Reviewerly/exHarmony huggingface dataset:
Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data

The availability of high-resolution LiDAR (Light Detection And Ranging) geospatial data has increased immensely, providing new opportunities to solve challenges in the field of spatial energy planning. This paper presents a new method for large-scale placement of photovoltaic arrays over buildings' rooftops in an optimal manner by using the global optimisation approach. The position, aspect and slope are the ey geometrical parameters being optimised for each photovoltaic array. The predicted energy generation (i.e. photovoltaic potential) is simulated by using state-of-the-art hourly shadowing estimation from the surroundings, anisotropic diffuse, reflected, and direct irradiances that are based on a Typical Meteorological Year, and non-linear efficiency characteristics of a considered photovoltaic system configuration. The optimisation performs multiple simulation scenarios throughout an entire year for each photovoltaic array, in order to maximise its photovoltaic potential. The method was tested over three LiDAR datasets with different landscape topographies and urban densities. In comparison to the methods for photovoltaic arrays' fixed optimal slope estimation, the proposed method is substantially more suitable for application in urban environments.
Optimization of the area under the ROC curve using neural network supervectors for text-dependent speaker verification

This paper explores two techniques to improve the performance of text-dependent speaker verification systems based on deep neural networks. Firstly, we propose a general alignment mechanism to keep the temporal structure of each phrase and obtain a supervector with the speaker and phrase information, since both are relevant for a text-dependent verification. As we show, it is possible to use different alignment techniques to replace the global average pooling providing significant gains in performance. Moreover, we also present a novel Back-end approach to train a neural network for detection tasks by optimizing the Area Under the Curve (AUC) as an alternative to the usual triplet loss function, so the system is end-to-end, with a cost function close to our desired measure of performance. As we can see in the experimental section, this approach improves the system performance, since our triplet neural network based on an approximation of the AUC (aAUC) learns how to discriminate between pairs of examples from the same identity and pairs of different identities. The different alignment techniques to produce supervectors in addition to the new Back-end approach were tested on the RSR2015-Part I and RSR2015-Part II database for text-dependent speaker verification, providing competitive results compared to similar size networks using the global average pooling to extract supervectors and using a simple Back-end or triplet loss training.
An Analysis of Smart Contracts Security Threats Alongside Existing Solutions

Smart contracts have gained a lot of popularity in recent times as they are a very powerful tool for the development of decentralised and automatic applications in many fields without the need for intermediaries or trusted third parties. However, due to the decentralised nature of the blockchain on which they are based, a series of challenges have emerged related to vulnerabilities in their programming that, given their particularities, could have (and have already had) a very high economic impact. This article provides a holistic view of security challenges associated with smart contracts, as well as the state of the art of available public domain tools.
Integration of mobile augmented reality (MAR) applications into biology laboratory: Anatomic structure of the heart

The purpose of the current study is to design and develop a sample Mobile Augmented Reality (MAR) application addressing the anatomic structure of the heart in a way suitable for laboratory learning for pre-service science teachers to achieve learning by constructing information in biology instruction. The implementation of the MAR design activity was conducted with the participation of 30 pre-service teachers taking the biology laboratory course. The implementation process of the activity consists of four stages. The first stage includes the introduction of the MAR application program and marker; the second stage includes the use of the MAR application in a laboratory environment; the third stage includes the operation of dissection and the last stage includes the association of the MAR application with the operation of dissection and general evaluation. Then, semi-structured interviews were conducted by involving pre-service teachers and the data obtained from these interviews revealed that integration of heart dissection with MAR application helped the pre-service teachers to better understand the anatomic structure of the heart and the related concepts. Thus, a sample activity demonstrating how MAR, which is an instructional method with strong potential for reification and visualisation, can be integrated into the teaching of concepts in laboratory settings was developed.
Generic Deep Learning-Based Linear Detectors for MIMO Systems Over Correlated Noise Environments

To support the and development and application of the fifth-generation (5G) communication and internet of things (IoT) networks, high data-rate wireless transmission is required.To meet the demand of high data-rate, multiple antennas are equipped at the transmitter and receiver, forming multiple-input multiple-output (MIMO) systems.A big challenge of MIMO is the detector design in correlated noise environments, which should achieve a fine performance with moderate computational complexity.To this end, we employ an iterative framework of a deep convolutional neural network (DCNN) and a linear detector for MIMO systems over correlated noise environments.In this framework, the linear detector can be zero-forcing (ZF), minimum mean square error (MMSE), ZF with successive interference cancellation (ZF-SIC), or MMSE-SIC, which produces an initial estimate of transmitted signals.The DCNN is used to capture the local correlation among noise, and it can produce a more accurate estimate of transmitted signals.Simulation results are finally provided to show that the proposed detector can outperform the conventional linear detectors substantially through capturing the local correlation characteristics among noise.
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once

Modern deep neural networks are often vulnerable to adversarial samples. Based on the first optimization-based attacking method, many following methods are proposed to improve the attacking performance and speed. Recently, generation-based methods have received much attention since they directly use feed-forward networks to generate the adversarial samples, which avoid the time-consuming iterative attacking procedure in optimization-based and gradient-based methods. However, current generation-based methods are only able to attack one specific target (category) within one model, thus making them not applicable to real classification systems that often have hundreds/thousands of categories. In this paper, we propose the first Multi-target Adversarial Network (MAN), which can generate multi-target adversarial samples with a single model. By incorporating the specified category information into the intermediate features, it can attack any category of the target classification model during runtime. Experiments show that the proposed MAN can produce stronger attack results and also have better transferability than previous state-of-the-art methods in both multi-target attack task and single-target attack task. We further use the adversarial samples generated by our MAN to improve the robustness of the classification model. It can also achieve better classification accuracy than other methods when attacked by various methods.
Comprehensive and Systematic Study on the Fault Tolerance Architectures in Cloud Computing

Providing dynamic resources is based on the virtualization features of the cloud environment. Cloud computing as an emerging technology uses a high availability of services at any time, in any place and independent of the hardware. However, fault tolerance is one of the main problems and challenges in cloud computing. This subject has an important effect on cloud computing, but, as far as we know, there is not a comprehensive and systematic study in this field. Accordingly, in this paper, the existing methods and mechanisms are discussed in different groups, such as proactive and reactive, types of fault detection, etc. Various fault tolerance techniques are provided and discussed. The advantages and disadvantages of these techniques are shown on the basis of the technology that they have used. Generally, the contributions of this research provide a summary of the available challenges associated with fault tolerance, a description of several important fault tolerance methods in the cloud computing and the key regions for the betterment of fault tolerance techniques in the future works. The advantages and disadvantages of the selected articles in each category are also highlighted and their significant challenges are discussed to provide the research lines for further studies.
A permutational-based Differential Evolution algorithm for feature subset selection

This paper describes a permutational-based Differential Evolution algorithm implemented in a wrapper scheme to find a feature subset to be applied in the construction of a near-optimal classifier. In this approach, the relevance of a feature chosen to build a better classifier is represented through its relative position in an integer-valued vector, and by using a permutational-based mutation operator, it is possible to create new feasible candidate solutions only. Furthermore, to provide a controlled diversity rate in the population, a straightforward repair-based recombination operator is utilized to evolve a population of candidate solutions. Unlike the other approaches in the existing literature using integer-valued vectors and requiring a predefined subset size, in this approach, this size is determined by an additional element included in the encoding scheme, allowing to find an adequate feature subset size to each specific dataset. Experimental results show that this approach is an effective way to create more accurate classifiers as they are compared with those obtained by other similar approaches.
Exploiting Anti-Monotonic Constraints in Mining Palindromic Motifs from Big Genomic Data

The advent of high-throughput technologies such as Illumina HiSeq X, mass spectrometry, and microarray heralds a new era of big biological datasets in computational biology. This digital revolution in bioinformatics has generated unprecedented volumes of omics data (e.g., transcriptomes, genomes, proteomes, metabolomes) with various degrees of veracities and values. These deluge of omics data are awash with a wealth of information in the form of frequently repeated contiguous patterns-namely, sequence motifs. Sequence motifs are short repeated contiguous subsequences located in the promoter region of a genome sequence. On some occasions, users are interested in mining only a particular type of sequence motifs (e.g., palindromic motifs). In genomics, palindromes are sequences from the nucleotide bases from deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) strands that are symmetrical in the sense that they read exactly the same as their complementary sequences in the reverse direction. The use of classical constraints (e.g., anti-monotonic, succinct, and/or convertible constraints) allows users to specify their interest in the universal search space, and thus enhancing distinct and effective pruning of the search space- leading to a reduction in the computational time required for the mining process. Despite several attempts made by existing algorithms for mining palindromic motifs from DNA sequences, a major drawback stems from the high volumes of the DNA sequences leading to high complexities and turnaround time of the algorithms. To this end, we propose a parallel scalable sequential mining algorithm that exploits some features of anti-monotonic constraints-using the in-memory computing model of the Apache Spark framework deployed on a cluster of a homogeneous distributed-memory system-for mining palindromic motifs from high volumes of DNA sequences. To evaluate our algorithm, we obtained the human genome (Homo sapiens) assemblies GRCh37 patch 13 (hg19), which is of size 3.2 GB from the Ensembl data repository. It contains 104,763 protein-coding sequences and 24,513 non-coding sequences. Evaluation results show that our algorithm extracts accurate palindromic motifs using a short turnaround time.
Learning to rank for multi-label text classification: Combining different sources of information

Abstract Efficiently exploiting all sources of information such as labeled instances, classes’ representation, and relations of them has a high impact on the performance of Multi-Label Text Classification (MLTC) systems. Most of the current approaches use labeled documents as the primary source of information for MLTC. We investigate the effectiveness of different sources of information— such as the labeled training data, textual labels of classes, and taxonomy relations of classes— for MLTC. More specifically, first, for each document–class pair, different features are extracted using different sources of information. The features reflect the similarity of classes and documents. Then, MLTC is considered to be a ranking problem, and a learning to rank (LTR) approach is used for ranking classes regarding documents and selecting labels of documents. An important characteristic of many MLTC instances is that documents can belong to multiple classes and there are implicit relations between classes. We apply score propagation on top of LTR to incorporate co-occurrence patterns of classes in labeled documents. Our main findings are the following. First, using an LTR approach integrating all features, we observe significantly better performance than previous systems for MLTC. Specifically, we show that simple classification approaches fail when there is a high number of classes. Second, the analysis of feature weights reveals the relative importance of various sources of evidence, also giving insight into the underlying classification problem. Interestingly, the results indicate that the titles of documents are more informative than all other sources of information. Third, a lean-and-mean system using only four features is able to perform at 96% of the large LTR model that we propose in this paper. Fourth, using the co-occurrence information of classes helps in classifying documents more accurately. Our results show that the co-occurrence information is more helpful when the underlying classifier has a poor performance.
Convolutional and recurrent neural network for human activity recognition: Application on American sign language

Human activity recognition is an important and difficult topic to study because of the important variability between tasks repeated several times by a subject and between subjects. This work is motivated by providing time-series signal classification and a robust validation and test approaches. This study proposes to classify 60 signs from the American Sign Language based on data provided by the LeapMotion sensor by using different conventional machine learning and deep learning models including a model called DeepConvLSTM that integrates convolutional and recurrent layers with Long-Short Term Memory cells. A kinematic model of the right and left forearm/hand/fingers/thumb is proposed as well as the use of a simple data augmentation technique to improve the generalization of neural networks. DeepConvLSTM and convolutional neural network demonstrated the highest accuracy compared to other models with 91.1 (3.8) and 89.3 (4.0) % respectively compared to the recurrent neural network or multi-layer perceptron. Integrating convolutional layers in a deep learning model seems to be an appropriate solution for sign language recognition with depth sensors data.
Identification and Prioritization of Agile Requirements Change Management Success Factors in the Domain of Global Software Development

The economic and strategic gains motivated the majority of software firms to adopt global software development (GSD). The adoption of GSD is not straightforward. The geographical distance between the GSD teams causes the various problems for the implementation of agile software development activities, particularly, that related to requirements change management in agile (ARCM). The objective of this study is to explore the success factors of ARCM in GSD context and prioritized them based on their significance. The study comprised of two steps: (1) identify the success factors of ARCM using systematic mapping study (SMS) and validate them with industry practitioners using questionnaire survey approach; (2) prioritize the identified success factors by applying the analytical hierarchy process (AHP) approach. A total of 21 success factors that are significant for ARCM process were identified using SMS and questionnaire survey study. The results of AHP shows that allocation of resources at overseas sites, 3C's (communication, coordination and control), geographically distributed change control board (CCB), RCM process improvement expertise and continuous top management support are the highest priority success factors of ARCM process in the context of GSD. The findings assist the practitioners to address the most priority success factors in order to successfully implement the ARCM activities in the domain of GSD.
Hierarchical Quantum Secret Sharing Based On Special High-Dimensional Entangled State

A hierarchical quantum secret sharing scheme is proposed, for which participants are divided into multiple layers. In this scheme, the number of participants and the shared secrets can be different for layers. The shared secret of a layer can only be recovered by the cooperation of all the participants at that layer. Moreover, each participant is able to obtain the shared secrets at lower layers, but not those at higher layers. The design is based on a special high-dimensional entangled state, in which the particles have different dimensions and can be set as subtle values to satisfy the hierarchical requirement. The correctness and security of the proposed scheme are analyzed. As compared to existing quantum secret sharing schemes, it possesses distinct features which are demanded by some specified scenarios.
Radar Gesture Recognition System in Presence of Interference using Self-Attention Neural Network

Gesture recognition provides an easy, convenient and intuitive way of remotely controlling several consumer electronics devices such as audio devices, television sets, projector or gaming consoles. In recent years, radar sensors have been shown to be effective sensing modality to sense and recognize fine-grained dynamic finger-gestures in watch or smartphone and thus offers an user-friendly human-computer interface in ultrashort range applications. However, hand-gesture recognition from a farther distance such as to control consumer devices like TV or projector pose challenge particularly arising due to interferences from multiple humans in the field of view. In this paper, we present a novel unguided spatio-Doppler attention mechanism to enable hand-gesture recognition in presence of multiple humans using a low power, compact 60-GHz FMCW radar operated in 500MHz ISM frequency band. The spatio-Doppler mechanism in 2D deep convolutional neural network with long short term memory (2D CNN-LSTM) makes use of the range-Doppler images and range-angle images. We experimentally present the classification accuracy of 94.75% of our proposed system on test dataset using eight gestures, namely wave, push forward, pull, left swipe, right swipe, clockwise rotate, anti-clockwise rotate, cross, in presence of interfering people, such as walking or arbitrary movements.
On market concentration and cybersecurity risk

ABSTRACT Market concentration affects each component of the cybersecurity risk equation (i.e. threat, vulnerability and impact). As the Internet ecosystem becomes more concentrated across a number of vectors from users and incoming links to economic market share, the locus of cyber risk moves towards these major hubs and the volume of systemic cyber risk increases. Mitigating cyber risk requires better measurement, diversity of systems, software and firms, attention to market concentration in cyber insurance pricing, and the deliberate choice to avoid ubiquitous interconnection in critical systems.
SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground truth labels are provided to the network. That being said, the network is unaware about any class instance or object present in the given data sample. So, we propose a convolutional neural network (CNN) based architecture for unsupervised segmentation. We used the squeeze and excitation network, due to its peculiar ability to capture the features’ interdependencies, which increases the network’s sensitivity to more salient features. We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation. Along with this CNN architecture, image enhancement and refinement techniques are exploited to improve the segmentation results. Our proposed algorithm produces improved segmented regions that meet the human level segmentation results. In addition, we evaluate our approach using different metrics to show the quantitative outperformance.
Scalability in Blockchain: Challenges and Solutions

Since its inception blockchain technologies have grown tremendously. Their vast success can be attributed to both the research community and the industry. Although, initially, blockchain was confined to financial sector only, its decentralized and immutable ledger availability has made it popular for nonfinancial services as well. These include applicability in energy and memory constrained Internet of Things (IoT) devices as well. However, the ever-increasing size of blockchains like Bitcoin, Ethereum, and so on has led to issues of scalability. Linear order of blockchain is now being changed to directed acyclic graphs (DAGs) based chains. Different approaches of side chain are also being explored to reduce processing time per transaction. Heterogeneous solutions are also being explored in this regard. It was observed that there are no descriptive work available explaining scalability concerns and solutions in present day blockchain scenario. In this chapter, we have covered chain partitioning-based scalability, DAGs-based scalability, and horizontal scalability through sharding and have discussed future directives.