Reviewerly/exHarmony huggingface dataset:
Representative Spatial Selection and Temporal Combination for 60fps Real-Time 3D Tracking of Twelve Volleyball Players on GPU

Real-time 3D players tracking plays an important role in sports analysis, especially for the live services of sports broadcasting, which have a strict limitation on processing time. For these kinds of applications, 3D trajectories of players contribute to high-level game analysis such as tactic analysis and commercial applications such as TV contents. Thus real-time implementation for 3D players tracking is expected. In order to achieve real-time for 60fps videos with high accuracy, (that means the processing time should be less than 16.67ms per frame), the factors that limit the processing time of target algorithm include: 1) Large image area of each player. 2) Repeated processing of multiple players in multiple views. 3) Complex calculation of observation algorithm. To deal with the above challenges, this paper proposes a representative spatial selection and temporal combination based real-time implementation for multi-view volleyball players tracking on the GPU device. First, the representative spatial pixel selection, which detects the pixels that mostly represent one image region to scale down the image spatially, reduces the number of processing pixels. Second, the representative temporal likelihood combination shares observation calculation by using the temporal correlation between images so that the times of complex calculation is reduced. The experiments are based on videos of the Final and Semi-Final Game of 2014 Japan Inter High School Games of Men's Volleyball in Tokyo Metropolitan Gymnasium. On the GPU device GeForce GTX 1080Ti, the tracking system achieves real-time on 60fps videos and keeps the tracking accuracy higher than 97%.
Complex systems of neural networks

The chapter presents applicative examples of artificial-neural networks (ANN) described in the contemporary literature and synthesizes the scientific and engineering information contained in a group of reviews assessing papers which apply artificial-neural networks. Various cases of ANN fuel cell modeling and pollutants modeling are considered. Some approaches apply the multilayer perceptron with unidirectional information flow. Hybrid models of ANN, with radial base function (RBF), are reviewed without going into detail regarding the network architecture and transition functions. The following topics are outlined: The role of ANN in the development of low pollution vehicles; NN hybrid model of direct internal reforming SOFC; NN-based dynamic control of PEM fuel cells; NN on-board FC power supply modeling; NN with SOFC control of power supply improvement; NN-based modeling of PEM FC and controller synthesis; NN model of drying and thermal degradation; NN modeling of SOFC; NN optimization of energy systems; NN model for fluidized bed; NN power control of a FC; performance prediction and analysis of the cathode catalyst layer; NN model for a PEM FC; hybrid NN models for fuel cells; ANN capillary transport characteristics of FC diffusion media; ANN of the mechanical behavior of PEM FC; ANN simulators for SOFC performance; ANN modeling the study and development of fuel cells; ANN to predict SOFC performance in residential spaces; NN modeling of polymer electrolyte membrane FC; GA-RBF neural networks modeling; and NN-based quality control with a SOFC plant. A review is given of central notions of the NN theory. A brief assessment of results obtained in these investigations is also provided. Finally, we direct the reader to a model of neural networks for emission prediction of dust pollutants.
Toward Generating Realistic Sounds for Soft Bodies

Generating realistic sounds for soft bodies is a challenging task due to the complexity of the interactions. Therefore, automatic audio generation based on procedural approach has become an attractive method for digital synthesis of soft-body sounds. In this paper, we present a comprehensive review in the field of procedural audio, with a special focus on synthesizing the sound of soft bodies. We first introduce the concept of procedural sound generation, including its advantages and challenges for soft-body sound synthesis. Next, we review the state-of-the-art in rigid/non-rigid-body sound synthesis techniques for computer animations and games. Thirdly, we summarize and survey a taxonomy of existing synthesis methods from previous literatures by analyzing their benefits and drawbacks for generating soft-body sounds. These methods include modal synthesis, sound texture modeling, motion-driven sound synthesis, wavelet tree learning, granular synthesis, and concatenative sound synthesis. Last but not least, we discuss several possible directions for future research in procedural soft-body sound synthesis.
FESC: Functionally Equivalent Service Composition

In the last few decades, researchers have focused on developing smart environment applications. In a smart environment, different kinds of smart devices are continuously working to make the lives of the inhabitants more comfortable. Smart environments are dynamic with respect to the context of user and availability of devices and services. A challenging problem is to satisfy a user’s request in case a service is unavailable in such environments. In this paper, we develop a novel method that provides a functionally equivalent service corresponding to an unavailable service. A knowledge-based system using a meta-reasoner tree is used to obtain a functionally equivalent service. The proposed approach is validated using a smart cooking system.
Enhancing the Performance of an Image Steganalysis Approach Using Variable Batch Size-Based CNN on GPUs

Blind image steganalysis is defined as the binary classification problem of predicting whether or not an image contains an embedded message. With the development of steganography, extracting powerful features from the stego-images becomes a challenge. Recently, convolutional Neural Networks (CNNs) are presented as a promising solution for such a challenge. Unlike traditional steganalysis approaches, CNN-based steganalysis approaches have the ability of extracting features automatically from input images. With such an ability, there is no need to handcraft feature extractors like those used by traditional steganalysis approaches. Despite its long clinical success, CNN-based steganalysis approaches are time consuming. Training on those approaches may stand for days and sometimes for weeks. It is necessary to accelerate the training on CNN-based approaches to make them more usable in practice, especially for some real-time applications. The purpose of this paper is to implement an enhanced version of the improved Gaussian-Neuron CNN (IGNCNN) steganalysis approach on GPUs, and to profiteer the parallel power of GPUS. In this paper two approaches for parallelizing the CNN training process are proposed. The first is to apply the concept of data parallelism with the feature extraction module and the second is to apply model parallelism with the classification module. Besides the parallelization approaches, a variable batch size is implemented as an optimization approach. Using a big batch size in fully-connected layers leads to faster convergence to a better minima, but it may negatively affect the accuracy. The results of the proposed approach show that it outperforms the IGNCNN in terms of accuracy and performance metrics.
Building Self-Play Curricula Online by Playing with Expert Agents in Adversarial Games

Multiagent reinforcement learning algorithms are designed to enable an autonomous agent to adapt to an opponent's strategy based on experience. However, most such algorithms require a relatively large amount of experience to perform well. This requirement is problematic when opponent interactions are expensive, for example, when the agent has limited access to the opponent during training. In order to make good use of the opponent as a resource to support learning, we propose SElf-PLay by Expert Modeling (SEPLEM), an algorithm that models the opponent policy in a few episodes, and uses it to train in a simulated environment where it is cheaper to perform learning steps than in the real environment. Our empirical evaluation indicates that SEPLEM, by iteratively building a Curriculum of simulated tasks, achieves better performance than both only playing against the expert and using pure Self-Play techniques. SEPLEM is a promising technique to accelerate learning in multiagent adversarial tasks.
Using Blockchain-based approach for building the system events logging service

Abstract The article deals with Blockchain technology, namely with its aspect of forming distributed registries. It serves as the platform for creating and storing corporate information resources which need higher protection. A lot of marketing uproar on cryptocurrency is feeding the interest in employing this technology for to enable cryptocurrency operation. But not only this factor explains the technology success. Blockchain technology also favorably compares to classic centralized registries and databases. The article describes the journaling service of data computing processes and events system, both based on the Blockchain technology. We display all the advantages of the existing Blockchain types for developing corporate journaling systems. And we also show the use of such a system for medium and large organizations, allowing for their computational capabilities and potential for scaling and developing such a system.
Removing moiré patterns from single images

Interference between the grids of the camera sensor and the screen cause moiré patterns to always appear on photographs captured from a screen, significantly affecting people’s ability to review images. We propose a novel method to remove such a screen moiré pattern from a single image. We characterize the degraded image as a composition of two layers: the latent layer and the moiré pattern layer. Because the screen moiré pattern is global and content-independent, we regard it as a group of sublayers, and we find that each sublayer after the shear transformation has a low-rank property. Combined with the piecewise constant feature of the latent layer, a convex model is proposed to solve the demoiréing problem. Experiments on synthetic and real data demonstrate its feasibility and efficiency.
The Rise of Smart Contract and Its Complex Impacts on the Digital Ecosystem

Nowadays, we are forced to join in a more and more complicated contract and a faster and faster transaction in the market. We are also seriously faced with security problems. As the society evolves, the related systems generally become complicated. The economic system, more specifically, the market system is not exempted from this law. This exactly means the advent of a new formula of it currently corresponding to the extent of complications. Without any incessant improvement of the digital technologies, our performance would not be smoothly achieved. Given an idea of smart contract, blockchain becomes a broader computational technology to realize a set of smarter systems/transactions associated with the Distributed Ledger Technologies (DLT). Such a well-known instance is the smart contracts on the Ethereum platform, that is associated with account information each. The smart contract tied together with DLT actually enables to design an economically well-behaved set of Peer to Peer (P2P) system, which ranges over from the micro market to the international currency transaction system. The introduction of this technology may suggest what a truly decentralized future is. This must cast a new light on the problem how to design either auction or vote of P2P, i.e., without auctioneer or chairperson. Either of them will possibly mean the change of existing social consensus. The DLT originally was designed by the presumption that someone always is malicious, as Byzantine Generals Problem suggests. The programming of smart contract is written in Turing complete language. A smarter solution against a complicated system actually then requires a stronger security against their vulnerabilities. Thus, the diffusion of blockchain technologies into the contract systems will demand a fully protective infrastructure. As Stephen Wolfram suggests, furthermore, “[a]s the number of DLTs grows, it becomes harder and harder to learn and build ecosystem tools to make monitoring transactions, smart contracts and services possible.” In other words, the diffusion of blockchain will bring newly some coordination problems as well as the new statistical examinations. In this paper, after looking briefly the current features of digital contract, we will argue some evolutionary impacts on the digital ecosystem in the context of either smart contracts on the Ethereum platform or the Byzantine Generals Problem.
Second Level Steganalysis - Embeding Location Detection Using Machine Learning

In recent years, various cloud-based services have been introduced in our daily lives, and information security is now an important topic for protecting the users. In the literature, many technologies have been proposed and incorporated into different services. Data hiding or steganography is a data protection technology, and images are often used as the cover data. On the other hand, steganalysis is an important tool to test the security strength of a steganography technique. So far, steganalysis has been used mainly for detecting the existence of secret data given an image, i.e., to classify if the given image is a normal or a stego image. In this paper, we investigate the possibility of identifying the locations of the embedded data if the a given image is suspected to be a stego image. The purpose is of two folds. First, we would like to confirm the decision made by the first level steganalysis; and the second is to provide a way to guess the size of the embedded data. Our experimental results show that in most cases the embedding positions can be detected. This result can be useful for developing more secure steganography technologies.
A new discrete-time neural network for quadratic programming with general linear constraints

This paper presents a discrete-time neurodynamic model to solve linear and quadratic programming with respect to linear equality and inequality constraints. The new model is obtained by using an auxiliary variable, and can be seen as the generalization of a neural model for bound constraints in the literature in the sense that bound constraints limit a linear function of the desired variable. The proposed neural solution is proved to be stable in the sense of Lyapunov and converges globally to the optimal solution of the given minimization by proper adjustment of a parameter. The model is further simplified for the case that the equality constraints entails a full row-rank linear mapping. The proposed neural solution is comparable with the state-of-the-art in terms of both the number of operations in each iteration and the required components for its circuit implementation. The experiments confirm the reasonable performance of the proposed neuaral network.
Using Augmented Reality in the Corporate Environment: A Proposed Study Protocol

AR provides opportunities to not only acquire information, but to experience it through visual, spatial, and sensorimotor feedback. Though this technology has been around for years and is professionally recognized as an effective tool to improve and enhance the learning experience, its benefits have gone widely unknown and underappreciated in the field of training and development. In order to maintain an advantage in a global economy, companies are always looking for more engaging and motivating training experiences for employees. As it becomes more accessible and popular, companies are enticed by the potentialities of Augmented Reality. The combination of print materials and augmented reality in education is becoming increasingly manageable due to advances in mobile technologies. This paper presents a proposed study protocol on an Augmented Reality training to deliver just-in-time training in the corporate environment using a marker-based system to enhance training in the context of the Human Capital Management industry.
Improving Virtual Machine Consolidation for Heterogeneous Cloud Computing Datacenters

Datacenters in cloud computing systems may consist of thousands of computing nodes and communication devices, requiring virtual machine placement to optimize resources utilization. Heterogeneous environments of current datacenters have not been properly evaluated yet when the number of processing nodes and network devices is considered. This paper presents an approach to place virtual machines to minimize both the number of employed computing nodes and communication costs in heterogeneous environments. We evaluate our results against other known algorithms to show the efficiency of the proposed approach, which outperforms a state-of-the-art algorithm with a result of up to 13% better for network improvement while achieves the same number of computing nodes.
Intellectual Property Protection for AI-Related Inventions in Japan

To increase the possibility of patent entitled of artificial intelligence related inventions at the Japanese patent office, this paper analyzes the Japanese patent act and patent examination guidelines. The approach for assessing whether a computer related invention belongs to a eligible subject-matter includes two steps. The first step is whether a computer related invention meets the definition of an "invention" that is "creation of a technical idea utilizing the laws of nature" . The second step is whether a computer related invention meets "idea based on the standpoint of software" . From the perspective of patent analysis, Japan's artificial intelligence technology is leading the world, second only to the United States. In this field, the Japanese patent office is one of the most important intellectual property offices, and its legislation and practice of patent eligibility review for artificial intelligence related inventions have an important impact on the world.
Comparative Analysis of K-Means Algorithm and Particle Swarm Optimization for Search Result Clustering

Clustering is being used to organize search results into clusters with an aim to help a user in accessing relevant information. The paper performs a comparative analysis of the most common traditional clustering algorithms: k-means and nature-inspired algorithm, and Particle Swarm Optimization (PSO). Experiments are conducted over the well-known dataset, AMBIENT, used for topic clustering. Experimental results show the highest recall and F-measure is achieved by the PSO. Though the highest precision is achieved by the k-means algorithm, in most of the topics, PSO shows a better result than the k-means algorithm.
Managing security risks

Threats that harm the security of software become more and more frequent. Such incidents can lead to substantial damage, not only financially, but also in terms of reputation loss. The combination of that consequence and the likelihood of an incident is called risk. Risk management processes describe coordinated activities to identify, evaluate and treat those risks. To reduce costs, it is necessary to concentrate on the most severe risks and to address those risks as early as possible by following the principle of security-by-design. In this paper, we introduce a template that enables security analysts to specify controls to treat risks in a systematic manner. Instances of the template make knowledge about controls reusable in future development projects. We designed our template for an application during requirements engineering. Our aim is to bridge the gap between functional requirements engineering and security. To do so, we consider controls as early aspects and relate them to relevant functional requirements and threats which can be treated by the control. Our template makes explicit how those aspects can be integrated into a requirements model by using a problem-based approach. To simplify the evaluation of controls, we adapt the concept of the Common Vulnerability Scoring System. The defined attributes will later allow a semi-automatic suggestion and evaluation of suitable controls.
Spatial Control Actions for a Physical Training with a Smart Walker

Research on smart walkers focuses mostly on low-level control methods using a variety of sensors to get inputs from a user or evaluate the user's gait. Our work investigates a possibility to use such a walker for a targeted robot-based motor training. In this paper a concept of control actions used as building blocks for the motor training are presented. These actions enable to challenge or support the user by influencing the output velocity of our smart walker-Robotrainer. The concept and evaluation of four types of control actions are presented in detail and their use in the training scenarios is discussed.
A solar optical hyperspectral library of rare-earth-bearing minerals, rare-earth oxide powders, copper-bearing minerals and Apliki mine surface samples

Abstract. Mineral resource exploration and mining is an essential part of today's high-tech industry. Elements such as rare-earth elements (REEs) and copper are, therefore, in high demand. Modern exploration techniques from multiple platforms (e.g., spaceborne and airborne), to detect and map the spectral characteristics of the materials of interest, require spectral libraries as an essential reference. They include field and laboratory spectral information in combination with geochemical analyses for validation. Here, we present a collection of REE- and copper-related hyperspectral spectra with associated geochemical information. The libraries contain reflectance spectra from rare-earth element oxides, REE-bearing minerals, copper-bearing minerals and mine surface samples from the Apliki copper–gold–pyrite mine in the Republic of Cyprus. The samples were measured with the HySpex imaging spectrometers in the visible and near infrared (VNIR) and shortwave infrared (SWIR) range (400–2500 nm). The geochemical validation of each sample is provided with the reflectance spectra. The spectral libraries are openly available to assist future mineral mapping campaigns and laboratory spectroscopic analyses. The spectral libraries and corresponding geochemistry are published via GFZ Data Services with the following DOIs: https://doi.org/10.5880/GFZ.1.4.2019.004 (13 REE-bearing minerals and 16 oxide powders, Koerting et al., 2019a), https://doi.org/10.5880/GFZ.1.4.2019.003 (20 copper-bearing minerals, Koellner et al., 2019), and https://doi.org/10.5880/GFZ.1.4.2019.005 (37 copper-bearing surface material samples from the Apliki copper–gold–pyrite mine in Cyprus, Koerting et al., 2019b). All spectral libraries are united and comparable by the internally consistent method of hyperspectral data acquisition in the laboratory.
Effective Learning Algorithms for Search and Rescue Missions in Unknown Environments

The following topics are dealt with: wireless sensor networks; learning (artificial intelligence); probability; autonomous aerial vehicles; radio networks; telecommunication control; radiowave propagation; wireless channels; ultra wideband communication; protocols.