Reviewerly/exHarmony huggingface dataset:
The State of Speech in HCI: Trends, Themes and Challenges

Speech interfaces are growing in popularity. Through a review of 68 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in HCI. We find that most studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes, or developed systems by using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, modality comparison, user speech production, assistive technology \& accessibility, design insight, experiences with interactive voice response (IVR) systems, using speech technology for development, people's experiences with intelligent personal assistants (IPAs) and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work, and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research.
Is Bitcoin a bubble?

The narrative of a Bitcoin is a bubble is very common. We employ statistical techniques to empirically evaluate such claim. A branch of literature links the existence of a bubble in some financial asset's price to strict local martingales — a finitely lived asset has a bubble if, and only if, it is a strict local martingale under the equivalent risk-neutral measure. A diffusion process is a strict local martingale if its volatility increases faster than linearly as its level grows. We apply a nonparametric method to estimate the volatility function of Bitcoin daily and high frequency prices, as well as of more traditional financial assets. We then estimate the stochastic volatility model of Andersen and Piterbarg (2007), whose parameter space has a specific subset under which the asset's price is a strict local martingale. Results suggest the existence of a bubble in Bitcoin prices from early 2013 to mid 2014, but, interestingly, not in late 2017.
A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter

Scene classification is a significant aspect of computer vision. Convolutional neural networks (CNNs), a development of deep learning, are a well-understood tool for image classification. But training CNNs requires large-scale datasets. Transfer learning addresses this problem and produces a solution for small-scale datasets. Because scene image classification is more complex than common image classification. We propose a novel ResNet based transfer learning model utilizing multi-layer feature fusion, taking full advantage of interlayer discriminating features and fusing them for classification by softmax regression. In addition, a novel data augmentation method with a filter useful for small-scale datasets is presented. New image patches are generated by sliding block cropping of a raw image, which are then filtered to insure that the new images sufficiently represent the original categorization. Our new ResNet based transfer learning model with enhanced data augmentation is evaluated on six benchmark scene datasets (LF, OT, FP, LS, MIT67, SUN397). Extensive experimental results show that on the six datasets our method obtains better accuracy than other state-of-the-art models.
A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction

Due to the rapid development of Internet technologies and social media, sentiment analysis has become an important opinion mining technique. Recent research work has described the effectiveness of different sentiment classification techniques ranging from simple rule-based and lexicon-based approaches to more complex machine learning algorithms. While lexicon-based approaches have suffered from the lack of dictionaries and labeled data, machine learning approaches have fallen short in terms of accuracy. This paper proposes an integrated framework which bridges the gap between lexicon-based and machine learning approaches to achieve better accuracy and scalability. To solve the scalability issue that arises as the feature-set grows, a novel genetic algorithm (GA)-based feature reduction technique is proposed. By using this hybrid approach, we are able to reduce the feature-set size by up to 42% without compromising the accuracy. The comparison of our feature reduction technique with more widely used principal component analysis (PCA) and latent semantic analysis (LSA) based feature reduction techniques have shown up to 15.4% increased accuracy over PCA and up to 40.2% increased accuracy over LSA. Furthermore, we also evaluate our sentiment analysis framework on other metrics including precision, recall, F-measure, and feature size. In order to demonstrate the efficacy of GA-based designs, we also propose a novel cross-disciplinary area of geopolitics as a case study application for our sentiment analysis framework. The experiment results have shown to accurately measure public sentiments and views regarding various topics such as terrorism, global conflicts, and social issues. We envisage the applicability of our proposed work in various areas including security and surveillance, law-and-order, and public administration.
Unified Spatio-Temporal Attention Networks for Action Recognition in Videos

Recognizing actions in videos is not a trivial task because video is an information-intensive media and includes multiple modalities. Moreover, on each modality, an action may only appear at some spatial regions, or only part of the temporal video segments may contain the action. A valid question is how to locate the attended spatial areas and selective video segments for action recognition. In this paper, we devise a general attention neural cell, called AttCell, that estimates the attention probability not only at each spatial location but also for each video segment in a temporal sequence. With AttCell, a unified Spatio-Temporal Attention Networks (STAN) is proposed in the context of multiple modalities. Specifically, STAN extracts the feature map of one convolutional layer as the local descriptors on each modality and pools the extracted descriptors with the spatial attention measured by AttCell as a representation of each segment. Then, we concatenate the representation on each modality to seek a consensus on the temporal attention, a priori, to holistically fuse the combined representation of video segments to the video representation for recognition. Our model differs from conventional deep networks, which focus on the attention mechanism, because our temporal attention provides a principled and global guidance across different modalities and video segments. Extensive experiments are conducted on four public datasets; UCF101, CCV, THUMOS14, and Sports-1M; our STAN consistently achieves superior results over several state-of-the-art techniques. More remarkably, we validate and demonstrate the effectiveness of our proposal when capitalizing on the different number of modalities.
Radial-Based oversampling for noisy imbalanced data classification

Imbalanced data classification remains a focus of intense research, mostly due to the prevalence of data imbalance in various real-life application domains. A disproportion among objects from different classes may significantly affect the performance of standard classification models. The first problem is the high imbalance ratios that pose a serious learning difficulty and require usage of dedicated methods, capable of alleviating this issue. The second important problem which may appear is noise, which may be accompanying the training data and causing strong deterioration of the classifier performance or increase the time required for its training. Therefore, the desirable classification model should be robust to both skewed data distributions and noise. One of the most popular approaches for handling imbalanced data is oversampling of the minority objects in their neighborhood. In this work we will criticize this approach and propose a novel strategy for dealing with imbalanced data, with particular focus on the noise presence. We propose Radial-Based Oversampling (RBO) method, which can find regions in which the synthetic objects from minority class should be generated on the basis of the imbalance distribution estimation with radial basis functions. Results of experiments, carried out on a representative set of benchmark datasets, confirm that the proposed guided synthetic oversampling algorithm offers an interesting alternative to popular state-of-the-art solutions for imbalanced data preprocessing.
Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities

With rapid development of compelling application scenarios of the Internet of Things (IoT), such as smart cities, it becomes substantially important to strengthen the management of data traffic in IoT networks. Traffic classification is beneficial in terms of both ensuring network security and improving quality of service. Traditional IoT traffic classification methods separate the classification algorithm and the design of feature engineering, which includes feature extraction and feature selection. Then, traffic identification or classification is performed by combining both. This paper proposes an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Our proposed traffic classification method beneficially eliminates the process of manually selecting traffic features, and is particularly applicable to smart city scenarios. To the best of our knowledge, this is the first time that capsule networks have been used in the context of traffic classification. Experimental results show the feasibility and effectiveness of our proposed traffic classification mechanism, which yields high classification accuracy.
Distributed Kalman Filtering and Control Through Embedded Average Consensus Information Fusion

This paper presents a unified framework for distributed filtering and control of state-space processes. To this end, a distributed Kalman filtering algorithm is developed via decomposition of the optimal centralized Kalman filtering operations. This decomposition is orchestrated in a fashion so that each agent retains a Kalman style filtering operation and an estimate of the state vector. In this setting, the agents mirror the operations of the centralized Kalman filter in a distributed fashion through embedded average consensus fusion of local state vector estimates and their associated covariance information. For rigor, closed-form expressions for the mean and mean square error performance of the developed distributed Kalman filter are derived. More importantly, in contrast to current approaches, due to the comprehensive framework for fusion of the covariance information, a duality between the developed distributed Kalman filter and decentralized control is established. Thus, resulting in an effective and all inclusive distributed framework for filtering and control of state-space processes over a network of agents. The introduced theoretical concepts are validated using the simulations that indicate a precise match between simulation results and the theoretical analysis. In addition, simulations indicate that performance levels comparable to that of the optimal centralized approaches are attainable.
Simulating Large Quantum Circuits on a Small Quantum Computer

Limited quantum memory is one of the most important constraints for near-term quantum devices. Understanding whether a small quantum computer can simulate a larger quantum system, or execute an algorithm requiring more qubits than available, is both of theoretical and practical importance. In this Letter, we introduce cluster parameters K and d of a quantum circuit. The tensor network of such a circuit can be decomposed into clusters of size at most d with at most K qubits of inter-cluster quantum communication. We propose a cluster simulation scheme that can simulate any (K,d)-clustered quantum circuit on a d-qubit machine in time roughly 2^{O(K)}, with further speedups possible when taking more fine-grained circuit structure into account. We show how our scheme can be used to simulate clustered quantum systems-such as large molecules-that can be partitioned into multiple significantly smaller clusters with weak interactions among them. By using a suitable clustered ansatz, we also experimentally demonstrate that a quantum variational eigensolver can still achieve the desired performance for estimating the energy of the BeH_{2} molecule while running on a physical quantum device with half the number of required qubits.
Comparing Data from Chatbot and Web Surveys

This study aims to explore the feasibility of a text-based virtual agent as a new survey method to overcome the web survey's common response quality problems, which are caused by respondents' inattention. To this end, we conducted a 2 (platform: web vs. chatbot) × 2 (conversational style: formal vs. casual) experiment. We used satisficing theory to compare the responses' data quality. We found that the participants in the chatbot survey, as compared to those in the web survey, were more likely to produce differentiated responses and were less likely to satisfice; the chatbot survey thus resulted in higher-quality data. Moreover, when a casual conversational style is used, the participants were less likely to satisfice-although such effects were only found in the chatbot condition. These results imply that conversational interactivity occurs when a chat interface is accompanied by messages with effective tone. Based on an analysis of the qualitative responses, we also showed that a chatbot could perform part of a human interviewer's role by applying effective communication strategies.
Deep face recognition using imperfect facial data

Today, computer based face recognition is a mature and reliable mechanism which is being practically utilised for many access control scenarios. As such, face recognition or authentication is predominantly performed using ‘perfect’ data of full frontal facial images. Though that may be the case, in reality, there are numerous situations where full frontal faces may not be available — the imperfect face images that often come from CCTV cameras do demonstrate the case in point. Hence, the problem of computer based face recognition using partial facial data as probes is still largely an unexplored area of research. Given that humans and computers perform face recognition and authentication inherently differently, it must be interesting as well as intriguing to understand how a computer favours various parts of the face when presented to the challenges of face recognition. In this work, we explore the question that surrounds the idea of face recognition using partial facial data. We explore it by applying novel experiments to test the performance of machine learning using partial faces and other manipulations on face images such as rotation and zooming, which we use as training and recognition cues. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the cheek. We also study the effect of face recognition subject to facial rotation as well as the effect of recognition subject to zooming out of the facial images. Our experiments are based on using the state of the art convolutional neural network based architecture along with the pre-trained VGG-Face model through which we extract features for machine learning. We then use two classifiers namely the cosine similarity and the linear support vector machines to test the recognition rates. We ran our experiments on two publicly available datasets namely, the controlled Brazilian FEI and the uncontrolled LFW dataset. Our results show that individual parts of the face such as the eyes, nose and the cheeks have low recognition rates though the rate of recognition quickly goes up when individual parts of the face in combined form are presented as probes.
A guideline to solar forecasting research practice: Reproducible, operational, probabilistic or physically-based, ensemble, and skill (ROPES)

Over the past decade, significant progress in solar forecasting has been made. Nevertheless, there are concerns about duplication, long-term value, and reproducibility; this is referred to as the “solar forecasting bubble.” There is an urgent need to reconcile and improve the current solar forecasting research practice. This discussion paper proposes a so-called “ROPES” guideline that describes the desirable characteristics of future solar forecasting studies. In short, ROPES stands for reproducible, operational, probabilistic and/or physically based, ensemble, and skill. This set of characteristics is intended to facilitate comparison, comprehension, and communication within the solar forecasting field and speed up its development. Compliance with ROPES is evaluated on 79 solar forecasting references published during 2017 January to 2018 July in 6 Elsevier energy journals. Although most current papers fall short of complying with ROPES, evidence suggests that a consensus is forming.
Fusion Methods for CNN-Based Automatic Modulation Classification

An automatic modulation classification has a very broad application in wireless communications. Recently, deep learning has been used to solve this problem and achieved superior performance. In most cases, the input size is fixed in convolutional neural network (CNN)-based modulation classification. However, the duration of the actual radio signal burst is variable. When the signal length is greater than the CNN input length, how to make full use of the complete signal burst to improve the classification accuracy is a problem needs to be considered. In this paper, three fusion methods are proposed to solve this problem, such as voting-based fusion, confidence-based fusion, and feature-based fusion. The simulation experiments are done to analyze the performance of these methods. The results show that the three fusion methods perform better than the non-fusion method. The performance of the two fusion methods based on confidence and feature is very close, which is better than that of the voting-based fusion.
Dual Cross-Entropy Loss for Small-Sample Fine-Grained Vehicle Classification

Fine-grained vehicle classification is a challenging topic in computer vision due to the high intraclass variance and low interclass variance. Recently, considerable progress has been made in fine-grained vehicle classification due to the huge success of deep neural networks. Most studies of fine-grained vehicle classification based on neural networks, focus on the neural network structure to improve the classification performance. In contrast to existing works on fine-grained vehicle classification, we focus on the loss function of the neural network. We add a regularization term to the cross-entropy loss and propose a new loss function, Dual Cross-Entropy Loss. The regularization term places a constraint on the probability that a data point is assigned to a class other than its ground-truth class, which can alleviate the vanishing of the gradient when the value of the cross-entropy loss is close to zero. To demonstrate the effectiveness of our loss function, we perform two sets of experiments. The first set is conducted on a small-sample fine-grained vehicle classification dataset, the Stanford Cars-196 dataset. The second set is conducted on two small-sample datasets, the LabelMe dataset and the UIUC-Sports dataset, as well as on one large-sample dataset, the CIFAR-10 dataset. The experimental results show that the proposed loss function improves the fine-grained vehicle classification performance and has good performance on three other general image classification tasks.
Distraction-Aware Shadow Detection

Shadow detection is an important and challenging task for scene understanding. Despite promising results from recent deep learning based methods. Existing works still struggle with ambiguous cases where the visual appearances of shadow and non-shadow regions are similar (referred to as distraction in our context). In this paper, we propose a Distraction-aware Shadow Detection Network (DSDNet) by explicitly learning and integrating the semantics of visual distraction regions in an end-to-end framework. At the core of our framework is a novel standalone, differentiable Distraction-aware Shadow (DS) module, which allows us to learn distraction-aware, discriminative features for robust shadow detection, by explicitly predicting false positives and false negatives. We conduct extensive experiments on three public shadow detection datasets, SBU, UCF and ISTD, to evaluate our method. Experimental results demonstrate that our model can boost shadow detection performance, by effectively suppressing the detection of false positives and false negatives, achieving state-of-the-art results.
CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries

Deep learning (DL) systems are widely used in domains including aircraft collision avoidance systems, Alzheimer's disease diagnosis, and autonomous driving cars. Despite the requirement for high reliability, DL systems are difficult to test. Existing DL testing work focuses on testing the DL models, not the implementations (e.g., DL software libraries) of the models. One key challenge of testing DL libraries is the difficulty of knowing the expected output of DL libraries given an input instance. Fortunately, there are multiple implementations of the same DL algorithms in different DL libraries. Thus, we propose CRADLE, a new approach that focuses on finding and localizing bugs in DL software libraries. CRADLE (1) performs cross-implementation inconsistency checking to detect bugs in DL libraries, and (2) leverages anomaly propagation tracking and analysis to localize faulty functions in DL libraries that cause the bugs. We evaluate CRADLE on three libraries (TensorFlow, CNTK, and Theano), 11 datasets (including ImageNet, MNIST, and KGS Go game), and 30 pre-trained models. CRADLE detects 12 bugs and 104 unique inconsistencies, and highlights functions relevant to the causes of inconsistencies for all 104 unique inconsistencies.
Explanatory Interactive Machine Learning

Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind predictions and queries is important when assessing how the learner works and, in turn, trust. Consequently, we propose the novel framework of explanatory interactive learning where, in each step, the learner explains its query to the user, and the user interacts by both answering the query and correcting the explanation. We demonstrate that this can boost the predictive and explanatory powers of, and the trust into, the learned model, using text (e.g. SVMs) and image classification (e.g. neural networks) experiments as well as a user study.