Reviewerly/exHarmony huggingface dataset:
Robot Multimodal Anomaly Diagnosis by Learning Time-lagged Complex Dynamics

Robots are prone to making anomalies when performing manipulation tasks in unstructured environments, it is often desirable that robots can detect, diagnose them, and then effectively provides prior experiences for rapidly adapting the robot anomalous behaviors. The traditional methods on anomaly diagnosis are to focus on univariate time series and ignore the modality correlation and time-dependent, which can't be applied in a Human-robot collaborative (HRC) system that is integrated with multiple sensors for improving autonomy and safety. The variability and temporal consistency of multimodal sensory data exacerbate anomaly diagnosis still an open problem in robotics. Here we propose a novel method of multimodal anomaly diagnosis by learning the time-lagged dynamics of anomalies detected during an HRC task. Specifically, a time-lagged variational auto-encoder model ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$tlVAE$</tex> ) is first proposed to compress complex multivariate dynamics into simpler manifolds, and the manifolds are used to fitting a dynamic time warping-based K-nearest neighbors model for anomaly diagnosis in a multi-classes classification scheme. A real-robot anomaly dataset is presented to evaluate the significance and effectiveness in extracting underlying time-dependent patterns, results indicate that the efficiency and precision of diagnosis can be improved by introducing a sparse representation of the multivariate sample. Meanwhile, we compare the accuracy of anomaly diagnosis with several commonly used sparse representation methods, including Principal Component Analysis (PCA), Time-lagged Independent Component Analysis (TICA), Autoencoder (AE) as well as Variational Autoencoder (VAE). The resulting anomaly diagnosis accuracy of 97.6% across 7 kinds of anomalies, which outperformed all the baselines.
Research on Traffic Target Detection Method Based on Improved YOLOv3

In order to solve the problem that it is difficult to balance the real-time and accuracy of the existing traffic target recognition methods in the field of automatic driving, an improved YOLOv3 target detection algorithm YOLO-R is proposed. The original three feature scales of YOLOv3 are increased to four to reduce the miss detection rate of small objects; K-means target frame clustering is used to get a new target detection candidate frame, which improves the detection accuracy; Through sparse training and pruning the unimportant channels in the model after sparse training, the model size can be reduced, the detection speed can be accelerated and over fitting can be prevented. On the Udacity data set used in the automatic driving algorithm competition, the experimental results show that under NVIDIA GTX 1080 Ti, the FPS of the new method is 42.42 frames/s, which is 1.44 frames/s higher than that of YOLOv3; the detection accuracy (mAP) is 93.61%, which is 3.43 % higher than that of YOLOv3. Compared with the original YOLOv3, the proposed YOLO-R has higher detection speed and accuracy in traffic environment.
Simplified quantum algorithm for the oracle identification problem

In the oracle identification problem we have oracle access to bits of an unknown string x of length n, with the promise that it belongs to a known set $$C\subseteq \{0,1\}^{n}$$ . The goal is to identify x using as few queries to the oracle as possible. We develop a quantum query algorithm for this problem with query complexity $$O\left (\sqrt {\frac {n\log M }{\log (n/\log M)+1}}\right )$$ , where M is the size of C. This bound is already derived by Kothari in 2014, for which we provide a more elegant simpler proof.
Re-Distributed Multi-Authority Attribute Based Encryption in Cloud Using CP-ABE Algorithm

Abstract Cloud Computing allows us to store and share the data easily with others. Redistributed “multi-authority Attribute-Based Encryption (ABE)” has importantly implemented for finding the solutions to the problem that which arises from sharing private corporate information in computing of cloud. For Re-distributed multi-authority ABE in cloud devices that don’t believe in a single main authority, collusion resistance are often done employing a universal identifier. so for that reason, identity must be globally managed, that ends up within the critical issues of privacy and protection. The new scheme is implemented that doesn’t use a single main authority in order to co “ordinate users and keys, and only normal trust relations got to be done with sharing the general public key in between each attribute authority” (AA). The “identities of the user are different and different by combination of the user’s identity along the identity of the AA where the user is found”. If a key request must be made to the authority outside of the domain, the request must be done by the authority within the present domain instead of by the client’s, so, the user identities should remain secured to the AA outside the domain that can strengthen privacy and protection. Along with that, the main issuing protocol in between Attribute Authority is straight forward because of the results of trusted AA relationship. On top of that, the authority’s extensibility is also supported by the method presented in our paper. The scheme is predicated on order of Composite in which the groups are bilinear. Validation of protection is given which uses the twin system methodology of encryption.
Generic Newton polygons for L-functions of (A,B)-exponential sums

In this paper, we consider the following (A,B)-polynomial f over finite field:f(x0,x1,⋯,xn)=x0Ah(x1,⋯,xn)+g(x1,⋯,xn)+PB(1/x0), where h is a Deligne polynomial of degree d, g is an arbitrary polynomial of degree less than dB/(A+B) and PB(y) is a one-variable polynomial of degree less than or equal to B. Let Δ be the Newton polyhedron of f at infinity. We show that Δ is generically ordinary if p≡1modD, where D is a constant determined by Δ. In other words, we prove that the Adolphson–Sperber conjecture is true for Δ.
A Framework for Supervised Heterogeneous Transfer Learning Using Dynamic Distribution Adaptation and Manifold Regularization

Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only a few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbors of a record, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is determined automatically. We evaluate TLF on seven publicly available datasets and compare performances of TLF and fourteen state-of-the-art techniques. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of TLF over the state-of-the-art techniques.
The Došen Square Under Construction: A Tale of Four Modalities

In classical modal logic, necessity \({\mathop {\Box } A}\), possibility \({\mathop {\Diamond } A}\), impossibility \({\mathop {\Box } \lnot A}\) and non-necessity \({\mathop {\Diamond } \lnot A}\) form a Square of Oppositions (SO) whose corners are interdefinable using classical negation. The relationship between these modalities in intuitionistic modal logic is a more delicate matter since negation is weaker. Intuitionistic non-necessity \({\mathop {\boxminus }}\) and impossibility Open image in new window , first investigated by Došen, have received less attention and—together with their positive counterparts \({\mathop {\Box }}\) and \({\mathop {\Diamond }}\)—form a square we call the Došen Square. Unfortunately, the core property of constructive logic, the Disjunction Property (DP), fails when the modalities are combined and, interpreted in birelational Kripke structures à la Došen, the Square partially collapses. We introduce the constructive logic \(\mathsf {CKD}\), whose four semantically independent modalities \({\mathop {\Box }}\), \({\mathop {\Diamond }}\), \({\mathop {\boxminus }}\), Open image in new window prevent the Došen Square from collapsing under the effect of intuitionistic negation while preserving DP. The model theory of \(\mathsf {CKD}\) involves a constructive Kripke frame interpretation of the modalities. A Hilbert deduction system and an equivalent cut-free sequent calculus are presented. Soundness, completeness and finite model property are proven, implying that \(\mathsf {CKD}\) is decidable. The logics \({\mathsf {HK} {\mathop {\boxminus }}}\), \({\mathsf {HK} {\mathop {\Box }}}\), Open image in new window and Open image in new window of Došen and other known theories of intuitionistic modalities are syntactic fragments or axiomatic extensions of \(\mathsf {CKD}\) .
Generalized Intersection Algorithms with Fixed Points for Image Decomposition Learning

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 23 October 2020Accepted: 21 June 2021Published online: 07 September 2021Keywordsregularization methods, cartoon-texture decomposition, filtering methodsAMS Subject Headings65D18, 68U10Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
The Impact of Smart Glasses on a New Generation of Users

The intelligent glasses are similar to the smart phone, which possess independent operational system. And it can use diverse functions and applications by installing software, Meanwhile the intelligent glasses also are regarded as one of popular intelligent equipment in the resent years. The intelligent glasses are in an early stage of development, So there are some core hardware and application field, which still be researching and exploring continually, But its development will be quite rapid and the scale of market also is expanding continually in near future. The intelligent glasses have a more higher requirement for the quality of data transmission. With the advent of 5G time, The experience and significance of intelligent glasses will be increase sharply.
Coevolution of Security’s Body of Knowledge and Curricula

Reviews efforts to capture the body of knowledge in cybersecurity or computer and Internet security (CIS) more specifically and discuss related curricular frameworks. The aim is to increase awareness of resources to guide topic selection of CIS courses for educational and training programs.
Physics-Guided Machine Learning for Prediction of Cloud Properties in Satellite-Derived Solar Data

With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the National Solar Radiation Database (NSRDB) is a vital public data asset. The NSRDB uses a two-step Physical Solar Model (PSM) that explicitly considers the effects of clouds and other atmospheric variables on radiative transfer. High-quality physical and optical cloud properties derived from satellite imagery are perhaps the most important data inputs to the PSM, representing the greatest source of radiation attenuation and scattering. However, traditional methods for cloud property retrieval have their own limitations and are unable to accurately predict cloud properties outside of nominal conditions. We introduce a physics-guided neural network that can accurately predict cloud properties when traditional methods fail or are inaccurate. Using this framework, we show reductions in relative Root Mean Square Error (RMSE) for Global Horizontal Irradiance (GHI) up to 13 percentage points for timesteps that previously had missing or low-quality cloud property data. We expect that this methodology will be effective in improving the quality of cloud property and solar irradiance data in the NSRDB.
Automatic Signature-Based Writer Identification in Mixed-Script Scenarios

Automated approach for human identification based on biometric traits has become popular research topic among the scientists since last few decades. Among the several biometric modalities, handwritten signature is one of the very common and most prevalent approaches. In the past, researchers have proposed different handcrafted feature-based techniques for automatic writer identification from offline signatures. Currently huge interests towards deep learning-based solutions for several real-life pattern recognition problems have been found which revealed promising results. In this paper, we propose a light-weight CNN architecture to identify writers from offline signatures written by two popular scripts namely Devanagari and Roman. Experiments were conducted using two different frameworks which are as follows: (i) In first case, signature script separation has been carried out followed by script-wise writer identification, (ii) Secondly, signature of two scripts was mixed together with various ratios and writer identification has been performed in a script independent manner. Outcome of both the frameworks have been analyzed to get the comparative idea. Furthermore, comparative analysis was done with recognized CNN architectures as well as handcrafted feature-based approaches and the proposed method shows better outcome. The dataset used in this paper can be freely downloaded from the link: https://ieee-dataport.org/open-access/multi-script-handwritten-signature-roman-devanagari for research purpose.
Communication-efficient Federated Learning via Quantized Clipped SGD

Communication has been considered as a major bottleneck of Federated Learning (FL) in mobile edge networks since participating workers iteratively transmit gradients to and receive models from the server. Compression technology like quantization that reduces the communication overhead and hyperparameter optimization technology like Clipped Stochastic Gradient Descent (Clipped SGD) that accelerates the convergence are two orthogonal approaches to improve the performance of FL. However, the combination of them has been little studied. To fill this gap, we propose Quantized Clipped SGD (QCSGD) to achieve communication-efficient FL. The major challenge of the combination lies in that the gradient quantization essentially affects the adjusting policy of step size in Clipped SGD, resulting in the lack of convergence guarantee. Therefore, we establish the convergence rate of QCSGD via a thorough theoretical analysis and exhibit that QCSGD has a comparable convergence rate as SGD without compression. We also conduct extensive experiments on various machine learning models and datasets and show that QCSGD outperforms state-of-the-art methods.
Inverse Function Theorem. Part I<sup>1</sup>

Summary In this article we formalize in Mizar [1], [2] the inverse function theorem for the class of C 1 functions between Banach spaces. In the first section, we prove several theorems about open sets in real norm space, which are needed in the proof of the inverse function theorem. In the next section, we define a function to exchange the order of a product of two normed spaces, namely 𝔼 ↶ ≂ ( x, y ) ∈ X × Y ↦ ( y, x ) ∈ Y × X , and formalized its bijective isometric property and several differentiation properties. This map is necessary to change the order of the arguments of a function when deriving the inverse function theorem from the implicit function theorem proved in [6]. In the third section, using the implicit function theorem, we prove a theorem that is a necessary component of the proof of the inverse function theorem. In the last section, we finally formalized an inverse function theorem for class of C 1 functions between Banach spaces. We referred to [9], [10], and [3] in the formalization.
Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.
Semantic Forgetting in Expressive Description Logics

Forgetting is an important ontology extraction technology. We present a semantic forgetting method for \(\mathcal {ALC}\) ontologies. The method forgets concept names, and captures the semantic content over the remaining vocabulary of an ontology, possibly, by introducing helper concept symbols. In an evaluation, the method performed well on large-scale ontologies when forgetting 10–50% of the vocabulary and the number of helper symbols occurring in the extracts decreased as the number of forgetting symbols increased. Against the forgetting tool Fame(Q), good performance was achieved while more semantic content was preserved.
Towards A Machine Learning Enabled Multi-Channel Messaging Framework for Financial Service Institutions: Preliminary Investigations

Messaging is essential when organizations dealing with financial customers need to share information. Technological innovations, such as machine learning (ML), have provided financial service institutions (FSIs) with the ability to reach out to consumers in more intelligent ways. Multi-Channel Messaging System (MCM), in use by FSI's, enables the seamless integration of disparate channels of communication within a single system. This problem has driven us to develop a machine learning-enabled channel assignment algorithm in the Multi-Channel Messaging System. The decision-making module would incorporate heterogeneous channels using an Enterprise Service Bus (ESB) layer, and use machine learning algorithms to assess channel capacity, dynamic assignment, and customer trends. Our framework would extend and maximize this approach, to achieve a fast balance between the exploration-exploitation dilemma, for channel selection. This research explores the problems and challenges a multi-channel messaging system currently used by financial services organizations, while proposing a multichannel framework which incorporates a machine learning channel selection and learning module. The analysis concentrates on the complex approach to channel selection and integration methods that can make the system effective and usable. The lessons learned from the design would be further refined to inspire future work in this field.
Scaling Blockchains: Can Elected Committees Help?

In the high-stakes race to develop more scalable blockchains, some platforms (Binance, Cosmos, EOS, TRON, etc.) have adopted committee-based consensus (CBC) protocols, whereby the blockchain's record-keeping rights are entrusted to a committee of elected block producers. In theory, the smaller the committee, the faster the blockchain can reach consensus and the more it can scale. What's less clear, is whether such protocols ensure that honest committees can be consistently elected, given blockchain users typically have limited information on who to vote for. We show that the approval voting mechanism underlying most CBC protocols is complex and can lead to intractable optimal voting strategies. We empirically characterize some simpler intuitive voting strategies that users tend to resort to in practice and prove that these nonetheless converge to optimality exponentially quickly in the number of voters. Exponential convergence ensures that despite its complexity, CBC exhibits robustness and has some efficiency advantages over more popular staked-weighted lottery protocols currently underlying many prominent blockchains such as Ethereum.
Multisensory Augmented Reality

Multisensory augmented reality enables content developers to generate more realistic, sensory rich user experiences. Applications and research related to multisensory augmented reality expands through several areas such as education, medicine, human-machine interactions, human-food interactions, marketing, and neuroscience. Aim of this workshop is to gather researchers and industry representatives who are involved in multisensory augmented reality research to discuss the current state-of-the-art in the field, define future research directions, form new collaborations, and come up with future publication plans. We believe that this workshop would enhance the participants’ experience of the Interact 2021 and encourage more participants to attend the main conference.
EnSOTA: Ensembled State of the Art Model for Enhanced Object Detection

With advancements in computation capabilities, the focus is diverted toward creating object detection models with better accuracy than speed. Through the medium of the research presented in this paper, we wish to establish a more robust and more accurate method for object detection using a different ensemble state-of-the-art object detection models. Earlier research had established the use of singular architectures and models to tackle object detection problems which later on presented with each model having their own bias and variance. Current research is now exploring the idea of “Ensemble Learning” after the success of simple ensembled models such as XGBoost. Through the medium of this research, we propose expanding the idea of ensembled learning in object detection by applying state-of-the-art object detection models together as an ensemble to reduce individual bias and variance, while achieving better metrics and accuracy. We have tested EnSOTA on the PASCAL VOC challenge, and in comparison to individual models, it was able to deliver an accuracy higher by 5-8% at solving the same challenge.