Reviewerly/exHarmony huggingface dataset:
Feasibility of All-Day Quantum Communication with Coherent Detection

Free-space global quantum communication networks of the future are expected to be robust under background radiation, including sunlight and moonlight, so as to be available at any time of day, but this is difficult to achieve. This study proposes exploiting the inherent resistance of the coherent detection used in continuous-variable quantum key distribution (CVQKD) against background light. The components of background noise in CVQKD are found to be extremely small, and to have a very subtle influence on system performance. These results suggest that CVQKD is a viable option for real-world quantum communication networks.
Optimizing Geo-Distributed Data Analytics with Coordinated Task Scheduling and Routing

Recent trends show that cloud computing is growing to span more and more globally distributed datacenters. For geo-distributed datacenters, there is an increasingly need for scheduling algorithms to place tasks across datacenters, by jointly considering WAN traffic and computation. This scheduling must deal with situations such as wide-area distributed data, data sharing, WAN bandwidth costs and datacenter capacity limits, while also minimizing makespan. However, this scheduling problem is NP-hard. We propose a new resource allocation algorithm called HPS+, an extension to Hypergraph Partition-based Scheduling. HPS+ models the combined task-data dependencies and data-datacenter dependencies as an augmented hypergraph, and adopts an improved hypergraph partition technique to minimize WAN traffic. It further uses a coordination mechanism to allocate network resources closely following the guidelines of task requirements, for minimizing the makespan. Evaluation across the real China-Astronomy-Cloud model and Google datacenter model show that HPS+ saves the amount of data transfers by upto 53 percent and reduces the makespan by 39 percent compared to existing algorithms.
Transfer Anomaly Detection by Inferring Latent Domain Representations

We propose a method to improve the anomaly detection performance on target domains by transferring knowledge on related domains. Although anomaly labels are valuable to learn anomaly detectors, they are difficult to obtain due to their rarity. To alleviate this problem, existing methods use anomalous and normal instances in the related domains as well as target normal instances. These methods require training on each target domain. However, this requirement can be problematic in some situations due to the high computational cost of training. The proposed method can infer the anomaly detectors for target domains without re-training by introducing the concept of latent domain vectors, which are latent representations of the domains and are used for inferring the anomaly detectors. The latent domain vector for each domain is inferred from the set of normal instances in the domain. The anomaly score function for each domain is modeled on the basis of autoencoders, and its domain-specific property is controlled by the latent domain vector. The anomaly score function for each domain is trained so that the scores of normal instances become low and the scores of anomalies become higher than those of the normal instances, while considering the uncertainty of the latent domain vectors. When target normal instances can be used during training, the proposed method can also use them for training in a unified framework. The effectiveness of the proposed method is demonstrated through experiments using one synthetic and four real-world datasets. Especially, the proposed method without re-training outperforms existing methods with target specific training.
A Discriminative Neural Model for Cross-Lingual Word Alignment

We introduce a novel discriminative word alignment model, which we integrate into a Transformer-based machine translation model.In experiments based on a small number of labeled examples (∼1.7K-5K sentences) we evaluate its performance intrinsically on both English-Chinese and English-Arabic alignment, where we achieve major improvements over unsupervised baselines (11-27 F1).We evaluate the model extrinsically on data projection for Chinese NER, showing that our alignments lead to higher performance when used to project NER tags from English to Chinese.Finally, we perform an ablation analysis and an annotation experiment that jointly support the utility and feasibility of future manual alignment elicitation.
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning

The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs).ADR classification helps to identify negative effects of drugs, which can guide health professionals and pharmaceutical companies in making medications safer and advocating patients' safety.Based on the observation that in social media, negative sentiment is frequently expressed towards ADRs, this study presents a neural model that combines sentiment analysis with transfer learning techniques to improve ADR detection in social media postings.Our system is firstly trained to classify sentiment in tweets concerning current affairs, using the SemEval17-task4A corpus.We then apply transfer learning to adapt the model to the task of detecting ADRs in social media postings.We show that, in combination with rich representations of words and their contexts, transfer learning is beneficial, especially given the large degree of vocabulary overlap between the current affairs posts in the SemEval17-task4A corpus and posts about ADRs.We compare our results with previous approaches, and show that our model can outperform them by up to 3% F-score.
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis

Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information.Existing research efforts mostly regard this joint task as a sequence labeling problem, building models that can capture explicit structures in the output space.However, the importance of capturing implicit global structural information that resides in the input space is largely unexplored.In this work, we argue that both types of information (implicit and explicit structural information) are crucial for building a successful targeted sentiment analysis model.Our experimental results show that properly capturing both information is able to lead to better performance than competitive existing approaches.We also conduct extensive experiments to investigate our model's effectiveness and robustness 1 .
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance).Automatically detecting these aspects can be useful for both users and downstream opinion mining applications.Current supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain.And, unfortunately, unsupervised topic models often fail to capture the aspects of interest.In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i.e., weakly positive indicators) for the aspects of interest.First, we show that current weakly supervised approaches do not effectively leverage the predictive power of seed words for aspect detection.Next, we propose a student-teacher approach that effectively leverages seed words in a bag-of-words classifier (teacher); in turn, we use the teacher to train a second model (student) that is potentially more powerful (e.g., a neural network that uses pre-trained word embeddings).Finally, we show that iterative co-training can be used to cope with noisy seed words, leading to both improved teacher and student models.Our proposed approach consistently outperforms previous weakly supervised approaches (by 14.1 absolute F1 points on average) in six different domains of product reviews and six multilingual datasets of restaurant reviews.
Themis: Predicting and Reining in Application-Level Slowdown on Spatial Multitasking GPUs

Predicting performance degradation of a GPU application when it is co-located with other applications on a spatial multitasking GPU without prior application knowledge is essential in public Clouds. Prior work mainly targets CPU co-location, and is inaccurate and/or inefficient for predicting performance of applications at co-location on spatial multitasking GPUs. Our investigation shows that hardware event statistics caused by co-located applications, which can be collected with negligible overhead, strongly correlate with their slowdowns. Based on this observation, we present Themis, an online slowdown predictor that can precisely and efficiently predict application slowdown without prior application knowledge. We first train a precise slowdown model offline using hardware event statistics collected from representative co-locations. When new applications co-run, Themis collects event statistics and predicts their slowdowns simultaneously. Our evaluation shows that Themis has negligible runtime overhead and can precisely predict application-level slowdown with prediction error smaller than 9.5%. Based on Themis, we also implement an SM allocation engine to rein in application slowdown at co-location. Case studies show that the engine successfully enforces fair sharing and QoS.
Ultrasensitive video extensometer using single-camera dual field-of-view telecentric imaging system

Video extensometer assesses mechanical properties of materials by measuring their length changes under different loadings with combined use of video recording and image processing. In this Letter, an ultrasensitive video extensometer implemented on a single-camera dual field-of-view (dual FOV) telecentric imaging system is proposed. The method integrates the idea of FOV separation and telecentric imaging, which can increase the strain resolution with an order of magnitude and enhance the strain accuracy significantly. Validation experiments indicate that the root mean square error of a video extensometer was effectively decreased from 25.2 to 4.4 microstrains after employing the proposed method. Time-varying thermal strains of Al alloy and alumina ceramic samples were also precisely measured by the established ultrasensitive video extensometer, confirming its accuracy and practicality.
Reservoir engineering using quantum optimal control for qubit reset

We determine how to optimally reset a superconducting qubit which interacts with a thermal environment in such a way that the coupling strength is tunable. Describing the system in terms of a time-local master equation with time-dependent decay rates and using quantum optimal control theory, we identify temporal shapes of tunable level splittings which maximize the efficiency of the reset protocol in terms of duration and error. Time-dependent level splittings imply a modification of the system-environment coupling, varying the decay rates as well as the Lindblad operators. Our approach thus demonstrates efficient reservoir engineering employing quantum optimal control. We find the optimized reset strategy to consist in maximizing the decay rate from one state and driving non-adiabatic population transfer into this strongly decaying state.
Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby &amp; Orca Sounds

The 2019 INTERSPEECH Computational Paralinguistics Challenge (ComParE) consists of four Sub-Challenges, where the tasks are to identify different German (Austrian) dialects, estimate how sleepy the speaker is, what type of sound a given baby uttered, and whether there is a sound of an orca (killer whale) present in the recording.Following our team's last year entry, we continue our research by looking for feature set types that might be employed on a wide variety of tasks without alteration.This year, besides the standard 6373-sized ComParE functionals, we experimented with the Fisher vector representation along with the Bag-of-Audio-Words technique.To adapt Fisher vectors from the field of image processing, we utilized them on standard MFCC features instead of the originally intended SIFT attributes (which describe local objects found in the image).Our results indicate that using these feature representation techniques was indeed beneficial, as we could outperform the baseline values in three of the four Sub-Challenges; the performance of our approach seems to be even higher if we consider that the baseline scores were obtained by combining different methods as well.
Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping

A computer vision method for measuring multiple pointer meters is proposed based on the inverse perspective mapping. First, the measured meter scales are used as the calibration objects to obtain the extrinsic parameters of the meter plane. Second, normal vector of the meter plane can be acquired by the extrinsic parameters, obtaining the rotation transformation matrix of the meter image. Then, the acquired meter image is transformed to a position both parallel to the meter plane and near the main point by the rotation transformation matrix and the extrinsic parameters, eliminating the perspective effect of the acquired image. Finally, the transformed image is tested by the visual detection method to obtain the readings of the pointer meter, improving measurement precision. The results of the measurement verify the effectiveness and accuracy of the method.
A novel deep learning architecture outperforming ‘off‑the‑shelf’ transfer learning and feature‑based methods in the automated assessment of mammographic breast density

Potentially suspicious breast neoplasms could be masked by high tissue density, thus increasing the probability of a false‑negative diagnosis. Furthermore, differentiating breast tissue type enables patient pre‑screening stratification and risk assessment. In this study, we propose and evaluate advanced machine learning methodologies aiming at an objective and reliable method for breast density scoring from routine mammographic images. The proposed image analysis pipeline incorporates texture [Gabor filters and local binary pattern (LBP)] and gradient‑based features [histogram of oriented gradients (HOG) as well as speeded‑up robust features (SURF)]. Additionally, transfer learning approaches with ImageNet trained weights were also used for comparison, as well as a convolutional neural network (CNN). The proposed CNN model was fully trained on two open mammography datasets and was found to be the optimal performing methodology (AUC up to 87.3%). Thus, the findings of this study indicate that automated density scoring in mammograms can aid clinical diagnosis by introducing artificial intelligence‑powered decision‑support systems and contribute to the 'democratization' of healthcare by overcoming limitations, such as the geographic location of patients or the lack of expert radiologists.
Do Human Rationales Improve Machine Explanations?

Work on "learning with rationales" shows that humans providing explanations to a machine learning system can improve the system's predictive accuracy.However, this work has not been connected to work in "explainable AI" which concerns machines explaining their reasoning to humans.In this work, we show that learning with rationales can also improve the quality of the machine's explanations as evaluated by human judges.Specifically, we present experiments showing that, for CNNbased text classification, explanations generated using "supervised attention" are judged superior to explanations generated using normal unsupervised attention.
Modeling the process of shaping the public opinion through Multilevel Fuzzy Cognitive Maps

The conformation of opinion in a society is analyzed in diverse studies, following diverse perspectives, methods and tools. Previous studies are basically descriptive, predictive, and not aiming at modeling the real situation. In this paper, we present a preliminary model representing the process of conformation of public opinion from the perspective of opinion mining, by using the Multilevel Fuzzy Cognitive Map (MFCM), which, in turn, is employed to evaluate the quality of the public opinion. This paper examines the capabilities of such model, for both, knowledge description and knowledge extraction about opinions presented in a certain topic, allowing the assessment of the quality of public opinion. This is very valuable for decision making, as well as for social and political analysis. We give special attention to the influence of the media on public opinion, because of its importance in the actual western society. The design of the MFCM, its concepts and relationships, are presented. The concepts describe the relevant aspects from which public opinion emerges, covering the social, technological and biological domains. The results are encouraging: the model permits to identify the media preferred by the public in order to understand better significant matters, including the high esteem that the new communication media hold, in contrast with the little merit that the traditional communication media enjoys, as well as the confidence and credibility in the information sources (from both, new and old media) diminish in time.
Survey on rain removal from videos or a single image

Rain can cause performance degradation of outdoor computer vision tasks. Thus, the exploration of rain removal from videos or a single image has drawn considerable attention in the field of image processing. Recently, various deraining methodologies have been proposed. However, no comprehensive survey work has yet been conducted to summarize existing deraining algorithms and quantitatively compare their generalization ability, and especially, no off-the-shelf toolkit exists for accumulating and categorizing recent representative methods for easy performance reproduction and deraining capability evaluation. In this regard, herein, we present a comprehensive overview of existing video and single image deraining methods as well as reproduce and evaluate current state-of-the-art deraining methods. In particular, these approaches are mainly classified into model- and deep-learning-based methods, and more elaborate branches of each method are presented. Inherent abilities, especially generalization performance, of the state-of-the-art methods have been both quantitatively and visually analyzed through thorough experiments conducted on synthetic and real benchmark datasets. Moreover, to facilitate the reproduction of existing deraining methods for general users, we present a comprehensive repository with detailed classification, including direct links to 85 deraining papers, 24 relevant project pages, source codes of 12 and 25 algorithms for video and single image deraining, respectively, 5 and 10 real and synthesized datasets, respectively, and 7 frequently used image quality evaluation metrics, along with the corresponding computation codes. Research limitations worthy of further exploration have also been discussed for future research along this direction.
Optimal Projection Guided Transfer Hashing for Image Retrieval

Recently, learning to hash has been widely studied for image retrieval thanks to the computation and storage efficiency of binary codes. Most existing learning to hash methods have yielded significant performance. However, for most existing learning to hash methods, sufficient training images are required and used to learn precise hashing codes. In some real-world applications, there are not always sufficient training images in the domain of interest. In addition, some existing supervised approaches need a amount of labeled data, which is an expensive process in terms of time, labor and human expertise. To handle such problems, inspired by transfer learning, we propose a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH) where we borrow the images of other different but related domain i.e., source domain to help learn precise hashing codes for the domain of interest i.e., target domain. In GTH, we aim to learn domain-invariant hashing functions. To achieve that, we propose to minimize the error matrix between two hashing projections of target and source domains. We seek for the maximum likelihood estimation (MLE) solution of the error matrix between the two hashing projections due to the domain gap. Furthermore, an alternating optimization method is adopted to obtain the two projections of target and source domains. By doing so, two projections can be progressively aligned. Extensive experiments on various benchmark databases for cross-domain visual recognition verify that our method outperforms many state-of-the-art learning to hash methods. The source code is available at https://github.com/liuji93/GTH.
Multi-SVM based Dempster–Shafer theory for gesture intention understanding using sparse coding feature

The Dempster–Shafer theory based on multi-SVM to deal with multimodal gesture images for intention understanding is proposed, in which the Sparse Coding (SC) based Speeded-Up Robust Features (SURF) are used for feature extraction of depth and RGB image. Aiming at the problems of the small sample, high dimensionality and feature redundancy for image data, we use the SURF algorithm to extract the features of the original image, and then perform their Sparse Coding, which means that the image is subjected to two-dimensional feature reduction. The dimensionally reduced gesture features are used by the multi-SVM for classification. A fusion framework based on D–S evidence theory is constructed to deal with the recognition of depth and RGB image to realize the gesture intention understanding. To verify the effectiveness of the proposal, the experiments on two RGB-D datasets (CGD2011 and CAD-60) are conducted. The results of 10-fold cross validation test show that the recognition rates were higher than those produced by other methods under the condition when each sensor was considered individually. Meanwhile, the preliminary experiments are also carried out in the developing emotional social robot system. The results indicate that the proposal can be applied to human–robot interaction.
‘This time with feeling?’ Assessing EU data governance implications of out of home appraisal based emotional AI

The boundaries of personal space and borders of bodily integrity are being tested by deployments of emotional artificial intelligence (EAI) in private and public spaces. By means of sensing, seeing and machine learning of facial expressions, voice, gaze, gestures and range of physiological signals (heart rate, skin conductivity and temperature, muscle activity, body temperature, respiration and other bio-signals), the goal is to make interior emotional life machine-readable for personal, commercial and security objectives.In this paper, we focus on computer vision and face-based analytics to consider the nature, method and development of facial coding, the potential demise of existing approaches, and the rise of even more invasive methods. Criticisms of facial coding have long existed, but recent scholarship and industrial development signals a lack of confidence in ‘basic emotions’ and a turn to appraisal-based accounts of emotion. This inevitably entails use of data about internal physiological and experiential contexts, but also factors external to an individual. To explore this, this paper asks and answers the following question: With regard to deployment in out-of-home situations, what are the legal and privacy implications of appraisal-based emotion capture?