Reviewerly/exHarmony huggingface dataset:
User adoption of physician's replies in an online health community: An empirical study

Abstract Online health question‐and‐answer consultation with physicians is becoming a common phenomenon. However, it is unclear how users identify the most satisfying reply. Based on the dual‐process theory of knowledge adoption, we developed a conceptual model and empirical method to study which factors influence adoption of a reply. We extracted 6 variables for argument quality (Ease of understanding, Relevance, Completeness, Objectivity, Timeliness, Structure) and 4 for source credibility (Physician's online experience, Physician's offline expertise, Hospital location, Hospital level). The empirical results indicate that both central and peripheral routes affect user's adoption of a response. Physician's offline expertise negatively affects user's adoption decision, while physician's online experience positively affects it; this effect is positively moderated by user involvement.
Detecting IoT User Behavior and Sensitive Information in Encrypted IoT-App Traffic

Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install on their smartphone or tablet to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer's cloud; we call this the "app-to-cloud way". In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g., login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g., user's location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy.
Sampling Wisely: Deep Image Embedding by Top-K Precision Optimization

Deep image embedding aims at learning a convolutional neural network (CNN) based mapping function that maps an image to a feature vector. The embedding quality is usually evaluated by the performance in image search tasks. Since very few users bother to open the second page search results, top-k precision mostly dominates the user experience and thus is one of the crucial evaluation metrics for the embedding quality. Despite being extensively studied, existing algorithms are usually based on heuristic observation without theoretical guarantee. Consequently, gradient descent direction on the training loss is mostly inconsistent with the direction of optimizing the concerned evaluation metric. This inconsistency certainly misleads the training direction and degrades the performance. In contrast to existing works, in this paper, we propose a novel deep image embedding algorithm with end-to-end optimization to top-k precision, the evaluation metric that is closely related to user experience. Specially, our loss function is constructed with wisely selected ``misplaced" images along the top k nearest neighbor decision boundary, so that the gradient descent update directly promotes the concerned metric, top-k precision. Further more, our theoretical analysis on the upper bounding and consistency properties of the proposed loss supports that minimizing our proposed loss is equivalent to maximizing top-k precision. Experiments show that our proposed algorithm outperforms all compared state-of-the-art deep image embedding algorithms on three benchmark datasets.
Real-time Serverless

Today's serverless provides "function-as-a-service" with dynamic scaling and fine-grained resource charging, enabling new cloud applications. Serverless functions are invoked as a best-effort service. We propose an extension to serverless, called real-time serverless that provides an invocation rate guarantee, a service-level objective (SLO) specified by the application, and delivered by the underlying implementation. Real-time serverless allows applications to guarantee real-time performance.
Peregrine

Database administrators (DBAs) were traditionally responsible for optimizing the on-premise database workloads. However, with the rise of cloud data services, where cloud providers offer fully managed data processing capabilities, the role of a DBA is completely missing. At the same time, optimizing query workloads is becoming increasingly important for reducing the total costs of operation and making data processing economically viable in the cloud. This paper revisits workload optimization in the context of these emerging cloud-based data services. We observe that the missing DBA in these newer data services has affected both the end users and the system developers: users have workload optimization as a major pain point while the system developers are now tasked with supporting a large base of cloud users.
Practical Cloud Workloads for Serverless FaaS

Serverless computing is gaining popularity with the Function-asa-Service (FaaS) execution model. Without incurring overheads involved in provisioning cloud instances and with high availability and scalability, serverless computing allows developers to focus on implementation of core application logic using other well-developed cloud services. By abstracting the complex resource management task, serverless computing opens new opportunities for the cloud service adoption even to non-cloud experts [2]. With the popularity, many research results have been published using the FaaS execution model. They include investigation of serverless computing opportunities [1], proposing new serverless applications, function run-time optimization, and public service comparison. Without a common test benchmark suite, authors in the previous work had evaluated proposed systems using fairly simple FaaS applications, such as micro-benchmarks that emphasize specific resources exclusively, e.g., CPU, disk I/O, and network. However, such simple workloads do not represent realistic FaaS system applications, and the evaluations might not compare proposed systems appropriately.
Cryptocurrency accepting venues, investor attention, and volatility

Using a novel dataset of cryptocurrency accepting business venues that accept cryptocurrencies as form of payments, we examine the relationship between new crypto accepting venues and crypto volatility. We argue that the number of new venues is a proxy for investor attention. We find that the number of new venues is a significant driver of crypto volatility. Moreover, venues that do not disclose their type of business as well as venues in Europe, North America and Oceania increase crypto volatility. Granger-causality, VAR estimation and a quasi-natural experiment validate our findings.
Analysis of back-face strain measurement for adhesively bonded single lap joints using strain gauge, Digital Image Correlation and finite element method

Back-face strain measurement is a widely used method for damage detection of adhesively bonded single lap joints. Often, a simple Strain Gauge is used to monitor in-situ structural adhesives bonds in the field. However, the extrinsic local placement of the Strain Gauge on the strain hot spot of the joint poses a major challenge. Furthermore, finite element method (FEM) simulations do not reproduce the adherents accurately enough due to inaccuracies in the manufacturing process. Therefore, an additional optical measurement methodology is required to precisely analyze and localize the strain hot spots on the adherent. This work deals with the most important theoretical background on the stiffness, strength and fracture behaviour of the single lap joint. Subsequently, the back-face strain along the joint part was simulated by means of FEM. The results were validated and implemented with the results of the Digital Image Correlation (DIC). The strains in the hot spot area were determined from both finite element (FE) and DIC methods. With this knowledge, the Strain Gauge is applied exactly to the location of the strain hot spot. The central result of the paper is the confirmation of the strain hot spot. It is shown that the calculated points and curves can be approximated very well.
Learning Chinese Word Embeddings from Stroke, Structure and Pinyin of Characters

Chinese word embeddings have recently attracted much attention in natural language processing (NLP). Existing researches learn Chinese word embeddings based on characters, radicals, components and stroke n-gram. Besides abovementioned features, Chinese characters also own structure and pinyin features. In this paper, we design feature substring, a super set of radicals, components and stroke n-gram with structure and pinyin information, to integrate stroke, structure and pinyin features of Chinese characters and capture the semantics of Chinese words. Based on the feature substring, we propose a novel method ssp2vec to predict the contextual words based on the feature substrings of the target words for learning Chinese word embeddings. It is based on our observation that exploiting the morphological information (stroke and structure) and the phonetic information (pinyin) is crucial for capturing the meanings of Chinese words. Meanwhile, the phonetic information (pinyin) can assist the model to distinguish Chinese words. Experimental results on word analogy, word similarity, text classification and named entity recognition tasks show that the proposed method obtains better results than state-of-the-art approaches.
Sentimental Analysis using Naive Bayes Classifier

Sentimental Analysis is mainly meant for classifying the text based on its polarity. Opinion Mining is one of the major categories in sentimental analysis. Opinion of any user in buying a product or rating a movie contributes highly to the product or movie For example: If a movie is given various levels of star rating, then the other audience who think of going for the movie might have a overview on the rating and then decide whether to prefer the movie or not, similarly the reviews of any kind of product might change the thought of any buyer in buying the product. In this project Naive Bayes classifier technique is being used for the purpose of classification. The dataset used here is the amazon product review dataset obtained from the UCI repository. The dataset consists of about 600 records and every record is being analyzed using the Naïve Bayes approach, this is a probabilistic approach and finally the result is obtained in a form of matrix. The accuracy of the proposed approach is finally calculated with the help of the confusion matrix.
Adaptive Sequentially Weighted Median Filter for Image Highly Corrupted by Impulse Noise

To tackle the difficulties in the detection and removal of impulse noise faced by the existing filters, and to further improve the denoising performance, we propose an adaptive sequentially weighted median filter for image corrupted by impulse noise.In the proposed method, a noise detector employing the 3σ principle of normal distribution and the local intensity statistics, is proposed; and a sequentially weighted median filter with a neighborhood of adaptive size, is proposed for noise removal, in which the weighted operator is derived in reference to the spatial distances from central noisy pixel, i.e., the weighting coefficients are sequentially inversely proportional to the spatial distances.The experimental results confirm that the proposed method outperforms the existing filters, excelling in the capability of noise removal, structure and edge information preservation.
Coded Computing for Distributed Machine Learning in Wireless Edge Network

In wireless mobile edge computing platforms, such as those supported by vehicular networks, location specific machine learning models can be trained by distributing computations using resources available at the edge. The computational and communication resources at the wireless edge are heterogeneous and unreliable, which can lead to straggler effects that significantly slow down recursive learning tasks, such as gradient descent. In this paper we propose a coded computation framework, which utilizes statistical knowledge of resource heterogeneity to determine optimal encoding and load balancing of training data using Random Linear codes, while avoiding an explicit step for decoding gradients. Results show that the proposed coding framework speeds up the training time for linear regression models, by up to ten times, when compared with repetition coding schemes.
Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN

This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects, regardless of their position in the scene. This problem is also known as image multi-labeling. (2) Methods: Our proposed deep learning solution is based on a light-weight pre-trained CNN called SqueezeNet. We improved the SqueezeNet architecture by resetting the last convolutional layer to free weights, replacing its activation function from a rectified linear unit (ReLU) to a LeakyReLU, and adding a BatchNormalization layer thereafter. We also replaced the activation functions at the output layer from softmax to linear functions. These adjustments make up the main contributions in this work. (3) Results: The proposed solution is tested on four image multi-labeling datasets representing different indoor environments. It has achieved results better than state-of-the-art solutions both in terms of accuracy and processing time. (4) Conclusions: The proposed deep CNN is an effective solution for predicting the presence of objects in a scene and can be successfully used as a module within BlindSys.
Distilling Channels for Efficient Deep Tracking

Deep trackers have proven success in visual tracking. Typically, these trackers employ optimally pre-trained deep networks to represent all diverse objects with multi-channel features from some fixed layers. The deep networks employed are usually trained to extract rich knowledge from massive data used in object classification and so they are capable to represent generic objects very well. However, these networks are too complex to represent a specific moving object, leading to poor generalization as well as high computational and memory costs. This paper presents a novel and general framework termed channel distillation to facilitate deep trackers. To validate the effectiveness of channel distillation, we take discriminative correlation filter (DCF) and ECO for example. We demonstrate that an integrated formulation can turn feature compression, response map generation, and model update into a unified energy minimization problem to adaptively select informative feature channels that improve the efficacy of tracking moving objects on the fly. Channel distillation can accurately extract good channels, alleviating the influence of noisy channels and generally reducing the number of channels, as well as adaptively generalizing to different channels and networks. The resulting deep tracker is accurate, fast, and has low memory requirements. Extensive experimental evaluations on popular benchmarks clearly demonstrate the effectiveness and generalizability of our framework.
Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network

Driver distraction has currently been a global issue causing the dramatic increase of road accidents and casualties. However, recognizing distracted driving action remains a challenging task in the field of computer vision, since inter-class variations between different driver action categories are quite subtle. To overcome this difficulty, in this paper, a novel deep learning based approach is proposed to extract fine-grained feature representation for image-based driver action recognition. Specifically, we improve the existing convolutional neural network from two aspects: (1) we employ multi-scale convolutional block with different receptive fields of kernel sizes to generate hierarchical feature map and adopt maximum selection unit to adaptively combine multi-scale information; (2) we incorporate an attention mechanism to learn pixel saliency and channel saliency between convolutional features so that it can guide the network to intensify local detail information and suppress global background information. For experiment, we evaluate the designed architecture on multiple driver action datasets. The quantitative experiment result shows that the proposed multi-scale attention convolutional neural network (MSA-CNN) obtains the state of the art performance in image-based driver action recognition.
Consensus Maximization Tree Search Revisited

Consensus maximization is widely used for robust fitting in computer vision. However, solving it exactly, i.e., finding the globally optimal solution, is intractable. A* tree search, which has been shown to be fixed-parameter tractable, is one of the most efficient exact methods, though it is still limited to small inputs. We make two key contributions towards improving A* tree search. First, we show that the consensus maximization tree structure used previously actually contains paths that connect nodes at both adjacent and non-adjacent levels. Crucially, paths connecting non-adjacent levels are redundant for tree search, but they were not avoided previously. We propose a new acceleration strategy that avoids such redundant paths. In the second contribution, we show that the existing branch pruning technique also deteriorates quickly with the problem dimension. We then propose a new branch pruning technique that is less dimension-sensitive to address this issue. Experiments show that both new techniques can significantly accelerate A* tree search, making it reasonably efficient on inputs that were previously out of reach. Demo code is available at https://github.com/ZhipengCai/MaxConTreeSearch.
Continual Learning by Asymmetric Loss Approximation With Single-Side Overestimation

Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suffers from the requirements of additional network components and the limited scalability to a large number of tasks. We propose a novel approach to continual learning by approximating a true loss function using an asymmetric quadratic function with one of its sides overestimated. Our algorithm is motivated by the empirical observation that the network parameter updates affect the target loss functions asymmetrically. In the proposed continual learning framework, we estimate an asymmetric loss function for the tasks considered in the past through a proper overestimation of its unobserved sides in training new tasks, while deriving the accurate model parameter for the observable sides. In contrast to existing approaches, our method is free from the side effects and achieves the state-of-the-art accuracy that is even close to the upper-bound performance on several challenging benchmark datasets.
Convolutional Networks With Channel and STIPs Attention Model for Action Recognition in Videos

With the help of convolutional neural networks (CNNs), video-based human action recognition has made significant progress. CNN features that are spatial and channel-wise can provide rich information for powerful image description. However, CNNs lack the ability to process the long-term temporal dependency of an entire video and further cannot well focus on the informative motion regions of actions. Aiming at the two problems, we propose a novel video-based action recognition framework in this paper. We first represent videos with dynamic image sequences (DISs), which effectively describe videos by modeling the local spatial-temporal dynamics and dependencies. Then a channel and spatial-temporal interest points (STIPs) attention model (CSAM) based on CNNs is proposed to focus on the discriminative channels in networks and the informative spatial motion regions of human actions. Specifically, channel attention (CA) is implemented by automatically learning channel-wise convolutional features and assigning different weights for different channels. STIPs attention (SA) is encoded by projecting the detected STIPs on frames of dynamic image sequences into the corresponding convolutional feature map space. The proposed CSAM is embedded after CNN convolutional layers to refine the feature maps, followed by global average pooling to produce effective feature representations for videos. Finally frame-level video representations are fed into an LSTM to capture the temporal dependencies and make classification. Experiments on three challenging RGB-D datasets show that our method has better performance and outperforms the state-of-the-art approaches with only depth data.
A predictive model on size of silver nanoparticles prepared by green synthesis method using hybrid artificial neural network-particle swarm optimization algorithm

The aim of this study is green synthesis of Ag nanoparticles (Ag NPs) and optimization of practical process by artificial neural network to prepare the minimum size of Ag. To decrease the issues of trapping in local minima, heuristic optimization methods, i.e., the particles swarm optimization (PSO) and modified PSO, were applied as training strategy for feed forward Neural Networks. To collect the experimental data, design of experiment with factorial D-optimal array was employed. Uv–visible, X-ray diffraction and field emission scanning electron microscopy were used to characterize the prepared samples. The results confirmed the possibility of Ag NPs preparation with the size about 4 nm at optimum condition. Based on the results of the optimal network sensetivity analysis, it was found that the feed rate, AgNO3 to opium ratio and agitation speed have the greatest impact on the particle size of the final product, respectively.
Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used.