Reviewerly/exHarmony huggingface dataset:
A secure medical image transmission scheme aided by quantum representation

Quantum information and quantum image processing have made immense progress in the most recent years. The proposed paper investigates the proficiency of quantum image encryption in the field of Telehealth which can be built by incorporating quantum block-based spatial transformations. The proposed quantum cryptosystem introduced two levels of security for medical image and its sensitive content. The secrecy of medical image is reinforced by choosing a plain image based initial seed values for encryption. It is found that the suggested quantum block-based scrambling can serve as a novel method for intensifying the privacy-preserving process of medical image. It additionally ensures the medical image integrity by introducing dedicated quantum encryption for ROI based regional data. With a bilateral image transmission perspective, the proposed paper introduced InterPlanetary Features based file system for proficient retrieval of medical images. The quantitative simulation and performance evaluation done on the Cancer Imaging Archive dataset manifests that the suggested quantum-based two-level security for medical images is suitable for reinforcing the confidentiality and privacy-preserving process. It also enlarges the effectiveness of quantum image processing applications in Telemedicine.
Viaduct: an extensible, optimizing compiler for secure distributed programs

Modern distributed systems involve interactions between principals with limited trust, so cryptographic mechanisms are needed to protect confidentiality and integrity. At the same time, most developers lack the training to securely employ cryptography. We present Viaduct, a compiler that transforms high-level programs into secure, efficient distributed realizations. Viaduct's source language allows developers to declaratively specify security policies by annotating their programs with information flow labels. The compiler uses these labels to synthesize distributed programs that use cryptography efficiently while still defending the source-level security policy. The Viaduct approach is general, and can be easily extended with new security mechanisms.
Quantum entanglement in the anisotropic Heisenberg model with multicomponent DM and KSEA interactions

Using group-theoretical approach, we found a family of four nine-parameter quantum states for the two-spin-1/2 Heisenberg system in an external magnetic field and with multiple components of Dzyaloshinsky–Moriya (DM) and Kaplan–Shekhtman–Entin–Wohlman–Aharony (KSEA) interactions. Exact analytical formulas are derived for the entanglement of formation for the quantum states found. The influence of DM and KSEA interactions on the behavior of entanglement and on the shape of disentangled region is studied. A connection between the two-qubit quantum states and the reduced density matrices of many-particle systems is discussed.
RISC-V Barrel Processor for Deep Neural Network Acceleration

This paper presents a barrel RISC-V processor designed to control a deep neural network accelerator. Our design has a 5-stage pipeline data path with 8 hardware threads (harts). Each thread is executed under a strict round robin scheduler and is responsible for providing data and control signals to a neural network processing element (PE). Each PE is capable of arbitrary precision GEneral Matrix Vector (GEMV) operations. The execution of each thread is independent of other threads and any communication between threads are sent through shared memory via software. To reduce the area required for implementation, our processor is an implementation of the RV32I plus a set of custom CSRs for controlling the PEs. Our design passes all riscv_test written in assembly and compiled with RISC-V gcc. Our 8-hart barrel processor runs at 250 MHz with CPI of 1 and consumes 0.372W. To demonstrate the capabilities of our design, we computed a GEMV operation with an input matrix size of 8 by 128 and a weight matrix size of 128 by 128 with two-bit precision in only 16 clock cycles.
Removing Adversarial Noise in Class Activation Feature Space

Deep neural networks (DNNs) are vulnerable to adversarial noise. Pre-processing based defenses could largely remove adversarial noise by processing inputs. However, they are typically affected by the error amplification effect, especially in the front of continuously evolving attacks. To solve this problem, in this paper, we propose to remove adversarial noise by implementing a self-supervised adversarial training mechanism in a class activation feature space. To be specific, we first maximize the disruptions to class activation features of natural examples to craft adversarial examples. Then, we train a denoising model to minimize the distances between the adversarial examples and the natural examples in the class activation feature space. Empirical evaluations demonstrate that our method could significantly enhance adversarial robustness in comparison to previous state-of-the-art approaches, especially against unseen adversarial attacks and adaptive attacks.
Impact of negative emotions on violations of information security policy and possible mitigations

Security research on the role of employees' negative emotions in their information security policy (ISP) violations is limited. In this study, we examine how employees' negative emotions influence their intention to violate ISP. To understand how to reduce employees' negative emotions, we investigate the effects of perceived organisational support, psychological ownership, and work engagement. We test our hypotheses using survey data of 318 employees from various organisations in China. Results indicate that employees with negative emotions are more likely to violate ISP, while perceived organisational support, psychological ownership, and work engagement can reduce employees' negative emotions. Furthermore, psychological ownership and work engagement partially mediate the relationship between perceived organisational support and negative emotions. The theoretical and practical significance of these results and the direction of future research are discussed.
Acceleration of the Secure Hash Algorithm-256 (SHA-256) on an FPGA-CPU Cluster Using OpenCL

The Secure Hash Algorithm-256 (SHA-256) is a cryptographic function used in a wide variety of applications ranging from Internet of Things micro-devices to highperformance systems. This paper studies a set of implementations of the SHA-256 on a field-programmable gate array (FPGA) elaborated using the Open Computing Language (OpenCL). These implementations apply several optimization techniques to improve their respective throughputs. Reported results show that a combination of OpenCL optimization techniques allows obtaining an implementation offering a 90x speed-up when compared to an unoptimized OpenCL implementation. Moreover, the best reported optimized implementation achieves a throughput of 3973 Mbps, which is 4.3 times higher than the best previously published HLS-based SHA-256 implementation and even higher than the previously published implementations using a hardware description language. To our knowledge, this work is the first that proposes an OpenCL-based FPGA implementation of SHA-256 and its OpenCL-based optimization methodology.
Short-Term Solar Power Forecasting Using Genetic Algorithms: An Application Using South African Data

Renewable energy forecasts are critical to renewable energy grids and backup plans, operational plans, and short-term power purchases. This paper focused on short-term forecasting of high-frequency global horizontal irradiance data from one of South Africa’s radiometric stations. The aim of the study was to compare the predictive performance of the genetic algorithm and recurrent neural network models with the K-nearest neighbour model, which was used as the benchmark model. Empirical results from the study showed that the genetic algorithm model has the best conditional predictive ability compared to the other two models, making this study a useful tool for decision-makers and system operators in power utility companies. To the best of our knowledge this is the first study which compares the genetic algorithm, the K-nearest neighbour method, and recurrent neural networks in short-term forecasting of global horizontal irradiance data from South Africa.
Efficient Word Size Modular Arithmetic

Modular multiplication is used in a wide range of applications. Most of the existing modular multiplication algorithms in the literature often focus on large size moduli. However, those large moduli oriented modular multiplication solutions are also used to implement modular arithmetic for applications requiring modular arithmetic on moduli of size inferior to a word size i.e., 32/64bits. As it happens, a large majority of applications are using word size modular arithmetic. In this work, we propose a new modular multiplication designed to be computed on one word size only. For word size moduli, in a large majority of instances, our solution outperforms other existing solutions including generalist solutions like Montgomery's and Barrett's modular multiplication as well as classes of moduli like Mersenne, Pseudo-Mersenne, Montgomery-Friendly and Generalized Mersenne.
Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning

In this study, we qualitatively and quantitatively examine the effects of COVID-19 on classrooms, students, and educators. Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic. Our contributions include a novel and open source geo-fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well. We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments. Moreover, we provide a comparative analysis of two widely used pre-trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests. Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions. We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions. We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi-dimensional analysis of pandemics.
Anonymity, User Engagement, Quality, and Trolling on Q&amp;A Sites

In online question and answer (Q&amp;A) communities, people ask questions and share answers at all levels of topic sensitivity. Identity options within these communities range from anonymity to real name. The amount of engagement, and the quality of engagement on Q&amp;A sites may differ depending on the identity options available. In this paper, we investigate the relationship between the amount of engagement, the quality of engagement, and different types of identity by analyzing three Q&amp;A sites with different identity policies. We find that highly sensitive questions are more likely to be asked anonymously. Furthermore, allowing anonymity does not affect answer quality and only has a weak, negative indirect effect on engagement. On the other hand, anonymity leads to more trolling. We suggest online communities provide a way for users to ask highly sensitive questions anonymously and pair this with moderation mechanisms to reduce trolling
An empirical investigation of domestic energy data visualizations

Which device in your home uses the most electricity? Many people have a poor understanding of their domestic energy consumption. In this paper, we evaluated three data visualizations used to deliver feedback. These were: (1) an aggregated line graph – showing changes in total electricity consumption over time, (2) a disaggregated line graph – showing changes in electricity consumed over time but separated out at the appliance-level, and (3) an area-based visualization – showing the cumulative energy consumed by different appliances over a given time period. In an experiment, 65 participants used one of these three visualizations to make sense of the same pattern of domestic electricity data. Participants who used the area-based visualization gained a more accurate understanding of how much electricity different domestic appliances were using compared to participants who were shown time series data. These results suggest that the choice of data visualization will impact people's understanding from smart metering systems, and that appliance-wise disaggregation offers the most promising approach for visualizing domestic electricity consumption data.
Crowdsourcing and Evaluating Concept-driven Explanations of Machine Learning Models

An important challenge in building explainable artificially intelligent (AI) systems is designing interpretable explanations. AI models often use low-level data features which may be hard for humans to interpret. Recent research suggests that situating machine decisions in abstract, human understandable concepts can help. However, it is challenging to determine the right level of conceptual mapping. In this research, we explore granularity (of data features) and context (of data instances) as dimensions underpinning conceptual mappings. Based on these measures, we explore strategies for designing explanations in classification models. We introduce an end-to-end concept elicitation pipeline that supports gathering high-level concepts for a given data set. Through crowd-sourced experiments, we examine how providing conceptual information shapes the effectiveness of explanations, finding that a balance between coarse and fine-grained explanations help users better estimate model predictions. We organize our findings into systematic themes that can inform design considerations for future systems.
An automatic method for removing empty camera trap images using ensemble learning

Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine-learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model with small errors on a small dataset. Using deep convolutional neural networks and a small-size dataset, we proposed an ensemble learning approach based on conservative strategies to identify and remove empty images automatically. Furthermore, we proposed three automatic identifying schemes of empty images for users who accept different omission errors of animal images. Our experimental results showed that these three schemes automatically identified and removed 50.78%, 58.48%, and 77.51% of the empty images in the dataset when the omission errors were 0.70%, 1.13%, and 2.54%, respectively. The analysis showed that using our scheme to automatically identify empty images did not omit species information. It only slightly changed the frequency of species occurrence. When only a small dataset was available, our approach provided an alternative to users to automatically identify and remove empty images, which can significantly reduce the time and personnel costs required to manually remove empty images. The cost savings were comparable to the percentage of empty images removed by models.
Cyberbullying Detection using LSTM-CNN architecture and its applications

In the present information age, there's been an increase in the use of social media, as a result, there have been multiple cyberbullying instances. To prevent or reduce cyberbullying, many existing approaches in the literature incorporate normal Machine Learning and Natural Language Processing text classification models without considering the sentence semantics. In this paper, we aim to target that issue. We have used word2vec to train our custom word embeddings upon which we built our LSTM - CNN architecture and finally it was trained on it. We made a comparative study of the above approaches by testing our model on Twitter posts and comments. The eminent performance achieved by our method has been observed in this study. We also created a web application that used the model to classify tweets as cyberbullying or not based on the toxicity score along with various features. The model was also implemented on Telegram Bot which is used to check and prevent cyberbullying. Two Chrome Extensions were built to redact and retract Not Safe For Work (NSFW) content and prevent cyberbullying on WhatsApp Web. We have been able to achieve excellent performance in the form of a 97% ROC AUC score for our model.
Structural analysis of mini drone developed using 3D printing technique

The drone is an emerging form of autonomous Unmanned Aerial Vehicle (UAV). The popularity of drones is increasing in the field of Systems of control are becoming more sophisticated. This work focus about 3D printing a quadrotor with a frame of 'X' shaped in structure and the dimension range is less than 100*100 mm. The drone structure designed using SOLIDWORKS. The frame of drone on 3D printer using Polylactic Acid (PLA) materials as the filament. The polylactic acid is a fully biodegradable thermoplastic. This drone is controlled by bluetooth based lumi controller integrated with monitoring and control a feature which provides the unique ability of flight stabilization and landing features in any terrain by the mini drone. The programming is done using the Arduino platform and a camera interface is used to capture the images. The parts used in the design are of low cost making the design economical which makes it ideal for production on an industrial scale which will aid in many applications like collecting data and surveillance in military operations.
Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem

Abstract The heat exchanger has been widely used in the energy and chemical industry and plays an irreplaceable role in the featured applications. The design of heat exchanger is a mixed integer complex optimization problem, where the efficient design significantly improves the efficiency and reduces the cost. Many intelligent methods have been developed for heat exchanger optimal design. In this paper, a novel variant of sine and cosine algorithm named EDOLSCA is proposed, enhanced by dynamic opposite learning algorithm and the elite strategy. The proposed method is tested in CEC2014 benchmark and proved to be of significant advantages over the original algorithm. The new algorithm is then validated in the plate-fin heat exchanger (PFHE) optimal design problem. The comparison results of the proposed algorithm and other algorithms prove that EDOLSCA also has demonstrated superiority in heat exchanger optimal design.
Design and implementation of a cost effective vacuum cleaner robot

Robot vacuum cleaners are among the first service robots to enter daily life. However, robot vacuum cleaners are currently inaccessible to many due to their high cost. In order for these robots to become widespread, they must be cheap and functional. In this study, the design and production of a low-cost, high-performance vacuum cleaner robot that can be controlled by smartphones is presented. This autonomous robot moves around the obstacles, vacuums the dust from the floor, performs basic navigation and also it is manually controllable. Designing of a vacuum cleaner robot is divided into three parts which are the mechanical, electrical and software design. In the mechanical design; the robot chassis and cleaning system have been designed and realized. In the electrical design; the electrical components such as batteries, motors, drivers, sensors and microcontroller have been selected and the power system has been designed by considering electrical requirements. In the software design; an easy to use Android application for remote control has been created, autonomous algorithms such as random walk and snake algorithms have been designed. Remote controlling of the robot has been provided by using Bluetooth connection between the Raspberry Pi and Android smartphone.
Validation of Forensic Crime Scene Images Using Watermarking and Cryptographic Blockchain

Images are critical part of investigations, they are referred to and regarded, to gather information, document and create “memory” of a crime scene. Crime scene images are considered as vital criminal evidence in the court of law; hence, it is imperative that the authenticity of such images be maintained and that they be verifiable. The advancement in technology in this digitally proliferated era allows for easy doctoring of crime scene images. Digital watermarking, and blockchain have been used in other research works to authenticate and verify digital content or digital documents. In this paper we propose the use of watermarking and cryptographic blockchain to validate forensic crime scene images.
ROBP a robust border-peeling clustering using Cauchy kernel

Recently a novel density-based clustering algorithm , namely, border-peeling (BP) clustering algorithm, is proposed to group data by iteratively identifying border points and peeling off them until separable areas of data remain. The BP clustering is able to correctly recognize the true structure of clusters and automatically detect the outliers on several test cases. However, there are some drawbacks in BP, and these may hinder its widespread application. The BP clustering might yield bad results on datasets with non-uniformly-distributed clusters. Especially, the BP clustering tends to over-partition the data with complex shape. To overcome these defects, a robust border-peeling clustering algorithm (named as ROBP) is proposed in this paper. Our method improves the BP clustering algorithm from two aspects: density influence (i.e. density estimation) and linkage criterion (i.e. association strategy). In density estimation, we use Cauchy kernel with longer tails instead of Gaussian kernel in the local scaling function, and further propose a kernel density estimator , i.e., the density estimator based on Cauchy kernel. It can calculate quickly and accurately the density influence value of each point. In association strategy, we design a linkage criterion based on the shared neighborhood information. The linkage criterion can create some links between peeled border points and their neighboring peeled border points, in order to avoid over-segmentation of the clusters. We integrate the proposed linkage criterion and the uni-directional association strategy, and further propose a bi-directional association strategy. In experiments, we compare ROBP with 7 representative density-based clustering (or hierarchical clustering) algorithms, including BP, DBSCAN, HDBSCAN, density peak (DP) clustering, DPC-KNN, DPC-DBFN and McDPC, on 8 synthetic datasets and 11 real-world datasets. Results show that the proposed algorithm outperforms 7 competitors in most cases. Moreover, we compare the robustness of ROBP and BP, and evaluate their running time. Experimental results indicate that ROBP is much more robust and reliable, as well as it is competitive to BP in computational efficiency.