Reviewerly/exHarmony huggingface dataset:
Real-Time Target Detection and Tracking System Based on Stereo Camera for Quadruped Robots

As quadruped robots are suitable to be used in unstructured environment, environment perception for them becomes a popular field of research nowadays. This paper presents a novel and effective detection and tracking system based on stereo camera for quadruped robots, which allows a particular user to designate himself as the robot's leader. Firstly, the object detection is carried out by YOLO-V3 method based on the newfangled residual network. Then, the object tracking is performed based on ECO method, which is an improved kernel algorithm. Finally, a IDLA and CCF cascaded pedestrian re-identification method is adopted to identify the navigator among the crowd. In this work, we tackle the key causes behind the problems of computational complexity and over-fitting, with the aim of simultaneously improving both speed and performance. The comprehensive experimental results validate the effectiveness and robustness of our algorithm. In addition, our system runs at a real-time of 30 frames per second.
Efficient Fair Multiparty Protocols Using Blockchain and Trusted Hardware

In ACM CCS'17, Choudhuri et al. designed two fair public-ledger-based multi-party protocols (in the malicious model with dishonest majority) for computing an arbitrary function f. One of their protocols is based on a trusted hardware enclave $$\mathcal {G}$$ (which can be implemented using Intel SGX-hardware) and a public ledger (which can be implemented using a blockchain platform, such as Ethereum). Subsequently, in NDSS'19, a stateless version of the protocol was published. This is the first time, (a certain definition of) fairness – that guarantees either all parties learn the final output or nobody does – is achieved without any monetary or computational penalties. However, these protocols are fair, if the underlying core MPC component guarantees both privacy and correctness. While privacy is easy to achieve (using a secret sharing scheme), correctness requires expensive operations (such as ZK proofs and commitment schemes). We improve on this work in three different directions: attack, design and performance. Our first major contribution is building practical attacks that demonstrate: if correctness is not satisfied then the fairness property of the aforementioned protocols collapse. Next, we design two new protocols – stateful and stateless – based on public ledger and trusted hardware that are: resistant against the aforementioned attacks, and made several orders of magnitude more efficient (related to both time and memory) than the existing ones by eliminating ZK proofs and commitment schemes in the design. Last but not the least, we implemented the core MPC part of our protocols using the SPDZ-2 framework to demonstrate the feasibility of its practical implementation.
String commitment scheme with low output locality

Commitment schemes are important tools for various protocols. However, no scheme with output locality have been proposed yet. Low output locality is a property of functions that every output bit to depend on a small number of input bits. In this paper, we construct a commitment scheme having low output locality from a modified lattice-based hash function for the first time. We also prove that our scheme satisfies the binding property by using the collision resistance of the lattice-based hash functions and the hiding property by using a modified version of the leftover hash lemma, respectively.
A Formally Verified Model of Web Components

The trend towards ever more complex client-side web applications is unstoppable. Compared to traditional software development, client-side web development lacks a well-established component model, i. e., a method for easily and safely reusing already developed functionality. To address this issue, the web community started to adopt shadow trees as part of the Document Object Model (DOM). Shadow trees allow developers to “partition” a DOM instance into parts that should be safely separated, e. g., code modifying one part should not unintentionally affect other parts of the DOM. While shadow trees provide the technical basis for defining web components, the DOM standard neither defines the concept of web components nor specifies the safety properties that web components should guarantee. Consequently, the standard also does not discuss how or even if the methods for modifying the DOM respect component boundaries. In this paper, we present a formally verified model of web components and define safety properties which ensure that different web components can only interact with each other using well-defined interfaces. Moreover, our verification of the application programming interface (API) of the DOM revealed numerous invariants that implementations of the DOM API need to preserve to ensure the integrity of components.
A Method for Position Estimation of Mobile Robot Based on Data Fusion

Triangulation is a commonly used method for position estimation of mobile robots. In this work, we describe the triangulation algorithms in detail and analyze the error by establishing a Gaussian error model. We perform multiple triangulation algorithms using angular information of landmarks in one scan. A different approach is proposed to fuse the estimated values which is acquired from multiple triangulation algorithms. The proposed algorithm effectively improves the accuracy of position estimation based on triangulation and reduces the uncertainty of position.
DCAV: A software system to evaluate next‐generation cloud data center availability through a friendly graphical interface

Summary To assess the availability of different data center configurations, understand the main root causes of data center failures and represent its low‐level details, such as subsystem's behavior and their interconnections, we have proposed, in previous works, a set of stochastic models to represent different data center architectures (considering three subsystems: power, cooling, and IT) based on the TIA‐942 standard. In this paper, we propose the Data Center Availability (DCAV), a web‐based software system to allow data center operators to evaluate the availability of their data center infrastructure through a friendly interface, without need of understanding the technical details of the stochastic models. DCAV offers an easy step‐by‐step interface to create and configure a data center model. The main goal of the DCAV system is to abstract low‐level details and modeling complexities, becoming the data center availability analysis a simple and less time‐consuming task.
A Solution to the Security Authentication Problem in Smart Houses Based on Speech

Voice-operated smart houses often face security problems when there is pathology in the voice of an individual. If an error occurs during the process of authentication, then the system should detect whether this error is due to pathology or someone is trying to deceive the system. In the case of successful pathology detection, the system offers a second option of the pin code. Pathology detection is one of the major components in this research work. Voice pathology is the disorder of the vocal fold, which is normally diagnosed in people working in different areas, such as education, courtroom, etc. Many researchers have proposed different voice pathology detection systems (VPDS) where they used a common feature extraction technique, i.e., Mel Frequency cepstral coefficient (MFCC), or compared it with their proposed technique and produced results that are different in almost every case. In this paper, we propose a system that is based on MFCC features fused with pitch and it achieves 99.97% accuracy of pathology detection for the MEEI data set. This voice pathology detection accuracy is better as compared to most of the VPDS available for MEEI.
Study on the strategy of 3D images based reconstruction using a camera attached to a robot arm

The metric distance error between the calculated point and the real point in the 3D measurement coordinate system is due to optimization methods carried out in 2D image plane. Euclidian image based 3D reconstruction is carried out in three major steps, geometric primitive extraction, correspondence, and triangulation. Extraction and triangulation are purely geometric tasks but the correspondence step is a challenge in precision. In this paper we study 3D object reconstruction based on a set of 2D images. We used a robot arm to accurately move the camera and we study the influence of the motion. It has been demonstrated that the impact of a correspondence error on the reconstruction accuracy of a 3D images-based reconstructed point may vary depending on the image capture strategy. Two modes of motion have been adopted. The first is an axial mode making the camera close or far from the object acting like a zoom. The second mode is lateral where the camera moves parallel to the object. It has been shown that axial mode leads to greater errors compared to the lateral mode. The impact of the geometric parameters of photographing on images based 3D reconstruction error is also discussed.
Dealing with Security in a Real DevOps Environment

Security is a hot topic in several domains especially in critical infrastructures such as the national health systems. Security practices, methods and tools enhance the resulting final products and services offered to citizens. There is no consensus on how security measures must be included within the DevOps pipeline. This paper provides a DevOps approach for managing security measures along the DevOps pipeline. This approach is based on source code analysis at the integration phase, and it is an initial step for injecting security along the DevOps process. This approach has been developed for a real scenario related to the health sector.
Convolutional End-to-End Memory Networks for Multi-Hop Reasoning

Machine reading and comprehension using differentiable reasoning models has recently been studied extensively, and memory networks have demonstrated promising performance on some reasoning tasks such as factual reasoning and basic deduction. However, as a natural language understanding model, memory networks still face challenges on the numeric representations for sentences, particularly the text representation method and the effectiveness of learned vector representations. In this paper, inspired by the convolution mechanism in the computer vision domain, a raw text representation architecture for question answering problem named convolutional end-to-end memory networks(CMemN2N) architecture is proposed. The convolutional architecture of the proposed model allows us to abstract the useful local information for reasoning to get the significant numeric sentence representation passed to the follow-up sub-tasks. Our experiments show that CMemN2N achieves better results on most of the 20 bAbI task dataset, yielding improvements for the average result compared to the state-of-the-art.
An explicit track continuity algorithm for the GM-PHD filter

In this study, an explicit track continuity algorithm is proposed for multi-target tracking (MTT) based on the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter. In this approach, the Gaussian components are classified and labeled, and multi-target state extraction is converted into multiple single-state extractions. This provides the identity label of the individual target and can shield against the negative effects of clutter in the prior density region on the estimates. Currently, trajectory maintenance and multi-target state extraction in the GM-PHD filter have not been effectively integrated. This paper realizes the integration of trajectory maintenance with state extraction in the GM-PHD filter. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity and obtain slightly lower accuracy than that of the generalized labeled multi-Bernoulli filter while maintaining the low computational cost of the GM-PHD filter.
Inverting and Modeling Morphological Inflection

Previous "wug" tests (Berko, 1958) on Japanese verbal inflection have demonstrated that Japanese speakers, both adults and children, cannot inflect novel present tense forms to "correct" past tense forms predicted by rules of existent verbs (de Chene, 1982;Vance, 1987Vance, , 1991;;Klafehn, 2003Klafehn, , 2013)), indicating that Japanese verbs are merely stored in the mental lexicon.However, the implicit assumption that present tense forms are bases for verbal inflection should not be blindly extended to morphologically rich languages like Japanese in which both present and past tense forms are morphologically complex without inherent direction (Albright, 2002).Interestingly, there are also independent observations in the acquisition literature to suggest that past tense forms may be bases for verbal inflection in Japanese (Klafehn, 2003;Murasugi et al., 2010;Hirose, 2017;Tatsumi et al., 2018).In this paper, we computationally simulate two directions of verbal inflection in Japanese, Present → Past and Past → Present, with the rule-based computational model called Minimal Generalization Learner (MGL; Albright and Hayes, 2003) and experimentally evaluate the model with the bidirectional "wug" test where humans inflect novel verbs in two opposite directions.We conclude that Japanese verbs can be computed online via some generalizations and those generalizations do depend on the direction of morphological inflection.
On the Use of Gaze as a Measure for Performance in a Visual Exploration Task

Visual exploration skill acquisition is important for many vocational professions, yet many apprentices struggle to acquire these skills, impacting both their grades and practical work. Traditionally, the learning of visual skills is facilitated through exercises where it can be difficult to identify struggling apprentices early. We propose the use of gaze patterns to identify apprentices who may need additional support. In this paper, we investigated differences in gaze patterns between teachers and apprentices and the relationship between gaze patterns and student performance. In a study with 18 fashion design apprentices and 16 fashion design teacher, we found teachers have a higher gaze coverage of an image than apprentices and there is a correlation between the verbalisation score of apprentices and their similarity of gaze patterns to the teacher average. Using these results, we may be able to adapt exercises to apprentices’ particular needs early in the learning process.
Hard Ship Detection via Generative Adversarial Networks

In optical remote sensing images, many ships have very similar shapes and textures with backgrounds. In this case, it is very hard to accurately detect these ships. In this paper, we introduce generative adversarial networks (GANs) to perform hard ship detection. GANs consist of one generative network and one discriminator network. We take state-of-the-art object (ship) detection network Faster R-CNN as the generative network, which outputs the detection results as fake samples. The ground-truth ships in the input image are set as the real samples. The discriminator network is responsible for distinguishing between fake samples and real samples. The two networks are simultaneously trained. Through continuous adversarial training, the fake samples generated by the generative network can be very similar to the real samples, and the discriminator network would not correctly distinguish between fake samples and real samples. As a result, the ship detection network (generative network) correctly recognizes hard-detection ships, producing satisfactory detection results. What's more, the discriminator network is only used in training process, and thus the proposed method not only improves detection accuracy, but also does not increase computational cost.
The Asynchronous Training Algorithm Based on Sampling and Mean Fusion for Distributed RNN

Training of large scale deep neural networks with distributed implementations is an effective way to improve the efficiency. However, high network communication cost for synchronizing gradients and parameters is a major bottleneck in distributed training. In this work, we propose an asynchronous training algorithm based on sampling and mean fusion for distributed recurrent neural network (RNN). In distributed RNN, multiple distributed neuron nodes and an interaction node work together to implement the training. The synchronization overhead is reduced by a unique asynchronous sampling strategy amongst the distributed neuron nodes. Then, in order to make up for the accuracy loss caused by the asynchronous parameter update, a mean fusion algorithm is proposed, where the interaction node averages all local parameters from the distributed neurons. We mathematically prove the convergence of the proposed algorithm. Experimental verification is performed on two language modeling benchmark datasets. The results demonstrate significant speed gains for distributed RNN, while the accuracy loss is less than 1% on average.
Hierarchical Recurrent and Convolutional Neural Network Based on Attention for Chinese Document Classification

Document classification is one of the foundational tasks of Natural Language Processing (NLP) applications. In this paper, we introduce an attention based hierarchical recurrent and convolutional neural network to do Chinese document classification. In our model, we not only apply a recurrent neural network to capture information of each sentence as far as possible when learning word representations, but also apply a convolutional neural network to get the contextual information of the whole document. Besides, we also employ an attention layer that automatically judges which words play key roles in each sentence to capture the key components in sentences. We conduct experiments on two commonly used Chinese document datasets. The experimental results show that our proposed method outperforms the baseline methods on these datasets in several indicators.
Deep Convolutional Neural Networks Features For Robust Foreground Segmentation

Background modeling and subtraction, the task to detect moving objects in a scene, is a fundamental and critical step for many high level computer vision tasks. However, background subtraction modeling is still an open and challenge problem particularly in practical scenarios with drastic illumination changes and dynamic backgrounds. In this paper, we propose a novel foreground detection method based on CNNs to deal with challenges confronted with background subtraction. Firstly, given the cleaned background image without moving objects, constructing adjustable neighborhood of each pixel in the background image to form windows; CNN features are extracted with pre-trained CNN model for each window to form the feature based background model. Secondly, for the current frame of a video scene, extracting features with the same operation as the background model. Euclidean distance is adopted to build the distmap for current frame and background image with CNN features. Thirdly, the distmap is fed into graph cut algorithm to obtain the foreground mask. In order to deal with the background changes, the background model is updated with a certain rate. Experimental results verify that the proposed approach is effective to detect foreground objects from complex background environments, and outperforms some state-of-the-art methods.
DAQAP: Defeasible Argumentation Query Answering Platform

In this paper we present the DAQAP, a Web platform for Defeasible Argumentation Query Answering, which offers a visual interface that facilitates the analysis of the argumentative process defined in the Defeasible Logic Programming (DeLP) formalism. The tool presents graphs that show the interaction of the arguments generated from a DeLP program; this is done in two different ways: the first focuses on the structures obtained from the DeLP program, while the second presents the defeat relationships from the point of view of abstract argumentation frameworks, with the possibility of calculating the extensions using Dung’s semantics. Using all this data, the platform provides support for answering queries regarding the states of literals of the input program.
Mining Frequent Patterns for ECG Multi-label Data by FP-Growth Algorithm Based on Spark

Chinese Cardiovascular Disease Database (CCDD), which contains 12-Lead electrocardiogram (ECG) data and detailed features with diagnosis, include more than 270 categories of ECG-type data. Each record can correspond to multiple diagnosis results. However, most of the current methods are for single-label data, few people study multi-label ECG data, especially the correlation in ECG type labels. As one of the top ten classic algorithms for data mining, association rule mining algorithm can be used to discover interesting associations or related information between item sets in a large amount of data. The association rule mining algorithm on a single machine is difficult to adapt to big data analysis and processing tasks because of its space-time complexity and I/O cost. This paper makes full use of the powerful data processing capabilities of Spark's cluster system, studies the frequent patterns for ECG big data.
Greenberger–Horne–Zeilinger state generation with linear optical elements

We propose a scheme to probabilistically generate Greenberger–Horne–Zeilinger states encoded on the path degree of freedom of three photons. These photons are totally independent from each other, having no direct interaction during the whole evolution of the protocol, which remarkably requires only linear optical devices to work and two extra ancillary photons to mediate the correlation. The efficacy of the method, which has potential application in distributed quantum computation and multiparty quantum communication, is analyzed in comparison with similar proposals reported in the recent literature. We also discuss the main error sources that limit the efficiency of the protocol in a real experiment and some interesting aspects about the mediator photons in connection with the concept of spatial nonlocality.
IP-GAN: Learning Identity and Pose Disentanglement in Generative Adversarial Networks

Synthesizing realistic multi-view face images from a single-view input is an effective and cheap way for data augmentation. In addition it is promising for more efficiently training deep pose-invariant models for large-scale unconstrained face recognition. It is a challenging generative learning problem due to the large pose discrepancy between the synthetic and real face images, and the need to preserve identity after generation. We propose IP-GAN, a framework based on Generative Adversarial Networks to disentangle the identity and pose of faces, such that we can generate face images of a specific person with a variety of poses, or images of different identities with a particular pose. To rotate a face, our framework requires one input image of that person to produce an identity vector, and any other input face image to extract a pose embedding vector. Then we recombine the identity vector and the pose vector to synthesize a new face of the person with the extracted pose. Two learning pathways are introduced, the generation and the transformation, where the generation path focuses on learning complete representation in the latent embedding space. While the transformation path focuses on synthesis of new face images with target poses. They collaborate and compete in a parameter-sharing manner, and in an unsupervised settings. The experimental results demonstrate the effectiveness of the proposed framework.