Reviewerly/exHarmony huggingface dataset:
Robust steady-state Kalman filter for descriptor system with random one-step measurement delays and uncertain noise variances

For the linear stochastic descriptor system with random one-step measurement delays and uncertain noise variances, the robust steady-state Kalman predictor is addressed. By new augmented state approach, the original system is transformed to the standard systems without measurement delays. The singular value decomposition (SVD) method, the randomization approach and the augmented state approach are presented, which are applied to transform the descriptor system with random one-step measurement delay to the standard non-descriptor system with fictitious white noises. Applying the presented approaches, the minimax robust steady-state Kalman predictor is presented in the sense that its actual prediction error variance is guaranteed to have the corresponding minimal upper bound for all admissible uncertainties. Their robustness is proved by the Lyapunov equation approach. A simulation example about circuits system verifies the correctness of the proposed results.
Relation Extraction with Proactive Domain Adaptation Strategy

Relation extraction is an important information extraction task in many Natural Language Processing (NLP) applications, such as automatic knowledge graph construction, question answering, sentiment analysis, etc. However, relation extraction suffers from inappropriate associations between entities when the background knowledge of corpus is insufficiency. Despite the preprocessed external word vector bases can ease this problem, how to find a single word vector base as domain knowledge that contains all the required knowledge features is a huge challenge, and relation extraction with background knowledge is still open to further optimization. To address this problem, in this paper, we propose Relation Extraction method with Proactive Domain Adaptation Strategy (REPDAS for short) to introduce more knowledge features from different knowledge bases. More specifically, firstly, a convolutional network with a parameter-sharing layer is introduced for relation extraction, and word seeds that are important to relational feature exploitation are proactively picked by an attention mechanism during training. Secondly, the proactively-chosen word seeds and the previous parameter-sharing layer are utilized to establish a map between different domains. Our proposed method selectively avails both background knowledge and contextual features for relation extraction by incorporating the convolutional neural network with the proactively domain adaptation strategy. Experiments show that our method effectively enhances the performance of relation extraction compared with other baselines.
DE/EI - A New Differential Evolution Selection Operator Based on Entropy and Index for Feature Ranking

The goal of feature ranking is to find the optimal list of features. The feature ranking methods use different search techniques to select features. An optimal feature selection results in an optimal feature ranking list, so, it is necessary to use a stochastic search method to select features. In this article, a new DE selection operator is introduced. To know the value of the features its fitness function is calculated using Shannon and singular value decomposition entropy. The index of the selected feature is computed by JI, MCI, and AA Index to know the feature list stability. Hence, DE/EI parent selection operator is proposed. The six fitness functions: SMCI, SVDMCI, SJI,SVDJI, SAA, and SVDAA, are thoroughly tested on ten UCI data sets and their performance is measured with different classifiers like Naive Bayes and Support Vector Machine. The experimental results show that the proposed method can efficiently be consolidated into any evolution that is based on a parent selection framework.
Arbitrary Oriented Ship Detection in Optical Remote Sensing Images via Partially Supervised Learning

To more accurately locate the arbitrary orientated ships in remote sensing images, recent methods turn to perform the detection via the rotated bounding box. However, these methods require all training samples to be annotated by rotated boxes. Compared with the traditional horizontal box, annotating with such a directional box is a laborious and time-consuming work. To solve this problem, we propose a novel partially supervised ship detection method by attaching an extra rbox (rotated bounding box) regression branch as well as a weight conversion function to the typical object detection network. The parameters of predicting horizontal bounding boxes in typical object detection network can be converted into those for rotated bounding box regression through the weight conversion function. With the help of this conversion, the models can be trained on a large number of samples all of which have horizontal box annotations, but only a small fraction of which have rotated box annotations. Experimental results demonstrate the effectiveness of the proposed method.
Experimental demonstration of in-service security monitoring using a quantum modulated signal

We experimentally demonstrate a method for in-service optical physical layer security monitoring with vacuum-noise sensitivity that can detect a 1% fiber tapping attack at 50km without classical security loopholes.
Fast Pathfinding in Knowledge Graphs Using Word Embeddings

Knowledge graphs, which model relationships between entities, provide a rich and structured source of information. Currently, search engines aim to enrich their search results by structured summaries, e.g., obtained from knowledge graphs, that provide further information on the entity of interest. While single entity summaries are available already, summaries on the relations between multiple entities have not been studied in detail so far. Such queries can be understood as a pathfinding problem. However, the large size of public knowledge graphs, such as Wikidata, as well as the large indegree of its major entities, and the problem of concept drift impose major challenges for standard search algorithms in this context. In this paper, we propose a bidirectional pathfinding approach for directed knowledge graphs that uses the semantic distance between entity labels, which is approximated using word vectors, as a search heuristics in a parameterized A*-like evaluation function in order to find meaningful paths between two entities fast. We evaluate our approach using different parameters against a set of selected within- and cross-domain queries. The results indicate that our approach generally needs to explore fewer entities compared to its uninformed counterpart and qualitatively yields more meaningful paths.
Multiple Graph Regularized Non-negative Matrix Factorization Based on L2,1 Norm for Face Recognition

In order to solve the problem that most non-negative matrix decomposition methods are sensitive to noise and outliers, resulting in poor sparsity and robustness, a multiple graph regularized non-negative matrix factorization based on L2,1 norm is proposed in this paper, and its performance is verified by face recognition. Firstly, the texture rich area is selected in the preprocessing stage. Secondly, L2,1 norm is used to improve the sparsity and robustness of decomposition results. Then, in order to better maintain the manifold structure of the data, the multi-graph constraint model is constructed. Furthermore, the corresponding multiplicative updating solution of the optimization framework is given, and the convergence proof is given. Finally, a large number of experimental results show that the superiority and effectiveness of the proposed approach.
A Two-stage Particle Filter for Equality Constrained Systems

This paper is concerned with the particle filtering problem for nonlinear dynamic systems with nonlinear equality constraints. It is well-known from the literature that filters incorporating constraint information can improve the accuracy of state estimation and that any true state should always satisfy these constraints in reality. However, it is difficult to obtain the particles naturally satisfying equality constraints from the importance density function (IDF) in the sampling procedure. To this end, this paper attempts to propose a novel constrained particle filter consisting of two stages. Considering that the dynamic model plays an important part in the sampling, the first stage incorporates the current measurement and constraint information to approximate the true dynamic model uncertainty. In the second stage, to sample the constrained particles, we construct a constrained optimization function from the perspective of IDF in the filtering. The performance of the proposed two-stage particle filter is demonstrated with simulated data in a target tracking application.
Automatic Source Code Generation from Owl Pseudocode

Algorithmics is a crucial step in programming learning processes. It helps learners to propose a better writing and understanding of solutions without worrying about programming language syntactic complexity. Once algorithm is clearly established, the next step is to translate it into a given programming language. When writing programs, syntax errors are common mainly due to the multiplicity of programming languages and strict syntactic rules. We propose in this work a web and mobile interface to automatically generate, from algorithms based on Pseudocode ontology, the corresponding codes in several programming languages such as C, Pascal and Python.
Factors Affecting an Organization’s Information Security Performance: The Characteristics of Information Security Officers

Information security breaches have damaged corporations’ images and caused share prices to fall, and breaches of information security are emerging as an organizational risk. Various laws mandate that businesses employ information security officers with specialized knowledge, such a CPO, CIO, CSO, CISO, CPO, etc. Information security breaches affect general citizens as customers. However, despite the growing importance of information security officers, there is a lack of research on their role and characteristics. The purpose of this study is to analyze the roles of information security officers in various legal systems and the impact of the characteristics of information security officers on an organization’s information security performance. A multiple linear regression is performed to validate the research model. The results of this study can act as a practical criterion for selecting information security officers who meet an organization’s information security objectives.
Extreme Learning Machines Classification of Kick Gesture

The induction self-starting trunk has been applied to the automotive field. The accurate classification of the foot signal is the primary task of sensing the selfstarting device. With the emergence of more and more car users, different car owners have different kicking modes. The traditional rule-based foot signal recognition has reached 94% for the fast kick foot signal, but the recognition rate for slow kick and sweep kick is less than 40%, and the recognition method for accurately identifying various kick styles is much more attention. This paper introduces a extreme learning machine based car tail door induction start algorithm, extracts the effective kick signal in the foot time series signal, analyzes the characteristics of standard kick, quick kick, slow kick, sweep kick, bend knee kick, and collect A variety of interfering signals are used as abnormal kick signals, and the samples were classified using an extreme learning machine (ELM). In the experiment, five volunteers participated in the test using different trunk kicking methods, and the accuracy of the car trunk opening was 98.33%.
Fault-Tolerant Semi-quantum Secure Direct Communication Scheme in Noisy Environment

We present an anti-noise quantum states, which is |φ1> = $$ \frac{ 1}{\sqrt 2} $$ (|0L0L> + |1L1L>) or |Ψ2> = $$ \frac{ 1}{\sqrt 2} $$ (|0L1L> + |1L0L>) to resist collective dephasing noise and |φ3> = $$ \frac{ 1}{\sqrt 2} $$ (|0R0R> + |1R1R>) or |Ψ4> = $$ \frac{ 1}{\sqrt 2} $$ (|0R1R> + |1R0R>) to resist collective rotation noise. With the anti-noise quantum states, Alice encode her information. She keep the first qubit and send the second qubit to Bob. Bob can choose to measure which means measure the second qubit by the basis {|0L>, |1L>} or {|0R>, |1R>}, or can choose to reflect which means send the qubit with undisturbed. About the transmission efficiency, when Alice transfers the information of 2n bits, Bob can get the information of n bits. Moreover, the security analysis of our scheme is also given.
Multi-sensor data augmentation for robust sensing

Data augmentation is a crucial technique for effectively learning deep models and for improving their generalization. It has shown remarkable performance gains on complex sets of problems, such as object detection and image classification. However, for sensor (time-series) data, its potential is not thoroughly explored even though the acquisition of large annotated sensor datasets is prohibitively expensive and challenging in real-life. In this work, we propose Sensor Augment - a generalized framework for automatically discovering data-specific augmentation strategies with black-box optimization search algorithms. Our approach makes use of the user-defined transformations to discover an optimal combination of the operations that can be used to train deep networks for a wide variety of tasks. Besides, we propose several augmentation operations that can be used to generate synthetic data and enrich the search space while harnessing existing functions. We show the efficacy of learned augmentation strategies on 7 multi-sensor datasets for 4 complex tasks. In our experiments, we see a substantial performance gain ranging from 1.5 to 10 F-score points over the baseline. We also show that the strategies can be learned from smaller subsets, and they can transfer well between related datasets.
A convergent framework with learnable feasibility for Hadamard-based image recovery

In this paper, we propose a framework for recovering image degradations that can be formulated by the Hadamard product of clear images with degradation factors. By training the mapping from datasets, we show that implicit feasibilities can be learned in forms of latent domains. Then with the feasibilities and acknowledged data priors, the recovery problems are formulated as a general optimization model in which the domain knowledge of degradations are also nicely involved. Then we solve the model based on the classical coordinate update with plugged-in networks so that all the variables can be well estimated. Even better, our updating scheme is designed under the guidance of theoretical analyses, thus its stability can always be guaranteed in practice. We show that different recovery problems can be solved under our unified framework, and the extensive experimental results verify that the proposed framework is superior to state-of-the-art methods in both benchmark datasets and real-world images.
PRTransE: Emphasize More Important Facts Based on Pagerank for Knowledge Graph Completion

Knowledge graph is a hot research field in the direction of artificial intelligence. The task of knowledge graph completion is to predict the links between entities. Translation-based models (such as TransE, TransH, and TransR) are a class of well-known knowledge graph completion methods. However, most existing translation-based models ignore the importance of triplets in the completion process. In this paper, we propose a novel knowledge graph completion model PRTransE, which considers the importance information of triplets based on PageRank and combines the importance information of triplets with knowledge graph embedding. Specifically, PRTransE integrates the entity importance and relationship importance of the triplet at the same time, and adopts different processing methods for the importance information of the positive and negative tuples, so that the proposed method pays adaptive attention to different triplet information in the learning process and improve learning performance to achieve better completion effect. Experimental results show that, in two real-world knowledge graph datasets, PRTransE has the best overall performance in terms of link prediction task compared to the five comparison models.
Creative Simulation Environment for Automation Control

Abstract Modern automation control design requires new technologies to develop, test and transfer technology from design to real world. One important step in automation design is testing automation algorithms in a simulated environment appropriate by real world. Our scope is to help designers to create a simulated environment for testing automation control. We created an interface and programming tool which help designers to simulate industrial installation and can connect with automation control equipment, using real electrical signals. Our base equipment is an industrial input/output controller, which connects with the computer. On the computer, is defined and runs a simulation environment, using a software tool. This environment communicates with the real world via an input/output controller. Design engineers can create their own simulation environment, using high level programming language (C++.Net, VB.Net or C#.Net) and can connect their software with external controller, using the proposed tool or a set of objects, from the library that might be added to it. This tool is useful for the creation of a simulation environment for industrial installation or subsection of a plant.
Particle Filter for Nonlinear Systems with Multi-Step Randomly-Delayed and Missing Measurements

For nonlinear dynamic systems, this paper develops a new particle filter to deal with the case that multi-step random measurement delay and missing exist simultaneously and are induced by the same reason. A measurement model with multi-step random delay and missing is formulated by introducing a sequence of independent and identically distributed Bernoulli variables with a certain latency probability. Based on this model, a new particle filter is proposed by exploiting a new weighting scheme for particles. When the latency probability is unknown, an identification method is designed by the maximum likelihood criterion. The superiority of the proposed particle filter and the effectiveness of the proposed identification method are illustrated in the univariate non-stationary growth model.
The Unsoundness of the Stylometric Case for Thomas Watson’s Authorship of <i>Arden of Faversham</i>

There is no external evidence of the authorship of the anonymous Elizabethan play Arden of Faversham. Thomas Watson (1555–1592) was an admired poet and translator of Latin in the Elizabethan era.1 ...
Printing Laboratories Practices: The i.E. Magazine Case Study

Successive technological advances and the overwhelming presence of digital tools in our daily lives necessarily have an impact on design practice, and there is a growing preference for immediate and ephemeral communication channels, instead of manual and traditional craft practices. This paper aims to address issues related to independent publishing and explore traditional production techniques in a workshop context, namely through the extracurricular project—i.E Magazine—that students of the Degree in Design and Graphic Arts Technology at the Polytechnic Institute of Tomar have developed to contribute to exploration and learning outside of the classroom and to encourage the use of equipment and materials provided by the printing laboratories at this institution. i.E. Magazine is owned, edited, designed and produced by students and acts as a platform for exploring the polytechnic laboratories at Tomar and as a way to express their ideas, without committing to a specific training, agenda or problem in the context of the classroom. In each edition of the magazine, the students are responsible for the choice of content, the writing or external solicitation of articles, the production of images and illustrations, the design and layout, the choice of materials, and final production. In recent editions, the students or alumni of the Design Degree or Master of Editorial Design have even designed unique type fonts to use in their projects.
Hardware/Software Co-design for Evolvable Hardware by Genetic Algorithm

A co-design architecture for the field programmable gate array (FPGA)-based evolvable hardware (EHW) system is proposed. In this architecture, a virtual reconfigurable circuit (VRC) is configured by the genetic algorithm (GA). The VRC is implemented in FPGA, and the GA is executed by a processor. The ARM processor and the NIOS II processor are used. This architecture maintains the flexibility and scalability of the VRC. This approach also has the flexibility to change the parameters or even the GA algorithm without affecting the FPGA part. Three circuits of different degrees of complexity are used to evaluate the system. Experimental results showed that the co-design was successfully implemented. In addition, compared to the NIOS II approach, the ARM approach had the advantages regarding the evolution speed and the logic utilization.
Museum Narration: A Memory-Driven Storyscape

Museums are integrated media that deliver stories and memories, and their narratives are not limited to their exhibitions or collections, but include buildings, catalogs, audio tours, educational materials, websites, and social media sitesSocial media site. Today’s digital domain exists in tandem with real-world physical assets, thereby expanding both into a new concept, “phygitalPhygital”, which combines the physical with the digital. The phygitalPhygital empowers museum narrationMuseum narration with integrated new media, facilitates its memory-driven storyscape with immersive and intuitive visiting experience, and extends museums’ beliefs, memories, and values to a broader public. This chapter focuses on phygitalPhygital aided museum narrationMuseum narration and discusses building memory-driven storyscapes with phygitalPhygital tools. The research approach extracts knowledge from the field of narrative environments by developing scenography designs and conducting in-the-wild studies at the Tsinghua University Art MuseumTsinghua University Art Museum. The four main contributions of this article are phygitalPhygital mediated communication and narration of museum exhibition, social media- aided co-creation for curatorial practices, the proposal of multi-media fusion for the memory-driven storyscape, and a summary of the qualities of memory-driven storyscapes in museums.
Higher-Order Interference in Quantum Mechanics Induced by Optical Nonlinearities

It has been proven theoretically and confirmed experimentally that quantum mechanics exhibits only second-order interference. However, this makes several implicit assumptions. Here we highlight these assumptions experimentally, showing that optical nonlinearities can induce higher-order interference.
A Free-Space Quantum Secure Direct Communication Scheme Based on Prefixed-Threshold Real-Time Selection Method

Quantum secure direct communication (QSDC) is a secure communication method that uses the quantum state as an information carrier to directly transmit the message between two remote anticipators. In the free-space quantum communication, atmospheric turbulence causes fluctuations in transmittance, which further affects the quantum bit error rate (QBER) and signal-to-noise ratio (SNR). Under such circumstances, the intensity of some optical signals arriving at the remote end may be weak, which makes it difficult to meet the requirements of secure communication. In order to solve this problem, a free-space QSDC scheme based on prefixed-threshold real-time selection (P-RTS) method is proposed. In this scheme, the sender transmits all photons in the pulse way, and the remote end receives these pulses responding to the optical threshold which is calculated by the P-RTS method. It shows that the proposed scheme has lower QBER and higher SNR than other QSDC scheme through analysis.