Reviewerly/exHarmony huggingface dataset:
Attack Hypothesis Generation

In recent years, the perpetrators of cyber-attacks have been playing a dynamic cat and mouse game with cybersecurity analysts who try to trace the attack and reconstruct the attack steps. While analysts rely on alert correlations, machine learning, and advanced visualizations in order to come up with sound attack hypotheses, they primarily rely on their knowledge and experience. Cyber Threat Intelligence (CTI) on past similar attacks may help with attack reconstruction by providing a deeper understanding of the tools and attack patterns used by attackers. In this paper, we present the Attack Hypothesis Generator (AHG) which takes advantage of a knowledge graph derived from threat intelligence in order to generate hypotheses regarding attacks that may be present in an organizational network. Based on five recommendation algorithms we have developed and preliminary analysis provided by a security analyst, AHG provides an attack hypothesis comprised of yet unobserved attack patterns and tools presumed to have been used by the attacker. The proposed algorithms can help security analysts by improving attack reconstruction and proposing new directions for investigation. Experiments show that when implemented with the MITRE ATT&CK knowledge graph, our algorithms can significantly increase the accuracy of the analyst's preliminary analysis.
Pterygium Tissues Segmentation using Densely Connected DeepLab

One of the eye conditions that are prevalent among the fishermen is pterygium. It happens when abnormal fibrovascular tissues grow towards the pupil region. It can cause vision problems if the tissues fully obstruct light from coming into the retina. Thus, the disease must be detected as early as possible so that the mitigation steps can be administered before it is too late. However, most of the fishermen are not aware of the pterygium existence. Therefore, an online screening tool should be developed to help them doing screening by themselves just by using a mobile phone. Taking inspiration from this need, an automatic pterygium tissue segmentation is proposed using a deep learning approach. The goal is to extract the right size of the pterygium infected tissues so that the users are aware of their disease severity level. The proposed method uses DeepLab V1 and DeepLab V2 as the foundation, in which densely connected layers are integrated to improve the segmentation performance. A set of four feedforward layers is added to the existing DeepLab architectures with the aim of reducing the zero gradient diminishing problem. Both DeepLab V1 and V2 have recorded performance improvement in terms of mean accuracy (acc) and mean intersection over union (IoU). Densely connected DeepLab V2 obtains the best testing results with acc and IoU of 0.9202 and 0.8381, respectively. Hence, the improved segmentation method is suitable to be implemented for the mobile pterygium screening system. The segmentation performance can be further enhanced by adding more parallel layers or convolution-based down-pooling operation.
A Discrete-Time Neurodynamic Approach to Sparsity-Constrained Nonnegative Matrix Factorization

Sparsity is a desirable property in many nonnegative matrix factorization (NMF) applications. Although some level of sparseness of NMF solutions can be achieved by using regularization, the resulting sparsity depends highly on the regularization parameter to be valued in an ad hoc way. In this letter we formulate sparse NMF as a mixed-integer optimization problem with sparsity as binary constraints. A discrete-time projection neural network is developed for solving the formulated problem. Sufficient conditions for its stability and convergence are analytically characterized by using Lyapunov's method. Experimental results on sparse feature extraction are discussed to substantiate the superiority of this approach to extracting highly sparse features.
Secure Cloud Storage With Data Dynamics Using Secure Network Coding Techniques

In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients' data at any point of time. Secure cloud storage protocols enable a client to check integrity of outsourced data. In this work, we explore the possibility of constructing a secure cloud storage for dynamic data by leveraging the algorithms involved in secure network coding. We show that some of the secure network coding schemes can be used to construct efficient secure cloud storage protocols for dynamic data, and we construct such a protocol (DSCS I) based on a secure network coding protocol. To the best of our knowledge, DSCS I is the first secure cloud storage protocol for dynamic data constructed using secure network coding techniques which is secure in the standard model. Although generic dynamic data support arbitrary insertions, deletions and modifications, append-only data find numerous applications in the real world. We construct another secure cloud storage protocol (DSCS II) specific to append-only data -- that overcomes some limitations of DSCS I. Finally, we provide prototype implementations for DSCS I and DSCS II in order to evaluate their performance.
SC-PSNET: A deep neural network for automatic P- and S-phase detection and arrival-time picker using 1C recordings

It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of [Formula: see text] ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.
Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning

We envision that in the near future, humanoid robots would share home space and assist us in our daily and routine activities through object manipulations. One of the fundamental technologies that needs to be developed for the robots is to enable them to detect objects and recognize them for effective manipulations and take real-time decisions involving the same. In this paper, we describe a novel architecture that enables multiple low-compute NAO robots to perform real-time detection, recognition and localization of objects in its camera view and take programmable actions based on the detected objects. The proposed algorithm for object detection and localization is an empirical modification of YOLOv3 along with a distributed architecture to operate multiple robots on a central “inference engine”, based on indoor experiments in multiple scenarios, with a smaller weight size and lesser computational requirements. YOLOv3 was chosen after a comparative study of bounding box algorithms was performed with an objective to choose one that strikes the perfect balance among information retention, low inference time and high accuracy for real-time object detection and localization. Quantization of the weights and re-adjusting filter sizes and layer arrangements for convolutions improved the inference time for low-resolution images from the robot's camera feed. The architecture also comprises of an effective end-to-end pipeline to feed the real-time frames from the camera feed to the neural net and use its results for guiding the robot with customizable actions corresponding to the detected class labels.
Framework for Underwater Image Enhancement

In this paper, we propose a framework for enhancement of underwater images. Underwater images suffer from low-contrast, blur and non-uniform illumination resulting in poor quality images. Red color in the atmospheric light is absorbed early due to its shorter wavelength, whereas the colors like blue and green penetrate deeper into water due to larger wavelength. As a result the underwater images appear bluish or greenish in color. Towards this, we propose a framework for enhancement of underwater images using color balance and Laplacian and Gaussian fusion pyramid. Here we aim to balance the color distribution of the underwater image in LAB color space, to remove the bluish-green tint caused due to atmospheric light attenuation. Focus is on sharpening the underwater image to enhance the edges distorted during the process of color balance. We emphasize on fusion of outputs obtained after color balancing and edge sharpening. We demonstrate the performance of the proposed framework using qualitative evaluation metrics and show, the results obtained through the proposed fusion framework outperforms the state of art enhancement methods. The proposed approach can also be used for enhancement of other kind of images like foggy or hazy on ground images.
Approximate to Be Great: Communication Efficient and Privacy-Preserving Large-Scale Distributed Deep Learning in Internet of Things

The increasing Internet-of-Things (IoT) devices have produced large volumes of data. A deep learning technique is widely used to analyze the potential value of these data due to its unprecedented performance in both the academic and industrial communities. However, the data generated from the IoT devices are distributed among different users. Directly combining these data to a central server will cause privacy leakage, especially for personal sensitive data. Rather than centralized training by getting access to all these raw data, an alternative is to collaboratively learn a model in a distributed manner. However, there exist two main challenges in a distributed learning setting. The first one is how to preserve the privacy of users. The second one is to reduce the communication burden (e.g., mobile users have limited bandwidth) due to high-frequent data exchange. To address these two challenges, we design a communication efficient and privacy-preserving framework to enable different participants to distributively learn a model with a privacy protection guarantee. In particular, we develop a differentially private approximate mechanism for the distributed deep learning. In addition, we design a new gradient sparsification method to, at the first time, reduce both upload and download communication costs. The performance of the proposed framework is tested under different neural network structures for different data sets including, image classification and mobile sensor data. The experimental results demonstrate that we can reduce the communication up to only 2% compared to the full gradients exchange and achieve up to 16% accuracy increase compared to the previous works.
The growth of open access publishing in geochemistry

In this communication, we look at Open Access (OA) publishing practices in geochemistry. We examine a list of 56 journals and assess whether Article Processing Charges (APCs) and Journal Impact Factors (JIFs) appear to influence publication or not. More than 40% of articles in 2018–2019 were published OA, and about 70% of that portion in fully OA journals. These had a mean APC of US$ 900, whereas the remaining were published in hybrid journals with a higher mean APC of more than $US 1,800. A moderate and positive correlation is found between the number of OA articles published in hybrids journals and their JIF, whereas there is a stronger positive relationship between the number of OA articles published in fully OA journals and the APC. For OA articles published in hybrid journals, it seems that the proportion of OA articles tends to increase in journals with higher JIF.
Deep Conformal Prediction for Robust Models

Deep networks, like some other learning models, can associate high trust to unreliable predictions. Making these models robust and reliable is therefore essential, especially for critical decisions. This experimental paper shows that the conformal prediction approach brings a convincing solution to this challenge. Conformal prediction consists in predicting a set of classes covering the real class with a user-defined frequency. In the case of atypical examples, the conformal prediction will predict the empty set. Experiments show the good behavior of the conformal approach, especially when the data is noisy.
Comparative Analysis of Human Face Recognition by Traditional Methods and Deep Learning in Real-Time Environment

In general, the field of face recognition has gain a lot of attention for researchers. As the crime increases day by day, the forensic surveillance is required to construct a facial attribute, a visual likeness of a victim's face. Many researchers have contributed their work in this field by using various methods. Real time face detection is still a tedious task and achieve a good performance of the system. In this paper we will compare two models of face recognition. First is traditional method and second is deep learning method on real time dataset. After comparing these methods with different algorithms, will find the accuracy of the models on same dataset and check whether it predict a correct face or not.
Video Anomaly Detection using Inflated 3D Convolution Network

The infrastructure of every city is getting smarter day by day. This infrastructure provides us with vital information. There is a demand for a real-time system that can help identify crimes as soon as they happen, which is now possible with the rise in popularity of AI. The data captured through the surveillance system can contain anomalous and normal videos. We propose to build an anomalous event detection system by using weakly labelled training videos, and after the detection of such an activity, an alarm would be raised. A Deep residual learning framework called I3D-Resnet- 50 is used for feature extraction. This network is pre-trained on the Kinetics video action dataset. Our dataset consists of 13 distinct anomalies. Anomalous events are Robbery, Assault, Shooting, Burglary, Stealing, Arrest, Fighting, Shoplifting, Arson, Explosion, Vandalism, Abuse, Road Accident. The introduced method for video anomaly detection attains significant improvement in the results both in terms of accuracy and recall.
Context awareness system in the use phase of a smart mobility platform: A vision system for a light-weight approach

Due to the development of image processing over the last years, the application of vision systems and image recognition technologies has been increasingly applied in many fields. In this paper, Machine Learning and Principal Component Analysis are utilized for context awareness at a smart mobility platform. The restrictions concern a rather dynamic environment and a relevantly small dataset of images. The aim is to determine the position of the camera itself and the appearance of obstacles within a predefined area. To examine the performance of the proposed approach Hierarchical Cluster Analysis has been applied to group similar observations into clusters.
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization

The majority of current object detectors lack context: class predictions are made independently from other detections. We propose to incorporate context in object detection by post-processing the output of an arbitrary detector to rescore the confidences of its detections. Rescoring is done by conditioning on contextual information from the entire set of detections: their confidences, predicted classes, and positions. We show that AP can be improved by simply reassigning the detection confidence values such that true positives that survive longer (i.e., those with the correct class and large IoU) are scored higher than false positives or detections with small IoU. In this setting, we use a bidirectional RNN with attention for contextual rescoring and introduce a training target that uses the IoU with ground truth to maximize AP for the given set of detections. The fact that our approach does not require access to visual features makes it computationally inexpensive and agnostic to the detection architecture. In spite of this simplicity, our model consistently improves AP over strong pre-trained baselines (Cascade R-CNN and Faster R-CNN with several backbones), particularly by reducing the confidence of duplicate detections (a learned form of non-maximum suppression) and removing out-of-context objects by conditioning on the confidences, classes, positions, and sizes of the co-occurrent detections. Code is available at https://github.com/LourencoVazPato/seeing-without-looking/.
Defending Blockchain Forking Attack by Delaying MTC Confirmation

In terms of security, blockchain technology faces the potential risks of enormous attacks, among which forking attack is the most common one. Because of the decentralized structure of a blockchain, the trust degree directly relies on the length of the blockchain, which makes the longest chain on the current network the most trusted chain (MTC). The forking attack is the kind of hazard behavior that aims to replace the MTC by launching an alternative chain to gain benefits. Blockchain plays a vital role in multiple applications, such as cryptocurrency, and hence such forking attack leads to huge financial loss if it succeeds. In this paper, we propose a novel MTC confirmation mechanism to defend the forking attack. First, we theoretically analyze the success probability distribution of the forking attack and propose an estimation model of the attack's expected profit. Then, we propose an arbitration mechanism to enforce branches from a fork to have competition. With the arbitration mechanism, we delay the confirmation of the MTC until the end of the competition. We set up an end threshold as the termination condition of the competition process to help the MTC become the winner. Finally, we define two dynamic difficulty factors and apply them for changing the difficulty of mining to reduce the value of the end threshold, which can improve the performance of the mechanism. The experiments demonstrate our proposed MTC confirmation mechanism can enhance the blockchain security by enforcing the expected profit of an attack to be negative. In the meantime, the mechanism balances well in both effectiveness and efficiency.
Dr. Agent: Clinical predictive model via mimicked second opinions

Abstract Objective Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seeking second opinions by training 2 agents with different focuses: the primary agent studies the most recent visit of the patient to learn the current health status, and then the second-opinion agent considers the entire patient history to obtain a more global view. Materials and Methods Our approach Dr. Agent augments recurrent neural networks with 2 policy gradient agents. Moreover, Dr. Agent is customized with various patient demographics information and learns a dynamic skip connection to focus on the relevant information over time. We trained Dr. Agent to perform 4 clinical prediction tasks on the publicly available MIMIC-III (Medical Information Mart for Intensive Care) database: (1) in-hospital mortality prediction, (2) acute care phenotype classification, (3) physiologic decompensation prediction, and (4) forecasting length of stay. We compared the performance of Dr. Agent against 4 baseline clinical predictive models. Results Dr. Agent outperforms baseline clinical prediction models across all 4 tasks in terms of all metrics. Compared with the best baseline model, Dr. Agent achieves up to 15% higher area under the precision-recall curve on different tasks. Conclusions Dr. Agent can comprehensively model the long-term dependencies of patients’ health status while considering patients’ demographics using 2 agents, and therefore achieves better prediction performance on different clinical prediction tasks.
Sentiment analysis in Nepali: Exploring machine learning and lexicon-based approaches

In recent times, sentiment analysis research has achieved tremendous impetus on English textual data, however, a very less amount of research has been focused on Nepali textual data. This work is focused towards Nepali textual data. We have explored machine learning approaches and proposed a lexicon-based approach using linguistic features and lexical resources to perform sentiment analysis for tweets written in Nepali language. This lexicon-based approach, first pre-process the tweet, locate the opinion-oriented features and then compute the sentiment polarity of tweet. We have investigated both conventional machine learning models (Multinomial Naïve Bayes (NB), Decision Tree, Support Vector Machine (SVM) and logistic regression) and deep learning models (Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM) for sentiment analysis of Nepali text. These machine learning models and lexicon-based approach have been evaluated on tweet dataset related to Nepal Earthquake 2015 and Nepal blockade 2015. Lexicon based approach has outperformed than conventional machine learning models. Deep learning models have outperformed than conventional machine learning models and lexicon-based approach. We have also created Nepali SentiWordNet and Nepali SenticNet sentiment lexicon from existing English language resources as by-product.
Ensemble model output statistics as a probabilistic site-adaptation tool for solar irradiance: A revisit

Previously, in the study by Yang [J. Renewable Sustainable Energy 12, 016102 (2020)], probabilistic site adaptation was demonstrated for the first time. This technique leverages the ensemble model output statistics (EMOS), post-processes the empirical distribution formed by m gridded solar irradiance estimates from different satellite-derived and reanalysis databases, and thus obtains a final predictive distribution of the site-adapted irradiance, which has a normal density. That said, three questions were later thought of: (1) can post-processing the clear-sky index, instead of irradiance, lead to better site-adaptation performance; (2) will the parameter estimation strategy substantially affect model performance; and (3) how does the normality assumption hold in reality? In this paper, I revisit the probabilistic site-adaptation problem and aim to address these questions. In summary, it is found that (1) building EMOS models on irradiance and on the clear-sky index leads to similar model performance; (2) the choice of minimizing the continuous ranked probability score and the ignorance score needs to be tailored to the problem at hand; and (3) using a truncated normal predictive distribution in EMOS does not seem to possess an advantage over using a normal predictive distribution.