Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Two Prominent Routing Protocols in IPv6 Network: OSPFv3 & EIGRPv6

Due to the huge demand of Internet, computer network has been transited from IPv4 to IPv6 environment. New routing protocols are also needed in IPv6 network. Among them two are very prominent: IETF’s OSPF and Cisco’s EIGRP. In IPv6 network, they are known as OSPFv3 and EIGRPv6 respectively. Though several researchers have worked in these area, but this paper have analyzed the comparisons between these two routing protocols more intensively. In this paper, packet loss, routing convergence speed and end to end delay have been considered as the parameters of the comparisons. The comparisons have been evaluated in Cisco’s simulation environment; Packet Tracer.
Application of interval decision concept lattice for quality-inventory cost

Concept lattice is an excellent tool of knowledge discovery and data mining.This paper established an interval concept lattice and applied it into the decision for quality-inventory cost.Specifically,it defined the interval formal context,the interval formal concept and established an interval concept lattice,and in the role of homomorphism,proposed the mining method of decision rules.Furthermore,from the start of the relationship between the quality and inventory cost,showed the impact factors and the relationships among them on the visualization of the concept lattice structure and the decision rules and extracted the hidden knowledge in the model and gave the specific implementation steps.Thus,for the effective development of quality management,this paper provided the basis for decision making.
An Improved Fuzzy-Based CPU Scheduling (IFCS) Algorithm for Real Time Systems

Till now various types of scheduling algorithms are used for determining which process should be executed by the CPU when there are multiple no. of processes to be executed.There are many conventional approaches to schedule the tasks but no one is absolutely ideal. In this paper an improved fuzzy technique has been proposed to overcome the drawbacks of other algorithms for better CPU utilization,throughput and to minimize waiting time and turn around time.
Energy Optimization of Hybrid electric Vehicles Using Deep Q-Network

Hybrid electric vehicles are positioned as an intermediate form between gasoline and electric vehicles, contributing to lower fuel consumption and emissions. Map control is used for conventional engine control. This method maps optimal values from experimental data, and it has been pointed out that the capacity of the map is increasing and that it is difficult to respond to increasingly sophisticated control objectives. In this paper, we first present a model of a series-parallel hybrid electric vehicle and propose a method using Deep Q-Network, a typical reinforcement learning technique. Through numerical simulation, we verify that the SOC is within an acceptable range throughout the entire run and that energy efficiency can be improved compared to existing map control.
Knowledge Bases as a Support to the New Distance Learning Concept

In this paper we deal with knowledge bases that can provide different decision making and participation in the system of distance learning. In addition to classical learning now it is necessary to acquire new knowledge and later in the workplace. The fact is that the employees who have been doing their job in one way now they have to perform it on completely different way and they must accept the new way of learning while working. The new concept of observing the system of distance learning that we propose here can to those who are thinking about organizing (implementation) and to those who wish to attend such an education in a faster and easier way reach the necessary knowledge on distance learning (e-m-Learning). For the knowledge base is used Protege platform of an open source. The knowledge base is updated with the sufficient amount of knowledge in order to be fully able to look at the new concept although some specific knowledge will not be covered.
Study on improved LLE algorithm based on a sample set of well-distributed and weights matrix

There are large amounts of data has accumulated along with technology of computer, information and network developed. How can we using these data and mining out the valuable information are hot topics in information processing field. There are some distress and difficulties caused by the high-dimensional data on data modelling and data analysis. In this paper, a local linear embedding algorithm based on the improved uniform sample set and the weight value matrix is proposed. The test shows that the improved dimensionality reduction algorithm accuracy is significantly higher than the original LLE algorithm.
The Teacher as a Lesson Designer

Teachers’ pedagogical design capacity is their ability to perceive and mobilise existing resources to create productive instructional episodes in the classroom. To a certain extent, this ability is dependent on the curricular resource used. As the textbook remains the most commonly used curricular resource in mathematics classrooms, the study reported in this paper investigates how and why one experienced mathematics teacher utilises the textbook. Data were gathered using lesson observations, as well as pre-lesson and post-lesson interviews. The teacher used offloading on the textbook, adapting the textbook content and improvising in the lessons to varying degrees, being aware of the affordances and constraints that the textbook has for her teaching practice. That approach to the textbook enabled the teacher to create various opportunities that enhance learning. The results of the study indicate that the mathematics teacher’s awareness of what a particular resource offers for teaching practice, and what constraints could be encountered on this journey are significant in terms of the teacher’s design capacity.
Ant Colony Optimization towards Image Processing

Soft Computing refers to the techniques of problem solving which are inspired from the human behavior, natural genetics and the behavior of insects. All these techniques are parallel computational techniques which aim to handle imprecise, incomplete, non-linear and complex data. This paper deals with one of the fields of soft computing- namely Ant Colony Optimization (ACO). ACO is a computational intelligence based approach which is used to solve combinatorial optimization problem. Due to its simplicity and optimal approach it has been applied to routing, scheduling, sub-set, assignment and classification problems.Focus of the current paper is onto the use of Ant Colony Optimization in the field of Image Processing. Edge detection, edge linking, feature extraction, segmentation and image compression are the various image processing tasks in which ACO has been applied successfully. The details pertaining to each of the approach have been discussed. Benefits of using ACO over the conventional techniques have also been presented.
PROPERTIES OF GLASSES FROM BaO-B2O3-TiO2 SYSTEM

LUCICA BOROICA∗, DOREL RADU, RAREŞ MEDIANU, ILEANA CRISTINA VASILIU, IOANA SÂNZIANA BOROICA Institutul Naţional de Fizica Laserilor, Plasmei şi Radiaţiilor, Str. Atomiştilor nr. 101-Măgurele, România Universitatea POLITEHNICA Bucureşti, Str. G. Polizu nr. 1, sector 1, Bucureşti, România Institutul Naţional de Optoelectronică INOE 2000, O.P. MG 5, RO-77125, Măgurele, România Universitatea Bucureşti, B-dul Mihail Kogălniceanu nr. 36-46, sector 5, Bucureşti, România
Improving K-copy intelligent caching efficiency based on multi-channel protocol in DTNs- A Survey

Mobile nodes in some challenging network scenarios suffer from intermittent connectivity and frequent partitions e.g. battlefield and disaster recovery scenarios. Disruption Tolerant Network (DTN) technologies are designed to enable nodes in such environments to communicate with one another. In this paper, our main focus is on improvement of caching efficiency. So, in this paper we use K- copy intelligent scheme for caching scheme. However in previous paper, author already use this caching scheme but he use in single channel. And we use this scheme for multiple channels. Along with Ad hoc on demand distance Vector (AODV) for message routing protocol. We conduct extensive simulation studies to evaluate efficiency of K-copy intelligent scheme for multi-channel over single channel. Keyword- Disruption Tolerant Network; multi-channel.
Dependability in cloud storage

The quantity of data in the world is steadily increasing bringing challenges to storage system providers to find ways to handle data efficiently in term of dependability and in a cost-effectively manner. We have been interested in cloud storage which is a growing trend in data storage solution. For instance, the International Data Corporation (IDC) predicts that by 2020, nearly 40% of the data in the world will be stored or processed in a cloud. This thesis addressed challenges around data access latency and dependability in cloud storage. We proposed Mistore, a distributed storage system that we designed to ensure data availability, durability, low access latency by leveraging the Digital Subscriber Line (xDSL) infrastructure of an Internet Service Provider (ISP). Mistore uses the available storage resources of a large number of home gateways and Points of Presence for content storage and caching facilities. Mistore also targets data consistency by providing multiple types of consistency criteria on content and a versioning system. We also considered the data security and confidentiality in the context of storage systems applying data deduplication which is becoming one of the most popular data technologies to reduce the storage cost and we design a two-phase data deduplication that is secure against malicious clients while remaining efficient in terms of network bandwidth and storage space savings.
A stepwise compositional approach to model and analyze system C designs at the transactional level and the delta cycle level

Embedded systems are increasingly integrated into existing real-time applications. They are usually composed of deeply integrated but heterogeneous hardware and software components. These components are developed under strict constraints. Accordingly, the work of design engineers became more tricky and challenging. To meet the high quality standards in nowadays embedded systems and to satisfy the rising industrial demands, the automatization of the developing process of those systems is gaining more and more importance. A major challenge is to develop an automated approach that can be used for the integrated verification and validation of complex and heterogeneous HW/SW systems.In this thesis, we propose a new compositional approach to model and verify hardware and software written in SystemC language. This approach is based on the SystemC Waiting State Automata (WSA). The SystemC Waiting State Automata are used to model the abstract behavior of hardware or software systems described in SystemC. They preserve the semantics of the SystemC scheduler at the temporal and the delta-cycle level. This model allows to reduce the complexity of the modeling process of complex systems due to the problem of state explosion during modeling while remaining faithful to the original system. The SystemC waiting state automaton is also compositional and supports refinement. In addition, this model is extended with parameters such as time and counters in order to take into account further aspects like temporality and other extra-functional properties such as QoS.In this thesis, we propose a stepwise approach on how to automatically extract the SystemC WSAs from SystemC descriptions. This construction is based on symbolic execution together with predicate abstraction. We propose a set of algorithms to symbolically compose and reduce the SystemC WSAs in order to study, analyze and verify concurrent behavior of systems as well as the data exchange between various components. We then propose to use the SystemC WSA to model and simulate hardware and software systems, and to compute the worst cas execution time (WCET) using the Timed SystemC WSA. Finally, we define how to apply model checking techniques to prove the correctness of the abstract analysis.
Man vs. Machine: Differences in SPARQL Queries.

Server-side SPARQL query logs have been a topic of study for some time now. The USEWOD collection of query logs is currently the primary source of information for researchers. A recurring problem is that these logs leave application queries and queries created by humans indistinguishable. In this paper, we present a new collection of manually created queries, that were collected through the YASGUI SPARQL interface. We show how these queries differ from those taken from server logs.
Natural Language and Universal Grammar: Essays in Linguistic Theory

John Lyons is recognized internationally as one of the most influential scholars in modern linguistics. This volume contains essays spanning many years of his thought and research, in addition to previously unpublished pieces. Chapters 2, 3 and 4 make their first appearance here, and set out the view of linguistics and linguistic theory which underlies the content of this and a (forthcoming) companion volume. The remaining six chapters have been either extensively revised or annotated to provide the reader with their historical context and to bring them into line with the author's current thinking.
A detailed survey on 2D and 3D still face and face video databases part I

This paper describes in detail about 2D and 3D face and video face databases developed in the year from 1992 to 2009. The still face database discussed here includes: Our Laboratory Database of Faces (ORL), Yale face database, Yale face databaseB(B stands for Big), Yale face database extendedB, FERET(Face Recognition Technology) face database and CASPEAL is a large scale Chinese face database with varying PEAL(Pose Expression Accessory, and Lighting). The 3D face databases like MIT-CBCL (Centre for Biological and Computational Learning) face recognition database, Georgia Tech face database, Cohn-Kanade Action Unit Face database and 3D BFM (Basel Face Model) are also discussed in this paper. In CK AU, CK+AU(Cohn-Kanade Action Unit) coded facial expression database, a 3D model is developed from a 2D model which is obtained from the captured video of different subjects. The other Face databases and development of new Face databases are discussed at the end of this paper. The application of proposed concept is to develop a new small size face database which includes all the important criteria included in the previous face and video face databases.
A radiology image retrieval system based on user preferences

This article introduces a computer-aided solution for radiology education integrated with the clinic practice, inherited from modern technologies that facilitate the processing of large amounts of stored information. Radiology training may have several challenges such as image retrieval, extraction of knowledge, education towards solving problems and the interaction with huge repositories known as Picture Archiving and Communication Systems (PACS). This project proposes a user-based system that learns from user interaction, retrieving not just the requested information but recommending related cases and interesting images. The recommended images are retrieved using a Click-through rate (CTR) strategy for defining the most similar cases in the database. This is a fully web-based proposal, potentially useful at classroom or home, that allows students to develop the clinical skills needed in a more realistic scenario.
EECS:an energy-efficient clustering scheme in wireless sensor networks

Clustering is an effective topology control approach which can increase network scalability and lifetime in Wireless Sensor Networks(WSNs). LEACH is an elegant clustering protocol that prolongs the network lifetime. The paper proposes a novel clustering scheme EECS for WSNs, which better suits the periodic data gathering applications. In the cluster head election phase it selects a small portion of nodes to join the election, adopts local radio communications method without iteration, and always selects cluster heads with more residual energy. Further, in the cluster set-up phase it uses a novel method for balancing the load of cluster heads. Its control message overhead is small, and the cluster head distribution is near uniform. EECS also has a high energy effective consuming rate across the network. Simulation results show that EECS outperforms LEACH more than 35% in prolonging the network lifetime under the same assumptions.
The Story of Forsythia

that no Forsythia species was common in cultivation until about 1850 and it was not until 1908 that the first really outstanding horticultural variety was imported into this country. Because of their ability to produce an abundant display of bright color so early in the season, few groups of shrubs have risen to favor as quickly as the Forsythias. The only other hardy early flowering shrubs that have yellow flowers are Cornus mas, the Cornelian Cherry; Lindera benzoin, Spice Bush; and Dirca palustris, Leatherwood. Delightful as each of these may be, none can match the bright display which most of the Forsythias produce. The following account is an attempt to describe the various species and cultivars of Forsythia which have played a role in the development of our current garden varieties, and also to describe a number of the best ones which are easily available
El ciclo de los negocios y los índices coincidentes. Aplicación de los filtros de Hodrick-Prescott y de Hamilton para México

El presente documento recopila diferentes métodos de construcción de un indicador de las etapas del ciclo de negocios para México. La importancia reviste en la oportunidad que brinda identificarlo y utilizarlo como insumo para establecer los tiempos de cambio de régimen y alertar sobre la importancia de este indicador, como punto de partida de nuevas alternativas de manejo de crisis y de expansión de la economía mexicana. Se utilizan los filtros de Hodrick-Prescott y de Hamilton para realizar este cálculo, para posteriormente someterlo a la estimación de las probabilidades de cambio de los regímenes clásicos: expansión y recesión mediante cadenas de Markov para México.
An Agent and Ontology based Generic Architecture for Domain Restricted Information Gathering on the Web

ABSTRACT Due to Web size and diversity of information, relevant information gathering on the Web is a very complex task. The main problem with most information retrieval approaches is neglecting the context of the pages, mainly because search engines are based on keyword indexing. Considering restricted domain, taking into account context may lead to more relevant information gathering in this paper, a specific cooperative information gathering approach based on the use of software agents and ontologies is proposed. To implement this approach, generic software architecture, named AGATHE system, permitting development of specific restricted-domain information gathering systems is presented in detail, with a focus on the extraction subsystem. Categories and Subject Descriptors H.3. 3 [ Information Search and Retrieval ]: Information filtering I.2. 11 [ Distributed Artificial Intelligence ]: Multiagent systems General Terms p Algorithms . Keywords Multiagents, information agents, agent-oriented software engineering, tcooperative information gathering, information extracti on, classification
Evaluating and benchmarking the performance of federated SPARQL endpoints and their partitioning using selected metrics and specific query types

The increasing amount of Linked Data and its inherent distributed nature have created for need to developing and researching querying technologies. Inspired by research results from traditional distributed databases, different approaches for managing federation over SPARQL Endpoints have been introduced. Such a system consists of a federated engine as the query mediator and a group of SPARQL endpoints as the data provider. SPARQL is the standardised query language for RDF, the default data model used in Linked Data deployments, and SPARQL endpoints are a popular access mechanism provided by many RDF repositories. The growth of the number of federated SPARQL query systems creates the necessity for benchmarking systems to evaluate their performance. Designing a benchmark for a federated SPARQL query system is a non-trivial task since it consists of heterogeneous systems (e.g. hardware, software, data structure and data distribution) which are also distributed. In this thesis, we design a comprehensive benchmark based on the dependencies between the metrics, datasets and queries. We initially investigate existing federated engines and compare their features and behaviours. Based on this investigation, we first identify the metrics that are suitable to assess the performance of federated SPARQL query systems. We introduce three types of metrics: independent metrics, semi-independent metrics and composite metrics. Thereafter, we investigate the benefits and the costs associated while federating a SPARQL query over multiple sources having links between them in the existing federated engines. Next, we present six approaches to generate a dataset for benchmarking a federated SPARQL queries. Thereafter, by using those approaches, we generate 9 datasets and then observe the relationship between the spreading factor of those datasets and the communication cost. The spreading factor is a dataset metric for computing the distribution of classes and properties throughout a set of data sources. Finally, we present QFed, a dynamic SPARQL query set generator for federated SPARQL query benchmarks that takes into account the characteristics of both datasets and queries along with the metrics.
A serious game empowering the prediction of potential terrorist actions

ABSTRACT Law Enforcement Agencies (LEAs) are nowadays taking advantage of a wide range of information and intelligence sources (e.g. human intelligence (HUMINT), open source intelligence (OSINT), image analysis (IMINT)) in their efforts to anticipate potential terrorist actions. However, activity prediction based on these sources requires datasets for training reasoning algorithms, which are not typically available. In this paper we introduce a gaming approach to the generation of datasets that can be used for training reasoning algorithms that are able to predict the likelihood of terrorist actions against specific assets and locations in urban environment. At the heart of our approach resides a two-player game, whose moves map to actions that are usually associated with the various stages of planning, preparing and executing terrorist attacks in urban environments. Apart from presenting the game, the paper introduces also the range of reasoning algorithms that have been trained based on its output. It also explains the terrorist semantics that underpin the implementation of the game and the design/modelling of the rules of the game. Early validation results demonstrate that the presented reasoning algorithms can successfully classify terrorist activity, through distinguishing it from seemingly suspicious but unrelated events that are typically generated by non-terrorist activities.
Hybrid location determination technology within urban and indoor environment based on Cell-ID and path loss

The location determination technology based on simple delay evaluations or GPS is not accurate enough or even impossible in urban and indoor environments due to the multi path propagation.To enhance the location accuracy and reduce the operation cost within these environments, this paper proposes a novel hybrid location determination technology which combines CELL ID with the database correlation method. The proposed method generate the prediction database of path loss according to CELL ID, and after the computation the smallest squared error of measured path loss and the prediction path loss, the location of the mobile terminal is decided by the coordinates of the best matching matrix entry.