Reviewerly/exHarmony huggingface dataset:
Experimental Analysis of Artificial Neural Networks Performance for Accessing Physical Activity Recognition in Daily Life

The importance of the Physical Activity (PA) study in the uncertain, dynamic environment of daily life has been drawn much attention. Traditional researches demonstrate that leveraging advanced on-body wearable sensors and effective machine learning algorithms enable monitoring and recognizing human daily PA in controlled environments with high accuracy. But they usually suffer from low accuracy and weak robustness of objectively qualifying PA in daily life due to restrictions like less-attachments, low sensitivity of consumable wearables and subject's variation. Thus, there is a lack of holistic investigation on how to improve accuracy of PA recognition in daily life using cost-effective wearable devices with feasible algorithms. In this paper, we use less-attached on-body consumable wearables like belt and wristband devices to collect data from two different groups of people(10 healthy people and 7 mild cognitive impairment patients). We compared the features from different types of sensors (accelerometer, gyroscope and magnetometer) and performed feature fusion. Then, by using Artificial Neural Network(ANN) to classify the features after fusion, we get a cost-effective wearable intelligence approaches for PA recognition in daily life. Parameters of achieving high recognition rate including time window sizes, features and activation functions. The experimental results indicate that PA containing 10 subjects collected by wrist in daily life environments with average accuracy up to 93%. In addition, ANN had good results in 7 mild cognitive impairment (MCI) patients, reaching 96%.
Finding Suitable Threshold for Support in Apriori Algorithm Using Statistical Measures

Data mining is one of the noticeable researches. Data mining is one of the most outstanding areas of research, striving to extract meaningful information and useful models from databases and to help managers to make better decisions. Mining association rules is a persistent and popular data mining process. It aims to find frequent models, association correlations, or causal structures between a set of objects in large transactional or relational databases and other data repositories. This paper provides an improvement of the Apriori algorithm, a classic rule extraction algorithm by finding appropriate minimum threshold values for the support automatically using different statistical measures depending on each dataset. The experiments on baseline datasets show a comparative analysis between different proposed methods of generation association rules using the automatic minimum support threshold.
An Improved Image Segmentation Algorithm CT Superpixel Grid Using Active Contour

The traditional CT image segmentation algorithm is easy to ignore image contour initialization, which leads to the problem of long time consuming and low accuracy. A superpixel mesh CT image improved segmentation algorithm using active contour was proposed. CT image superpixel gridding was carried out first; secondly, on the basis of gridding, the region growth criterion was improved by superpixel processing, the region growth graph was established, the image edge salient graph was calculated based on the growth graph, and the target edge was obtained as the initial contour; finally, the Mumford-Shah model in the active contour model was improved; the energy functional was constructed based on the improved model and transformed into the symbol distance function. The results show that the proposed algorithm takes less time to mesh superpixels, the accuracy of image edge calculation is high, the correct classification coefficient is as high as 0.9, and the accuracy of CT image segmentation is always higher than 90%, which has superiority.
Pedestrian Detection and Tracking in Challenging Conditions

Driver safety is essential in this time of the world as the roads are filled with vehicles and pedestrians and accidents frequently occur especially during tough weather conditions. We used the YOLOv4 model which is pretrained with generic images with the standard COCO weights to detect pedestrians. We chose a custom dataset consisting various other datasets which we used for training our new improved YOLOv4. We got the performance metrics of the YOLOv4 detection model which is compared with the Improved YOLOv4 detection model and resulted with an improvement of mean Average Precision (mAP) by 7%.
Solution of the optimal control problem by symbolic regression method

The paper introduces an approach to solve the optimal control problem applying symbolic regression methods. All known approaches to solve the optimal control problem are based on direct or indirect methods. Indirect methods may provide analytical solution but impose restrictions on the form and dimension of control object. In direct ones the optimal control problem is reduced to the nonlinear programming problem which provides general numerical solution. Also direct methods impose no restrictions on control object and have a wide range of application to complex applied problems. The approach proposed combines the advantages of both direct and indirect methods not involving the disadvantages mentioned previously. The suggested approach provides solution to the optimal control problem as a control function of time in explicit form. This approach benefits in case of applied optimal control problem requiring a solution in explicit form while indirect methods are inapplicable due to the restrictions imposed on the control system. The computational experiment of solving the optimal control problem for a mobile robot with phase constraints is presented. The results illustrate that the proposed approach finds an optimal control as a function of time which provides a mobile robot’s motion along the optimal trajectory avoiding phase constraints.
Read Textual features in Images and convert to Editable form by extended use of Artificial Neural Networks, Deep learning and Maximally Stable Extremal Region techniques

Abstract Nowadays most of the information is stored in images and there is a need to convert this information into an editable format. The objective of this paper includes the development of a user-friendly tool to extract text from the video or to recognize well-written handwriting in a scanned document to an editable form. The proposed methodology is robust and can provide high-grade performance with layout distortion. The explication utilizes modalities like Optical Character Recognition (OCR), Artificial Neural Networks (ANN), Deep Learning and Maximally Stable Extremal Regions (MSER) techniques for text detection. The video extraction can be attained by using video to frame conversion and then to use the Structural Similarity Index Measure (SSIM). The prototype is developed using MATLAB and provided with a GUI, which is deployable on a workstation. The generated GUI is employed to define Region of Interest (ROI), specify the required text layout, highlighting the specific portions of the image, select the appropriate language and also export the text to a word document or notepad applications where it can be edited.
Cross-Modality Graph Neural Network For Few-Shot Learning

Few-shot learning, which attempts to predict unlabeled samples with only a few labeled samples, has drawn more and more attention. Though recent works have achieved promising progress, none of them have noticed to establish consistency among episodes, leading to the ambiguity in latent embedding space. In this paper, we propose a novel Cross-Modality Graph Neural Network (CMGNN) to uncover the associations among episodes for consistent global embedding. Since the semantic information induced from NLP is relatively fixed compared to visual information space, we leverage it to construct meta nodes for each category to guide the corresponding visual feature learning through GNN. Moreover, to ensure global embedding, a distance loss function is designed to force the visual nodes closer to their associated meta nodes to a greater extent. Extensive experiments and ablation studies on four benchmark datasets show its superiority over many SOTA comparison methods.
Information needs and services for autism in China: is there any gap between them?

Purpose This study aims to explore the gap between information needs and services for autism in China. Design/methodology/approach The gap is revealed by investigating the status quo of autism information needs and services. The authors extracted categories and subcategories of information needs through content analysis of academic documents and then supplemented the subcategories through text mining of an online forum. Meanwhile, categories and subcategories of information services were extracted through content analysis of autism websites. Finally, the authors matched the two to explore the gap and designed a quantitative index to measure it. Findings A total of eight and ten categories of information needs and services are extracted, respectively. In total, six categories of information needs can be partly matched, but nearly half of the subcategories failed. Huge gaps in economic support, sociality and policy mechanisms categories are observed through the quantitative index and medium gaps in social resource services and employment categories while almost no gaps in psychological/emotional counseling, rehabilitation skills training and professional knowledge/information categories. Originality/value This study takes a deep insight into the gap between autism information needs and services in China, providing evidences and suggestions for information providers to improve their services. Academic documents and online forum data are adopted to avoid the impact of stigmatization, which provides a multi-source data analysis approach for the information needs of special groups.
Role of Additional Education in the Context of Labor Market Volatility

The article explores new trends that appear on the labor market and affect the structure of employment. The authors analyze the possible competencies that are necessary for graduates of universities of economic and legal specialties, taking into account new professions that will appear in the future. In order to be in demand on the labor market and not to remain without work, students need to engage in self-education and improve not only professional competencies, but also constantly improve their over-professional skills. The system of additional education at universities should be a useful assistant for students and teachers. The authors consider the system of continuing education as a flexible and dynamic structure that allows students to choose the course program themselves.
AMBTC-based visual secret sharing with different meaningful shadows

<abstract> Secret sharing based on Absolute Moment Block Truncation Coding (AMBTC) has been widely studied. However, the management of stego images is inconvenient as they seem indistinguishable. Moreover, there exists a problem of pixel expansion, which requires more storage space and higher transmission bandwidth. To conveniently manage the stego images, we use multiple cover images to make the stego images seem to be visually different from with each other. Futhermore, the stego images are different, which will not cause the attacker's suspicion and increase the security of the scheme. And traditional Visual Secret Sharing (VSS) is fused to eliminate pixel expansion. After images are compressed by AMBTC algorithm, the quantization levels and the bitmap corresponding to each block are obtained. At the same time, when the threshold is $ (k, k) $, bitmaps can be recovered losslessly, and the slight degradation of image quality is only caused by the compression itself. When the threshold is another value, the recovered image and the cover images can be recovered with satisfactory image quality. The experimental results and analyses show the effectiveness and advantages of our scheme. </abstract>
Double auction and profit maximization mechanism for jobs with heterogeneous durations in cloud federations

Abstract By sharing resources with each other, different cloud providers in a cloud federation can exploit their diversity in resource configuration and operational cost so as to improve service performance. They should consider the strategy of resource pricing, job scheduling and server provisioning altogether to maximize their own interests. On the other hand, they need to efficiently trade the resources with a suitable mechanism, typically auction, so as to guarantee the participants’ profits. Nevertheless, in consideration of the heterogeneous execution times of jobs, both the pricing strategy and trading mechanism should be delicately designed, which is obviously a challenging task. In this paper, we firstly propose a truthful, individual-rational and ex-post budget-balanced auction mechanism for selecting pairs of buyer and seller winners to trade virtual machines for different durations. Then, to maximize the individual profits, we propose a dynamic resource bidding scheme and a job scheduling strategy based on our importance model of jobs with heterogeneous execution times and resource requirements. The simulation results show that, compared with existing ones, our design can better handle varieties of both execution time and resource requirement and make the participants obtain more individual profits.
Can Immersive Systems Help Address Sustainability Goals? Insights from Research in Information Systems

Abstract Several developments in recent years have highlighted the urgent need for an increase in sustainable behavior. In an effort to systematically achieve global human well-being and create a more sustainable and equal world by 2030, the United Nations passed the Sustainable Development Goals in 2015. In this chapter, we discuss several different approaches for Information Systems (IS) to contribute to these goals. We present ideas and first results from different research approaches in which we analyze how immersive systems, in particular, are capable of addressing challenges regarding awareness, motivation, information transfer, and educating citizens to act in a sustainable manner. We conclude that the main features of immersive systems are particularly suited to approach challenges related to sustainable behavior and attitude change. With this chapter, we hope to inspire further research efforts on how IS can make a contribution to a more sustainable world.
Scalable logistic regression with crossed random effects

The cost of both generalized least squares (GLS) and Gibbs sampling in a crossed random effects model can easily grow faster than N3∕2 for N observations. Ghosh et al. (2022) develop a backfitting algorithm that reduces the cost to O(N). Here we extend that method to a generalized linear mixed model for logistic regression. We use backfitting within an iteratively reweighted penalized least squares algorithm. The specific approach is a version of penalized quasi-likelihood due to Schall (1991). A straightforward version of Schall’s algorithm would also cost more than N3∕2 because it requires the trace of the inverse of a large matrix. We approximate that quantity at cost O(N) and prove that this substitution makes an asymptotically negligible difference. Our backfitting algorithm also collapses the fixed effect with one random effect at a time in a way that is analogous to the collapsed Gibbs sampler of Papaspiliopoulos et al. (2020). We use a symmetric operator that facilitates efficient covariance computation. We illustrate our method on a real dataset from Stitch Fix. By properly accounting for crossed random effects we show that a naive logistic regression could underestimate sampling variances by several hundred fold.
DeCapsGAN: generative adversarial capsule network for image denoising

A convolutional capsule network that characterizes small objects previously has shown remarkable performance in small object classification. We extend the idea of capsules to the image denoising task and combine it with the generative adversarial network to develop a generative adversarial capsule network (DeCapsGAN). Both the generator and discriminator adopt the capsule network architecture. The convolutional capsule network is used to capture richer image features. We introduce deconvolution into the generator and propose a convolutional–deconvolutional capsule block. Skip connections are beneficial to transfer image features to deeper networks. A pretrained residual network (ResNet) is implemented as a feature extractor that captures features from the denoised image and reference image to measure the difference in perceptual information in the feature space. The performance of the proposed model is evaluated on the image with synthetic noise (Gaussian noise and mixed Gaussian with impulse noise) and real noise. Extensive experiments show that our model achieves excellent denoising performance in terms of both visual quality and quantitative metrics.
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network

Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in re-cent years. However, the network’s performance depends on the distribution of the training sets and degrades on out-of-distribution samples. This paper adopts a Bayesian approach for estimating uncertainty associated with output and applies it in a deep image super-resolution model to address the concern mentioned above. We use the uncertainty estimation technique using the batch-normalization layer, where stochasticity of the batch mean and variance generate Monte-Carlo (MC) samples. The MC samples, which are nothing but different super-resolved images using different stochastic parameters, reconstruct the image, and provide a confidence or uncertainty map of the reconstruction. We propose a faster approach for MC sample generation, and it allows the variable image size during testing. Therefore, it will be useful for image reconstruction domain. Our experimental findings show that this uncertainty map strongly relates to the quality of reconstruction generated by the deep CNN model and explains its limitation. Furthermore, this paper proposes an approach to reduce the model’s uncertainty for an input image, and it helps to defend the adversarial attacks on the image super-resolution model. The proposed uncertainty reduction technique also improves the performance of the model for out-of-distribution test images. To the best of our knowledge, we are the first to propose an adversarial defense mechanism in any image reconstruction domain.
Learning Embedding Adaptation for ISAR Image Recognition with Few Samples

Inverse Synthetic Aperture Radar (ISAR) image recognition with few samples is a key challenge for space target recognition. Prototype network addresses this challenge by learning an embedding function from training set and applying the function to targets from test set with few samples, but the learned embedding function is not optimal relative to unknown tasks. To this end, this paper employs a type of triple attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by establishing the dimensional dependency between labeled samples and targets to be identified in the task. The experimental results show that proposed method improves recognition accuracy by 24.92% in 5-way 1-shot task and 18.35% in 5-way 5-shot task, which verifies the effectiveness of the method.
Towards Sharing Student Models Across Learning Systems

Modern AIED systems develop sophisticated and multidimensional models of students. However, what is learned about students in one system—their skills, behaviors, and affect—is not carried over to other systems that could benefit students by using the information, potentially reducing both the effectiveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. In this paper, we discuss existing progress towards resolving this challenge, break down five sub-challenges, and propose how to address the sub-challenges.
Individualization of Bayesian Knowledge Tracing Through Elo-infusion

For as long as the Bayesian Knowledge Tracing (BKT) approach is known, so are the attempts to account for not only skill-level but individual student factors. A lot of computational methods to implement individualization in BKT were proposed over the past 25 years as BKT existed. To this day, virtually all individualization approaches were not suited for easy implementation. Either they were purely analytical (only fit for post-hoc analyses) or required significant computational effort to realize (e.g., calibrating individual factors as students cleared units of content). In this work, we discuss implementing the individualization of BKT using a mechanism of an Elo rating schema. Elo has been established in the educational domain for some time and offers tangible theoretical and practical benefits. We show that infusing BKT even with an Elo component using a single parameter to track student-specific factors results in significant quantitative and qualitative improvements to modeling student learning. This approach is easy to implement in a system already featuring BKT.
Non-Equivocation in Blockchain: Double-Authentication-Preventing Signatures Gone Contractual

Equivocation is one of the most fundamental problems that need to be solved when designing distributed protocols. Traditional methods to defeat equivocation rely on trusted hardware or particular assumptions, which may hinder their adoption in practice. The advent of blockchain and decentralized cryptocurrencies provides an auspicious breakthrough paradigm to resolve the problem above. In this paper, we propose a blockchain-based solution to address contractual equivocation, which supports user-defined fine-grained policy-based equivocation. Specifically, users will be de-incentive if the statements they made breach the predefined access rules. The core of our solution is a newly introduced primitive named Policy-Authentication-Preventing Signature (PoAPS), which combined with a deposit mechanism allows a signer to make conflict statements corresponding to a policy to be penalized. We present a generic construction of PoAPS based on Policy-Based Verifiable Secret Sharing (PBVSS) and demonstrate its practicality via a concrete implementation in the blockchain. Compared with the existing solutions that only handle specific types of equivocation, our proposed approach is more generic and can be instantiated to deal with various kinds of equivocation.