Reviewerly/exHarmony huggingface dataset:
GARN: A Novel Generative Adversarial Recognition Network for End-to-End Scene Character Recognition

Deep neural networks have shown their powerful ability in scene character recognition tasks; however, in real life applications, it is often hard to find a large amount of high-quality scene character images for training these networks. In this paper, we proposed a novel end-to-end network named Generative Adversarial Recognition Networks (GARN) for accurate natural scene character recognition in an end-to-end way. The proposed GARN consists of a generation part and a classification part. For the generation part, the purpose is to produce diverse realistic samples to help the classifier overcome the overfitting problem. While in the classification part, a multinomial classifier is trained along with the generator in the form of a game to achieve better character recognition performance. That is, the proposed GARN has the ability to augment scene character data by its generation part and recognize scene characters by its classification part. It is trained in an adversarial way to improve recognition performance. The experimental results on benchmark datasets and the comparisons with the state-of-the-art methods show the effectiveness of the proposed GARN in scene character recognition.
QoE-based enhancements of Chunked CMAF over low latency video streams

5G infrastructures are in the roadmap of content delivery services, aiming to forward all broadcast and broadband video traffic using a common telecommunication network architecture. Streaming services will benefit from 5G networks which promise higher capacity, higher bandwidth and lower latency than current infrastructures. However, the widely employed streaming technologies, such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH), require an intrinsic high latency of tens of seconds to enforce the Quality of Experience (QoE). These conditions turn MPEG-DASH unfavourable when compared with a traditional broadcast pipeline for live events in terms of latency. Therefore, improvements on latency of streaming technologies are necessary to deliver live broadcast services over 5G networks. The media industry proposed a Chunked Common Media Application Format (Chunked CMAF) in order to achieve latency under a second. In this paper, we show an implementation of a Chunked CMAF for MPEG-DASH live videos in a real deployment. To further evaluate the benefits of CMAF we evaluate the QoE results when delivering a legacy MPEG-DASH live content compared to a Chunked CMAF-powered one.
Detecting spam campaign in twitter with semantic similarity

Abstract Twitter is a widespread supply for real-time news distribution between individuals. Furthermore, spammers could post any kinds of spam content to users, and a variant of incidents are committed on Twitter against users. These threats aren’t restricted to the social media platforms however they threaten the safety of Twitter users. Most of the researches use deep learning techniques to detect Twitter spammer activities. The traditional solutions check the behavior of each account or campaign of similar purpose accounts. The number of solutions concentrate on detecting spam campaign based on URL only and ignoring text in a tweet. In this paper, the lightweight framework is proposed to take tweet text into consideration for optimizing spam campaign detection methods based on deep learning techniques. The main contribution of this work summarized in two key points. First one is to summarize text of the tweets to cluster them. The second one is to find similar tweets based on Siamese Recurrent Network. Experimental results show the ability of the proposed technique to extract the right candidate campaign and classify them as spam or not with high recall and precision.
Optical Terminal for Canada's Quantum Encryption and Science Satellite (QEYSSat)

Honeywell Aerospace has been selected by the Canadian Space Agency to implement the Quantum Encryption and Science Satellite (QEYSSat), a Canadian-owned and operated scientific and technology demonstration mission towards the next-generation of secure communications infrastructure. Quantum key distribution (QKD) is a method for distributing, via single-photon transfer, verifiably-confidential encryption keys between two parties separated by large distances. This capability is a powerful tool for the transmission of sensitive data (e.g. financial transactions, personal health records, etc.), however current ground-based QKD networks are limited in geographic reach to roughly 200 kilometres. The QEYSSat mission will use a satellite receiver to demonstrate the distribution of secure keys between ground stations separated by at least 400 km. In addition, Honeywell intends to fly an optical intersatellite link (OISL) terminal as a hosted payload on this mission. The QEYSSat mission will implement both weak coherent pulse (WCP) sources and entangled photon sources to study the performance of QKD, and to perform Bell tests of long-range quantum entanglement. Honeywell is building the free-space QKD terminal consisting of a large-diameter front-end telescope, a precision pointing and tracking system, single-photon receiver, and a downlink source. Major technical challenges include accurate pointing and tracking, polarization-management throughout the optical chain, and deep suppression of background and stray light sources, given the nature of single photon exchange over large distances. To address these challenges, Honeywell is leveraging its existing optical communications solutions to meet the enhanced performance requirements for space-based QKD. A large-format telescope for geostationary optical communications forms the basis for the terminal's front-end optics, and Honeywell's commercially-focused Optical Pointing and Tracking Relay Assembly for intersatellite Communications (OPTRAC) is being adapted as a high-performance, quantum-ready pointing and tracking unit (QTRAC). This paper discusses the design of the QKD terminal and will highlight results from the recently completed QEYSSat Phase A study.
Multi Robot Route Planning (MRRP): Extended Spatial-Temporal Prioritized Planning

Autonomous vehicles are, in contrast to classical automated guided vehicles (AGVs), less predictable in their behavior and drive time. Therefore, the issue of how to efficiently control these vehicles arises, because autonomous agents need to be coordinated and not controlled, to give autonomous behaviors and actions space. The scientific contribution of this paper is a novel approach, based on prioritized planning to target this issue as well as an open source framework for evaluation and comparison. Prioritized planning has the disadvantage of being neither optimal nor complete, however, it has the advantage of being computationally feasible. This work utilizes prioritized planning to significantly increase the set of feasible scenarios through collision prevention: by locally finding alternative routes and adding them to the search graph. The paper clearly formulates the extensions needed and delineates the approach's limits, as it is neither optimal nor complete. More importantly, however, our method calculates routes for each vehicle with inter-vehicle synchronization, enabling vehicles to execute the plan in a distributed fashion without centralized control, thereby allowing autonomous behavior. Finally, results are verified by comparing our Multi Robot Router (MRR) proposed in this work to classical approaches. The software developed as well as the test sets are publicly available for ROS and the simulation environment.
Effects of Past Interactions on User Experience with Recommended Documents

Recommender systems are commonly used in entertainment, news, e-commerce, and social media. Document recommendation is a new and under-explored application area, in which both re-finding and discovery of documents need to be supported. In this paper we provide an initial exploration of users' experience with recommended documents, with a focus on how prior interactions influence recognition and interest. Through a field study of more than 100 users, we investigate the effects of past interactions with recommended documents on users' recognition of, prior intent to open, and interest in the documents. We examined different presentations of interaction history, and the recency and richness of prior interaction. We found that presentation only influenced recognition time. Our findings also indicate that people are more likely to recognize documents they had accessed recently and to do so more quickly. Similarly, documents that people had interacted with more deeply were also more frequently and quickly recognized. However, people were more interested in older documents or those with which they had less involved interactions. This finding suggests that in addition to helping users quickly access documents they intend to re-find, document recommendation can add value in helping users discover other documents. Our results offer implications for designing document recommendation systems that help users fulfil different needs.
Meter Digit Recognition Via Faster R-CNN

The current method of meter reading is manual and error-prone in developing countries. A meter reader logs the reading to calculate the cost of electricity. In recent years, there have been multiple efforts to provide automated solutions to read the meter digits. However, the existing systems extract reading based on a specific meter topology. In this paper, we propose an approach based on Faster R-CNN to extract and recognize digits in an electric meter. We compared our method against several state-of-the-art object detection methods. The proposed approach is robust against different lightening conditions, severe perspective distortions and blurred images. In addition, it is scaleinvariant. Furthermore, we created a new dataset consisting of 10310 images taken from electricity companies in Pakistan to benchmark meter digit recognition task. Experimental results shows the high accuracy of the proposed approach on the created electricity meter dataset.
Augmented Reality Controlled Smart Wheelchair Using Dynamic Signifiers for Affordance Representation

The design of augmented reality interfaces for people with mobility impairments is a novel area with great potential, as well as multiple outstanding research challenges. In this paper we present an augmented reality user interface for controlling a smart wheelchair with a head-mounted display to provide assistance for mobility restricted people. Our motivation is to reduce the cognitive requirements needed to control a smart wheelchair. A key element of our platform is the ability to control the smart wheelchair using the concepts of affordances and signifiers. In addition to the technical details of our platform, we present a baseline study by evaluating our platform through user-trials of able-bodied individuals and two different affordances: 1) Door Go Through and 2) People Approach. To present these affordances to the user, we evaluated fixed symbol based signifiers versus our novel dynamic signifiers in terms of ease to understand the suggested actions and its relation with the objects. Our results show a clear preference for dynamic signifiers. In addition, we show that the task load reported by participants is lower when controlling the smart wheelchair with our augmented reality user interface compared to using the joystick, which is consistent with their qualitative answers.
Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors

This article revisits the problem of Bayesian shape-restricted inference in the light of a recently developed approximate Gaussian process that admits an equivalent formulation of the shape constraints in terms of the basis coefficients. We propose a strategy to efficiently sample from the resulting constrained posterior by absorbing a smooth relaxation of the constraint in the likelihood and using circulant embedding techniques to sample from the unconstrained modified prior. We additionally pay careful attention to mitigate the computational complexity arising from updating hyperparameters within the covariance kernel of the Gaussian process. The developed algorithm is shown to be accurate and highly efficient in simulated and real data examples.
Object Detection in Rainy Condition from Video Using YOLO Based Deep Learning Model

Video surveillance is one of the primary and practical actions to prevent criminal and terrorist attacks. Nowadays all public areas are under video surveillance. Most of the surveillance cameras are installed in open spaces. However, the video data captured by the surveillance camera can be affected by the weather condition. In this paper, we are concentrating on the video data captured by surveillance cameras in rainy situation. We have proposed a deep learning-based method to detect object in rainy situations from videos. However, object detection in deep learning is the ubiquitous research topic in computer vision and analysis. Much work has been done in that area. However, deep learning-based object detection in rainy condition remains untouched. We have applied our proposed method for both daytime and nighttime. The method produces excellent results in all conditions.
Review of the Blockchain Technology in the Energy Sector

With the appearance of Blockchain technology, besides cryptocurrency, interest in the use of this technology in various fields has increased, including in the energy sector. Since Blockchain technology has repeatedly proven its importance in various fields, now the advantages of technology have become obvious in large energy companies. Many developed countries are massively starting to introduce Blockchain technology into the energy industry. While it is mainly pilot projects or just technology testing but already now Blockchain can qualify for creating new energy markets. The energy sector is currently at the stage of transition from a centralized system to a decentralized one in which complete autonomy will be achieved through the production and storage of energy.
A Comparison of Machine Learning Algorithms to Estimate Effort in Varying Sized Software

Software Effort Estimation is the most crucial task in software engineering and project management. It is very essential to estimate cost and required people properly for a project. Nowadays software is developed in more complexly and its success depends on proper estimation. In this research, we have compared the estimated result in varying software among three algorithms. These algorithms can be used in the early stages of software life cycle and can help project managers to conduct effort estimation efficiently before starting the project. It avoids project overestimation and underestimation among other benefits. Software size, productivity, complexity and requirement stability are the input factors of these three models. Softwares are classified into three categories (i.e. small, medium, large) based on software size. The effort has been measured using Radial Basis Function Neural Network, Extreme Learning Machine and Decision Tree for each category of software. The Root Mean Square Error has been calculated for the algorithms. The result shows that Decision Tree provides minimum 10% and 6% better result for small and medium sized software respectively. For large sized software Extreme Learning Machine gives 10% better result than Decision Tree.
A Traffic Classification Method Based on Packet Transport Layer Payload by Ensemble Learning

Network traffic classification is an important research topic for computer network, such as QoS detection and admission monitoring. Traditional classification methods, such as port-based and DPI(deep packet inspect)-based, are out-of-date due to the computational expensiveness and inaccuracy. In this paper, we propose a novel traffic classification approach based on packet transport layer payload by ensemble learning. We use three kinds of base neural networks to form a strong classifier. Each model is trained separately and the final prediction result is decided by weight voting. The raw traffic data are reshaped into the format of sequence and matrix as the input, which avoids the TCP stream feature selection and extraction process. Our approach is applicable to both TCP and UDP, which means that it doesn't require a distinction between transport layer protocols. The experiment results show that our approach can reach the high accuracy of 96.38%, and is better than the state-of-the-art methods based on the same dataset. Besides, our proposed model can select packet samples randomly avoiding tracing the whole stream and the model works well even there's packet loss and disorder.
Red blood cell classification on thin blood smear images for malaria diagnosis

Abstract Parasite detection is important for the diagnosis of many blood-borne diseases including malaria. As part of a program to develop a fast, accurate, and affordable automatic device for diagnosing malaria, a critical step is to automatically classify individual red blood cells in thin blood smear images. To automatically recognize malaria parasites in an image, this paper presents a red blood cell classification study for malaria diagnosis. To diagnose malaria, the threshold-based segmentation is implemented using the Otsu’s method succeeded by the distance transform and statistical classifier. The methods are applied to red blood cell images obtained from Kaggle. These experimental results show that the classification recognizes malaria parasite with 94.60% accuracy, 96.20% specificity, and 93% sensitivity.
Multilingual Sentiment Analysis on Social Media Disaster Data

The use of social media in disaster situations is inevitable, but it is also the case that information presented through this medium can include both public opinion and general information. In a multicultural nation like Malaysia, people like to use codeswitch sentences, through which they mix several languages to express their opinions. Sentiment analysis can be used to classify the subjectivity of social media data, by considering the multilingual aspect of Malaysian users who may experience disaster. In this paper, the authors propose a multilingual sentiment classifier used to understand how Malaysians react during a disaster. The proposed model collects disaster data from social media, which is then classified through a deep learning algorithm, so as to analyze the sentiments of people affected by disasters. The experiment results show that a multilingual sentiment classifier can achieve 0.862 accuracy and 0.864 F1-score which is considered suitable for analyzing social media data. The classification result shows that most Malaysians use social media to disseminate information during disaster periods.
Machine learning models for renewable energy forecasting

As the world unanimously works towards utilizing non-conventional energy for powering industries, households, vehicles, etc., one of the key limiting factors is the fluctuation of energy availability by non-conventional energy sources. The power generated by wind turbines or photovoltaic cells is dependent upon factors that can’t be controlled such as wind speed, humidity, solar irradiance. In such a situation the integration of renewable energy into grids becomes difficult. Eventually, maintaining an equilibrium between the energy supply and demand can be erratic. This calls for forecasting the amount of energy available via non-conventional energy sources like wind and sun so that the transition to renewable energy can be done highly efficiently without destabilizing the power grid. As the renewable power industry has abundant data that can be exploited in renewable energy forecasting, machine learning techniques can revolutionize the way we deal with renewable energy. This paper describes the efficiency of Linear Regression, Neural Networks Regression, Random Forest Regression, and Extra Tree Regression models for forecasting solar irradiation available on Earth’s surface.
An Efficient Cryptosystem based on the Logistic-Chebyshev Map

We utilise the Logistic-Chebyshev map as a cascade discrete-time chaotic system to design a new form of a cryptosystem. Based on the Logistic-Chebyshev map, we present a novel method for constructing substitution boxes or S-boxes. The performance of the proposed S-box scheme is investigated. Our results shows that the constructed S-box has significant qualities for viable applications in security related domains. Additionally, we utilise a new pseudo-random number generator (PRNG) scheme for the purpose of generating keystreams for further encryption. We utilise this technique to demonstrate how a PRNG based chaotic system can be utilsied for image encryption. Our simulation results and analysis show that the proposed scheme has efficient performance for cryptographic based image processing applications.
Privacy-preserving Network Path Validation

The end-users communicating over a network path currently have no control over the path. For a better quality of service, the source node often opts for a superior (or premium) network path to send packets to the destination node. However, the current Internet architecture provides no assurance that the packets indeed follow the designated path. Network path validation schemes address this issue and enable each node present on a network path to validate whether each packet has followed the specific path so far. In this work, we introduce two notions of privacy— path privacy and index privacy —in the context of network path validation. We show that, in case a network path validation scheme does not satisfy these two properties, the scheme is vulnerable to certain practical attacks (that affect the privacy, reliability, neutrality and quality of service offered by the underlying network). To the best of our knowledge, ours is the first work that addresses privacy issues related to network path validation. We design PrivNPV, a privacy-preserving network path validation protocol, that satisfies both path privacy and index privacy. We discuss several attacks related to network path validation and how PrivNPV defends against these attacks. Finally, we discuss the practicality of PrivNPV based on relevant parameters.
Grouped Correlational Generative Adversarial Networks for Discrete Electronic Health Records

Using Generative Adversarial Networks (GANs) to generate synthetic Electronic Health Records (EHR) has attracted increasing attention. However, in existing approaches, the events in EHRs are treated as separate variables which are indiscriminately entered into the model, without taking into account the meaning and grouping of them. Besides, the efficacy of treatment is often neglected. In this paper, we first embed the efficacy information into the disease diagnosis, and then propose Grouped Correlational GAN (GcGAN) to explicitly learn inherent correlations between different groups of variables. We also introduce a dense connection to strengthen the generator capacity in GcGAN. Experimental results on real-world data demonstrate that the generated data from GcGAN are able to simulate real-world data in terms of distribution statistics. The results on multi-label treatment recommendation tasks show that GcGAN can boost the performances by augmenting the training dataset with the generated data and outperforms state-of-the-art approaches. It can also automatically distinguish between disease-specific drugs and adjuvant drugs, which enhances the model interpretability.