Reviewerly/exHarmony huggingface dataset:
Banknote Object Detection for the Visually Impaired using a CNN

Computer Vision (CV) is an area within the field of Artificial Intelligence (AI) which analyses images and video and attempts to identify and interpret the data contained in these. It aims to match or better the results a human could achieve given the same dataset. CV technology has major applications within the area of assistive technology. It has the potential to make the lives of disabled people easier by making the objects and systems they interact with more accessible. The aim of this project is to create a Convolutional Neural Network (CNN) suitable for use in a mobile bank note recognition application which can alleviate the struggle visually impaired people experience when trying to identify different bank note values. Limited previous studies have attempted to tackle this problem using a CNN. Additionally, these past studies have often neglected to include partial currency images in their datasets. This study uses data augmentation techniques to simulate partial currency images resembling those a blind or visually impaired person would take. The model created for this study achieved an average accuracy rate of 94% and was deemed suitable for use in a real-time currency recognition application.
Optimising Connection Weights in Neural Networks Using a Memetic Algorithm Incorporating Chaos Theory

Learning in feed-forward neural networks is a crucial and challenging task. Gradient descent-based approaches are among the most commonly employed learning algorithms but suffer from difficulties such as ending up in local optima. To cope with this, swarm intelligence (SI) algorithms can be employed. Memetic algorithms integrate an SI technique with a problem-specific method to improve the exploration ability of the algorithm. In this chapter, we present a chaos-enhanced memetic algorithm for feed-forward neural network learning. To this end, the colonial competitive algorithm (CCA) is employed as a powerful SI algorithm, while gradient descent back-propagation is used as a competent local search operator to move the best candidate solution towards the negative gradient of the error function. Furthermore, chaotic maps are utilised for improved exploration. We compare our algorithm with several competitors such as original CCA, particle swarm optimisation (PSO), genetic algorithm (GA), ant colony optimisation (ACO), evolution strategy (ES), population-based incremental learning (PBIL), biogeography-based optimisation (BBO), and grey wolf optimiser (GWO). Experimental results on several function approximation problems clearly confirm highly competitive performance of the proposed algorithm compared to others.
Coupling-based Convergence Assessment of some Gibbs Samplers for High-Dimensional Bayesian Regression with Shrinkage Priors

Abstract We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the choice of the number of iterations to perform. This is critical when each iteration is expensive, as is the case when dealing with modern data sets, such as genome-wide association studies with thousands of rows and up to hundreds of thousands of columns. We develop coupling techniques tailored to the setting of high-dimensional regression with shrinkage priors, which enable practical, non-asymptotic diagnostics of convergence without relying on traceplots or long-run asymptotics. By establishing geometric drift and minorization conditions for the algorithm under consideration, we prove that the proposed couplings have finite expected meeting time. Focusing on a class of shrinkage priors which includes the ‘Horseshoe’, we empirically demonstrate the scalability of the proposed couplings. A highlight of our findings is that less than 1000 iterations can be enough for a Gibbs sampler to reach stationarity in a regression on 100,000 covariates. The numerical results also illustrate the impact of the prior on the computational efficiency of the coupling, and suggest the use of priors where the local precisions are Half-t distributed with degree of freedom larger than one.
First Trimester Gaze Pattern Estimation Using Stochastic Augmentation Policy Search for Single Frame Saliency Prediction

While performing an ultrasound (US) scan, sonographers direct their gaze at regions of interest to verify that the correct plane is acquired and to interpret the acquisition frame. Predicting sonographer gaze on US videos is useful for identification of spatio-temporal patterns that are important for US scanning. This paper investigates utilizing sonographer gaze, in the form of gaze-tracking data, in a multimodal imaging deep learning framework to assist the analysis of the first trimester fetal ultrasound scan. Specifically, we propose an encoderdecoder convolutional neural network with skip connections to predict the visual gaze for each frame using 115 first trimester ultrasound videos; 29,250 video frames for training, 7,290 for validation and 9,126 for testing. We find that the dataset of our size benefits from automated data augmentation, which in turn, alleviates model overfitting and reduces structural variation imbalance of US anatomical views between the training and test datasets. Specifically, we employ a stochastic augmentation policy search method to improve segmentation performance. Using the learnt policies, our models outperform the baseline: KLD, SIM, NSS and CC (2.16, 0.27, 4.34 and 0.39 versus 3.17, 0.21, 2.92 and 0.28).
Multi-scale Salient Features for Person Re-identification

Abstract Person re-identification is a technology that uses computer vision to match and retrieve the same target person across cameras, which has a wide range of applications in the fields of intelligent video surveillance, intelligent security, and unmanned driving. Aiming at the problem of low accuracy caused by complex background information, occlusion and other factors in person re-identification, the extracted features are not discriminative and robust, resulting in low accuracy, a person re-identification method based on multi-scale saliency features is proposed. Firstly, the saliency detection is used to extract the distinctive feature areas with discriminative power in person, so that the features of distinguishing person inages are more prominent. Secondly, these saliency features are merged with global features and the fused features are divided into different scales, and the global features are divided into three different scales to enhance the expression of features. Finally, according to the difference of global and local features, three kinds of loss functions are combined for learning. In the inference stage, the global feature and the local feature are fused into a new feature vector for metric learning. The proposed method has been used in the person re-identification public datasets Market1501 and DukeMTMC-reID to do a lot of effective verification experiments. The experimental results show that the features extracted by the proposed method have strong distinguishability and robustness.
Tactile Heatmaps: A Novel Visualisation Technique for Data Analysis with Tactile Charts

Analysing large data sets for various purposes is a growing requirement for many professions. Tactile charts are suitable to enable people with visual impairment and blindness performing data analysis tasks. However, only a few approaches focus on the development of tactile charts for data analysis purposes. Concepts are needed to represent a sufficient amount of data with tactile charts and address arising challenges, such as information overload. In this paper, we first discuss and analyse the scalability of data represented by tactile charts using tactile scatterplots. We further address the data size limitations and present methods to identify critical, tactile representation with limited readability respecting the analysis task. Moreover, we propose methods to increase the amount of data represented in tactile scatterplots. We further introduce tactile heatmaps as an innovative and new concept for haptic data representation that utilises different elevation levels. We evaluated our design concept as well as the feasibility of varying elevation levels with 11 blind and visually impaired people. We compared four design conditions for embossed tactile heatmaps as well as the suitability of 3D-printed heatmaps. The results show that tactile heatmaps are suitable for representing more data than previously known tactile representation methods. They support obtaining an overview of a high amount of data and can be applied for data analysis purposes.
Image Classification with Convolutional Neural Networks

The world, as we know, it would not exist without the development of machine learning (ML). Its application in processing information gives the human more advantages than in any other age. The use of ML brings the opportunity to perform categorizations into a big amount of elements and into areas that the human simply can not do it without this technology. But the development of ML algorithms is complex since it requires specific theoretical bases and the development of skills to achieve the tuning of these algorithms. Given the wide variety of algorithms and techniques to perform element classification, this chapter focuses on the study of Convolutional Neural Networks (CNN), and the development of tests that facilitate the understanding of the effects of their basic parameters. The classification objects used are images, and only the multiple class classification model is considered.
Single Image Super-Resolution Method Based on Bilinear Interpolation and U-Net Combination

The single image super-resolution issue is studied in this paper. We propose a new method based on a combination of bilinear interpolation and the U-Net neural network to solve this problem. The image is enlarged by bilinear interpolation, then its quality is improved by the neural network. This approach allows improving reconstruction quality by 1.00-6.56% compared with known methods.
Smart Home Based Sleep Disorder Recognition for Ambient Assisted Living

As the age profile of many societies continues to increase, supporting health, both mental and physical, is of increasing importance if independent living is to be maintained. Sensing and, ultimately, recognising activities of daily living has been perceived as a prerequisite for detecting tasks that people avoid or find increasingly difficult to perform, as well as being indicators of certain illnesses. To date, extensive research efforts have been made in activity monitoring, recognition and assistance in indoor scenarios, frequently through smart home initiatives. Moreover, certain behaviours and activities may indicate certain disease especially for elderly people, such as sleep disorder. Thus this paper advocates a need for platforms that enable activity monitoring, in particularly, this sleep disorder, in home environment, thereby enabling the construction of more complex yet realistic activity models and behaviours patterns.
Impact of programming languages on machine learning bugs

Machine learning (ML) is on the rise to be ubiquitous in modern software. Still, its use is challenging for software developers. So far, research has focused on the ML libraries to find and mitigate these challenges. However, there is initial evidence that programming languages also add to the challenges, identifiable in different distributions of bugs in ML programs. To fill this research gap, we propose the first empirical study on the impact of programming languages on bugs in ML programs. We plan to analyze software from GitHub and related discussions in GitHub issues and Stack Overflow for bug distributions in ML programs, aiming to identify correlations with the chosen programming language, its features and the application domain. This study's results enable better-targeted use of available programming language technology in ML programs, preventing bugs, reducing errors and speeding up development.
BLSTM-CNN Relationship Classification Network Model

Relationship classification tasks are the process of extracting the relationship between two or more entities described in a natural language. Entity relationship classification can be used for search algorithms, structured knowledge extraction and knowledge map construction. The traditional relationship classification algorithm based on language characteristics can not meet the needs of entity relationship classification, and the use of deep learning method has become an important means to solve the task of relationship classification. In order to solve this problem effectively, the relationship classification model of Bi-LSTM-CNN structure is proposed. Experimental results show that the model has a high F1 score on the task data set of SemEval2010_task8 entity relationship extraction, which is better than the existing relationship classification task model.
Opportunities and Challenges in Securing Critical Infrastructures Through Cryptography

Components of modern critical infrastructures (CIs), like water treatment and smart grids, are potential targets for attackers. We present the state of cryptographic techniques with timeliness and availability being the key focus to suit the unique needs of CI systems.
Class Retrieval of Detected Adversarial Attacks

Adversarial attack is a genuine threat compromising the safety of many intelligent systems curbing the standardization of using neural networks in security-critical applications. Since the emergence of adversarial attacks, the research community has worked relentlessly to avert the malicious damage of these attacks. Here, we present a new, additional and required element to ameliorate adversarial attacks: the recovery of the original class after a detected attack. Recovering the original class of an adversarial sample without taking any precautions is an uncharted concept which we would like to introduce with our novel class retrieval algorithm. As case studies, we demonstrate the validity of our approach on MNIST, CIFAR10 and ImageNet datasets where recovery rates were 72%, 65% and 65%, respectively.
Power and Performance Evaluation of Memory-Intensive Applications

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.
Empowering NGOs in countering online hate messages

Studies on online hate speech have mostly focused on the automated detection of harmful messages. Little attention has been devoted so far to the development of effective strategies to fight hate speech, in particular through the creation of counter-messages. While existing manual scrutiny and intervention strategies are time-consuming and not scalable, advances in natural language processing have the potential to provide a systematic approach to hatred management. In this paper, we introduce a novel ICT platform that NGO operators can use to monitor and analyze social media data, along with a counter-narrative suggestion tool. Our platform aims at increasing the efficiency and effectiveness of operators' activities against islamophobia. We test the platform with more than one hundred NGO operators in three countries through qualitative and quantitative evaluation. Results show that NGOs favor the platform solution with the suggestion tool, and that the time required to produce counter-narratives significantly decreases.
Blockchain Smart Contracts Static Analysis for Software Assurance

This paper examines blockchain smart contract software assurance through the lens of static analysis. Smart contracts are immutable. Once they are deployed, it is impossible to patch or redevelop the smart contracts on active chains. This paper explores specific blockchain smart contract bugs to further understand categories of vulnerabilities for bug detection prior to smart contract deployment. Specifically, this work focuses on smart contract concerns in Solidity v0.6.2 which are unchecked by static analysis tools. Solidity, influenced by C++, Python and JavaScript, is designed to target the Ethereum Virtual Machine (EVM). Many, if not all, of the warnings we categorize are currently neither integrated into Solidity static analysis tools nor earlier versions of the Solidity compiler itself. Thus, the prospective bug detection lies entirely on smart contract developers and the Solidity compiler to determine if contracts potentially qualify for bugs, concerns, issues, and vulnerabilities. We aggregate and categorize these known concerns into categories and build a model for integrating the checking of these categories into a static analysis tool engine. The static analysis engine could be employed prior to deployment to improve smart contract software assurance. Finally, we connect our fault categories with other tools to show that our introduced categories are not yet considered during static analysis.
An Entity Resolution Approach Based on Word Embeddings and Knowledge Bases for Microblog Texts

In the context of data management, several proposals for entity resolution usually perform on structured data or on long texts that contain contextual information. In short texts, such as microblogs, the lack of context may complicate the disambiguation of named entities mentioned in these texts. On the other hand, word embeddings have been demonstrated as promising techniques for enriching contextual information or being used on similarity estimations. Thus, in this work, we propose an approach for disambiguating named entities gathered from short texts, linking them to documents in a knowledge base using word embeddings and three strategies to find the correct document. Strategy 1 is based on other entity names in the short text. Strategy 2 exploits categories from the candidate documents. And Strategy 3 is based on similarity between documents associated to other named entities from the text and the candidate documents to be linked to the target named entity. In our experimental evaluation, our proposed approach outperforms other approaches usually used in the entity resolution task.
Fast Function Instantiation with Alternate Virtualization Approaches

This paper focuses on the need for emerging domains such as serverless and in-network computing, where applications are often hosted on virtualized compute instances (e.g., containers and unikernels), to have applications startup as quickly as possible. We provide a qualitative and quantitative analysis of containers and unikernels with regard to the startup time. We analyze these in-depth and identify the key components and their impact under scale on the startup latency. We study how startup time scales as we launch multiple instances concurrently. We study the contribution of popular Container Networking Interfaces (CNIs), to the startup time.
Development of Obstacle Avoidance for Autonomous Vehicles and an Optimization Scheme for the Artificial Potential Field Method

The research on the driving process of autonomous vehicles involves the combination of multiple subjects including sensor perception, artificial intelligence, high-precision map, and intelligent obstacle avoidance, which is an important development direction of current road driving. To achieve the purpose of autonomous driving without human control, the high adaptability and robustness of the vehicle, as well as the ability to identify and avoid obstacles while driving are required. The purpose of this paper is to study the development process of the obstacle avoidance system for autonomous vehicles and propose an optimization scheme for the obstacle avoidance algorithm.
Hierarchical clustered multiclass discriminant analysis via cross-validation

Linear discriminant analysis (LDA) is a well-known method for multiclass classification and dimensionality reduction. However, in general, ordinary LDA does not achieve high prediction accuracy when observations in some classes are difficult to be classified. A novel cluster-based LDA method is proposed that significantly improves prediction accuracy. Hierarchical clustering is adopted, and the dissimilarity measure of two clusters is defined by the cross-validation (CV) value. Therefore, clusters are constructed such that the misclassification error rate is minimized. The proposed approach involves a heavy computational load because the CV value must be computed at each step of the hierarchical clustering algorithm. To address this issue, a regression formulation for LDA is developed and an efficient algorithm that computes an approximate CV value is constructed. The performance of the proposed method is investigated by applying it to both artificial and real datasets. The proposed method provides high prediction accuracy with fast computation from both numerical and theoretical viewpoints.
User Identification in Dynamic Web Traffic via Deep Temporal Features

Modern web applications rely heavily on dynamic content, i.e., page updates made by the browser using an XMLHttpRequest and more recently the JavaScript Fetch API. These requests are often made on behalf of user actions, such as typing on the keyboard or pointing at an HTML element. As a result, the timings of the user’s actions are strongly correlated with the timings of packets that carry these events. In this work, we examine several dynamic web applications and the ability to measure human behavior in encrypted network traffic by using deep temporal features. Our approach relies on the ability to accurately detect a subset of packets that correspond to user actions. Leveraging recent work in keystroke dynamics, we show that user identification can be performed with modest accuracy utilizing the packet timings induced by a user typing into a search engine. While this tool could be used by forensic investigators to perform target identification among encrypted network traffic, it also raises a privacy concern in which an on-path remote adversary able to detect these packets may infer user behaviors.