Reviewerly/exHarmony huggingface dataset:
A Comprehensive Performance Evaluation of Different Kubernetes CNI Plugins for Edge-based and Containerized Publish/Subscribe Applications

The growing number of data- and latency-sensitive Internet of Things (IoT) applications is posing significant challenges for the edge and cloud deployment of publish/subscribe services, which are required by these applications. Two independently developed technologies show promise in addressing these challenges. First, Kubernetes (K8s) provides a de-facto standard for container orchestration that can manage and scale distributed applications in the cloud. Second, OMG's Data Distribution Service (DDS), a standardized real-time, data-centric and peer-to-peer publish/subscribe middleware, is being used in thousands of critical systems around the world. However, the feasibility of running DDS applications within K8s for latency-sensitive edge computing, and specifically the performance overhead of K8s' network virtualization on DDS applications is not yet well-understood. To address this, in this paper we evaluate the performance overhead of several container network interface (CNI) plugins including Flannel, WeaveNet and Kube-Router installed on a hybrid (ARM+AMD) edge/cloud K8s cluster. The paper reports results from a comprehensive set of experiments conducted to measure and analyze the performance (throughput, latency, and CPU/memory usage) of containerized DDS applications from the perspectives of virtualization overhead, reliability (DDS Reliable and BestEffort QoS), transport mechanisms (UDP unicast and multicast), and security. The insights derived from this study provide concrete guidance to developers of DDS-based applications in choosing the right virtual network plugin and configurations when hosting their real-time IoT applications in real-world containerized environments.
Simulated Annealing Strategy in Chaotic Neural Network with Legendre Function

As to the monotonous activation funcation of chaotic neural network, a new transient chaotic neuron model is constructed by combining multiple Legendre polynomials and sigmoid functions as a new non-monotonic excitation function, and the dynamic characteristics of chaotic neurons are analyzed. A new transient chaotic neuron network model is constructed by using this model. This model improves the ability of the network to fall into local minima by using the idea of piecewise simulated annealing. The effectiveness of this model has been verified by the application of multi-user detectors in nonlinear function optimization, traveling salesman problem (TSP) and direct sequence-code division multiple access (DS-CDMA) on multi-user detector.
Noise-residue learning convolutional network model for magnetic resonance image enhancement

Abstract Magnetic Resonance Image (MRI) is an important medical image acquisition technique used to acquire high contrast images of human body anatomical structures and soft tissue organs. MRI system does not use any harmful radioactive ionized material like x-rays and computerized tomography (CT) imaging techniques. High-resolution MRI is desirable in many clinical applications such as tumor segmentation, image registration, edges &amp; boundary detection, and image classification. During MRI acquisition, many practical constraints limit the MRI quality by introducing random Gaussian noise and some other artifacts by the thermal energy of the patient body, random scanner voltage fluctuations, body motion artifacts, electronics circuits impulse noise, etc. High-resolution MRI can be acquired by increasing scan time, but considering patient comfort, it is not preferred in practice. Hence, postacquisition image processing techniques are used to filter noise contents and enhance the MRI quality to make it fit for further image analysis tasks. The main motive of MRI enhancement is to reconstruct a high-quality MRI while improving and retaining its important features. The new deep learning image denoising and artifacts removal methods have shown tremendous potential for high-quality image reconstruction from noise degraded MRI while preserving useful image information. This paper presents a noise-residue learning convolution neural network (CNN) model to denoise and enhance the quality of noise-corrupted low-resolution MR images. The proposed technique shows better performance in comparison with other conventional MRI enhancement methods. The reconstructed image quality is evaluated by the peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics by optimizing information loss in reconstructed MRI measured in mean squared error (MSE) metric.
Explainable Collaborative Filtering Recommendations Enriched with Contextual Information

Today, the most important requirement of intelligent systems is to be able to explain their decisions to the end-user. Fulfilling this requirement is the goal of explainable AI (XAI) that proposes to produce explainable models that enable end-users to understand and trust the models. This research addresses the explainability of the recommendations. This paper presents an explainable recommender system for point of interest recommendations taking into account the context of the user. In our experiments we have used the STS (South Tyrol Suggests) dataset. The following major steps are part of our methodology: i) presenting the dataset, ii) using Restricted Boltzmann Machine based collaborative filtering recommendations, iii) using contextual information, and iv) extracting and presenting explanations for recommendations based on contextual information. The novelty that we propose in explainable recommender systems is a new explainable recommendation technique, which is quantitative and qualitative, providing both the list of top-n recommendations and the explanations of the recommendations based on context. This paper also provides an overview of research on this topic.
Work With What You've Got: An Approach for Resource-Driven Adaptation

Resource-driven systems are affected by resource variability, which prevents the timely completion of important tasks. This paper presents BOND, a hyBrid resOurce-driveN aDaptation approach which addresses the issue of resource variability by (i) prioritising tasks and making resources available for tasks with higher priorities, (ii) considering alternative task executions when resources are not available, (iii) substituting resources with alternative ones, and (iv) changing tasks into similar ones. The approach supports a proactive and reactive adaptation plan. A prototype tool has been implemented as a proof of concept and used for an initial evaluation of the approach in terms of its feasibility and scalability.
Application of Reverse Engineering in Software Engineering Teaching

In the teaching of software engineering for computer related majors, there is a gap between high-level logic design and implementation, which leads to the students' unreasonable software design, and the implementation code does not follow the design scheme. Therefore, it is necessary to carry out heuristic teaching for students, starting from the programming students are most familiar with, gradually transition to software design, using reverse thinking teaching mode, so that students can master the transformation relationship between design and implementation. In the implementation of case teaching, it is necessary to restore high-level design or demand analysis through code, realize the conversion from code to document, and effectively improve the theoretical level of students; in terms of practical skills, implement task driven teaching, deepen the understanding of cases and knowledge transfer, and exercise the practical ability of students. Through the performance analysis of 2015 and 2016 students, the reverse engineering case teaching and task driven practice can significantly improve students' practical ability, stimulate students' enthusiasm for software development, and highlight students' software development ability in computer skills competition, college students' innovation training project and graduation design.
Information technology for career guidance of creatively gifted university students. Developmental activities

The article concludes the description of the standard regional infocommunication system “Student and Labor” (ST), begun by the authors in previous publications in the “Informatics and Education” journal. This system is an integral part of the regional system for identifying, developing and involving creatively gifted youth in labor activity, starting from high school age. The S&amp;T system is aimed at identifying and developing university students who are creatively gifted in the field of science and technology in the process of their gradual involvement in real labor activities in cooperation with leading enterprises in the region. The S&amp;T system is aimed at identifying and developing university students who are creatively gifted in the field of science, technology and technology in the process of their gradual involvement in real labor activity in interaction with the leading enterprises of the region. Organizational and informational issues of its creation and functioning were discussed in previous articles, this article presents tools that allows a student to most effectively plan the developmental content of his activities, taking into account individual goals and interests, available resources, as well as a wide range of opportunities provided by the surrounding socio-economic environment. The central part of the toolkit is an individually adjustable optimization mathematical model. The article provides a formalized description of this model and considers an example of its use.
G-DAN-SNR: An improved Personalized POI recommendation algorithm based on gating and attention mechanism

In the field of POI recommendation, some scholars proposed the DAN-SNR model, which constructs the deep learning network by self-attention mechanism and designs dual channels to capture users' long and short interest preferences and social influence. Based on the improvement of the DAN-SNR model, the gating mechanism is introduced in this paper based on the self-attentive mechanism before its long- and short-term interest preference features are fused, and the optimal features are filtered by weighting the feature parameters.
Research on ShadowsocksR Traffic Identification Based on DART Algorithm

Shadowsocks (SS) is a new popular anonymous communication software in recent years. The traffic generated by SS is very difficult to identify. There is also an enhanced version of SS, called ShadowsocksR(SSR), which can disguise SS traffic as traditional protocol traffic, such as HTTP traffic, TLS traffic, etc. This makes the identification of SS traffic more difficult. In reference [16], an identification method of HTTP camouflaging traffic of SS is proposed for the first time. Here, a new identification method is proposed based on dart algorithm. Compared with reference [16], this method has more types and wider range of SSR obfuscated traffic, and has better identification effect for recent SSR obfuscated traffic, with the accuracy, the recall and the precision are all above 98.5%.
Energy-Efficient Programming Languages for Mobile Applications

Mobile devices, particularly cellular phones, play a significant part in people's lives everywhere. However, people in remote rural areas lack access to power and face power outages, which make it hard for them to charge their devices. Therefore, applications running on these devices must be as energy efficient as possible. In this paper, we analyze how different programming languages affect the energy consumption of applications in mobile devices. Considering that watching videos is one of the most commonly performed and power-hungry activities on mobile devices, we compare the implementation of video operations using three different programming languages: Java, Kotlin, and React Native. We show that the choice of programming language may make an application more energy efficient. Besides, the device model, Android version, and API used all affect battery usage. We also show that not all programming languages yield battery savings when compromising video quality. We believe these results will provide an important insight for application developers on the factors to consider when developing energy-efficient applications for mobile devices.
Design Of Medi-Chain: A Blockchain and Cloud Based Health Record System

Today to give better health facilities, governments require input data to analyze the needs and requirements of their citizens and how they can be benefited. If this data is not analyzed properly, it creates a burden on health facilities as the government and health facilities cannot expand properly. As the system starts collecting large scale of Electronic Health Records (EHR) they are prone to attacks and the data can be misused So, to create highly abled EHR facilities we are thinking of using the latest secure technique called Blockchain in our project to secure the EHR data that is generated by the communication between various entities in health infrastructure. We will use the latest cloud infrastructure available today to build a robust cloud and block-chain combination EHR retrieval system. The cloud we are considering is Google cloud which is secured and very robust. Our system will use multiple Google clouds to achieve distributed architecture to store blocks which will make it more secure and effective to use. Our EHR infrastructure will have various entities like doctors, patients, medical staff, admin, etc. working together with a combination of mobile computing and standalone application together. We will apply the Smart Contracts system to communicate between these health care entities. Thus, in our paper, we will cover all the possible scenarios in a secured EHR system to design a Medi-Chain framework using blockchain, cryptography, and cloud computing together.
A Secure Blockchain-Powered Land Record Modernization System for Documenting Land User's Rights

Proving ownership of the land should preferably be done with a legal document that proves it decisively. Many authorities retain various documents, any of which could be used to assert a claim on the land. To prevent document falsification, the land administration mechanism ought to be robust, accessible at all times, and quick to accomplish exercises. But, any such solutions are prone to a slew of issues, including data accuracy, security, and dispute resolution. Usage of blockchain technology in land administration is considerably advanced to solve issues that plague current LAS. With the adoption of blockchain, the problem of cooperation among a variety of land records is articulated. The proposed model has integrated units to digitally sign the land assets to store securely into the blockchain using cryptography algorithms after which land assets are verified. The proposed approach eliminates deception, improves administration. The results show that the time complexity for registering, signing, and verifying land facts to establish a system using blockchain is relatively secure.
Convergence of Information and Social Technologies in the Digital Age

The article discusses the specifics of current convergent development of information (IT) and social technologies (ST) and their socialization as well as defines the characteristic features of ST implemented on a digital platform. The authors analyze the reasons for the increasing demand for ST and explicate their role in post-industrial society. The authors believe that the boom of ST over the recent decades is associated with the need to transform “vertical” management practices toward distributed and maximally individualized usage. The network communication format in modern society, established due to IT development, is correlated with “soft” social design technologies, which provide not just subordination and discipline, but highly motivated work, initiative, emotional involvement, and creativity. At the same time, personal boundaries, which have become the most important component of psychological well-being for a modern person, are quite permeable to the combined impact of ST and IT. The “capitalization of human capital” in the information age has obtained quite a literal meaning because individual knowledge, skills, and initiative become the main resource and competitive base in digital economy. Using the example of Agile, one of the leading practices in modern IT industry, the authors analyzed the specifics of work organization of compact creative groups. The article reveals the ethical aspects of using convergent information and social technologies. The authors conclude that the task of countering destructive influences from ST on the information platform is relevant and even urgent. However, the society has yet to formulate humanistic guidelines for constructive socio-technological design practices.
Improved dark channel prior single image defogging

The foggy sea image has low contrast, color distortion, blurred edges and other problems. Sea fog brings great difficulties to ocean observation. In response to these problems, this paper proposes an improved single sea fog defogging method based on domain decomposition and dark and bright channel priors. First, it is proposed to segment the sky area through gradient information and edge tracking, and combine the dark channel of the sky area to determine the atmospheric light value. Secondly, it is proposed to optimize the transmission map based on the bright channel of the sky area and the dark channel of the non-sky area, and use the guided filtering to optimize the edge. Finally, the fog-free image is obtained by combining the atmospheric scattering model. Evaluate the performance of our algorithm from a comprehensive test. Experiments on images under different sea fog scenes show that the algorithm can not only effectively overcome the sky color distortion and halo phenomenon, but also restore the details of non-sky areas.
MM-FPN: Multi-path and Multi-scale Feature Pyramid Network for Object Detection

Small and multi-scale objects are always dilemmas for object detection. However, small objects may disappear and cannot be detected because it is arduous to differentiate information from a small part of the original image. To alleviate the issue, an image pyramid is utilized to build a feature pyramid to detect across a range of scales. Instead, we combine image pyramid and feature pyramid with a Contextually Enhanced Module (CEM) to extract contextual information. Furthermore, we propose Unidirectional Bottom-up Connections (UBC) to extract more distinct features. A novel Multi-path and Multi-scale Feature Pyramid Network (MM-FPN) is proposed to improve the performance of both small-sized and large-sized objects. Experiments and ablation studies are performed on PASCAL VOC, which surpass most of the existing competitive single-stage and two-stage methods.
Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion

One of the most difficult jobs in remote sensing is dealing with traffic bottlenecks at airports. This fact has been confirmed by several studies attempting to resolve this issue. Among a wide range of approaches employed, the most successful methods have been based on airplane object recognition using satellite images as datasets for deep learning models. Airplane object identification is not a viable method for resolving traffic congestion. There are several types of airplanes at the airport, each with its own set of requirements and specifications. Utilising satellite pictures will require the use of complex equipment, which is a financial burden. In this work, a universal, low-cost and efficient solution for airport traffic congestion is proposed. Drone-captured aerial pictures are used to train and assess a Mask Region Convolution Neural Network model. This model can detect the presence of any aircraft in a photograph and pinpoint its location. It also includes mask estimations to properly identify each detected airplane type based on the estimated surface area and cabin length, which are crucial variables in distinguishing planes. The study is conducted using Microsoft's Common Object in Context (COCO) metrics, average accuracies and a confusion matrix.
Constraint-based Whole-Body-Control of Mobile Manipulators in Human-Centered Environments

In this work, we describe a ROS-based method for whole-body control (WBC) of mobile manipulators in the context of safe human-robot interaction. Our method is based on cyclic quadratic programming (QP) with a set of simultaneously active tasks that define constraints. The importance of different tasks is captured through priorities and weights. Robot behavior can be changed at run-time by re-configuring the active tasks through ROS interfaces. We evaluate the suitability of our method for safe human-robot collaboration in a Gazebo simulation. We show that our method lets the mobile manipulator perform evasive motions while staying consistent with other tasks if possible. At the same time, self-collisions and static obstacles are avoided. If a given safety threshold is crossed, the robot comes to a safe stop. Operation continues once the distance is high enough again.
Application of Generative Adversarial Networks and Latent Space Exploration in Music Visualisation

Generative Adversarial Networks (GANs) are a very recent chapter in generative models and music visualisation. This paper explores the application of GANs in music visualisation via latent space exploration using latent directions. The output music visualisation tool is then evaluated via a user study to verify its effectiveness.
SensorMov, mobile devices as/for tangible user interfaces

This short paper presents a preliminary implementation of SensorMov, a native library for Android mobile devices, which provides development tools for creating tangible user interfaces connected to smartphones. SensorMov brings together a set of functionalities aimed at extending various mobile sensors through physical objects. We seek to diversify mobile phone use in interaction processes, inspiring mobile technologies’ heterogeneity while reducing development times and complexities. The library understands tangibles as the smartphone itself or passive physical objects that do not require power supply through electrical energy, such as magnets. The paper describes the library, a preliminary application case, and future work.
Malaria Cell Image Classification Using Convolutional Neural Networks (CNNs)

This study provides an insight into malaria as a disease. Malaria is a disease caused due to plasmodium parasite. It requires a type of mosquito as its host. Hence, the bite of the mosquito leads to malaria. The impact it carries on the health of people around the world is extremely large and cannot be curtailed or controlled without quick and efficient diagnostics and treatment. Subsequent topics dwell on the constraints of detection of the malaria parasite. These constraints may include problems with the feasibility of certain types of tests, or not having access to a diagnostics center or problems with transportation of necessary infrastructure. We also must understand that traditional prognosis methods are very tedious and hence always have a chance for human error or oversight leading to devastating consequences. The ease or simplification of diagnosis of malaria upon the use of machine learning and deep learning is undeniable; hence, in our project, we aim to create a model, using CNN, which using feature extraction, can predict whether a sample image of a Red Blood Cell provided to the model is parasitized or unhealthy. This model has a primary goal of detecting malaria in Red Blood Cells from blood smears with the least number of losses. This allows for the most minimal number of malaria-infected cell to be mistakenly passed off as healthy cells. There will be a further comparison between the custom CNN model, VGG19 model with no fine-tuning, VGG19 model fine-tuned, and a ResNet50 model. All of these are models which have been pre-trained on a vast number of images previously with a set of weights termed as Imagenet.