Reviewerly/exHarmony huggingface dataset:
Context-based Smart Contracts For Appendable-block Blockchains

Currently, blockchain proposals are being adopted to solve security issues, such as data integrity, resilience, and non-repudiation. To improve certain aspects, e.g., energy consumption and latency, of traditional blockchains, different architectures, algorithms, and data management methods have been recently proposed. For example, appendable-block blockchain uses a different data structure designed to reduce latency in block and transaction insertion. It is especially applicable in domains such as Internet of Things (IoT), where both latency and energy are key concerns. However, the lack of some features available to other blockchains, such as Smart Contracts, limits the application of this model. To solve this, in this work, we propose the use of Smart Contracts in appendable-block blockchain through a new model called context-based appendable-block blockchain. This model also allows the execution of multiple smart contracts in parallel, featuring high performance in parallel computing scenarios. Furthermore, we present an implementation for the context-based appendable-block blockchain using an Ethereum Virtual Machine (EVM). Finally, we execute this implementation in four different testbed. The results demonstrated a performance improvement for parallel processing of smart contracts when using the proposed model.
A Robust Method of Eye Torsion Measurement for Medical Applications

The detection of eye torsion is an important element for diagnosis of balance disorders, although it is rarely available in existing eye tracking systems. A novel method is proposed in this paper to provide robust measurement of torsional eye movements. A numerical approach is presented to estimate the iris boundary only according to the gaze direction, so the segmentation of the iris is more robust against occlusions and ambiguities. The perspective distortion of the iris pattern at eccentric eye positions is also corrected, benefiting from the transformation relation that is established for the iris estimation. The angle of the eye torsion is next measured on the unrolled iris patterns via a TM (Template Matching) technique. The principle of the proposed method is validated and its robustness in practice is assessed. A very low mean FPR (False Positive Rate) is reported (i.e., 3.3%) in a gaze test when testing on five participants with very different eye morphologies. The present method always gave correct measurement on the iris patterns with simulated eye torsions and rarely provided mistaken detections in the absence of eye torsion in practical conditions. Therefore, it shows a good potential to be further applied in medical applications.
A Feature-Enhanced Anchor-Free Network for UAV Vehicle Detection

Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task. One reason is that the objects are small size, low-resolution, and large scale variations, resulting in weak feature representation. Another reason is the imbalance between positive and negative examples. In this paper, we propose a novel architecture for UAV vehicle detection to solve above problems. In detail, we use anchor-free mechanism to eliminate predefined anchors, which can reduce complicated computation and relieve the imbalance between positive and negative samples. Meanwhile, to enhance the features for vehicles, we design a multi-scale semantic enhancement block (MSEB) and an effective 49-layer backbone which is based on the DetNet59. The proposed network offers appropriate receptive fields that match the small-sized vehicles, and involves precise localization information provided by the contexts with high resolution. The MSEB strengthens discriminative feature representation at various scales, without reducing the spatial resolution of prediction layers. Experiments show that the proposed method achieves the state-of-the-art performance. Particularly, the main part of vehicles, much smaller ones, the accuracy is about 2% higher than other existing methods.
P4TC—Provably-Secure yet Practical Privacy-Preserving Toll Collection

Abstract Electronic toll collection (ETC) is widely used all over the world not only to finance our road infrastructures, but also to realize advanced features like congestion management and pollution reduction by means of dynamic pricing. Unfortunately, existing systems rely on user identification and allow tracing a user’s movements. Several abuses of this personalized location data have already become public. In view of the planned Europeanwide interoperable tolling system EETS and the new EU General Data Protection Regulation, location privacy becomes of particular importance. In this paper, we propose a flexible security model and crypto protocol framework designed for privacy-preserving toll collection in the most dominant setting, i.e., Dedicated Short Range Communication (DSRC) ETC. A major challenge in designing the framework at hand was to combine provable security and practicality, where the latter includes practical performance figures and a suitable treatment of real-world issues, like broken onboard units etc. To the best of our knowledge, our work is the first in the DSRC setting with a rigorous security model and proof and arguably the most comprehensive formal treatment of ETC security and privacy overall. Additionally, we provide a prototypical implementation on realistic hardware which already features fairly practical performance figures. An interaction between an onboard unit and a road-side unit is estimated to take less than a second allowing for toll collection at full speed assuming one road-side unit per lane.
AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING

Abstract. Label placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been relatively less studied than that of point-feature and line-feature. Most of the existing approaches have adopted a rule-based algorithm, and there are limitations in expressing the characteristics of label placement for area-features of various shapes utilizing handcrafted rules, criteria, objective functions, etc. Hence, we propose a novel approach for automatic label placement of area-feature based on deep learning. The aim of the proposed approach is to obtain the complex and implicit characteristics of area-feature label placement by manual operation directly and automatically from training data. First, the area-features with vector format are converted into a binary image. Then a key-point detection model, which simultaneously detect and localize specific key-points from an image, is applied to the binary image to estimate the candidate positions of labels. Finally, the final label placement positions for each area-feature are determined via simple post-process. To evaluate the proposed approach, the experiments with cadastral data were conducted. The experimental results show that the ratios of the estimation errors within 1.2 m (corresponding to one pixel of the input image) were 92.6% and 94.5% in the center and upper-left placement style, respectively. It implies that the proposed approach could place the labels for area-features automatically and accurately.
Classification and identification of unknown network protocols based on CNN and T-SNE

Abstract With the continuous development of users’ demands and network technology, more and more new network protocols emerge, which poses great challenges to network protocol classification and identification. An artificial intelligence method was used to explore autonomous classification and identification of unknown network protocols in this paper in order to reduce the time and labor cost of network protocol classification and identification. In this paper, firstly, the network traffic was converted into grayscale images, and through transfer learning, the Convolutional Neural Networks (CNN) pre-trained model was used to extract the protocol features, so as to reduce the time and the amount of labeled data needed for the artificial neural network training. Finally, with the improved unsupervised hybrid clustering algorithm based on T-SNE and K-means, the types and number of protocols were autonomously identified and the network traffic was classified simultaneously. In this way, we can identify unknown protocols without prior knowledge and the protocol identification adaptability for big data was also greatly improved. Experimental results show this method has high accuracy and robustness in identifying unknown network protocols.
Adaptive Guidance-based Differential Evolution with Iterative Feedback Archive Strategy for Multimodal optimization Problems

Multimodal optimization problems (MMOPs) target to locate multiple global optima simultaneously, which require the algorithms not only can maintain the population diversity to locate the global optima as many as possible, but also can ensure the convergence on each found optimal region to refine the solutions as high accuracy as possible. Aiming to these two goals and to efficiently deal with MMOPs, an adaptive guidance-based differential evolution (AGDE) with archive strategy is proposed in this paper, including three novel components. Firstly, an adaptive mutation strategy (AMS) is introduced, which guides the current individual to move towards the peak that is closest to itself. Secondly, an iterative feedback archive (IFA) strategy is used to store the global optima of the population in every iteration. Thirdly, a Gaussian disturbance-based elite learning (GDEL) strategy is performed on the archive to refine the accuracy of the solutions. The AMS strategy helps to locate more peaks, while the IFA and GDEL strategies help to maintain the found solutions and refine their accuracy. The performance of AGDE is tested on 20 widely used multimodal benchmark functions of CEC'2013. The experimental results of AGDE are competitive with the results obtained by the state-of-the-art multimodal algorithms.
SOCIAL MEDIA USAGE, OVERLOAD AND EXHAUSTION: A PERFORMANCE PERSPECTIVE

This study is focused on investigating the negative consequences of social media usage at work through overloads that are a cause of exhaustion and ultimately impact performance. Performance of employee is taken as criterion of exhaustion. Three categories of overloads social overload, communication overload and information overloads serve as mechanism of negative consequences of social media during working hours. A sample of 300 employees of telecom sector of Pakistan contributed in the study. Data was analyzed by using partial least squares structural equation modelling (PLS-SEM) technique. Results revealed that excessive use of social media positively influence information, communication and social overload. Information and communication overload were also found to have an enhanced effect on exhaustion of social media while social overload could not. Performance of employee was also found to be negatively influenced by exhaustion of social media. Study limitations and future directions are also discussed. Keywords: Performance; Social Media; Exhaustion; Telecom Sector JEL Classification: D89 DOI: https://doi.org/10.32479/irmm.10190
Surveillance in the COVID-19 Normal

The COVID-19 pandemic has accelerated the adoption of surveillance technologies in cities around the world. The new surveillance systems are unfolding at unprecedented speed and scale in response to the fears of COVID-19, yet with little discussion about long-term consequences or implications. The authors approach the drivers and procedures for COVID-19 surveillance, addressing a particular focus to close-circuit television (CCTV) and tracking apps. This paper describes the technologies, how they are used, what they are capable of, the reasons why one should be concerned, and how citizens may respond. No commentary should downplay the seriousness of the current pandemic crisis, but one must consider the immediate and longer-term threats of insinuated enhanced surveillance, and look to how surveillance could be managed in a more cooperative social future.
Detection of Phishing URLs by Using Deep Learning Approach and Multiple Features Combinations

Phishing detection is mostly performed through the usage of blacklists.However, blacklists cannot be exhaustive and lack the ability to detect newly generated phishing URLs.In recent years, increased attention has been given to exploring machine learning techniques in order to improve the universality of phishing URL detectors.This article aims at presenting our results on phishing URLs classification where three different features: lexical features, character level embeddings, and word level embeddings were compared with the view to find an approach that maximizes the ratio of phishing URL detection.In addition, a new deep neural network architecture for that problem was suggested.The said deep neural network consists of combined multiple CNN and LSTM layers.The 94.4% accuracy was achieved by combining character and word level embeddings.
Comments on “A new approach for the verification of infinite-step and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e50" altimg="si3.svg"><mml:mi>K</mml:mi></mml:math>-step opacity using two-way observers” [Automatica 80 (2017) 162–171]

In this note, we point out that the observer of a given discrete event system and the observer of the reversed system can be used together to verify both infinite-step opacity and K-step opacity without constructing the two-way observer in Yin and Lafortune (2017). In this way, the complexity of checking the two opacity properties can be reduced.
A Weakly Supervised Deep Learning Approach for Detecting Malaria and Sickle Cells in Blood Films

Machine vision analysis of blood films imaged under a brightfield microscope could provide scalable malaria diagnosis solutions in resource constrained endemic urban settings. The major bottleneck in successfully analyzing blood films with deep learning vision techniques is a lack of object-level annotations of disease markers such as parasites or abnormal red blood cells. To overcome this challenge, this work proposes a novel deep learning supervised approach that leverages weak labels readily available from routine clinical microscopy to diagnose malaria in thick blood film microscopy. This approach is based on aggregating the convolutional features of multiple objects present in one hundred high resolution image fields. We show that this method not only achieves expert-level malaria diagnostic accuracy without any hard object-level labels but can also identify individual malaria parasites in digitized thick blood films, which is useful in assessing disease severity and response to treatment. We demonstrate another application scenario where our approach is able to detect sickle cells in thin blood films. We discuss the wider applicability of the approach in automated analysis of thick blood films for the diagnosis of other blood disorders.
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L 2,1 -norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.
Camouflage: Hardware-assisted CFI for the ARM Linux kernel

Software control-flow integrity (CFI) solutions have been applied to the Linux kernel for memory protection. Due to performance costs, deployed software CFI solutions are coarse grained. In this work, we demonstrate a precise hardware-assisted kernel CFI running on widely-used off-the-shelf processors. Specifically, we use the ARMv8.3 pointer authentication (PAuth) extension and present a design that uses it to achieve strong security guarantees with minimal performance penalties. Furthermore, we show how deployment of such security primitives in the kernel can significantly differ from their user space application.
FlexReduce: Flexible All-reduce for Distributed Deep Learning on Asymmetric Network Topology

We propose FlexReduce, an efficient and flexible all-reduce algorithm for distributed deep learning under irregular network hierarchies. With ever-growing deep neural networks, distributed learning over multiple nodes is becoming imperative for expedited training. There are several approaches leveraging the symmetric network structure to optimize the performance over different hierarchy levels of the network. However, the assumption of symmetric network does not always hold, especially in shared cloud environments. By allocating an uneven portion of gradients to each learner (GPU), FlexReduce outperforms conventional algorithms on asymmetric network structures, and still performs even or better on symmetric networks.
Linearly Convergent Frank-Wolfe with Backtracking Line-Search

Structured constraints in Machine Learning have recently brought the Frank-Wolfe (FW) family of algorithms back in the spotlight. While the classical FW algorithm has poor local convergence properties, the Away-steps and Pairwise FW variants have emerged as improved variants with faster convergence. However, these improved variants suffer from two practical limitations: they require at each iteration to solve a 1-dimensional minimization problem to set the step-size and also require the Frank-Wolfe linear subproblems to be solved exactly. In this paper, we propose variants of Away-steps and Pairwise FW that lift both restrictions simultaneously. The proposed methods set the step-size based on a sufficient decrease condition, and do not require prior knowledge of the objective. Furthermore, they inherit all the favorable convergence properties of the exact line-search version, including linear convergence for strongly convex functions over polytopes. Benchmarks on different machine learning problems illustrate large performance gains of the proposed variants.
Towards Queryable and Traceable Domain Models

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.
Use of Augmented Reality to Improve Specific and Transversal Competencies in Students

Work in higher educational centres implicates the use of different types of methodology. The digital society demands students with a digital competence, and it is the responsibility of the university institution to ensure they succeed in obtaining it. This article presents an innovative immersive experience developed in a seminar on emerging technologies with 231 university students, and records their perceptions of some augmented reality apps used on the Social Education degree course and the Social Work and Social Education joint degree course at Pablo de Olavide University in Seville (Spain) in the academic years 2016-2018. A qualitative exploratory descriptive study was used to analyse the experience. The study presents the most important findings of this experience with augmented reality at the university, and proposes some didactic uses for the most interesting of these apps, with attention to their usability, ease of use and capacity to help early-stage learners to acquire new knowledge. Students considered that AR apps would provide educational scenarios that were more stimulating, collaborative and interactive, and would foment a more open type of education (N= 159/68.8%). The principal conclusion is using this technology in the psychosocial treatment of problems could help social area professionals to sharpen their competences and, at the same time, reinforce support and improve the conditions and treatments of aspects identified in these at risk groups. https://doi.org/10.26803/ijlter.19.8. 21
Unsupervised concept drift detection based on multi-scale slide windows

In the past few decades, research related to concept drift learning has been increasing, and many concept drift learning algorithms have also been developed and applied to actual data stream processing. In general, concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. This paper focuses on concept drift detection, and proposes an unsupervised concept drift detection algorithm based on multi-scale slide windows, where the total average distance is obtained through k-means clustering and multi-scale windows and is used as a detection index for concept drift, and then uses the statistical process control system to determine the range of index thresholds. Proved by experiments of detecting the gradual and abrupt concept drift with five datasets of different dimensions, including Sin, Circle, Gaussian, Radar and Motion Sense datasets, the algorithm has a good concept drift detection effect.