Reviewerly/exHarmony huggingface dataset:
Petersen Graph Multi-Orientation Based Multi-Scale Ternary Pattern (PGMO-MSTP): An Efficient Descriptor for Texture and Material Recognition

Classifying and modeling texture images, especially those with significant rotation, illumination, scale, and view-point variations, is a hot topic in the computer vision field. Inspired by local graph structure (LGS), local ternary patterns (LTP), and their variants, this paper proposes a novel image feature descriptor for texture and material classification, which we call Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP). PGMO-MSTP is a histogram representation that efficiently encodes the joint information within an image across feature and scale spaces, exploiting the concepts of both LTP-like and LGS-like descriptors, in order to overcome the shortcomings of these approaches. We first designed two single-scale horizontal and vertical Petersen Graph-based Ternary Pattern descriptors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGTP_{h}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGTP_{v}$ </tex-math></inline-formula> ). The essence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGTP_{h}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGTP_{v}$ </tex-math></inline-formula> is to encode each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> image patch, extending the ideas of the LTP and LGS concepts, according to relationships between pixels sampled in a variety of spatial arrangements (i.e., up, down, left, and right) of Petersen graph-shaped oriented sampling structures. The histograms obtained from the single-scale descriptors <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGTP_{h}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PGTP_{v}$ </tex-math></inline-formula> are then combined, in order to build the effective multi-scale PGMO-MSTP model. Extensive experiments are conducted on sixteen challenging texture data sets, demonstrating that PGMO-MSTP can outperform state-of-the-art handcrafted texture descriptors and deep learning-based feature extraction approaches. Moreover, a statistical comparison based on the Wilcoxon signed rank test demonstrates that PGMO-MSTP performed the best over all tested data sets.
A Link Analysis Algorithm for Identification of Key Hidden Services

The Tor dark web network has been reported to provide a breeding ground for criminals and fraudsters who are exploiting the vulnerabilities in the network to carry out illicit and unethical activities. The network has unfortunately become a means to perpetuate crimes like illegal drugs and firearm trafficking, violence and terrorist activities among others. The government and law enforcement agencies are working relentlessly to control the misuse of Tor network. This is a study in the similar league, with an attempt to suggest a link-based ranking technique to rank and identify the influential hidden services in the Tor dark web. The proposed method considers the extent of connectivity to the surface web services and values of the centrality metrics of a hidden service in the web graph for ranking. The modified PageRank algorithm is used to obtain the overall rankings of the hidden services in the dataset. Several graph metrics were used to evaluate the effectiveness of the proposed technique with other commonly known ranking procedures in literature. The proposed ranking technique is shown to produce good results in identifying the influential domains in the tor network.
Knowledge Management Strategies Through Educational Digital Platforms During Periods of Social Confinement

During COVID-19 the world experienced an unprecedented educational crisis, where conventional systems based on presence were fractured by long periods of confinement. In order to face this situation, the benefits of the educational digital platforms were used. In this sense, this research aimed to analyze knowledge management strategies through the use of educational digital platforms during periods of social confinement in Mexico, Colombia and Peru. A survey composed of 22 items with a Likert scale was applied to 396 teachers in the period from 01/09/2020 to 15/11/2020. It was concluded that schools had difficulties in connecting to the Internet, lack of electronic devices, lack of information technology skills for the use of educational digital platforms.
Implicit Emotion Relationship Mining Based on Optimal and Majority Synthesis From Multimodal Data Prediction

Emotion is precious, useful for many applications such as public opinion detection and psychological disease prediction. Emotion recognition on multimodal data has attracted extensive attention. Although modifying model structure or multimodal feature fusion methods have contributed a lot to emotion recognition, little attention is paid to mining implicit emotion relationship. In this article, implicit emotion relationship consists of emotion distribution, confusion, and transfer. Emotion distribution allows multiple emotions in one sample, while confusion and transfer imply the prediction confusion and bias. In order to mine implicit emotion relationship in multimodal data, this article employs three image and two text classification models to recognize emotions, respectively. Two prediction emotion synthesis methods (optimal prediction emotion synthesis and majority prediction emotion synthesis) are proposed to synthesize the outputs of multiple models. Based on the results of two emotion synthesis methods, emotion distribution on samples is obtained. Emotion confusion and transfer among different emotion samples are analyzed by relative entropy and Jensen-Shannon divergence. Implicit emotion relationship mining has potential not only in the interpretation of model performance, but also in guiding the development of emotion recognition as prior knowledge. Finally, we take topic scenario as an instance to mine implicit emotion relationships.
Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells

Morphologic rare cell detection is a laborious, operator-dependent process which has the potential to be improved by the use of image analysis using artificial intelligence. Detection of rare hemoglobin H (HbH) inclusions in red cells in the peripheral blood is a common screening method for alpha-thalassemia. This study aims to develop a convolutional neural network-based algorithm for the detection of HbH inclusions.Digital images of HbH-positive and HbH-negative blood smears were used to train and test the software. The software performance was tested on images obtained at various magnifications and on different scanning platforms. Another model was developed for total red cell counting and was used to confirm HbH cell frequency in alpha-thalassemia trait. The threshold minimum red cells to image for analysis was determined by Poisson modeling and validated on image sets.The sensitivity and specificity of the software for HbH+ cells on images obtained at ×100, ×60, and ×40 objectives were close to 91% and 99%, respectively. When an AI-aided diagnostic model was tested on a pilot of 40 whole slide images (WSIs), good inter-rater reliability and high sensitivity and specificity of slide-level classification were obtained. Using the lowest frequency of HbH+ cells (1 in 100,000) observed in our study, we estimated that a minimum of 2.4 × 106 red cells would need to be analyzed to reduce misclassification at the slide level. The minimum required smear size was validated on 78 image sets which confirmed its validity.WSI image analysis can be utilized effectively for morphologic rare cell detection. The software can be further developed on WISs and evaluated in future clinical validation studies comparing AI-aided diagnosis with the routine diagnostic method.
A Knowledge Management Approach Supporting Model-Based Systems Engineering

Model-based Systems Engineering (MBSE) is a noval approach to support complex system development by formalizing system artifacts and development using models. Though MBSE models provide a completely structural formalisms about system development for system developers, such large of domain specific knowledge represented by models cannot be captured as what the developers expect. This leads to a big challenge when MBSE can be widely used for complex system development. In this paper, a knowledge management approach is proposed to support an intelligent question answering scenario when implementing MBSE in system lifecycle. We make use of the GOPPRR approach to support MBSE formalisms which are transformed to knowledge graph models. Then such models provide cues for intelligent question answers through reasoning. In the case study, we make use of an auto-braking system scenario to develop MBSE models and to implement the intelligent question answering. Finally, we find the availability of our approach is evaluated which the domain engineers enable to capture their domain knowledge more efficiently.
CARLA: A Convolution Accelerator With a Reconfigurable and Low-Energy Architecture

Convolutional Neural Networks (CNNs) have proven to be extremely accurate for image recognition, even outperforming human recognition capability. When deployed on battery-powered mobile devices, efficient computer architectures are required to enable fast and energy-efficient computation of costly convolution operations. Despite recent advances in hardware accelerator design for CNNs, two major problems have not yet been addressed effectively, particularly when the convolution layers have highly diverse structures: (1) minimizing energy-hungry off-chip DRAM data movements; (2) maximizing the utilization factor of processing resources to perform convolutions. This work thus proposes an energy-efficient architecture equipped with several optimized dataflows to support the structural diversity of modern CNNs. The proposed approach is evaluated on convolutional layers of VGGNet-16 and ResNet-50. Results show that the architecture achieves a Processing Element (PE) utilization factor of 98% for the majority of 3×3 and 1×1 convolutional layers, while limiting latency to 396.9 ms and 92.7 ms when performing convolutional layers of VGGNet-16 and ResNet-50, respectively. In addition, the proposed architecture benefits from the structured sparsity in ResNet-50 to reduce the latency to 42.5 ms when half of the channels are pruned.
An Improved Dummy Generation Approach for Enhancing User Location Privacy

Location-based services (LBS), which provide personalized and timely information, entail privacy concerns such as unwanted leak of current user locations to potential stalkers. Existing works have proposed dummy generation techniques by creating a cloaking region (CR) such that the user’s location is at a fixed distance from the center of CR. Hence, if the adversary somehow knows the location of the center of CR, the user’s location would be vulnerable to attack. We propose an improved dummy generation approach for facilitating improved location privacy for mobile users. Our performance study demonstrates that our proposed approach is indeed effective in improving user location privacy.
Security Threats in Cloud Computing

In today's world, data is increasing day by day. Every business has so much of sensitive data that now they need space to store it somewhere. But data is so large that it is impossible for an enterprise to maintain that data itself. Cloud computing is the one technology through which the sensitive data can be stored and maintained by the cloud service provider hosted remotely. Since, all this data which is present on the cloud is very confidential, there is huge responsibility on cloud service provider to secure this data efficiently. In this paper we will learn about cloud computing, challenges in cloud computing, security of these clouds, and the current security threats in cloud computing and proposed work ending with some conclusions and lessons learned in this work.
Adversarial Attacks Against Reinforcement Learning-Based Portfolio Management Strategy

Many researchers have incorporated deep neural networks (DNNs) with reinforcement learning (RL) in automatic trading systems.However, such methods result in complicated algorithmic trading models with several defects, especially when a DNN model is vulnerable to malicious adversarial samples.Researches have rarely focused on planning for long-term attacks against RL-based trading systems.To neutralize these attacks, researchers must consider generating imperceptible perturbations while simultaneously reducing the number of modified steps.In this research, an adversary is used to attack an RL-based trading agent.First, we propose an extension of the ensemble of the identical independent evaluators (EIIE) method, called enhanced EIIE, in which information on the best bids and asks is incorporated.Enhanced EIIE was demonstrated to produce an authoritative trading agent that yields better portfolio performance relative to that of an EIIE agent.Enhanced EIIE was then applied to the adversarial agent for the agent to learn when and how much to attack (in the form of introducing perturbations).In our experiments, our proposed adversarial attack mechanisms were > 30% more effective at reducing accumulated portfolio value relative to the conventional attack mechanisms of the fast gradient sign method (FSGM) and iterative FSGM, which are currently more commonly researched and adapted to compare and improve.
Deep Neural Networks Based on Transfer Learning Approaches to Classification of Gun and Knife Images

Most of the criminal acts are performed using criminal tools. One of the most effective ways of preventing crime is to observe and detect offensive weapons by security camera systems. Deep learning techniques can show very high-performance in observing and perceiving objects. In the current study, the performances of the pre-trained AlexNet, VGG16, and VGG19 models based on convolutional neural networks, were tested for the detection and classification of criminal tools such as guns and knives. In the study, the training process was carried out using transfer learning approaches such as Fine-tuning and Training from scratch based on deep architectures. To test the deep architectures used in the proposed study, the gun and knife datasets frequently used in the literature were collected and combined with new datasets obtained originally from search engines and videos, and then their performances were tested. In the experimental results, the VGG16 model based on fine-tuning for the two and three classes achieved the highest accuracy in detecting criminal devices with a rate of 99.73% and 99.67%, respectively. As a result, the study has observed that offensive weapons could be detected with security cameras using learned weights of deep architectures
Empirical analysis on productivity prediction and locality for use case points method

Use case points (UCP) method has been around for over two decades. Although there was a substantial criticism concerning the algebraic construction and factor assessment of UCP, it remains an efficient early size estimation method. Predicting software effort from UCP is still an ever-present challenge. The earlier version of UCP method suggested using productivity as a cost driver, where fixed or a few pre-defined productivity ratios have been widely agreed. While this approach was successful when not enough historical data is available, it is no longer acceptable because software projects are different in terms of development aspects. Therefore, it is better to understand the relationship between productivity and other UCP variables. This paper examines the impact of data locality approaches on productivity and effort prediction from multiple UCP variables. The environmental factors are used as partitioning factors to produce local homogeneous data either based on their influential levels or using clustering algorithms. Different machine learning methods, including solo and ensemble methods, are used to construct productivity and effort prediction models based on the local data. The results demonstrate that the prediction models that are created based on local data surpass models that use entire data. Also, the results show that conforming to the hypothetical assumption between productivity and environmental factors is not necessarily a requirement for the success of locality.
An effective fuzzy clustering algorithm with outlier identification feature

DKIFCM (Density Based Kernelized Intuitionistic Fuzzy C Means) is the new proposed clustering algorithm that is based on outlier identification, kernel functions, and intuitionist fuzzy approach. DKIFCM is an inspiration from Kernelized Intuitionistic Fuzzy C Means (KIFCM) algorithm and it addresses the performance issue in the presence of outliers. It first identifies outliers based on density of data and then clusters are computed accurately by mapping the data to high dimensional feature space. Performance and effectiveness of various algorithms are evaluated on synthetic 2D data sets such as Diamond data set (D10, D12, and D15), and noisy Dunn data set as well as on high dimension real-world data set such as Fisher-Iris, Wine, and Wisconsin Breast Cancer Data-set. Results of DKIFCM are compared with results of other algorithms such as Fuzzy-C-Means (FCM), Intuitionistic FCM (IFCM), Kernel-Intuitionistic FCM (KIFCM), and density-oriented FCM (DOFCM), and the performance of proposed algorithm is found to be superior even in the presence of outliers and noise. Key advantages of DKIFCM are outlier identification, robustness to noise, and accurate centroid computation.
Parallel Quadri-valent Quantum-Inspired Gravitational Search Algorithm on a heterogeneous platform for wireless sensor networks

Sensor nodes in a wireless sensor Network are assigned for different operational modes to perfume application-specific objectives. The decision to assign operational modes to nodes is a challenging problem in the presence of multiple criteria including energy-efficient, maintaining network connectivity, and fulfilling application goals. Several metaheuristic methods are introduced in the literature to address this NP-hard problem, however, these methods require further improvements in execution-time and finding the optimum solution. In this research, we propose an improved version of a metaheuristic method called Quadri-valent Quantum-Inspired Gravitational Search Algorithm (QQIGSA) to solve Quadri-valent problems by applying a Not Q-Gate and paralleling QQIGSA method on the graphics processing unit. The proposed method employs a heterogeneous platform and justifies its parameters. The experimental results show that the performance enhancement from 1.8 to 2.25 compared to the previous parallel implementations. Moreover, we achieve the speedup of 8 by using the proposed heterogeneous paralleling technique.
Fighting terrorism in Africa: Complementarity between inclusive development, military expenditure and political stability

This study examines complementarities between inclusive development, military expenditure and political stability in the fight against terrorism in 53 African countries for the period 1998–2012. Hence the policy variables employed in the study are inclusive development, military expenditure and political stability. The empirical evidence is based on Generalised Method of Moments (GMM) with forward orthogonal deviations. The paper reports three main findings. Firstly, military expenditure and inclusive development are substitutes and not complements. Secondly, it is more relevant to use political stability as a complement of inclusive development than to use inclusive development as a complement of political stability. Thirdly, it can be broadly established that military expenditure and political stability are complementary. In the light of the sequencing, complementarity and substitutability, when the three policy variables are viewed within the same framework, it is more feasible to first pursue political stability and then complement it with military expenditure and inclusive development.
Biometric keystroke barcoding: A next-gen authentication framework

Investigation of new intelligent solutions for user identification and authentication is and will be essential for enhancing the security of the alphanumeric passwords entered on touchscreen and traditional keyboards. Extraction of the keystrokes has been very beneficial given the intelligent authentication protocols operating in time-domain; while the time-domain solutions drastically lose their efficiency over time due to converging inter-key times. Realistically reflecting the habitual traits, the frequency-domain solutions, however, reveal unique biometric characteristics better, without any risk of convergence. On the contrary, the existing frequency-based frameworks don’t provide storable biometric data for further classification of the attempts. Therefore, we propose a novel barcoding framework converting habitual biometric information into storable barcodes as very low-size barcode images. The key-press times are extracted and turned into pseudo-signals exhibiting binary-train characteristics for continuous wavelet transformation (CWT). The transformed signals are primarily categorized with 4-scale scalograms by various complex frequency B-spline wavelets and subsequently superposed to create the unique barcodes. One-class support vector machines (SVM) is employed as the main classifier for training and testing the barcodes and very promising results are achieved given the lowest equal error rate (EER) of 1.83%.