Reviewerly/exHarmony huggingface dataset:
Joint uneven channel information network with blend metric loss for person re-identification

Abstract Person re-identification, one of the most challenging tasks in the field of computer vision, aims to recognize the same person cross different cameras. The local feature information has been proved that can improve performance efficiently. Image horizontal even division and pose estimation are two popular methods to extract the local feature. However, the former may cause misalignment, the latter needs much calculation. To fill this gap and improve performance, an efficient strategy is proposed in this work. First, a joint uneven channel information network consisting of an uneven channel information extraction network and a channel information fusion network is designed. Different from the traditional image division, the former can divide images horizontally and unevenly with strong alignment based on weak pose estimation, and extract multiple channel information. The latter can joint channel information based on channel validity and generate an efficient similarity descriptor. To optimize the joint uneven channel information network efficiently, this work proposes a blend metric loss. The extra image information is utilized to dynamically adjust the penalty for the sample distance and the distance margin based on the outlier of the hardest sample to construct i-TriHard loss. Besides, softmax loss and center loss are embedded in the blend metric loss, which can guide the network to learn more discriminative features. Our method achieves 89.6% mAP and 95.9% Rank-1 on Market-1501, 79.9% mAP and 89.4% Rank-1 on DukeMTMC. The proposed method also performs excellently on occluded datasets.
Prediction of dengue infection using Machine Learning

Dengue is an vector-borne sickness introduced via the female anopheles mosquito because the provider. The world fitness agency (WHO) stated a every year price of around one hundred-four hundred million illnesses in 2019 that&#x0027;s the largest variety of dengue instances at any point introduced internationally and incited WHO to proclaim the contamination as the arena&#x0027;s fine 10 preferred well-being risks. It tends to be transformed into normal female dengue cold along with fever which will similarly develops into dengue shock syndrome. Integral treasured apparatuses that will be unequivocally apprehend dengue and its subtypes in the starting phase of infection movement need necessary for helpful very an awful lot planned regular consideration and treatment. The sort of flare-up can make destruction inside the public arena. In view of the absence of immunization and antiviral medications, an opportune expectation of the dengue flare-up is, consequently, pressing to lessening the loss. Proposed technique in time collection and direct relapse calculation is applied in our paper. The information will be surrendered to making ready and testing structure subsequent to giving the information missing records ascription cycle could be occur in 2 urban communities Iquitos and solar JUAN improvement of hotness planning approval is completed and NDVI() stage charting is likewise applied. The normalization and standardization with the mean and fashionable deviation scores can be befell so the eventual effects will be the all out variety of cases you&#x0027;re impacted by means of the dengue may be identified in the city groups by using making use of the directly relapse time series.
Improved Segmentation of Cardiac MRI Using Efficient Pre-Processing Techniques

Cardiac magnetic resonance imaging is a popular non-invasive technique used for assessing the cardiac performance. Automating the segmentation helps in increased diagnosis accuracy in considerably less time and effort. In this paper, a novel approach has been proposed to improve the automated segmentation process by increasing the accuracy of segmentation and laying focus on efficient pre-processing of the cardiac magnetic resonance (MR) image. The pre-processing module in the proposed method includes noise estimation and efficient denoising of images using discrete total variation-based non-local means method. Segmentation accuracy is evaluated using measures such as average perpendicular distance and dice similarity coefficient. The performance of all the segmentation techniques is improved. Further segmentation comparison has also been performed using other state-of-the art noise removal techniques for pre-processing, and it was observed that the proposed pre-processing technique outperformed other noise removal techniques in improving the segmentation accuracy.
Bitcoin'de Haftanın Günü Ve Yılın Ayı Anomalilerinin Varlığının İncelenmesi

The main purpose of this study is to reveal whether seasonal/time-oriented/calendar anomalies affect the price and transaction volume of Bitcoin. Day of the week and month of the year anomalies are examined in this context. The data for the years 2013-2021 are handled in 3 different sampling periods, consisting of the whole of this time period and each of its divided parts. The existence of these anomalies is analyzed with EGARCH models created separately. The most important conclusion reached in this study is that the analyzed anomalies differ according to the sampling periods. The common findings reached as a result of the analyzes for all three time intervals are as follows: It has been determined that Monday has positive effects in terms of both Bitcoin return and transaction volume, while Saturday has negative effects only regarding transaction volume. Mondays, Tuesdays, and Wednesdays create volatility-increasing effects concerning returns, Friday, Saturday and Sunday reduce volatility. In terms of trading volume, Monday and Tuesday reduce volatility, while Thursday and Friday increase volatility. Whereas March has a positive effect on return volatility, it has a negative effect on trading volume volatility, and September has only a negative effect on return volatility.
Empirical Assessment and Comparison of Educational Efficiency between Major Countries across the World

Education is a fundamental factor to enhance a country’s comprehensive national strength and international competitiveness. Recently, several governments have been attracting investments in educational sectors in contemplation of meliorating a country’s overall strength. This study empirically assesses and compares the educational efficiency of 29 major countries across the world using panel data for 2010–2016 by employing data envelopment analysis (DEA) and the super-slacks-based measure (super-SBM) model at the static level combined with the Malmquist index (MI) to investigate educational efficiency at the dynamic level. The results indicate, inter alia, huge average education efficiency differences existed among the studied countries, the highest being Japan (3.2845) and lowest Norway (0.4137), there are differences in the bias of technological progress among the studied countries during the sample period and technological progress directly affects the sustainability of educational efficiency, the growth rate of total factor productivity (TFP) index has been reduced in 2010–2013 but increased in 2014–2016 and technological progress has been the dominant factor influencing the rise of the education TFP index. Based on the results, this study identifies the merits and drawbacks of education efficiency across the sample countries and presents relevant recommendations to promote investment in the education sector and human capital.
Autonomous Motion Control Using Deep Reinforcement Learning for Exploration Robot on Rough Terrain

In this paper, we propose a novel approach to allow exploration robots to solve navigation problems in complex environments including rough terrain. Previous studies show that it is difficult for robots to autonomously navigate without prior information such as an environmental map. Recently, advances in deep reinforcement learning (DRL) have made it possible to complete the autonomous motion control without the map. In this respect, we apply DRL to realize fully autonomous navigation on rough terrain for the exploration robot. The exploration robot can generate suitable control motion converted from observed depth information through a neural network. The simulation results show that our DRL-based approach can successfully solve the collision-free navigation of robots on rough terrain.
Improvised multi-factor user authentication mechanism using defense in depth strategy with integration of passphrase and keystroke dynamics

As the technology has emerged, the need for computer systems and computer networks has increased, and so has the requirement for authentication been escalated. Multi-Factor Authentication has been used since the past decade and still is the best type of authentication available to us today. The only disadvantage is the type of factors being used in the multi factor architecture and mechanism. If better and complex factors are not engaged in proper order or sequence in any multi-factor authentication, it can jeopardize the information systems and result in data, especially the digital identity of user, being compromised. On the note for improving authentication systems, the proposed work introduces a 3 factor authentication system with 3 levels of security. Each level being more secure than the last one, thus implementing a defense in depth strategy. Every level uses different factor for the purpose of authentication. The factors being used in the mechanism include a 4 digit passcode, passphrase, and keystroke dynamics. These factors are considered to be economical and easy to integrate in multi factor authentication. Further, the proposed work’s security strength has been evaluated with the help of Shannon’s entropy theory and is compared with other recent works on multi factor authentication. The proposed work is sought to provide a multi factor authentication mechanism which is economical and provides robust security in this emerging technological era.
Semantic Management of Domain Modification in a Virtual Environment for Modeling Vulnerable Information Subjects

The paper deals with the development of information technology tools for semantic stabilization of interaction in virtual environments. The basis of the approach is the development and systematic application of a semantic network of processes. As part of the model, the subjects who are vulnerable to targeted information influence are described. The paper proposes a semantic model of the virtual environment, which allows registering the occurrence of a channel of potential undesirable effects. One of the distinctive features of the use of semantic information during conceptual modeling is the generation of a family of related concepts along pre-laid “trajectories”. In this regard, the conceptual design of a virtual environment is constructed as a sequence of several stages: (1) the virtual world is conceptually modeled using the generated semantic data; (2) semantic data is used to generate families of concepts; (3) this families form a synonyms of subject concepts. After generation, the main displaced concepts that are characteristic of the subject application of the virtual environment are formed. The paper offers a tool that provides control of domain modifications using handlers and adapters. Interaction with the environment is carried out by matching the selected network fragment (specified in the user interface) with a generalized domain network that implements the semantic interaction environment (the system and subject semantic components available in the environment). The system configuration of the environment is performed by specifying the system of states and possible transitions, which are controlled by a family of semantic handlers. The subject configuration of the environment is performed by means of families of semantic adapters.
Semi-Supervised Label Distribution Learning with Co-regularization

Label Distribution Learning (LDL) is a machine learning paradigm which is recently proposed to deal with the more ambiguity label. This paradigm assigns the distribution-level label to an instance so that it can exploit the relative importance of every candidate labels to a particular instance. Previous studies always concentrate on the methods under strong supervision, which requires a large number of tagged training data. In real-world applications, it is usually difficult to collect numerical precise labels owing to the large costs in labor and time spent on the label annotation. To this end, this paper proposes a novel algorithm named Semi-Supervised Label Distribution Learning with Co-regularization (S2LDL-CO). To benefit from all available information, ensemble of two different models is utilized to deal with the labeled and unlabeled data, respectively. More specifically, the co-regularization framework is adopted to combine these two different models, which can process both the labeled and unlabeled data with good robustness and consistency. What’s more, manifold regularization and l2,1-norm are also added into the objective function, which can fully exploit the implicit information in instances. Finally, the well-designed objective function is optimized by an Alternating Direction of Method of Multipliers (ADMM) algorithm. Experimental results tested on thirteen benchmark datasets illustrate its effectiveness over several state-of-the-art methods.
Are structural equation models theories and does it matter?

In their introductory marketing, management, and social psychology courses, undergraduates learn that correlation coefficients provide weak evidence for causal conclusions. Nonetheless, researchers conclude causally from correlation coefficients by drawing causal arrows in their structural equation models (SEMs). Although most researchers avoid describing their findings in causal language, obligatory recommendations for applying those findings insert causation. Researchers’ standard rejoinder to validity challenges is “the critics have ignored theory’s role in rendering our SEM internally and externally valid”. To evaluate this rejoinder, we explore SEMs based on comprehensive underlying theories and as stand-alone and testable context-specific theories that blend previously published hypotheses and findings. In most cases, the rejoinder is unconvincing.
Algorithm for generating negative cases for collaborative filtering recommender

Abstract Most collaborative filtering recommenders based on deep learning utilize implicit feedback information such as likes and bookmarks to infer user preferences rather than explicit feedback such as ratings provided by users. However, collaborative filtering models utilizing implicit feedback pose a chronic problem, that is, the “one‐class” problem (also known as the one‐class collaborative filtering problem), which causes trivial solutions due to lack of negative examples. A one‐class problem free‐boosting (OCF‐B) algorithm is proposed in this study to solve the one‐class problem by considering a user's preference pattern when generating negative cases and training the model with both positive and negative cases. More specifically, the OCF‐B algorithm iteratively selects negative cases that exacerbate the loss value of the object function and replace them with other negative cases when extracting negative cases from unknown cells of the user–item interaction matrix. In an experiment using four datasets, the results show that negative cases selected meticulously via the OCF‐B algorithm improve the prediction performance not only for negative cases, but also for positive cases. In addition, the prediction performance was better than that of the existing zero‐injection method.
When Reality Kicks In: Exploring the Influence of Local Context on Community-Based Design

Social sustainability is becoming an increasingly important topic in design practice, calling for more contextual perspectives on the process of design for social sustainability. This paper presents a retrospective case study analyzing the design process of a serious game which aimed to empower teenagers to organize events to strengthen community bonds. The community context in which the collaborative project took place underwent significant contextual changes due to the COVID-19 pandemic. Analysis using the Ecologies of Contestation framework shows the influence of multiple contextual levels (Socio-cultural, Power, Constructed, and Values-based) on the design process. Moreover, the paper discusses multiple contextual factors which influenced the design process and presents four suggestions for designers to anticipate and benefit from dynamics in these contextual elements. The suggestions regard (1) integrating the temporal dimension in the collaborative design processes, (2) carefully considering (value) alignment between actors, (3) leveraging values in the collaborative design process, and (4) acknowledging and responding to the multilayered nature of communities throughout the design process. As such, this paper explores the relationships between the community context and the collaborative design process to contribute to more resilient design practices.
Removal and Recovery of the Human Invisible Region

The occlusion problem is one of the fundamental problems of computer vision, especially in the case of non-rigid objects with variable shapes and complex backgrounds, such as humans. With the rise of computer vision in recent years, the problem of occlusion has also become increasingly visible in branches such as human pose estimation, where the object of study is a human being. In this paper, we propose a two-stage framework that solves the human de-occlusion problem. The first stage is the amodal completion stage, where a new network structure is designed based on the hourglass network, and a large amount of prior information is obtained from the training set to constrain the model to predict in the correct direction. The second phase is the content recovery phase, where visible guided attention (VGA) is added to the U-Net with a symmetric U-shaped network structure to derive relationships between visible and invisible regions and to capture information between contexts across scales. As a whole, the first stage is the encoding stage, and the second stage is the decoding stage, and the network structure of each stage also consists of encoding and decoding, which is symmetrical overall and locally. To evaluate the proposed approach, we provided a dataset, the human occlusion dataset, which has occluded objects from drilling scenes and synthetic images that are close to reality. Experiments show that the method has high performance in terms of quality and diversity compared to existing methods. It is able to remove occlusions in complex scenes and can be extended to human pose estimation.
Secured Land Title Transfer System in Australia using VPN based Blockchain Network

Blockchain technology has evolved as a secure method for storing important private and public record transactions. Transfer of land title is one such important public record. To this end, a blockchain-based system is proposed in the context of NSW (New South Wales) Australia in this paper. However, due to the decentralized and distributed nature of the blockchain network, it is prone to several security attacks. We have focused on the routing attacks, particularly on the delay attacks for the blockchain network. A VPN (Virtual Private Network) based blockchain network is proposed and implemented in this paper to mitigate the routing attacks. The results show that the proposed VPN-based blockchain network outperforms the traditional blockchain-based network in the presence of a routing attack.
A UX 3-Factor Hierarchical Model for Understanding, Designing and Evaluating the UX of Software Products

This study used a literature review technique to acquire a better grasp of the available literatures on user experience modeling. The findings support a 3-factor user experience (UX) paradigm, which includes expected monovalent ergonomic, expected bivalent hedonomic, and unexpected monovalent hedonomic qualities, according to the findings. These attributes are arranged in a hierarchical fashion, according to the review. Each rung of the hierarchy is crucial for software product design and evaluation in order to improve UX design and assessment. As a result, software designers and assessors should consider this model. The study is limited however, because the conclusions, albeit interesting, were derived from a literature survey rather than an empirical investigation. This conceptual model will be empirically verified and validated in future works.
A High-Precision Method for Segmentation and Recognition of Shopping Mall Plans

Most studies on map segmentation and recognition are focused on architectural floor plans, while there are very few analyses of shopping mall plans. The objective of the work is to accurately segment and recognize the shopping mall plan, obtaining location and semantic information for each room via segmentation and recognition. This work can be used in other applications such as indoor robot navigation, building area and location analysis, and three-dimensional reconstruction. First, we identify and match the catalog of a mall floor plan to obtain matching text, and then we use the two-stage region growth method we proposed to segment the preprocessed floor plan. The room number is then obtained by sending each segmented room section to an OCR (optical character recognition) system for identification. Finally, the system retrieves the matching text to match the room number in order to obtain the room name, and outputs the needed room location and semantic information. It is considered a successful detection when a room region can be successfully segmented and identified. The proposed method is evaluated on a dataset including 1340 rooms. Experimental results show that the accuracy of room segmentation is 92.54%, and the accuracy of room recognition is 90.56%. The total detection accuracy is 83.81%.
A Continuous Gesture Segmentation and Recognition Method for Human-Robot Interaction

Abstract The process of human-computer cooperation using gesture recognition can make people get rid of the limitations of traditional input devices such as mouse and keyboard, and control artificial intelligence devices more efficiently and naturally. As a new way of human-robot interaction (HRI), gesture recognition has made some progress. There are many ways to realize gesture recognition combined with visual recognition, motion information acquisition and EMG signal. The research on isolated language gesture recognition has been quite mature, but the expression semantics of isolated gestures is single. In order to improve the interaction efficiency, the application of continuous gesture recognition is essential. This paper studies its application in continuous sign language sentence recognition based on inertial sensor and rule matching recognition algorithm. The recognition rate of nine single HRI gestures is 92.7%, and the HRI of combined gestures is realized.