Reviewerly/exHarmony huggingface dataset:
Perceptual Image Hashing Based on Weber Local Binary Pattern and Color Angle Representation

This paper proposes an efficient scheme for generating image hashing by combining the local texture and color angle features. During the stage of texture extraction, using Weber's Law, the difference ratios between the center pixels and their surrounding pixels are calculated and the dimensions of these values are further reduced by applying principal component analysis to the statistical histogram. In the stage of color feature extraction, the color angle of each pixel is computed before dimensional reduction and is carried out using a discrete cosine transform and a significant coefficients selection strategy. The main contribution of this paper is a novel construction for image hashing that incorporates texture and color features by using Weber local binary pattern and color angular pattern. The experimental results demonstrate the efficacy of the proposed scheme, especially for the perceptual robustness against common content-preserving manipulations, such as the JPEG compression, Gaussian low-pass filtering, and image scaling. Based on the comparisons with the state-of-the-art schemes, receiver operating characteristic curves and integrated histograms of normalized distances show the superiority of our scheme in terms of robustness and discrimination.
CHERI Concentrate: Practical Compressed Capabilities

We present CHERI Concentrate, a new fat-pointer compression scheme applied to CHERI, the most developed capability-pointer system at present. Capability fat pointers are a primary candidate to enforce fine-grained and non-bypassable security properties in future computer systems, although increased pointer size can severely affect performance. Thus, several proposals for capability compression have been suggested elsewhere that do not support legacy instruction sets, ignore features critical to the existing software base, and also introduce design inefficiencies to RISC-style processor pipelines. CHERI Concentrate improves on the state-of-the-art region-encoding efficiency, solves important pipeline problems, and eases semantic restrictions of compressed encoding, allowing it to protect a full legacy software stack. We present the first quantitative analysis of compiled capability code, which we use to guide the design of the encoding format. We analyze and extend logic from the open-source CHERI prototype processor design on FPGA to demonstrate encoding efficiency, minimize delay of pointer arithmetic, and eliminate additional load-to-use delay. To verify correctness of our proposed high-performance logic, we present a HOL4 machine-checked proof of the decode and pointer-modify operations. Finally, we measure a 50 to 75 percent reduction in L2 misses for many compiled C-language benchmarks running under a commodity operating system using compressed 128-bit and 64-bit formats, demonstrating both compatibility with and increased performance over the uncompressed, 256-bit format.
Inducing Document Structure for Aspect-based Summarization

Automatic summarization is typically treated as a 1-to-1 mapping from document to summary.Documents such as news articles, however, are structured and often cover multiple topics or aspects; and readers may be interested in only some of them.We tackle the task of aspect-based summarization, where, given a document and a target aspect, our models generate a summary centered around the aspect.We induce latent document structure jointly with an abstractive summarization objective, and train our models in a scalable synthetic setup.In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspectbased summaries; and (c) that structure is particularly advantageous for summarizing long documents.All results transfer from synthetic training documents to natural news articles from CNN/Daily Mail and RCV1.
Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed.Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images.In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE).Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality.In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously.In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE.The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding.The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE.The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches.
Low-Resource Corpus Filtering Using Multilingual Sentence Embeddings

In this paper, we describe our submission to the WMT19 low-resource parallel corpus filtering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an encoder-decoder architecture trained on a parallel corpus to obtain multilingual sentence representations. We then use the representations directly to score and filter the noisy parallel sentences without additionally training a scoring function. We contrast our approach to other promising methods and show that LASER yields strong results. Finally, we produce an ensemble of different scoring methods and obtain additional gains. Our submission achieved the best overall performance for both the Nepali-English and Sinhala-English 1M tasks by a margin of 1.3 and 1.4 BLEU respectively, as compared to the second best systems. Moreover, our experiments show that this technique is promising for low and even no-resource scenarios.
Structuring Latent Spaces for Stylized Response Generation

Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
An Ultra-Low Power Binarized Convolutional Neural Network-Based Speech Recognition Processor With On-Chip Self-Learning

Always-on speech interfaces are prevailing in human-machine interaction, especially on wearable devices, Internet of Things, etc., which benefits from the recent breakthroughs in deep learning. For battery-powered devices, ultra-low power and real-time processing are critical. However, the massive memory access and computation of deep neural networks (DNNs) lead to long latency and huge energy consumption, which hinder their further integration in battery-powered devices. Extremely low-bit quantization shows the potential to enhance energy efficiency and speed by orders of magnitudes, but it suffers from the degradation of recognition accuracy. In this paper, we propose a binarized convolutional neural network (BCNN) based speech recognition processor, integrated with on-chip self-learning mechanism to compensate the accuracy loss caused by low-precision. We optimize the BCNN architecture by eliminating the computation redundancy, compressing the weights and tailoring approximate circuits. Fabricated in 28 nm CMOS, this processor supports real time speech recognition with power consumption of $141~\mu \text{W}$ and energy efficiency of 2.46 pJ/Neuron. Compared with state-of-the-art speech recognition implementations, this processor achieves $2.5\times $ reduction on energy consumption per neuron, and $8.0\times $ energy reduction per speech frame.
A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.
Fast Multi-View Semi-Supervised Learning With Learned Graph

Multi-view semi-supervised learning (SSL) has attracted great attention due to its effectiveness in information utilization of multiple views and labeled and unlabeled data to solve practical problems. However, most existing methods exhibit high computational complexity. Effective integration of the information on different views to achieve enhanced performance remains a challenging task. In this study, we combine an anchor-based approach with multi-view semi-supervised learning to address these problems. A novel multi-view SSL method called fast multi-view SSL (FMSSL) based on learned graph is proposed. Starting from the affinity graphs constructed by using an anchor-based strategy, FMSSL learns an optimal multi-view consensus graph by using feature and label information. The learned graph can jointly consider the relation of multiple views to approximate the manifold structure. The learned graph is then introduced into the SSL model as the weight matrix of a bipartite graph to simultaneously perform separate classification on the original samples and anchors. Accordingly, multi-view SSL can be efficiently performed, and the computational complexity can be significantly reduced. We propose an effective algorithm to optimize the objective function. Extensive experimental results on different real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
Building and Interpreting Deep Similarity Models

Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on distances or similarities. Before similarities are used for training an actual machine learning model, we would like to verify that they are bound to meaningful patterns in the data. In this paper, we propose to make similarities interpretable by augmenting them with an explanation. We develop BiLRP, a scalable and theoretically founded method to systematically decompose the output of an already trained deep similarity model on pairs of input features. Our method can be expressed as a composition of LRP explanations, which were shown in previous works to scale to highly nonlinear models. Through an extensive set of experiments, we demonstrate that BiLRP robustly explains complex similarity models, e.g., built on VGG-16 deep neural network features. Additionally, we apply our method to an open problem in digital humanities: detailed assessment of similarity between historical documents, such as astronomical tables. Here again, BiLRP provides insight and brings verifiability into a highly engineered and problem-specific similarity model.
Software defect prediction based on correlation weighted class association rule mining

Software defect prediction based on supervised learning plays a crucial role in guiding software testing for resource allocation. In particular, it is worth noticing that using associative classification with high accuracy and comprehensibility can predict defects. But owing to the imbalance data distribution inherent, it is easy to generate a large number of non-defective class association rules, but the defective class association rules are easily ignored. Furthermore, classical associative classification algorithms mainly measure the interestingness of rules by the occurrence frequency, such as support and confidence, without considering the importance of features, resulting in combinations of the insignificant frequent itemset. This promotes the generation of weighted associative classification. However, the feature weighting based on domain knowledge is subjective and unsuitable for a high dimensional dataset. Hence, we present a novel software defect prediction model based on correlation weighted class association rule mining (CWCAR). It leverages a multi-weighted supports-based framework rather than the traditional support-confidence approach to handle class imbalance and utilizes the correlation-based heuristic approach to assign feature weight. Besides, we also optimize the ranking, pruning and prediction stages based on weighted support. Results show that CWCAR is significantly superior to state-of-the-art classifiers in terms of Balance, MCC, and Gmean.
Multi-Task Learning for Recommendation Over Heterogeneous Information Network

Traditional recommender systems (RS) only consider homogeneous data and cannot fully model heterogeneous information of complex objects and relations. Recent advances in the study of Heterogeneous Information Network (HIN) have shed some light on how to leverage heterogeneous information in RS. However, existing HIN-based recommendation models assume HIN is invariable and merely use HIN as a data source for assisting recommendation, which limits their performance. In this paper, we propose a multi-task learning framework, called MTRec, for recommendation over HIN. MTRec relies on self-attention mechanism to learn the semantics of meta-paths in HIN and jointly optimizes the tasks of both recommendation and link prediction. Using a Bayesian task weight learner, MTRec is able to achieve the balance of two tasks during optimization automatically. Moreover, MTRec provides good interpretabilities of recommendation through a “translation” mechanism which is used to model the three-way interactions among users, items and the meta-paths connecting them. Experimental results demonstrate the superiority of MTRec over state-of-the-art HIN-based recommendation models, and the case studies we provide illustrate that MTRec enhances the explainability of RS.
On the Security of Reversible Data Hiding in Encrypted Images by MSB Prediction

The reversible data hiding in encrypted images by MSB prediction of P. Puteaux and W. Puesch not only provides high embedding bit-rates, but also entails a very low mathematical complexity. This correspondence investigates its security and shows flaws in embedding imperceptibility, unauthorized detection/removal of embedded data and unauthorized access to image content. Secure solutions are discussed.
Twitter Sentiment Analysis Approaches: A Survey

Twitter is one of the most popular microblogging and social networking platforms where massive instant messages (i.e. tweets) are posted every day. Twitter sentiment analysis tackles the problem of analyzing users’ tweets in terms of thoughts, interests and opinions in a variety of contexts and domains. Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event. The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter. The reviewed articles are categories into four categories based on the approach they use. Furthermore, we discuss directions for future research on how twitter sentiment analysis approaches can utilize theories and technologies from other fields such cognitive science, semantic Web, big data and visualization.
Practical Privacy-Preserving K-means Clustering

Abstract Clustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacypreserving context. Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the amortizing setting, when one needs to compute the distance from the same point to other points. Furthermore, we construct a customized garbled circuit for computing the minimum value among shared values.We believe these new constructions may be of independent interest. We implement and evaluate our protocols to demonstrate their practicality and show that they are able to train datasets that are much larger and faster than in the previous work. The numerical results also show that the proposed protocol achieve almost the same accuracy compared to a K-means plain-text clustering algorithm.
Reinforcement Learning Interpretation Methods: A Survey

Reinforcement Learning (RL) systems achieved outstanding performance in different domains such as Atari games, finance, healthcare, and self-driving cars.However, their black-box nature complicates their use, especially in critical applications such as healthcare.To solve this problem, researchers have proposed different approaches to interpret RL models.Some of these methods were adopted from machine learning, while others were designed specifically for RL.The main objective of this paper is to show and explain RL interpretation methods, the metrics used to classify them, and how these metrics were applied to understand the internal details of RL models.We reviewed papers that propose new RL interpretation methods, improve the old ones, or discuss the pros and cons of the existing methods.
A Novel Pure Pursuit Algorithm for Autonomous Vehicles Based on Salp Swarm Algorithm and Velocity Controller

Pure pursuit algorithm is one of the most effective ways of path tracking in autonomous vehicles. Nevertheless, the tracking accuracy of the existing pure pursuit algorithm is limited by the look-ahead distance. In this paper, to improve the tracking accuracy of the pure pursuit algorithm, a novel pure pursuit algorithm based on the optimized look-ahead distance named OLDPPA is proposed. Four improvements are presented in OLDPPA. Firstly, to find a better look-ahead distance of pure pursuit algorithm, salp swarm algorithm (SSA) is used in pure pursuit algorithm. Secondly, Brownian motion, a random motion mechanism of particles, is introduced in SSA to enhance its exploitation and exploration capabilities. Thirdly, to accelerate the convergence speed of SSA, a weighted mechanism which uses two different weights in the search process to adjust the salps closer to the food source quickly is assigned. Based on innovations 2 and 3, adaptive Brownian motion salp swarm algorithm (ABMSSA) is proposed and applied to pure pursuit algorithm. Finally, a velocity controller which outputs the speed of the next moment according to the distance and time interval between the look-ahead point and the current vehicle position is designed in OLDPPA, to ensure that the vehicle reaches its destination at a specified time. To verify the effectiveness and efficiency of OLDPPA, OLDPPA is applied in four different paths and the corresponding results are compared with other pure pursuit algorithms that use different look-ahead distances. Experimental results show that the tracking accuracy of OLDPPA is better than other algorithms.
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition

Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become promising and important on mobile devices such as smart phones. However, previous RNN compression techniques either suffer from hardware performance overhead due to irregularity or significant accuracy loss due to the preserved regularity for hardware friendliness. In this work, we propose RTMobile that leverages both a novel block-based pruning approach and compiler optimizations to accelerate RNN inference on mobile devices. Our proposed RTMobile is the first work that can achieve real-time RNN inference on mobile platforms. Experimental results demonstrate that RTMobile can significantly outperform existing RNN hardware acceleration methods in terms of both inference accuracy and time. Compared with prior work on FPGA, RTMobile using Adreno 640 embedded GPU on GRU can improve the energy-efficiency by 40× while maintaining the same inference time.
To err is human: Tolerate humans instead of machines in service failure

Full automation and self-service technologies have become popular in service marketing. However, customers often face multiple issues when dealing with self-service technologies. This paper examines the effect of service-failure type (employee failure vs. self-service technology failure) on customers' negative responses (dissatisfaction, forgiveness, willingness to switch between employee and self-service technology, and negative word of mouth). Through four experiments with Amazon Mechanical Turk workers and undergraduate students, this research finds that customers have more negative responses for a self-service technology failure than for an employee failure. This is because they get angrier with machines' mistakes than with those of humans. Moreover, empathy alleviates anger and customers’ negative responses in employee failure, but not in self-service technology failure. This research offers service providers new insights by scrutinizing the flip side of complete automation in service marketing.
Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation

In this paper, we present Goal-GAN, an interpretable and end-to-end trainable model for human trajectory prediction. Inspired by human navigation, we model the task of trajectory prediction as an intuitive two-stage process: (i) goal estimation, which predicts the most likely target positions of the agent, followed by a (ii) routing module which estimates a set of plausible trajectories that route towards the estimated goal. We leverage information about the past trajectory and visual context of the scene to estimate a multi-modal probability distribution over the possible goal positions, which is used to sample a potential goal during the inference. The routing is governed by a recurrent neural network that reacts to physical constraints in the nearby surroundings and generates feasible paths that route towards the sampled goal. Our extensive experimental evaluation shows that our method establishes a new state-of-the-art on several benchmarks while being able to generate a realistic and diverse set of trajectories that conform to physical constraints.