Reviewerly/exHarmony huggingface dataset:
PFCBAS: Pairing Free and Provable Certificate-Based Aggregate Signature Scheme for the e-Healthcare Monitoring System

Recently, one of the most popular technologies of the modern era, the Internet of Things, allows the deployment and usage of various real-time test beds in various smart applications. One such application is the e-healthcare, in which patients' healthcare related data are transmitted to the nearest base station and then to a local or remote server as per the requirements. The data related to patients' health are sensitive and need special protection, therefore, the integrity and authentication of the sources of data generation are paramount concerns. However, several authentication or signature schemes that have been introduced in the past for this purpose are ID-based or having certificate-less settings. In these settings, a central authority, known as a trusted authority (TA), creates and distributes the secret key of every user. Thus, knowing the secrete key by the TA is called key escrow problem. But, these proposed schemes suffer from key distribution problems, which limit their applications in various applications. To mitigate these issues, this paper presents a certificate-based pairing free aggregate signature scheme (CBPFAS). The proposed scheme uses the merits of public key cryptography (PKC) and identity-based PKC (IDBPKC). The scheme is proven to be unforgeable, assuming the hardness of elliptic curve discrete log problem (ECDLP). The performance analysis shows that the proposed CBPFAS scheme executes in $0.78(n+1)$ ms in comparison to $9.63+1.17$n ms in [1], $9.63+0.78$n ms in [2], $9.63+3.39$n ms in [3], and $9.63+1.17$n ms in [4]. From these results, it is concluded that the proposed pairing free certificate-based aggregate signature scheme performs better than its counterparts.
Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility &amp; Patient Privacy

There is increasing interest in the potential of synthetic data to validate and benchmark machine learning algorithms as well as reveal any biases in real-world data used for algorithm development. This paper discusses the key requirements of synthetic data for such purposes and proposes an approach to generating and evaluating synthetic data that meets these requirements. We propose a framework to generate and evaluate synthetic data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data whilst also ensuring privacy. We include as a case study, a proof-of-concept synthetic dataset modelled on UK primary care data to demonstrate the application of this framework.
Spectra data classification with kernel extreme learning machine

One of the most important issues in chemometrics is how to design a robust classifier for spectra data with high classification accuracy. Extreme learning machine (ELM) has proved to be an effective method for numerous pattern recognition problems. However, the randomness of ELM’s hidden layer nodes setting would give rise to the oscillation of the hidden layer output matrix, which may bring down the stability of this model. This paper proposes a framework for spectra data classification using the kernel extreme learning machine (KELM) that can circumvent the calculation of hidden layer outputs and encode it in a kernel matrix inherently. Moreover, KELM doesn’t need to specify the hidden layer feature mapping function, and the number of hidden nodes doesn’t need to be given, which may avoid an extensive mapping calculation. The proposed framework contains two stages: spectral preprocessing and dimensionality reduction, as well as classification implementation with KELM. The experimental results on four datasets reveal that the radial basis function (RBF) should be a priority candidate kernel function for this kind of applications, and the suggestion of parameter selection is also given experimentally. Furthermore, the classification accuracy of KELM is superior to ELM in most cases, and KELM is significantly faster than ELM when the number of the training samples is relatively small, which is a common case for spectra data in chemometrics. Therefore, KELM may be a promising method for rapid spectra data classification.
On the performance of distributed ledgers for Internet of Things

As proof-of-work blockchains are inherently energy greedy and offer probabilistic guarantees, permissioned distributed ledgers based on the classic deterministic consensus of the distributed computing literature appear as promising solutions to record securely the information produced by a large amount of connected Internet of Things (IoT) devices. As of today, it remains unclear whether these solutions can scale to large networks. In this paper, we evaluate the performance of prominent distributed ledgers using classic consensus protocols. We select five mainstream distributed ledgers, namely Ripple, Tendermint, Corda and v0.6 and v1.0 of Hyperledger Fabric, and evaluate both the throughput and the latency of clusters with different numbers of nodes (ranging from 2 to 32) for each of them. Our results show that, while offering sometimes thousands of transactions per second throughput, their performance usually does not scale to tens of devices as it drops dramatically when the number of devices increases. This study motivates the need for alternative solutions that solve the Blockchain consensus problem, a scalable variant of the classic consensus problem dedicated to blockchains.
STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection

While depth cameras and inertial sensors have been frequently leveraged for human action recognition, these sensing modalities are impractical in many scenarios where cost or environmental constraints prohibit their use. As such, there has been recent interest on human action recognition using low-cost, readily-available RGB cameras via deep convolutional neural networks. However, many of the deep convolutional neural networks proposed for action recognition thus far have relied heavily on learning global appearance cues directly from imaging data, resulting in highly complex network architectures that are computationally expensive and difficult to train. Motivated to circumvent the challenges associated with training complex network architectures, we introduce the concept of spatio-temporal activation reprojection (STAR). More specifically, we reproject the spatio-temporal activations generated by human pose estimation layers in space and time using a stack of 3D convolutions. Experimental results on UTD-MHAD and J-HMDB demonstrate that an end-to-end architecture based on the proposed STAR framework (which we nickname STAR-Net) is proficient in single-environment and small-scale applications. On UTD-MHAD, STAR-Net outperforms several methods using richer data modalities such as depth and inertial sensors.
Evaluating the Agile-Stage-Gate Hybrid Model: Experiences From Three SME Manufacturing Firms

Agile-Stage-Gate is a hybrid product development model that integrates elements of both Agile and Stage-Gate to help companies realize the strengths of both. Recent studies show positive results in manufacturing companies, although SMEs are notably absent despite being the majority. This paper reports results of a test of the model in three deliberately chosen manufacturing SMEs. Results were improved: time to market, overall new product process, higher success rate. Agile required adaptations, and novel solutions were found by the test firms. The positive results suggest that Agile-Stage-Gate must be considered as a recommended product development approach in SME manufacturers.
Disaster Victims Detection System Using Convolutional Neural Network (CNN) Method

Natural disasters are one of the things that cannot be predicted. Natural disasters can cause losses, both assets and objects can even take lives. To reduce the number of losses, rapid evacuation handling from the Search and Rescue (SAR) team is needed to help victims of natural disasters. But in fact, there are often obstacles in the evacuation process. Such obstacles are such as bad weather conditions, disconnection of telecommunications networks, difficulty access to the victims of natural disasters and the spread of SAR teams that are not evenly distributed throughout the disaster area. Convolutional Neural Network is one of the developments of Artificial Neural Networks for image classification, image segmentation, and object recognition with high accuracy and high performance. CNN can learn to detect various images according to images from the dataset studied. So, this paper designed a system for detecting victims of natural disasters using the CNN method and implemented it on a raspberry pi which can detect victims of natural disasters through streaming cameras placed on UAVs. In this paper, the Convolutional Neural Network (CNN) method with 100% accuracy with distance object 1-4 m uses the Mobile-net SSD model.
Semantic Segmentation for Ships Detection from Satellite Imagery

Ships detection, as well as other object detection, and localization tasks in satellite images are the central problems in the field where remote sensing and computer vision coalesce. They are commonly used in different areas like environment monitoring, fishery management, logistics, insurance and many others. This paper provides an approach based on the Convolutional Neural Networks (CNN) as the main algorithm/instrument for detecting ships in optical satellite images of different spatial resolution. For achieving the best performance, we divided the problem into stages, which gave a possibility to control the quality of intermediate outcomes. The proposed method contains two parts: 1) building a classifier based on XCeption, 2) using baseline Unet model with Resnet18 as encoder for exact segmentation which allow us to achieve accuracy of more than 84%.
Accuracy comparison of navigation local planners on ROS-based mobile robot

The aim of this work is to investigate and analyze the performance of ROS-based navigation local planners in a simulated environment and on a real research platform the TurtleBot 3 mobile robot. Three planners were subject to the study: DWA, EBand and TEB; the accuracy and repeatability were measured for position and orientation of the robot, as well as time of travel. Tests were based on continuous movements between three waypoints placed in a confined space, in both static and dynamic environments. The motion capture system based on AR tags was used in the real world. The obtained results do not indicate a specific comparison winner. All tested planners in most cases met their requirements.
Computer Aided System of Time Series Analysis Methods for Forecasting the Epidemics Outbreaks

The research is devoted to the problem of predicting the incidence of gastroenterocollitis. The paper discusses the forecasting of time series by statistical models. The methods of exponential smoothing and adaptive exponential smoothing are proposed, applied to additive and multiplicative time series models. The analysis of the results obtained using the developed computer aided system.
Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration

Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.
Multi-Label Image Recognition with Joint Class-Aware Map Disentangling and Label Correlation Embedding

Multi-label image recognition is a fundamental but challenging computer vision task. Great progress has been achieved by exploring the label correlation among these multiple labels which is the most crucial issue for multi-label recognition. In this paper, we propose a unified deep learning framework to jointly disentangle class-specific maps corresponding to discriminative category-wise information and then evaluate the label co-occurrence of these maps. Specifically, after obtaining the general deep image features and conducting multi-label classification, we employ the classification weights to reform the feature maps into class-aware disentangled maps (CADMs). Then, based on CADMs, we first transfer them into label vectors and then formulate the label correlation dependency from an embedding perspective. The whole model is driven by both the classification loss and the label correlation embedding loss, which is end-to-end trainable with only image-level supervisions. Extensive quantitative results of two benchmark multi-label image datasets show our model consistently outperforms other competing methods by a large margin. Meanwhile, qualitative analyses also demonstrate our model can effectively capture relatively pure class-aware maps and model label correlation dependency as well.
Bug Model Based Intelligent Recommender System with Exclusive Curriculum Sequencing for Learner-Centric Tutoring

Face to face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's requirements and tutor in a manner that best suits the learner. Reaching this level in an intelligent tutoring system is a challenge even today given the advanced developments in the field. This article focuses on ITS, mimicking a human tutor in terms of providing a curriculum sequence exclusive for the learner. Unsuitable courseware disorients the learner and thus degrades the overall performance. A bug model approach has been used for curriculum design and its re-alignment as per requirements and is demonstrated through a prototype tutoring recommender system, SeisTutor, developed for this purpose. The experimental results indicate an enhanced learning gain through a curriculum recommender approach of SeisTutor as opposed to its absence.
Predicting pull request completion time: a case study on large scale cloud services

Effort estimation models have been long studied in software engineering research. Effort estimation models help organizations and individuals plan and track progress of their software projects and individual tasks to help plan delivery milestones better. Towards this end, there is a large body of work that has been done on effort estimation for projects but little work on an individual checkin (Pull Request) level. In this paper we present a methodology that provides effort estimates on individual developer check-ins which is displayed to developers to help them track their work items. Given the cloud development infrastructure pervasive in companies, it has enabled us to deploy our Pull Request Lifetime prediction system to several thousand developers across multiple software families. We observe from our deployment that the pull request lifetime prediction system conservatively helps save 44.61% of the developer time by accelerating Pull Requests to completion.
Who Is Democracy Good For? Elections, Rural Bias, and Health and Education Outcomes in Sub-Saharan Africa

How do African governments respond to democratic electoral competition? Although the common perception is that African governments have sought to win elections by combining various types of electoral fraud, clientelism, and ethnic mobilization, I argue that democratic elections in Africa have also induced governments to compete for votes by providing basic services. One implication of this is a rural bias in the impact of democracy on basic health and education outcomes. Using individual-level data from 27 African countries, I investigate the theoretical claim that competitive elections create incentives for African governments to implement prorural policies in order to satisfy the rural majority. The results demonstrate that democratic elections significantly increase access to primary education and reduce infant mortality rates, but only for children in rural areas. As the argument expects, these effects are conditional on the level of urbanization.
A Non-Stationary Office Desk Substitution for Desk-Based and HMD-Projected Virtual Reality

The ongoing migration of HMDs to the consumer market also allows the integration of immersive environments into analysis workflows that are often bound to an (office) desk. However, a critical factor when considering VR solutions for professional applications is the prevention of cybersickness. In the given scenario the user is usually seated and the surrounding real world environment is very dominant, where the most dominant part is maybe the desk itself. Including this desk in the virtual environment could serve as a resting frame and thus reduce cybersickness next to a lot of further possibilities. In this work, we evaluate the feasibility of a substitution like this in the context of a visual data analysis task involving travel, and measure the impact on cybersickness as well as the general task performance and presence. In the conducted user study ( n=52), surprisingly, and partially in contradiction to existing work, we found no significant differences for those core measures between the control condition without a virtual table and the condition containing a virtual table. However, the results also support the inclusion of a virtual table in desk-based use cases.
A Blockchain-Based Peer-to-Peer Trading Scheme Coupling Energy and Carbon Markets

Climate change enforces the integration of distributed renewable energy sources and development of carbon price scheme. Whilst the energy is traded among distributed prosumers, the carbon responsibilities and corresponding allowances trading need to be transferred from large-scale energy suppliers to prosumers. During this transformation, the issues of energy imbalance, carbon reduction imbalance, and residential privacy leakage in centralised trading market present serious challenges. In this paper, we propose a fully decentralised blockchain-based peer-to-peer trading scheme coupling energy and carbon markets. We implement pay-to-public-key-hash with multiple signatures as a transaction standard to realise a more secure transaction and reduced storage burden of senders. A script is hashed during the wallet address generation for each new transaction to protect residential privacy. A novel carbon accounting method and corresponding incentive mechanism for carbon reduction are designed to evaluate emission behaviours of distributed prosumers. Case studies demonstrate that the proposed scheme leads a reduced costs and carbon emissions compared to centralised trading systems and existing blockchain-based trading schemes.
ADONiS—Adaptive Online Nonsingleton Fuzzy Logic Systems

Nonsingleton fuzzy logic systems (NSFLSs) have the potential to capture and handle input noise within the design of input fuzzy sets (FSs). In this article, we propose an online learning method that utilizes a sequence of observations to continuously update the input FSs of an NSFLS, thus providing an improved capacity to deal with variations in the level of input-affecting noise, common in real-world applications. The method removes the requirement for both a priori knowledge of noise levels and relying on offline training procedures to define input FS parameters. To the best of our knowledge, the proposed ADaptive, ONline Nonsingleton (ADONiS) fuzzy logic system (FLS) framework represents the first end-to-end framework to adaptively configure nonsingleton input FSs. The latter is achieved through online uncertainty detection applied to a sliding window of observations. Since real-world environments are influenced by a broad range of noise sources, which can vary greatly in magnitude over time, the proposed technique for combining online determination of noise levels with associated adaptation of input FSs provides an efficient and effective solution which elegantly models input uncertainty in the FLS's input FSs, without requiring changes in any other part (e.g., antecedents, rules or consequents) of the FLS. In this article, two common chaotic time series (Mackey-Glass, Lorenz) are used to perform prediction experiments to demonstrate and evaluate the proposed framework. Results indicate that the proposed adaptive NSFLS framework provides significant advantages, particularly in environments that include high variation in noise levels, which are common in real-world applications.
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

Deep learning provides a powerful tool for robotic perception in the open world. However, real-world robotic systems, especially mobile robots, must be able to react intelligently and safely even in unexpected circumstances. This requires a system that knows what it knows, and can estimate its own uncertainty for unfamiliar, out-of-distribution observations. Approximate Bayesian approaches are commonly used to estimate uncertainty for neural network predictions, but struggle with out-of-distribution observations. Generative models can in principle detect out-of-distribution observations as those with a low estimated density, but overly pessimistic as an uncertainty measure, since the mere presence of an out-of-distribution input does not by itself indicate an unsafe situation. Intuitively, we would like a perception system that can detect when task-salient parts of the image are unfamiliar or uncertain, while ignoring task-irrelevant features. In this paper, we present a method for uncertainty-aware robotic perception that combines generative modeling and model uncertainty. Our method estimates an uncertainty measure about the model's prediction, taking into account an explicit generative model of the observation distribution to handle out-of-distribution inputs. We evaluate our method on an action-conditioned collision prediction task with both simulated and real data, and demonstrate that our approach improves on a variety of Bayesian neural network techniques.