Reviewerly/exHarmony huggingface dataset:
An Asymptotically Optimal Algorithm For Classification of Data Vectors with Independent Non-Identically Distributed Elements

In this paper, we propose a classifier for classification of data vectors with mutually independent but not identically distributed elements. For the proposed classifier, we prove that the error probability goes to zero as the length of the data vectors goes to infinity, even when there is only one training data vector per label available. Finally, we present numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of training data is small.
A dynamic window-size based segmentation technique to detect driver entry and exit from a car

In-built hardware sensors of a smartphone have emerged as a useful sensor platform for human activity recognition and context awareness. Most of available research in human activity recognition domain is expressed as a classification problem where a classifier’s performance is directly co-related upon the amount of inter-class variance which feature engineering can bring. Several feature extraction techniques have been used by existing studies, including dynamic and static activity-window sizes. In our study, we propose a dynamic non-overlapping window based approach which spans across the entire time-series data representing the activity (DyNOEA). The objective is to detect driver entry and exit from a car using smartphone sensors, while the smartphone is in the driver’s pocket. In comparison to earlier works, which used static and overlapping window-size for activity recognition, the proposed feature extraction technique is able to classify activities with extremely high accuracy in perfectly naturalistic setup achieving up to 100% accuracy for classifiers like logistic regression, decision tree and k-NN.
A quantum edge detection algorithm for quantum multi-wavelength images

Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis.
Similarity-evaluation-based evolving of flexible neural trees for imbalanced classification

A flexible neural tree (FNT) is a special and effective kind of artificial neural network that can search optimal network structures using tree structure evolving algorithms, leading to its high performance in many real classification and prediction problems. However, the standard FNT model does not take into account tree structure similarities in its evolving process. This may result to a rapid and significant decrease of its tree structure population. Standard FNT also suffers from imbalanced data. In this study, we propose a new similarity evaluation method for FNT (SEFNT) to keep the population diversity and deal with the imbalanced data. The main idea of SEFNT is twofold. First, the difference between two nodes is introduced for similarity measurement. Second, the node position and height of trees are also taken into account for accurate tree structure distance measurement. SEFNT uses imbalanced fitness function to control its evolving procedure to deal with imbalanced problems. We compare SEFNT with 10 imbalanced methods on 46 KEEL datasets and 10 UCI datasets. The experimental results show that our approach can significantly improve the classification performance of FNT. In the comparisons with other algorithms, SEFNT shows significantly better performance. We also apply the proposed method to a practical imbalanced classification problem, that is, Internet video traffic identification. The results imply that our method is effective in dealing with practical problems.
Automatic Unusual Activities Recognition Using Deep Learning in Academia

In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.
Speech Recognition Using Artificial Neural Network

Hussain, Shoeb Nazir, Ronaq Javeed, Urooj Khan, Shoaib Sofi, RumaisaSpeech recognition can be an important tool in today’s society for hand-free or voice-driven implementation. Using simple commands or triggers, it is possible for speech impaired human beings to communicate with increased ease of understanding. With the advent of various soft computing methods, a large class of nonlinearities can be handled. Artificial Neural Networks (ANN) have been applied in finding the solution for speech recognition. A lot of work is going on in this regard and mostly positive results have been achieved. Now, the research is being done to minimize the rate of error in obtaining the solution. In this paper, a comprehensive study of use of artificial neural networks in speech recognition is studied and proposes methods for training of the neural network so that an appropriate neural output can be obtained which is as close to the desired output. The paper demonstrates that ANN can indeed form the basis for a general-purpose speech recognition and neural network offers clear advantages over conventional methods. MATLAB simulation has been carried out to validate the results.
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)

Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GANs to time series modeling, including forecasting. In this work, we present the Mixture Density Conditional Generative Adversarial Model (MD-CGAN), with a focus on time series forecasting. We show that our model is capable of estimating a probabilistic posterior distribution over forecasts and that, in comparison to a set of benchmark methods, the MD-CGAN model performs well, particularly in situations where noise is a significant component of the observed time series. Further, by using a Gaussian mixture model as the output distribution, MD-CGAN offers posterior predictions that are non-Gaussian.
Asynchronous Guessing Subject to Distortion

The problem of guessing subject to distortion is considered, and the performance of randomized guessing strategies is investigated. A one-shot achievability bound on the guessing moment (i.e., moment of the number of required queries) is given. Applying this result to i.i.d. sources, it is shown that randomized strategies can asymptotically attain the optimal guessing moment. Further, a randomized guessing scheme which is feasible even when the block size is extremely large is proposed, and a single-letter characterization of the guessing moment achievable by the proposed scheme is obtained.
Scrum for Safety: Agile Development in Safety-Critical Software Systems

The adoption of agile methodologies in all domains of software development is a desired goal. Unfortunately, many obstacles have been meet in the past for a full adoption in secure and safe systems, where different standards and operational constraints apply. In this paper we propose a novel agile methodology to be applied in the development of safety critical systems. In particular, we developed an extension of the well-known Scrum methodology and discussed the complete workflow. We finally validated the applicability of the methodology over a real case study from the railway domain.
Digital Skills in Times of the COVID-19 Pandemic

This chapter describes the digital competencies that have become essential in the workforce and how higher education institutions (HEIs) are trying to keep up in a moment in which faculty members have been acquiring digital skills alongside students. A field study was conducted with faculty from HEIs in Mexico to identify the differences between the digital skills that faculty possessed previous to the pandemic and those acquired as a result of remote work. It also analyzes the digital tools they have been provided with to perform their jobs, the training they have received, and the digital skills that they still lack to help students acquire the digital competencies demanded in the workforce. The objective is to identify areas of opportunity and create general guidelines that will help develop critical digital skills. A literature review of the most relevant aspects of digital dexterity and digital competence in higher education (HE) is presented. An analysis of the current context and how it is producing changes faster than before is also included.
Multi-level Graph Attention Network based Unsupervised Network Alignment

Network alignment is the matching of two networks with corresponding nodes that belong to the same user or entity.The most common application is to analyze which accounts belong to the same user in two social networks.Most of existing techniques rely on matrix factorization so that they cannot be scaled to large-scale networks, are constrained by strict constraints, and cannot learn node embedding without a training set.In this paper, we propose an unsupervised network alignment model based on multi-level graph attention networks.The model uses multi-level graph attention network to learn the embedded representation of nodes, satisfying attribute and structure constraints of alignment.Augmented learning process is proposed to simulate attribute noise and structural noise to improve adaptability of the model.Extensive experiments on real datasets show that the proposed model performs better than the state-of-the-art network alignment model.We also demonstrate the robustness of the proposed model.
Efficient Decoding of Gabidulin Codes over Galois Rings

This paper presents the first decoding algorithm for Gabidulin codes over Galois rings with provable quadratic complexity in the code length. The new method consists of two steps: (1) solving a syndrome-based key equation to obtain the annihilator polynomial of the error and therefore the column space of the error, (2) solving a key equation based on the received word in order to reconstruct the error vector. This two-step approach became necessary since standard solutions as the Euclidean algorithm do not properly work over rings.
The Mechatronic system design of the bed-chair integration system E-Bed

With the advent of an aging society, there are more and more disabled elderly people, and the cost of care is getting higher and higher, causing a great burden to the society. In order to improve the quality of care for the disabled elderly and reduce the cost of care, this paper developed a modular and deformable multifunctional bed and chair integration system, which mainly includes a multifunctional nursing bed system, an omnidirectional wheelchair system and corresponding control systems. This paper starts from the demand analysis, carries out detailed design and integrated verification and debugging for each subsystem, and develops a prototype of the integrated service robot for bed and chair E-Bed. The bed-chair integration robot system E-Bed can effectively meet the actual needs of nursing homes, reduce the labor intensity of nursing staff and improve the self-care ability of the elderly.
A 4-Approximation of the $$\frac{2\pi }{3}$$-MST

Bounded-angle (minimum) spanning trees were first introduced in the context of wireless networks with directional antennas. They are reminiscent of bounded-degree (minimum) spanning trees, which have received significant attention. Let P be a set of n points in the plane, and let $$0< \alpha < 2\pi $$ be an angle. An $$\alpha $$ -spanning tree ( $$\alpha $$ -ST) of P is a spanning tree of the complete Euclidean graph over P, with the following property: For each vertex $$p_i \in P$$ , the (smallest) angle that is spanned by all the edges incident to $$p_i$$ is at most $$\alpha $$ . An $$\alpha $$ -minimum spanning tree ( $$\alpha $$ -MST) is an $$\alpha $$ -ST of P of minimum weight, where the weight of an $$\alpha $$ -ST is the sum of the lengths of its edges. In this paper, we consider the problem of computing an $$\alpha $$ -MST for the important case where $$\alpha = \frac{2\pi }{3}$$ . We present a 4-approximation algorithm, thus improving upon the previous results of Aschner and Katz and Biniaz et al., who presented algorithms with approximation ratios 6 and $$\frac{16}{3}$$ , respectively. To obtain this result, we devise an O(n)-time algorithm that, given any Hamiltonian path $$\varPi $$ of P, constructs a $$\frac{2\pi }{3}$$ -ST $$\mathcal{T}$$ of P, such that $$\mathcal{T}$$ ’s weight is at most twice that of $$\varPi $$ and, moreover, $$\mathcal{T}$$ is a 3-hop spanner of $$\varPi $$ . This latter result is optimal in the sense that for any $${\varepsilon }> 0$$ there exists a polygonal path for which every $$\frac{2\pi }{3}$$ -ST (of the corresponding set of points) has weight greater than $$2-{\varepsilon }$$ times the weight of the path.
Discriminative Feature Spamming Technique for Roman Urdu Sentiment Analysis

Term weighting is one of the most commonly used approaches, which works by assigning weights to terms, that aims to improve the performance of information retrieval or text categorization tasks. In this paper, we present a novel term weighting technique, called discriminative feature spamming technique (DFST), which identifies distinctive terms, based on a term utility criteria (TUC), and then spams them to increase their discriminative power. The experimental results show that the DFST outperformed a set of time-tested term weighting schemes, from the information retrieval field. All the experiments were performed on the largest ever Roman Urdu (RU) dataset of 11000 reviews, which was collected and annotated for this work. In addition, a custom tokenizer was built, which further improved classification accuracy. A cross-scheme comparison was performed, which showed that the results obtained by using the newly proposed DFST, were statistically significant and better than previous approaches.
Integrity and Confidentiality in Cloud Outsourced Data

Cloud services have become an increasingly popular solution to provide different services to clients. One of the cloud services is database as a service (DBaaS), in which the service provider offers different resources such as software, hardware and network to the clients to be able to manage and administer the database. However, the data and the execution of database queries are under control of the distrustful service provider. This lack of trust opens up new security issues and serves as the chief motivation of our work. This study presents an overview of different cryptographic algorithms, based on different schemes in the outsourced database security and query authentication. We conclude the paper by proposing a new architecture that achieves the confidentiality and integrity of query results of the outsourced database.
Livestock detection in aerial images using a fully convolutional network

In order to accurately count the number of animals grazing on grassland, we present a livestock detection algorithm using modified versions of U-net and Google Inception-v4 net. This method works well to detect dense and touching instances. We also introduce a dataset for livestock detection in aerial images, consisting of 89 aerial images collected by quadcopter. Each image has resolution of about 3000×4000 pixels, and contains livestock with varying shapes, scales, and orientations. We evaluate our method by comparison against Faster RCNN and Yolo-v3 algorithms using our aerial livestock dataset. The average precision of our method is better than Yolo-v3 and is comparable to Faster RCNN.
Big Data and blockchain supported conceptual model for enhanced healthcare coverage

Purpose Significant advances have been made in the field of healthcare service delivery across the world; however, health coverage particular for the poor and disadvantaged still remains a distant dream in developing world. In large developing countries like India, disparities in access to healthcare are pervasive. Despite recent progress in ensuring improved access to health care in past decade or so, disparities across gender, geography and socioeconomic status continue to persist. Fragmented and scattered health records and lack of integration are some of the primary causes leading to uneven healthcare service delivery. The devised framework is intended to address these challenges. The paper aims to discuss these issues.
Split-CNN

We present an interdisciplinary study to tackle the memory bottleneck of training deep convolutional neural networks (CNN). Firstly, we introduce Split Convolutional Neural Network (Split-CNN) that is derived from the automatic transformation of the state-of-the-art CNN models. The main distinction between Split-CNN and regular CNN is that Split-CNN splits the input images into small patches and operates on these patches independently before entering later stages of the CNN model. Secondly, we propose a novel heterogeneous memory management system (HMMS) to utilize the memory-friendly properties of Split-CNN. Through experiments, we demonstrate that Split-CNN achieves significantly higher training scalability by dramatically reducing the memory requirements of training algorithms on GPU accelerators. Furthermore, we provide empirical evidence that splitting at randomly chosen boundaries can even result in accuracy gains over baseline CNN due to its regularization effect.
Cyberbiosecurity: A New Perspective on Protecting U.S. Food and Agricultural System

Our national data and infrastructure security issues affecting the "bioeconomy" are evolving rapidly. Simultaneously, the conversation about cyber security of the U.S. food and agricultural system (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse and they require advanced technologies and efficiencies that rely on computer technologies, big data, cloud-based data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bio economy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safety management Hazard Analysis Critical Control Point system concept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). This analysis explores the relevant concepts of cyber biosecurity from food production to the end product user (such as the consumer) and considers the integration of diverse transportation, supplier, and retailer networks. We describe common challenges and unique barriers across these systems and recommend solutions to advance the role of cyber biosecurity in the food and agricultural sectors.
Remote Sensing Airport Detection Based on End-to-End Deep Transferable Convolutional Neural Networks

Rapid intelligent detection of airports from remote sensing images is required to accomplish autonomous intelligent landing of unmanned aerial vehicles (UAVs) and other tasks. To address the insufficiency of traditional models in detecting airports under complicated backgrounds from remote sensing images, we propose an end-to-end remote sensing airport hierarchical expression and detection model based on deep transferable convolutional neural networks. Based on transfer learning, we solve the fundamental problem of overfitting due to the inadequate number of labeled remote sensing images by transferring the network model from natural image source domain to remote sensing image target domain. In addition, we introduce a cascade region proposal network with soft-decision nonmaximal suppression to improve the network structure and the performance of our method under complex backgrounds. Moreover, we use skip-layer feature fusion and hard example mining methods to improve the object expression ability and the training efficiency. Finally, the experimental results demonstrate that the method established in this letter can quickly and effectively detect different types of airports over complex backgrounds and obtain better detection performance than the other detection methods.