Reviewerly/exHarmony huggingface dataset:
Asymmetry-induced nonclassical correlation

In quantum resource theory (QRT), asymmetry recognized as a valid resource for the advantage of various quantum information processing. In this paper, we establish the resource theory of asymmetry using quantum Fisher information (QFI). By defining the average Fisher information as a measure of asymmetry, it is shown that the discrepancy of bipartite global and local asymmetries naturally induces the nonclassical correlation between the subsystems. This measure satisfies all the necessary axioms of a faithful measure of bipartite quantum correlation. As an illustration, we have studied the proposed measure for an arbitrary pure state and Bell diagonal state.
Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve as a memory to process dependent sentences to infer implicit aspects. It can identify implicit aspects from four types of sentences, including independent and three types of dependent sentences. The study is evaluated on a large movie reviews dataset with 50k examples. The experimental results showed that the explicit aspect identification method achieved 89% F1-score and implicit aspect extraction methods achieved 76% F1-score. In addition, the proposed approach also performs better than the state-of-the-art techniques (NMFIAD and ML-KB+) on the product review dataset, where it achieved 93% precision, 92% recall, and 93% F1-score.
Application Research on Extreme Learning Machine in Rapid Detection of Tool Wear in Machine Tools

Abstract In order to put an end to the product quality accidents caused by cutting tool breakage or severe wear in machining process, the paper explored an ELM model detection method based on voice recognition. In this paper, firstly it analysed the features of cutting sound signal in time-frequency domain, then discussed the extraction method of tool working state sensitive spectral energy statistical feature based on wavelet packet decomposition, and finally constructed an ELM fast detection model based on sound feature recognition. The experimental results demonstrated that the ELM detection model could achieve higher detection accuracy and faster response time. The simulation results show that the ELM model is effective and applicable in detecting tool wear with the help of sound recognition.
Smart Contracts Based on Multi-agent Negotiation

Decision-making processes and collaboration scenarios expect users to trust the negotiation process and respective fulfilment of its outcomes. Agents inherit the preferences of the entities that they represent and can engage in negotiation processes to fulfil their goals, or the objectives of a group. When faced with the challenges of multi-agent systems and group decision-making processes and negotiation, the traditional solutions to trust issues are supported by an inclusion of a third-party entity that, consequently, raises new trust challenges. In this work, we propose an alternative solution to this problem, based on a combination of Smart Contracts and blockchain. The immutable and distributed characteristics of these technologies provide a trustworthy support for the negotiation process, including knowledge representation. Additionally, by focusing on the terms used during the negotiation, we can improve Smart Contracts through the automatization of their contractual terms, where their parameters are derived from the output of the negotiation process between agents. This mainly reinforces that negotiation can benefit from the inclusion of Smart Contracts, and vice versa. The proposed model should be independent of protocol, language, and decision processes, however special attention should be addressed to communication, namely to the simplification and generalization of locutions.
Simultaneous Patch-Group Sparse Coding with Dual-Weighted ℓp Minimization for Image Restoration

Sparse coding (SC) models have been proven as powerful tools applied in image restoration tasks, such as patch sparse coding (PSC) and group sparse coding (GSC). However, these two kinds of SC models have their respective drawbacks. PSC tends to generate visually annoying blocking artifacts, while GSC models usually produce over-smooth effects. Moreover, conventional ℓ1 minimization-based convex regularization was usually employed as a standard scheme for estimating sparse signals, but it cannot achieve an accurate sparse solution under many realistic situations. In this paper, we propose a novel approach for image restoration via simultaneous patch-group sparse coding (SPG-SC) with dual-weighted ℓp minimization. Specifically, in contrast to existing SC-based methods, the proposed SPG-SC conducts the local sparsity and nonlocal sparse representation simultaneously. A dual-weighted ℓp minimization-based non-convex regularization is proposed to improve the sparse representation capability of the proposed SPG-SC. To make the optimization tractable, a non-convex generalized iteration shrinkage algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed SPG-SC model. Extensive experimental results on two image restoration tasks, including image inpainting and image deblurring, demonstrate that the proposed SPG-SC outperforms many state-of-the-art algorithms in terms of both objective and perceptual quality.
An Experimental Study on Replay Attack Detection Using Spoofing Clues from both Voiced and Non-Voiced Segments

The spoofing clues with reverberation, channel and environmental noise are intertwined with the genuine speaker voice, making the task for replay attack detection challenging. In this study, we propose a novel approach to make full use of the replay clues of a whole utterance, by separately extracting different features from voiced and non-voiced segments and training separate Gaussian Mixed Models. First, a joint voice activity detector is adopted to get accurate boundaries of the different segments. Then this paper extracts Constant-Q Cepstral Coefficients and Inverse Mel Frequency Cepstral Coefficients from voiced and non-voiced segments respectively. Finally, a Score Calibrator Toolkit is used to fuse the scores of voiced and non-voiced segments. The result on evaluation set of ASVspoof 2017 V2.0 corpus shows that our proposed method yields an 18.4% relative reduction in equal error ratecompared to the CQCC-CMVN baseline system.
cgSpan: Pattern Mining in Conceptual Graphs

Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm. It extends the DMGM-GSM algorithm that takes taxonomy-based labeled graphs as input; it includes three more kinds of knowledge of the CG formalism: (a) the fixed arity of relation nodes, handling graphs of neighborhoods centered on relations rather than graphs of nodes, (b) the signatures, avoiding patterns with concept types more general than the maximal types specified in signatures and (c) the inference rules, applying them during the pattern mining process. The experimental study highlights that cgSpan is a functional CG Frequent Pattern Mining algorithm and that including CGs specificities results in a faster algorithm with more expressive results and less redundancy with vocabulary.
Sonic Straws: A beverage-based playful gustosonic system

We present Sonic Straws, a beverage-based gustosonic system that allows users to experience playful personalized sounds via drinking through straws. The system consists of two straws, a customized lid, and a holder containing a microcontroller. It performs a sensing process via the two straws connected to the microcontroller to sense capacitance, which then generates sounds played back through a speaker. Our aim is to support a playful way of drinking in everyday life because we believe that interactive technology offers unique opportunities to enrich drinking experiences. Ultimately, with our work, we aim to inspire and guide designers working with playful gustosonic interactions to experience eating/drinking as play.
A Topology-Based Stereo Matching Method for One Shot 3D Measurement Using Coded Spot-Array Structured Light

In this paper, a topology-based stereo matching method for 3D measurement using a single pattern of coded spot-array structured light is proposed. The pattern of spot array is designed with a central reference ring spot, and each spot in the pattern can be uniquely coded with the row and column indexes according to the predefined topological search path. A method using rectangle templates to find the encoded spots in the captured images is proposed in the case where coding spots are missing, and an interpolation method is also proposed for rebuilding the missing spots. Experimental results demonstrate that the proposed technique could exactly and uniquely decode each spot and establish the stereo matching relation successfully, which can be used to obtain three-dimensional (3D) reconstruction with a single-shot method.
Window-Level Is a Strong Denoising Surrogate

CT image quality is heavily reliant on radiation dose, which causes a trade-off between radiation dose and image quality that affects the subsequent image-based diagnostic performance. However, high radiation can be harmful to both patients and operators. Several (deep learning-based) approaches have been attempted to denoise low dose images. However, those approaches require access to large training sets, specifically the full dose CT images for reference, which can often be difficult to obtain. Self-supervised learning is an emerging alternative for lowering the reference data requirement facilitating unsupervised learning. Currently available self-supervised CT denoising works are either dependent on foreign domains or pretexts that are not very task-relevant. To tackle the aforementioned challenges, we propose a novel self-supervised learning approach, namely Self-Supervised Window-Leveling for Image DeNoising (SSWL-IDN), leveraging an innovative, task-relevant, simple, yet effective surrogate—prediction of the window-leveled equivalent. SSWL-IDN leverages residual learning and a hybrid loss combining perceptual loss and MSE, all incorporated in a VAE framework. Our extensive (in- and cross-domain) experimentation demonstrates the effectiveness of SSWL-IDN in aggressive denoising of CT (abdomen and chest) images acquired at 5% dose level only (Code available at https://github.com/ayaanzhaque/SSWL-IDN).
Recommending Metadata Contents for Learning Objects Through Linked Data

Attach metadata to digital objects effectively underlies the development of high-quality services in systems. This work explores how the metadata of a learning object represented as linked data, in a brand new repository, can be a facilitator to a more complete catalog and search with contents recommendations. The proposed approach underlies in DBpedia Spotlight for unstructured text annotation to deliver recommendations at the learning object cataloging phase and GEMET, a marine domain thesaurus, to expand marine searching terms. Each learning object is described with OBAA metadata as a set of triples stored in Resource Description Framework format to deliver interoperability and Linked Data compatibility.
A Deep Reinforced Tree-Traversal Agent for Coronary Artery Centerline Extraction

Vessel centerline extraction is fundamental for plentiful medical applications. Majority of current methods require pre-segmentations, distance maps or similar sorts of scanning whole volume action and followed by minimal-path or skeletonization algorithms. In this paper, we demonstrate a deep reinforced tree-traversal agent that automatically traces tree-structure centerlines assuming no post-prune or post-merging. It takes raw images as input and generates tree-structure centerlines naturally. To this end, road mark and dynamic reward mechanisms are proposed to make tree-structure vessels learnable and impart the agent how to learn correspondingly. Besides, a multi-task discriminator is raised to simultaneously detect bifurcations and decide terminations. We experimentally show that traced centerlines have an overlap of more than 90% and a distance less than 0.25 mm with annotated reference centerlines on coronary arteries. Beyond the promising accuracy, the proposed method also surpasses other existing methods by a large margin in terms of the time and memory efficiency. And a flexible trade-off between accuracy and time efficiency is exhibited at the inference. Codes are available at https://github.com/LzVv123456/Deep-Reinforced-Tree-Traversal.
Data driven conditional optimal transport

A data-driven procedure is developed to compute the optimal map between two conditional probabilities $$\rho (x|z_{1},\ldots ,z_{L})$$ and $$\mu (y|z_{1},\ldots ,z_{L})$$ , known only through samples and depending on a set of covariates $$z_{l}$$ . The procedure is tested on synthetic data from the ACIC Data Analysis Challenge 2017 and it is applied to non-uniform lightness transfer between images. Exactly solvable examples and simulations are performed to highlight the differences with ordinary optimal transport.
A non-linear mapping representing human action recognition under missing modality problem in video data

Human action recognition by using standard video files is a well-studied problem in the literature. In this study, we assume to have access to single modality standard data of some actions (training data). Based on this data, we aim at identifying the actions that are present in a target modality video data without any explicit source–target relationship information. In this case, the training and test phases of the recognition task are based on different imaging modalities. Our goal in this paper is to introduce a mapping (a nonlinear operator) on both modalities such that the outcome shares some common features. These common features were then used to recognize the actions in each domain. Simulation results on MSRDailyActivity3D, MSRActionPairs, UTKinect-Action3D, and SBU Kinect interaction datasets showed that the introduced method outperforms state-of-the art methods with a success rate margin of 15% on average.
Deep Learning based Automated Waste Segregation System based on degradability

Rapidly increasing rate of consumption of resources by the growing population has led to a drastic spike in the amount of waste generated. Due to lack of effective management of waste at the initial stage of waste disposal from domestic as well as industrial sector causes segregation and recycling process to be challenging. A model is suggested in this paper which focuses on the software portion of an automatic waste segregation system where a camera and Raspberry Pi are mounted to detect and classify the individual waste item placed on a conveyor belt that carries the waste to the respective dustbin based on the classification done by Pi module. The software module consists of a Deep Learning algorithm called Single Shot Detector model with MobileNet as base network to classify the waste into biodegradable and non-biodegradable.
Thermal state quantum key distribution

We analyse a central broadcast continuous variable quantum key distribution protocol in which a beam produced by a thermal source is used to create a secret key between two parties, Alice and Bob. A beam splitter divides the initial beam into a pair of output beams, which are sent to Alice and Bob, with Eve intercepting Bob's beam. We investigate the protocol in detail, calculating mutual informations through a pair of analytic methods and comparing the results to the outputs of a Monte Carlo simulation of the protocol. In a lossless system, we find that a lower bound on the key rate remains positive in the protocol under a beam splitter attack, provided Bob receives a nonzero proportion of the beam initially sent to him. This suggests that the thermal state protocol could be used experimentally to produce secure keys.
Paratick: Reducing Timer Overhead in Virtual Machines

To this day, efficient timer management is a major challenge in virtualized environments. Contemporary timekeeping techniques in guest kernels frequently interact with timer hardware, which requires continual and costly hypervisor interference.
A Branch Elimination-based Efficient Algorithm for Large-scale Multiple Longest Common Subsequence Problem

It is a key issue to find out all longest common subsequences of multiple sequences over a set of finite alphabets, namely MLCS problem, in computational biology, pattern recognition and information retrieval, to name a few. However, it is very challenging to tackle the large-scale MLCS problem effectively and efficiently due to the high complexity of time and space. To this end, this paper will therefore propose a Branch Elimination based Space and Time efficient algorithm called BEST-MLCS, which includes the following four key strategies: 1) Estimation scheme for the lower bound of the length of MLCS. 2) Estimation scheme for the upper bound of the length of the paths through the current node. 3) Branch elimination strategy by finding all useless match points and removing the branches not on the longest paths. 4) A new Directed Acyclic Graph (DAG) construction method for constructing the smallest DAG among the existing ones. As a result, the proposed algorithm BEST-MLCS can save a lot of space and time and can handle much larger scale MLCS problems than the existing algorithms. Extensive experiments conducted on biological DNA sequences show that the performance of the proposed algorithm BEST-MLCS outperforms three state-of-the-art algorithms in terms of run-time and memory consumption.
BehavE: Behaviour Understanding Through Automated Generation of Situation Models

Automated systems for assisting persons to achieve their everyday tasks are gaining popularity, both in the application domains for supporting healthy persons, as well as for assisting people with impairments. The development of such assistive systems is a challenging task associated with a lot of time and effort and often requires the involvement of domain experts. To address this problem, different works have investigated the automated knowledge extraction and model generation for behaviour interpretation and assistance. Existing works, however, usually concentrate on one source of data for the task of automated knowledge generation, which could potentially result in simpler models that are unable to adequately support the person. To address this problem, in this work we present the BehavE methodology, which proposes the extraction of knowledge from different types of sources and its consolidation into a unified semantic model that is used for behaviour interpretation and generation of assistance strategies.
Using the Improved Mask R-cnn and Softer-nms for Target Segmentation of Remote Sensing Image

Recently, the combination of remote sensing image processing and deep learning methods is an increasingly popular trend. In this paper, we combine the existing instance segmentation model Mask R-CNN and the target detection algorithm Softer-NMS, and propose a model based on the improved Mask R-CNN. The model can capture the edges well, thereby segmenting the target more accurately. The experimental results also verify the effectiveness of our method. Compared with the prototype Mask R-CNN, our method improves the overall segmentation accuracy, and the loss function of the mask branch and the loss function of the candidate frame of the RPN drop faster than the original model. Incidentally, there is also a good improvement in the accuracy of detection.
Gaussian Fusion: Accurate 3D Reconstruction via Geometry-Guided Displacement Interpolation

Reconstructing delicate geometric details with consumer RGB-D sensors is challenging due to sensor depth and poses uncertainties. To tackle this problem, we propose a unique geometry-guided fusion framework: 1) First, we characterize fusion correspondences with the geodesic curves derived from the mass transport problem, also known as the Monge-Kantorovich problem. Compared with the depth map back-projection methods, the geodesic curves reveal the geometric structures of the local surface. 2) Moving the points along the geodesic curves is the core of our fusion approach, guided by local geometric properties, i.e., Gaussian curvature and mean curvature. Compared with the state-of-the-art methods, our novel geometry-guided displacement interpolation fully utilizes the meaningful geometric features of the local surface. It makes the reconstruction accuracy and completeness improved. Finally, a significant number of experimental results on real object data verify the superior performance of the proposed method. Our technique achieves the most delicate geometric details on thin objects for which the original depth map back-projection fusion scheme suffers from severe artifacts (See Fig. 1).
Design and evaluation of a noninvasive tongue-computer interface for individuals with severe disabilities

Tongue-computer interfaces have shown the potential to control assistive devices developed for individuals with severe disabilities. However, current efficient tongue-computer interfaces require invasive methods for attaching the sensor activation units to the tongue, such as piercing. In this study, we propose a noninvasive tongue-computer interface to avoid the requirement of invasive activation unit attachment methods. We developed the noninvasive tongue-computer interface by integrating an activation unit on a frame, and mounting the frame on an inductive tongue-computer interface (ITCI). Thus, the users are able to activate the inductive sensors on the interface by positioning the activation unit with their tongue. They also do not need to remount the activation unit before each use. We performed pointing tests for controlling a computer cursor and number typing tests with two able-bodied participants, where one of them was experienced with using invasive tongue-computer interfaces and other one had no experience. We measured throughput and movement error for pointing tasks, and speed and accuracy for number typing tasks for the evaluation of the feasibility and performance of the developed noninvasive system. Results show that the inexperienced participant achieved similar results with the developed noninvasive tongue-computer interface compared to the current invasive version of the ITCI, while the experienced participant performed better with the invasive tongue-computer interface.