Reviewerly/exHarmony huggingface dataset:
A Systematic Mapping Study: How Can UX Design Be Adapted to Improve the Design of Meaningful Gamified Solutions?

A meaningful gamified solution design is not limited to the use of points and rewards, but rather focuses on the intrinsic motivations for a more engaging user experience. Furthermore, the implementation of this concept is subject to difficulties given the lack of unified, effective, and efficient tools that are integrated into a process that covers the life cycle of a product or a service. Indeed, the existing gamification frameworks are limited to guidelines and recommendations that set up game elements for specific needs and contexts. However, the methods and the best practices that can lead to a meaningful gamified solution still cannot be concluded. Moreover, the user experience is currently a fundamental concept that has changed the focus on the processes of a generation of products and services; putting the user at the center of the design process. The main objective of this paper is to study the relevance of adapting or adopting UX design elements to improve the design of gamified products. To this end, a systematic mapping of the literature was carried out with a selection of 77 articles published between 2010 and March 2021. The results obtained enabled the identification of the adaptation or adoption proposals related to the following three levels: (1) UX design process and framework, (2) UX design process phases, in particular the phase of understanding the user need and usability testing, and (3) user’s research tools such as the persona, the journey map, and the questionnaire. The adaptation of these design elements was not systematic and depended on the context of the study.
Regularization paths of <i>L</i><sub>1</sub>‐penalized ROC Curve‐Optimizing Support Vector Machines

The receiver operator characteristic (ROC) curve is one of the most popular tools to evaluate the performance of binary classifiers in a variety of applications. Rakotomamonjy (2004) proposed the ROC‐SVM that directly optimizes the area under the ROC curve instead of the prediction accuracy. In this article, we study the L 1 ‐penalized ROC‐SVM that directly optimizes the ROC curve. We first show that the L 1 ‐penalized ROC‐SVM has piecewise linear regularization paths and then develop an efficient algorithm to compute the entire paths, which greatly facilitates its tuning procedure.
A systematic review of automated cooking machines and foodservice robots

A colossal volume of automated applications is being created every day in this epoch-making millennium. Robots are powerful, critical for driving businesses and shaping major industries today. Just like one is the sculptor of his persona, Robotics is the essence of new-age technology. This paper carefully peruses and examines the literature on automated cooking machines and cooking service robots. Classifications are made fundamentally on their purpose, the technology used, and the cuisine they cater to. Present-day affirmative outcomes, future implications, and limitations are discussed further in a coherent manner. Further, A model is designed and simulated that takes care of flagged limitations in cooking applications to cook specific dishes.
A Conceptual Foundation for Blockchain Development: The Contribution of Ibn Khaldun

Blockchain is a game-changing technology that has the ability to solve plenty of real-world issues in the digital age. Blockchain is a subject of huge interest in many industries and academia in terms of discovering technology and classifying challenges and innovative practical application for the industry. This study addresses the challenges that are of main concern in designing a Blockchain platform. In this regard, the problems such as privacy, regulation, security, lack of adequate skill sets, energy consumption, inefficient technology design, the criminal connection, scalability, energy consumption, and public view are discovered to be important. Due to such challenges, the blockchain technologies have emitted a negative impression due to its incapability to be successfully applied while, at the same time, its benefits could not be fully gained by its investors. The objective of this study, hence, is to assess the blockchain advantages and growth in light of the eight foundations for economic development as advocated by Ibn Khaldun. Expending Ibn Khaldun’s philosophy, each challenge is deliberated and investigated to find the answers and solutions for addressing and overcoming the afore-mentioned challenges.
FedMDR: Federated Model Distillation with Robust Aggregation

This paper presents FedMDR, a federated model distillation framework with a novel, robust aggregation mechanism that exploits transfer learning and knowledge distillation. FedMDR adopts a weighted geometric-median-based aggregation with trimmed prediction accuracy on the server-side, which orchestrates communication-efficient training on both heterogeneous model architectures and non-i.i.d. data. The aggregation provides resilience to sharp accuracy drop of corrupted models. We also extend FedMDR to support differential privacy by adding Gaussian noise to the aggregated consensus. Results show that FedMDR achieves significant robustness gain and satisfactory accuracy, and outperforms the existing techniques.
Visual Vehicle Tracking via Deep Learning and Particle Filter

Visual vehicle tracking is one of the most challenging research topics in computer vision. In this paper, we propose a novel and efficient approach based on the particle filter technique and deep learning for multiple vehicle tracking, where the main focus is to associate vehicles efficiently for online and real-time applications. Experimental results illustrate the effectiveness of the system we are proposing.
Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.
Analyzing the Attacks on Blockchain Technologies

Blockchain technology has gained significant interest because of a wide variety of possible uses. It was first developed as a Bitcoin cryptocurrency but has since been used in many other business and non-business applications. There are various innovations available from Bitcoin to financial innovations, risk management, IoT, and public and social services. The blockchain infrastructure, instead of most current systems based on center-based architectures, combines peer-to-peer networks and distributed platforms that use blockchain registers to store transactions. But, like the Internet in 1990, the blockchain is regarded as still growing. In the future, it can alter so many aspects of technology. However, there are many setbacks, mainly due to the defense area, as a new undeveloped sector. So, it becomes important to analyze their success in multiple use cases and scenarios as more and more diverse blockchain technologies have appeared. We address the security concerns of blockchain technology in this survey. In this paper, with a focus on shared blockchains, we systematically analyze the attack surface of blockchain technologies.
Implementation of post processing hardware for real-time object detection in CNN acceleration system

In neural network based real-time object detection and recognition systems, the scaling of layer processing acceleration hardware can be one way to achieve realtime goal. However, this approach places burden on hardware resources, and consequently requires bulky hardware on the system. In this paper, we propose a post processing hardware that can improve the operation speed of an application while using less hardware resources. The proposed hardware implements the most time-consuming post processing part in our previous study. The proposed hardware achieves about 93.8% better than the conventional method in Xilinx Zynq Ultrascale + MPSoC ZCU106 processing system (PS).
Making the invisible visible: New perspectives on the intersection of human–environment interactions of clinical teams in intensive care

Understanding human behaviour is essential to the successful adoption of new technologies, and for the promotion of safer care. This requires capturing the detail of clinical workflows to inform the design of new human-technology interactions. We are interested particularly in the possibilities for touchless technologies that can decipher human speech, gesture and motion and allow for interactions that are free of contact. Here, we employ a new approach by installing a single 360° camera into a clinical environment to analyse touch patterns and human-environment interactions across a clinical team to recommend design considerations for new technologies with the potential to reduce avoidable touch.
Psychological Effects in Flipped Classroom Teaching of College English Based on Big Data

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Connecting Sequent Calculi with Lorenzen-Style Dialogue Games

Abstract Lorenzen has introduced his dialogical approach to the foundations of logic in the late 1950s to justify intuitionistic logic with respect to first principles about constructive reasoning. In the decades that have passed since, Lorenzen-style dialogue games turned out to be an inspiration for a more pluralistic approach to logical reasoning that covers a wide array of nonclassical logics. In particular, the close connection between (single-sided) sequent calculi and dialogue games is an invitation to look at substructural logics from a dialogical point of view. Focusing on intuitionistic linear logic, we illustrate that intuitions about resource-conscious reasoning are well served by translating sequent calculi into Lorenzen-style dialogue games. We suggest that these dialogue games may be understood as games of information extraction, where a sequent corresponds to the claim that a certain information package can be systematically extracted from a given bundle of such packages of logically structured information. As we will indicate, this opens the field for exploring new logical connectives arising by consideration of further forms of storing and structuring information.
Graph Representation Learning

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years.
Can Color Visual Cryptography Be Truly Random?

The concept of color visual cryptography with truly random shares is proposed. The randomness was confirmed with the battery of 15 NIST randomness tests. The two shares are generated as two equal purely random sequence of R, G, B and black pixels. Coding is performed by swapping the pixels in the second share in small subregions called segments. The criterion for swapping is that after superimposing the shares, some pixels remain color and the remaining ones are covered so that they appear as black. The color pixels form a color image in the way of dithering in the RGB space. The secret is in principle decrypted with the unarmed human eye; however, it can be easily further reconstructed numerically to attain the quality very close to that of the dithered source image. It was observed that the coding can be optimized by varying the dimensions of the segments and the palettes in the dithering.
Improvisation of RSA Algorithm in Respect to Time and Security with the Proposed (AEA) Algorithm

Abstract The security of digital information has become increasingly vital in the digital world, which is currently evolving and changing at such a quick speed. With the rapid growth of hidden contact and communication around the world, the need for communication security is becoming increasingly important. The main motive of cloud service providers is to collect the information and details of the material uploaded on their cloud servers. One of the major concern is security of the contents uploaded on the cloud servers. Although, they challenge about the security provided by them to the individuals but they are the owner of servers and the information on it. The same has been observed when one of our server crashed and we consulted about the incident with the customer care department of our CSP. We use to back up our university servers backup on cloud for which we pay some amount as decided by our management as well as CSP. The aim of this research is that the cloud owners cannot have access to data of the users. I proposed a new robust encryption technique in this paper to improve the security of the key generating algorithm. To achieve this, an algorithm has been implemented which performs much better than renowned algorithm i.e. RSA algorithm. This algorithm achieves better response time than RSA algorithm which is described and proved in this paper. This paper uses ASCII and EBCDIC codes while encryption and decryption.
Lorenzen Between Gentzen and Schütte

Abstract We discuss Lorenzen’s consistency proof for ramified type theory without reducibility, published in 1951, in its historical context and highlight Lorenzen’s contribution to the development of modern proof theory, notably by the introduction of the $$\omega $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ω</mml:mi> </mml:math> -rule.
Real-Time 3d Face Reconstruction From Single Image Using End-To-End Cnn Regression

This paper presents a learning-based method for detailed 3D face reconstruction from a single unconstrained image. The core of our method is an end-to-end multi-task network architecture. The purpose of the proposed network is to predict a geometric representation of 3D face from a given facial image. Unlike most existing reconstruction methods using low-dimension morphable models, we propose a pixel-based multi-scale representation of a detailed 3D face to ensure that our reconstruction results are not limited by the expressiveness of linear models. We break the task of high-fidelity face reconstruction into three subtasks, which are face region segmentation, coarse-scale reconstruction and detail recovery. So the end-to-end network is constructed as a multitask mode, which contains three subtask networks to deal with different subtasks respectively. A backbone network with feature pyramid structure is proposed as well to provide different levels of feature maps required by the three subtask networks. We train our end-to-end network in the spirit of the recent photo-realistic data generation approach. The experimental results demonstrate that our method can work with totally unconstrained images and produce high-quality reconstruction but with less runtime compared to the state-of-the-art.
Optimized Color Correlogram based CBIR to beat the hole of Information and Interpretation

This Efficient Systems are the requirement of this digitalized word due to its numerous applications. This Digital World spurts images and raises systems' demand to deal with the image data. The content-based image retrieval system (CBIR) is one of them and also an on-demand system. This system helps the user retrieve similar information or image data based on the provided image and makes life easy to solve many daily life tasks. Government, architecture, academics, engineering, fashion, journalism, and hospitals are examples where the CBIR system efficiently works and reduces manual work requirements. With this heightened trade, the dependency on the automated systems increases, making the systems complex and more vulnerable to complexity issues. CBIR uses multiple features to extract and information and provide consistent and relevant retrieval in revert. Still, the matter of accuracy and complexity is part of it and needs attention from the researchers. This work proposes an optimized color correlogram based CBIR system that utilizes the color and spatial information to contribute efficiency and accuracy in the CBIR system. Ant lion algorithm is here used to optimize low-level and high-level semantics. This proposed system resolves the complexity issue and determines the gap of information and interpretation to provide accurate outcomes. This proposed system's performance was evaluated using the Correl dataset and hence revealed the fitness of the proposed method in terms of precision.
Multi-Camera Video Scene Graphs for Surveillance Videos Indexing and Retrieval

Modern video surveillance systems often consist of multiple cameras capturing continuous videos. However, existing systems do not fully take advantage of shared scene semantics across multiple video streams, and hence querying the captured videos to find an event or object of interest can be a time-consuming task. In this paper, we propose a compact representation of objects and their relationships in the scene under surveillance, over time, and across multiple cameras, using a combined global spatio-temporal scene graph. The same objects that appear in multiple cameras are stored only once, reducing the storage required and speeding up the query when compared to storing the scene graph from each camera individually. Our experiments show that in a 5-camera system our proposed representation can speed up querying time by a factor of 3.9 times.
Guided Facial Skin Color Correction

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.
Recommending Changes on QoE Factors with Conditional Variational AutoEncoder

Increasing complexity in management of immense number of network elements and their dynamically changing environment necessitates machine learning based recommendation models to guide human experts in setting appropriate network configurations to sustain end-user Quality of Experience (QoE). In this paper, we present and demonstrate a generative Conditional Variational AutoEncoder (CVAE)-based technique to reconstruct realistic underlying QoE factors together with improvement suggestions in a video streaming use case. Based on our experiment setting consisting of a set of what-if scenarios, our approach pinpointed the potential required changes on the QoE factors to improve the estimated video Mean Opinion Scores (MOS).
Construction and Application of a Tree Knowledge Graph

At present, tree information on the Internet is not systematic enough. In order to solve this problem, we propose a tree knowledge graph namely TreeKG. After obtaining a mass of data, We construct TreeKG based on rules and deep learning and develop some application. This paper introduce the construction and application of TreeKG in detail, providing a new idea for the research on forestry knowledge graph.
Segmentation of the Left Ventricle in Cardiac MRI Using Random Walk Techniques

As a regular tool for assessing and diagnosing cardiovascular disease (CVD), medical professionals and health care centers, are highly dependent on cardiac imaging. The purpose of dividing the cardiac images is to paint the inner and outer walls of the heart to divide all or part of the limb’s boundaries. In order to enhance cardiologist in the process of cardiac segmentation, new and accurate methods are needed to divide the selected object, which is the left ventricle (LV). Segmentation techniques aim to provide a fast segmentation process and improve the reliability of the process. In this paper, a comparative study is made on basic random walk (BRW), extended random walk with priors (ERW), and high-speed random walk (HSRW) techniques. In the presented paper, we have applied three different types of medical image segmentation techniques to many Cardiovascular Magnetic Resonance images (CMRIs) in our experimental evaluation to lead statistically significant conclusion and confirm that our results are generalized. We have used 125 sets of CMRIs generated from five groups of patients with different types of cardiovascular disease to get the precise capacity of the productivity of the introduced method. In this paper, several performance metrics are used for instance correspondence coefficient D, distance, and PSNR. In the presented paper, a short-axis 3D multilayer CMRIs database has been taken and applied on many case studies to decide the outcomes of several segmentation methods. Throughout the experiments, the performance time for three segmentation methods are also calculated and utilized in the comparison process as another important performance factor. The experimental results show that ERW technique is the furthermost accurate segmentation technique among all the approaches.