Reviewerly/exHarmony huggingface dataset:
Geospatial-Temporal Convolutional Neural Network for Video-Based Precipitation Intensity Recognition

In this work, we propose a new framework, called Geospatial-temporal Convolutional Neural Network (GT-CNN), and construct the video-based geospatial-temporal precipitation dataset from the surveillance cameras of the eight weather stations (sampling points) to recognize the precipitation intensity. GT-CNN has three key modules: (1) Geospatial module, (2) Temporal module, (3) Fusion module. In the geospatial module, we extract the precipitation information from each sampling point simultaneously, and that is used to construct the geospatial relationships using LSTM between various sampling points. In the temporal module, we take 3D convolution to grab the precipitation features with time information, considering a series of precipitation images for each sampling point. Finally, we generate the fusion module to fuse the geospatial and temporal features. We evaluate our framework with three metrics and compare GT-CNN with the state-of-the-art methods using the self-collected dataset. Experimental results demonstrated that our approach surpasses state-of-the-art methods concerning various metrics.
An efficient method for scanned images by using color-correction and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e732" altimg="si262.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>gradient minimization

To improve the quality of scanned image captured by cameras, we propose a novel and efficient method for image processing by using color-correction and L0 gradient minimization. Our method is divided into two steps. To derive a colorimetric mapping between digital RGB signals and real image values, we use a polynomial model by considering the interrelations among the standard color spaces. A L0 gradient minimization is used to remove the image noises. Based on the half-quadratic splitting method, an iterative algorithm for our proposed method is developed. The iterative algorithm is easy to implement with the optimal complexity O(NlogN). Our method is particularly beneficial to correct image color and remove image noises. Various tests are presented to demonstrate the robustness and efficiency of our method.
Parasitized Cell Recognition Using AlexNet Pre-trained Model

Malaria is globally known as one of the most prevalent diseases that kill thousands of people every year. Plasmodium parasites are the product of malaria disease that infects the red blood cells of humans. These parasites are transmitted by a female mosquito class that is known as anopheles. The diagnostic process of malaria involves isolation and manual counts in microscopic bloodstreams of parasitized cells by medical practitioners. In large-scale screening, Malaria diagnostic accuracy is largely affected because of resource unavailability. In this paper, we proposed an intelligent diagnosis system using advanced techniques based on a deep learning algorithm precisely AlexNet pre-trained model. As the bright side of machine learning techniques, CNN has greatly led to numerous image recognition activities. This method shows encouraging results. In terms of accuracy, the proposed model achieved 97.33% in the validation phase. Therefore, in some places where there are no medical services, this approach can be widely used for diagnosing parasitized cells.
Fusion Coding of 3D Real and Virtual Scenes Information for Augmented Reality-Based Holographic Stereogram

In this paper, an optical field coding method for the fusion of real and virtual scenes is proposed to implement an augmented reality (AR)-based holographic stereogram. The occlusion relationship between the real and virtual scenes is analyzed, and a fusion strategy based on instance segmentation and depth determination is proposed. A real three-dimensional (3D) scene sampling system is built, and the foreground contour of the sampled perspective image is extracted by the Mask R-CNN instance segmentation algorithm. The virtual 3D scene is rendered by a computer to obtain the virtual sampled images as well as their depth maps. According to the occlusion relation of the fusion scenes, the pseudo-depth map of the real scene is derived, and the fusion coding of 3D real and virtual scenes information is implemented by the depth information comparison. The optical experiment indicates that AR-based holographic stereogram fabricated by our coding method can reconstruct real and virtual fused 3D scenes with correct occlusion and depth cues on full parallax.
Machine Learning and its role in Diverse Business Systems

Machine learning as a branch of Artificial Intelligence is growing at a very rapid pace. It has shown significant benefits across a number of different industry verticals in helping them improve their productivity and making them less reliant on humans. The success and the growth of any industry depends on the manageability of massive data, using the data for predictions and deriving business value, automating the processes without the need of human intervention, provide satisfactory services to their clients and the security of client's information. Machine learning is a method that provides a way to transform the processes that leads to growth by using the statistical methods. The focus of this paper is to provide an overview of machine learning and highlight the various areas where machine learning is implemented by the business organizations and industries.
Violence Detection from Video under 2D Spatio-Temporal Representations

Action recognition in videos, especially for violence detection, is now a hot topic in computer vision. The interest of this task is related to the multiplication of videos from surveillance cameras or live television content producing complex $2D+t$ data. State-of-the-art methods rely on end-to-end learning from 3D neural network approaches that should be trained with a large amount of data to obtain discriminating features. To face these limitations, we present in this article a method to classify videos for violence recognition purpose, by using a classical 2D convolutional neural network (CNN). The strategy of the method is two-fold: (1) we start by building several 2D spatio-temporal representations from an input video, (2) the new representations are considered to feed the CNN to the train/test process. The classification decision of the video is carried out by aggregating the individual decisions from its different 2D spatio-temporal representations. An experimental study on public datasets containing violent videos highlights the interest of the presented method.
ATRIA: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-DRAM CNN Processing

With the rapidly growing use of Convolutional Neural Networks (CNNs) in real-world applications related to machine learning and Artificial Intelligence (Al), several hardware accelerator designs for CNN inference and training have been proposed recently. In this paper, we present ATRIA, a novel bit-pArallel sTochastic aRithmetic based In-DRAM Accelerator for energy-efficient and high-speed inference of CNNs. ATRIA employs light-weight modifications in DRAM cell arrays to implement bit-parallel stochastic arithmetic based acceleration of multiply-accumulate (MAC) operations inside DRAM. ATRIA significantly improves the latency, throughput, and efficiency of processing CNN inferences by performing 16 MAC operations in only five consecutive memory operation cycles. We mapped the inference tasks of four benchmark CNNs on ATRIA to compare its performance with five state-of-the-art in-DRAM CNN accelerators from prior work. The results of our analysis show that ATRIA exhibits only 3.5% drop in CNN inference accuracy and still achieves improvements of up to 3.2× in frames-per-second (FPS) and up to 10× in efficiency (FPS/W/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), compared to the best-performing in-DRAM accelerator from prior work.
Classify and Localize Threat Items in X-Ray Imagery With Multiple Attention Mechanism and High-Resolution and High-Semantic Features

No doubt incorporating deep learning in security public transportation is an attractive and challenging research direction. In this article, we incorporate multiple attention mechanisms with high-resolution and high-semantic features into neural networks to further classify and localize threat items automatically. We propose our efficient model located in different network layers. To be specific, our model is involved in three modules called the spatial attention module (SAM), the channel attention module (CAM), and the high-resolution and high-semantic module (HRHSM). For shallow and deep layers, we adapt SAM and CAM to mine the long-range spatial and channel information of the network, respectively. With regard to the last few layers with different sizes, we leverage HRHSM to fuse their features so that the output contains high-resolution and high-semantic features. Due to the generalizability, our model can be broadly applied to many commonly used networks. Experimental results on the SIXray dataset that is consisted of six classes of threat item in X-ray images, CIFAR-10, and CIFAR-100 datasets demonstrate that our modules can improve the performance of networks in image classification and object localization without ground-truth bounding boxes.
The Stress as Adversarial Factor for Cyber Decision Making

There are several factors that make cyber operations stressful, which include their complexity, unpredictability, and a continuum of decisions involving high risk and fast cost-benefit reasoning. These operations are subject to the reception of a large number of events to which the operator must learn (feedback) and respond appropriately in a timely manner, presenting particular cyber stressors able to trigger combat exhaustion battle fatigue. Their consequences will vary and evolve according to changes in the operational context, which makes them difficult to prevent, detect and mitigate. Among others, these attenuate the perception of a lack of self-efficacy, reduces the cyber decision-maker ability of distinguishing ally, neutral and hostile assets; or tend to wrongly perceive the decision cost (effort, time, self-protection, etc.) as much higher than the expected benefits. With the motivation of facilitating the understanding of the impact of the combat and operational stress at cyber operations, this paper discusses the related recent insights for cognitive dominance at decision making on the cyberspace.
The Connection of IoT to Big Data–Hadoop Ecosystem in a Digital Age

The massive amount of data gathered by physical devices in the modern IoT world carries hidden valuable information. Extracting these gems has become a focused task for data scientists and researchers around the globe. Meanwhile, since its appearance in early 2000s, the Hadoop ecosystem has set up a solid foundation for big data processing, directing tech experts to a prevalent stream. While one could take advantages of the various tools, Hadoop provides to conjure up quick fixes for specific real-life matters; there is a lack of common methods presented in literature to deal with sets of alike problems. This creates a confused scenario for researchers and impedes the advancement of solution-designers in the IoT-big data field. In this paper, we have conducted a thorough review of the aspects related to handling IoT's big data in the Hadoop ecosystem, including data storage, data processing, and data analytics. We also remark some significant works introduced in previous studies and suggest directions for future research.
Research on the Improvement of MTCNN ALGORITHM: Face Recognition with Mask

During the 2019-nCoV epidemic, in order to effectively prevent the spread of the virus, people generally wore masks when entering public places, rendering traditional facial recognition technology ineffective. This paper constructs a dataset of face images with masks, and proposes a face recognition algorithm for masks based on deep learning. Applying the TensorFlow framework and proposing an improved MTCNN algorithm to cluster the effective feature regions of the face; using the FaceNet model shortens the time of face detection and improves the efficiency of face recognition. The test results show that the improved model has an average accuracy of 91% in recognition of faces wearing masks, and an average recall rate of 92%. Compared with the unimproved algorithm, the candidate frame of the improved algorithm focuses on important feature information to make it accurate. The rate increased by an average of 3%.
Pseudo-feasible solutions in evolutionary bilevel optimization: Test problems and performance assessment

This work presents a study about a special class of infeasible solutions called here as pseudo-feasible solutions in bilevel optimization. This work is focused on determining how such solutions can affect the performance of an evolutionary algorithm. After its formal definition, and based on theoretical results, two conditions to detect and deal with them are proposed. Moreover, a novel and scalable set of test problems with characterized pseudo-feasible solutions is introduced. Furthermore, an algorithm designed to solve bilevel optimization problems (BOP) is adapted with the above mentioned conditions and tested in already known test problems and also in the new testbed so as to analyze its performance when compared with state-of-the-art evolutionary approaches for BOPs. The obtained results suggest that the presence of pseudo-feasible solutions can be considered as a source of difficulty in this type of optimization problems, since their presence may lead to incorrect comparisons among algorithms.
Novel Modular Adder Based on Thermometer Coding for Residue Number Systems Applications

Residue Number Systems (RNS) was an efficient substitute to Positional Number Systems. Now-a-days RNS used in different fields in technology like Communications, Digital Signal Processing (DSP), Digital Image Processing (DIP), Cryptography, Network Security, Internet of Things (IoT), Embedded Systems (ES), etc., because it provides an efficient computational system in terms of speed, power, energy, area, etc. Modular Adder is an important element in RNS systems, like forward converters, reverse converters, modulo channels, etc. One of RNS applications is low power embedded systems where the dynamic range is less or moderate, Thermometer Coding (TC) is used. In this paper, a new Thermometer Coding based modular adder was proposed. This design has been simulated using NC Launch – Encounter tool in Cadence.
PAN: Personalized Attention Network For Outfit Recommendation

Recent years have witnessed the dramatic development of e-fashion industry, it becomes essential to build an intelligent fashion recommender system. Most of existing works on fashion recommendation focus on modeling the general compatibility while ignoring the user preferences. In this paper, we present a Personalized Attention Network (PAN) for fashion recommendation. The key component of PAN includes a user encoder, an item encoder and a preference predictor. To modeling users’ diverse interests, we develop an attention network to incorporate the learnt user representation into the item encoder component. More specifically, the attention module consists of a sequential user-aware channel-level and a spatial-level sub-module. Moreover, a novel ranking an user-specific loss, is proposed to capture the interest of different users on the same outfit. To make the training more effective and efficient, a novel user-aware online hard negative mining strategy is proposed. Extensive experiments on Polyvore-U dataset demonstrate the excellence of the proposed system and the effectiveness of different modules.
Fusion based approach for Underwater Image Enhancement

Underwater images are affected by factors such as haze, insufficient light, high concentration of various impurities which leads to a blue/green tinge, and blurriness. Majority of image enhancement techniques have been developed for atmospheric images but comparatively, very few techniques have been proposed for underwater image enhancement. This paper proposes a fusion algorithm which can enhance underwater images shot under various environmental conditions. This method uses Multi-Scale Retinex with Color Restoration (MSRCR) to obtain an image with improved color retention and better dynamic range. Then, the image blur is removed by using Dark Channel Prior (DCP). The results show that the proposed method gives 10% better Structural Similarity Index (SSIM) for the fusion-based algorithm.
A Hybrid Large Neighborhood Search for Solving the Unmanned Aerial Vehicle Routing Problem with Multi-depot

The Unmanned aerial vehicles (UAVs) are widely used for inspection tasks both in the military and civilian areas in which the route planning problem could be critical. The Unmanned Aerial Vehicle Routing Problem with Multi-depot (MDUAVRP) is a combination and extension of the Unmanned Aerial Vehicle Route Planning (UAVRP) and the Multi-depot Vehicle Routing Problem (MDVRP), which determines a set of the route visited by a fleet of UAV to perform a set of tasks. The objective of the MDUAVRP is to minimize the total flight cost. A novel hybrid heuristic approach is proposed aiming at this problem. Our proposal combines Adaptive Large Neighborhood Search (ALNS) and Variable Neighborhood Descent (VND) algorithm. The experimental results verify the effectiveness of the proposed algorithm.
Data Visualization Analysis of Knowledge Graph Application

In recent years, the application of knowledge graph has gradually become an important field of computer research. In this paper, a visual data analysis tool is used to conduct data analysis on the literature samples. The analysis focuses on solving the following problems :(1) Knowledge Graphic provides a selection mechanism for academic research and corporate practice in the application of artificial intelligence;(2) The development process, technical framework and co-citation network of application fields of knowledge graph were analyzed.
Formal Modelling of the Impact of Cyber Attacks on Railway Safety

Modern railway signaling extensively relies on wireless communication technologies for efficient operation. The communication infrastructures that they rely on are increasingly based on standardized protocols and are shared with other users. As a result, it has an increased attack surface and is more likely to become the target of cyber attacks that can result in loss of availability and, in the worst case, in safety incidents. While formal modeling of safety properties has a well-established methodology in the railway domain, the consideration of security vulnerabilities and the related threats lacks a framework that would allow a formal treatment. In this paper, we develop a modeling framework for the analysis of the potential of security vulnerabilities to jeopardize safety in communications-based train control for railway signaling, focusing on the recently introduced moving block system. We propose a refinement-based approach enabling a structured and rigorous analysis of the impact of security on system safety.
Digital Competencies Of Public Administration Employees Related To Cybersecurity

This paper presents an analysis of the second part of the results of a survey on digital competencies related to security conducted in 2020. The survey involved representatives of public administration and students on the Bachelor degree study programme of "Information Technology in Industry" -and on the Master degree study programme of "Computer and Software Engineering" -, research area 5.3 "Communication and computer equipment" at the Technical University of Sofia.
Zero-Shot Embedding via Regularization-Based Recollection and Residual Familiarity Processes

The goal of zero-shot learning (ZSL) is to transfer knowledge learned from seen classes during training to unseen classes for testing, with the help of auxiliary information, such as attributes and descriptions. Most of the existing methods view ZSL as a label-embedding problem, in which class and image representations are embedded to a common space. However, many methods either show a bias toward seen classes caused by the projection domain-shift problem, or sacrifice the performance of seen classes to generalize to unseen ones. In this article, we present an embedding approach for ZSL, which is motivated by human recognition memory, namely, recollection and familiarity (R&F). We propose a decoder to regularize the nonlinear mapping between the semantic space and the visual space, which represents the reasonable recollection process, and use a residual block to refine the recognition ability for seen classes, which indicates the familiarity process. R&F can generalize well to unseen classes, while retaining the discriminative ability for the seen classes. Extensive experiments are conducted on Animals with Attribute (AwA1), Animals with Attributes 2 (AwA2), Attribute Pascal&Yahoo (aPY), SUN Attribute (SUN), Caltech-UCSD-Birds 200-2011 (CUB), and ImageNet databases. As qualitative and quantitative results show, the proposed approach outperforms state-of-the-art embedding-based methods by a large margin and significantly alleviates the projection domain-shift problem.
How Enhancing Gender Inclusion Affects Inequality: Thresholds of Complementary Policies for Sustainable Development

This study investigates how enhancing gender inclusion affects inequality in 42 African countries for the period 2004-2014. The empirical evidence is based on the Generalized Method of Moments. Three inequality indicators are used, namely, the: Gini coefficient, Atkinson index, and Palma ratio. The two gender inclusion measurements used include female labour force participation and female employment. The following main findings are established. There are positive net effects on inequality from the enhancement of gender inclusion dynamics. An extended threshold analysis is used to assess critical masses at which further increasing gender inclusion enhances inequality. The established thresholds are: (i) 55.555 “employment to population ratio, 15+, female (%)”for the nexus with the Gini coefficient. (ii) 50 “labor force participation rate, female (% of female population ages 15+)” and between 50 to 55 “employment to population ratio, 15+, female (%)”, for the Atkinson index. (iii) 61.87 “labor force participation rate, female (% of female population ages 15+)” for the Palma ratio.These established thresholds are worthwhile for sustainable development because, beyond the critical masses, policy makers should complement the gender inclusion policy with other measures designed to reduce income inequality. Some complementary measures that can be taken on board beyond the established thresholds could focus on enhancing, inter alia: information and communication technology, infrastructural development; financial inclusion and inclusive education.
Active learning from label proportions via pSVM

Learning from label proportions (LLP), in which the training data is divided into different bags and only the proportions of samples belonging to certain categories in each bag are known, has attracted widespread interest in many application scenarios. However, the existing researches ignore the importance of part instances in which the labels can be easily obtained. In this paper, we propose a novel method called active-pSVM, which incorporates active learning into the large margin framework to solve LLP problem. In detail, we first label the most valuable instances that are selected by QUIRE or uncertainty sampling query strategy. Then, the obtained labeled instances and the given unlabeled data are simultaneously used to construct the prediction model under the large margin framework. Extensive experiments on benchmark datasets show that the proposed active-pSVM outperforms the state-of-the-art approaches for solving LLP problem, i.e. InvCal, alter-∝SVM and LLP-NPSVM.