Reviewerly/exHarmony huggingface dataset:
Sixteen Years of Phishing User Studies: What Have We Learned?

Several previous studies have investigated user susceptibility to phishing attacks. A thorough meta-analysis or systematic review is required to gain a better understanding of these findings and to assess the strength of evidence for phishing susceptibility of a subpopulation, e.g., older users. We aim to determine whether an effect exists; another aim is to determine whether the effect is positive or negative and to obtain a single summary estimate of the effect. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OBJECTIVES:</i> We systematically review the results of previous user studies on phishing susceptibility and conduct a meta-analysis. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">METHOD:</i> We searched four online databases for English studies on phishing. We included all user studies in phishing detection and prevention, whether they proposed new training techniques or analyzed users’ vulnerability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FINDINGS:</i> A careful analysis reveals some discrepancies between the findings. More than half of the studies that analyzed the effect of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">age</i> reported no statistically significant relationship between age and users’ performance. Some studies reported older people performed better while some reported the opposite. A similar finding holds for the gender difference. The meta-analysis shows: 1) a significant relationship between participants’ age and their susceptibility 2) females are more susceptible than males 3) users training significantly improves their detection ability.
Data Processing in Cloud Computing Model on the Example of Salesforce Cloud

Data processing is integrated with every aspect of operation enterprises—from accounting to marketing and communication internal and control of production processes. The best place to store the information is a properly prepared data center. There are a lot of providers of cloud computing and methods of data storage and processing. Every business must do the right thing, which is to think over how the data at your disposal are to be managed. The main purpose of this paper is research and the comparison of available methods of data processing and storage outside the enterprise in the cloud computing model. The cloud in SaaS (software as a service) model—Salesforce.com and a free platform development offered by Salesforce.com—force.com were used to perform the research. The paper presents the analysis results of available methods of processing and storing data outside the enterprise in the cloud computing model on the example of Salesforce cloud. Salesforce.com offers several benefits, but each service provider offers different services, systems, products, and forms of data protection. The choice of customer depends on individual needs and business plans for the future. A comparison of available methods of data processing and storage outside the enterprise in the cloud computing model was presented. On the basis of collected results, it was determined for what purposes the data processing methods available on the platform are suitable and how they can meet the needs of enterprises.
RATNet: A deep learning model for Bengali handwritten characters recognition

The Bengali language is based on a set of symbols for basic characters, modifiers, compound characters, and numerals. The recognition rates of handwritten basic characters and numerals are very high. However, the recognition rates of compound characters and modifiers are still poor. This might be due to their large class size with huge writing styles, much similarity, and unavailability of sufficient data for deep learning. In fact, there are some compound characters which appear very rare in practice. A proper selection of frequently used characters may reduce class size, and hence improving the accuracy. In this study, we performed a statistics on the frequency of compound characters, we developed two datasets for modifiers and compound characters, and finally we proposed a heterogeneous deep learning model (RATNet) for characters recognition. A statistics was performed on two daily Bengali newspapers, and characters with frequency ≥ 5% were selected. The handwriting of selected characters was collected from 130 writers of different ages and professions. The performance of RATNet model was evaluated on the proposed datasets and also three other existing datasets (i.e., ISI, CMATERdb, BanglaLekha-Isolated). In addition, the performance of RATNet was also compared with LeNet-5, VGG-16, ResNet-50, and DenseNet-121 models. We selected 87 out of 107 compound characters. The proposed RATNet model outperforms other models providing 99.66%, 99.27%, 98.78%, and 97.70% accuracy, respectively for the recognition of numerals, basic characters, modifiers, and compound characters on the CMATERdb dataset while keeping the number of parameters relatively low likely due to layer heterogeneity.
Multi-view Shape Generation for a 3D Human-like Body

Three-dimensional (3D) human-like body reconstruction via a single RGB image has attracted significant research attention recently. Most of the existing methods rely on the Skinned Multi-Person Linear model and thus can only predict unified human bodies. Moreover, meshes reconstructed by current methods sometimes perform well from a canonical view but not from other views, as the reconstruction process is commonly supervised by only a single view. To address these limitations, this article proposes a multi-view shape generation network for a 3D human-like body. Particularly, we propose a coarse-to-fine learning model that gradually deforms a template body toward the ground truth body. Our model utilizes the information of multi-view renderings and corresponding 3D vertex transformation as supervision. Such supervision will help to generate 3D bodies well aligned to all views. To accurately operate mesh deformation, a graph convolutional network structure is introduced to support the shape generation from 3D vertex representation. Additionally, a graph up-pooling operation is designed over the intermediate representations of the graph convolutional network, and thus our model can generate 3D shapes with higher resolution. Novel loss functions are employed to help optimize the whole multi-view generation model, resulting in smoother surfaces. In addition, two multi-view human body datasets are produced and contributed to the community. Extensive experiments conducted on the benchmark datasets demonstrate the efficacy of our model over the competitors.
Design of stabilized fuzzy relation-based neural networks driven to ensemble neurons/layers and multi-optimization

In this study, a design methodology based on fuzzy relation-based neural networks for stabilized network structure is introduced to cope with over-fitting as well as multi-collinearity problems which generally appear in conventional fuzzy neural networks. The design method of the proposed self-organizing network structure provides an efficient solution to construct the stabilized Fuzzy Relation-based Neural Networks (FRNN) through a synergy of multi-techniques such as ensemble neurons/layers, L2-norm regularization, compromise technique, and multi-optimization, in order to generate the structure of the multi-layered self-organizing network designed with the aid of the learning as well as novel structural design. The overall network structure is realized with the aid of parallel network structure with newly added layers as well as effective node selection method through the combination technique of both a sum of squared coefficients (SSC) and performance index (PI) as a node selection criterion for each layer in FRNN. Ensemble neurons consist of the current inputs and the original inputs, and they serve to reduce the bias of the proposed FRNN by maintaining the information of the original inputs. Also, ensemble layers stand for the combination of the current layer and the front layer, and they play a role for control the variance of the model by taking into account the output of the previous layers. The least square error estimation (LSE)-based learning method with L2-norm regularization is used for constructing the stabilized network architecture, and their ensuring design methodologies result in alleviating the overfitting phenomenon and also enhancing the generalization ability. For the performance enhancement of FRNN directly affected by some parameters such as the number of input variables, collocation of the specific subset of input variables, the number of membership functions per each variable, and the order of polynomial in the consequent parts of the fuzzy rules, multi-particle swarm optimization (MPSO) is exploited for the effectively structural as well as parametric optimization of the proposed networks. That is, the multi-optimization helps achieve a compromise between the better generation performance and the alleviated over-fitting leading to the stabilization of the proposed multi-layered self-organizing network structure realized with the aid of synergistic multi-techniques and ensemble structures such as a) L2-norm regularization-based LSE learning, b) combination technique for effective node selection through the combination of SSC and PI, and c) a novel parallel network structure including newly added layers and node selection method. The performance of the proposed network structure is quantified by comprehensive experiments and comparative analysis.
Multi-Criteria Methodology for the Location of Photovoltaic Solar Energy Production Facilities in Tenerife (Spain)

This paper presents a multi-criteria methodology for the detection of optimal locations for solar photovoltaic installations connected to the electrical grid. The proposed methodology has been applied to the island of Tenerife, as it is one of the territories in Spain with the greatest solar potential. This methodology integrates an Aptitude Model (which covers variables such as connections to the electrical grid, accessibility, cloudiness, solar irradiation and slope) together with an Impact Model (which considers variables such as landscape vulnerability, land use and hydrology). Each one of the variables considered has been transformed into standardized decision criteria, which have been weighted by means of Saaty’s pair method, having also assigned them relative weights by means of expert consultation. The integration of both Models in a Hosting Capacity Model makes it possible to consider urban and environmental constraints in different possible scenarios. Finally, the Hosting Capacity Model generated is implemented through a Geographic Information System (GIS) on the island of Tenerife, so that it has been possible to detect the optimum locations for each municipality and region.
A New Fractional-Order Map with Infinite Number of Equilibria and Its Encryption Application

The study of the chaotic dynamics in fractional-order discrete-time systems has received great attention over the last years. Some efforts have been also devoted to analyze fractional maps with special features. This paper makes a contribution to the topic by introducing a new fractional map that is characterized by both particular dynamic behaviors and specific properties related to the system equilibria. In particular, the conceived one dimensional map is algebraically simpler than all the proposed fractional maps in the literature. Using numerical simulation, we investigate the dynamic and complexity of the fractional map. The results indicate that the new one-dimensional fractional map displays various types of coexisting attractors. The approximate entropy is used to observe the changes in the sequence sequence complexity when the fractional order and system parameter. Finally, the fractional map is applied to the problem of encrypting electrophysiological signals. For the encryption process, random numbers were generated using the values of the fractional map. Some statistical tests are given to show the performance of the encryption.
Recognition of occluded objects by slope difference distribution features

Object recognition under occlusion is a key issue in computer vision. Since one can recognize an occluded object solely based on the shape, one ultimate goal of artificial intelligence is to find an automatic method that could recognize the object solely based on its shape with equal recognition accuracy. In this paper, slope difference distribution (SDD) is used to extract the shape features of the object as its sparse representation. One or several scale-invariant shape models are defined with the general SDD features for each shape class. The object is recognized based on the minimum distances between its detected SDD features and the SDD features of all the shape models. To increase the generality, we propose a two-dimensional SDD feature extraction method that computes the SDD features directly from the two-dimensional contours. Experimental results showed that the proposed object recognition method could recognize the object under significant occlusion robustly. It achieved 100% recognition and retrieval accuracy on three public datasets, Kimia99, Kimia216 and MPEG-7. For the fine-grained object classification, the proposed method achieved 90.6% accuracy on CUB-200-2011, which is also better than existing methods.
EasyDeep: An IoT Friendly Robust Detection Method for GAN Generated Deepfake Images in Social Media

Advancements in artificial intelligence, and especially deep learning technology have given birth to a new era of multimedia forgery. Deepfake takes it to a whole new level. This deep learning based technology creates new images with features which have been acquired from a different set of images. The rapid evolution of Generative Adversarial networks (GANs) provides an available route to create deepfakes. They generate highly sophisticated and realistic images through deep learning and implement deepfake using image-to-image translation. We propose a novel, memory-efficient lightweight machine learning based deepfake detection method which is successfully deployed in the IoT platform. A detection API is proposed along with the detection method. To the best of the authors' knowledge, this effort is the first ever for detecting highly sophisticated GAN generated deepfake images at the edge. The novelty of the work is achieving a considerable amount of accuracy with a short training time and inference at the edge device. The total time for sending the image to the edge, detecting and result display through the API is promising. Some discussion is also provided to improve accuracy and to reduce the inference time. A comparative study is also made by performing a three-fold textural analysis - computation of Shannon's entropy, measurement of some of Haralick's texture features (like contrast, dissimilarity, homogeneity, correlation) and study of the histograms of the generated images. Even when generated fake images look similar to the corresponding real images, the results present clear evidence that they differ significantly from the real images in entropy, contrast, dissimilarity, homogeneity, and correlation.
An integrated calibration technique for variable-boresight three-dimensional imaging system

There is an emerging need for multiview stereo systems achieved with high accuracy, wide coverage, and flexible perspective in spatially-limited vision and close-range photogrammetry. Accurate and efficient calibration is a critical prerequisite that is always performed to ensure high-performance object reconstruction and situational awareness. This paper proposes an integrated calibration technique based on rigorous modeling for the variable-boresight three-dimensional imaging system composed of a single camera and a wedge prism. A unified self-calibration framework which considers the misalignment between camera and prism is systematically demonstrated such that all involved parameters are estimated by minimizing the statistical discrepancy between model prediction and reference information. Moreover, a novel approach is presented to construct calibration reference through adaptive generation and flexible transfer. Experiments have validated that the proposed technique can offer a viable solution to multi-parameter identification for the imaging system, which therefore contributes to the recovery of any spatial object with competitive geometric accuracy and satisfactory robustness to potential error sources.
Identifying Ethereum traffic based on an active node library and DEVp2p features

With the prevalence of blockchain technology, Ethereum, as the second-largest blockchain, with the capabilities of running smart contracts, grows fast. A user on the Ethereum platform can implement smart contracts as well as remit its cryptocurrency. Cyber-attacks often target cryptocurrency. Besides, Ethereum may be exploited by malicious users for launching attacks. Therefore, the demand for Ethereum network supervision is increasing. To protect interior users on Ethereum from attacks, an Internet Service Provider (ISP) should supervise the Ethereum traffic from or to its internal users. Ethereum traffic identification is the first step towards supervision. Ethereum is a peer-to-peer communication network implemented by DEVp2p. DEVp2p is designed to realize data interaction and adopts encryption to enhance anonymity on Ethereum. It is difficult to identify Ethereum traffic accurately at line speed in a high-speed network. So far, Ethereum traffic identification has not been explored yet. This paper designs an efficient Ethereum traffic identification system with high identification accuracy, dubbed ETI (Ethereum Traffic Identification), for an ISP to supervise its internal users on Ethereum without Deep Packet Inspection (DPI). ETI first constructs an active node library consisting of core nodes on Ethereum and active Ethereum peers in the ISP. Then potential Ethereum flows are filtered by checking if their sources or destinations are in the active node library. As active Ethereum nodes may generate non-Ethereum traffic, machine learning classifiers are further adopted to judge if the potential Ethereum traffic is actually from Ethereum. The classification is based on the unique packet/payload-length and port number-related traffic features on Ethereum implemented by DEVp2p. Our experimental studies validate that ETI outperforms state-of-the-art network traffic classification methods in identification accuracy and time efficiency. ETI achieves an identification accuracy of 0.9998 and relatively high time efficiency.
GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major parameters that need to be determined during the design of an ANN are weights and biases. Various gradient-based optimization algorithms have been proposed by researchers in the past to generate an optimal set of weights and biases. However, due to the tendency of gradient-based algorithms to get trapped in local minima, researchers have started exploring metaheuristic algorithms as an alternative to the conventional techniques. In this paper, we propose the GGA-MLP (Greedy Genetic Algorithm-Multilayer Perceptron) approach, a learning algorithm, to generate an optimal set of weights and biases in multilayer perceptron (MLP) using a greedy genetic algorithm. The proposed approach increases the performance of the traditional genetic algorithm (GA) by using a greedy approach to generate the initial population as well as to perform crossover and mutation. To evaluate the performance of GGA-MLP in classifying nonlinear input patterns, we perform experiments on datasets of varying complexities taken from the University of California, Irvine (UCI) repository. The experimental results of GGA-MLP are compared with the existing state-of-the-art techniques in terms of classification accuracy. The results show that the performance of GGA-MLP is better than or comparable to the existing state-of-the-art techniques.
QIRO: A Static Single Assignment-based Quantum Program Representation for Optimization

We propose an IR for quantum computing that directly exposes quantum and classical data dependencies for the purpose of optimization. The Quantum Intermediate Representation for Optimization (QIRO) consists of two dialects, one input dialect and one that is specifically tailored to enable quantum-classical co-optimization. While the first employs a perhaps more intuitive memory-semantics (quantum operations act on qubits via side-effects), the latter uses value-semantics (operations consume and produce states) to integrate quantum dataflow in the IR’s Static Single Assignment (SSA) graph. Crucially, this allows for a host of optimizations that leverage dataflow analysis. We discuss how to map existing quantum programming languages to the input dialect and how to lower the resulting IR to the optimization dialect. We present a prototype implementation based on MLIR that includes several quantum-specific optimization passes. Our benchmarks show that significant improvements in resource requirements are possible even through static optimization. In contrast to circuit optimization at run time, this is achieved while incurring only a small constant overhead in compilation time, making this a compelling approach for quantum program optimization at application scale.
National and Higher Education Institutions (HEIs) IP Policies: Comparison of Indian HEIs’ IP Policies from a Global Perspective

Abstract Until the end of the eighteenth century, Higher Education Institutions (HEIs) were restricted to generate knowledge for free access to industries which they use for making commercial products. Though HEIs have the potential to contribute to the national economy from their creative and commercially viable R&amp;D output through commercialization of research and Intellectual Property Rights, it was not given the needed importance due to lack of supportive policies. In many developed countries, innovation strategies, Science, Technology, and Innovation (STI) and national Intellectual Property Rights (IPR) policies provided opportunities to HEIs to own and commercialize knowledge they generate. National IPR policy and formulation and implementation of HEI IP policies became crucial for effective implementation, promotion of innovation, generation, and protection of IP in the HEIs. Efficiency of HEI in commercializing research is dependent on national IP policy framework aligning vision and goals of HEI IP policy with national IP policy, objectives, ownership, and revenue sharing mechanism. These are the key indicators for comparing the institutions’ innovation and IP capacity. This paper uses these indicators to discuss the impact of different countries’ (developed, developing, and least developed) national IPR policy frameworks on top-performing HEIs of those countries and to address the research question, how national and HEI IP policies have worked in achieving envisaged economic growth by improving innovation output of the HEIs. It further compares Indian HEI IP policies with global HEIs. We found that there is a greater need for local governments and HEIs to make their incentive strategies and policies in line with their corresponding national frameworks for facilitating the collaboration and commercialization of innovations. Implementing a robust IP policy framework at state/region and HEI level plays a pivotal role in creating an innovation ecosystem supportive to envisaged national economic growth.