Reviewerly/exHarmony huggingface dataset:
Cut-Elimination for Provability Logic by Terminating Proof-Search: Formalised and Deconstructed Using Coq

Recently, Brighton gave another cut-admissibility proof for the standard set-based sequent calculus GLS for modal provability logic GL. One of the two induction measures that Brighton uses is novel: the maximum height of regress trees in an auxiliary calculus called RGL. Tautology elimination is established rather than direct cut-admissibility, and at some points the input derivation appears to be ignored in favour of a derivation obtained by backward proof-search. By formalising the GLS calculus and the proofs in Coq, we show that: (1) the use of the novel measure is problematic under the usual interpretation of the Gentzen comma as set union, and a multiset-based sequent calculus provides a more natural formulation; (2) the detour through tautology elimination is unnecessary; and (3) we can use the same induction argument without regress trees to obtain a direct proof of cut-admissibility that is faithful to the input derivation.
A generic framework for Generation of Summarized Video Clips using Transfer Learning (SumVClip)

Video summarization aims to produce highlights of the original video showing informative key events. Now a days video content is increasing enormously therefor to store, browse and share videos is time consuming and a challenging task. A generic technique named SumVClip is proposed in this study. The generic video summary clips are produced by selecting key-frames based on classification through deep learning i.e. AlexNet. The model is trained for a dataset in different categories such as surveillance and lectures having two main classes positive and negative. To generate summarized video clips, threshold is set according to video length for generation of each video clip; hence frames are classified and added sequentially into the output video. For the evaluation of the proposed algorithm, various metrics i.e. accuracy, precision, recall and F1 scores is computed. The quantitative F1 score shows the encouraging results to generate summarized video clips.
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our model can outperform various methods. We also conduct zero-shot setting experiments on the Argumentative Dialogue Summary Corpus, the results show that our model can better generalized to the new domain.
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We prove that under the action of GL(2,q6) there are ⌊(q2+q+1)(q−2)/2⌋ equivalence classes of maximum scattered subspaces of the form Ub={(x,bxq+xq4):x∈Fq6} in Fq6×Fq6. This verifies a conjecture of Csajbók, Marino, Polverino and Zanella from 2018.
Exploring the Embodiment of a Virtual Hand in a Spatially Augmented Respiratory Biofeedback Setting

Enhancing the embodiment of artificial limbs—the individuals' feeling that a virtual or robotic limb is integrated in their own body scheme—is an impactful strategy for improving prosthetic technology acceptance and human-machine interaction. Most studies so far focused on visuo-tactile strategies to empower the embodiment processes. However, novel approaches could emerge from self-regulation techniques able to change the psychophysiological conditions of an individual. Accordingly, this pilot study investigates the effects of a self-regulated breathing exercise on the processes of body ownership underlying the embodiment of a virtual right hand within a Spatially Augmented Respiratory Biofeedback (SARB) setting. This investigation also aims at evaluating the feasibility of the breathing exercise enabled by a low-cost SARB implementation designed for upcoming remote studies (a need emerged during the COVID-19 pandemic). Twenty-two subjects without impairments, and two transradial prosthesis users for a preparatory test, were asked (in each condition of a within-group design) to maintain a normal (about 14 breaths/min) or slow (about 6 breaths/min) respiratory rate to keep a static virtual right hand “visible” on a screen. Meanwhile, a computer-generated sphere moved from left to right toward the virtual hand during each trial (1 min) of 16. If the participant's breathing rate was within the target (slow or normal) range, a visuo-tactile event was triggered by the sphere passing under the virtual hand (the subjects observed it shaking while they perceived a vibratory feedback generated by a smartphone). Our results—mainly based on questionnaire scores and proprioceptive drift—highlight that the slow breathing condition induced higher embodiment than the normal one. This preliminary study reveals the feasibility and potential of a novel psychophysiological training strategy to enhance the embodiment of artificial limbs. Future studies are needed to further investigate mechanisms, efficacy and generalizability of the SARB techniques in training a bionic limb embodiment.
UR5 Robot Manipulation using Matlab/Simulink and ROS

This paper shows the implementation of a control of a real UR5 robot from Matlab/Simulink using ROS. The desired trajectories are designed in Matlab, tested in Simulink and the ROS Toolbox is used to send these trajectories from Simulink to ROS, using a Python file to make the connection. The robot simulator CoppeliaSim is used to test the movement of the robot before sending it to the real manipulator. Different trajectories are tested on a UR5 robot. This paper hopes to help the researchers that have experience in Matlab and want to move a UR5 robot without the use of other platforms.
Design and Application of Electronic Rehabilitation Medical Record (ERMR) Sharing Scheme Based on Blockchain Technology

As the value of blockchain has been widely recognized, more and more industries are proposing their blockchain solutions, including the rehabilitation medical industry. Blockchain can play a powerful role in the field of rehabilitation medicine, bringing a new research idea to the management of rehabilitation medical data. The electronic rehabilitation medical record (ERMR) contains rich data dimensions, which can provide comprehensive and accurate information for assessing the health of patients, thereby enhancing the effect of rehabilitation treatment. This paper analyzed the data characteristics of ERMR and the application requirements of blockchain in rehabilitation medicine. Based on the basic principles of blockchain, the technical advantages of blockchain used in ERMR sharing have been studied. In addition, this paper designed a blockchain-based ERMR sharing scheme in detail, using the specific technologies of blockchain such as hybrid P2P network, block-chain data structure, asymmetric encryption algorithm, digital signature, and Raft consensus algorithm to achieve distributed storage, data security, privacy protection, data consistency, data traceability, and data ownership in the process of ERMR sharing. The research results of this paper have important practical significance for realizing the safe and efficient sharing of ERMR, and can provide important technical references for the management of rehabilitation medical data with broad application prospects.
Extracting per Query Valid Explanations for Blackbox Learning-to-Rank Models

Learning-to-rank (LTR) is a class of supervised learning techniques that apply to ranking problems dealing with a large number of features. The popularity and widespread application of LTR models in prioritizing information in a variety of domains makes their scrutability vital in today's landscape of fair and transparent learning systems. However, limited work exists that deals with interpreting the decisions of learning systems that output rankings. In this paper we propose a model agnostic local explanation method that seeks to identify a small subset of input features as explanation to the ranked output for a given query. We introduce new notions of validity and completeness of explanations specifically for rankings, based on the presence or absence of selected features, as a way of measuring goodness. We devise a novel optimization problem to maximize validity directly and propose greedy algorithms as solutions. In extensive quantitative experiments we show that our approach outperforms other model agnostic explanation approaches across pointwise, pairwise and listwise LTR models in validity while not compromising on completeness.
Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes

We consider the problem of private distributed matrix multiplication under limited resources. Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against the workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we propose the use of recently-introduced bivariate polynomial codes to further speed up private distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them. We show that the proposed approach reduces the average computation time of private distributed matrix multiplication compared to its competitors in the literature while improving the upload communication cost and the workers' storage efficiency.
Gatekeepers of toxicity: Reconceptualizing Twitter's abuse and hate speech policies

Policy & InternetVolume 13, Issue 4 p. 502-521 RESEARCH ARTICLE Gatekeepers of toxicity: Reconceptualizing Twitter's abuse and hate speech policies Daniel Konikoff, Corresponding Author Daniel Konikoff Daniel.konikoff@mail.utoronto.ca orcid.org/0000-0002-5515-4985 Centre for Criminology and Sociolegal Studies, University of Toronto, Toronto, Ontario, Canada Correspondence Daniel Konikoff, Centre for Criminology and Sociolegal Studies, University of Toronto, Second Floor, Canadiana Gallery, 14 Queen's Park Crescent West, Toronto, ON M5S 3K9, Canada. Email: Daniel.konikoff@mail.utoronto.caSearch for more papers by this author Daniel Konikoff, Corresponding Author Daniel Konikoff Daniel.konikoff@mail.utoronto.ca orcid.org/0000-0002-5515-4985 Centre for Criminology and Sociolegal Studies, University of Toronto, Toronto, Ontario, Canada Correspondence Daniel Konikoff, Centre for Criminology and Sociolegal Studies, University of Toronto, Second Floor, Canadiana Gallery, 14 Queen's Park Crescent West, Toronto, ON M5S 3K9, Canada. Email: Daniel.konikoff@mail.utoronto.caSearch for more papers by this author First published: 29 August 2021 https://doi.org/10.1002/poi3.265Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Twitter has become the platform of choice for journalists, politicians, and citizens because of the ways that it encourages expression, participation, and debate. In recent years, however, Twitter's "zeal for free speech" has turned the platform into a breeding ground for abuse, harassment, and hate speech. Through a discourse analysis of Twitter's policies, rules, and enforcement guidelines, this project asks (1) What role do Twitter's policies, rules, and terms of service play in instances where Twitter does not successfully address online abuse, hate speech, and harassment? and (2) What role does Twitter's technological architecture play in both propagating or preventing online hate speech and abuse? By invoking traditional and contemporary gatekeeping theories, theories of deterrence, and responsibilization, this paper will demonstrate that Twitter's policies perpetuate abuse and hateful conduct by splitting the gatekeeping role between the platform and its users. The study sheds light on the blind spots in Twitter's policies, such as their permissive free speech rhetoric and their nondeterrent enforcement options, to provide an empirical basis for how Twitter can detoxify user experience through revisions to the platform's policy framework. Volume13, Issue4December 2021Pages 502-521 RelatedInformation
A Verifiable Federated Learning Scheme Based on Secure Multi-party Computation

Federated learning ensures that the quality of the model is uncompromised while the resulting global model is consistent with the model trained by directly collecting user data. However, the risk of inferring data considered in federated learning. Furthermore, the inference to the learning outcome considered in a federated learning environment must satisfy that data cannot be inferred from any outcome except the owner of the data. In this paper, we propose a new federated learning scheme based on secure multi-party computation (SMC) and differential privacy. The scheme prevents inference during the learning process as well as inference of the output. Meanwhile, the scheme protects the user's local data during the learning process to ensure the correctness of the results after users' midway exits through the process.
DEAttack: A differential evolution based attack method for the robustness evaluation of medical image segmentation

Deep learning is an effective tool to assist doctors with many time-consuming and error-prone medical image analytical tasks. However, deep models are shown to be vulnerable to adversarial attacks, posing significant challenges to clinical applications. Existing works regarding the robustness of deep learning models are scarce, where most of them focus on the attack of medical image classification models. In this paper, a differential evolution attack (DEAttack) method is proposed to generate adversarial examples for medical image segmentation models. Our method does not require extra information such as the network’s structures and weights compared with the most widely investigated gradient-based attack methods. Additionally, benefit from the embedded differential evolution algorithm, which can preserve diversities of the optimization space. The proposed method can achieve better results than gradient-based methods, which can successfully attack the segmentation model with only perturbing a small fraction of the image pixels, demonstrating that the medical image segmentation model is more susceptible to adversarial examples. In addition to evaluating model robustness attack with public datasets, our DEAttack method was also tested on the clinical diagnostic dataset, demonstrating its superior performance and elegant process for the robustness evaluation of deep models in medical image segmentation.
Recent advances in 3D object detection based on RGB-D: A survey

3D object detection is a critical part of environmental perception systems and one of the most fundamental tasks in understanding the 3D visual world, which benefit a series of downstream real-world applications. RGB-D images include object texture and semantic information, as well as depth information describing spatial geometry. Recently, numerous 3D object detection models for RGB-D images have been proposed with excellent performance, but summaries in this area are still absent. To stimulate future research, this paper provides a detailed analysis of current developments in 3D object detection methods for RGB-D images to motivate future research. It covers three major parts, including background on 3D object detection, RGB-D data details, and comparative results of state-of-the-art methods on several publicly available datasets, with an emphasis on contributions, design ideas, and limitations, as well as insightful observations and inspiring future research directions.
Monitoring Fast Superconducting Qubit Dynamics Using a Neural Network

Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. To recover individual quantum trajectories from these noisy signals, traditional methods require slow qubit dynamics and substantial prior information in the form of calibration experiments. Monitoring rapid qubit dynamics, e.g., during quantum gates, requires more complicated methods with increased demand for prior information. Here, we experimentally demonstrate an alternative method for accurately tracking rapidly driven superconducting qubit trajectories that uses a long short-term memory (LSTM) artificial neural network with minimal prior information. Despite few training assumptions, the LSTM produces trajectories that include qubit-readout resonator correlations due to a finite detection bandwidth. In addition to revealing rotated measurement eigenstates and a reduced measurement rate in agreement with theory for a fixed drive, the trained LSTM also correctly reconstructs evolution for an unknown drive with rapid modulation. Our work enables new applications of weak measurements with faster or initially unknown qubit dynamics, such as the diagnosis of coherent errors in quantum gates.6 MoreReceived 6 September 2021Revised 21 April 2022Accepted 17 June 2022DOI:https://doi.org/10.1103/PhysRevX.12.031017Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum information processingQuantum measurementsPhysical SystemsSuperconducting qubitsTechniquesWeak measurementsQuantum Information
Geological mapping of basalt using stream sediment geochemical data: Case study of covered areas in Jining, Inner Mongolia, China

Multidisciplinary exploration data have been widely and successfully applied when using machine learning methods to conduct geological mapping. However, in covered areas such as Jining, Inner Mongolia, China, where remote sensing and geophysical data are unavailable or difficult to obtain, geochemical data become more important. In addition, previous studies have often selected data labels based on geological maps, which are generally obtained by interpolation or extrapolation of field lithological points and so are inherently uncertain. This study collected seven types of 2341 field lithological points and evaluated the errors of each lithological unit, based on a confusion matrix. Using these field lithological points, we applied the random forest (RF) and support vector machine (SVM) methods to delineate basalt in the Jining region by integrating 1:50,000 stream sediment geochemical data. The evaluation indexes of accuracy, precision, recall, and the receiver operating characteristic curve (ROC) all indicated that RF outperformed SVM. Based on the predictions of RF, five types of target areas were generated, which were further verified using Sentinel-2 images. This research highlights that using lithological points as data labels and trace-element stream sediment data as a training dataset can provide encouraging results when conducting lithological mapping in covered areas.
Blockchain Applications in UAV-Towards Aviation 4.0

Aviation 4.0Aviation 4.0 is one of the most important contexts that started to attract many researchers in recent years. The main element of Aviation 4.0Aviation 4.0 is to employ and manage unmanned aircraft vehicles (UAVs). Employing UAVs in the Aviation 4.0Aviation 4.0 era will minimize the human dependency level of the aviation management systems. Embedding UAVs with BlockchainBlockchain technology (BCT) might become an increasing phenomenon since the UAVs should be operated in an environment with many obstacles and possibilities of cyber-attacks. Therefore, using BCT, regulators in the whole world will be able to track the identities of UAVs flying in their territories. This chapter provides detailed information about the BCT and explains its implementation architecture, and the potential applications of this technology in the UAV in the Aviation 4.0Aviation 4.0 era are also stated.
Multivariate analysis of dilution-corrected residuals to improve the interpretation of geochemical anomalies and determine their potential sources: The Mingardo River case study (Southern Italy)

Stream sediment geochemical prospecting is historically acknowledged as a reliable tool to investigate the influence of geology and to assess the presence of natural and/or anthropogenic anomalies within river catchment basins at different scales. Several methods have been developed during decades to seek for geochemical anomalies especially those considered with a high potential for mineral resources. In the last decades, the Sample Catchment Basin (SCB) method, based on the use of dilution-corrected residuals (DCRs), has been developed, improved, and extensively used in mineral exploration, mostly at regional scale. In this paper the method was applied to a small catchment basin in southern Italy (Mingardo River) with the purposes of testing its reliability and improving the interpretation of geochemical patterns. The SCBs method was performed by using as input both unprocessed (raw) and centered log-ratio (clr) transformed data, and the obtained DCRs were classified through the concentration-area (C-A) method. After verifying the inadequacy of raw data, Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) on PCA loadings were used to address and improve the interpretation processes in a multivariate perspective. The method has proved to be effective in identifying anomalies associated with lithological layers that are often not reported on the medium scale and which, however, have the ability to condition the geochemical patterns of the basin to which they belong. The geochemical footprint of anthropic presence (road, villages, etc.) was also detected although its impact on the study area is limited.
Towards a Set of Metrics for Quantum Circuits Understandability

Quantum computing is the basis of a new revolution. Several quantum computers are already available and, with them, quantum programming languages, quantum software development kits and platforms, quantum error correction and optimization tools are proposed and presented continuously. In connection with this, disciplines such as the Quantum Software Engineering are appearing for applying the knowledge acquired through time in their corresponding classical relatives. Besides, measurement is well known as a key factor for assessing, and improving if needed, the quality of any model in terms of, for instance, its understandability. The easier to understand a model is, the easier to maintain, reuse, etc. In this work, we present the definition of a set of metrics for assessing the understandability of quantum circuits. Some examples of the calculation of the metrics are also presented. This is just the beginning of a more thorough process in which they will be empirically validated by the performance of empirical studies, especially experiments.