Reviewerly/exHarmony huggingface dataset:
Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

Image dehazing plays a pivotal role in numerous computer vision applications such as object recognition, surveillance systems, and security systems, where it can be considered as an introductory stage. Recently, many proposed learning-based works address this significant task; however, most of them neglect the atmospheric light estimation and fail to produce accurate transmission maps. To address such a problem, in this paper, we propose a two-stage dehazing system. The first stage presents an accurate atmospheric light algorithm labeled “A-Est” that employs hazy image blurriness and quadtree decomposition. Te second stage represents a cascaded multi-scale CNN model called CMT n e t that consists of two subnetworks, one for calculating rough transmission maps (CMCNN t r ) and the other for its refinement (CMCNN t ). Each subnetwork is composed of three-layer D-units (D indicates dense). Experimental analysis and comparisons with state-of-the-art dehazing methods revealed that the proposed system can estimate AL and t efficiently and accurately by achieving high-quality dehazing results and outperforms state-of-the-art comparative methods according to SSIM and MSE values, where our proposed achieves the best scores of both (91% average SSIM and 0.068 average MSE).
Sentiment Analysis for Roman Urdu

The majority of online comments/opinions are written in text-free format. Sentiment Analysis can be used as a measure to express the polarity (positive/negative) of comments/opinions. These comments/ opinions can be in different languages i.e. English, Urdu, Roman Urdu, Hindi, Arabic etc. Mostly, people have worked on the sentiment analysis of the English language. Very limited research work has been done in Urdu or Roman Urdu languages. Whereas, Hindi/Urdu is the third largest language in the world. In this paper, we focus on the sentiment analysis of comments/opinions in Roman Urdu. There is no publicly available Roman Urdu public opinion dataset. We prepare a dataset by taking comments/opinions of people in Roman Urdu from different websites. Three supervised machine learning algorithms namely NB (Naive Bayes), LRSGD (Logistic Regression with Stochastic Gradient Descent) and SVM (Support Vector Machine) have been applied on this dataset. From results of experiments, it can be concluded that SVM performs better than NB and LRSGD in terms of accuracy. In case of SVM, an accuracy of 87.22% is achieved.
Sampling-Based Optimal Motion Planning With Smart Exploration and Exploitation

In this article, a novel sampling framework with smart exploration and exploitation (sampling-SEE) is proposed for sampling-based optimal motion planning problem. Exploration represents uniform sampling in the configuration space to find a path with better performance than the previous one. Exploitation denotes biased sampling in the surrounding region of the current path to rapidly improve this path. Sampling-SEE fully utilizes the current path cost and location information to decrease the space for exploration and increase the capability for exploitation. Additionally, a biasing ratio is introduced to dynamically adjust the sampling proportion for the two procedures. Sampling-SEE can be easily integrated into the single or batch sampling-based optimal anytime planners to improve the sampling efficiency and accelerate the convergence. Meanwhile, the property of asymptotic optimality remains unchanged. Simulation experiments are carried out in various environments to verify the effectiveness of the proposed framework.
<b>NeuralSens</b>: Sensitivity Analysis of Neural Networks

Neural networks are important tools for data-intensive analysis and are commonly applied to model non-linear relationships between dependent and independent variables. However, neural networks are usually seen as "black boxes" that offer minimal information about how the input variables are used to predict the response in a fitted model. This article describes the \pkg{NeuralSens} package that can be used to perform sensitivity analysis of neural networks using the partial derivatives method. Functions in the package can be used to obtain the sensitivities of the output with respect to the input variables, evaluate variable importance based on sensitivity measures and characterize relationships between input and output variables. Methods to calculate sensitivities are provided for objects from common neural network packages in \proglang{R}, including \pkg{neuralnet}, \pkg{nnet}, \pkg{RSNNS}, \pkg{h2o}, \pkg{neural}, \pkg{forecast} and \pkg{caret}. The article presents an overview of the techniques for obtaining information from neural network models, a theoretical foundation of how are calculated the partial derivatives of the output with respect to the inputs of a multi-layer perceptron model, a description of the package structure and functions, and applied examples to compare \pkg{NeuralSens} functions with analogous functions from other available \proglang{R} packages.
A Real-Time Naive Bayes Classifier Accelerator on FPGA

In this paper, we propose a real-time hardware naive Bayes classifier (NBC) which is implemented on field programmable gate array (FPGA).We first use logarithm transformation based look-up table and float-to-fixed point process to simplify the calculations in naive Bayes classification algorithm.The methods clear up the multiplication and division operations of floating points completely.Based the simplified algorithm, we design our hardware architecture which includes both training and inference part.A novel format of logarithm look-up table with very limited items and a shifter in it are working together to calculate the logarithm value of any number.There are several processing element (PE) arrays in the accelerator where each PE in an array is running in parallel, which speed up the classification process remarkably.The experiments prove that the proposed accelerator has much better real-time efficiency than the general processor, some hardware Bayes classifiers and convolutional neural network (CNN) accelerators.It outperforms the NBC and semi-NBC accelerators and costs far less resources on chip than many CNN accelerators.Its utilization of LUT, FF and BRAM is only 10%, 0.05% and 2% of CNN accelerators on average.The experimental results over five datasets of different magnitudes show the accelerator has almost no loss of classification accuracy comparing with ARM Cortex-A9 processor.Their deviation of the classification accuracy is only 0.39% on average.What's more, it improves the performance of the training phase and the inference phase about 7.9+e4 and 8.3+e4 on average, respectively.
A Parallel Recommender System Using a Collaborative Filtering Algorithm with Correntropy for Social Networks

Recently, the extraction of valid information from big data has witnessed a growing interest. Nowadays, in social networks, large parts of websites collect user profiles to provide some valuable information through personalized recommendation. Among the available recommendation algorithms, collaborative filtering (CF) is one of the most popular algorithms due to its simple framework. However, in some practices, the computational time of CF may be unsatisfactory. Meanwhile, in some cases there are noises in data, i.e., some data are invalid, it also has a great impact on algorithm performance. To speed up the time it takes to make recommendation and tackle the noise issue more effectively, we developed a novel parallel recommender system based on CF with correntropy. Instead of traditional measures used in recommendation algorithms, the correntropy was employed to compute the similarity of two items or users to achieve insensitive performance to outliers. Moreover, to reduce the computational cost, we employed the Spark framework to facilitate parallel computing. The experiments on three datasets consisting data collected from actual social networks were conducted and the experimental results showed that for social networks application, the proposed system could effectively improve the computational time and achieve satisfactory performance though invalid data existed.
A general-purpose distributed pattern mining system

Abstract This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern mining problems by reducing the enumeration search space. Thus, it derives the relevant patterns by studying the different correlation among the transactions. It first decomposes the set of transactions into several clusters of different sizes, and then explores heterogeneous architectures, including MapReduce, single CPU, and multi CPU, based on the densities of each subset of transactions. To evaluate the DT-DPM framework, extensive experiments were carried out by solving five pattern mining problems (FIM: Frequent Itemset Mining, WIM: Weighted Itemset Mining, UIM: Uncertain Itemset Mining, HUIM: High Utility Itemset Mining, and SPM: Sequential Pattern Mining). Experimental results reveal that by using DT-DPM, the scalability of the pattern mining algorithms was improved on large databases. Results also reveal that DT-DPM outperforms the baseline parallel pattern mining algorithms on big databases.
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizing the quality of recommendation for each user. We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. In this framework, a collection of recommenders is trained with data from all users, on top of which a model selector is trained via meta-learning to select the best single model for each user with the user-specific historical data. We conduct extensive experiments on two public datasets and a real-world production dataset, demonstrating that our proposed framework achieves improvements over single model baselines and sample-level model selector in terms of AUC and LogLoss. In particular, the improvements may lead to huge profit gain when deployed in online recommender systems.
Multiple social platforms reveal actionable signals for software vulnerability awareness: A study of GitHub, Twitter and Reddit

The awareness about software vulnerabilities is crucial to ensure effective cybersecurity practices, the development of high-quality software, and, ultimately, national security. This awareness can be better understood by studying the spread, structure and evolution of software vulnerability discussions across online communities. This work is the first to evaluate and contrast how discussions about software vulnerabilities spread on three social platforms-Twitter, GitHub, and Reddit. Moreover, we measure how user-level e.g., bot or not, and content-level characteristics e.g., vulnerability severity, post subjectivity, targeted operating systems as well as social network topology influence the rate of vulnerability discussion spread. To lay the groundwork, we present a novel fundamental framework for measuring information spread in multiple social platforms that identifies spread mechanisms and observables, units of information, and groups of measurements. We then contrast topologies for three social networks and analyze the effect of the network structure on the way discussions about vulnerabilities spread. We measure the scale and speed of the discussion spread to understand how far and how wide they go, how many users participate, and the duration of their spread. To demonstrate the awareness of more impactful vulnerabilities, a subset of our analysis focuses on vulnerabilities targeted during recent major cyber-attacks and those exploited by advanced persistent threat groups. One of our major findings is that most discussions start on GitHub not only before Twitter and Reddit, but even before a vulnerability is officially published. The severity of a vulnerability contributes to how much it spreads, especially on Twitter. Highly severe vulnerabilities have significantly deeper, broader and more viral discussion threads. When analyzing vulnerabilities in software products we found that different flavors of Linux received the highest discussion volume. We also observe that Twitter discussions started by humans have larger size, breadth, depth, adoption rate, lifetime, and structural virality compared to those started by bots. On Reddit, discussion threads of positive posts are larger, wider, and deeper than negative or neutral posts. We also found that all three networks have high modularity that encourages spread. However, the spread on GitHub is different from other networks, because GitHub is more dense, has stronger community structure and assortativity that enhances information diffusion. We anticipate the results of our analysis to not only increase the understanding of software vulnerability awareness but also inform the existing and new analytical frameworks for simulating information spread e.g., disinformation across multiple social environments online.
Shorter Non-interactive Zero-Knowledge Arguments and ZAPs for Algebraic Languages

We put forth a new framework for building pairing-based non-interactive zero-knowledge ( $$\mathsf {NIZK}$$ ) arguments for a wide class of algebraic languages, which are an extension of linear languages, containing disjunctions of linear languages and more. Our approach differs from the Groth-Sahai methodology, in that we rely on pairings to compile a $$\varSigma $$ -protocol into a $$\mathsf {NIZK}$$ . Our framework enjoys a number of interesting features: Our proof system relies on a new (static, falsifiable) assumption over pairing groups which generalizes the standard kernel Diffie-Hellman assumption in a natural way and holds in the generic group model (GGM) and in the algebraic group model (AGM). Replacing Groth-Sahai $$\textsf {NIZKs}$$ with our new proof system allows to improve several important cryptographic primitives. In particular, we obtain the shortest tightly-secure structure-preserving signature scheme (which are a core component in anonymous credentials), the shortest tightly-secure quasi-adaptive $$\mathsf {NIZK}$$ with unbounded simulation soundness (which in turns implies the shortest tightly-mCCA-secure cryptosystem), and shorter ring signatures.
Natural language modeling with syntactic structure dependency

In natural language, the relationship among the constituents of a sentence is usually tree-like: words, phrases, and clauses constitute a sentence hierarchically, and the dependency between different constituents induces the syntactic structure. Such a complex tree-like structure is vital for understanding natural languages. However, recurrent neural networks (RNNs) model languages sequentially and fail to encode a hierarchical syntactic dependency comprehensively, therefore causing the networks to underperform on comprehension-based tasks. In this paper, we propose a novel neural language model, called relative syntactic distance LSTM (RSD-LSTM), to capture the syntactic structure dependency dynamically. RSD-LSTM employs a convolutional neural network to compute the relative syntactic distance between sentences to represent the degree of dependency between words and modifies the gating mechanism of LSTM through the relative syntactic distance. Furthermore, we add a direct connection between hidden states to fuse high-level and low-level syntactic features. We conducted extensive experiments on language modeling. The results suggest that RSD-LSTM achieves improvements of 1.82 and 2.03 in perplexity compared with current top methods on the Penn Treebank and WikiText-2 datasets, respectively. Moreover, we conducted experiments on a machine translation application task. Experimental results of this task also show significant improvements of RSD-LSTM compared with baseline models.
Using Feature Selection for Local Causal Structure Learning

Local causal structure learning aims to discover and distinguish the direct causes and direct effects of a target variable. However, the state-of-the-art local causal structure learning algorithms need to perform an exhaustive subset search within the currently selected variables for PC (i.e., parents and children) discovery. In this article, we propose an efficient local causal structure learning algorithm around a target variable, called LCS-FS (Local Causal Structure learning by Feature Selection). First, to construct the local causal skeleton of the target, we employ feature selection for finding PC without searching for conditioning sets to speed up PC discovery, leading to improve the skeleton construction efficiency. Second, to orient edges in this local causal skeleton, we propose an efficient method to find separating sets from the subsets of PC for identifying V-structures. With the integration of feature selection and the new way of finding separating sets, LCS-FS recursively finds the spouses of Markov blankets in local causal skeleton for edge orientations, until the direct causes and direct effects of the target are distinguished. The experiments on five benchmark Bayesian networks with the number of variables from 35 to 801 validate that our algorithm achieves higher efficiency and better accuracy than the state-of-the-art local causal structure learning algorithms.
RDF graph summarization for first-sight structure discovery

To help users get familiar with large RDF graphs, RDF summarization techniques can be used. In this work, we study quotient summaries of RDF graphs, that is: graph summaries derived from a notion of equivalence among RDF graph nodes. We make the following contributions: (i) four novel summaries which are often small and easy-to-comprehend, in the style of E–R diagrams; (ii) efficient (amortized linear-time) algorithms for computing these summaries either from scratch, or incrementally, reflecting additions to the graph; (iii) the first formal study of the interplay between RDF graph saturation in the presence of an RDFS ontology, and summarization; we provide a sufficient condition for a highly efficient shortcut method to build the quotient summary of a graph without saturating it; (iv) formal results establishing the shortcut conditions for some of our summaries and others from the literature; (v) experimental validations of our claim within a tool available online.
All In One Network for Driver Attention Monitoring

Nowadays, driver drowsiness and driver distraction is considered as a major risk for fatal road accidents around the world. As a result, driver monitoring identifying is emerging as an essential function of automotive safety systems. Its basic features include head pose, gaze direction, yawning and eye state analysis. However, existing work has investigated algorithms to detect these tasks separately and was usually conducted under laboratory environments. To address this problem, we propose a multi-task learning CNN framework which simultaneously solve these tasks. The network is implemented by sharing common features and parameters of highly related tasks. Moreover, we propose Dual-Loss Block to decompose the pose estimation task into pose classification and coarse-to-fine regression and Objectcentric Aware Block to reduce orientation estimation errors. Thus, with such novel designs, our model not only achieves SOA results but also reduces the complexity of integrating into automotive safety systems. It runs at 10 fps on vehicle embedded systems which marks a momentous step for this field. More importantly, to facilitate other researchers, we publish our dataset FDUDrivers which contains 20000 images of 100 different drivers and covers various real driving environments. FDUDrivers might be the first comprehensive dataset regarding driver attention monitoring.
Predicting hypoglycemic drugs of type 2 diabetes based on weighted rank support vector machine

Diabetes has become a disease that seriously endangers people’s health, then how to control the content of glycemic is an important issue. Since the treatment scheme of patient is usually a combination of multiple hypoglycemic drugs, multi-label learning is an effective method to solve this problem. By analyzing the type 2 diabetes data set including 2443 diabetics provided by the Chinese People’s Liberation Army General Hospital, we find that the defined daily dose system (DDDs) of drugs is an imbalanced problem, traditional multi-label methods easily leads to poor prediction results. In order to overcome the shortcoming, a weighted rank support vector machine (WRank-SVM) is proposed in this paper. We firstly define the weight of each label and then give each sample different weight according to relevant–irrelevant label pair. This method ensures that the prediction results on drugs with higher DDDs are as accurate as possible. Compared with the other six popular multi-label methods, our WRank-SVM can effectively predict the schemes for hypoglycemic drugs of type 2 diabetes. Meanwhile, receiver operating characteristic (ROC) curve is employed to statistically show the effectiveness of the model. Finally, the correlation between labels and features is further analyzed, and 13 important features are selected to improve the average precision of our proposed algorithm.
Irony Detection in a Multilingual Context

This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system. We employ both feature-based models and neural architectures using monolingual word representation. We compare the performance of these systems with state-of-the-art systems to identify their capabilities. We show that these monolingual models trained separately on different languages using multilingual word representation or text-based features can open the door to irony detection in languages that lack of annotated data for irony.
Coupled Real-Synthetic Domain Adaptation for Real-World Deep Depth Enhancement

Advances in depth sensing technologies have allowed simultaneous acquisition of both color and depth data under different environments. However, most depth sensors have lower resolution than that of the associated color channels and such a mismatch can affect applications that require accurate depth recovery. Existing depth enhancement methods use simplistic noise models and cannot generalize well under real-world conditions. In this paper, a coupled real-synthetic domain adaptation method is proposed, which enables domain transfer between high-quality depth simulators and real depth camera information for super-resolution depth recovery. The method first enables the realistic degradation from synthetic images, and then enhances degraded depth data to high quality with a color-guided sub-network. The key advantage of the work is that it generalizes well to real-world datasets without further training or fine-tuning. Detailed quantitative and qualitative results are presented, and it is demonstrated that the proposed method achieves improved performance compared to previous methods fine-tuned on the specific datasets.
The problems with contact-tracing apps

As countries look to ease restrictions and manage second waves of cases, apps are appealing but fraught with issues, reports Adam Vaughan.