Reviewerly/exHarmony huggingface dataset:
ARC: Adversarially Robust Control Policies for Autonomous Vehicles

Deep neural networks have demonstrated their capability to learn control policies for a variety of tasks. However, these neural network-based policies have been shown to be susceptible to exploitation by adversarial agents. Therefore, there is a need to develop techniques to learn control policies that are robust against adversaries. We introduce Adversarially Robust Control (ARC), which trains the protagonist policy and the adversarial policy end-to-end on the same loss. The aim of the protagonist is to maximise this loss, whilst the adversary is attempting to minimise it. We demonstrate the proposed ARC training in a highway driving scenario, where the protagonist controls the follower vehicle whilst the adversary controls the lead vehicle. By training the protagonist against an ensemble of adversaries, it learns a significantly more robust control policy, which generalises to a variety of adversarial strategies. The approach is shown to reduce the amount of collisions against new adversaries by up to 90.25%, compared to the original policy. Moreover, by utilising an auxiliary distillation loss, we show that the fine-tuned control policy shows no drop in performance across its original training distribution.
Analysis of Car Damage for Personal Auto Claim Using CNN

In the world of vehicle insurance and rental industries, detecting damages on vehicles is one of the most important activities. These damages are identified and inspected by the drivers and insurance companies to determine the suitable monetary compensation, and by vehicle rental companies to assign the responsibility to guilty customers. Since the current system is time consuming, wherein the inspectors have to manually inspect the damages before appraising, this identification can be performed by object recognition systems. The intricacy of these systems rests in the image feature determination and extraction techniques. A more novel approach to detecting the severity of damage and predicting the repair costs is using 2D image recognition. This way, the driver would not have to wait for the appraisal of insurance companies to determine the rough estimate of cost of repairs. After a picture is uploaded to the framework, the picture is then processed and the vehicular damage is identified. The image is then classified into relevant damage severity classes. Afterward, the damage severity that is detected in the image, is then mapped to the approximate cost values. Then finally, the user is presented with a report of the vehicular damage severity classification as well as an average expense cost report from which the vehicle can then be recuperated from the damages.
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication

Gesture-based authentication has emerged as a non-intrusive, effective means of authenticating users on mobile devices. Typically, such authentication techniques have relied on classical machine learning techniques, but recently, deep learning techniques have been applied this problem. Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial domain for behavioral biometrics. In this research, we collect tri-axial accelerometer gesture data (TAGD) from 46 users and perform classification experiments with both classical machine learning and deep learning models. Specifically, we train and test support vector machines (SVM) and convolutional neural networks (CNN). We then consider a realistic adversarial attack, where we assume the attacker has access to real users’ TAGD data, but not the authentication model. We use a deep convolutional generative adversarial network (DC-GAN) to create adversarial samples, and we show that our deep learning model is surprisingly robust to such an attack scenario.
A Secured Distributed Ledger Based Fundraising Framework Using Smart Contracts

We make a living by what we get, we make a life by what we give. Giving is not about making a donation, it's about making a difference. To make this process highly accountable and transparent we propose the following system. The solution we have been working on consists of a blockchain-based application that brings the donor and the fundraiser on a common platform. From a broader perspective, this application can be scaled up to meet national or global level fundraising with accountability as one of its core pillars. Hence this motivated us to learn how crowd-sourcing and fundraising happens and how we could provide higher authenticity and accountability with the onset of Blockchain Technology. This paper proposes a blockchain-based charity framework and describes the platform's design pattern, architecture, and operational phase. Additionally, we plunge into how businesses can make use of our proposed system for their Corporate Social Responsibility. By sandwiching a layer of Blockchain Technology between each stakeholder, we affirm change that empowers people in the right direction.
An experimental analysis on evolutionary ontology meta-matching

Abstract Every year, new ontology matching approaches have been published to address the heterogeneity problem in ontologies. It is well known that no one is able to stand out from others in all aspects. An ontology meta-matcher combines different alignment techniques to explore various aspects of heterogeneity to avoid the alignment performance being restricted to some ontology characteristics. The meta-matching process consists of several stages of execution, and sometimes the contribution/cost of each algorithm is not clear when evaluating an approach. This article presents the evaluation of solutions commonly used in the literature in order to provide more knowledge about the ontology meta-matching problem. Results showed that the more characteristics of the entities that can be captured by similarity measures set, the greater the accuracy of the model. It was also possible to observe the good performance and accuracy of local search-based meta-heuristics when compared to global optimization meta-heuristics. Experiments with different objective functions have shown that semi-supervised methods can shorten the execution time of the experiment but, on the other hand, bring more instability to the result.
Optimized Energy Extraction in Tidal Current Technology using Evolutionary Algorithm

Renewable energy is gaining more popularity recently. Tidal currents are driven by two different connected bodies trying to equalize their level differences, hence there will be a flow of water from the high-pressure head to the low-pressure head. It is this kind of water flow that makes tidal current suitable for power generation. The main advantage of tidal power is that it can be forecasted easily. Aside from that, sea water has higher density as compared to air, therefore for the same amount of power, the power can be generated at a lower speed. The tidal current model is composed of a permanent magnet synchronous generator, tidal velocity profile, and another two sub-systems. This model is simulated in Matlab. The resultant tidal velocity is made up of 5 different partial tides. The tidal current turbine model is tested with different inputs of pitch angle and tidal current speed. The results show that the maximum generated output power is 295kW when the pitch angle is 2.77°. Furthermore, the higher the tidal current speed, the higher the generated output power. Aside from that, as the pitch angle is gradually increased while keeping the tidal speed constant, the power coefficient will decrease. Maximum Power Point Tracking algorithm which is based on Perturb and Observe (P&O) is used to locate the maximum power coefficient of the system. It can track the maximum power coefficient successfully but there will be oscillation at the steady state. Cuckoo Search via Levy Flight is able to overcome this problem as there will be no oscillation at steady state and this can prevent power loss. The convergence of Cuckoo Search via Levy Flight is two times faster than P&O.
Predicting Instance Type Assertions in Knowledge Graphs Using Stochastic Neural Networks

Instance type information is particularly relevant to perform reasoning and obtain further information about entities in knowledge graphs (KGs). However, during automated or pay-as-you-go KG construction processes, instance types might be incomplete or missing in some entities. Previous work focused mostly on representing entities and relations as embeddings based on the statements in the KG. While the computed embeddings encode semantic descriptions and preserve the relationship between the entities, the focus of these methods is often not on predicting schema knowledge, but on predicting missing statements between instances for completing the KG. To fill this gap, we propose an approach that first learns a KG representation suitable for predicting instance type assertions. Then, our solution implements a neural network architecture to predict instance types based on the learned representation. Results show that our representations of entities are much more separable with respect to their associations with classes in the KG, compared to existing methods. For this reason, the performance of predicting instance types on a large number of KGs, in particular on cross-domain KGs with a high variety of classes, is significantly better in terms of F1-score than previous work.
Predictive text analysis using eye blinks

The current work aims to facilitate interaction with others to those with the inability to perform activities requiring motor skills or those who cannot speak. It proposes a modus operandi or a system based on Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM), which automatically identifies eye blinks in real-time to predict a lexicon. The system implements an auxiliary input that enables individuals to interact with others with the help of a device, where voluntary long blinks help in transition from a counter to a predictive table, while the short blinks are used to make the counter stop and select the lexicon. The system does not require prior manual initialization, special lighting, or previous face detection as it can calibrate it if the user is in the camera region and close. The proposed user interface makes the process of words detection by blinking easier with 74% accuracy.
Reputation Equity in Ranking Systems

The impact of ranking systems on humans is an aspect that is getting a lot of attention. In this paper, we consider a class of algorithms, known as reputation-based ranking systems, which rank the items based on a reputation score automatically computed for each user. Recent literature introduced the concept of reputation independence, which considers a sensitive attribute of the users (such as gender or age) and makes the reputation scores independent from that attribute. Here, we show that if we consider a different sensitive attribute w.r.t. a user to introduce independence, reputation scores are still biased. To overcome this issue, we propose an approach to attain equity in the reputation scores computation, independently of any sensitive attribute that characterizes the users.
Automatic Gate using Face Recognition Technique using HAAR Cascade Algorithm

Now a day, in every single person households it is important to check regularly regarding their safety. Especially for elderly people it is mandatory, because they have become a target for certain burglars which leads to higher accidents/robberies in almost all the areas. To decrease the risk of such unwanted happenings in living space for single-person households, the hybrid security system should be adopted. The automatic personal identification has become the popular instead of using passwords or pattern in this days. This paper addresses the development of a face recognition technique for the above mentioned purpose.
A small object detection algorithm based on improved faster RCNN

Faster RCNN, as a classical detection algorithm, still faces a huge challenge in detecting small objects. Therefore, we introduce a multi-scale auxiliary feature fusion strategy to make sure that each layer of features contains rich semantic and spatial information. Firstly, we introduce shallow features extracted by a multi-scale auxiliary feature network into the backbone network, as a way to ensure that there is sufficient spatial information for detecting small objects even for the deepest feature. Secondly, we design a fusion module to fuse the auxiliary feature and backbone feature. Finally, to make the object proposal boxes positioning more precise in the ROI classification and regression network, replace RoIPool with RoIAlign. Our experiments are conducted on PASCAL VOC and KITTI autopilot datasets. Compared with the conventional methods, the improved Faster RCNN algorithm has 2.48% and 3.09% improved in mean average precision on PASCAL VOC and KITTI datasets, respectively.
AdaptiveGCN

Graph Convolutional Networks (GCNs) have become the prevailing approach to efficiently learn representations from graph-structured data. Current GCN models adopt a neighborhood aggregation mechanism based on two primary operations, aggregation and combination. The workload of these two processes is determined by the input graph structure, making the graph input the bottleneck of processing GCN. Meanwhile, a large amount of task-irrelevant information in the graphs would hurt the model generalization performance. This brings the opportunity of studying how to remove the redundancy in the graphs. In this paper, we aim to accelerate GCN models by removing the task-irrelevant edges in the graph. We present AdaptiveGCN, an efficient and supervised graph sparsification framework. AdaptiveGCN adopts an edge predictor module to get edge selection strategies by learning the downstream task feedback signals for each GCN layer separately and adaptively in the training stage, then only inference with the selected edges in the test stage to speed up the GCN computation. The experimental results indicate that AdaptiveGCN could yield 43% (on CPU) and 39% (on GPU) GCN model speed-up averagely with comparable model performance on public graph learning benchmarks.
A Soft Drone with Multi-modal Mobility for the Exploration of Confined Spaces

In post-disaster scenarios, rescuers are often confronted with the challenge of accessing confined and cluttered environments including long and narrow passageways, gaps in walls or ceilings. Because of their mobility and versatility, there is a growing interest in developing drones for the remote exploration of these dangerous and often difficult to access places. However, the mechanical design and locomotion strategies of current drones limit the size of the confined space that can be explored. In this work, we present a quadcopter capable of traversing long passageways 34% smaller than its nominal size. The combination of a soft morphing frame and multi-modal mobility allows the drone to exploit a new dynamic strategy for passageway traversal. The drone flies at a given speed towards the entrance of the passageway until it collides with it. The momentum and ability of the frame to soften allow the drone to passively fold and enter. Once the drone is squeezed between the walls of the passageway, it uses two tracks to crawl through. Through experiments, we characterize the main mechanical systems of the drone and study the entry into crevices of different sizes.
Selecting a Family Planning Method for Various Age Groups of Different States in India

Couples do not have proper knowledge about the family planning methods that leads to unplanned pregnancy, which is the main reason of population growth. Women as well as men must be educated regarding the issues related to pregnancy that leads to population explosion and is a matter of great concern. Keeping in view all the mentioned issues, there is a strict requirement of a Smart System that guides the couples. The system calculates percentage of population growth for various age groups for different family planning methods for two cases: a. Delivered any baby b. Not Delivered any baby by using aggregate function of Rapid Miner Tool used in Data Mining. The percentage is used to determine the most and least effective contraceptive methods and can serve as a useful statistic to choose a family planning method. The datasets have been taken from Kaggle website for different states in India.
Won’t Somebody Please Think of the Tests? A Grounded Theory Approach to Industry Challenges in Continuous Practices

Continuous integration and delivery are well-established paradigms in the software development community. With these continuous practices come many challenges; while some of these challenges are immediate and well documented in literature, others may be revealed only after sustained application of these practices in large-scale and complex contexts. Based on researcher observations and interviews with 22 senior professionals from four companies, all with significant but varied experiences of continuous practices, we present a set of underreported challenges with continuous practices observed in multiple industry settings. Through a grounded theory approach we construct the Tapco model — Test Automation Progression in Continuous Practices — identifying two distinct ways in which companies progress towards continuous delivery, onto which the studied cases are mapped. This model is then validated by presenting it to the four studied companies, operating in disparate industry segments, and to three additional industry cases, letting them evaluate its relevancy, accuracy and novelty. We find that the model provides industry professionals with essential guidance on how to avoid common pitfalls, as well as an understanding of their causes and possible remediation.
Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework

There are generally many redundant and irrelevant features in high-dimensional datasets, which leads to the decline of classification performance and the extension of execution time. To tackle this problem, feature selection techniques are used to screen out redundant and irrelevant features. The artificial bee colony (ABC) algorithm is a popular meta-heuristic algorithm with high exploration and low exploitation capacities. To balance between both capacities of the ABC algorithm, a novel ABC framework is proposed in this paper. Specifically, the solutions are first updated by the process of employing bees to retain the original exploration ability, so that the algorithm can explore the solution space extensively. Then, the solutions are modified by the updating mechanism of an algorithm with strong exploitation ability in the onlooker bee phase. Finally, we remove the scout bee phase from the framework, which can not only reduce the exploration ability but also speed up the algorithm. In order to verify our idea, the operators of the grey wolf optimization (GWO) algorithm and whale optimization algorithm (WOA) are introduced into the framework to enhance the exploitation capability of onlooker bees, named BABCGWO and BABCWOA, respectively. It has been found that these two algorithms are superior to four state-of-the-art feature selection algorithms using 12 high-dimensional datasets, in terms of the classification error rate, size of feature subset and execution speed.
Adversarial Learning with Bidirectional Attention for Visual Question Answering

In this paper, we provide external image features and use the internal attention mechanism to solve the VQA problem given a dataset of textual questions and related images. Most previous models for VQA use a pair of images and questions as input. In addition, the model adopts a question-oriented attention mechanism to extract the features of the entire image and then perform feature fusion. However, the shortcoming of these models is that they cannot effectively eliminate the irrelevant features of the image. In addition, the problem-oriented attention mechanism lacks in the mining of image features, which will bring in redundant image features. In this paper, we propose a VQA model based on adversarial learning and bidirectional attention. We exploit external image features that are not related to the question to form an adversarial mechanism to boost the accuracy of the model. Target detection is performed on the image-that is, the image-oriented attention mechanism. The bidirectional attention mechanism is conducive to promoting model attention and eliminating interference. Experimental results are evaluated on benchmark datasets, and our model performs better than other models based on attention methods. In addition, the qualitative results show the attention maps on the images and leads to predicting correct answers.
Design and Implementation of Human Computer Interactive Gesture Recognition System

With the continuous development of science and technology, computer technology is also further improved, more and more operating instructions, and its functions are more comprehensive. Human computer interaction is the process that people adapt to the computer from the past to the computer constantly adapt to people. For example, human-computer interaction through graphics is attracting people to research. In this paper, raspberry PI combined with camera is used to collect the two-dimensional model data of gesture, and the feature processing of gesture information is completed. Hand gesture recognition is carried out through OpenCV image preprocessing, the boundary of input image is selected, the existence of hand is scanned, and the contour of hand is drawn to recognize and display finger. Using Spyder integrated development environment to complete the design and implementation of human-computer interactive gesture application system, which can effectively identify different types of gestures, so that it can be applied to real life to solve the needs of special environment.
HTF

Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.