Reviewerly/exHarmony huggingface dataset:
Adaptive infinite dropout for noisy and sparse data streams

The ability to analyze data streams, which arrive sequentially and possibly infinitely, is increasingly vital in various online applications. However, data streams pose various challenges, including sparse and noisy data as well as concept drifts, which easily mislead a learning method. This paper proposes a simple yet robust framework, called Adaptive Infinite Dropout (aiDropout), to effectively tackle these problems. Our framework uses a dropout technique in a recursive Bayesian approach in order to create a flexible mechanism for balancing between old and new information. In detail, the recursive Bayesian approach imposes a constraint on the model parameters to make a regularization term between the current and previous mini-batches. Then, dropout whose drop rate is autonomously learned can adjust the constraint to new data. Thanks to the ability to reduce overfitting and the ensemble property of Dropout, our framework obtains better generalization, thus it effectively handles undesirable effects of noise and sparsity. In particular, theoretical analyses show that aiDropout imposes a data-dependent regularization, therefore, it can adapt quickly to sudden changes from data streams. Extensive experiments show that aiDropout significantly outperforms the state-of-the-art baselines on a variety of tasks such as supervised and unsupervised learning.
Obstacle Detection and Walkable Path Detection

Object detection is a computer vision technique which enables to locate and identify the labelled objects in an image or video. This kind of identification and localization through object detection, detects objects in a scene and determines their precise locations. In this paper, implementation of an object detection Application Programming Interface (API) using Single Shot Detector (SSD) inception to detect obstacles in a path is discussed. The scope of the paper covers how the corresponding walkable path is planned according to the detected obstacles and this project can be used as an application for navigation assistance.
Kicking in Virtual Reality: The Influence of Foot Visibility on the Shooting Experience and Accuracy

When playing sports in virtual reality foot interaction is crucial for many disciplines. We investigated how the visibility of the foot influences penalty shooting in soccer. In a between-group experiment, we asked 28 players to hit eight targets with a virtual ball. We measured the performance, task load, presence, ball control, and body ownership of inexperienced to advanced soccer players. In one condition, the players saw a visual representation of their tracked foot which improved the accuracy of the shots significantly. Players with invisible foot needed 58% more attempts. Further, with foot visibility the self-reported body ownership was higher.
Navigating the Mobile Applications: The Influence of Interface Metaphor and Other Factors on Older Adults’ Navigation Behavior

The interface metaphor was suggested to facilitate a user's mental model development while navigating, whereas its effectiveness is still unknown among the group of older adults. We developed an experiment to investigate how older adults navigate the mobile interfaces with and without metaphors, by evaluating the possible effects of users' perceptual speed, task complexity, content similarity, and other related user characteristics in an integrated fashion. Results indicated that the use of interface metaphor could assist in the older adults' navigation performance, but only for those with a higher level of perceptual speed. Additionally, we found that the older adult's navigation behavior was significantly influenced by the task complexity, content similarity and the users' level of technology experience. These findings can help researchers and practitioners to evaluate the effectiveness of interface metaphor on older adults' mobile navigation behavior by identifying the possible influential factors and interpreting the behind reasons.
Predictors of Employees’ Mobile Security Practice: An Analysis of Personal and Work-Related Variables

Personal mobile devices form an integral part of business activities today. Mobile devices, nevertheless, pose various security issues and data privacy threats, which require a close attention. The rational choice theory was utilized to examine the determinants of employees’ security behavior in relation to mobile device usage. Employees were postulated to rationally evaluate the costs and benefits of mobile security measures and decide on the option that is perceived to provide the best expected outcome. Twelve out of thirteen hypotheses examined in this study were found to be significant. We also hypothesized that demographics and work-related variables significantly affect employees’ mobile security practices, examined using ordinal logistic regression analysis. The findings indicate the efficacy of the rational choice theory in explaining mobile security behavior. Security inconvenience has been found to be a significant cost to information security measures. Moreover, the findings also showed the influence of gender, job function, past security experience, and perceived risk on the dependent variable. In conclusion, we would like to draw considerable attention to the contribution of security awareness programs and security training to good mobile security behaviors.
Higher Immersive Profiles Improve Learning Outcomes in Augmented Reality Learning Environments

Augmented reality (AR)-based learning environments are argued to foster cognitive and emotional involvement. Immersion has been identified as one of the driving forces that promote learning in technology-based learning environments. This study evaluated the learning effectiveness and immersion appeal of an AR-marker-based learning activity targeted at practicing basic chemistry concepts. Data were collected from a cohort of 124 middle school students in Mexico and analyzed using pretest–posttest comparisons and cluster analysis. The results suggest that students with higher immersive learning profiles achieve better learning outcomes compared with those with lower immersive profiles.
Efficient Opportunities and Boundaries towards Internet of Things (IoT) Cost Adaptive Model

Inside the previous decade, the description has been more complete covering a wide reach out of utilizations like transport, utilities, medical services, and so forth for the Internet of Things (IoT). Inside IoT perspective, various research papers that envelop us will be coordinated in some casing. In any case, when the data advances toward the cloud for examination, the chance to follow up on it very well may be gone. This white paper, focusing on IT and functional advancement specialists, explains another model for examining and following up on IoT data. Our diversion occurs to create the impression that the used systems decline the control group overhead, benefit conjuring deferral, and imperativeness usage. This paper concentrate on advances in IoT-based prosperity care developments and reviews the cutting edge coordinated models/stages, applications, and mechanical examples in IoT-based prosperity care courses of action.
Monitoring Supply Chain of Pharmaceutical Drugs Using Blockchain

In the last two decades, the global value of pharmaceutical goods traded increased by six times. As a result of this growth, the pharmaceutical supply chains have become more complicated and ambiguous. The number of counterfeit drug-related incidents have also increased. Counterfeit drugs when consumed can have adverse effects on individuals and sometimes can even lead to death. The solution to this problem is leveraging blockchain technology. The paper proposes a supply chain management system using a permissioned blockchain network which will consist of role-based authorization to avoid tampering with the data available in the network. The blockchain-based decentralized solution will help to improve collaboration and trust issues by minimizing the consequences caused by information asymmetry by introducing transparency, traceability, and consistency of all the item units.
A retrieval and ranking method of mathematical documents based on CA-YOLOv5 and HFS

In a retrieval system for mathematical documents based on mathematical expressions, the input and matching of mathematical expressions are key steps that affect the system's usability, accessibility and efficiency because of their special attributes. Therefore, this paper mainly focuses on improving the input efficiency and matching accuracy of mathematical expressions. This paper proposes a method for retrieval and ranking of mathematical documents based on CA-YOLOv5 and HFS (hesitation fuzzy set) by utilizing the advantages of CA (coordinate attention) model and YOLOv5 in target detection and the superiority of HFS in multiattribute decision-making. By embedding the CA model into the YOLOv5 network, the mathematical expressions in layout images are extracted and recognized to form mathematical query expressions. These expressions are then analyzed to obtain similarity evaluation features and matched with the candidate mathematical expressions indexed with the same features in a library of mathematical documents by employing the HFS as the similarity evaluation measure. Experiments were performed based on the TFD-ICDAR2019v2 dataset and the NTCIR dataset. The F1-score of the mathematical expression detection result was 76.54%, the MAP (mean average precision) of the mathematical documents retrieval result was 71.73%, and the average nDCG of mathematical documents ranking was 80.89%.
Value-semantic model of formation of the successive results of pedagogue training

The problem of ensuring the continuous training of pedagogic staff is one of the most significant for improving the system of continuing education. How can continuity be ensured in a situation where individual stages of pedagogic training are regulated by different regulatory documents containing specific requirements for the results of mastering educational programmes of a certain level by students? The answer to this question presupposes allocation of the basic foundations for the design of targeted, meaningful, operational-activity and effective components based on the principles of continuity and continuity. In order to develop the basic foundations for combining educational programmes for pedagogic training at different stages and levels of education, it is advisable to use a value-semantic model of the formation of successive results. The model was developed by a problem group of lecturers based on the results of the study, which included conducting focus groups with the participation of students and practical pedagogues, comparative analysis of normative documents, content analysis of the products of the activities of existing teachers, conducting a survey among students of psychological and pedagogic classes, pedagogues and representatives of school administration, students and lecturers of pedagogic universities. Universal pedagogic competences (anthropocentric, social and acmeological) are defined as the basic components of the model. Indicators have been developed for each level of education that characterise the manifestation of competences in the knowledge, attitudes and qualities of future pedagogues. The value-semantic model of the formation of successive results can be used to assess the quality of pedagogic education at a separate level of training and when designing individual professional educational routes for future and current teachers, taking into account the identified deficits and problems.
Online streaming feature selection for multigranularity hierarchical classification learning

Abstract Hierarchical classification learning is a hot research topic in machine learning and data mining domains, and many feature selection algorithms with category hierarchy have been proposed. However, existing algorithms assume that the feature space of data is completely obtained in advance, and ignore its uncertainty and dynamicity. To address these problems, we propose an online streaming feature selection framework with a hierarchical structure to solve the above two problems simultaneously. First, we apply the hierarchical relationship between nodes in a hierarchical structure to the Relief algorithm, so that it can be used to compute the weights of dynamic features. Second, we dynamically select important features for each internal node via comparing the magnitude of the weights of features on these nodes with their parent and sibling nodes. In addition, we perform redundancy analysis of features by calculating the covariance between features to obtain a superior online feature subset for each internal node. Finally, the proposed algorithm is compared with six online streaming feature selection methods on six hierarchical data sets, and experimental results shows that the classification performance of the proposed algorithm is effective.
Measurement of two-photon position–momentum Einstein–Podolsky–Rosen correlations through single-photon intensity measurements

The measurement of the position-momentum EPR correlations of a two-photon state is important for many quantum information applications ranging from quantum key distribution to coincidence imaging. However, all the existing techniques for measuring the position-momentum EPR correlations involve coincidence detection and thus suffer from issues that result in less accurate measurements. In this letter, we propose and demonstrate an experimental scheme that does not require coincidence detection for measuring the EPR correlations. Our technique works for two-photon states that are pure, irrespective of whether the state is separable or entangled. We theoretically show that if the pure two-photon state satisfies a certain set of conditions then the position-momentum EPR correlations can be obtained by doing the intensity measurements on only one of the photons. We experimentally demonstrate this technique for pure two-photon states produced by type-I spontaneous parametric down-conversion, and to the best of our knowledge, we report the most accurate measurement of position-momentum EPR correlations so far.
A quick look at Cryptocurrency Mining: Proof of Work

Cryptocurrency, a name heard in the news as well as social media. But what exactly is cryptocurrency? Cryptocurrency is a decentralized digital asset on the blockchain, which means, that it is not controlled by an entity or an institution. Therefore, users of cryptocurrency enjoy financial freedom. But how are most of the transactions related to cryptocurrency processed on the blockchain? There is a term called proof of work as well as proof of stake. This paper has focused on the proof of work aspect of processing the cryptocurrency transactions, also specified the currencies that support the proof of work consensus. It also distinguishes between the two types of hardware that can be used to mine the currencies, and also using the past one-year data recorded by the author to analyze whether mining can be fruitful with any hardware in the future. Also, commenting on which type hardware can yield the best ROI. This type of thorough research on PoW consensus has not been found, so this paper is the first of its kind. A prediction has also been made using Python, whether mining in the near future will be of great potential or not. Also highlighted why mining revenue fluctuates.
The Cobb-Douglas Learning Machine

• A novel Minimum Error Minimax Probability Machine (MEMPM) method is presented. • The Cobb-Douglas production function is extended to machine learning. • The proposal is a robust formulation for linear and kernel-based classification. • The method is solved via a self-developed two-step alternating algorithm. • We prove that the optimization scheme converges to the optimal solution of the problem. • Best performance is achieved in experiments carried out on 17 benchmark datasets. In this paper, we propose a novel machine learning approach based on robust optimization. Our proposal defines the task of maximizing the two class accuracies of a binary classification problem as a Cobb-Douglas function. This function is well known in production economics and is used to model the relationship between two or more inputs as well as the quantity produced by those inputs. A robust optimization problem is defined to construct the decision function. The goal of the model is to classify each training pattern correctly, up to a given class accuracy, even for the worst possible data distribution. We demonstrate the theoretical advantages of the Cobb-Douglas function in terms of the properties of the resulting second-order cone programming problem. Important extensions are proposed and discussed, including the use of kernel functions and regularization. Experiments performed on several classification datasets confirm these advantages, leading to the best average performance in comparison to various alternative classifiers.
A Deep OCR for Degraded Bangla Documents

Despite the significant success of document image analysis techniques, efficient Optical Character Recognition (OCR) of degraded document images still remains an open problem. Although a body of work has been reported on degraded document recognition for English language, only little attention has been paid to Indic scripts. In this work, we focus on developing a degraded OCR for Bangla, a major Indian language. In general, an OCR system includes segmentation of the foreground text part from the background followed by recognition of the extracted text. The text segmentation module aims to assign the foreground or background label to each pixel of the document image. In this paper, we present a new OCR system which is particularly suitable for degraded quality Bangla document images. The contribution is two fold. In the first phase, we use a semi-supervised Markov Random Field (MRF)- based Generative Adversarial Network (GAN) model (which we call MRF-GAN ) for foreground segmentation of texts from degraded text. In the proposed MRF-GAN , we extend the concept of GAN to a multitask learning mechanism where discriminator-classifier networks differentiate between real/fake images and also assign a foreground or background label to each pixel. In the second phase, we propose to use a new encoder-decoder based recognizer that incorporates an attention-based character to a word prediction model, which has the capability of minimizing Word Error Rate (WER) . We optimize this network using a Multitask based Transfer Learning scheme (MTTL) . We perform experiments on a publicly available degraded Bangla document image dataset as well as on a new degraded printed Hindi document image dataset, which has been created as a part of the present study. Results of the experimentations demonstrate the efficacy of the proposed OCR.
Concept Formation and Quantum-like Probability from Nonlocality in Cognition

Abstract Human decision-making is relevant for concept formation and cognitive illusions. Cognitive illusions can be explained by quantum probability, while the reason for introducing quantum mechanics is based on ad hoc bounded rationality (BR). Concept formation can be explained in a set-theoretic way, although such explanations have not been extended to cognitive illusions. We naturally expand the idea of BR to incomplete BR and introduce the key notion of nonlocality in cognition without any attempts on quantum theory. We define incomplete bounded rationality and nonlocality as a binary relation, construct a lattice from the relation by using a rough-set technique, and define probability in concept formation. By using probability defined in concept formation, we describe various cognitive illusions, such as the guppy effect, conjunction fallacy, order effect, and so on. It implies that cognitive illusions can be explained by changes in the probability space relevant to concept formation.
Bugtrac – A New Improved Bug Tracking System

Software testing is a concept that is associated with the identification and detection of bugs. Most software bugs emerge from mix-ups and mistakes made by individuals in either a program's source code or in its technical design. Bugs are found in a variety of forms while using software applications and therefore it becomes difficult to detect and manage them properly for an individual/user of an application. In order to resolve this issue, bug tracking systems have been introduced to monitor and keep track of the reported bugs in an application. There exist many proprietary and open-source bug tracking systems today and some new systems are also under development to cater to the variety of bugs that keep originating due to the changing requirements of software. There is a need for a tool that will help in fixing and tracking the progress of bug fixes with the ever-changing types of software applications that keep evolving every day. In this paper, a comparative study has been performed between five defect tracking systems, a combination of both open source and proprietary. A new defect tracking system “Bugtrac” has been proposed taking into consideration all types of software applications and the defects that may originate from these applications. The proposed system is expected to become a more promising defect tracking system when compared with the already existing ones.
Process-Driven Modelling of Media Forensic Investigations-Considerations on the Example of DeepFake Detection

Academic research in media forensics mainly focuses on methods for the detection of the traces or artefacts left by media manipulations in media objects. While the resulting detectors often achieve quite impressive detection performances, when tested under lab conditions, hardly any of those have yet come close to the ultimate benchmark for any forensic method, which would be courtroom readiness. This paper tries first to facilitate the different stakeholder perspectives in this field and then to partly address the apparent gap between the academic research community and the requirements imposed onto forensic practitioners. The intention is to facilitate the mutual understanding of these two classes of stakeholders and assist with first steps intended at closing this gap. To do so, first a concept for modelling media forensic investigation pipelines is derived from established guidelines. Then, the applicability of such modelling is illustrated on the example of a fusion-based media forensic investigation pipeline aimed at the detection of DeepFake videos using five exemplary detectors (hand-crafted, in one case neural network supported) and testing two different fusion operators. At the end of the paper, the benefits of such a planned realisation of AI-based investigation methods are discussed and generalising effects are mapped out.
Dynamics Modeling and Adaptive Sliding Mode Control of a Hybrid Condenser Cleaning Robot

This study examines the pose control of the 4-RPU redundant parallel mechanism of a hybrid condenser cleaning robot in response to the poor control accuracy of current cleaning robots. The kinematics of the 4-RPU mechanism is analysed, and its dynamics model is constructed using the virtual work principle. The theoretical calculation and virtual prototype simulation of the constructed model are conducted in MATLAB and ADAMS, which yield basically consistent results, demonstrating the precision of the model. Based on this model, an adaptive sliding mode control method is proposed that can estimate and compensate for parameter uncertainties and load perturbations simultaneously. The system stability is analysed using Lyapunov functions. The results suggest that the adaptive sliding mode control method can significantly reduce the average tracking error of each degree of freedom of the moving platform and exhibits higher control stability and convergence accuracy than the conventional sliding mode control algorithm. This study provides a reference and research basis for attitude control of the cleaning robots affected by uncertainties such as water backlash during operation.