Reviewerly/exHarmony huggingface dataset:
Machine Learning Classifiers for Symptom-Based Malaria Prediction

A patient's previous history of malaria plays an important role in malaria prediction based on symptoms. Malaria patients who have previously suffered from malaria are highly likely to develop different symptoms from those without a previous history of malaria. To predict the malaria presence for both patients with and without a previous history of malaria, we build two separate malaria classifiers based on two different sets of symptoms using four machine learning techniques including neural networks (NNs), logistic regression (LR), support vector machines (SVMs) and k-nearest neighbors. These malaria classifiers are built using medical records collected from patients suffering from malaria and other febrile diseases. Extensive experiments conducted show that the two NN classifiers slightly outperform the other classifiers. The NN classifier for patients with a previous history of malaria achieves excellent performance for accuracy, recall and F1-score with 95.76%, 95.41% and 95.76% respectively. The LR classifier outperforms the other three classifiers for precision with 97.14%. The NN classifier for patients without a previous history of malaria also achieves superior performance in accuracy and precision with 88.48% and 87.34% respectively. The SVM classifier outperforms other classifiers in terms of recall and F1-score with 94.62% and 88.04% respectively. With high recall rates, the four classifiers are suitable for symptom-based malaria prediction. These results justify the development of two separate malaria classifiers and provide valuable insights into building symptom-based malaria classifiers based on patients' previous history of malaria.
Towards Robust Certified Defense via Improved Randomized Smoothing

Deep learning models change their prediction through a carefully optimized imperceptible change in the input termed as an adversarial perturbation. Researchers have been focusing on developing methods to counter such effects. Recently, randomized smoothing a highly scalable technique to develop a certified classifier was introduced. However, this technique involves training a neural network from scratch. In this paper we present an empirical insight into the technique of randomized smoothing and propose a framework for a generalizable defence that works on a novel way of adding gaussian noise to the randomized smoothing procedure and is applicable to black-box pre-trained classifiers. We perform extensive experimentation across a variety of classification models and multiple datasets such as Cifar10 and ImageNet. Our framework is model-agnostic and out-performs the recent state-of-the-art certified defence methods by a large margin without the requirement of any intensive training, thus achieving high certified as well as unperturbed performance.
Low Cost Implementation of Deep Neural Network on Hardware

Recently, deep learning framework gained extreme importance in various domains such as Computer Vision, Natural Language Processing, Bioinformatics, etc. The general architecture of deep learning framework is very complex that includes various tunable hyper-parameters and millions/billions of learnable weight parameters. In many of these Deep Neural Network (DNN) models, a single forward pass requires billions of operations such as multiplication, addition, comparison and exponentiation. Thus, it requires large computation time and dissipates huge amount of power even at the inference/prediction phase. Due to the success of DNN models in many application domains, the area and power efficient hardware implementations of DNNs in resource constraint systems have recently become highly desirable. To ensure the programmable flexibility and shorten the development period, field-programmable gate array (FPGA) is suitable for implementing the DNN models. However, the limited bandwidth and low on-chip memory storage of FPGA are the bottlenecks for deploying DNN on these FPGAs for inferencing. In this paper, Binary Particle Swarm Optimization (PSO) based approach is presented to reduce the hardware cost in terms of memory and power consumption. The number of weight parameters of the model and floating point units are reduced without any degradation in the generalization accuracy. It is observed that 85% of the weight parameters are reduced with 1% loss in accuracy.
General Object Pose Transformation Network from Unpaired Data

Object pose transformation is a challenging task. Yet, most existing pose transformation networks only focus on synthesizing humans. These methods either rely on the keypoints information or rely on the manual annotations of the paired target pose images for training. However, collecting such paired data is laboring and the cue of keypoints is inapplicable to general objects. In this paper, we address a problem of novel general object pose transformation from unpaired data. Given a source image of an object that provides appearance information and a desired pose image as reference in the absence of paired examples, we produce a depiction of the object in that specified pose, retaining the appearance of both the object and background. Specifically, to preserve the source information, we propose an adversarial network with $${\textbf {S}}$$ patial- $${\textbf {S}}$$ tructural (SS) block and $${\textbf {T}}$$ exture- $${\textbf {S}}$$ tyle- $${\textbf {C}}$$ olor (TSC) block after the correlation matching module that facilitates the output to be semantically corresponding to the target pose image while contextually related to the source image. In addition, we can extend our network to complete multi-object and cross-category pose transformation. Extensive experiments demonstrate the effectiveness of our method which can create more realistic images when compared to those of recent approaches in terms of image quality. Moreover, we show the practicality of our method for several applications.
Determination of Avalanche Effect to Compute the Efficiency of Association Rule Hiding Algorithms

Protection of sensitive information is a primary issue in data mining. Association rule hiding is a popular technique to stop exposure of sensitive information while mining the desired data from data repositories. It preserves the confidentiality of rules as per certain interestingness criteria. Many techniques and algorithms are available for hiding sensitive rules in order to keep sensitive information hidden. Increase support left (ISL) and decrease support right (DSR) are popular and widely used algorithms to implement association rule hiding during data mining. As an advancement of the existing algorithms, a novel algorithm selective flip bit was developed. It is used to generate better results in terms of lost rules, false rules, and ghost rules as compared to algorithms in the same field. In this paper, the avalanche effect has been calculated of rule hiding algorithms in order to evaluate their performances. A database of supermarket store was taken to evaluate performance in the real market domain. Results indicated that selective flip bit algorithm returns the least avalanche effect which makes it better than other algorithms in the same field.
Dual Control Design: TS fuzzy model transformation based approach

The paper would like introduce the key steps of the Dual Control Design that is based on the fact that if the controller and the observer are derived from different TS fuzzy models of the given model the resulting control solution yield a better control performance. The motivation is based on the fact that finding the best controller and observer requires different features of the TS fuzzy model.
Ethereum Contract Honeypot Risk Analysis

In recent years, smart contracts on the Ethereum platform have attracted considerable attention, and smart contracts have increasingly become targets of cyberattacks for the purpose of stealing cryptoassets. One of the emerging attack methods is to intentionally deploy contracts that appear to contain vulnerabilities but have backdoors, and that, lure attackers who are targeting vulnerable contracts to steal cryptoassets. These are called smart contract honeypots (henceforth referred to simply as “contract honeypots”). Torres et al. analyzed contract honeypots for the first time at USNIX Security 2019. In this study, we look at eight types of contract honeypots organized by Torres et al. and calculate the damages caused by each of them. We also analyze the code of contract honeypots by focusing on the arguments of the money transfer process, and we discovered a new type of contract honeypot. This analysis suggests that smart contracts with “this.balance” in the money transfer process may be contract honeypots. Furthermore, we discuss the impact of contract honeypots on general users.
Distributed ledger-based authentication and authorization for Industrie 4.0 components

In the connected world of Industrie 4.0, not only the access to information becomes more and more important, but also secure, standardized, and machine-interpretable communication between assets. The identification of assets forms the basis for all emerging processes, which leads to new requirements. The authors developed a concept to establish the identity of Industrie 4.0 components in decentralized systems by providing a decentralized identity and access management based on distributed ledger technology and integrated this into the concept of the Industrie 4.0 language. The use of decentralized identifiers guarantees more security and reliability for the involved actors in the value chain. Thus, authentication and authorization can be carried out solely between the involved actors without the use of centralized identity providers or certification authorities.
Texture Segmentation and Features of Medical Images

Texture analysis is one of the basic aspects of human visual system by which one can differentiate the objects and homogenous areas in an image. Manual diagnosis is not possible for huge database of images. Automatic diagnosis is required for greater accuracy in a shorter time. Texture analysis is required for effective diagnosis of medical images like functional MRI (magnetic resonance image) and diffusion tensor MRI, where only visualization is not sufficient to get the pathological information. This chapter explains the basic concepts of texture analysis and features available for analysis of medical images. Specifically, the intense review of texture segmentation and texture feature extraction and entropy measures of medical images have been done. The chapter also explores the available techniques for it. Common findings, comparative analysis, and gaps identified have also been mentioned on both issues.
Decision Modeling for an ISA-95 based Production Ontology

Decision support systems in production and manufacturing help to achieve economic and ecological objectives and increase flexibility and resilience. Semantic models as knowledge base for these systems enable and empower analytical components such as machine learning, simulation, and optimization algorithms. This paper discusses some of the relevant aspects of such semantic models. Existing solutions and standards for common data models in manufacturing are explored, and based on that, alternatives and decisions for managing manufacturing operations are modeled in an ontology. Specifically, a general ontology for handling decisions in any type of factory is introduced.
Gabor CNN Based Intelligent System for Visual Sentiment Analysis of Social Media Data on Cloud Environment

Social media contains a plethora of information in the form of text, images, videos, and other data. Users across the globe are increasingly sharing their data on various social media platforms. Sentiment analysis of data, such as text, images, and videos are widely used to understand the feelings of users. In recent years, the convolutional neural network (CNN) has been extensively applied for various applications. The cloud computing environment is a popular service due to its reliability, availability, and easy software integration. However, CNN models are deep neural networks that have a high computational cost. There is a need for CNN models which utilize lesser computational resources especially when these models are deployed in a cloud environment due to the remote physicality of servers, resource optimization, and infrastructure cost reduction. In this research, Gabor filters are integrated with CNN models to improve image sentiment analysis in a cloud environment, with advantages such as the reduction in computation energy and time, the elimination of the need for pre-trained models, and a perceived accuracy improvement. Two variants of Gabor-CNN (G-CNN) models with a different number of pooling and normalization layers are developed. The proposed G-CNN is trained and tested using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">five</i> standard databases as SentiBank, Twitter, MVSO, MultiView_I, and MultiView_II. Maximum classification accuracies of 91.71%, 92.52%, 97.39%, 90.88%, and 91.31% are obtained on SentiBank, Twitter, MVSO, MultiView_I, and MultiView_II databases respectively using the developed models. The proposed G-CNN model has provided an accuracy of 92.76% on average.
Persistent Monitoring for Indoor Farming using Static and Mobile Sensors

This paper considers the use of static and mobile sensors (quadrotors) for persistent monitoring in an indoor farming scenario. The focus is on autonomous navigation of the quadrotors to monitor the coverage holes generated by a number of broken static sensors. To that end, a three-layer strategy is proposed by (i) dividing the coverage holes into several region-of-interests (ROIs); (ii) selecting the quadrotor(s) responsible for monitoring each ROI using a novel Modified Ant Colony Optimization; (iii) designing coverage control with time-varying density function for ensuring persistent monitoring. Simulation results demonstrate the efficacy of the proposed strategy in persistently minimizing the coverage holes over time.
Towards a General-Purpose Dynamic Information Flow Policy

Noninterference offers a rigorous end-to-end guarantee for secure propagation of information. However, real-world systems almost always involve security requirements that change during program execution, making noninterference inapplicable. Prior works alleviate the limitation to some extent, but even for a veteran in information flow security, understanding the subtleties in the syntax and semantics of each policy is challenging, largely due to very different policy specification languages, and more fundamentally, semantic requirements of each policy. We take a top-down approach and present a novel information flow policy, called Dynamic Release, which allows information flow restrictions to downgrade and upgrade in arbitrary ways. Dynamic Release is formalized on a novel framework that, for the first time, allows us to compare and contrast various dynamic policies in the literature. We show that Dynamic Release generalizes declassification, erasure, delegation and revocation. Moreover, it is the only dynamic policy that is both applicable and correct on a benchmark of tests with dynamic policy.
YouMVOS: An Actor-centric Multi-shot Video Object Segmentation Dataset

Many video understanding tasks require analyzing multishot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos. To address this challenge, we collected a new dataset-YouMVaS-of 200 popular YouTube videos spanning ten genres, where each video is on average five minutes long and with 75 shots. We selected recurring actors and annotated 431K segmentation masks at a frame rate of six, exceeding previous datasets in average video duration, object variation, and narrative structure complexity. We incorporated good practices of model architecture design, memory management, and multi-shot tracking into an existing video segmentation method to build competitive baseline methods. Through error analysis, we found that these baselines still fail to cope with cross-shot appearance variation on our YouMVOS dataset. Thus, our dataset poses new challenges in multi-shot segmentation towards better video analysis. Data, code, and pre-trained models are available at https://donglaiw.github.io/proj/youMVOS
Reliable Multi-Factor User Authentication With One Single Finger Swipe

Multi-factor user authentication becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing multi-factor user authentication methods usually require multiple interactions between users and different authentication components when inputting the multiple factors, leading to extra overhead and bad user experience. In this paper, we propose a secure and user-friendly multi-factor user authentication system named BioDraw. It utilizes four categories of biometrics (impedance, geometry, behavior, and composition) of human hand plus the pattern-based password to identify and authenticate users. User only needs to draw a pattern on a radio frequency identification tag array, while four biometrics can be collected simultaneously. Specifically, we first design a gradient-based pattern recognition algorithm to precisely extract user’s secret pattern. Then, a convolutional neural network- and long short-term memory-based classifier is utilized for user recognition. Furthermore, to guarantee the systemic security, an anti-replay method called Binary ALOHA is proposed to detect replayed signals. We conduct extensive experiments with 30 volunteers. The experiment results show that BioDraw can achieve high authentication accuracy (with a 2%– false reject rate) and is effective in defending against various attacks.
Website Creation Software and Web Browsers

This chapter introduces a couple of no-cost/low-cost tools for creating websites or blogging, as well as free web browsers for security and privacy.
Only Tablet with Dark Hair (Pictorial)

This pictorial takes the form of an original poem and photographs. Each illustrates the other. The theme concerns how contemporary ontological conditions may be induced by preoccupation with digital devices and online media. There is an element of dark humour. There is also a sense in which the design of our photographic narrative is an appeal to return to a healthy balance between virtual and physical. We also describe how this pictorial situates in the historical context of pictorials. We reflect on how the pictorial relates to selected conference themed design modes, technical considerations, and the photographic narrative as a form of design fiction.
Development of sentiment analysis and sarcasm detection systems for social network users

As one of the biggest data sources, conversations on social media can be used for various purposes, one of which is sentiment analysis. By utilizing sentiment analysis, we can obtain an overview of public opinion on certain topics, such as business and politics. Through sentiment analysis, text can be grouped into positive, negative, or neutral sentiments. Sarcasm can cause inaccurate classification of sentiment. In this study, we examined the effect of sarcasm detection on sentiment analysis accuracy. The dataset used in this study contains a group of 1000 Indonesian language Tweet. The features used for sarcasm detection are sentiment score and interjection words. The algorithm used for sarcasm detection is Random Forest. Sentiment analysis was performed using the TF-IDF as the feature and the NaYve Bayes as the classification algorithm. The test results show that the sarcasm detection does not improve the accuracy of sentiment analysis. The average sentiment analysis accuracy is 74.3%. The average sentiment analysis accuracy with sarcasm detection is 72.9%. The average accuracy of sarcasm detection is 71.7%.
Ad Service Detection - A Comparative Study Using Machine Learning Techniques

The use of web-based services has been soaring since the internet has come up. Hence, traditional forms of advertisements have moved on to the web, leading to a rising number of ad services and trackers affecting user privacy. Ad blockers were developed in order to prevent these. However, the currently available ad blockers are purely based on a predefined set of blacklist domains. This study proposes a machine learning-based approach to detect web-based ad services and trackers. In order to help us explore the area using machine learning techniques, a dataset was created. The developed dataset contained 74,000 entries comprising both domains that served advertisements and normal domains. In addition, we have also compared the scores that were obtained by various supervised machine learning models, and the best model was identified. The best model offered up to 88% accuracy in classifying ad services and normal websites. The model contributed by this paper could be further developed into a machine learning-based advertisement blocker system.
Maximizing Output Power for Solar Panel using Grey Wolf Optimization

Obtaining the highest output power delivered from solar panel that leads to an amelioration in the efficiency of the solar system related to the weather conditions and the rate of solar radiation falling on the panel. The optimization in the solar system efficiency by MPPT is performed using the gray wolf optimization method by applying the proposed algorithm in order to maximize the output power. This means that the gray wolf technologies must increase the power received from the solar panels in order to increase the efficiency
Privacy and Data Protection in COVID-19 Contact Tracing Apps: An Analysis from a Socio-Technical System Design Perspective

This paper examines privacy and data protection concerns of the public in relation to COVID-19 contact tracing apps. In addition, the role played by these concerns in the adoption of contact tracing apps has been investigated. Further emphasis has been directed at the limitations of contact tracing apps that could stem from privacy and data protection accommodations. Regarding socio-technical system design, this paper attempts to identify mechanisms preserving privacy in contact tracing apps. It has been a further research aim to determine factors that assist and hinder adoption. A mixed methods approach utilising a survey including both qualitatively and quantitatively evaluable questions was employed. Contact tracing apps have been a highly topical subject during the COVID-19 pandemic. This research found that privacy and data protection are indeed important factors in people's decision-making about whether to use a contact tracing app. While certain privacy trade-offs are inevitable when it comes to contact tracing, this research found that a decentralised design approach characterised by full anonymity for users and the largest amount of data possible remaining on the device is best suited to achieve widespread adoption and approval with a privacy-conscious public that is concerned with data protection.