Reviewerly/exHarmony huggingface dataset:
Distributed Incremental Clustering Algorithms: A Bibliometric and Word-Cloud Review Analysis

“Incremental Learning (IL)” is the niche area of “Machine Learning.” It is of utmost essential to keep learning incremental for ever-increasing data from all domains for effectual decisions, predications and solving problems. This can be achieved effectually by applying “Incremental Clustering” methods on real-time data sources. IL can be achieved by “Incremental Clustering” easily as well as effectively. To achieve worldwide data analysis related to the data and to achieve broader perspectives, it is essential to deploy “Incremental Clustering” algorithms on distributed platforms, which will enable them to accept data from varied sources; analyze it and produce distributed worldwide solutions. This paper hence focuses on understanding the current status of “Distributed Incremental Clustering Algorithms (DICA),” its scope, limitations and other details so as to formulate better than the best algorithm in future. To enhance the analysis further Word-Clouds of impactful papers were explored and added in this paper, along with the details about platforms used to implement DICA by various upcoming researchers, readers and authors.
Modeling data-driven sensor with a novel deep echo state network

Data-driven approach has been widely utilized in modeling soft sensor for predicting key quality variables in process engineering area. The soft sensor is generally a time dependent dynamical model between the input and the output. Echo state network (ESN) is a typical data-driven modeling tool, which has exhibited excellent performance in temporal data processing area. However, the memory mode in the traditional ESN lacks flexibility. It is sometimes hard to preserve sufficient input features in the states, especially for modeling long-term dependent soft sensors. To solve this problem, this paper proposes an asynchronously deep echo state network (ADESN), which is composed of a number of sub-reservoirs that are connected one by one in sequence. Additionally, time delay modules are inserted between every two adjacent layers. The ADESN scheme preserves more input history in the states. Moreover, it can realize a selective memory. The validity of the ADESN is demonstrated on modeling a number of numerical and real-life soft sensors.
An Expressive “Test-Decrypt-Verify” Attribute-Based Encryption Scheme With Hidden Policy for Smart Medical Cloud

With the rapid development of cloud computing and the Internet of Things, many companies or individuals store their data in the cloud server, which brings new challenges to the security and privacy. Although traditional encryption schemes could solve some problems, data owners (DOs) might lose the access control over the data, which is important in some specific application scenarios, such as the smart medical cloud system. In order to address these problems, some ciphertext-policy attribute-based encryption (CP-ABE) schemes have been proposed to protect the privacy and security of data; but these schemes still have the following defects: 1) most of the existing hidden policy CP-ABE schemes only enable restricted access structure, such as “AND” gate; 2) several schemes supporting flexible access control are inefficient in decryption, because most of them are constructed in the composite order bilinear group; and 3) many of the proposed schemes fail to check the correctness of decryption message. In this article, we construct a “test-decrypt-verify” CP-ABE scheme based on prime order bilinear group to solve the above-mentioned problems. The proposed scheme supports secure outsourcing decryption, because the return value is an intermediate value unrelated to the encapsulation value of message encrypted by the DO.
High Security User Authentication Enabled by Piezoelectric Keystroke Dynamics and Machine Learning

With the rapid development of electric and information technologies, security issues are becoming a pressing concern. This has given rise to a plethora of authentication techniques, leading among them being the fixed-password method due to its simple mechanism and independence from any specific hardware in the modern smartphone. While fixed-password methods enjoy benefits such as ease of use, potential security issues associated with password leakage cannot be ignored. This article, investigates an alternative strategy based on user keystrokes. Here, the user touch times and force features are extracted from a piezoelectric force touch panel which is an integral part of the hardware. Three broadly adopted machine learning classifiers are used for the collected data set, finally achieving an Equal Error Rate (EER) of 0.720%.
Establishing prison-led contact tracing to prevent outbreaks of COVID-19 in prisons in Ireland

Abstract Background Preventing transmission of COVID-19 within prisons is both important and challenging. The confined conditions in prisons can encourage person-to-person spread with the potential for outbreaks occurring. Contact tracing is an important contributor to the longer-term management strategy for COVID-19 in prisons as well as in the community but is highly resource-intensive. This paper describes the approach to contact tracing taken by the Irish Prison Service (IPS). Methods The IPS National Infection Control Team, in collaboration with the National Quality Improvement (QI) team and Health Service Executive (HSE) in Ireland, implemented a programme to develop and train in-prison contact tracing teams (CTTs). CTTs were run by prison staff with experience of working with detainees, prison IT systems and CCTV. Protocols for undertaking contact tracing for both detainee and staff cases of COVID-19 were established. Results All prisons, and two support agencies, within the IPS now have fully functional in-prison CTTs. Every CTT has responded to at least one case COVID-19, undertaken contact tracing and instigated quarantine of contacts. Conclusions A partnership approach with development of prison-led CTTs can provide an effective mechanism for contact tracing of COVID-19 cases within the prison setting.
Telecom Fraud Identification Based on ADASYN and Random Forest

With the development of information and communication technology, the situation of communication frauds is becoming more and more serious, how to identify fraudulent telephone accurately and effectively has become an urgent task in telecom operation at present. Affected by the power law distribution, existing machine learning methods are used to identify the unbalanced distribution data set of positive and negative samples with low recognition accuracy. This paper proposes ADASYN+RF model. First of all, for the problem of unbalanced data sets, this paper chooses the ADASYN(Adaptive Synthetic Sampling) algorithm to rebalance the original data set. Secondly, we choose the random forest algorithm is employed to train the new data set to avoid overfitting. Finally, two groups of comparative experiments are carried out respectively, and the results show that: (1) For the processing of biased data, the ADASYN algorithm used in this paper is more advantageous than the traditional SMOTE algorithm;(2) Compared with Nonintegrated learning model, the accuracy, recall rate and F1 value of the ADASYN+RF model are significantly improved.
Detecting GANs and Retouching based Digital Alterations via DAD-HCNN

While image generation and editing technologies such as Generative Adversarial Networks and Photoshop are being used for creative and positive applications, the misuse of these technologies to create negative applications including Deep-nude and fake news is also increasing at a rampant pace. Therefore, detecting digitally created and digitally altered images is of paramount importance. This paper proposes a hierarchical approach termed as DAD-HCNN which performs two-fold task: (i) it differentiates between digitally generated images and digitally retouched images from the original unaltered images, and (ii) to increase the explainability of the decision, it also identifies the GAN architecture used to create the image. The effectiveness of the model is demonstrated on a database generated by combining face images generated from four different GAN architectures along with the retouched images and original images from existing benchmark databases.
Adversarial Jamming Attacks on Deep Reinforcement Learning Based Dynamic Multichannel Access

Adversarial attack strategies have been widely studied in machine learning applications, and now are increasingly attracting interest in wireless communications as the application of machine learning methods to wireless systems grows along with security concerns. In this paper, we propose two adversarial policies, one based on feed-forward neural networks (FNNs) and the other based on deep reinforcement learning (DRL) policies. Both attack strategies aim at minimizing the accuracy of a DRL-based dynamic channel access agent. We first present the two frameworks and the dynamic attack procedures of the two adversarial policies. Then we demonstrate and compare their performances. Finally, the advantages and disadvantages of the two frameworks are identified.
A Clustering-Based Framework for Improving the Performance of JPEG Quantization Step Estimation

Quantization plays a pivotal role in JPEG compression with respect to the tradeoff between image fidelity and storage size, and the blind estimation of quantization parameters has attracted considerable interest in the fields of image steganalysis and forensics. Existing estimation methods have made great progress, but they usually suffer a sharp decline in accuracy when addressing small-size JPEG decompressed bitmaps due to the insufficiency of coefficients. Aiming to alleviate this issue, this paper proposes a generic clustering-based framework to improve the performance of the existing methods. The core idea is to gather as many coefficients as possible by clustering subbands before feeding them into a step estimator. The proposed framework is implemented using hierarchical clustering with two kinds of histogram-like features. Extensive experiments are conducted to validate the effectiveness of the proposed framework on a variety of images of different sizes and quality factors, and the results show that notable improvements can be achieved. In addition to quantization step estimation, we believe the idea behind the proposed framework might provide inspiration for other forensic tasks to alleviate their performance issues induced by sample insufficiency.
Facial plastic surgery and face recognition algorithms: Interaction and challenges. A scoping review and future directions

Face recognition (FR) technology can be used in wide range of applications such as identity authentication, access control, and surveillance. Interests and research activities in face recognition have increased significantly over the past twenty years. Plastic surgery procedures can significantly alter facial appearance, thereby posing a serious challenge even to the state-of-the-art face matching algorithms. The purpose of this work was to detail the interaction between facial plastic surgery and facial recognition software and discuss the new challenges of this interaction. The authors critically reviewed the literature from January 2000 to September 2019, to identify articles reporting interactions between facial plastic surgery and facial recognition algorithms and discuss the new challenges of these interactions. Controlled vocabulary terms and keywords were used in the search strategy and two authors independently analyzed data. Factors included in the analysis were: Author, Journal, Year, Scope, Study design, Plastic surgery, Data (volume, origin and processing), Identification accuracy and Conclusion (interaction/challenge). Forty-three research articles underwent data extraction and 28 articles were included in quantitative synthesis. Of the 28 articles, the most common study designs were experimental evaluation (n = 15, 53,5%), Evaluation Study studies (n = 7, 25%) and Review studies (n = 4, 14,3%). Fifty percent of the articles have been published in the last 4 years (14 articles, 50%). Most of the study scope was informatics (64,3%). Only 10 articles were published in medical journals. Rhytidectomy (face lift) is the most challenging procedure for the FR algorithms. Data volume varied from 4 to 2878 subjects. The proposed algorithms provide at least 15 to 99% better identification performance. Among these, only two papers discuss the new challenges of the interaction between facial plastic surgery and Face Recognition Algorithms. In the context of advances in artificial intelligence, Internet connectivity and data integration, the purpose of this review is, to look forward to analyze the new interactions of facial plastic surgery and facial recognition algorithms, and to suggest avenues for future research and clinical application of this technology. Furthermore, to evaluate if plastic surgeons are prepared to discuss this technology with their patients. Plastic surgeons should be prepared to answer questions from patients about the fundamentals of facial recognition technology, and the potential effects of plastic surgery on facial recognition technology performance. Continued efforts are needed to provide scientifically rigorous data of facial biometric identification after facial plastic surgery and to include these notions in the routine consultation or consent process for patients seeking aesthetic facial surgery.
Interobserver Variability in Ductal Carcinoma In Situ of the Breast

Abstract Objectives Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic assessment has never sounded louder. Methods Here, the available classification systems for DCIS are discussed in depth. Results This comprehensive review illustrates that histopathologic evaluation of DCIS is characterized by significant interobserver variability. Future digitalization of pathology, combined with development of deep learning algorithms or so-called artificial intelligence, may be an innovative solution to tackle this problem. However, implementation of digital pathology is not within reach for each laboratory worldwide. An alternative classification system could reduce the disagreement among histopathologists who use “conventional” light microscopy: the introduction of dichotomous histopathologic assessment is likely to increase interobserver concordance. Conclusions Reproducible histopathologic assessment is a prerequisite for robust risk stratification and adequate clinical decision-making. Two-tier histopathologic assessment might enhance the quality of care.
A Variational Algorithm for Quantum Neural Networks

Quantum Computing leverages the laws of quantum mechanics to build computers endowed with tremendous computing power. The field is attracting ever-increasing attention from both academic and private sectors, as testified by the recent demonstration of quantum supremacy in practice. However, the intrinsic restriction to linear operations significantly limits the range of relevant use cases for the application of Quantum Computing. In this work, we introduce a novel variational algorithm for quantum Single Layer Perceptron. Thanks to the universal approximation theorem, and given that the number of hidden neurons scales exponentially with the number of qubits, our framework opens to the possibility of approximating any function on quantum computers. Thus, the proposed approach produces a model with substantial descriptive power, and widens the horizon of potential applications already in the NISQ era, especially the ones related to Quantum Artificial Intelligence. In particular, we design a quantum circuit to perform linear combinations in superposition and discuss adaptations to classification and regression tasks. After this theoretical investigation, we also provide practical implementations using various simulation environments. Finally, we test the proposed algorithm on synthetic data exploiting both simulators and real quantum devices.
“Smart” quarantine and “blanket” quarantine: the Czech response to the COVID-19 pandemic

By the end of May 2020, the Czech response to the COVID-19 pandemic has been recognized as a "success" following the fast introduction of strict nationwide preventive measures, nicknamed a "blanket quarantine". This article focuses on the alternative and rival concept of a "smart quarantine", which emerged at the beginning of the lockdown. Inspired by Korean and Singaporean anti-COVID-19 smart city solutions, a group of ICT professionals volunteered to develop a system that promised to help limit the spread of the infection and, at the same time, ease the nationwide lockdown within a foreseeable time. The idea was received enthusiastically, yet, two months later, smart solutions are still not fully integrated. This article reconstructs the story of the smart quarantine in Czechia and suggests considering possible societal consequences of unsatisfactorily valid smart tracing methodologies. Rather than seeing lockdown and smart solutions as opposite approaches to the current risk, it shows that a hybrid strategy may be considered, if not necessary, especially in contexts where smart solutions have been previously applied only to a limited extent.
Wearable Internet-of-Things platform for human activity recognition and health care

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.
Performance Analysis of Blockchain based Smart Grids with Ethereum and Hyperledger Implementations

Smart grids lay the foundation for future communities. Smart homes, smart buildings, smart streets, and smart offices are built when intelligent devices piles on intelligent devices. To reach the maximum capacity, they all must be supported by an intelligent power supply. For optimal and real-time electricity consumption, monitoring and trading, blockchain posses several potential benefits in its application to electricity infrastructure. To analyze the performance of the blockchain-based smart grid, this paper presents a virtual smart grid. A smart grid equipped with smart contracts, capable of executing virtual activities is evaluated and possible strengths and weaknesses are discussed. The paper draws a performance analysis of the blockchain-based smart grid by using the Ethereum and Hyperledger Fabric-based implementations.
HCA: Hierarchical Compare Aggregate model for question retrieval in community question answering

We address the problem of finding similar historical questions that are semantically equivalent or relevant to an input query question in community question-answering (CQA) sites. One of the main challenges for this task is that questions are usually too long and often contain peripheral information in addition to the main goals of the question. To address this problem, we propose an end-to-end Hierarchical Compare Aggregate (HCA) model that can handle this problem without using any task-specific features. We first split questions into sentences and compare every sentence pair of the two questions using a proposed Word-Level-Compare-Aggregate model called WLCA-model and then the comparison results are aggregated with a proposed Sentence-Level-Compare-Aggregate model to make the final decision. To handle the insufficient training data problem, we propose a sequential transfer learning approach to pre-train the WLCA-model on a large paraphrase detection dataset. Our experiments on two editions of the Semeval benchmark datasets and the domain-specific AskUbuntu dataset show that our model outperforms the state-of-the-art models.
Convolutional neural network architecture for beam instabilities identification in Synchrotron Radiation Systems as an anomaly detection problem

Solaris National Synchrotron Radiation Centre is a research facility that provides high quality synchrotron light. To control such a complex system it is necessary to monitor signals from various devices and subsystems. Despite the high demand for solutions to monitor the operation of centres, little work has concentrated on automatic analysis and fault detection. Anomaly detection prevents from financial loss, unplanned downtimes and in extreme cases cause damage. To address the problem a convolutional neural network (CNN) for fault detection in time series data has been proposed. The aim of the system is to identify abnormal status of sensors in certain time steps. In this study, we deploy transfer learning by examining pre-trained VGG-16, VGG-19, InceptionV3 and Xception CNN models with an adjusted densely-connected classifiers. Our database contains 336 h of signals in total which have been divided into 6300 time windows of 3 min length. The proposed solution, based on the VGG-16 architecture, detects anomalies in diagnostics signals with 92% accuracy and 85.5% precision what is a state-of-the-art result.
Cross-Attentional Bracket-shaped Convolutional Network for semantic image segmentation

As perception-related applications are of great importance in industrial production and daily life nowadays, solutions for understanding given images semantically receive numerous attention from the literature. To this end, significant accomplishments have been reached for such pixel-wise segmentation problem thanks to novel manipulations of integrating global context into local details in convolutional neural networks. However, this strategy in the existing work did not exhaustively exploit middle-level features, which carry reasonable balance between fine-grained and semantic information. Therefore, this paper introduces a Cross-Attentional Bracket-shaped Convolutional Network (CAB-Net) to leverage their contribution to the tournament of constructing pixel-wise labeled map. In concrete, fine-to-coarse feature maps of interest from the backbone network are densely combined by an efficient fusion of channel-wisely and spatially attentional schemes in crossing manner, namely Cross-Attentional Fusion, to embed semantically rich features into finer patterns. Continuously, these newly decoded outputs repeat the same procedure round-by-round until shaping a final feature map having finest resolution for complete scene understanding. Consequently, the proposed CAB-Net achieves competitive mean Intersection of Union performance on PASCAL VOC 2012 (83.6% without MS-COCO pretraining), CamVid (76.4%) and Cityscapes (78.3%) datasets.