Reviewerly/exHarmony huggingface dataset:
Creating Cybersecurity Knowledge Graphs From Malware After Action Reports

After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect or find patterns indicative of a cyber-attack. In this paper, we describe a system to extract information from AARs, aggregate the extracted information by fusing similar entities together, and represent that extracted information in a Cybersecurity Knowledge Graph (CKG). We extract entities by building a customized named entity recognizer called `Malware Entity Extractor' (MEE). We then build a neural network to predict how pairs of `malware entities' are related to each other. When we have predicted entity pairs and the relationship between them, we assert the `entity-relationship set' in a CKG. Our next step in the process is to fuse similar entities, to improve our CKG. This fusion helps represent intelligence extracted from multiple documents and reports. The fused CKG has knowledge from multiple AARs, with relationships between entities extracted from separate reports. As a result of this fusion, a security analyst can execute queries and retrieve better answers on the fused CKG, than a knowledge graph with no fusion. We also showcase various reasoning capabilities that can be leveraged by a security analyst using our fused CKG.
Breast Cancer Classification in Ultrasound Images using Transfer Learning

Computer-aided detection of malignant breast tumors in ultrasound images has been receiving growing attention. In this paper, we propose a deep learning methodology to tackle this problem. The training data, which contains several hundred images of benign and malignant cases, was used to train a deep convolutional neural network (CNN). Three training approaches are proposed: a baseline approach where the CNN architecture is trained from scratch, a transfer-learning approach where the pre-trained VGG16 CNN architecture is further trained with the ultrasound images, and a fine-tuned learning approach where the deep learning parameters are fine-tuned to overcome overfitting. The experimental results demonstrate that the fine-tuned model had the best performance (0.97 accuracy, 0.98 AUC), with pre-training on US images. Creating pre-trained models using medical imaging data would certainly improve deep learning outcomes in biomedical applications.
Regional metallogeny of Tungsten-tin-polymetallic deposits in Nanling region, South China

The Nanling region gains its reputation for abundant mineral resources, especially W, Sn, Sb, Bi, Pb, Zn, Li, Be, Ta, Nb and ion-adsorption REE within weathering crust as well as uranium. These dominant mineral resources are mainly formed in the Yanshanian period with intimate genetic relationship to Yanshanian granitoids. Spatially, tungsten and tin deposits separately occurred in the eastern and western parts of Nanling region respectively. The complicated genetic relationships between tungsten-tin polymetallic deposits and Nanling granitoids demonstrate a regularity for mineralization (we named this “metallogenic specialization”), reflected in the aspects of specialized metallogenic compositions, age, spatiality, alteration and its geological background. The metallogenic specialization (regional metallogeny) is important to discover mineralization lay the foundation. A new exploration model (named “Five Levels + Basement”) was proposed in this paper. Now, it became a prospecting guidance in Nanling region. Recently, the ongoing prospecting of large tungsten deposits in Nanling and its adjacent region using this model indicates a northward expanding for tungsten deposits in the south part of study area and westward expanding for that in the eastern part. In practice, the model also can be used in deep prospecting (e.g., deep-seated bedded or bed-like orebodies) as well as, not limited to tungsten deposits, other mineral deposits.
Indian Sign Language Gesture Recognition using Image Processing and Deep Learning

Speech impaired people use hand based gestures to communicate. Unfortunately, the vast majority of the people are not aware of the semantics of these gestures. In a attempt to bridge the same, we propose a real time hand gesture recognition system based on the data captured by the Microsoft Kinect RGB-D camera. Given that there is no one to one mapping between the pixels of the depth and the RGB camera, we used computer vision techniques like 3D contruction and affine transformation. After achieving one to one mapping, segmentation of the hand gestures was done from the background noise. Convolutional Neural Networks (CNNs) were utilised for training 36 static gestures relating to Indian Sign Language (ISL) alphabets and numbers. The model achieved an accuracy of 98.81% on training using 45,000 RGB images and 45,000 depth images. Further Convolutional LSTMs were used for training 10 ISL dynamic word gestures and an accuracy of 99.08% was obtained by training 1080 videos. The model showed accurate real time performance on prediction of ISL static gestures, leaving a scope for further research on sentence formation through gestures. The model also showed competitive adaptability to American Sign Language (ASL) gestures when the ISL models weights were transfer learned to ASL and it resulted in giving 97.71% accuracy.
SMSH: Secure Surveillance Mechanism on Smart Healthcare IoT System With Probabilistic Image Encryption

The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient's information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient's information in the IoT enabled healthcare system.
An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing

Cloud computing is a new distributed environment to provide on-demand services through the internet and has become popular due to this specific feature. By increasing the count of user requests and different criteria in applying the cloud resources, there exist challenges for managing these requests and their optimal allocation. Some of these challenges consist of considering the dynamic nature of the cloud environment, finding the values of the different criteria and the scheduling tasks by considering the user’s Quality of Service (QoS) preferences, load balancing, etc. Static load balancers distribute requests based on pre-known server capability ratios and these models do not produce optimal throughput when the server capabilities change overtime. None of the other scheduling algorithms is able to provide an adaptive approach to consider both of the load balancing and optimizing the QoS requirements. To solve this problem, this paper proposes a new adaptive approach based on a combination of the concept of the best-worst multi criteria decision-making method (BWM), and compromise ranking method (VIKOR). The VIKOR method is implemented as a decision maker to specify the task priorities. The proposed approach is validated with numerical experiments and is compared with the existing scheduling algorithms over different performance metrics. The simulation results indicate that the proposed approach improves the performance metrics like throughput, makespan, waiting time, virtual machine (VM) utilization and VM usage cost for all considered experimental scenarios in comparison with its counterparts.
Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.
Geometric calibration for LiDAR-camera system fusing 3D-2D and 3D-3D point correspondences

Calibrating the extrinsic parameters on a system of 3D Light Detection And Ranging (LiDAR) and the monocular camera is a challenging task, because accurate 3D-2D or 3D-3D point correspondences are hard to establish from the sparse LiDAR point clouds in the calibration procedure. In this paper, we propose a geometric calibration method for estimating the extrinsic parameters of the LiDAR-camera system. In this method, a novel combination of planar boards with chessboard patterns and auxiliary calibration objects are proposed. The planar chessboard provides 3D-2D and 3D-3D point correspondences. Auxiliary calibration objects provide extra constraints for stable calibration results. After that, a novel geometric optimization framework is proposed to utilize these point correspondences, thus leading calibration results robust to LiDAR sensor noise. Besides, we contribute an automatic approach to extract point clouds of calibration objects. In the experiments, our method has a superior performance over state-of-the-art calibration methods. Furthermore, we verify our method by computing depth map and improvements can also be found. These results demonstrate that our method performance on the LiDAR-camera system is applicable for future advanced visual applications.
Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models

A deep learning (DL) model is inherently imprecise. To address this problem, existing techniques retrain a DL model over a larger training dataset or with the help of fault injected models or using the insight of failing test cases in a DL model. In this paper, we present Apricot, a novel weight-adaptation approach to fixing DL models iteratively. Our key observation is that if the deep learning architecture of a DL model is trained over many different subsets of the original training dataset, the weights in the resultant reduced DL model (rDLM) can provide insights on the adjustment direction and magnitude of the weights in the original DL model to handle the test cases that the original DL model misclassifies. Apricot generates a set of such reduced DL models from the original DL model. In each iteration, for each failing test case experienced by the input DL model (iDLM), Apricot adjusts each weight of this iDLM toward the average weight of these rDLMs correctly classifying the test case and/or away from that of these rDLMs misclassifying the same test case, followed by training the weight-adjusted iDLM over the original training dataset to generate a new iDLM for the next iteration. The experiment using five state-of-the-art DL models shows that Apricot can increase the test accuracy of these models by 0.87%-1.55% with an average of 1.08%. The experiment also reveals the complementary nature of these rDLMs in Apricot.
Graph-regularized least squares regression for multi-view subspace clustering

Many works have proven that the consistency and differences in multi-view subspace clustering make the clustering results better than the single-view clustering. Therefore, this paper studies the multi-view clustering problem, which aims to divide data points into several groups using multiple features. However, existing multi-view clustering methods fail to capturing the grouping effect and local geometrical structure of the multiple features. In order to solve these problems, this paper proposes a novel multi-view subspace clustering model called graph-regularized least squares regression (GLSR), which uses not only the least squares regression instead of the nuclear norm to generate grouping effect, but also the manifold constraint to preserve the local geometrical structure of multiple features. Specifically, the proposed GLSR method adopts the least squares regression to learn the globally consensus information shared by multiple views and the column-sparsity norm to measure the residual information. Under the alternating direction method of multipliers framework, an effective method is developed by iteratively update all variables. Numerical studies on eight real databases demonstrate the effectiveness and superior performance of the proposed GLSR over eleven state-of-the-art methods.
Improving solar forecasting using Deep Learning and Portfolio Theory integration

Solar energy has been consolidated as one of the main renewable energy sources capable of contributing to supply global energy demand. However, the solar resource has intermittent feature in electricity production, making it difficult to manage the electrical system. Hence, we propose the application of Deep Learning (DL), one of the emerging themes in the field of Artificial Intelligence (AI), as a solar predictor. To attest its capacity, the technique is compared with other consolidated solar forecasting strategies such as Multilayer Perceptron, Radial Base Function and Support Vector Regression. Additionally, integration of AI methods in a new adaptive topology based on the Portfolio Theory (PT) is proposed hereby to improve solar forecasts. PT takes advantage of diversified forecast assets: when one of the assets shows prediction errors, these are offset by another asset. After testing with data from Spain and Brazil, results show that the Mean Absolute Percentage Error (MAPE) for predictions using DL is 6.89% and for the proposed integration (called PrevPT) is 5.36% concerning data from Spain. For the data from Brazil, MAPE for predictions using DL is 6.08% and 4.52% for PrevPT. In both cases, DL and PrevPT results are better than the other techniques being used.
Privacy-Preserving Auto-Driving: A GAN-Based Approach to Protect Vehicular Camera Data

The autonomous driving (auto-driving) technology has been promoted significantly by the rapid advances in computer vision and deep neural networks. Auto-driving vehicles, nowadays, are fully equipped with numerous sensors such as cameras, geo-sensors, and radar sensors, to capture real-time data inside the vehicles and outside surroundings. Meanwhile, the captured data contains lots of private information about vehicles, drivers and passengers and thus faces a high risk of privacy breaches. Especially, side-channel information can be mined from camera data to identify vehicles' locations and even trajectories, raising serious privacy issues. Unfortunately, the issue, how to resist location-inference attack for camera data in auto-driving, has never been addressed in literature. In this paper, we intend to fill this blank by developing a GAN-based image-toimage translation method named Auto-Driving GAN (ADGAN). Through performance comparisons between ADGAN and the state-of-the-art, the superiority of ADGAN can be validated - offering an effective tradeoff between recognition utility and privacy protection for camera data.
Reaching Broader Audiences With Data Visualization

The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this article, we discuss four research topics-personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization-that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible.
Deep Generalized Max Pooling

Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pooled and thus activations of different locations are pooled together. In contrast, we propose Deep Generalized Max Pooling that balances the contribution of all activations of a spatially coherent region by re-weighting all descriptors so that the impact of frequent and rare ones is equalized. We show that this layer is superior to both average and max pooling on the classification of Latin medieval manuscripts (CLAMM'16, CLAMM'17), as well as writer identification (Historical-WI'17).
Taxonomical classification of barriers for scaling agile methods in global software development environment using fuzzy analytic hierarchy process

Increasingly, software development organizations are scaling agile practices in the global software development (GSD) environment in order to meet the requirements of the quickly changing and regularly developing business environment. The main objectives of this study are to investigate the key barriers and develop a prioritization-based taxonomy of the barriers for scaling agile development in the GSD environment. Total twenty-two barriers were extracted from the available literature and categorized into five categories, i.e. “human resources management”, ‘coordination”, “technology”, “project management”, and “software methodology”. In the next phase, the identified barriers and their categories were further validated using the questionnaire survey. In the final phase, fuzzy-AHP method, a multi-criterion decision making (MCDM) technique, was applied to prioritize and taxonomy of identified barriers and their related categories was designed. The contribution of this study is not limited to investigate the barriers, but it also provides the roadmap to tackle the issues related to the scaling agile methods in the GSD environment.
Estimating the Optimum Tilt Angles for South-Facing Surfaces in Palestine

The optimum tilt angle of solar panels or collectors is crucial when determining parameters that affect the performance of those panels. A mathematical model is used for determining the optimum tilt angle and for calculating the solar radiation on a south-facing surface on a daily, monthly, seasonal, semi-annual, and annual basis. Photovoltaic Geographical Information System (PVGIS) and Photovoltaic Software (PVWatts) is developed by the NREL (US National Renewable Energy Laboratory) are also used to calculate the optimum monthly, seasonal, semi-annual, and annual tilt angles and to compare these results with the results obtained from the mathematical model. The results are very similar. PVGIS and PVWatts are used to estimate the solar radiation on south-facing surfaces with different tilt angles. A case study of a mono-crystalline module with 5 kWP of peak power is used to find out the amount of increased energy (gains) obtained by adjusting the Photovoltaic (PV) tilt angles based on yearly, semi-annual, seasonal, and monthly tilt angles. The results show that monthly adjustments of the solar panels in the main Palestinian cities can generate about 17% more solar energy than the case of solar panels fixed on a horizontal surface. Seasonal and semi-annual adjustments can generate about 15% more energy (i.e., it is worth changing the solar panels 12 times a year (monthly) or at least 2 times a year (semi-annually). The yearly optimum tilt angle for most Palestinian cities is about 29°, which yields an increase of about 10% energy gain compared to a solar panel fixed on a horizontal surface.
Is Big Digital Data Different? Towards a New Archaeological Paradigm

Archaeological data is always incomplete, frequently unreliable, often replete with unknown unknowns, but we nevertheless make the best of what we have and use it to build our theories and extrapolations about past events. Is there any reason to think that digital data alter this already complicated relationship with archaeological data? How does the shift to an infinitely more flexible, fluid digital medium change the character of our data and our use of it? The introduction of Big Data is frequently said to herald a new epistemological paradigm, but what are the implications of this for archaeology? As we are increasingly subject to algorithmic agency, how can we best manage this new data regime? This paper seeks to unpick the nature of digital data and its use within a Big Data environment as a prerequisite to rational and appropriate digital data analysis in archaeology, and proposes a means towards developing a more reflexive, contextual approach to Big Data.
Deep Reinforcement Learning for Market Making in Corporate Bonds: Beating the Curse of Dimensionality

In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for bonds to asset managers. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. The existing models, mostly inspired by the Avellaneda-Stoikov model, describe the complex optimization problem faced by market makers: proposing bid and ask prices for making money out of the difference between them while mitigating the market risk associated with holding inventory. While most of the models only tackle one-asset market making, they can often be generalized to a multi-asset framework. However, the problem of solving the equations characterizing the optimal bid and ask quotes numerically is seldom tackled in the literature, especially in high dimension. In this paper, we propose a numerical method for approximating the optimal bid and ask quotes over a large universe of bonds in a model à la Avellaneda–Stoikov. As classical finite difference methods cannot be used in high dimension, we present a discrete-time method inspired by reinforcement learning techniques, namely, a model-based deep actor-critic algorithm.