Miracle-dz/UniFAR-FTUs huggingface dataset:
Stabilization of Discrete-Time Delayed Systems in Presence of Actuator Saturation Based on Wirtinger Inequality

This paper examines the stability analysis of discrete-time control systems particularly during the event of actuator saturation and time-varying state delay. With the help of Wirtinger inequality along with Lyapunov-Krasovskii functional gain of state feedback controller is determined for stabilization of above system. The saturation nonlinearity is represented in the terms of convex hull. A new linear matrix inequality (LMI) criterion is settled with reciprocally convex combination based inequality which is dependent on delay. The proposed criterion is less conservative in concern to increase the delay bound and a controller is also simulated for real time problem of missile control system in this paper. It is also attained that projected stability criterion is less conservative compared to other outcomes. Furthermore, an optimization procedure together with LMI constraints has been proposed to maximize the attraction of domain.
STSM: An Infrastructure for Unifying Steel Knowledge and Discovering New Knowledge

There are rich data resources in materials science, but these data resources are heterogeneous in level of system, structure, syntax, and semantics. Therefore, a domain ontology is necessary and helpful for the integration of these heterogeneous data resources, and it is also one of the main tasks of materials informatics. In this paper, we propose a steel semantic model (named STSM) based on ontology and logic rules for the representation of the steel knowledge. STSM is developed with the consideration of the features of materials data and the developed process is presented. Then, we describe the content and organization of STSM which covers the basic knowledge in steel domain. Further, domain axioms and logic rules are designed to enhance the reasoning ability of STSM. STSM is built and tested in protege, and an experimental prototype based on Jena is also developed to demonstrate the effectiveness of STSM.
• A Comparison of the HINT and Quick SIN Tests • Comparaison entre les tests HINT et Quick SIN

Speech-in-noise measures are gaining relevance as audiologists understand the advantages of using outcome measures that demonstrate the need for and benefit from amplification. Two such speechin-noise measures are the Hearing in Noise Test (HINT) and the Quick Speech-In-Noise (Quick SIN) test. This study was conducted to determine how HINT and Quick SIN performance among young adults with normal hearing (N=15) compared to normative values, as well as to reach conclusions about the clinical utility of both tests. Results showed that Quick SIN measures and normative values were not statistically significantly different while the HINT measures and normative values were. The Quick SIN was found to have some advantages over the CD version of the HINT in terms of clinical
Middleware-Oriented Government Interoperability Frameworks : A Comparison 1

We discuss deployment solutions for e-Government Interoperability Frameworks (GIFs). We concentrate on middleware-oriented GIFs, i.e., those in which middleware modules act as intermediaries among information systems that need to exchange data and services. A prominent example is the Italian SPCoop interoperability framework. We review the SPCoop architecture, and two popular open-source implementations of its core modules, called OpenSPCoop and freESBee. We argue that the comparison of these two solutions is relevant since they obey to radically different philosophies, both in terms of the relationship to the underlying J2EE container, and of their internal module organization. Then, we discuss one of the main problems in large-scale deployment of SPCoop-like GIFs, namely the need to quickly deploy a large number of middleware instances over a relatively small number of servers. We report a number of experiments to discuss how the different design choices impact performance. To the best of our knowledge, this is the first large-scale test of the framework, from which a number of important lessons can be learned.
How to Make Operating Systems for Smart Cards Open ?

Nowadays the world of smart cards aimed for the market of ID-cards and e-administration services is divided (roughly) into two main categories: native cards and java cards. The first choice imposes lower requirements to the chip area and its energy consumption (which is especially important in case of contactless cards), whereas the second choice allows third parties to develop applications for the card, making the product less dependent on the developer of its operating system (OS). This dependency may be a crucial factor for administration of a country planning to deploy a new ID-card, for example. However, apart from increased requirements towards hardware, java cards do not provide access to low level libraries, which affects flexibility and development of new applications. In this paper we propose a new business model for smart cards’ OS developers, namely: “earn on the service” instead of “earn on the product”. Applying the signature scheme described below, OS developers will have interest in publishing at least a partial specification of internal, low level API of their native OS. On the algorithmic side, the main, two layered structure used by the signature scheme may be discerned as an extension of CMSS and GMSS variants of Merkle Signatures Scheme, and outperforms them in terms of speed of the structure (re)initialization and the number of signatures available with a single structure.
Aggregating unsupervised provenance anomaly detectors

System-level provenance offers great promise for improving security by facilitating the detection of attacks. Unsupervised anomaly detection techniques are necessary to defend against subtle or unpredictable attacks, such as advanced persistent threats (APTs). However, it is difficult to know in advance which views of the provenance graph will be most valuable as a basis for unsupervised anomaly detection on a given system. We present baseline anomaly detection results on the effectiveness of two existing algorithms on APT attack scenarios from four different operating systems, and identify simple score or rank aggregation techniques that are effective at aggregating anomaly scores and improving detection performance.
Gradient-Based Clustering Routing Protocol for Wireless Sensor Networks

A routing protocol based on gradient and cluster in wireless sensor network is analyzed.An energy-efficient gradient-based clustering routing protocol(GBCRP) is proposed and evaluated.The new protocol combines the advantages of gradient and cluster.It saves the energy and improves the reliability by the technologies of clustering,creating gradient based on the hop,key nodes transmitting and the adaptive power optimal routing of cluster header nodes.Simulation results show that the new routing protocol provides longer lifetime and higher reliability.
A Survey on Anomaly Detection Methods for System Log Data

System logs are often a collection of unrelated print statements which records certain events that occur while the system is running. Log file analysis often can be crucial for finding system faults which can otherwise be quite difficult to detect. Traditional log analysis involves analyzing line by line until a discrepancy is spotted. This process is tedious, time consuming and is prone to human errors. With the advent of machine learning, several new methods have been devised which can make anomaly detection much easier. Further, in the past decade, deep learning has evolved so much that new techniques and algorithms spring every now and then. This paper examines several existing techniques that can be used for system log analysis.
Optimized Human Movement Gesture Recognition Algorithm Based on Hu Invariant Moments Features

The body's movement process is relatively complex,so there are many similar movements in the images,forming interferences on the characteristics of the traditional recognition,and causing low recognition accuracy.In order to improve its recognition accuracy,this paper put forward a kind of Hu moment invariants and artificial fish optimization support vector machine(SVM)model for human motion recognition(Hu-AFSA-SVM). First of all,based on the twodimensional continuous images,extracted the image 7Hu moment invariants of the human body movement gesture recognition,and then input into SVM to train,and picked a AFSA to SVM parameter optimization,to find an optimal hyperplane,in as much as possible meet the constraints of classification,all concentrated human motion data classification categories,complete recognition.Finally carried out the simulation experiment.Simulation results show that,compared with other identification model,Hu-AFSA-SVM improves the human motion recognition accuracy,speeds up the recognition,and is an effective method for human movement gesture recognition.
Binary Control-Flow Trimming Masoud Ghaffarinia

A new method of automatically reducing the attack surfaces of binary software is introduced, affording code consumers the power to remove features that are unwanted or unused in a particular deployment context. The approach targets stripped binary native code with no source-derived metadata or symbols, can remove semantic features irrespective of whether theywere intended and/or known to code developers, and anticipates consumers who can demonstrate desired features (e.g., via unit testing), but who may not know the existence of specific unwanted features, and who lack any formal specifications of the code’s semantics. Through a combination of runtime tracing, machine learning, in-lined reference monitoring, and contextual control-flow integrity enforcement, it is demonstrated that automated code feature removal is nevertheless feasible under these constraints, even for complex programs such as compilers and servers. The approach additionally accommodates consumers whose demonstration of desired features is incomplete; a tunable entropy-based metric detects coverage lapses and conservatively preserves unexercised but probably desired flows. A prototype implementation for Intel x86-64 exhibits low runtime overhead for trimmed binaries (about 1.87%), and case studies show that consumer-side control-flow trimming can successfully eliminate zero-day vulnerabilities.
Generate Semantically Similar Images with Kernel Mean Matching

We propose a novel procedure which adds “contentaddressability” to any given unconditional implicit model e.g., a generative adversarial network (GAN). The procedure allows users to control the generative process by specifying a set (arbitrary size) of desired examples based on which similar samples are generated from the model. The proposed approach, based on kernel mean matching, is applicable to any generative models which transform latent vectors to samples, and does not require retraining of the model. Experiments on various high-dimensional image generation problems (CelebA-HQ, LSUN bedroom, bridge, tower) show that our approach is able to generate images which are consistent with the input set, while retaining the image quality of the original model. To our knowledge, this is the first work that attempts to construct, at test time, a content-addressable generative model from a trained marginal model.
A Method for Calculating Similarity among Glyphs using Cross Correlation

In this paper, a method for measuring similarity among glyph contours is proposed. A predominance measure of each contour point is defined and computed at the first place, then we adopt cross correlation between glyph contour pairs as the metric of similarity. The proposed method has several attractive properties: firstly, it is invariable to scales and rotations. Secondly, it is automatically normalized. Thirdly, it can be accelerated by FFT algorithm. These characteristics are of great use in many document image processing procedures, especially in applications where small deviations or rotations is a commonplace. Experiments show that our method can effectively figure out degree of similarity among glyph contours.
Access control for network management

ion will also support better automation. Current network technologies leave too much room for human error. Some tasks, such as adding a new airline or shop, may be repeated many times and should become routine; dependencies with local implementation detail must be better hidden to make this simple and dependable. The main driver in day-to-day operations is cost. The air transport industry operates on tight margins; in Heathrow’s case the operator is heavily indebted following a takeover and was forced to divest other London airports by the competition authorities. Investments in new technologies will only happen if there is an unavoidable regulatory mandate, or to save money. 4.3 Hierarchical design Our detailed threat model therefore assumes physical compromise of devices, along with associated attacks that can be done by having physical access. It follows that some devices (the ones ‘in the field’) are unsafe. Once an attacker gains physical access to a 4.3. HIERARCHICAL DESIGN 79 device, he can open, modify, remove, or replace it. It is reasonable to say a number of switches are compromised at a given time, and sometimes controllers at the bottom of the hierarchy are compromised too (since they are deployed near switches). We also assume the communication channels connecting an unsecured switch to be insecure. If an attacker can get access to a device, he also has access to its cabling. Imagine an ISP needing to change a switch at a customer’s site by ordering the equipment from a contractor. There is no guarantee of security at any of the steps from factory and shipping company to contractors and subcontractors and eventually to the customer. Even the customer may modify the equipment if there is incentive to do so. It is not just a matter of occasional access by secret policemen at points of presence in less well-governed countries; recent Edward Snowden files reveal that NSA tampered with Cisco supply chain of hardware exported to other countries. In a time of conflict, the opportunity would exist for an opponent who had taken over one of the routers to use it to cause havoc, by inserting false rules, or removing and replacing legitimate ones, and thus generally disrupting the switching fabric. If a network has a thousand routers and any one of them can disrupt it, as is the case today, then an attacker need compromise a few routers for the whole network to become vulnerable.
HIGH AVAILABILITY TELEMATIC MANAGEMENT SYSTEM

An efficient use of ITS resources depends on the information and communications architecture that enables the optimal use of technology, information and, increasingly, services, available across the variety of ITS applications and systems. This is especially true for quality of service requirements, namely availability. The current article focuses on architectural concerns in the development and deployment of the VMS operation component of Traffic Atlas. This component is cross-cutting in regards to the multi-layer service oriented architecture of Traffic Atlas; in order to simplify our approach a VMS-centric approach is taken at each layer.
The Efficient Prediction Review of Difficult Keyword Queries over Databases

Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. Further, we present a suite of optimizations to minimize the incurred time overhead.
Modelling the Generation and Retrieval of Word Associations with Word Embeddings A Case Study for a Guesser Agent in the Location Taboo Game

Citation for published version (APA): Dankers, V. , Bilgin, A. , & Fernández, R. (2017). Modelling the Generation and Retrieval of Word Associations with Word Embeddings: A Case Study for a Guesser Agent in the Location Taboo Game. In B. Verheij, & M. Wiering (Eds.), BNAIC 2017: Benelux Conference on Artificial Intelligence: Preproceedings of the 29th Benelux Conference on Artificial Intelligence, November 8–9, 2017 in Groningen, The Netherlands (pp. 401-402). (BNAIC; Vol. 29). University of Groningen. http://bnaic2017.ai. rug.nl/preproceedings.pdf
Isolation and Characterization of Bacteria of Mangrove Rhizosphere in the Mekong Delta , Vietnam

Total of 125 rhizospheric bacterial isolates were isolated from 18 rhizospheric soil samples of mangrove at Ca Mau Peninsula (Ca Mau province, Mekong Delta of Vietnam). Although all of them had the abilities of ammonium synthesis and phosphate solubilization, the isolated strains had excellent phosphate solubilizing potential. The sequences from selected nitrogen-fixing and phosphate-solubilizing bacteria (22 isolates) showed high degrees of similarity to those of the GenBank reference strains (between 97% and 99%). From 22 isolates, 16 strains belonged to Bacilli, and 6 strains were Gamma-Proteobacteria. Whereas our results showed that there were some good strains for nitrogen fixation as Bacillus subtilis DLB4b, Bacillus sp. VAB2b, Enterobacter sp. MAB1b and Bacillus sp. MDB1c and strains for phosphate solubilation as Bacillus subtilis MLN1b, Bacillus sp. MLN1c, Bacillus subtilis VDN1f and Bacillus subtilis MDN1c, all of them tolerated at a concentration of 4% NaCl. However, the strain Bacillus subtilis MAB2b revealed as a promising candidate with multiple beneficial characteristics (both good nitrogen fixation and phosphate solubilization). Besides, the isolated bacterial strain has the potential for application as inoculants adapted to poor, soil salinity as well many kinds of crop because it is not only famous strain but also safe strain for sustainable agriculture in “sea level rise” condition.
Unsupervised Anomaly Detection Based on Principal Components Analysis

Intrusion detection systems need a mass of the labeled data in the process of training It hampers the application and popularity of traditional IDSs A study was conducted to realize the automation of the learning process of the detection models where the training data is unsupervised A novel method of unsupervised anomaly detection based on principal components analysis (PCA) is presented The main characteristics of the training samples are learned under the principle of least mean square errors The information of the samples is duplicated in the process of encoding and decoding The anomaly behaviors can be detected according to the anomaly factor defined by the square errors between the original vector and the resultant one The experiment of the simulation system proves that the method of unsupervised anomaly detection based on PCA does not need the participation of experts in the prophase The experimental result shows its effectiveness
PERFORMANCE ANALYSIS ON OBLIQUE IMAGERY Dense Image Matching

3D reconstruction using dense image matching is a hot topic as it enables the automatic extraction of 3D urban models, notably from airborne oblique imagery. However, applying DIM algorithms to oblique imagery is challenging because of large scale variations, illumination changes and the many occlusions. Here, the authors present a methodology for evaluating the performance of DIM algorithms and show the initial results from four DIM packages: Agisoft PhotoScan, Leica Xpro SGM, OpenCV StereoSGBM and SURE.
Inter-Frame Compression of 3 D Point Cloud Sequences

In this paper, we propose a 3D point cloud sequence compression algorithm based on inter-frame coding. First, we create an octree structure for the 3D point cloud. For the first frame, we compress geometric and color attributes through intraframe coding. Then we apply inter-frame coding to the rest of the frames. We compress the geometric attributes using a dual octree and compress the color attributes using the k-means clustering. Both intra-frame coding and inter-frame coding are performed with arithmetic codes. Experimental results demonstrate that the proposed algorithm compresses point cloud sequences more efficiently than the algorithm using only intra-frame coding. Keywords—point cloud; dual octree; intra-frame coding; interframe coding
Shenhuang Powder reduces the intestinal inflammatory response and ameliorates impaired gastrointestinal motility in a rat model of postoperative ileus

Postoperative ileus (POI) is a temporary disturbance in gastrointestinal motility following surgery, and there is no ideal pharmacological agent available to shorten POI. We aimed to investigate whether Shenhuang Powder (SHP) can ameliorate the intestinal inflammation and improve gastrointestinal transit in the rats. POI was induced in adult male rat by standardized small intestinal manipulation (IM). SHP was administered transdermally 24 h prior to the surgery. POI in rat was characterized by decreased gastrointestinal transit, reduced body weight and marked upregulation of mRNA expression of inflammatory mediators including TNF-α, IL-1β, IL-6, and IL-12, IL10, iNOS and ICAM-1. SHP treatment could ameliorate the intestinal inflammatory response and delayed intestinal transit induced by IM, as well as promote the recovery of normal body weight in POI animals. Our study provides evidence that SHP has a potential clinical utility to shorten POI.
Weakly-Supervised Network Alignment With Adversarial Learning

Network alignment, the task of seeking the hidden underlying correspondence between nodes across networks, has become increasingly studied as an important task to multiple network analysis. A few of the many recent applications of network alignment include protein network alignment, social network reconciliation, and computer vision. However, traditional methods which are based on matrix factorization directly work on networks themselves rather than exploit their intrinsic structural consistency, and thus their performance is sensitive to structural variations of networks. Recently, many supervised approaches which leverage latent representation have been proposed. Although they can handle large-scale datasets, most of them rely on a large number of parallel anchor links which are unavailable or expensive to obtain for many domains. Therefore, in this paper, we propose the WENA Framework, a representation learningbased network alignment, in which we study how to design weakly-supervised methods to align large-scale networks with a limit of ground truth available. Empirical results show that, with only two anchor links, WENA significantly outperforms existing unsupervised aligners and even outperforms state-of-the-art supervised methods that use richer resources in terms of both noise robustness and accuracy.