Reviewerly/exHarmony huggingface dataset:
UAV Based Survivor Search during Floods

This paper addresses the problem of searching for survivors in flood affected regions. These search missions are carried out by UAVs (Unmanned Aerial Vehicles) in order to search faster and cover larger distances. The proposed search algorithm receives information regarding survivor location from the ground based observers. The observers report the location of survivors when the survivor is within its field of view. In such a situation when the location information about the survivor is received by the UAV, the survivor might have moved to a different location when the UAV tries to go to the last spotted location to search. Hence, it is necessary to have a prediction algorithm to estimate the future position of the survivor. The algorithms implemented in this paper is a weight age based approach and a truncated weight based approach to predict the future location of the survivor. The performance of these algorithms are compared with a tradition lawnmower search pattern. These algorithms are implemented by taking a real life flood scenario and the effectiveness and efficiency of the implemented algorithm is shown by an extensive Monte Carlo simulation.
Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military

Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.
A Deep Multimodal Investigation To Determine the Appropriateness of Scholarly Submissions

Present day peer review is a time-consuming process and is still the only gatekeeper of scientific knowledge and wisdom. However, the rapid increase in research article submissions these days across different fields is posing significant challenges to the current system. Hence the incorporation of Artificial Intelligence (AI) techniques to better streamline the existing peer review system is an immediate need in this age of rapid scientific progress. Among many, one particular challenge these days is that the journal editors and conference program chairs are overwhelmed with the ever-increasing rise in article submissions. Studies show that a lot many submissions are not well-informed and do not fit within the scope of the intended journal or conference. Here in this work, we embark on to investigate how an AI could assist the editors and program chairs in identifying potential out-of-scope submissions based on the past accepted papers of the particular journal or conference. We design a multimodal deep neural architecture and investigate the role of every possible channel of information in a research article (full-text, bibliography, images) to determine its appropriateness to the concerned venue. Our approach does not involve any handcrafted features, solely depends on the past accepting activity of the venue, and thereby achieves significant performance on two real-life datasets. Our findings suggest that a system of this kind is possible and with reasonable accuracy could assist the editors/chairs in flagging out inappropriate submissions.
“Soft law” and innovations: empirical analysis of ICO-related statements

Purpose The purpose of the paper is to explore the initial coin offering (ICO) statements as “soft law” instrument used to regulate disruptive innovations. Design/methodology/approach The research is based on the qualitative content analysis of 40 ICO statements issued by regulators in 37 countries by applying a custom-made coding table. Findings The research shows that “soft law” is used predominantly by high-capacity jurisdictions. “Soft law” allows for more flexibility and less technological and business neutrality. The findings also show the contradiction between empirical evidence and public sentiment: it seems that the widespread notion that virtual currencies have connotations with money laundering/financing of terrorism (ML/FT) is not shared by the regulators, who are more concerned by the fraud. Finally, it was found that the standard-setting bodies are lagging behind in providing guidance on the emergence technologies. Research limitations/implications The content analysis is based on 40 statements, which is a limited set of data. The method might be subject to interpersonal bias, although arrangements were made to ensure the uniformity of coding process. Practical implications The findings imply that soft law is an attractive risk-mitigation tool when the object of regulation is still evolving but the risks are present. Soft law also might contradict with the “technology and business neutrality” principle which requires further research. Finally, the findings show the need for more active involvement of the standard setting bodies. Originality/value This is the first in-depth research of the ICO-related statements as “soft law” instruments. It also offers a new perspective on the issue of financial innovations regulation.
A Memetic Algorithm for Symbolic Regression

This research aims to address the practical difficulties of computational heuristics for symbolic regression, which models data with algebraic expressions. In particular we are motivated by cases in which the target unknown function may be best represented as the ratio of functions. We propose an alternative general approach based on a different representation of mathematical models with an analytic continued fraction representation, from which rational function models can be extracted. A memetic algorithm, which is a paradigm of meta-heuristic optimization based on the evolution of solutions by a set of computational agents, is implemented to generate solutions in this representation. A population of computational agents with problem domain knowledge improves feasible solutions using local search heuristics and produces models that fit the data better. In addition, the agents compete in searching for function models with fewer number of variables. Agent interactions are constrained by a population structure which has been previously used in several successful MAs for other combinatorial optimization problems. We utilize a tree-based population structure to improve the algorithm's consistency and performance. Data from real-world applications are used to measure the potential of our approach and benchmark its performance against other approaches in symbolic regression.
Efficient Computation of Optimal UAV Routes for Persistent Monitoring of Targets

In this article, we consider a routing problem that arises in the scenario of persistently monitoring a set of targets using an unmanned vehicle. A UAV is tasked with monitoring a set of targets, by frequently visiting them for data collection. The UAV has a limited fuel capacity, which is specified in terms of the number of visits it makes, at the end of which it must be recharged at a depot. The problem considered here is to plan an optimal sequence of visits (to targets) for the UAV, such that the maximum time between consecutive revisits to the targets is minimized. The problem is a generalization of the Traveling Salesman Problem (TSP) and is computationally challenging; It is significantly difficult to compute optimal solutions using standard formulations. However, the authors recently developed a set of theoretical results characterizing the structure of optimal solutions for this problem. Using these results, in this article, we propose a new formulation to solve the problem. Extensive numerical simulations suggest that the average computation time required to solve the problem using the proposed formulation is within a fraction of a second on a standard laptop (here, a MacBook Pro with Intel Core i7 processor and 16 GB RAM was used).
Making Online Sketching Hashing Even Faster

Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However, they still suffer from several obstacles: First, most of existing hashing methods are trained in a batch mode, yielding inefficiency for training streaming data. Second, the computational cost and the memory consumption increase extraordinarily in the big data setting, which perplexes the training procedure. Third, the lack of labeled data hinders the improvement of the model performance. To address these difficulties, we utilize online sketching hashing (OSH) and present a FasteR Online Sketching Hashing (FROSH) algorithm to sketch the data in a more compact form via an independent transformation. We provide theoretical justification to guarantee that our proposed FROSH consumes less time and achieves a comparable sketching precision under the same memory cost of OSH. We also extend FROSH to its distributed implementation, namely DFROSH, to further reduce the training time cost of FROSH while deriving the theoretical bound of the sketching precision. Finally, we conduct extensive experiments on both synthetic and real datasets to demonstrate the attractive merits of FROSH and DFROSH.
Testing A Multi-Operator based Differential Evolution Algorithm on the 100-Digit Challenge for Single Objective Numerical Optimization

Although over the past one decades, several variants of Differential Evolution (DE) have been introduced for solving the global optimization functions, no single variant of DE shows better performance on a variety of optimization problems. During the last five years, to lighten this deficiency, many variants of DE which employ multiple mutation and crossover strategies in a single structure of algorithm, called as multi-operators variant of DE (MODE), have been proposed. In this work, ESHADE, an enhanced version of a MODE, is introduced including various mutation strategies and an exponential population size reduction (EPSR) technique is utilized to reduce size of the population for the next iteration. Additionally, a version of uni-variate sampling method is employed in later iterations to provide a balance between exploitative and explorative search. To perform the comparative analysis, the proposed algorithm is benchmarked on the problem suite of the 100-digit challenge on single objective numerical optimization at CEC-2019. Comparative analysis reveals that the ESHADE can provide high-quality solutions as compared to state-of-the-art algorithms.
Gas Consumption-Aware Dynamic Load Balancing in Ethereum Sharding Environments

Advances in blockchain technology have made a significant impact on a wide range of research areas due to the features such as transparency, decentralization and traceability. With the explosive growth of blockchain transactions, there has been a growing interest in improving the scalability of blockchain network. Sharding is one of the methods to solve this scalability problem by partitioning the network into several shards so that each shard can process the transactions in parallel. Ethereum places each transaction statically on a shard based on its account address without considering the complexity of the transaction or the load generated by the transaction. This causes the transaction load on each shard to be uneven, which makes the transaction throughput of the network decrease. In this paper, we propose a dynamic load balancing mechanism among Ethereum shards called D-GAS. The D-GAS dynamically balances the transaction load of each shard by relocating the accounts based on the gas consumption to maximize the transaction throughput. Ethereum gas is a unit that represents the amount of computational effort needed to execute operations in a transaction. Benchmarking results show that the D-GAS outperforms existing techniques by up to 12% in transaction throughput and decreases the makespan of transaction latency by about 74% under various conditions.
A two-sample test for the equality of univariate marginal distributions for high-dimensional data

A recurring theme in modern statistics is dealing with high-dimensional data whose main feature is a large number, p, of variables but a small sample size. In this context our aim is to address the problem of testing the null hypothesis that the marginal distributions of p variables are the same for two groups. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. The asymptotic normality of the test statistic is derived under mixing conditions. In our asymptotic analysis the number of variables tends to infinity, while the size of individual samples remains fixed. In order to obtain a practical test several estimators of the variance are proposed, leading to three somewhat different versions of the test. An alternative global test based on the P-values derived from permutation tests is also proposed. A simulation study to investigate the finite sample properties of the proposed tests is carried out, and a practical illustration involving microarray data is provided.
Dynamic Traffic Scene Classification with Space-Time Coherence

This paper examines the problem of dynamic traffic scene classification under space-time variations in viewpoint that arise from video captured on-board a moving vehicle. Solutions to this problem are important for realization of effective driving assistance technologies required to interpret or predict road user behavior. Currently, dynamic traffic scene classification has not been adequately addressed due to a lack of benchmark datasets that consider spatiotemporal evolution of traffic scenes resulting from a vehicle's ego-motion. This paper has three main contributions. First, an annotated dataset is released to enable dynamic scene classification that includes 80 hours of diverse high quality driving video data clips collected in the San Francisco Bay area. The dataset includes temporal annotations for road places, road types, weather, and road surface conditions. Second, we introduce novel and baseline algorithms that utilize semantic context and temporal nature of the dataset for dynamic classification of road scenes. Finally, we showcase algorithms and experimental results that highlight how extracted features from scene classification serve as strong priors and help with tactical driver behavior understanding. The results show significant improvement from previously reported driving behavior detection baselines in the literature.
Compromising Flight Paths of Autopiloted Drones

While more and more consumer drones are abused in recent attacks, there is still very little systematical research on countering malicious consumer drones. In this paper, we focus on this issue and develop effective attacks to common autopilot control algorithms to compromise the flight paths of autopiloted drones, e.g., leading them away from its preset paths. We consider attacking an autopiloted drone in three phases: attacking its onboard sensors, attacking its state estimation, and attacking its autopilot algorithms. Several first-phase attacks have been developed (e.g., [1]-[4]); second-phase attacks (including our previous work [5], [6]) have also been investigated. In this paper, we focus on the third-phase attacks. We examine three common autopilot algorithms, and design several attacks by exploiting their weaknesses to mislead a drone from its preset path to a manipulated path. We present the formal analysis of the scope of such manipulated paths. We further discuss how to apply the proposed attacks to disrupt preset drone missions, such as missing a target in searching an area or misleading a drone to intercept another drone, etc. Many potential attacks can be built on top of the proposed attacks. We are currently investigating different models to apply such attacks on common drone missions and also building prototype systems on ArduPilot for real world tests. We will further investigate countermeasures to address the potential damages.
Visual Attention Modeling for Autism Spectrum Disorder by Semantic Features

Autism spectrum disorder (ASD) is a common mental illnesses for children. Existing studies show that visual attention of children with ASD is different from that for normal children. Thus, it is meaningful to design an effective visual attention model for children to diagnose ASD. In this paper, we propose a saliency detection model for children with ASD by small-scale image samples. In the proposed model, we design a deep neural network by high-level semantic features to saliency detection in data-driven way. The U-net is used for construct the deep neural network for semantic feature learning, where a loss function of positive and negative equilibrium mean square-error (PN-MSE) is designed in the constructed deep neural network. Experimental results show that the proposed model can obtain better performance than other existing models.
An Attribute-Based Privacy-Preserving Ethereum Solution for Service Delivery with Accountability Requirements

The main benefit of smart contracts over Ethereum is that different parties with conflicting interests can exchange value without trusting each other. As a matter of fact, solutions in which service delivery is regulated by smart contracts are proliferating. Sometimes, services can be negotiated and delivered only on the basis of some attributes, without disclosing the identity of the customer to the service supplier. However, accountability is still required, so that, in case of need, the identity of the customer should be linked to the service delivered and communicated to the appropriate parties. In this paper, we propose a practical solution to the above problem that integrates the features of Ethereum with a (Ciphertext-Policy) Attribute-Based Encryption scheme. To show the effectiveness of our proposal, we instantiate the general model to a significant use case.
Sentiment Analysis and Extractive Summarization Based Recommendation System

With the commencementRoul, Rajendra Kumar of new technology and increase of online shopping companies like Amazon, E-Bay, and Flipkart, people give a wide range of reviews for the products they purchase. Some are too long, some too short, someSahoo, Jajati Keshari difficult to understand while some are totally irrelevant. Thus, there is a pressing need to reduce the diversity of reviews for a particular product and to show the users only the gist of most useful and important reviews about a product. The proposed work focuses to summarize the reviews for movies purchased from Amazon using a combination of four state-of-the-arts algorithms and a feature selection technique. Sentiment analysis has been performed to categorize the reviews into positives and negatives. Also, a novel method named hierarchical summarization is proposed to summarize large reviews into summary of few sentences. The results of this summary are compared with the existing algorithms using the ROUGE score to determine the best summary. Experimental results show that the proposed approach is promising.
Reusability in Artificial Neural Networks

Machine learning, especially deep learning has aroused interests of researchers and practitioners for the last few years in development of intelligent systems such as speech, natural language, and image processing. Software solutions based on machine learning techniques attract more attention as alternatives to conventional software systems. In this paper, we investigate how reusability techniques are applied in implementation of artificial neural networks (ANNs). We conducted an empirical study with an online survey among experts with experience in developing solutions with ANNs. We analyze the feedback of more than 100 experts to our survey. The results show existing challenges and some of the applied solutions in an intersection between reusability and ANNs.
Using Convolutional Neural Networks for Classifying Malicious Network Traffic

As the reliance on the Internet and its constituent applications increase, so too does the value in exploiting these networking systems. Methods to detect and mitigate these threats can no longer rely on singular facets of information, they must be able to adapt to new threats by learning from a diverse range of information. For its ability to learn complex inferences from large data sources, deep learning has become one of the most publicised techniques of machine learning in recent years. This chapter aims to investigate a deep learning technique typically used for image classification, the convolutional neural network (CNN), and how its methodology can be adapted to detect and classify malicious network traffic.
Building a Robust Mobile Payment Fraud Detection System with Adversarial Examples

Mobile payment is becoming a major payment method in many countries. However, the rate of payment fraud with mobile is higher than with credit card. One potential reason is that mobile data is easier to be modified than credit card data by fraudsters, which degrades our data-driven fraud detection system. Supervised learning methods are pervasively used in fraud detection. However, these supervised learning methods used in fraud detection have traditionally been developed following the assumption that the environment is benign; there are no adversaries trying to evade fraud detection system. In this paper, we took potential reactions of fraudsters into consideration to build a robust mobile fraud detection system using adversarial examples. Experimental results showed that the performance of our proposed method was improved in both benign and adversarial environments.
Combining Safe Interval Path Planning and Constrained Path Following Control: Preliminary Results

We study the navigation problem for a robot moving amidst static and dynamic obstacles and rely on a hierarchical approach to solve it. First, the reference trajectory is planned by the safe interval path planning algorithm that is capable of handling any-angle translations and rotations. Second, the path following problem is treated as the constrained control problem and the original flatness-based approach is proposed to generate control. We suggest a few enhancements for the path planning algorithm aimed at finding trajectories that are more likely to be followed by a robot without collisions. Results of the conducted experimental evaluation show that the number of successfully solved navigation instances significantly increases when using the suggested techniques.
Fine Detection and Classification of Multi-class Barcode in Complex Environments

Barcode, including one-dimensional (1D) barcode and two-dimensional (2D) barcode, can be seen almost anywhere in our lives. In many barcode-based mobile systems, different barcodes will appear simultaneously with different angles, shapes, and image quality. Barcode localization is a significant prerequisite for barcode decoding in these applications. In this paper, we use a region-based end-to-end network with a quadrilateral regression layer to finely localize and classify 1D barcode and Quick Response (QR) code. In addition, we use a multi-scale feature fusion layer to improve the detection accuracy of small scale barcode in complex environments. Extensive experiments on existing public datasets and our own dataset demonstrate the effectiveness of the proposed method.