Reviewerly/exHarmony huggingface dataset:
Distributed Learning using Consensus on Edge AI

Moving machine learning services such as inference and training from the cloud layer to the edge layer is a complex task, but necessary to guarantee the quality of service of many Internet of Things (IoT) applications. However, running machine learning models in edge computing using lighter (limited) hardware ends up being an obstacle to applying powerful models that have better accuracy. In this context, distributed machine learning techniques aim to mitigate such limitations, being federated learning, model compression and model ensemble some of the existing alternatives. The present work proposes a new distributed machine learning technique focused on inference, which improves the accuracy of the final response of the models respecting the limitations of commonly used hardware in edge computing through a consensus algorithm.
Deep learning-based video coding optimisation of H.265

Today's multi-media applications need high video quality with low bitrates. However, it is restricted in its capacity to provide higher quality than earlier coding methods. Deep learning (DL) approaches for video coding have shown compression capacities equal to or better than traditional methods, including high-efficiency video coding (HEVC) methods. The trade-off between compression efficiency and encoding/decoding complexity, optimisation for perceptual nature of semantic dependability, specialisation, and universality, the federalised layout of various deep toolkits, etc. remains unclear. HEVC encoding is more efficient than previous standards. Improved efficiency is driven by intra image prediction, which incorporates more prior directions (35 modes) than previous standards. Its high efficiency comes from balancing encoder complexity and dependability. This article presents DL, which uses a convolutional neural network to predict the best model with the least rate-distortion (RD) and further promotes study into deep learning video coding (DLVC).
Power Usage Effectiveness Analysis of a High-Density Air-Liquid Hybrid Cooled Data Center

Abstract Environmental impacts of ever-growing computational requirements have raised worldwide concerns and significant efforts have been dedicated to reducing power consumption, water usage, and eventually the carbon footprint. Cloud architecture and services have undergone substantial development to become more sustainable and reliable with implementing advanced cooling solutions. In this research, a bottom-up approach has been presented to investigate the energy optimization opportunities of an air-liquid hybrid cooling method compared to the pure air cooling for a 1.7 MW data center. A gradual transition from 100% air cooling to 25%–75% air and liquid cooling has been studied to capture the changes in IT, fan, facility, and the total data center power consumption. Various system design optimizations such as supply air temperature (SAT), facility chiller water temperature, economization and secondary fluid temperature are embedded in this work to highlight the importance of proper setpoint conditions on both primary and secondary sides. Computational fluid dynamics (CFD) and flow network modeling (FNM) are utilized to precisely assess the performance of air and liquid cooling by evaluating the required flow rate, pressure drop, and critical case temperature of computing components as well as temperature change of cooling medium. Energy consumption of the selected cooling equipment is measured based on the BIN data for CRAH and CDU’s performance models. Power usage effectiveness (PUE) measured and compared with Total Usage Effectiveness (TUE) which appears to be a more suitable metric to weigh a data center’s design efficiency by not limiting the fan power to the IT boundary. For the most optimized case, we obtained up to 27% lower consumption in the facility power and 15.5% lower usage in the whole data center site. Increasing the percentage of liquid cooling contribution significantly diminishes the power intake which addresses concerns about natural resources limit as one of the most critical requirements of a sustainable design.
Vision based Abnormal Action Detection of Children in Wards

In clinical care, hospitalized children are prone to get secondary injury such as falling from the bed. Avoiding secondary injury to patients has become the core indicator of medical care quality evaluation. Falls are the most common and most dangerous behavior of children in hospital. And vision based abnormal action detection of children in wards is very challenging because of the unnoticeable appearance and the complex ward environment. Previous approaches achieve anomaly action detection in urban and industry by the skeleton of human. However, they cannot effectively detect and alarm abnormal actions of hospitalized children in the ward, because their model or Kinect cameras cannot obtain the accurate skeleton of the child in real time. In this paper, we propose an intelligent video surveillance system aimed at detecting and alarming abnormal actions of children in wards. Experimental results demonstrate that our system achieve 85% accuracy in detecting and alarming abnormal actions of children in wards, and our system can process information from two beds simultaneously with a speed of 12ms per frame. The daily video data of children in the ward is also saved locally, which is exceedingly useful for augmenting risk action datasets of hospitalized children. In the future, we will improve our proposed system by obtaining state information of children, thereby reducing the false positive rate of the system.
PipeDevice

Containers are prevalently adopted due to the deployment and performance advantages over virtual machines. For many containerized data-intensive applications, however, bulky data transfers may pose performance issues. In particular, communication across co-located containers on the same host incurs large overheads in memory copy and the kernel's TCP stack. Existing solutions such as shared-memory networking and RDMA have their own limitations, including insufficient memory isolation and limited scalability.
Comparative Analysis on Various CSS and JavaScript Frameworks

Web development is a huge part of the current world, affecting all parts of the world and human interaction. Learning about frameworks used in web development allows for increased development efficiency and increased website performance and functionality, based on knowing what technologies to apply when and where. This paper will examine various web framework technologies, specifically in the realm of CSS and JavaScript, and various frameworks that make use of them such as AJAX, Node JS, Bootstrap, and AngularJS, among others. This paper will provide analysis and explanation of these technologies’ designs, useful features, and potential shortcomings, and recommend which technology to use depending on various situations and web development environments.
An Attractive Proposal Based on Big Data for Sentiment Analysis Using Artificial Intelligence

Global data traffic is now growing at a pace that has not been seen in the previous decade. Different organizations extract valuable information after analyzing it. Among these analyses, we find the sentiment analysis to measure the level of satisfaction. Sentiment analysis is a branch of research that examines people’s written thoughts, sentiments, assessments, attitudes, and emotions using natural language processing and text analysis to find, measure, and study emotions in textual data. This study looks at how sentiment analysis is done and stresses how important it is to deal with natural language processing problems. The contest is centered on big data and artificial intelligence. We plan to implement the results of our study on the Hadoop platform. This new proposal considers the polarity result to modify the dataset using sentiment synthesis and change or remove the negative polarity.
Efficient flow equations for dissipative systems

Open quantum systems provide an essential theoretical basis for the development of novel quantum technologies, since any real quantum system inevitably interacts with its environment. Lindblad master equations capture the effect of Markovian environments. Closed quantum systems can be treated using flow equations with the particle conserving generator. We generalize this generator to non-Hermitian matrices and open quantum systems governed by Lindbladians, comparing our results with recently proposed generators by Rosso et al. (arXiv:2007.12044). In comparison, we find that our advocated generator provides an efficient flow with good accuracy in spite of truncations.
Motion Planning in the Area of Robotics and Automation

Motion Planning is computational problem of geometry to find continuous and optimal path from source to destination in multidimensional environment. Today’s automation world for industry 4.0 works on multiple technologies where robotics is core part of industry 4.0. To achieve optimal solution with robotics and automation motion planning is crucial area of research. This paper proposes study about motion planning sampling-based algorithm and latest research and development of new variant of probabilistic roadmap algorithm in which researcher achieve optimal solution and reduce time complexity. Main logic behind PRM algorithm is learning phase and query phase. In learning phase, construction of basic road map take place and in query phase, different techniques are used to reach destination by optimal path for different environment.
An immediate-return reinforcement learning for the atypical Markov decision processes

The atypical Markov decision processes (MDPs) are decision-making for maximizing the immediate returns in only one state transition. Many complex dynamic problems can be regarded as the atypical MDPs, e.g., football trajectory control, approximations of the compound Poincaré maps, and parameter identification. However, existing deep reinforcement learning (RL) algorithms are designed to maximize long-term returns, causing a waste of computing resources when applied in the atypical MDPs. These existing algorithms are also limited by the estimation error of the value function, leading to a poor policy. To solve such limitations, this paper proposes an immediate-return algorithm for the atypical MDPs with continuous action space by designing an unbiased and low variance target Q-value and a simplified network framework. Then, two examples of atypical MDPs considering the uncertainty are presented to illustrate the performance of the proposed algorithm, i.e., passing the football to a moving player and chipping the football over the human wall. Compared with the existing deep RL algorithms, such as deep deterministic policy gradient and proximal policy optimization, the proposed algorithm shows significant advantages in learning efficiency, the effective rate of control, and computing resource usage.
Stacked encoder–decoder transformer with boundary smoothing for action segmentation

In this work, a new stacked encoder–decoder transformer (SEDT) model is proposed for action segmentation. SEDT is composed of a series of encoder–decoder modules, each of which consists of an encoder with self-attention layers and a decoder with cross-attention layers. By adding an encoder with self-attention before every decoder, it preserves local information along with global information. The proposed encoder–decoder pair also prevents the accumulation of errors that occur when features are propagated through decoders. Moreover, the approach performs boundary smoothing in order to handle ambiguous action boundaries. Experimental results for two popular benchmark datasets, “GTEA” and “50 Salads”, show that the proposed model is more effective in performance than existing temporal convolutional network based models and the attention-based model, ASFormer.
Autonomous Robot Project Based on the Robot Operating System Platform

This article presents the concept of an autonomous mobile robot running on the ROS system and using advanced algorithms for 2D map generation and autonomous navigation. The authors focused on presenting the hardware platform, the Linux-based software and the Robot Operating System (ROS) platform. This article also introduces an algorithm that provides the generation of a 2D map of the surroundings, autonomous robot driving and remote control of the device. Measurements of the temperature of the computer helped in the decision on which cooling system to use.
A new method for hosting and sharing MATLAB Web App

Currently, using MATLAB Web App Server to deploy MATLAB Web applications for hosting and sharing interactive Web applications involves the following problems: slow loading of applications, incompatibility with some browser versions, and various shortcomings of deployment tools, such as poor scalability and difficulty in optimizing black boxes. These problems adversely affect the user experience. To address this situation, we propose the use of front-end technology to design application layouts and build an application hosting platform with front-end and back-end separation using Nginx and Python. This method is shown to be simple and efficient, and it can successfully overcome the aforementioned problems while providing new ideas for hosting and sharing.
The Limits of Learning and Planning: Minimal Sufficient Information Transition Systems

In this paper, we view a policy or plan as a transition system over a space of information states that reflect a robot’s or other observer’s perspective based on limited sensing, memory, computation, and actuation. Regardless of whether policies are obtained by learning algorithms, planning algorithms, or human insight, we want to know the limits of feasibility for given robot hardware and tasks. Toward the quest to find the best policies, we establish in a general setting that minimal information transition systems (ITSs) exist up to reasonable equivalence assumptions, and are unique under some general conditions. We then apply the theory to generate new insights into several problems, including optimal sensor fusion/filtering, solving basic planning tasks, and finding minimal representations for feasible policies.
Ubimus Strategies for Colocated and Remote Educational Activities

The present chapter offers a review of current concepts of ubiquitous music (ubimus) – based on ecological cognition and semantics-oriented strategies – as they apply to educational artistic practices. Two case studies are presented and analyzed. The first one is Harmonic Walk, a large-scale system that tracks the users’ movements to enable harmonic-training activities. Preestablished landmarks serve to encourage the participants to move within the space while following harmonic changes. This metaphor has also been deployed as a web application (Harmonic Touch). Preliminary results are presented and discussed. The second study examines synchronous, remote live-patching sessions based on the usage of the graphical programming environment “Kiwi.” An intercontinental live-patching experience engaged two groups of music-programming students located in Brazil and France. The results indicate that this interaction modality facilitates the creative exchanges among the stakeholders. These two ubimus-based educational experiences are discussed in light of the current demands for distance-learning support and remote socialization.
Socio-conversational systems: Three challenges at the crossroads of fields

Socio-conversational systems are dialogue systems, including what are sometimes referred to as chatbots, vocal assistants, social robots, and embodied conversational agents, that are capable of interacting with humans in a way that treats both the specifically social nature of the interaction and the content of a task. The aim of this paper is twofold: 1) to uncover some places where the compartmentalized nature of research conducted around socio-conversational systems creates problems for the field as a whole, and 2) to propose a way to overcome this compartmentalization and thus strengthen the capabilities of socio-conversational systems by defining common challenges. Specifically, we examine research carried out by the signal processing, natural language processing and dialogue, machine/deep learning, social/affective computing and social sciences communities. We focus on three major challenges for the development of effective socio-conversational systems, and describe ways to tackle them.
A Blockchain Approach to Improving Digital Linked Management Information Systems (MIS)

In the era of intelligent automation (industry 5.0), every Management Information System (MIS) espouses with disruptive technologies such as deep learning, big data and artificial intelligence and will be able to do real time data manipulation that raises a new range of novelties, newer business processes and models and larger business perfections. MIS are commonly managed by particular organizations which exhibit the central control and participants are limited to the organization itself whereas blockchain systems are managed in a distributed environment which exhibits public control and anyone can join and partake. Conversely, the amalgamation of MIS with blockchain will enjoy the shield of blockchain Trinity (Decentralization, Trust & Immutability). Traditional MIS are the participatory social web kind we are currently using, whereas the proposed digital linked Management Information System based on blockchain incorporates its merits. This chapter comes out with the framework of integrating traditional MIS web 2.0 applications with web 3.0 digital linked advanced metadata applications. Furthermore, the work consists of upgraded PINSI algorithm for a transaction process and a new smart contract was implemented to trade-off. The new smart contracts diminish the amount of data and improves query efficiency. Finally, an information query system has been added as a new feature in the proposed system and after being examined the middling time for every query is 30.210 milliseconds in the blockchain system which has greater performance and improved query efficiency.
Artificial Bee Colony with Crossover Operations for Discrete Problems

The Artificial Bee Colony (ABC) is an algorithm designed to solve continuous problems. ABC has been proven to be more effective than other biological-inspired algorithms. However, it is needed to modify its functionality in order to solve a discrete problem. In this work, a natural modification to the original ABC is made to make it able to solve discrete problems. Six neighborhood operators are proposed to simulate the original behavior of ABC. Moreover, several Traveling Salesman Problem Library (TSPLIB) problems were used to examine the proposed method. The results of the proposed method are promising.
Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification

In daily life, when taking photos of scenes containing glass, the images of the dominant transmission layer and the weak reflection layer are often blended, which are difficult to be uncoupled. Meanwhile, because the reflection layer contains sufficient important information about the surrounding scene and the photographer, the problem of recovering the weak reflection layer from the mixture image is of importance in surveillance investigations. However, most of the current studies mainly focus on extracting the transmission layer while often ignoring the merit of the reflection layer. To fill that gap, in this paper, we propose a network framework that aims to accomplish two tasks: (1) for general scenes, we attempt to recover reflection layer images that are as close as possible to the ground truth ones, and (2) for scenes containing portraits, we recover the basic contour information of the reflection layer while improving the defects of dim portraits in the reflection layer. Through analyzing the performance exhibited by different levels of feature maps, we present the first transmission removal network based on an image-to-image translation architecture incorporating residual structures. The quality of generated reflection layer images is improved via tailored content and style constraints. We also use the patch generative adversarial network to increase the discriminator’s ability to perceive the reflection components in the generated images. Meanwhile, the related information such as edge and color distribution of transmission layer in the mixture image is used to assist the overall reflection layer recovery. In the large-scale experiments, our proposed model outperforms reflection removal-based SOTAs by more than 5.356 dB in PSNR, 0.116 in SSIM, and 0.057 in LPIPS.
A Comparison of Policies for International Education between Taiwan and South Korea

In the face of globalization, Taiwan and South Korea promote international education in primary and secondary schools. The authors conduct a comparative study of international education and its policies in Taiwan and South Korea based on relevant theories and literature. This paper is based on the part of the findings from the comparative study. The purpose of the comparative study was to examine the similarities and differences in the international education policies of Taiwan and South Korea under different social and cultural environments. For the above purpose, methods used include document analysis and comparative research. Concluding remarks are made based on the results of the comparison of policies for international education between Taiwan and Korea.
Deepfake Caricatures: Human-guided Motion Magnification Improves Deepfake Detection by Humans and Machines

Deepfake videos pose a threat to the integrity of our digital society by fueling the spread of misinformation. It is essential to develop techniques that increase both human and machine detection of doctored videos. Here, we introduce a new deepfake detection framework that uses human supervision to estimate locations of video artifacts, and increases their detectability. Specifically, we introduce a semi-supervised artifact attention module, which combines human annotations of distortions in deepfakes with internally-learned salience maps, to create artifact attention maps that highlight distorted regions of videos. This module makes two contributions. First, it improves model detection of deepfakes: a deepfake detector that leverages the artifact attention module in its self-attention layers achieves 98.21% detection accuracy, averaged over four different datasets, outperforming five baseline comparisons. Additionally, our detector generalizes better: when trained on one dataset and tested on another, we observe a 2% to 13% increase in generalization performance compared to competing approaches. Second, it allows us to generate novel ”Deepfake Caricatures”, video transformations where subtle unnatural movements are exacerbated to improve human detection. Videos are passed through a dedicated distortion module that detects and amplifies artifacts by taking the difference between consecutive frames and weighting them by the artifact attention maps. This procedure magnifies artificial distortions while leaving real videos untouched. In a behavioral experiment (N= 41, 2-alternative forced choice over 200 real and 200 deepfake videos), human participants were able to correctly detect 71% of deepfakes to which the module had been applied, compared to 42% without artifact magnification. Overall, our approach blends human and artificial supervision to yield high deepfake detection, and crucially, grants humans the ability to make their own judgment about the trustworthiness of visual media.