Reviewerly/exHarmony huggingface dataset:
Vision-Based Obstacle Avoidance Strategies for MAVs Using Optical Flows in 3-D Textured Environments

Due to payload restrictions for micro aerial vehicles (MAVs), vision-based approaches have been widely studied with their light weight characteristics and cost effectiveness. In particular, optical flow-based obstacle avoidance has proven to be one of the most efficient methods in terms of obstacle avoidance capabilities and computational load; however, existing approaches do not consider 3-D complex environments. In addition, most approaches are unable to deal with situations where there are wall-like frontal obstacles. Although some algorithms consider wall-like frontal obstacles, they cause a jitter or unnecessary motion. To address these limitations, this paper proposes a vision-based obstacle avoidance algorithm for MAVs using the optical flow in 3-D textured environments. The image obtained from a monocular camera is first split into two horizontal and vertical half planes. The desired heading direction and climb rate are then determined by comparing the sum of optical flows between half planes horizontally and vertically, respectively, for obstacle avoidance in 3-D environments. Besides, the proposed approach is capable of avoiding wall-like frontal obstacles by considering the divergence of the optical flow at the focus of expansion and navigating to the goal position using a sigmoid weighting function. The performance of the proposed algorithm was validated through numerical simulations and indoor flight experiments in various situations.
Attention-based Conditioning Methods for External Knowledge Integration

In this paper, we present a novel approach for incorporating external knowledge in Recurrent Neural Networks (RNNs).We propose the integration of lexicon features into the self-attention mechanism of RNN-based architectures.This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand.We introduce three methods, namely attentional concatenation, feature-based gating and affine transformation.Experiments on six benchmark datasets show the effectiveness of our methods.Attentional feature-based gating yields consistent performance improvement across tasks.Our approach is implemented as a simple add-on module for RNN-based models with minimal computational overhead and can be adapted to any deep neural architecture.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network

In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews.This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications.This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews.In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research.On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise.Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.
Multiqubit randomized benchmarking using few samples

Randomized benchmarking (RB) is an efficient and robust method to characterize gate errors in quantum circuits. Averaging over random sequences of gates leads to estimates of gate errors in terms of the average fidelity. These estimates are isolated from the state preparation and measurement errors that plague other methods such as channel tomography and direct fidelity estimation. A decisive factor in the feasibility of randomized benchmarking is the number of sampled sequences required to obtain rigorous confidence intervals. Previous bounds were either prohibitively loose or required the number of sampled sequences to scale exponentially with the number of qubits in order to obtain a fixed confidence interval at a fixed error rate. Here, we show that, with a small adaptation to the randomized benchmarking procedure, the number of sampled sequences required for a fixed confidence interval is dramatically smaller than could previously be justified. In particular, we show that the number of sampled sequences required is essentially independent of the number of qubits and scales favorably with the average error rate of the system under investigation. We also investigate the fitting procedure inherent to randomized benchmarking in light of our results and find that standard methods such as ordinary least squares optimization can give misleading results. We therefore recommend moving to more sophisticated fitting methods such as iteratively reweighted least squares optimization. Our results bring rigorous randomized benchmarking on systems with many qubits into the realm of experimental feasibility.
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization

This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations.Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form.Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words.The overall approach is evaluated on two popular datasets with encouraging results.
Driver Gaze Detection Based on Deep Residual Networks Using the Combined Single Image of Dual Near-Infrared Cameras

Research into the prevention of driver inattention by detecting the driver gaze has become more vital, as traffic accidents due to driver inattention have increased. In a vehicle environment, the conventional gaze-detection methods include detecting the driver gaze using single or multiple cameras. When a single camera is used to detect the driver gaze, excessive rotation of the driver's head may prevent the eye region from being accurately detected, thereby reducing the gaze-detection accuracy. To address this issue, researchers previously attempted gaze detection using dual cameras. However, these methods selectively use the information obtained from each camera; thus, accuracy improvement is limited because the information is not simultaneously used. In addition, the processing complexity increases when images obtained from dual cameras are simultaneously processed. Accordingly, this paper proposes a method to detect the driver's gaze position in the vehicle. This is the first study to calculate the driver gaze via a deep convolutional neural network (CNN) that simultaneously uses image information acquired from the dual near-infrared light cameras. Previous research selectively used one of the images acquired from the dual cameras, and the existing CNN-based gaze-detection methods use multiple deep CNNs for the driver eyes and facial images. However, the proposed method uses one CNN model that integrates all information acquired from the dual cameras into one three-channel image and uses it as an input for the network, thereby increasing the recognition reliability and reducing the computational cost. We conducted experiments based on a self-built driver database that comprised the images from 26 participants (Dongguk dual-camera-based gaze database) and the Columbia gaze dataset, which is an open database. The results demonstrate that the proposed method shows better performance than the existing methods.
A key-sharing based secure deduplication scheme in cloud storage

The data deduplication technique can efficiently eliminate redundant data by keeping only one copy of the duplicate data. Convergent encryption (CE) has been widely used in secure deduplication to save storage space and reduce data upload bandwidth, but it still faces two problems. One is that CE is not semantically secure, and suffers from an offline brute-force attack when the data is selected from a predictable set. Another is the convergent key (CK) management problem. CE requires each user holds an independent master key to encrypt its CK then stores them in the cloud, thus different users would store the same key for the duplicate copies. As the scale of users and data expand, the number of CK increase linearly. Therefore, so many keys stored repeatedly is a type of redundancy and brings about a key management issue. To enhance the security of CE, current schemes usually interact with a third party to generate a CK, but this brings an additional burden to the system. Recently, several schemes have been proposed for efficient CK management, but these schemes are confronted with a heavy computation and communication overhead and cannot resist the collusion attack. To deal with the above two problems, we propose a key-sharing method based on proof of ownership for secure deduplication. In the new scheme, only the initial uploader of the data owner encrypts the data with a randomly-chosen CK and then distributes the CK in the cloud, and only the users possessing the claimed data can retrieve the CK. The CK only needs to store once for a single duplicate data. Furthermore, our scheme adopts a deduplication check on the plaintexts and the consistency policy, and only a few owners need to encrypt the duplicate data. Analysis shows that our scheme is more efficient and remains secure in the proposed security model.
A novel interface for generating choreography based on augmented reality

Choreography is a creative art of defining sequences of movement. The choreographing process benefits a lot from the use of digital applications. In this study, we investigate the effects of different interactive techniques on user experience for a choreography generator interface. We develop a virtual reality-based and an augmented reality-based interface and compare them with 1) a personal computer-based choreography generator interface and 2) a touch-based mobile application for choreography generation. We evaluate user performance and user experience on the interfaces in terms of task completion time, as well as subjective criteria, like mental stress, physical stress, and pleasure. Our research contributes to the study of how different interaction methods of the same application affect user experience. This research also contributes to the study of virtual and augmented reality in education and training applications. The results verify the effectiveness of augmented reality in developing training and design applications, particularly choreography. The augmented reality interface emerged as the preferred choice for users. Furthermore, the results demonstrate the effectiveness of natural user interaction approaches in interface design.
Differentiated Fashion Recommendation Using Knowledge Graph and Data Augmentation

E-commerce recommender systems (RSs) can help users quickly find what they need or new products they might be interested in.To continuously enhance user trust in the website, improve page visits and dwell time, and most importantly, increase gross merchandise value (GMV), it is crucial to understand and capture the important information hidden in the data, which has a great impact on user choice.The fashion e-commerce websites can collect the attributes of items and users as well as the user purchase behaviors, but lack the fine-grained classification of the items and the implicit relationship between items and users.This paper focuses on Amazon fashion dataset, one of the most widely used datasets in the fashion field.A differentiated recommendation framework is proposed that provides different recommendation paths for active and inactive users to improve the overall recommendation quality.In the framework, a data augmentation algorithm based on transfer learning is proposed to filter out the irrelevant items and label items with fine-grained tags, and a user-item knowledge graph is built to discover the potential relationship between items and users.Finally, a differentiated recommendation strategy is put forward to make different recommendations for users with different characteristics.The experimental results show that through data augmentation algorithm to improve data quality, factorization machine model produces higher recommendation accuracy, the constructed knowledge graph can alleviate the cold start problem for recommendation, and the differentiated recommendation strategy has achieved better recommendations for both active and inactive users.
Crossing generative adversarial networks for cross-view person re-identification

Person re-identification (re-id) refers to matching pedestrians across disjoint yet non-overlapping camera views. The most effective way to match these pedestrians undertaking significant visual variations is to seek reliably invariant features that can describe the person of interest faithfully. Most of existing methods are presented in a supervised manner to produce discriminative features by relying on labeled paired images in correspondence. However, annotating pair-wise images is prohibitively expensive in labours, and thus not practical in large-scale networked cameras. Moreover, seeking comparable representations across camera views demands a flexible model to address the complex distributions of images. In this work, we study the co-occurrence statistic patterns between pairs of images, and propose to crossing Generative Adversarial Network (Cross-GAN) for learning a joint distribution for cross-image representations in an unsupervised manner. Given a pair of person images, the proposed model consists of the variational auto-encoder to encode the pair into respective latent variables, a proposed cross-view alignment to reduce the view disparity, and an adversarial layer to seek the joint distribution of latent representations. The learned latent representations are well-aligned to reflect the co-occurrence patterns of paired images. We empirically evaluate the proposed model against challenging datasets, and our results show the importance of joint invariant features in improving matching rates of person re-id with comparison to semi/unsupervised state-of-the-arts.
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model

We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks. The method we propose can be used to extend any closedvocabulary generative model, but in this paper we specifically consider the case of neural language modeling. Our Bayesian generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNNbased spelling model (generating the letters in each word type). These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics. By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model. For known words, embeddings are naturally inferred by combining evidence from type spelling and token context. Comparing to baselines (including a novel strong baseline), we beat previous work and establish state-of-the-art results on multiple datasets.
Phase retrieval of real-valued signals in a shift-invariant space

In this paper, we consider an infinite-dimensional phase retrieval problem to reconstruct real-valued signals living in a shift-invariant space from their phaseless samples taken either on the whole line or on a discrete set with finite sampling density. We characterize all phase retrievable signals in a real-valued shift-invariant space using their nonseparability. For nonseparable signals generated by some function with support length L, we show that they can be well approximated, up to a sign, from their noisy phaseless samples taken on a discrete set with sampling density 2L−1. In this paper, we also propose an algorithm with linear computational complexity to reconstruct nonseparable signals in a shift-invariant space from their phaseless samples corrupted by bounded noises.
Unsupervised Hierarchical Story Infilling

Story infilling involves predicting words to go into a missing span from a story. This challenging task has the potential to transform interactive tools for creative writing. However, state-of-the-art conditional language models have trouble balancing fluency and coherence with novelty and diversity. We address this limitation with a hierarchical model which first selects a set of rare words and then generates text conditioned on that set. By relegating the high entropy task of picking rare words to a word-sampling model, the second-stage model conditioned on those words can achieve high fluency and coherence by searching for likely sentences, without sacrificing diversity.
IOWA-SVM: A Density-Based Weighting Strategy for SVM Classification via OWA Operators

A weighting strategy for handling outliers in binary classification using support vector machine (SVM) is proposed in this article. The traditional SVM model is modified by introducing an induced ordered weighted averaging (IOWA) operator, in which the hinge loss function becomes an ordered weighted sum of the SVM slack variables. These weights are defined using IOWA quantifiers, while the order is induced via fuzzy density-based methods for outlier detection. The proposal is developed for both linear and kernel-based classification using the duality theory and the kernel trick. Our experimental results on well known benchmark datasets demonstrate the virtues of the proposed IOWA-SVM, which achieved the best average performance compared to other machine learning approaches of similar complexity.
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.
Optical single-channel color image asymmetric cryptosystem based on hyperchaotic system and random modulus decomposition in Gyrator domains

An enhanced optical single-channel asymmetric cryptosystem for color image is proposed by using hyperchaotic system and random modulus decomposition (RMD) in gyrator domains. For increasing the security of the encryption scheme, a novel 4D four-winged hyperchaotic system is utilized to generate several hyperchaotic phases in the encryption approach. Subsequently, the RMD is implemented to obtain the effective trapdoor one-way function in the asymmetric process. Finally, a scrambling encoding algorithm is designed to combine and randomize the intermediate and hyperchaotic data, and the result can be regarded as the ciphertext and private key of the cryptosystem. In the decryption approach, the ciphertext and private key are imported into the input plane of gyrator transform. In fact, the proposed cryptosystem is also applicable in information authentication since the hyperchaotic data is prerequisite both in encryption and decryption approaches. Various numerical simulations are made to demonstrate the validity and capability of the proposed cryptosystem.
Head Pointer or Eye Gaze: Which Helps More in MR Remote Collaboration?

This paper investigates how two different unique gaze visualizations (the head pointer(HP), eye gaze(EG)) affect table-size physical tasks in Mixed Reality (MR) remote collaboration. We developed a remote collaborative MR Platform which supports sharing of the remote expert's HP and EG. The prototype was evaluated with a user study comparing two conditions: sharing HP and EG with respect to their effectiveness in the performance and quality of cooperation. There was a statistically significant difference between two conditions on the performance time, and HP is a good proxy for EG in remote collaboration.
Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle

Small unmanned aircraft vehicles (SUAVs) or drones are very useful for visual detection and tracking due to their efficiency in capturing scenes. This paper addresses the detection and tracking of moving pedestrians with an SUAV. The detection step consists of frame subtraction, followed by thresholding, morphological filter, and false alarm reduction, taking into consideration the true size of targets. The center of the detected area is input to the next tracking stage. Interacting multiple model (IMM) filtering estimates the state of vectors and covariance matrices, using multiple modes of Kalman filtering. In the experiments, a dozen people and one car are captured by a stationary drone above the road. The Kalman filter and the IMM filter with two or three modes are compared in the accuracy of the state estimation. The root-mean squared errors (RMSE) of position and velocity are obtained for each target and show the good accuracy in detecting and tracking the target position—the average detection rate is 96.5%. When the two-mode IMM filter is used, the minimum average position and velocity RMSE obtained are around 0.8 m and 0.59 m/s, respectively.
Evaluating Perceptual Bias During Geometric Scaling of Scatterplots

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.