Reviewerly/exHarmony huggingface dataset:
Design Space Analysis of Health Technologies for Families

Diverse disciplines, including Human-Computer Interaction have explored how health technologies can support families in dealing with health conditions and in promoting healthy living. With the changing of family structures and the increase of variety in family backgrounds, however, there is still a need to understand the extent which existing studies have addressed family health needs by proposing technological solutions. In this paper, we present a systematic review of the literature in order to see which family health topics have been researched extensively or not, and to identify hidden aspects and opportunities for family-centered health technology research. We had a final sample of 55 papers from diverse sources in this process. Our findings highlight characteristics of the studies, families, and health design. We also describe existing technological solutions and evaluation measures. Based on our findings, we discuss factors that were comprehensively studied and others that need further exploration in future research related to family-centered health technology.
A Comparative Study of Nighttime Object Detection With Datasets From Australia and China

Intelligent traffic surveillance (ITS) in nighttime has increasingly become important in advanced-driving-assisted-systems (ADAS). ITS relies on efficient detection of objects and adaptability to changing environments in the nighttime. However, detecting the visual features of the objects in nighttime poses challenge due to insufficient illumination conditions. With the development of deep learning object detectors, higher accuracies have been achieved in object detection from nighttime images. In this paper, we employ convolutional neural network (CNN) object detectors such as Faster R-CNN (with various feature extractors), Feature Pyramid Network, and Single Shot Detector to analyse their performance in terms of speed and accuracy using our datasets for nighttime object detection. We carry out the image quality assessment of our datasets and perform an automatic parameter tuning for re-training the object detectors. Experimental results show that the Faster R-CNN with inception has higher detection performance compared with other CNN based detectors.
Transfer Learning for Breast Cancer Classification using Small Dataset of Ultrasound Images

Globally, breast cancer has been identified as probably the most deadly type of cancer among women. This proposed work implements a sophisticated Breast Cancer Prognosis from Ultrasound Dataset using Transfer Learning. The two great challenges for the Researchers are Breast cancer analysis and the Prognosis. Malignancy patients are influenced by many aspects. All these factors and the outward symptoms in the individual are recorded making use of scanning devices such as Ultrasound pictures, histopathology, cytology, mammogram, etc. After that this data has been used for the medical diagnosis of cancer utilizing the Transfer learning method. This paper aims to compare & analyze our suggested model Transfer learning and deep learning techniques for Breast cancer Recognition from Ultrasound images predicated on parameters viz. Accuracy, Precision, Losses, Recall, and Area under Curve (AUC). The outcome of the proposed method results in best accuracy and obtains minimum losses when dealing with machine learning and deep learning approaches for the diagnosis of breast cancer in Ultrasound Images.
ARAIL: Learning to rank from incomplete demonstrations

Generative Adversarial Imitation Learning (GAIL) has been successfully applied to imitation learning in control tasks. However, most GAIL-like approaches require complete and high-quality demonstrations that are scarcely available in practice, which leads to unsatisfactory performances. Researches have proposed algorithms for incomplete demonstrations, which, however, are supposed to be effective only when exceptionally high-quality demonstrations are provided. To solve the problem, the Action-Rank Adversarial Imitation Learning (ARAIL) algorithm is introduced to target the issue of incomplete demonstrations. By reconstructing the standard GAIL framework and introducing the ranker model, ARAIL reshapes the reward function from the discriminator and auxiliary information from the ranker. The primary insight is that the ranker makes a better assessment of missing actions, which in turn helps to learn a better policy. We empirically compare our approach with SOTA algorithms on Atari and Mujoco platforms with imitation learning benchmarks, demonstrating that ARAIL improves both performance and robustness on various levels of incompleteness of actions in demonstrations.
Learning Rule for a Quantum Neural Network Inspired by Hebbian Learning

Utilizing the enormous potential of quantum computers requires new and practical quantum algorithms. Motivated by the success of machine learning, we investigate the fusion of neural and quantum computing, and propose a learning method for a quantum neural network inspired by the Hebb rule. Based on an analogy between neuron-neuron interactions and qubit-qubit interactions, the proposed quantum learning rule successfully changes the coupling strengths between qubits according to training data. To evaluate the effectiveness and practical use of the method, we apply it to the memorization process of a neuro-inspired quantum associative memory model. Our numerical simulation results indicate that the proposed quantum versions of the Hebb and anti-Hebb rules improve the learning performance. Furthermore, we confirm that the probability of retrieving a target pattern from multiple learned patterns is sufficiently high.
SLC: A Permissioned Blockchain for Secure Distributed Machine Learning against Byzantine Attacks

As data volume and complexity of the machine learning model increase, designing a secure and effective distributed machine learning (DML) algorithm is in direct need. Most traditional master-worker type of DML algorithms assume a trusted central server and study security issues on workers. Several researchers bridged DML and blockchain to defend against malicious central servers. However, some critical challenges remain, such as not being able to identify Byzantine nodes, not being robust to Byzantine attacks, requiring large communication overhead. To address these issues, in this paper, we propose a permissioned blockchain framework for secure DML, called Secure Learning Chain (SLC). Specifically, we design an Identifiable Practical Byzantine Fault Tolerance (IPBFT) consensus algorithm to defend against malicious central servers. This algorithm can also identify malicious central servers and reduce communication complexity. In addition, we propose a Mixed Acc-based multi-Krum Aggregation (MAKA) algorithm to prevent Byzantine attacks frommalicious workers. Finally, our experiment results demonstrate our proposed model's efficiency and effectiveness.
Ensemble Model Approach For Imbalanced Class Handling on Dataset

In the field of machine learning, the distribution of classes in the dataset is important to produce a good model. The existence of class imbalances in the dataset is often ignored by researchers in the field of machine learning. This will certainly make the resulting model have less than maximum performance because theoretically, the single classifier has a weakness to the class imbalance conditions in datasets, this is because the majority of single classifier tends to work by recognizing patterns in the majority class in datasets that are not balanced so that the performance is not can be the maximum. Therefore, it is necessary to deal with these problems. In this study proposing an algorithmic level approach using Random Forest and Stacking to deal with these problems, the basic idea of using an algorithmic level approach is not to change the composition or pattern contained in the dataset itself. Based on tests using 5 datasets with different imbalance ratios, it shows that Random Forests or Bagging Tree and Stacking Naïve Bayes and Decision Tree C4.5 can produce better performance than single classifiers such as SVM, Naïve Bayes, and Decision Tree C4.5. So, the proposed method can be a solution in handling class imbalance in the dataset with different imbalance ratios.
Flexible Genetic Algorithm Operators for Task Scheduling in Cloud Datacenters

Cloud computing is the backbone of the modern information technology industry. Due to the increase in internet usage, social media, and smart phones, a large amount of data is producing. Cloud datacenters can provide resources to handle this data. If data is not properly handled, it can cause overhead on cloud servers and can increase operational costs. Genetic algorithm is used to solve scheduling problem efficiently, but they take a lot of time to find an optimal solution. In this paper, we proposed Flexible Genetic Algorithm Operators (FGAO) for Task Scheduling in Cloud Datacenters. This algorithm changes crossover and mutation operators according to the quality of scheduling solutions. Instead of giving a fixed stopping criteria algorithm uses flexible crossover and mutation operators as a stopping criterion. Experimental results show that the proposed FGAO algorithm reduces 40% execution time and 33% iterations as compared to the genetic algorithm.
COVID-19 Risk Management: A Mobile Application to Notify Alerts and Provide Real-Time COVID-19 Updates

The sudden outbreak of coronavirus has left people unanswered till date. COVID-19 virus cases are drastically increasing day by day. The only way to protect ourselves from COVID-19 is by maintaining social distancing and taking preventive measures. A mobile application-based solution is proposed for risk management of COVID-19. The objective of the proposed model is to help people with the status of cases in their region. The proposed model notifies the user regularly about the COVID-19 cases in nearby locations which help him/her to be cautious. Android studio and Google Firebase are used to develop the mobile application. COVID-19 information and state-wise cases are updated in the database regularly, and users are notified. The results are good in terms of response time and delivery of static alerts.
An optimization of im2col, an important method of CNNs, based on continuous address access

Convolutional neural networks (CNNs) are now widely used in various common tasks such as image classification, semantic segmentation, and face recognition. Convolution layers are the core layers of CNNs, the computing speed of the convolution layer will directly affect the computing speed of the entire network, thereby affecting the real-time performance. The current general convolutional layer acceleration method is to use the image to column (im2col) algorithm to split the input image into a column matrix, then use the general matrix multiplication (GEMM) to perform matrix multiplication on the column vector and the convolution kernel. This operation can greatly improve the computing speed of the convolutional layer because most computing platforms have more mature optimizations for GEMM. However, DSP is very fast for vector multiplication and addition. In the inference of the convolutional layer, the memory access of the im2col algorithm consumes far more time than the GEMM. This has become a bottleneck for further optimization of computing speed. In this article, I will present an im2col algorithm acceleration method in the case of a single stride based on continuous memory address read. With this method, the speed of the im2col algorithm can be increased by more than 10 times when processing a single-step convolutional layer. This is a portable method. In this article, I'11 show the optimization effects on Xtensa BBE64ep DSP cores and stm32f4 processors.
Towards a Hybrid Recommendation System On Apache Spark

Now a day's success of internet business depends on its capability in providing personalized experiences for the users. In the era of SMAC, Social, Mobile, Analytics & Cloud the data is dynamic. But as the digital data is exponentially increasing users are having a deluge of options for services and commodities. Recommender Systems help users in overcoming the paradox of alternatives. This paper precis different approaches for Content based, Collaborative and hybrid recommendation systems to handle the usual problems of cold start and data sparsity. To generate accurate recommendation a hybrid frame work is proposed on the score. Experiments on movie lens dataset justify that the model proposed bring out better recommendations than the standard methods.
Liver Pathological States Identification in Diffuse Diseases with Self-Organization Models Based on Ultrasound Images Texture Features

The article deals with the construction of the norm-pathology states classifiers according to statistical features of the ultrasound images texture in diffuse liver diseases. A number of new features are proposed to distinguish the texture of classes. Classifiers are constructed in the form of analytical expressions using the GMDH Shell DS software and in the form of a forest, the trees of which are obtained in accordance with the GMDH principles. The work was performed on data, had provided by the Nuclear Medicine and Radiation Diagnostics Institute of the National Academy of Medical Sciences of Ukraine.
Analysis of Gender Identification in Bahasa Indonesia using Supervised Machine Learning Algorithm

Gender classification or identification is an interesting research area in speech or voice signal processing. It is a promising research area, and there still a room for improvements, especially in the localization context. There are not many researches related gender identification in Bahasa Indonesia. Most of the research found are in English, some are in Chinese, Korea, Arab, France. This paper will used primary data, self-collected in Bahasa Indonesia to identify the gender using the supervised machine learning algorithm. MFCC is used as the feature extraction algorithm for input in the machine learning. After comparing several algorithms: Artificial Neural Network, SVM and K-Nearest Neighbors (KNN) algorithm, ANN shows more promising result then others. There are 2.735 primary data used in this research. The result in this research will be used in future experiment about the impact of gender classification in voice recognition in Bahasa Indonesia.
Secure Data Analytics for IoT Cloud-enabled Framework Using Intel SGX

Cloud infrastructure capabilities, including massive, scalable and elastic computing resources, have led to the widespread adaption of Internet of Things (IoT) cloud-enabled services. This involves moving the storage and processing of sensitive IoT data to Cloud Service Providers (CSPs) that gain complete access to outsourced IoT data in the cloud. An efficient and lightweight Advanced Encryption Standard (AES) cryptosystem can play a major role in protecting IoT data from being exposed to CSPs by protecting the privacy of sensitive outsourced data. However, AES cryptosystems lack computation capabilities, which is a critical factor that prevents us taking full advantage of cloud computing services. When used with AES cryptosystems, Intel Software Guard Extensions (SGX) can provide a comprehensive solution to building secure data analytics framework for IoT-enabled application in various domains. In this paper, we develop a secure data analytics framework that relies on a hyper-integrated approach where both software- and hardware-based solutions are applied to protect and process sensitive outsourced data in the cloud.
A Study of Current Trends Writer Identification in Large-scale Across Three World Major Languages with Retrieval Approaches

Recent developments in writer identification have led new directions, which is writer identification integrated with retrieval mechanism for boosting the performance applications of writer identification when a database is getting larger in size and retrieval of documents from expanding databases is becoming difficult. Issues on datasets used by previous studies directly impacts accuracy of the writer identification deteriorates as database size increases. Until now, in the research on writer identification researchers have been mainly interested in “writer identification without retrieval mechanism” approach. This paper provides a review on the state of the art for writer identification with retrieval mechanism throughout three major languages, namely English, Chinese, and Arabic. Up to date, truly little attention has been paid to the role of retrieval mechanism or documents in English language identification; the growth on Arabic and Chinese is far from satisfactory in comparison to its extensive usage. A search of the literature revealed few studies for writer identification with single retrieval mechanism. Seamless integration of such strategies in current forensic handwriting experience is yet unclear. A consistently reliable and efficient, fully automated writer recognition (AWR) and a powerful handwritten document retrieval system as an emerging technology are real challenging issues might be taken by upcoming researchers. It is still considered a fresh field and still has large room for research.
Optimized Version of Tree based Support Vector Machine for Named Entity Recognition in Medical Literature

Medical literature comprises valued information, like clinical symptoms, diagnosis, dosage, and treatment for specific diseases of a particular disease. Named Entity Recognition (NER) is a primary process involved in the extraction of knowledge from unstructured text and providing it as a Knowledge Graph (KG). Several existing works of NER suffers from small scale human- labeled training dataset. Since the extraction of knowledge from medical literature is a difficult process, this paper aims to focus on the development of the NER model using machine learning (ML) approaches to improve efficiency. The proposed model is based on the optimized version of support vector machine (O-SVM) where the optimal parameters of the tree based SVM are tuned by the particle swarm optimization (PSO) algorithm. In addition, the medical dataset is initially preprocessed and then the classification process takes place via the O-SVM model. The weight and bias parameters in tree based SVM model are tuned by the PSO algorithm. The experimental results analysis of the O-SVM model is carried out and the results are compared with the state of art approaches in terms of diverse measures.
Visualization for Spectators in Cybersecurity Competitions

The goal is to raise awareness and encourage learning cybersecurity principles by making competitions appealing to a wider audience. In an effort to make events compelling, attractive, and watchable, the researchers will develop systems to support visualizations and make the transactions between teams in different cybersecurity competitions easy to comprehend. In informing and educating the audience on the intricacies of the competition through engaging visualizations, cybersecurity competitions will be opened up to a world beyond just participants. In doing so, we can potentially attract new talent into the field. Our team seeks to make prototype visualizations for key actions in various student cybersecurity competitions and assess spectator understanding of key principles of the competition.
Research on Identification of Fraud in Financial Reporting Based on Adaptive Group Lasso-Logistic Regression

Non-financial indicators are an important basis for fraud Identification in financial reports, but the quantification of non-financial indicators will also cause problems such as more dummy variables in the identification model, the correlation of explanatory variables and the decrease of identification rate, this makes it in the financial report fraud Identification application is more limited. In view of this situation, this paper proposes a fraud Identification model based on Lasso-Logistic regression of adaptive group, and selects the data of listed companies in China from 2010 to 2018 as a sample, considering both financial and non-financial indicators, an empirical study is carried out by using the model. The results show that this model has higher recognition rate and robustness than the previous Logistic model, Lasso-Logistic model and adaptive Lasso-Logistic model when several non-financial indicators are introduced, it solves the problem of the application of non-financial index in fraud Identification effectively, and has certain application value.
Method for measuring the radius of mean curvature of a spherical surface based on phase measuring deflectometry

In this paper, a method for measuring the radius of mean curvature ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) of the spherical surface based on phase measuring deflectometry is proposed. With our method, the average of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> in a small region is constrained by a preknown radius of curvature ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ), and the height of an anchor point in this region can be calculated with iterative and optimization methods. Then both the height and slope data of the spherical surface can be obtained, and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> of each point of the spherical surface can be calculated by a differential geometric method. The feasibility of the proposed method is verified by simulation. In the experiment, a spherical surface with 88.652 mm radius of curvature is measured to demonstrate the effectiveness of this method, which can be used to accurately measure the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>R</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>m</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> of each point on the spherical surface by online or in situ measurement.
The investigation on cowrie honeypot logs in establishing rule signature snort

Abstract The attack of brute force is still one of the popular attacks used to hack into your account unauthorized by a computer system. Brute force is also the most crucial attack and has a high risk of the system being taken over. Investigating brute force attacks is useful for building strong computer network defense systems. In this study, Snort acts as an intrusion prevention system and Cowrie Honeypot as a tool to investigate anomalous behavior that occurs when a brute force attack happened. The aim of this research is to improve Snort's rule signature performance from brute force attacks by relying on the results of the Cowrie Honeypot log investigation. The results obtained, namely Snort rule signature successfully improved detection capabilities with performance in matching the same packet only requires a short processing time, respectively: 3.5 microsecs in Hydra attacks, 3.8 microsecs in Medusa attacks and 2.3 microsecs in Ncrack attacks.