Reviewerly/exHarmony huggingface dataset:
Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier's predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors.
Looking for a Deal?

Whereas social visual attention has been examined in computer-mediated (e.g., shared screen) or video-mediated (e.g., FaceTime) interaction, it has yet to be studied in mixed-media interfaces that combine video of the conversant along with other UI elements. We analyzed eye gaze of 37 dyads (74 participants) who were tasked with negotiating the price of a new car (as a buyer and seller) using mixed-media video conferencing under competitive or cooperative negotiation instructions (experimental manipulation). We used multidimensional recurrence quantification analysis to extract spatio-temporal patterns corresponding to mutual gaze (individuals look at each other), joint attention (individuals focus on the same elements of the interface), and gaze aversion (an individual looks at their partner, who is looking elsewhere). Our results indicated that joint attention predicted the sum of points attained by the buyer and seller (i.e., the joint score). In contrast, gaze aversion was associated with faster time to complete the negotiation, but with a lower joint score. Unexpectedly, mutual gaze was highly infrequent and unrelated to the negotiation outcomes and none of the gaze patterns predicted subjective perceptions of the negotiation. There were also no effects of gender composition or negotiation condition on the gaze patterns or negotiation outcomes. Our results suggest that social visual attention may operate differently in mixed-media collaborative interfaces than in face-to-face interaction. As mixed-media collaborative interfaces gain prominence, our work can be leveraged to inform the design of gaze-sensitive user interfaces that support remote negotiations among other tasks.
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks

Neural have achieved remarkable performance in computer vision, however, they are vulnerable to adversarial examples. Adversarial examples are inputs that have been carefully perturbed to fool classifier networks, while appearing unchanged to humans. Based on prior works on detecting adversaries, we propose a structured methodology of augmenting a deep neural network (DNN) with a detector subnetwork. We use adversarial noise sensitivity (ANS), a novel metric for measuring the adversarial gradient contribution of different intermediate layers of a network. Based on the ANS value, we append a detector to the most sensitive layer. In prior works, more complex detectors were added to a DNN, increasing the inference computational cost of the model. In contrast, our structured and strategic addition of a detector to a DNN reduces the complexity of the model while making the overall network adversarially resilient. Through comprehensive white-box and black-box experiments on MNIST, CIFAR-10, and CIFAR-100, we show that our method improves state-of-the-art detector robustness against adversarial examples. Furthermore, we validate the energy efficiency of our proposed adversarial detection methodology through an extensive energy analysis on various hardware scalable CMOS accelerator platforms. We also demonstrate the effects of quantization on our detector-appended networks.
What would it take for us to abandon Construction Grammar?

Abstract One of the hallmarks of scientific theories is their falsifiability, i.e. the fact that they make predictions that can objectively be proven wrong. Thus, it is paramount that researchers, including linguists, are able to state what kind of evidence would lead them to abandon their scientific theory. Yet, researchers just like all other human beings are susceptible to confirmation bias, i.e. the fact that they only seek evidence that supports their existing views. In this squib, I will raise the question whether Construction Grammar can become a falsifiable theory.
The Effect of Feature Characteristics on the Performance of Feature Location Techniques

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature Location (FL)</i> is a core software maintenance activity that aims to locate observable functionalities in the source code. Given its key role in software change, a vast array of Feature Location Techniques (FLTs) have been proposed but, as more and more FLTs are introduced, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">selection of an appropriate FLT</i> is an increasingly difficult problem. One consideration is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">characteristics of the features</i> being sought. For example, in the code associated with the feature, programmers may have named identifiers consistently, and with meaningful naming conventions, or not, and this may impact on the suitability of different FLTs. The suggestion that such characteristics matter has implicit support in the literature: An analysis of existing FLT empirical studies reveals that the system under study can often have a stronger impact on FLT performance than differing FLTs themselves. To understand this interaction between feature characteristics and FLTs better, this paper proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a suite of feature-characteristic metrics</i> that are postulated to control FLTs’ performance, holistically across FLTs and impacting on individual FLTs to different degrees. To evaluate the suite, a controlled experiment is performed, using 878 features, to probe the relationship between the metrics and the performance of four FTL techniques: three commonly-used techniques and one state-of-the-art technique. The evaluation is performed using four commonly used evaluation measures and extended by employing 41 other established source-code metrics as extraneous variables. Results of the empirical evaluation suggest that the feature-metric suite presented impacts FLT performance holistically, and impacts different FLTs to different degrees. Thus, this paper moves towards the more standard selection of appropriate FLTs, with respect to the prominent feature characteristics in the software systems under study, and more rigorous consideration of the features selected to compare FLTs.
New fault detection and fault-tolerant scheme for Doppler velocity logger outage in ocean navigation systems

Abstract The inertial navigation system/Doppler velocity logger (INS/DVL) plays an important role in ocean navigation. Any DVL malfunction poses serious risks to navigation. A precise detection system is required to detect the initial moments of DVL signal malfunctions; moreover, with loss of DVL, a fault-tolerant scheme (FTS) is necessary for DVL signal reconstruction. In this paper, an evolutionary knowledge-based method, namely improved evolutionary TS-fuzzy (I-eTS), is adopted to build an artificial intelligence (AI)-based pseudo DVL to deal with long-term outage of DVL. By employing Gaussian process regression (GPR) models for fault detection, a new FTS is constructed. To verify the effectiveness of the new fault detection and fault tolerance system, navigation data is gathered by a test setup and algorithms are performed in the laboratory. In the tests, it is demonstrated that the proposed FTS leads to rapid detection of both gradual and abrupt faults, which leads to less interaction between fault detection and FTS.
Graph-CAT: Graph Co-Attention Networks via local and global attribute augmentations

Graph neural networks have achieved tremendous success in semi-supervised node classification. In this paper, we firstly analyse the propagation strategies in two milestone methods, Graph Convolutional Network (GCN) and Graph Attention Network (GAT), to reveal their underlying philosophies. According to our analysis, the propagations in GAT can be interpreted as learnable and asymmetric local attribute augmentations, while that of GCN can be interpreted as fixed and symmetric local attribute smoothing. Unfortunately, the local attribute augmentations in GAT is not adequate in certain circumstances, because the nodes tend to possess similar attributes in local neighbourhoods. With a toy experiment, we manage to demonstrate the necessity to incorporate global information. Therefore, we propose a novel Graph Co-ATtention Network (Graph-CAT), which performs both the local and global attribute augmentations based on two different yet complementary attention schemes. Extensive experiments in both the transductive and inductive tasks demonstrate the superiority of our Graph-CAT compared to the state-of-the-art methods.
Effects of Random Seeds on the Accuracy of Convolutional Neural Networks

The domain of Artificial Neural Networks is growing very fast and new research ideas and results are published constantly. The advent of big data brought more complex network architectures and with that complexity, the training time often runs into multiple days. In such scenarios, a significant number of published papers only report the result on the basis of a single run. We explore the possibility whether such a reported result is robust enough and we investigate whether the variations in the result are significant. In this paper, we explore the effects of random seeds and different optimizers on the accuracy of different network architectures.
A Secured Supply Chain Network for Route Optimization and Product Traceability Using Blockchain in Internet of Things

The conventional method of implementing supply chain management suffers several limitations like cost, unpredictability, complicated environment, and wide-open access to sensitive information. To defeat these issues, the supply chains should be progressively more intelligent. To build a large-scale smart infrastructure of physical objects, products, and all the other supply chain-related processes, supply chain management can be integrated with the Internet of things (IoT). Open interrelation communication opens several threats in the supply chain environment. In the proposed work, we construct a supply chain network (SCN) secured with blockchain to find the optimum route of a product from a manufacturer to customer and to trace back a product to its origin. A genetic algorithm-based scheme is used to find the optimum route for a product and approach to trace back the origin of a product is proposed, which is especially beneficial in cases like a product turning out to be infected and the need to track the rest of the products of that batch. Simulation results are compared using two different consensuses for verification of transactions: PoW and proof of elapsed time. Graphical results show that proof of elapsed time outperforms PoW in terms of cost, processing power, and transaction verify per second.
An Evaluation of Serverless Data Processing Frameworks

Serverless computing is a promising cloud execution model that significantly simplifies cloud users' operational concerns by offering features such as auto-scaling and a pay-as-you-go cost model. Consequently, serverless systems promise to provide an excellent fit for ad-hoc data processing. Unsurprisingly, numerous serverless systems/frameworks for data processing emerged recently from research and industry. However, systems researchers, decision-makers, and data analysts are unaware of how these serverless systems compare to each other.
Novel View Synthesis via Depth-guided Skip Connections

We introduce a principled approach for synthesizing new views of a scene given a single source image. Previous methods for novel view synthesis can be divided into image-based rendering methods (e.g., flow prediction) or pixel generation methods. Flow predictions enable the target view to re-use pixels directly, but can easily lead to distorted results. Directly regressing pixels can produce structurally consistent results but generally suffer from the lack of low-level details. In this paper, we utilize an encoder-decoder architecture to regress pixels of a target view. In order to maintain details, we couple the decoder aligned feature maps with skip connections, where the alignment is guided by predicted depth map of the target view. Our experimental results show that our method does not suffer from distortions and successfully preserves texture details with aligned skip connections.
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task

In this paper, we propose an approach to improve few-shot classification performance using a composite rotation based auxiliary task. Few-shot classification methods aim to produce neural networks that perform well for classes with a large number of training samples and classes with less number of training samples. They employ techniques to enable the network to produce highly discriminative features that are also very generic. Generally, the better the quality and generic-nature of the features produced by the network, the better is the performance of the network on few-shot learning. Our approach aims to train networks to produce such features by using a self-supervised auxiliary task. Our proposed composite rotation based auxiliary task performs rotation at two levels, i.e., rotation of patches inside the image (inner rotation) and rotation of the whole image (outer rotation) and assigns one out of 16 rotation classes to the modified image. We then simultaneously train for the composite rotation prediction task along with the original classification task, which forces the network to learn high-quality generic features that help improve the few-shot classification performance. We experimentally show that our approach performs better than existing few-shot learning methods on multiple benchmark datasets.
Self-Guided Actor-Critic: Reinforcement Learning from Adaptive Expert Demonstrations

This paper develops a novel Reinforcement Learning from Demonstration (RLfD) algorithm, called Self-Guided Actor-Critic (SGAC), with the purpose of enhancing exploration of Reinforcement Learning by imitating expert policies from demonstration data. Instead of using human experts or other control algorithms as demonstrations in most of existing methods, SGAC can generate high quality expert data online by continuously querying and adaptively updating an expert that is given by model predictive Deep Deterministic Policy Gradient (MP-DDPG). In this way, the training cost of SGAC is reduced, and distribution mismatch problem leading to unstable learning process is alleviated. In addition, the optimality assumption of the expert in typical RLfD methods is relaxed since the adaptive expert of SGAC can make self-improvement. A non-trivial example of applying SGAC to ship berthing control problem is present. The simulation results show the learning process of SGAC is faster and steadier than typical Reinforcement Learning algorithms and MP-DDPG.
High-quality Frame Interpolation via Tridirectional Inference

Videos have recently become an omnipresent form of media, gathering much attention from industry as well as academia. In the video enhancement field, video frame interpolation is a long-studied topic that has dramatically improved due to the advancement of deep convolutional neural networks (CNN). However, conventional approaches utilizing two successive frames often exhibit ghosting or tearing artifacts for moving objects. We argue that this phenomenon comes from the lack of reliable information provided only by two frames. With this motivation, we propose a frame interpolation method by utilizing tridirectional information obtained from three input frames. Information extracted from triplet frames allows our model to learn rich and reliable inter-frame motion representations, including subtle nonlinear movement, which can be easily trained via any video frames in a self-supervised manner. We demonstrate that our method generalizes well to high-resolution content by evaluating on FHD resolution, and illustrates our approach's effectiveness via comparison to state-of-the-art methods on challenging video content.
Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese

Textual emotion recognition has been a promising research topic in recent years. Many researchers aim to build more accurate and robust emotion detection systems. In this paper, we conduct several experiments to indicate how data pre-processing affects a machine learning method on textual emotion recognition. These experiments are performed on the Vietnamese Social Media Emotion Corpus (UIT-VSMEC) as the benchmark dataset. We explore Vietnamese social media characteristics to propose different pre-processing techniques, and key-clause extraction with emotional context to improve the machine performance on UIT-VSMEC. Our experimental evaluation shows that with appropriate pre-processing techniques based on Vietnamese social media characteristics, Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40%, a significant improvement of 4.66% over the CNN model built by the authors of UIT-VSMEC (59.74%).
Predicting Emotion Reactions for Human–Computer Conversation: A Variational Approach

Although emotional conversation generation has attracted widespread attention in recent years, research works on the emotional interactional mechanism are still scarce, which makes it difficult for existing emotional dialogue system to automatically determine a suitable emotion type for conversation generation. Such a response emotion planning task is often difficult due to the “gap” problem: we need to predict the emotional probability distribution of the upcoming responses, which have not yet been generated. In this article, we propose a novel method, namely interactional emotion learning (IEL), which adopts an intuitive way to eliminate the “gap” problem: we design a variational learning network, called potential response learning, to learn the latent distribution of responses for given conversation in a semantic space. Then, we predict an appropriate emotion for response generation based on both the dialogue context and the learned latent distribution. Extensive experiments have been performed on three off-the-shelf conversation datasets, and the experimental results show that the proposed variational learning network significantly boosts the prediction ability of our approach, and our IEL method outperforms the state-of-the-art dialogue classification methods in the emotion planning task.
A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble

Fully Autonomous Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an essential component for network security resisting attacks, such as collision attack and password blasting.As a recently emerged CAPTCHA technology, drag-and-drop interactive CAPTCHA has been successfully employed in great number of practical applications. However, there are still some problems involved in the architecture and back-end anomaly detection model of the interactive CAPTCHA that need to be addressed: excessive concentration of computing pressure on cloud system, poor accuracy of anomaly detection model, and huge cost of the labelling for the attack sample. To this end, a novel cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble (MVSE) is proposed in this paper. In particular, a novel cloud endpoint coordinating CAPTCHA architecture is designed to make most use of the computing power of endpoint devices and reduce the calculation pressure of cloud system. Meanwhile, a multi-view stacking ensemble learning-based user action anomaly detection model is proposed for the cloud endpoint coordinating CAPTCHA architecture. Finally, an iterative top-k training (ITK-training) semi-supervised learning algorithm is employed for data enhancement and make the most use of un-labeled samples in order to reduce the deploy cost of drag-and-drop CAPTCHA system. A real-world data from one of the biggest Internet companies of China is used to validate the effectiveness of our proposed model. We can obtain that the computing pressure of the cloud can reduce nearly 95% and the accuracy of the proposed CAPTCHA system can reach 96.77% using MVSE learning and 98.67% using MVSE learning with the ITK-training based data enhancement.
Keyword extraction from Tweets using NLP tools for collecting relevant news

Keywords play a major role in representing the gist of a document. Therefore, a lot of Natural Language processing tools have been implemented to identify keywords in both structured and unstructured texts. Text that appears in social media platforms such as twitter is mostly unstructured because of the character limitation. Consequently, a lot of short terms and symbols such as emoticons and URLs are included in tweets. Keyword extraction from grammatically ambiguous text is not easy compared to structured text since it is hard to rely on the linguistic features in unstructured texts. But when it comes to news on twitter, it may contain somewhat structured text than informal text does but it depends on the tweeter, the person who posts the tweet. In this paper, a methodology is proposed to extract keywords from a given tweet to retrieve relevant news that has been posted on twitter, for fake news detection. The intention of extracting keywords is to find more related news efficiently and effectively. For this approach, a corpus that contains tweet texts from different domains is built in order to make this approach more generic instead of making it a domain-specific approach. In fact, the Stanford Core NLP tool kit, Wordnet linguistic database and statistical method are used for extracting keywords from a tweet. For the system evaluation, the Turing test which has human intervention is used. The system was able to acquire an accuracy of 67.6% according to the evaluation conducted.
Traffic Congestion Detection from Twitter Using word2vec

Event detection from social media is a highly important field of study. The main goal of the event detection is to detect events (e.g., traffic, disaster) automatically from social media like Twitter. In the area of supervised event detection from Twitter, word2vec model is not yet used by the researchers as a feature extraction technique. Since word2vec introduces a new approach on feature extraction from words and documents, in this work, we propose a near real-time traffic congestion detection system from the Twitter data stream using word2vec model as our feature extraction method. The system fetches the continuous stream of tweets from Twitter, preprocess and extract features from tweets using word2vec, classifies traffic congestion related tweets and notifies the occurrence of traffic congestion in a particular region. For the classification task, we compared the performance of Support Vector Machine (SVM), Logistic Regression (LR) and Naive Bayes (NB) algorithms. Our experimental results have shown that SVM outperforms the other two algorithms achieving an accuracy of 91.73%. Later on, the SVM has been selected to build the actual system.