Reviewerly/exHarmony huggingface dataset:
Distributed ImageJ(Fiji): a framework for parallel image processing

ImageJ is an open-source application widely used for image processing. It has developer API that can be used to implement new plugins for specific image processing tasks. However, ImageJ wasn't designed to work on distributed systems. Currently, it is still being used on single machines to process large medical images, which takes several hours to complete. In this article, we present the approaches to make several essential and widely used ImageJ plugins to work in a cluster. As the cluster nodes parallelly run the existing plugins for image processing, they write the results on a shared drive. But one of the main challenges is, merging those results with high accuracy. Several ImageJ plugins were developed to distribute tasks and generate combined results efficiently. The existing 3D-Object-Counter plugin was used for testing the designed system. The experimental results on the test images of 3D objects stored in Tagged Image File Format(TIFF) show faster processing time with high accuracy and similarity compared to the single machine-based results. The image processing time depends on the number of nodes in the cluster. So, we present a mathematical model that determines the cluster size automatically for optimizing the overall image processing time.
Circuit Component Detection in Offline Handdrawn Electrical/Electronic Circuit Diagram

Detection of circuit components from handdrawn circuit image refers to the process of automatic separation of all the circuit components belonging to a circuit. In this work, we have devised an algorithm that accepts a hand-drawn circuit image as input and returns the circuit components marked with bounding box. We have proposed a method where we have used the morphological `close' operator to create a structural dissimilarity between the circuit components and the wires. Then we have used run length smoothing algorithm (RLSA) based technique to remove all the wires. Finally, we have used the spatial coordinates of the circuit components in the wire-removed circuit diagram to localize them in the actual circuit diagram. The proposed methodology is evaluated over a database consisting of 60 circuit images collected from the copies of engineering students. Considering the complexity of the research topic, the result thus obtained is encouraging.
Sample-Based Motion Planning for Multi-Robot Systems

We present a methodology for robot path planning and smoothing based on the probabilistic roadmap algorithm and simulated annealing algorithm. Furthermore, a fast and accurate collision detection and a suitable format for the abstract description of robot problems are introduced. The methods are tested on the automotive sealing process by applying them to a practical problem.
TargetNet Backdoor

Deep neural networks (DNNs) provide good performance in image recognition, speech recognition, and pattern analysis. However, DNNs are vulnerable to backdoor attacks. Backdoor attacks allow attackers to proactively access DNN training data to train it on additional data that are malicious, including a specific trigger. Normally, DNNs correctly classify normal data, but malicious data with a specific trigger trained by attackers can cause misclassification by DNNs. For example, if an attacker sets up a road sign that includes a specific trigger, an autonomous vehicle equipped with a DNN may misidentify the road sign and cause an accident. Thus, an attacker can use a backdoor attack to threaten the DNN at any time. However, in certain cases, when an attacker wants to perform a targeted attack, it may be desirable for the data introduced through the backdoor to be misrecognized as a particular class chosen by the attacker according to the position of a trigger. For example, if a specific trigger is attached to the top right side of the road sign, it may be misunderstood as a left-turn sign; if a specific trigger is attached to the top left side of the road sign, it may be misunderstood as a right-turn sign; and if a specific trigger is attached to the bottom left side of the road sign, it may be misunderstood as a U-turn sign. In this paper, we propose the TargetNet backdoor, which is designed to be misidentified as a particular target class chosen by the attacker according to a specific trigger location. The proposed method additionally trains the target classifier on the TargetNet backdoor data so that data with a trigger at a specific location will be misidentified as the target class selected by the attacker. We used MNIST and Fashion-MNIST as experimental datasets and Tensor-flow as a machine learning library. Experimental results show that the proposed method applied to MNIST and Fashion-MNIST has a 100% attack success rate for the TargetNet backdoor and 99.17% and 91.4% accuracy rates on normal test data, respectively.
A Study of CNN Architectures over Two Hand Indian Sign Language Dataset

Sign language is a nonverbal way of communication with the speech-impaired and hearing-disabled people. Hand gestures play a significant role in establishing the connection between humans and machines. Gesture recognition poses a serious challenge to the system due to improper light and background conditions. Recognition of gestures using two hands is more difficult than using one hand gestures. In the present work, we have used Two Hand Indian Sign Language (THISL) dataset and have added two more gestures to it. The modified dataset consists of approximately 12,600 images which split into 80% for training set and the remaining 20% for the test set. The aim is to validate THISL dataset on various convolutional neural network models and analyze the model’s performance in terms of accuracy and loss, computational time, and memory usage.
Characterizing the Cost of Introducing Secure Programming Patterns and Practices in Ethereum

Ethereum is blockchain-based platform which enables the development and deployment of smart contracts. Smart contracts are computer programs that provide automation for the governance of decentralized autonomous organizations (DAO). However, while the Blockchain technology is secure, smart contracts are only as secure as the programmers has designed it to be. Therefore, smart contract exposes vulnerabilities that can be exploited by attackers and threaten the viability of the DAOs. This study presents a case study which investigated how security programming patterns and practices from other programming languages can be applied in Solidity – Ethereum programming language. We have characterized the cost of introducing these patterns and practices. We identified 30 security programming patterns and practices from C++, JAVA which can be applicable to Solidity and implemented ten in a representative smart contract. The results show that the application of the ten security patterns and practices identified and implemented increases the cost of the smart contract (when compared to the baseline). Furthermore, we argue that this difference is not significant and should not deter any programmers into introducing the security patterns and practices into their smart contracts.
Inductive Guided Filter: Real-Time Deep Matting with Weakly Annotated Masks on Mobile Devices

Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficult to attain in practice. An efficient image matting method based on a weakly annotated mask is in demand for mobile applications. In this paper, we propose a novel method called Inductive Guided Filter, which tackles the real-time general image matting task with weakly annotated masks on mobile devices. The Inductive Guided Filter exploits the gradient prior implicit in Guided Filter to reduce the computational burden tremendously in a deep learning manner. The use of Gabor loss is also proposed for complicated textures in image matting. Moreover, we create an image matting dataset MAT-2793 with a variety of foreground objects. Experimental results demonstrate that our proposed method massively reduces running time with robust accuracy.
Increasing distillable key rate from bound entangled states by using local filtration

We show the enhancement of distillable key rate for quantum key distribution(QKD), by local filtering, for several bound entangled states. Through our work it becomes evident that the local filtration operations, while transforming one bound entangled state to another, have the potential to increase the utility of the new state for QKD. We demonstrate three examples of `one way distillable key rate' enhancement by local filtering and in this process, discover new bound entangled states which are key distillable.
An Effective Content Based Image Retrieval Model using Improved Memetic Algorithm

Content Based Image Retrieval (CBIR) technique retrieves relevant images based on a query image (QI) from larger databases. The existing CBIR models filter only a minimal number of features and tends to decrease the efficiency. This paper proposes an effective CBIR model by the use of Improved Memetic Algorithm (IMA). At first, feature extraction process takes place when the features exist in the database are extracted and saved in a feature repository. These feature sets are comprised with color signatures along with the shape and color texture features. At the same time, the features present in the QI are also extracted. Then, new similarity estimation has been developed by applying a meta-heuristic technique named as IMA. The presented IMA-CBIR model is tested using a benchmark dataset and the results indicated that the proposed IMA-CBIRS are highly effective in terms of precision and recall when compared with existing techniques.
Extractive summarization using siamese hierarchical transformer encoders

In this paper, we present an extractive approach to document summarization, the Siamese Hierarchical Transformer Encoders system, that is based on the use of siamese neural networks and the transformer encoders which are extended in a hierarchical way. The system, trained for binary classification, is able to assign attention scores to each sentence in the document. These scores are used to select the most relevant sentences to build the summary. The main novelty of our proposal is the use of self-attention mechanisms at sentence level for document summarization, instead of using only attentions at word level. The experimentation carried out using the CNN/DailyMail summarization corpus shows promising results in-line with the state-of-the-art.
Adaptive multilevel thresholding based on multiobjective artificial bee colony optimization for noisy image segmentation

Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perform multilevel thresholding under several segment ation requirements. In order to solve these problems, an adaptive multilevel thresholding algorithm based on multiobjective artificial bee colony optimization (AMT-MABCO) segmentation is presented for noisy image in this paper. To improve the robustness of AMT-MABCO to image noise, a line intercept histogram which considers both the intensity and coordinate information in the neighborhood of the pixels is firstly utilized to define a novel between-class variance function as one fitness function. Then, an interval-valued fuzzy entropy function is constructed as another fitness function to deal with the blurred characteristic in images. AMT-MABCO tries to obtain a compromising multilevel thresholding result under these two segmentation requirements. To adaptively determine the number of thresholds, a grouping population initialization and evaluation strategies are proposed in AMT-MABCO. Furthermore, two novel search equations are constructed in AMT-MABCO to generate candidate solutions in the employed bees and onlookers phases, respectively. Experimental results show that AMT-MABCO outperforms state-of-the-art thresholding methods in noise robustness and segmentation performance.
Physical Layer Anomaly Detection Mechanisms in IoT Networks

With the advent of IoT and wireless mesh networks, security risks inherent to these types of networks have grown in number, severity, and potential for harm. Most of the approaches currently available for anomaly detection in IoT networks perform frame and packet inspection, which may inadvertently reveal private behavioural patterns of its users.This paper proposes a privacy-focused framework for anomaly detection which analyses radio activity at the physical layer, measuring silence and activity periods, and extracts relevant features for training One-Class Classification (OCC) models.We train our models with data captured from interactions with an Amazon Echo with multiple devices generating background noise, thus simulating a business-like environment, and test them against a similar scenario with a tampered network node periodically uploading data to a local machine. Our data show that the best performing model is able to detect anomalies with a 99% precision rate.
DA-cGAN: A Framework for Indoor Radio Design Using a Dimension-Aware Conditional Generative Adversarial Network

A novel "physics-free" approach of designing indoor radio dot layout for a floor plan is introduced by formulating it as an image-to-image translation problem and solved with customized dimension-aware conditional generative adversarial networks (DA-cGANs). The proposed model generates a desirable radio heatmap and its respective radio dot layout from a given floor plan with wall types, physical dimension, and macro-cell interference, by learning from the accumulated indoor radio designs by human experts. Considering the nature of radio propagation, two new loss functions and a two-stage training strategy are proposed for the generator to learn the right direction of signal propagation and precise dot locations, in addition to a sectional analysis for dealing with large floor plans. Experimental results show that the new model is effectively generating acceptable dot layout designs and that dimension-awareness is a key enabler for this type of prediction.
MULTI-OBJECTIVE OPTIMIZATION OF HOSE ASSEMBLY ROUTING FOR VEHICLES

Abstract This paper presents a method for multi-objective optimization of hose assembly routing. Hose routing is a non-trivial task which demand a lot of iterations, especially with the increased complexity in modern vehicles. The proposed method utilizes design automation through multi-objective optimization of routing assemblies containing multiple hoses. The method is intended as a decision support and automation-tool, that reduces the number of iterations needed. The method has been implemented and tested on a case, concerning a set of hoses in an engine compartment, showing credible results.
Towards Interactive Pattern Search in Massive Graphs

We present the design overview of a pattern matching engine for labeled graphs that supports interactive search: the user, based on feedback received from the search system, repeatedly revises her search template until s/he is satisfied with the results. To this end, we have developed a distributed memory solution that supports human-in-the-loop processing. Our solution embraces a number of design principles to offer high-performance, scalability and efficiency: (i) fast parallel processing - we adopt a vertex parallel computation model; (ii) aggressive search space reduction - using lightweight routines, we identify and prune away the non-matching part of the graph early; (iii) redundant work elimination - a revised query is likely to share label(s) and/or substructure(s) with its predecessor(s); therefore, whenever possible, we avoid redundant computation by reusing (partial) match information from earlier searches. Our preliminary evaluation highlights the effectiveness of the proposed approach: using a 257 billion edge real-world webgraph, on a 128 node (4,608 cores) deployment, we demonstrate the advantage of our technique over a naive approach (that uses an exact matching solution to independently search the original query and each of its revisions).
A Cloud-Agnostic Framework to Enable Cost-Aware Scheduling of Applications in a Multi-Cloud Environment

We have witnessed a surge in both the big data applications being hosted by an assortment of cloud vendors, and in the astronomical amount of data they produce and consume on a daily basis. Traditional cluster computing frameworks can hardly cope with the unprecedented data volume and the geo-distributed, cross-cloud data distribution due to their limited scalability and adaptability across the heterogeneous clouds. Moreover, running data-intensive applications across clouds at will is extremely cost-inefficient and likely to incur outrageous expenses. Hence, we introduce our cloud-agnostic system PIVOT with the novel cost-aware scheduling algorithm, which enables data-intensive applications to run and scale across clouds instantly in a cost-efficient manner. We evaluate our system and scheduling algorithm extensively with the Alibaba production cluster trace, as well as real-world big data applications on a 100-node deployment across 11 regions (31 availability zones) on AWS and GCP. The experimental results show that PIVOT achieves up to 90.8% saving in expense for VM subscription and 99.2% for egress network traffic compared to the state-of-the-art baselines. Notably, the cost-aware scheduling also achieves over 4x speedup in data transfers for data-intensive applications.
EEG-based intelligent system for cognitive behavior classification

Intelligence and learning styles are among most widely studied traits in cognitive psychology. Currently, both aspects of cognition can only be assessed using paper-based psychometric tests. The methods however, are exposed to inconsistency issues due to the variation of examination format and lang uage barriers. Hence, this study proposes an intelligent system for assessing intelligence quotient (IQ) level and learning style from the resting brainwaves using artificial neural network (ANN). Eighty-five individuals from varying educational backgrounds have participated in this study. Resting electroencephalogram (EEG) is recorded from the left prefrontal cortex using NeuroSky. Control groups are established using Kolb's Learning Style Inventory (LSI) and a model developed based on Raven's Progressive Matrices (RPM). Subsequently, theta, alpha and beta power ratio is extracted from the pre-processed EEG. Distribution and pattern of features show a correlation with the Neural Efficiency Hypothesis of intelligence and Alpha Suppression Theory. The power ratio features are then used to train, validate and test the ANN model. The system has demonstrated satisfactory performance for IQ classification with accuracies of 98.3% for training and 94.7% for testing. The proposed model is also able to classify learning style with accuracies of 96.9% for training and 80.0% for testing.
Question answering model based on machine reading comprehension with knowledge enhancement and answer verification

Summary Deep learning has led to important breakthroughs in natural language processing and obtained the state‐of‐the‐art results on machine reading comprehension. However, it is essential to consider the entity recognition and the detection of unanswerable questions for accuracy improvement. A novel question answering model is proposed with knowledge enhancement and answer verification to promote the performance of reading comprehension. With knowledge enhancement, the proposed model is able to recognize entities from the passage and detect word boundary precisely. To deal with unanswerable questions, the answerability of questions is evaluated based on the textual entailment. Empirical studies suggest that the proposed model has better ability of reading comprehension than others, with improvement on question answering tasks.
Regional Relation Modeling for Visual Place Recognition

In the process of visual perception, humans perceive not only the appearance of objects existing in a place but also their relationships (e.g. spatial layout). However, the dominant works on visual place recognition are always based on the assumption that two images depict the same place if they contain enough similar objects, while the relation information is neglected. In this paper, we propose a regional relation module which models the regional relationships and converts the convolutional feature maps to the relational feature maps. We further design a cascaded pooling method to get discriminative relation descriptors by preventing the influence of confusing relations and preserving as much useful information as possible. Extensive experiments on two place recognition benchmarks demonstrate that training with the proposed regional relation module improves the appearance descriptors and the relation descriptors are complementary to appearance descriptors. When these two kinds of descriptors are concatenated together, the resulting combined descriptors outperform the state-of-the-art methods.
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings

We introduce a new video synthesis task: synthesizing time lapse videos depicting how a given painting might have been created. Artists paint using unique combinations of brushes, strokes, and colors. There are often many possible ways to create a given painting. Our goal is to learn to capture this rich range of possibilities. Creating distributions of long-term videos is a challenge for learning-based video synthesis methods. We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process. We implement this model as a convolutional neural network, and introduce a novel training scheme to enable learning from a limited dataset of painting time lapses. We demonstrate that this model can be used to sample many time steps, enabling long-term stochastic video synthesis. We evaluate our method on digital and watercolor paintings collected from video websites, and show that human raters find our synthetic videos to be similar to time lapse videos produced by real artists.
Q-Learning Applied to Soft-Kill Countermeasures For Unmanned Aerial Vehicles (UAVs)

This work presents a three-dimensional control algorithm using reinforcement learning to guide an attacking hunter drone capable of performing a global navigation satellite systems (GNSS) repeater attack on the GNSS receiver of a target invader drone. Considering the mission and movement requirements of the hunter drone, a Q-learning algorithm was developed, for which the table with the possible transitions of the states and actions is obtained by the actions that the vehicle can take considering directions and the respective consequences of each action. The learning capability of the proposed algorithm arises from the trial and error by an agent. The penalty calculation is based on the error of the invader position to the hunter's desired position of the attacked drone. The developed algorithm is tested using a software-in-the-loop (SITL) implementation, which is based on the Ardupilot platform. SITL simulations are performed in a developed testbed to emulate operational scenarios, where an unmanned aerial vehicle (UAV) is hijacked and then controlled by an attacking UAV until it reaches the final position desired by the hunter, usually a secure area where the vehicle can be captured without being destroyed. Results, including error metrics and action time, are discussed for different mission scenarios.