Reviewerly/exHarmony huggingface dataset:
Multilevel Thresholding Segmentation for Color Image Using Modified Moth-Flame Optimization

Multilevel thresholding has got more attention in the field of image segmentation recently.However, it is still challenging and complicated for color image segmentation in many applications.To mitigate the above conditions, a novel multilevel thresholding algorithm consists of two innovative strategies is proposed on the basis of moth-flame optimization (MFO) to develop the SAMFO-TH algorithm.On one hand, a creative self-adaptive inertia weight scheme is used to enhance both the exploration and exploitation, on the other hand, a newly proposed thresholding (TH) heuristic is embedded into MFO to improve the global performance in multilevel thresholding.To find the optimal threshold values of an image, Otsu's variance, and Kapur's entropy criteria are employed as fitness functions.The experiments have been performed on ten color images including six natural images and four satellite images at different threshold levels with a comparison of other eight meta-heuristic algorithms: multi-verse optimizer (MVO), whale optimization algorithm (WOA), standard MFO, and so on.The experimental results are presented in terms of computational time (CPU time), mean value to reach (MVTR), standard deviation (STD), mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), probability rand index (PRI), the variation of information (VoI), and threshold value distortion (TVD).The results demonstrate that the proposed SAMFO-TH outperforms other competitive algorithms and has superiority concerning stability, accuracy, and convergence rate, which can be applied to practical engineering problems.
Tourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews

Tourist reviews are information sources for travelers to know about tourist places. Unfortunately, some reviews are irrelevant and become noisy data. Aspect-based sentiment classification methods have shown promise in suppressing the noise. However, not much research has been done on automatic aspect identification, and identification of implicit, infrequent and co-referential aspects, resulting in misclassifications. This paper presents a framework of aspect-based sentiment classification that will not only identify the aspects very efficiently but can perform classification task with high accuracy. The framework has been implemented as a mobile app that helps tourists find the best restaurant or hotel in a city, and performance has been evaluated by conducting experiments on real-world datasets with excellent results (85% identification and 90% classification).
Acute lymphoblastic leukemia segmentation using local pixel information

The severity of acute lymphoblastic leukemia depends on the percentages of blast cells (abnormal white blood cells) in bone marrow or peripheral blood. The manual microscopic examination of bone marrow is less accurate, time-consuming, and susceptible to errors, thus making it difficult for lab workers to accurately recognize the characteristics of blast cells. Researchers have adopted different computational methods to identify the nature of blast cells; however, these methods are incapable of accurately segmenting leukocyte cells due to some major disadvantages, such as lack of contrast between objects and background, sensitivity to gray-scale, sensitivity to noise in images, and large computational size. Therefore, it is indispensable to develop a new and improved technique for leukocyte cell segmentation. In the present research, an automatic leukocyte cell segmentation process was introduced that is based on machine learning approach and image processing technique. Further, the characteristics of blast cells were extracted using 4-moment statistical features and artificial neural networks (ANNs). It was found that the proposed method yielded a blasts cell segmentation accuracy of 97% under different lighting conditions.
The moderating role of governmental support in the relationship between entrepreneurship and economic growth

Purpose The purpose of this paper is to find out the extent to which governments of the Gulf Cooperation Council (GCC) countries play a moderating role in the relationship between entrepreneurship and economic growth. Design/methodology/approach The study uses a 10-year time series (2006-2015) for six GCC countries: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates. Secondary sources of data were collected from The World Bank database, general available statistics on the GCC, the Global Entrepreneurship Index from the Global Entrepreneurship and Development Institute (GEDI) and the Global Entrepreneurship Monitor (GEM) database. Findings Results indicate that governmental support has a significant moderating effect on the relationship between entrepreneurship and economic growth in the GCC. Furthermore, the strongest indicators of entrepreneurial investments in the Gulf have been found to be risk capital and high growth, which indicate a rapid growth in entrepreneurial investments. The lowest scoring indicators were found to be technology absorption and innovation process. Research limitations/implications Despite the necessary measures taken to assure standard results such as testing data validity, care should be taken when generalizing the research results mainly because the time series of the study (2006-2015) could have been affected by the International and Financial Crisis, though the study has taken this into consideration. Originality/value This study has clarified the significant role of GCC governments in moderating the relationship between entrepreneurship and economic growth. Thus, the findings of this study are important because they help the GCC governments recognize their significant role and hence to utilize this role by supporting new and existing entrepreneurs particularly through regulatory quality, risk capital, technology absorption and process innovation. Furthermore, this study proves the extent to which entrepreneurship can help enhance the GCC economic growth, hence elaborating the importance of the sustainable resource, such as the human capital, in achieving diversification of sources to move from an oil-based to a more diversified economy.
A Lightweight Privacy-Preserving CNN Feature Extraction Framework for Mobile Sensing

The proliferation of various mobile devices equipped with cameras results in an exponential growth of the amount of images. Recent advances in the deep learning with convolutional neural networks (CNN) have made CNN feature extraction become an effective way to process these images. However, it is still a challenging task to deploy the CNN model on the mobile sensors, which are typically resource-constrained in terms of the storage space, the computing capacity, and the battery life. Although cloud computing has become a popular solution, data security and response latency are always the key issues. Therefore, in this paper, we propose a novel lightweight framework for privacy-preserving CNN feature extraction for mobile sensing based on edge computing. To get the most out of the benefits of CNN with limited physical resources on the mobile sensors, we design a series of secure interaction protocols and utilize two edge servers to collaboratively perform the CNN feature extraction. The proposed scheme allows us to significantly reduce the latency and the overhead of the end devices while preserving privacy. Through theoretical analysis and empirical experiments, we demonstrate the security, effectiveness, and efficiency of our scheme.
An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition

The optimal generation of initial connection weight parameters and dynamic updating strategies of connection weights are critical for adjusting the performance of back-propagation (BP) neural networks. This paper presents an adaptive fractional-order BP neural network abbreviated as PEO-FOBP for handwritten digit recognition problems by combining a competitive evolutionary algorithm called population extremal optimization and a fractional-order gradient descent learning mechanism. Population extremal optimization is introduced to optimize a large number of initial connection weight parameters and fractional-order gradient descent learning mechanism is designed to update these connection weight parameters adaptively during the evolutionary process of fractional-order BP neural network. The extensive experimental results for a well-known MNIST handwritten digits dataset have demonstrated that the proposed PEO-FOBP outperforms the original fractional-order BP neural network and the traditional integer-order BP neural network in terms of training and testing accuracies.
Fractional-Order Fusion Model for Low-Light Image Enhancement

In this paper, a novel fractional-order fusion model (FFM) is presented for low-light image enhancement. Existing image enhancement methods don’t adequately extract contents from low-light areas, suppress noise, and preserve naturalness. To solve these problems, the main contributions of this paper are using fractional-order mask and the fusion framework to enhance the low-light image. Firstly, the fractional mask is utilized to extract illumination from the input image. Secondly, image exposure adjusts to visible the dark regions. Finally, the fusion approach adopts the extracting of more hidden contents from dim areas. Depending on the experimental results, the fractional-order differential is much better for preserving the visual appearance as compared to traditional integer-order methods. The FFM works well for images having complex or normal low-light conditions. It also shows a trade-off among contrast improvement, detail enhancement, and preservation of the natural feel of the image. Experimental results reveal that the proposed model achieves promising results, and extracts more invisible contents in dark areas. The qualitative and quantitative comparison of several recent and advance state-of-the-art algorithms shows that the proposed model is robust and efficient.
The Effect of Stereo Display Deficiencies on Virtual Hand Pointing

The limitations of stereo display systems affect depth perception, e.g., due to the vergence-accommodation conflict or diplopia. We performed three studies to understand how stereo display deficiencies impact 3D pointing for targets in front of a screen and close to the user, i.e., in peripersonal space. Our first two experiments compare movements with and without a change in visual depth for virtual respectively physical targets. Results indicate that selecting targets along the depth axis is slower and has less throughput for virtual targets, while physical pointing demonstrates the opposite result. We then propose a new 3D extension for Fitts' law that models the effect of stereo display deficiencies. Next, our third experiment verifies the model and measures more broadly how the change in visual depth between targets affects pointing performance in peripersonal space and confirms significant effects on time and throughput. Finally, we discuss implications for 3D user interface design.
Towards Collaboration Translucence

Collocated, face-to-face teamwork remains a pervasive mode of working, which is hard to replicate online. Team members' embodied, multimodal interaction with each other and artefacts has been studied by researchers, but due to its complexity, has remained opaque to automated analysis. However, the ready availability of sensors makes it increasingly affordable to instrument work spaces to study teamwork and groupwork. The possibility of visualising key aspects of a collaboration has huge potential for both academic and professional learning, but a frontline challenge is the enrichment of quantitative data streams with the qualitative insights needed to make sense of them. In response, we introduce the concept of collaboration translucence, an approach to make visible selected features of group activity. This is grounded both theoretically (in the physical, epistemic, social and affective dimensions of group activity), and contextually (using domain-specific concepts). We illustrate the approach from the automated analysis of healthcare simulations to train nurses, generating four visual proxies that fuse multimodal data into higher order patterns.
Scalable min-max multi-objective cyber-security optimisation over probabilistic attack graphs

We present a framework to efficiently solve a multi-objective optimisation problem for cyber-security defence. Facing an attacker who can mount a multi-stage attack (modelled using attack graphs), the defence problem is to select a portfolio of security controls which minimises the security risk and the (direct and indirect) costs of the portfolio of controls. The main challenges for the optimisation are: (a) the effect of the security controls is in general probabilistic, for example, the effect of staff anti-phishing training; moreover, some controls like taking regular back-ups do not have an attack-preventing effect, but rather, mitigate the losses of a successful attack; (b) each control may affect multiple vulnerabilities; and each vulnerability may be affected by multiple controls; (c) there can be a prohibitively large number of attack paths, each involving exploitation of different vulnerabilities. Our mathematical framework deals with all these problems. In particular, we model the problem as a min-max multi-objective optimisation. Using techniques such as ILP conversion, exact LP relaxation and dualisation, we convert the problem into a very efficient MILP. For instance, it returns the optimal solution for attack graphs with 20,000 nodes in less than four minutes typically.
Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models

In this study, the cyclic of a spark ignition engine using octane fuel is modeled using extreme learning machine, an emergent technology related to single-hidden layer feedforward neural networks (SLFNs). The experimental engine case study was operated with five different engine speeds from 1000 to 3000 rpm, and crankshaft angle from −360° to 360° without exhaust gas recirculation. The mean effective pressure was used to indicate the cyclic variability for the mean of 100 consecutive cycles. In this study the extreme learning machine (ELM), the regularized extreme learning machine and the outlier robust extreme learning machine were applied to predict the conditions of a combustion parameter used to reflect pressure information for entire cycle in a single-cylinder compression ignition naturally aspirated engine. Prediction by ELM models is normally faster than mathematical models employed to solve a set of differential equations by iterative numerical methods. The essence of ELM is that the hidden layer of SLFNs need not be tuned. Nevertheless, the selection of an appropriate ELM topology is crucial in terms of simplicity, velocity and accuracy. The suitable determination of the number of hidden layer nodes (neurons), type of activation function, and sparse connection structure of weights and biases were obtained using a modified biogeography-based optimization approach (BBO), a population-based metaheuristic algorithm inspired on the mathematical model of organism distribution in biological systems. The experimental dataset were used to train ELM models, and the reliability of these models was assessed and compared for two case studies based on performance criteria related to accuracy, sparsity and complexity using a cross-validation procedure. After training, experimental results show that the pressure can be modeled with reasonable accuracy. The results analysis indicated that the proposed optimized ELM and its variants optimized by BBO approaches have potential for prediction the mean effective pressure showed reasonable consistency with the experimental results.
The 3D modular chaotic map to digital color image encryption

The cryptography is one of the important security mechanisms for the confidentiality, integrity and authentication of text and multimedia data. This study proposes a five-step color images encryption method in 3D space based on substitution and permutation operations for diffusion and confusion properties. To transform the image to a 3D space, in the first step, the red, green, and blue spectra of the color image are subdivided into the sub-images, in this case the rows, columns, and the number of sub-images are specified in 3D axis coordinates for carrying out 3D substitution and permutation operations. In the second and fifth steps of encryption, 3D substitution operations are performed by using logical exclusive OR and circular shift operators on the sub-images pixels. In the middle steps, the permutation operations of sub-images pixels are accomplished with the help of 3D permutation and 3D modular chaotic map. The 3D modular chaotic presented in this research is multiplicative reversible based on modular arithmetic, which improves speed, period, and space of the key. The proposed method, with the help of permutations and substitutions in 3D space, improves the performance parameters and increases the key space in comparison to other image cryptography researches.
Timely daily activity recognition from headmost sensor events

Smart homes are designed to promote safe and comfortable living for inhabitants without any manual intervention. The performance of approaches for daily activity recognition is therefore crucial, but current real-time approaches have to wait until a daily activity ends before performing recognition. We present an approach for timely daily activity recognition from an incomplete stream of sensor events, by which the recognition process can start as soon as a daily activity begins. Activity features are generated from several headmost sensor events rather than from all sensor events that a daily activity activated. A public dataset was utilized to evaluate the presented method. Experimental findings show its effectiveness for timely daily activity recognition in terms of precision, recall, average saved time, and saved time proportion.
Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors

Human activity recognition has become an active research field over the past few years due to its wide application in various fields such as health-care, smart home monitoring, and surveillance. Existing approaches for activity recognition in smart homes have achieved promising results. Most of these approaches evaluate real-time recognition of activities using only sensor activations that precede the evaluation time (where the decision is made). However, in several critical situations, such as diagnosing people with dementia, “preceding sensor activations” are not always sufficient to accurately recognize the inhabitant's daily activities in each evaluated time. To improve performance, we propose a method that delays the recognition process in order to include some sensor activations that occur after the point in time where the decision needs to be made. For this, the proposed method uses multiple incremental fuzzy temporal windows to extract features from both preceding and some oncoming sensor activations. The proposed method is evaluated with two temporal deep learning models (convolutional neural network and long short-term memory), on a binary sensor dataset of real daily living activities. The experimental evaluation shows that the proposed method achieves significantly better results than the real-time approach, and that the representation with fuzzy temporal windows enhances performance within deep learning models.
Self-Tuning Algorithms for Multisensor-Multitarget Tracking Using Belief Propagation

Situation-aware technologies enabled by multitarget tracking algorithms will create new services and applications in emerging fields such as autonomous navigation and maritime surveillance. The system models underlying multitarget tracking algorithms often involve unknown parameters that are potentially time-varying. A manual tuning of unknown model parameters by the user is prone to errors and can, thus, dramatically reduce target detection and tracking performance. We address this challenge by proposing a framework of “self-tuning” multisensor-multitarget tracking algorithms. These algorithms adapt in an online manner to time-varying system models by continuously inferring unknown model parameters along with the target states. We describe the evolution of the parameters by a Markov chain and incorporate them in a factor graph that represents the statistical structure of the tracking problem. We then use a belief propagation scheme to efficiently calculate the marginal posterior distributions of the targets and model parameters. As a concrete example, we develop a self-tuning tracking algorithm for maneuvering targets with multiple dynamic models and sensors with time-varying detection probabilities. The performance of the algorithm is validated for simulated scenarios and for a real scenario using measurements from two high-frequency surfacewave radars.
Homomorphic Consortium Blockchain for Smart Home System Sensitive Data Privacy Preserving

The relative low level of smart home system (SHS) device information security may threaten the privacy of users.In this paper, we propose a homomorphic consortium blockchain for SHS sensitive data privacy preserving (HCB-SDPP), which is based on the traditional smart home system.We add verification services, which are composed of verification nodes, to our model to verify working nodes and transactions in SHS.In order to record the SHS device information transaction, we propose a new block data structure based on homomorphic encryption (HEBDS).Using the HCB-SDPP model, we design an encrypted algorithm based on Paillier encrypted for privacy protection.To verify the validity of the HCB-SDPP model, we firstly encrypt sensitive data of all gateway peers and upload them to the consortium blockchain.Then, we validate the security of sensitive data after homomorphic encryption processing.In the experiment, we also design attack experiments to attack different types of peers on the consortium blockchain in the HCB-SDPP model.If these nodes are insecure, the influence on the whole model will be analyzed.The simulation result shows that the HCB-SDPP model can protect customer privacy more effectively than SHS.
A set of benchmarks for Handwritten Text Recognition on historical documents

Handwritten Text Recognition is a important requirement in order to make visible the contents of the myriads of historical documents residing in public and private archives and libraries world wide. Automatic Handwritten Text Recognition (HTR) is a challenging problem that requires a careful combination of several advanced Pattern Recognition techniques, including but not limited to Image Processing, Document Image Analysis, Feature Extraction, Neural Network approaches and Language Modeling. The progress of this kind of systems is strongly bound by the availability of adequate benchmarking datasets, software tools and reproducible results achieved using the corresponding tools and datasets. Based on English and German historical documents proposed in recent open competitions at ICDAR and ICFHR conferences between 2014 and 2017, this paper introduces four HTR benchmarks in order of increasing complexity from several points of view. For each benchmark, a specific system is proposed which overcomes results published so far under comparable conditions. Therefore, this paper establishes new state of the art baseline systems and results which aim at becoming new challenges that would hopefully drive further improvement of HTR technologies. Both the datasets and the software tools used to implement the baseline systems are made freely accessible for research purposes.