Reviewerly/exHarmony huggingface dataset:
Two-Stage CNN Model for Joint Demosaicing and Denoising of Burst Bayer Images

In the classical image processing pipeline, demosaicing and denoising are separated steps that may interfere with each other. Joint demosaicing and denoising utilizes the shared image prior information to guide the image recovery process. It is expected to have better performance by the joint optimization of the two problems. Besides, learning recovered images from burst (continuous exposure images) can further improve image details. This article proposes a two-stage convolutional neural network model for joint demosaicing and denoising of burst Bayer images. The proposed CNN model consists of a single-frame joint demosaicing and denoising module, a multiframe denoising module, and an optional noise estimation module. It requires a two-stage training scheme to ensure that the model converges to a good solution. Experiments on multiframe Bayer images with simulated Gaussian noise show that the proposed method has obvious performance advantages and speed advantages compared with similar approaches. Experiments on actual multiframe Bayer images verify the denoising effect and detail retention ability of the proposed method.
A Privacy-Preserving Auditable Approach Using Threshold Tag-Based Encryption in Consortium Blockchain

The rising attention of deploying consortium blockchain in the industry has facilitated a wide scope of enterprise-level applications. In consortium blockchain, each participant’s identity needs to be verified before it joins the blockchain network, which implies both identity leakage and the linkability between entites are targets for privacy attackers. In this paper, we propose a novel approach to hide both identity and linking relations between participants. An auditor role is developed in our solution to trace down suspicious transactions to identify malicious participants in a decentralized manner. Security analysis and experiment evaluations are given for evidencing the effectiveness of our approach in this work.
Multi-Class Voice Disorder Classification Using OpenL3-SVM

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DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform

With the introduction of whole slide imaging (WSI) systems, several digital pathology applications have emerged. Despite all benefits, lacking appropriate infrastructure to process proprietary WSI file formats for remote diagnosis and annotation is a constraint for widespread application of digital pathology. The joint imaging platform (JIP) already includes a wide range of solutions for digital medical image processing, mainly focused on radiology. We extend the infrastructure in the JIP for accessing, storage, remote analysis and deep learningbased processing of pathological data. By converting proprietaryWSI file formats into the DICOM standard, we enable the linkage of radiology and pathology on the JIP and show potential applications in current research studies.
The Influence of HRIS Usage on Employee Performance and Mediating Effects of Employee Engagement in Five Stars Hotels in Jordan

The purpose of this study is to evaluate the role of employee engagement with their work as a mediating factor in the relationship between utilisation of a human resource information system (HRIS) and employee performance in five-star hotels in Jordan. A conceptual framework based on social exchange theory and content analysis of similar previous studies was developed. A quantitative approach and simple random sampling were used to obtain data via questionnaires completed by 221 human resources (HR) employees working at 21 five-star hotels in Jordan. The response rate was 71%. Structural equation modelling (SEM) was used to analyse and verify the study variables. The findings revealed that use of a HRIS had a significant positive influence on both employee performance and work engagement, while the latter acted as a partial mediator between HRIS utilisation and employee performance.
Research Challenges in Breast Cancer Classification through Medical Imaging Modalities using Machine Learning

Breast cancer (BrC) ensues in the breast cells besides it is the utmost predominant disease in females in the biosphere later skin cancer. Breast malignant growth is the most widely-recognized form of disease, and in women it is regular to the extent where one is determined around the world to have a bite of dust conviction, and the resulting driving cause of death in women is essentially due to 60 per cent of the detection. Key challenges in the identification and remediation of malignancy cells are the upgrading of the research pipeline, the advancement of disease wonders, the production of preclinical models, the unmistakable handling of complex tumors, early care, innovative strategies for preparing and conveying clinical preliminary results, and the improvement of precision that will be of benefit to physicians as a second and early evaluation. The study illustrates research challenges when how disease analysis, remedial action is supported by the usage of machine learning (ML) also deep learning (DL) techniques using different dataset.
II-UserCF: Optimizing movie recommendation systems based on UserCF improvement algorithm

With the advent of the e-commerce era and the development of computer technology. Recommendation systems are widely used in people's lives. Traditional user-based collaborative filtering recommendations often produce prediction errors because they do not take into account the popularity of the item. In order to improve this drawback, a new collaborative filtering recommendation algorithm based on a combination of traditional usercf, and item importance is proposed. User relevance is first calculated. Then a new value is obtained based on the different popularity levels of the items. The two are combined in order to obtain a new user similarity. To better predict the similarity between users and to produce more accurate recommendation results. Research shows that adding a value related to the popularity of the product can effectively improve the accuracy of the recommendation.
Cascaded Feature-Mask Fusion for Foreground Segmentation

Foreground segmentation aims at extracting moving objects from the background in a robust manner under various challenging scenarios.The deep learning-based methods have achieved remarkable improvement in this field.These methods produce semantically correct predictions based on extracted rich semantic features yet perform poorly on segmentation of edge details.The main reason is that the high-level features extracted by the deep network lose the high-frequency information for the successful edge segmentation.On this basis, we propose a novel segmentation network with a cascade architecture to refine segmentation results step by step by introducing detailed information into high-level features.The network recorrects and optimizes the segmentation maps in each step so that more accurate segmentation results are obtained.Furthermore, we evaluate our approach on the challenging CDnet2014 dataset and achieve an F-measure of 0.9868.Our approach thus outperforms previous methods, such as FgSegNet_v2, FgSegNet, BSPVGan, Cascade CNN, IUTIS-5, WeSamBE, DeepBS, and GMM-Stauffer.
Enable data privacy, dynamics, and batch in public auditing scheme for cloud storage system

Abstract With the popularity of cloud computing, cloud storage technology has also been widely used. Among them, data integrity verification is a hot research topic. At present, the realization of public auditing has become the development trend of integrity verification. Most existing public auditing schemes rarely consider some indispensable functions at the same time. Thus, in this article, we propose a comprehensive public auditing scheme (PDBPA) that can simultaneously support data privacy protection, data dynamics, and multi‐user batch auditing. To guarantee privacy protection during the audit process, our PDBPA design a new method of constructing audit proof, which combines random masking techniques and bilinear properties of bilinear pairing. Not only can it ensure that TPA performs audits correctly, but it can also prevent it from exploring the user's sensitive data. In addition, by utilizing the modified dynamic hash table, which is a novel and small two‐dimensional data structure, data dynamics can be effectively achieved. Furthermore, we provide a detailed process for the third‐party auditors to perform batch audits for multiple users. Moreover, we give the detailed and rigorous security analysis in defending against forgery attack, replace attack, and replay attack. Performance evaluations demonstrate that our PDBPA scheme is effective and feasible.
Non-parametric scene parsing: Label transfer methods and datasets

Scene parsing is the problem of densely labeling every pixel in an image with a meaningful class label. Driven by powerful methods, remarkable progress has been achieved in scene parsing over a short period of time. With growing data, non-parametric scene parsing or label transfer approach has emerged as an exciting and rapidly growing research area within Computer Vision. This paper constitutes a first survey examining label transfer methods through the lens of non-parametric, data-driven philosophy. We provide insights on non-parametric system design and its working stages, i.e. algorithmic components such as scene retrieval, scene correspondence, contextual smoothing, etc. We propose a synthetic categorization of all the major existing methods, discuss the necessary background, the design choices, followed by an overview of the shortcomings and challenges for a better understanding of label transfer. In addition, we introduce the existing standard benchmark datasets, the evaluation metrics, and the comparisons of model-based and data-driven methods. Finally, we provide our recommendations and discuss the current challenges and promising research directions in the field.
An approach to email categorization and response generation

The creation of automatic e-mail responder systems with human-quality responses is challenging due to the ambiguity of meanings and difficulty in response modeling. In this paper, we present the Personal Email Responder (PER); a novel system for email categorization and semi-automatic response generation. The key novelty presented in this paper is an approach to email categorization that distinguishes query and non-query email messages using Natural Language Processing (NLP) and Neural Network (NN) methods. The second novelty is the use of Artificial Intelligence Markup Language (AIML)- based chatbot for semiautomatic response creation. The proposed methodology was implemented as a prototype mobile application, which was then used to conduct an experiment. Email messages logs collected in the experimental phase are used to evaluate the proposed methodology and estimate the accuracy of the presented system for email categorization and semi-automatic response generation.
The TSC-PFed Architecture for Privacy-Preserving FL

In this paper we will introduce our system for trust and security enhanced customizable private federated learning: TSC-PFed. We combine secure multiparty computation and differential privacy to allow participants to leverage known trust dynamics which allow for increased ML model accuracy while preserving privacy guarantees and introduce an update auditor to protect against malicious participants launching dangerous label flipping data poisoning. We additionally introduce customizable modules into the TSC-PFed ecosystem which (a) allow users to customize the type of privacy protection provided and (b) provide a tiered participant selection approach which considers variation in privacy budgets.
Improved Reranking Approach for Person Re-identification System

Safety is ensured in all the public places with the help of surveillance cameras. It can be used to find any person involved in crime or lost person. Human operators do this job by enabling correspondence between images of same person captured across diffrent cameras. Automation of the same is called as person re-identification (PRID). One of the major challenges of the PRID process is to determine the representation of images which discriminates the person identity irrespective of the view-points, poses, illumination variations, and occlusions. With this motivation, a new deep learning-based inception network (DLIN) with ranking is proposed for person re-identification (PRID) in surveillance videos. The DLIN technique mainly uses Adadelta with Inception-v4 model as feature extraction technique in which the hyperparameters of the Inception-v4 model are optimally tuned by the use of Adadelta technique. The features are extracted from the probe and gallery images. Euclidean distance-based similarity measurement and expanded neighborhood distance reranking (ENDRR) is employed for determining the similarity and ordering the output of the PRID process along with Mahalanobis distance. A wide range of simu-lations are carried out using CUHK01 benchmark dataset and the experimental outcomes ensured the goodness of the DLIN technique over the other existing techniques.
An Integrated Machine Learning Framework for Fraud Detection

The research develops a practical Machine Learning framework with a comparative and comprehensive approach to sequence-learn and then detect the online banking payment fraud. The integrated framework introduces exploratory analysis and feature engineering, multiple modelling and performance comparison, and model robustness, uncertainty and sensitivity analysis toward a systematic approach for Machine Learning applications. For demonstration purpose, the framework is implemented on a set of real-life online banking transaction datasets obtained from a UK-based bank through three models, i.e., Support Vector Machine, Markov Model and LSTM model, with various combinational features of the datasets evidenced in the exploratory analysis and modelling with noise ratios of datasets, range values of model parameters and confidence intervals of prediction results. The modelling results show that overall, the LSTM model achieves the best performance, with outcome accuracy of 97.7%, indicating its advantage in modelling sequential data such as customer behaviours.
A Practical Framework for General Dialogue-Based Bilateral Interactions

For autonomous agents and services to cooperate and interact in multi-agent environments they require well-defined protocols. A multitude of protocol languages for multi-agent systems have been proposed in the past, but they have mostly remained theoretical or have limited prototypical implementations. This work proposes a practical realisation of a general framework for defining dialogue-based bilateral interaction protocols which supports arbitrary agent-based interactions. Crucially, this work is tightly integrated with a modern framework for the creation of autonomous agents and multi-agent systems, making it possible to go from protocols’ specification to their implementation and usage by agents, and enables evaluation of protocols’ effectiveness and applicability in real-world use cases.
New (k,l,m)-verifiable multi-secret sharing schemes based on XTR public key system

Secret sharing was proposed primarily in 1979 to solve the problem of key distribution. In recent decades, researchers have proposed many improvement schemes. Among all these schemes, verifiable multi-secret sharing (VMSS) schemes are studied sufficiently, which share multiple secrets simultaneously and perceive malicious dealer as well as participants. By pointing out that the schemes presented by Dehkordi and Mashhadi in 2008 cannot detect some vicious behaviors of the dealer, we propose two new VMSS schemes by adding new validity check in the verification phase to overcome this drawback. Our new schemes are based on XTR public key system, and can realize GF(p6) security by computations in GF(p2) without explicit constructions of GF(p6), where p is a prime. Compared with the VMSS schemes using RSA and linear feedback shift register (LFSR) public key cryptosystems, we can achieve the same security level with shorter parameters by using trace function. What's more, our schemes are much simpler to operate than those schemes based on Elliptic Curve Cryptography (ECC). In addition, our two schemes are dynamic and threshold changeable, which means that it is efficient to implement our schemes when participants, secrets or the threshold needs to be changed.
Effect of Feature Selection on Software Fault Prediction

Software is composed of several modules. Software reliability, quality, and maintenance cost are drastically affected by the existence of software glitches. Achieving bug-free software is hard as most of the time there are hidden defects. Several techniques have been introduced to overcome the software defect problem. Predicting the rate of faultiness in software modules before commercializing is still a challenge to software engineers. The main objective of the software fault prediction module is not only to detect the faultiness of the software but also to identify the components which are likely to be defective. In this study, we aim to apply and compare different machine learning algorithms such as Support Vector Machine and Random Forest to develop software fault, prediction models. The developed models are applied on fault datasets collected from different open source repositories such as the PROMISE repository. We analyze the results of the developed models using different performance measures such as Accuracy, AUC, and F1-Score.
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network

Incremental text-to-speech (TTS) synthesis generates utterances in small linguistic units for the sake of real-time and low-latency applications. We previously proposed an incremental TTS method that leverages a large pre-trained language model to take unobserved future context into account without waiting for the subsequent segment. Although this method achieves comparable speech quality to that of a method that waits for the future context, it entails a huge amount of processing for sampling from the language model at each time step. In this paper, we propose an incremental TTS method that directly predicts the unobserved future context with a lightweight model, instead of sampling words from the large-scale language model. We perform knowledge distillation from a GPT2-based context prediction network into a simple recurrent model by minimizing a teacher-student loss defined between the context embedding vectors of those models. Experimental results show that the proposed method requires about ten times less inference time to achieve comparable synthetic speech quality to that of our previous method, and it can perform incremental synthesis much faster than the average speaking speed of human English speakers, demonstrating the availability of our method to real-time applications.
Sign Language Recognition Using Diverse Deep Learning Models

In society, communication is a very essential thing. The deaf, dumb peoples face different communication issues. The only means used by these peoples for the communication is by the sign language. It is a very challenging task to develop an automated model for sign language recognition. Presently for analyzing the visual imagery, a convolutional neural network (CNN) model has been used. It is a class of deep neural network. In this paper, we have analyzed various gestures of sign language and elaborate the comparative study of diverse convolution neural network models. The CNN models (AlexNet, VGGNet, GoogLeNet, and EfficientNet) have been used and analyzed for the sign language recognition system. The benchmarks ASL alphabet dataset has been used for quantitative experimentation.
Representation learning via consistent assignment of views to clusters

We introduce Consistent Assignment for Representation Learning (CARL), an unsupervised learning method to learn visual representations by combining ideas from self-supervised contrastive learning and deep clustering. By viewing contrastive learning from a clustering perspective, CARL learns unsupervised representations by learning a set of general prototypes that serve as energy anchors to enforce different views of a given image to be assigned to the same prototype. Unlike contemporary work on contrastive learning with deep clustering, CARL proposes to learn the set of general prototypes in an online fashion, using gradient descent without the necessity of using non-differentiable algorithms or K-Means to solve the cluster assignment problem. CARL surpasses its competitors in many representations learning benchmarks, including linear evaluation, semi-supervised learning, and transfer learning.
Spatial-Temporal Constrained Pseudo-labeling for Unsupervised Person Re-identification via GCN Inference

Most existing unsupervised person re-identification (Re-ID) methods primarily depend on the cluster distance, and merely exploit the available source labeled data to assign pseudo labels for the unannotated data. Whereas, the cluster distance usually fails to adapt to different datasets due to the domain gap. Besides, learning exclusively from the source data can not generate accurate pseudo labels for the lack of the target data information. To address this problem, we propose to exploit the spatial-temporal constraints to facilitate the pseudo label generation process. Specifically, graphs for the labeled source data are constructed and the graph convolution network (GCN) is used to learn graph embeddings. Based on these graph embeddings, the likelihood of linkages between graph nodes is estimated and utilized to assign pseudo labels for the unlabeled data. Then, with the pseudo labels, a smoothed spatial-temporal probability distribution model is generated to amend the likelihood of linkages between graph nodes as well as correct the visual similarity scores for person Re-ID. Finally, we optimize the pseudo label assignment, feature extraction networks, and spatial-temporal model alternatively and iteratively to improve the person Re-ID performance. Comprehensive experiments demonstrate that the proposed method outperforms state-of-the-art methods.
Does the introduction of stock exchange markets boost economic growth in African countries?

We assess whether the introduction of private equity capital markets affects economic growth in African countries. We address this issue by focussing on stock exchange markets as the predominant type of new equity markets, using a Diff-in-Diff regression method. The analysis uses a panel data set from 48 Sub-Saharan countries over the time range of 1970–2018. 23 countries are part of the “treated” group – which introduced international stock exchanges – and 25 “untreated” countries serve as the control group. Our results show that when compared with the time period prior to the introduction of stock exchange markets, GDP per capita rises by the amount of 532 US$ (around 40% of the Sub-Saharan average) after the introduction of equity capital markets in the treated countries. Over the ten years post introduction, the effect is hump-shaped, with effects becoming statistically significant from the first year after implementation, with a peak in the 5th year, and it then becomes statistically insignificant from then onwards.