Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and stabilization for networked control systems with time-triggered asynchronous sampling and network induced delay

The modeling and stabilization for the networked control systems with numbers of subsystems, are investigated in this paper. In order to eliminate conflictions and reduce network induced delay, a time-triggered asynchronous sampling mechanism is introduced, and the feature of network induced delay is analyzed. Then a distributed control law which uses the newest sampling date for control purpose, is delivered. And the networked control system is modeled as a discrete time system with uncertainty. A stability criterion is presented based on a Lyapunov function and matrix inequalities. An iterative algorithm for controller design is presented. Finally, a numerical example is given to validate the approach.
Unmanned Autonomous Driving Method Based on Content-centric Network

Aiming at the problem that the existing wireless network can not transfer the massive amount of traffic information received by unmanned driving vehicles effectively,the unmanned autonomous driving method based on Content-centric Network(CCN)is proposed.It sends and receives real-time traffic information to other vehicles and the road infrastructure rapidly by using the Interest packet and Data packet of the CCN.After gathering the entire traffic information of the whole region,the path for the vehicle to the destination can be generated according to the user demand.And during the traveling to the destination,the paths are exchanged among the vehicles,and the route is adjusted according to the real-time generated traffic information,which is used to improve the safety of the driving.Experimental result shows that,compared with the autonomous driving method based on802.11 communication,the method can reduce the receiving time of traffic information,packet loss rate and cost,and improve the driving safety significantly.
Study on a Support Service System under the New “MOOC+SPOC”-Based Mixed Teaching Mode

A support service system is an important condition for guaranteeing teaching and learning effects. Based on theories and concepts concerning new mixed teaching modes, the concept of the new “MOOC+SPOC”-based mixed teaching mode is analyzed and defined, and coupled with the MOOC+SPOC-based computer course practice of H university in China, a support service system model is constructed. A questionnaire survey method is adopted to learn about students’ various demands for support services and in view of their demands, measures for improving the support service system are put forward.
Implementation of Sine&Cosine Function Based on Look-up Ttable and Taylor Expansion

SineCosine function has important application in modern digital signal processing.A method is proposed for implementing SineCosine function based on look-up table and taylor expansion.It can calculate the Sine and Cosine value from 0~360°. The input and output are 16 bits,and the precision of output is 0.000 03.This paper implements the hardware on the stratix device of Altera Corporation.Comparing with other methods,this method needs less resource and has higher precision,the speed of implementation is faster.
Design and Implementation of New Type Cloud Computing Server

It exists problems of big size,high power and low efficiency when cloud computing center is composed of traditional sever.This paper designs and implements a new cloud computing server.It discusses the key technologies,such as cloud processors,3D-torus net,hardware virtualization,etc.Compared with traditional servers,the new one has the characteristics of small size and low power.
Complex Network Community Mining Based on Particle Swarm Optimization

In order to solve the problem of community mining optimization from complex network,according to the prior knowledge of the topology structure of complex network,a complex network community mining algorithm based on Particle Swarm Optimization(PSO)is proposed. In the proposed algorithm,particle's position and velocity are redefined in discrete case,particle's update principles is redesigned,the proposed algorithm can automatically determine the best community numbers without knowing it in advance. In order to improve the global search ability of the proposed algorithm,a local search operator is designed,and this operator can help the algorithm to jump out of local optimum and improves the convergence speed. Experimental results demonstrate that the proposed algorithm can efficiently dig out the community structures hidden behind complex networks,and the execution speed is much faster than that of i Meme-net algorithm.
Synthesis, characterization, and application of yeast-based magnetic bionanocomposite for the removal of Cu(II) from water

Abstract Due to the amount of yeast residues generated by the industries and their high capacity as biosorbent, this work proposes the use of yeast biomass in natura (YB) or modified with ferromagnetic nanoparticles (Fe3O4) (YB-MNP) for Cu(II) removal from water. Synthesized YB-MNP, YB, and magnetite alone (MNP) were characterized by FTIR, XRD, and SEM. It was observed the efficient impregnation of magnetite on the surface of the biomass. Copper sorption was evaluated in batch tests using 100 mg/L of Cu(II) solution at pH 5.5, and the supernatant was analyzed by FAAS for Cu determination. Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) isotherm models were applied to fit the experimental data. A favorable sorption process was observed for all the materials, mainly for the D–R model adjusted to the bionanocomposite data (YB-MNP), with r2 = 0.9950 and a low error (χ2 = 0.0427) associated to the model. Theoretical and experimentally Cu(II) sorption capacities were fairly similar, 8.6 ± 0.1 and 8.3 ± 0.2 mg/g, respectively.
A C4.5 Decision Tree Based Algorithm for Web Pages Categorization

Web text categorization can be applied to many domains such as information retrieval,news categorization,etc.Decision tree algorithm is a simple method for categorization and has been used extensively.This paper investigates the basic method and process to build a web classifier by means of C4.5 decision tree,which has various merits such as high categorization precision,high categorization speed,etc.Moreover,this paper proposes a C4.5 decision tree based frame of web pages classifier,and implements it on a web crawler.The experimental results show that this algorithm is highly effective.
Adaptive iterative learning control combined with discrete-time sliding mode control for piezoelectric nanopositioning

In this paper, an innovative controller is proposed to compensate for both the nonlinear hysteresis and the vibration dynamics effects of a nanopositioning stage driven by piezoelectric actuators. With the increasing of operating bandwidth and range, large positioning errors are produced because of the hysteresis, vibration, external disturbance and noise, as well as uncompleted modeled dynamic errors. The main contribution of this work is that an adaptive approach based on iterative learning control (ILC) combined with discrete-time variable structure control is proposed for the disturbance estimation and disturbance rejection. The discrete-time sliding mode controller is applied to corporate with ILC, which is effective to compensate for nonrepeating disturbance. In addition, the proposed control algorithm is applied to experimentally validate the effectiveness of enhancing the performance in positioning control of a nanopositioning stage.
A Negotiation Model Based on Bayesian Learning

In Multi-Agent systems where each Agent has a different goal, Agent must be able to solve conflicts aris- ing in the process of achieving its goal, with incomplete knowledge about other Agents. Negotiation is an effective ap- proach to solve these problems. This paper introduces a negotiation model based on Bayesian learning, called NMBL. Agent gets information of the negotiation opponents in every iteration by means of Bayesian learning, updates the pri- or knowledge of the negotiation opponents and then brings forward the offer of the next iteration according to negotia- tion strategies based on the conflicting point and un-compromising degree. NMBL regards the whole negotiation pro- cess as a dynamic interaction conduct, which reveals the dynamic characteristic of Multi-Agent systems' NMBL also has a relatively strong learning ability. The experiments show that this model has good negotiation performance.
Part-of-speech tagging based on dictionary and statistical machine learning

Part-of-speech tagging is the basis of Natural Language Processing, and is widely used in information retrieval, text processing and machine translation fields. The traditional statistical machine learning methods of POS tagging rely on the high quality training data, but obtaining the training data is very time-consuming. The methods of POS tagging based on dictionaries ignore the context information, which lead to lower performance. This paper proposed a POS tagging approach which combines methods based on dictionaries and traditional statistical machine learning. The experimental results show that the approach not only can solve the problem that the training data are insufficient in statistical methods, but also can improve the performance of the methods based on dictionaries. The People's Daily corpus in January 1998 is used as testing data, and the accurate rate of POS tagging achieves 95.80%. For the ambiguity word POS tagging, the accuracy achieves 88%.
Real-time human activity recognition in smart home with binary tree SVM

In this paper, binary tree support vector machines (BT-SVM) and a sliding sensor window have been applied to recognize the human activities in real time. To construct the optimal binary tree, genetic algorithm (GA) has been employed. The real data collected from participants performing activities was used to train the SVM models and evaluate the accuracy of the recognition algorithms. Compared with one-against-one SVM and BT-SVM with human knowledge, the experiment results show that the proposed BT-SVM with GA has better activity recognition performances in smart home.
Data Integrity Verification in Cloud Storage without using Trusted Third Party Auditor

Data security is biggest problem. In cloud storage User can store their data remotely without maintaining local copy of data. So the integrity verification of the data is major problem in cloud storage. We ensure the data not tampered with other because cloud provider is not completely trusted. Integrity Verification can be managed by the use of TPA (Third Party Auditor) or without TPA. In this paper we review integrity verification with TPA and non TPA with security.
Relocalización de paradas de transporte público para ser incorporado dentro planes de contingencia como resultado de la construcción de obras de mejora vial

Las paradas de transporte publico (TP), tanto en su distribucion como en su localizacion, son generalmente analizadas desde un punto de vista macroscopico, utilizando modelos analiticos para observar tanto una linea de buses, en particular, o la red de transporte publico, en general. Sin embargo, la importancia y complejidad de lograr una buena operacion de las paradas de transporte publico y en particular lograr que estas tengan una buena accesibilidad, requiere de un analisis mas detallado. Actualmente no existen desarrollos metodologicos que solucionen este problema, menos aun en el caso de proyectos viales de mejora de la infraestructura urbana que implican una intervencion importante de un sector de la ciudad, por lo que en este trabajo se propone una metodologia de localizacion de paradas de transporte publico que a traves de un modelo matematico de optimizacion permita ubicar dichos paraderos provisorios a lo largo de las calles propuestas en un Plan de Desvios de manera tal de optimizar la accesibilidad de los usuarios al transporte publico. Esta metodologia fue aplicada a un caso real de estudio en la interseccion de las avenidas 5 de Abril – Esquina Blanca con Avenida Del Ferrocarril de Santiago, Chile, y los resultados muestran que se puede mantener y, en algunos casos, mejorar el nivel de servicio del transporte publico desde el punto de vista de la accesibilidad al mismo. Por lo tanto, se demuestra la importancia y el aporte que esta metodologia puede tener para mejorar la planificacion de los Planes de Desvio por obras de mejoramiento vial al incorporar la variable accesibilidad al transporte publico.
Consistency of a gauged two-Higgs-doublet model: Scalar sector

We study the theoretical and phenomenological constraints imposed on the scalar sector of the gauged two Higgs doublet model proposed recently as a variant of the popular inert Higgs doublet model of dark matter. The requirements of tree-level vacuum stability and perturbative unitarity in the scalar sector are analyzed in detail. Furthermore, taking into account the constraints from the 125 GeV Higgs boson measurements at the Large Hadron Collider, we map out the allowed ranges for the fundamental parameters of the scalar potential in the model.
Communication in distributed simulation environment

Growing complexity of the systems determines the important rule of the simulation, because specification possibilities of classical analytical methods are quite limited. The analysis and operation of the real sociotechnical systems ask for cooperation among experts of technical and social (environmental) systems. Serious amount of simulation tools and simulators are created for modelling technical as well as social systems, nevertheless designing of heterogeneous and distributed simulation models is still difficult. One of the reasons is lack of suitable techniques for communications and synchronization of the various models inside the joint systems model. The authors offer the CORBA solution for communication inside distributed simulation model of Ligatne Natural Trails - an integrated tourism object for recreation and ecosystems research.
Projectively generated $d$-abelian categories are $d$-cluster tilting

Building on work of Jasso, we prove that any projectively generated $d$-abelian category is equivalent to a $d$-cluster tilting subcategory of an abelian category with enough projectives. This supports the claim that $d$-abelian categories are good axiomatizations of $d$-cluster tilting subcategories.
Adaptive unsupervised classification of polarimetric SAR images using the improved affinity propagation clustering algorithm

ABSTRACT A robust automatic classification system is critical for polarimetric synthetic aperture radar (POLSAR) terrain processing. In most of the conventional classification methods, the number of classes could not be calculated before classification. In this article, we present a new unsupervised classification algorithm with an adaptive number of classes for POLSAR data which is capable of estimating the class numbers automatically. The approach is mainly composed of three operations. First, region-based feature map combining the polarimetric statistical and spatial information is constructed based on the turbopixel method. This is followed by a clustering step performed through an improved affinity propagation clustering with Wishart distance. Finally, the result of the improved affinity propagation clustering is classified using Wishart classifier. The proposed approach takes the spatial information into consideration and makes good use of the inherent statistical characteristics of POLSAR data. The performance of the proposed classification approach on three real datasets is presented and analysed, and the experimental results show that the approach provides more accurate estimation under the condition of various numbers of classes compared with existing methods.
Using SCTP as a Transport Layer Protocol for HTTP

Hyper-Text Transfer Protocol (HTTP) [RFC2116] requires a reliable transport for end-to-end communication. While historically TCP has been used for this purpose, this document proposes an alternative -- the Stream Control Transmission Protocol (SCTP) [RFC4960]. Similar to TCP, SCTP offers a reliable end-to-end transport connection to applications. Additionally, SCTP offers other innovative services unavailable in TCP. The objectives of this draft are three-fold: (i) to highlight SCTP services that better match HTTP's needs than TCP, (ii) to propose a design for persistent and pipelined HTTP 1.1 transactions over SCTP's multistreaming service, and (iii) to share some lessons learned from implementing HTTP over SCTP. Finally, open issues warranting more discussion and/or investigation are listed.
The Annual Report Algorithm: Retrieval of Financial Statements and Extraction of Textual Information

U.S. corporations are obligated to file financial statements with the U.S. Securities and Exchange Commission (SEC). The SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system containing millions of financial statements is one of the most important sources of corporate information available. The paper illustrates which financial statements are publicly available by analyzing the entire SEC EDGAR database since its implementation in 1993. It shows how to retrieve financial statements in a fast and efficient way from EDGAR. The key contribution however is a platform-independent algorithm for business and research purposes designed to extract textual information embedded in financial statements. The dynamic extraction algorithm capable of identifying structural changes within financial statements is applied to more than 180,000 annual reports on Form 10-K filed with the SEC for descriptive statistics and validation purposes.
Do you trust the cryptography used in your devices

The goal of COSICs research activities is to create a secure electronic equivalent for interactions in the physical world such as confidentiality, signatures, identification, anonymity, payment and elections. The research concentrates on the design, evaluation, and implementation of cryptographic algorithms and protocols, on the development of security architectures for information and communication systems, on the development of security mechanisms for embedded systems and the design and analysis of privacy preserving systems.
Optimization of FFT Parallel Algorithm on Multi-Core CPUs

Fast Fourier Transformation is widely applied in fields of digital signal processing and communication.Multi-core CPU is increasingly common,and based on the parallelism of FFT algorithm,butterfly algorithm can be flexibly decomposited;we can improve the computational efficiency of FFT via investigating the allocation of parallel blocks and nested relations to optimize the algorithm,and reasonably allocating threads to realize parallel computing of multi-core CPU.
Using automatic speech transcriptions in lecture recommendation systems

The research leading to these results has received funding from the PASCAL2 Network of Excellence under the PASCAL Harvest Project La Vie, the EU 7th Framework Programme (FP7/2007-2013) under grant agreement no. 287755 (transLectures), the ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no. 621030 (EMMA), the Spanish MINECO Active2Trans (TIN2012-31723) research project, and by the Spanish Government with the FPU scholarship AP2010-4349.
Flight MH370 community structure

Studying community structure has proven efficient in understanding social forms of interactions among people, quantitatively investigating some of the well-known social theories and providing useful recommendations to users in communities based on common interests. Another important feature for community structure is that it allows for classification of vertices according to their structural positions in clusters such that some vertices may have an important function of control and stability within the community while others may play an important role of leading relationships and exchanges between different communities. Studying the community structure of Flight MH370 will help us finding patterns that emerge from that structure which can lead to demystify some of the many ambiguous aspects of that flight. The aim of this study is to analyze the mesoscopic and macroscopic features of that community using social network analysis. Pajek, which is a program for social network analysis, is used to generate a series of social networks that represent the different network communities.
The Top-down Visual Attention Method with Known Interference Target

The simulation of visual attention can improve the efficiency of relevant intelligent applications by finding out most salient locations quickly in images or videos while ignoring redundant information. The paper can reason key features of target based on task scenario, filter the bottom-up visual attention feature channel by top-down method, and propose the top-down visual attention method with known interference target. The proposed method can minimize the time of generating visual saliency map by ensuring the hit rate of mission targets, and improve the efficiency of the search targets based on visual attention
Searchable Privacy-Enabled Information and Event Management Solution.

With network traffic proliferating over the last couple of decades, there is an increasing need to monitor security information in order to prevent and resolve network security threats. A Security Information and Event Management (SIEM) solution collects all the alerts that the various Intrusion Detection and Prevention Systems (IDS/IDP or IDPS) generates, as well as security logs from various other systems, into one database so that the security analyst (SA) can more easily get an overview of the threat activity. A privacy enhanced anonymization and deanonymization protocol (Anonymiser/ Reversible Anonymiser) has been used to prevent a first-line security analyst, without proper clearance, getting access to personal identifiable information (PII) and/or other types of confidential information that are not allowed to leave the network perimeter. Some examples may be PII sampled in IP packets, critical address information and network architecture. This thesis proposes an architectural design for a new SIEM solution which utilises a reversible anonymizer (RA) for enabling privacy-enhanced data collection and on demand deanonymization of anonymized alarms.
FRIEND MATCHING USING PROBABILISTIC TOPIC MODEL

Recommender Systems provide suggestions for users to guide in various decision-making processes.The recommender systems can be defined by the purpose of recommendation, mechanism and data gathering. Recommendation system for social networks are different since the item recommended are rational human beings. The paper focuses on designing a friend matching system by analyzing user lifestyles as common criteria. Large amount of data collected from various users create high dimensional data. In order to resolve this, probabilistic topic modeling is used. Content based machine learning approaches are used to find out suspicious users in the recommendation system. The results are evaluated based on the datasets created from the real world users.