to put in some changes on the original matrix for encryption and send the encrypted matrix to the cloud. The cloud then uses any LU decomposition algorithm on the encrypted matrix and divide the matrix for calculating the log-determinant. After returning from the cloud some transformations are applied to the returned result to get the accurate log-determinant of the original matrix.
Groove Depth Detection Method Base on the Line Laser Vision Measurement on Cement Concrete Pavement

Abstract The groove depth has a decisive influence on the anti-sliding performance of cement concrete pavement, and it also plays an important role on the noise of cement concrete pavement. Aiming the shortcomings of traditional measurement methods of groove depth detection on the cement concrete pavement, a measurement method base on the structure light vision model was proposed. This method was realized by that the groove depth was obtained by 3D coordinate of the light strip according to the camera model and the structure light plane equation. A new algorithm based on double 2D plane pane target was proposed as the key step of this method.
Reply to “Comments on ‘Traffic Sign Recognition Using Kernel Extreme Learning Machines With Deep Perceptual Features”’

This paper addresses the questions and concerns raised in the comment paper entitled “Comments on `Traffic Sign Recognition Using Kernel Extreme Leaning Machines With Deep Perceptual Features”'. It is analyzed that the major questions and concerns in the comment paper are due to some misunderstanding on the statements and the main contributions of the original paper by Zeng et al.. The main clarifications are as follows: (1) The weight update rule used by Zeng et al. is a standard form of kernel extreme learning machine (KELM) but not the rule of reduced KELM mentioned by the comment paper. (2) The aim and main contribution of the original paper by Zeng et al. are to improve the classification precision of traffic sign recognition (TSR) based on convolutional neural networks without dramatically increasing the scale or complexity of the deep neural network model, which leads to a relatively less training cost. The authors of the comment paper focused on the reduction of KELM's computational costs but it is beyond the topic of the original paper. In addition, the reduction of KELM's computational costs has been well studied in the literature. (3) The supplementary experiment is provided to show that the TSR method proposed by Zeng et al. is valid whether or not the training samples are much more than the needed kernels.
Development of low computational power vision tracking algorithm on embedded system

Object tracking is one of the perennial tasks in the field of Computer Vision. Great advancement has been made in the recent years due to the Deep Learning revolution, but in return Deep learning models significantly lags behind in terms of processing time and computational efficiency. In this work, we present a novel object tracking model, the Buffered Object Box Boundary Generator (BOBBY) that is capable of achieving a ∼7x speedup as compared to its current state-of-the-art competitors. The model is trained and validated on the VisDrone2018 dataset which is dedicated to providing aerial footages for supervised Deep Learning training. We demonstrate that it is possible to design and implement a Deep Learning based object tracking model that could actually achieve real-time computation requirements on a low-power CPU without the need of a dedicated GPU albeit with lower accuracy, with a resulting performance of more than 20 frames-per-second (FPS).
LineIT: Similarity Search and Recommendation Tool for Photo Lineup Assembling

In this paper we focus on the problem of assembling unbiased photo lineups. Photo lineups are an important method in the identification process for the prosecution and possible conviction of suspects. Incorrect lineup assemblies have led to the false identification and conviction of innocent persons. One of the significant errors which can occur is the lack of lineup fairness, i.e., that the suspect significantly differs from other candidates. Despite the importance of the task, few tools are available to assist police technicians in creating lineups. Furthermore, these tools mostly focus on fair lineup administration and provide only a little support in the candidate selection process. In our work, we first summarize key personalization and information retrieval (IR) challenges of such systems and propose an IR/Personalization model that addresses them. Afterwards, we describe a LineIT tool that instantiate this model and aims to support police technicians in assembling unbiased lineups.
Dual-Level Cooperative Game Approach for Energy-Aware Resource Allocation in Data Centers

Cloud computing is the next major paradigm shift in information and communication technology. Today, data center (DC) is pivotal to the effective implementation of the cloud computing paradigm. To deliver the necessary cloud services, the DC faces major reliability and robustness challenges. In this study, we concentrate upon the efficient DC resource allocation technique by considering the synergy of the performance and energy efficiency. To achieve the maximum system efficiency, thermal-aware control mechanism is developed based on the dual-level cooperative game. Proactively, we adaptively assign the workload among different servers using the concept of relative utilitarian bargaining solution. Reactively, we maintain a thermal equilibrium according to the characteristics of τ-value. The main advantage of our dual-level game model is to provide an axiom-based strategic solution for the DC resource allocation problem while dynamically responding to the current workload and thermal conditions. The effectiveness of our proposed protocol is verified through simulation evaluations under different workload scenarios. Results demonstrate that the proposed approach significantly outperforms the existing state-of-the-art DC control schemes from energy consumption and load balancing perspectives. Last, some open problems are identified and we summarize possible research issues for the future study.
Large-scale resource scheduling method using improved genetic algorithm combined with secondary coding in cloud computing environment

Abstract This paper proposes a large-scale resource scheduling method based on improved genetic algorithm combined with secondary coding. This method is used to solve the problem that traditional genetic algorithm can not meet the resource scheduling problem in large-scale cloud computing environment under multi-user. In the selective replication phase, a dual fitness function based on minimum task completion time and matching degree is used to screen the populations by double criteria. Next, the cross-mutation probability of the algorithm is adaptively optimized, and its adaptive ability is further improved, which ensures that the algorithm converges to the optimal solution as soon as possible. Finally, the improved genetic algorithm (IGA) is analyzed on the CloudSim platform. It shows that the improved genetic algorithm can be well applied to large-scale resource scheduling, and the result is better than the comparison algorithm.
Resource Demand Management in Java Ecosystem

The evolution of Software Ecosystems (SECO) requires to satisfy community's needs based on platform's and applications' requirements, known as SECO demands. Such SECO demands must be evaluated, approved and subsequently translated into platform resources (e.g., API, framework, library). In this context, this work applies a research approach based on primary and secondary studies to investigate how resource demands are managed in Java SECO. To do so, we conducted a systematic mapping study on the existing models, methods and conditioning factors for supporting emergence and/or inclusion of SECO platforms' resources. Based on the results, we planned and executed a survey research with members of a real SECO, more especifically Java Community Process (JCP) Committee members. From the studies, we identified seven methods for collecting data related to the emergence of new APIs, including a method for architecture assessment based on quality attributes, and finally ten conditioning factors for API management that can support SECO evolution. The results also allowed us to organize a process for managing demands of resources in Java SECO platform. We found out that the lack of standardization for management of SECO demands can hinder platforms/applications management. Therefore, this field is worth of further in-depth studies.
THE ORIGIN AND DEVELOPMENT OF MEDIA FOR CHILDREN IN UKRAINE

More than 70 kinds of children's magazines and newspapers are featured in the Ukrainian press.At the moment, they are able to satisfy even the most demanding readers.Such presses most clearly and in a special way respond to events occurring in society, has always been and remains one of the main communicative channels through which the child receives information of different content and form.There are no exceptions to television broadcasts, which eventually rallied in full-fledged television channels for children.Modern children do not remain indifferent to social events and expect answers to their questions from their favorite magazines and newspapers and TV channels.It is they who replace children's books, parents in their free time, teach and educate, that is, with their help the child forms his own outlook, built on the information that it absorbs.Therefore, research and study of topical issues of TB, magazines and newspapers intended for the children's audience is very important.Today, newspapers and TV channels for children are flooded with pictures and games, which looses and weakens the cognitive function of editions, and this is an important topic for research at present.The aim is to explore the features of the preparation of magazines, newspapers and television programs for children.To study the areas of improvement of journalistic skills for the preparation of media production and its successful implementation.Find shortcomings and ways to solve them.Deepening into history, one can not but notice that the first sources of information materials for the younger generation reach the far 1117 and presented in the form of "Poucheniya" and "Letters" written by Volodymyr Monomakh.From year to year children's magazines popularize the world's leading pedagogical ideas, placing them in their materials.Children are keen to get acquainted with the works of writers of different epochs and countries, learn about new events in the life of mankind and absorb a lot of valuable information that the authors of the magazine provide for them in a playful and interesting manner.Researchers note that the number of children's periodicals on the territory of the domestic press indicates the level of future education in the country.In addition, they note that the development of the press is an important factor in the diverse upbringing of the child, because television can not meet the need for knowledge and development of logical thinking in the right amount.Therefore, with the advent and entry into the masses of television channels
Tracking-Tolerant Visual Cryptography

We introduce a novel secure display system, which uses visual cryptography [4] with tolerance for tracking. Our system brings cryptographic privacy from text to virtual worlds [3]. Much like traditional encryption that uses a public key and a private key, our system uses two images that are both necessary for visual decryption of the data. The public image could be widely shared on a printed page, on a traditional display (desktop, tablet, or smartphone), or in a multi-participant virtual world, while the other private image can be exclusively on a user's personal AR or VR display. Only the recipient is able to visually decrypt the data by fusing both images. In contrast to prior art, our system is able to provide tracking tolerance, making it more practically usable in modern VR and AR systems. We model the probability of misalignment caused by head or body jitter as a Gaussian distribution. Our algorithm diffuses the second image using the normalized probabilities, thus enabling the visual cryptography to be tolerant of alignment errors due to tracking.
Dataflow synchronization mechanism for H.264 hardware video codec

Modern video compression standards require significant computational costs for their implementation. With a high rate of receipt of video data and significant computational costs, it may be preferable to use hardware rather than software compression tools. The article proposes a method for synchronizing data streams during hardware implementation of compression / decompression in accordance with the H.264 standard. The developed video codec is an IP core as part of an 1892ВМ14Я microcircuit operating under the control of an external processor core. The architecture and the main characteristics of the video codec are presented. To synchronize the operation of the computing blocks and the controller of direct access to the video memory, the video codec contains an event register, which is a set of data readiness flags for the blocks involved in processing. The experimental results of measuring performance characteristics on real video scenes with various formats of the transmitted image, which confirmed the high throughput of the developed video codec, are presented.
Multi-attending Memory Network for Modeling Multi-turn Dialogue

Modeling and reasoning about the dialogue history is a main challenge for building a good multi-turn conversational agent. End-to-end memory networks with recurrent or gated architectures have been demonstrated promising for conversation modeling. However, it still suffers from relatively low computational efficiency for its complex architectures and costly strong supervision information or fixed priori knowledge. This paper proposes a multi-head attention based end-to-end approach called multi-attending memory network without additional information or knowledge, which can effectively model and reason about multi-turn history dialogue. Specifically, a parallel multi-head attention mechanism is introduced to model conversational context via attending to different important sections of a full dialog. Thereafter, a stacked architecture with shortcut connections is presented to reason about the memory (the result of context modeling). Experiments on the bAbI-dialog datasets demonstrate the effectiveness of proposed approach.
Inhuman power: artificial intelligence and the future of capitalism, digital barricades: Interventions in digital culture and politics

A slim excerpt buried within the hefty Grundrisse, Karl Marx’s posthumously published collection of working notes, has elicited an outsized influence amidst the communist philosopher’s otherwise to...
Removing Spinning Motion from Spherical Video Using a Rotationally Invariant Feature Extractor

This paper presents a method for removing rotational motion from an omnidirectional video captured from the viewpoint of a spinning object. For instance, the technique could be used to recover stable footage from spherical video captured from within a football used for competitive sports. The methodology uses a rotationally-invariant algorithm to obtain feature points and descriptors for pairs of successive frames. This information is then abstracted into three-dimensional point clouds, from which the Kabsch algorithm can infer the rotational motion between frames. The resulting rotational matrices are used to remap each equirectangular frame to a reference frame, and thereby offset the effect of frame-to-frame camera rotations. The algorithm has been successfully tested with various styles of footage. Additionally, the codebase implementing our algorithm has been freely shared through an open online repository.
Novel Wavelet Domain Based Adaptive Thresholding Using Bat Algorithm for Image Compression

Image compression is the significant study in the arena of image processing owing to its enormous usages and its ability to reduce the storage prerequisite and communication bandwidth. Thresholding is a kind of image compression in which computational time increases for multilevel thresholding and hence optimization techniques are applied. The quality of reconstructed image is superior when discrete wavelet transform based thresholding is used as compared to when it is not applied. Both particle swarm optimization and fire fly algorithm becomes unstable when the velocity of the particle becomes maximum and when there is no bright firefly in the search space respectively. To overcome the above-mentioned drawbacks bat algorithm based thresholding in frequency domain is proposed. Echolocation is the sort of sonar used by micro-bats. The way they throng their prey, overcoming the hurdles they come across, pinpointing nestling gaps have become the main motivation research in artificial intelligence. With the feature of frequency tuning and having the benefit of automatic zooming, bat algorithm produces superior PSNR values and quality in reconstructed image and also results in fast convergence rate as compared to state of art of optimization techniques.