Reviewerly/exHarmony huggingface dataset:
Exploiting Simulation for MAS Development and Execution—The JaCaMo-Sim Approach

Simulation can be an important conceptual and practical tool to support the engineering of multi-agent systems (MAS), in different ways. In this paper we consider the case in which simulation is applied and exploited directly upon a MAS developed using an existing agent/MAS programming platform. That is: without requiring to model and simulate agents and their environment using a different platform, e.g. an agent-based simulation one. In particular, we describe the design of JaCaMo-sim, an extension of the JaCaMo platform that makes it possible to both run and simulate the execution of MAS programs based on BDI agents written in Jason, situated in artifact-based environments developed in CArtAgO. The tool can be useful for different aspects that concern MAS engineering, from MAS testing/debugging at development time to agent decision making support at runtime.
Understanding the performance of container execution environments

Many application server backends leverage container technologies to support workloads formed of short-lived, but potentially I/O-intensive, operations. The latency at which container-supported operations complete impacts both the users' experience and the throughput that the platform can achieve. This latency is a result of both the bootstrap and execution time of the containers and is impacted greatly by the performance of the I/O subsystem. Configuring appropriately the container environment and technology stack to obtain good performance is not an easy task, due to the variety of options, and poor visibility on their interactions.
Research on monitoring volleyball players’ competition load based on intelligent tracking technology

To evaluate volleyball players' sports activities using the author's own video recording system. They tested 28 volleyball players in four games for the Polish Volleyball League Plus and the Federation of Europe. Record data were analyzed for groups in matches given the average total distance covered by the volleyball players in different meetings' specialties. The research programs that extend the finals' players' distance set indicate these individual meetings' long-term trend. It is crucial to monitor the training load to essential the state together to prevent unnecessary changes. Knowledge of these variables during the volleyball season is still lacking—objective: To describe competitive volleyball players' training load and recovery status during the monitoring season. The purpose of this research is to describe and explore professional volleyball players throughout the season for Internal Training Load (ITL), recovery and body distribution. Recipe: 15 professional male Brazilian volleyball players participated. The target's three-dimensional position monitoring technology is beneficial for analysis and professional understanding of volleyball games. Still, many factors make any change in the rate of sports volleyball tracking. This article describes the image processing technology combined with 3D space matching technology. It features a particle filter, effectively enhancing volleyball tracking accuracy, tracking a game system's structure. Second, the proposed similarity prediction method is based on the properties of volleyball itself, mainly to solve supercomputing in real-time monitoring based on the similarity prediction of alkali and HSV color spaces. Finally, the simulation test results show that this method can significantly improve the tracking success rate—high efficiency and high accuracy dynamic video game tracking properties unexpected value development value volleyball.
What matters for financial inclusions? Evidence from emerging economy

Abstract The major objective of this research is to examine the influence of cybercrimes on financial inclusion in a developing country. Furthermore, the study also tends to examine the role of cybersecurity in controlling the impact of cybercrimes on financial inclusion. Data were collected from the banking sector of Pakistan using a close‐ended questionnaire. Partial least square‐structural equation modelling (PLS‐SEM) technique was employed to analyse the data. The findings of the study confirm that strong cybersecurity reduces the consequences of cyber threats to financial inclusion. This study suggests that numerous external and internal factors lead to criminals for illegal activities in the banking sector. Therefore, it is obligatory for the banks to enhance their cybersecurity control, to minimize the occurrences of cyber‐attacks. The present study focuses on the banking sector, so its finding cannot be generalized in other sectors. Further in‐depth comparative studies in other sectors with different cultural settings will help to authenticate the research findings.
Low-Illumination Image Enhancement in the Space Environment Based on the DC-WGAN Algorithm

Owing to insufficient illumination of the space station, the image information collected by the intelligent robot will be degraded, and it will not be able to accurately identify the tools required for the robot’s on-orbit maintenance. This situation increases the difficulty of the robot’s maintenance in a low-illumination environment. We proposes a novel enhancement method for images under low-illumination, namely, a deep learning algorithm based on the combination of deep convolutional and Wasserstein generative adversarial networks (DC-WGAN) in CIELAB color space. The original low-illuminance image is converted from the RGB space to the CIELAB color space which is relatively close to human vision, to accurately estimate the illumination image, and effectively reduce the effect of uneven illumination. DC-WGAN is applied to enhance the brightness component by increasing the width of the generation network to obtain more image features. Subsequently, the LAB is converted into RGB space to obtain the final enhanced image. The feasibility of the algorithm is verified by experiments on low-illuminance image under general, special, and actual conditions and comparing the experimental results with four commonly used algorithms. This study lays a technical foundation for robot target recognition and on-orbit maintenance in a space environment.
Using Machine Learning to Address Customer Privacy Concerns: An Application with Click-stream Data

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Representation learning from videos in-the-wild: An object-centric approach

We propose a method to learn image representations from uncurated videos. We combine a supervised loss from off-the-shelf object detectors and self-supervised losses which naturally arise from the video-shot-frame-object hierarchy present in each video. We report competitive results on 19 transfer learning tasks of the Visual Task Adaptation Benchmark (VTAB), and on 8 out-of-distribution-generalization tasks, and discuss the benefits and shortcomings of the proposed approach. In particular, it improves over the baseline on all 18/19 few-shot learning tasks and 8/8 out-of-distribution generalization tasks. Finally, we perform several ablation studies and analyze the impact of the pretrained object detector on the performance across this suite of tasks.
Modeling the Perceptual Quality for Viewport-Adaptive Omnidirectional Video Streaming Considering Dynamic Quality Boundary Artifact

Instead of streaming the entire omnidirectional video that induces a waste of bandwidth, the tile-based viewport adaptive streaming is preferred in practice. Tiles within users’ viewport are transmitted in high quality, while tiles elsewhere are delivered in lower quality to reduce the network bandwidth consumption. In this setup, when users change their viewport, they may potentially be disturbed by the exposure of low-quality tiles and the subsequent quality refinement in a period of delay. We defined this phenomenon in the viewport as a Dynamic Quality Boundary (DQB) artifact, as there exists a clear boundary between low-quality tiles and high-quality tiles which appears, moves, and disappears along with the change of users’ viewport. The DQB artifact is a specific quality degradation in the viewport-adaptive omnidirectional video streaming whose impact on users’ perceptual quality has not been adequately studied. In this paper, we focused on investigating and modeling its impact on the quality perception of users when streaming omnidirectional videos. More specifically, systematic subjective quality evaluation experiments towards DQB artifact were designed and conducted. Accordingly, a perceptual quality metric was proposed considering the quality of tiles, the proportion of low-quality tiles, and the refinement duration. Experimental results, measured in various aspects, showed that the proposed metric can accurately predict users’ perceptual quality of the tile-based viewport-adaptive omnidirectional video streaming service.
Closed form word embedding alignment

We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as absolute orientation, which are popular for aligning objects in three dimensions, and generalize an approach by Smith et al. (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then, we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.
Study on ray-tracing-based 3D reconstruction method for underwater measurement in glass-flume experiments

Stereo-vision-based measurement is affected by refraction of light in hydrodynamics and ocean-engineering experiments that are performed in glass flumes. Using the conventional 3D reconstruction strategy in measurement of underwater objects will lead to considerable errors. In this paper, a new reconstruction method based on ray tracing is proposed for measurement in the flume experiments. The refractive interfaces of the experiments are estimated by a robust algorithm, so that light rays corresponding to the image points can be recovered reliably. In the estimation algorithm, differential evolution is employed to compute globally optimal parameters from general image correspondences. To determine the more reasonable reconstructed points, a modified triangulation approach is also developed. The intersection of the back-projected rays is computed by correcting the corresponding image points. Experiments are conducted with synthetic data and real underwater objects. The results demonstrate the effectiveness and convenience of the proposed method in such underwater measurement tasks.
The Emerging Science of Content Labeling: Contextualizing Social Media Content Moderation

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Lightweight Action Recognition in Compressed Videos

Most existing action recognition models are large convolutional neural networks that work only with raw RGB frames as input. However, practical applications require lightweight models that directly process compressed videos. In this work, for the first time, such a model is developed, which is lightweight enough to run in real-time on embedded AI devices without sacrifices in recognition accuracy. A new Aligned Temporal Trilinear Pooling (ATTP) module is formulated to fuse three modalities in a compressed video. To remedy the weaker motion vectors (compared to optical flow computed from raw RGB streams) for representing dynamic content, we introduce a temporal fusion method to explicitly induce the temporal context, as well as knowledge distillation from a model trained with optical flows via feature alignment. Compared to existing compressed video action recognition models, it is much more compact and faster thanks to adopting a lightweight CNN backbone.
Securing Technique Using Pattern-Based LSB Audio Steganography and Intensity-Based Visual Cryptography

With the increasing need of sensitive or secret data transmission through public network, security demands using cryptography and steganography are becoming a thirsty research area of last few years.These two techniques can be merged and provide better security which is nowadays extremely required.The proposed system provides a novel method of information security using the techniques of audio steganography combined with visual cryptography.In this system, we take a secret image and divide it into several subparts to make more than one incomprehensible sub-images using the method of visual cryptography.Each of the sub-images is then hidden within individual cover audio files using audio steganographic techniques.The cover audios are then sent to the required destinations where reverse steganography schemes are applied to them to get the incomprehensible component images back.At last, all the sub-images are superimposed to get the actual secret image.This method is very secure as it uses a two-step security mechanism to maintain secrecy.The possibility of interception is less in this technique because one must have each piece of correct sub-image to regenerate the actual secret image.Without superimposing every one of the sub-images meaningful secret images cannot be formed.Audio files are composed of densely packed bits.The high density of data in audio makes it hard for a listener to detect the manipulation due to the proposed time-domain audio steganographic method.
Non-singleton fuzzification made simpler

Non-singleton fuzzification is used in rule-based fuzzy systems when the measurements that activate them are imperfect or uncertain or when their inputs are words. It models such measurements or words as fuzzy numbers or more general fuzzy sets so that, regardless of the cause of a measurement’s or word’s imperfections or uncertainties, they are treated within the framework of fuzzy sets and systems. Since 2011, there has been a resurgence of interest in both type-1 and interval type-2 non-singleton fuzzy systems. This paper removes a computational bottleneck associated with computing the firing level or firing interval for such fuzzy systems, by providing closed-form formulas for them when the involved fuzzy sets are trapezoidal or triangular, which are widely used fuzzy sets. This is done for both the minimum and product t-norms. It is also demonstrated that a non-singleton fuzzy system that uses the product t-norm has the potential to outperform a non-singleton fuzzy system that uses the minimum t-norm. The results in this paper greatly simplify non-singleton fuzzy systems, which should make them much more popular.
Open Experimental AGV Platform for Dynamic Obstacle Avoidance in Narrow Corridors

Automated Guided Vehicles (AGVs) are a promising solution to automation in the view of Industry 4.0. The amount of goods that can be automatically transported can be further increased by efficient path planning and tracking methods. The efficiency is always a trade off in terms of cost, accuracy and flexibility, but should never influence safety. This paper proposes a flexible path planning and tracking solution, aiming to be applicable to several application domains, and which is able to dynamically avoid an (unforeseen) obstacle by an overtake manoeuvre. The approach is based on Model Predictive Control (MPC), consisting of multi-domain objectives, applicable to multiple vehicle models and is fast in calculation time due to an adjusted multiple shooting approach, which guarantees constraint satisfaction over the entire time domain. Further, a dynamic maximum velocity approach is proposed, which adapts the maximum velocity constraint to the environmental circumstances, such that an emergency brake can be applied if a human would appear behind a corner or obstacle. These algorithms are implemented on an autonomous forklift. The overall system performance is measured by the time-of-arrival of an obstacle avoidance manoeuvre. To evaluate the influence of usage of a low-cost ultra wideband (UWB) localization technology, the same algorithms and platforms are used in combination with standard off-the-shelf laser based localization technology. The UWB technology does lead to a slightly larger spread in terms of time-of-arrival, but is on average very much comparable to the laser-based setup.
Singability-enhanced lyric generator with music style transfer

The lyrics generator should consider the context and the singability of the songs because every song expresses a story through the context of lyrics, and the lyrics should sound with the music well. Therefore, this study proposes a framework to generate the singable lyrics, and the context of lyrics should fit the given musical style. For the context, this study adopts the GPT-2 model which is powerful for text generation. The conditional GPT-2 model can be used to generate lyrics according to the given style. For suitable for singing, this study adjusts the structure and rhyme of lyrics through the use of a syntactic parser and a rhyme modification module. With automatic and human evaluations, the experimental results show that the proposed method can generate lyrics with high structural consistency, rhyme consistency, and originality according to the given music style.
Robot navigation on star worlds using a single-step Navigation Transformation

This work presents a single-step diffeomorphic transformation from a known star world to a trivial domain called the point world, where the navigation task is reduced to connecting the images of the initial and destination configurations by a straight line. Obstacle potential functions - derived using Zenkin's formulas - are used to define a transformation activation region around each obstacle. Configurations in this region are radially mapped with respect to the center of the corresponding obstacle. The proposed transformation guarantees almost global navigation. The provided theoretical results are backed by analytical proofs, while the effectiveness of the method is demonstrated by a series of simulation studies.
Corporate accounting information disclosure based on FPGA and neural network

Corporate accounting information is a measure of the company's accounting and external reporting systems. It is routinely disclosed, which is quantitative data on its financial position and performance audit. The corporate accounting information system contains confidential information that needs to be secured. The consequences of unauthorized access are data loss from identity theft issues. To solve the problem, encrypt and decrypt the sensitive corporate accounting information and product the data using the proposed algorithm Neural Network (NN) and Field Programmable Gate Array (FPGA) is used to classify the corporate accounting information authorized person and unauthorized person. When one authorized user accesses the corporate account information, it generates the secret critical process. The proposed algorithm unauthorized person cannot access the information is not allowed for stealing. Encryption is the process of converting to something as random and meaningless as direct text data. Decryption is the process of restoring the ciphertext plaintext.
Comparison of workload consolidation algorithms for cloud data centers

Abstract Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation‐based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT‐CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population‐based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real‐world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population‐based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.
Complex network perspective on modelling chaotic systems via machine learning*

Recent advances have demonstrated that a machine learning technique known as “reservoir computing” is a significantly effective method for modelling chaotic systems. Going beyond short-term prediction, we show that long-term behaviors of an observed chaotic system are also preserved in the trained reservoir system by virtue of network measurements. Specifically, we find that a broad range of network statistics induced from the trained reservoir system is nearly identical with that of a learned chaotic system of interest. Moreover, we show that network measurements of the trained reservoir system are sensitive to distinct dynamics and can in turn detect the dynamical transitions in complex systems. Our findings further support that rather than dynamical equations, reservoir computing approach in fact provides an alternative way for modelling chaotic systems.
Deep multiple instance learning for airplane detection in high-resolution imagery

Automatic airplane detection in aerial imagery has a variety of applications. Two of the significant challenges in this task are variations in the scale and direction of the airplanes. To solve these challenges, we present a rotation-and-scale invariant airplane proposal generator. We call this generator symmetric line segments (SLS) that is developed based on the symmetric and regular boundaries of airplanes from the top view. Then, the generated proposals are used to train a deep convolutional neural network for removing non-airplane proposals. Since each airplane can have multiple SLS proposals, where some of them are not in the direction of the fuselage, we collect all proposals corresponding to one ground-truth as a positive bag and the others as the negative instances. To have multiple instance deep learning, we modify the loss function of the network to learn from each positive bag at least one instance as well as all negative instances. Finally, we employ non-maximum suppression to remove duplicate detections. Our experiments on NWPU VHR-10 and DOTA datasets show that our method is a promising approach for automatic airplane detection in very high-resolution images. Moreover, we estimate the direction of the airplanes using box-level annotations as an extra achievement.