Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Assignment Procedure based on Categories' Boundaries

We presented a procedure for multicriteria assignment problems. Assignment to a category is based on the comparison between an action and categories’s boundaries. Boundary is represented by the least typical action that can be included in the category, and is considered as the category’s inclusion threshold. Comparison is executed considering performance on evaluation criteria and calculation of a fuzzy inclusion relation, generalizing preference relations model with concordance and non-discordance concepts as used in ELECTRE methods. An illustrating example is also presented to demonstrate method’s application to assignment problems. The proposed procedure can be applied to a variety of domains, such as production and operations, as well as financial and human resources.
Power System Control with an Embedded Neural Network in Hybrid System Modeling

The output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to non-smooth nonlinearities from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures can be used. A feedforward neural network (FFNN) (with a structure of multilayer perceptron neural network) is applied to identify the dynamics of an objective function formed by the states, and thereafter to compute the gradients required in the nonlinear parameter optimization. Moreover, its derivative information is used to replace that obtained from the trajectory sensitivities based on the hybrid system model with the differential-algebraic-impulsive-switched (DAIS) structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in both a single machine infinite bus system (SMIB) and a multi-machine power system (MMPS)
A study of partitioning and parallel UDF execution with the SAP HANA database

Large-scale data analysis relies on custom code both for preparing the data for analysis as well as for the core analysis algorithms. The map-reduce framework offers a simple model to parallelize custom code, but it does not integrate well with relational databases. Likewise, the literature on optimizing queries in relational databases has largely ignored user-defined functions (UDFs). In this paper, we discuss annotations for user-defined functions that facilitate optimizations that both consider relational operators and UDFs. In this paper we focus on optimizations that enable the parallel execution of relational operators and UDFs for a number of typical patterns. A study on real-world data investigates the opportunities for parallelization of complex data flows containing both relational operators and UDFs.
Collision-Free Trajectory Planning for Manipulator Using Virtual Force based Approach

This paper presents a virtual force based approach for real-time collision-free trajectory planning for manipulators. This approach defines a virtual attractive force to drive an end-effecter of a manipulator along a preplanned path and uses virtual repulsive force calculated based on 3-dimensional force field method (3D-F 2 ) to protect the whole robot arm. When all these external virtual forces are applied to a manipulator's dynamic model, the wire model of the manipulator, which is modeled by using virtual joints with stiffness and damping, will move to the steady state of the manipulator's dynamics model at the target point. At every time step, this approach outputs a pose or joint angles of the robot arm to the robotic manipulator controller to control the real robot arm through. Simulation results show that this approach can manage to find the trajectory of a robot arm in a very narrow environment without colliding into obstacles.
Conference on Prognostic Factors and Staging in Cancer Management: Contributions of Artificial Neural Networks and Other Statistical Methods

Received December 18, 2000; accepted January 26, 2001. BACKGROUND. The Commission on Cancer data from the National Cancer Data Base (NCDB) for patients with colon carcinoma was used to develop several artificial neural network and regression-based models. These models were designed to predict the likelihood of 5-year survival after primary treatment for colon carcinoma. METHODS. Two modeling methods were used in the study. Artificial neural networks were used to select the more important variables from the NCDB database and model 5-year survival. A standard parametric logistic regression also was used to model survival and the two methods compared on a prospective set of patients not used in model development. RESULTS. The neural network yielded a receiver operating characteristic (ROC) area of 87.6%. At a sensitivity to mortality of 95% the specificity was 41%. The logistic regression yielded a ROC area of 82% and at a sensitivity to mortality of 95% gave a specificity of 27%. CONCLUSIONS. The neural network found a strong pattern in the database predictive of 5-year survival status. The logistic regression produced somewhat less accurate, but good, results. Cancer 2001;91:1673– 8. © 2001 American Cancer Society.
Prediction of Airborne Particulate Matter PM 10 Based on Main Curve

It’s very practical significance to predict the density of hazardous substance(such as PM10) in the air, However, in most cases, this kind of data have the characteristics of imbalance and sequential arrived online, It’s difficult to realize rapid and effective prediction by traditional supervised learning methods. In order to solve this problem, a PM10 prediction method which based on the principal curve, build a PM10 model of PM10 from 2010 to 2012, received corresponding parameters by fitting, Finally, the main curve is obtained. corresponding threshold values of different density of PM10 respectively by a lot of experiment. The results show that the PM10 prediction model based on the principal curve predicts rapidly and a lower prediction error, meanwhile, the network structure is more compact. Introduction In recent years, environmental degradation caused by the concentration of harmful substances in the air increased, the physical health of urban residents have a significant impact. Predicting the concentration of contaminants will help to prepare precautions in advance and alleviate the symptoms of the disease, with significant practical significance. Therefore, this study is the hotspot of current air quality assessment. At present, a variety of improved algorithms are proposed for the classification of unbalanced data. The improvement direction can be divided into two categories, one is from the perspective of the data set, the other is from the perspective of the algorithm. The processing method of data plane [1] mainly includes over-sampling strategy and under-sampling strategy, which improves the unbalanced data set through some mechanism in order to obtain a balanced data distribution. easily lead to the loss of important sample information. The algorithmic angle [2] mainly includes changing the probability density, single class learning classification, cost-sensitive learning and kernel method of integration algorithm. However, these methods are analyzed for single-core case. However, using single-core mapping method, It is not reasonable to proceed with the process. In addition, in the practical application of all the data is not a visit, and requires online prediction; in order to solve this problem liang [3] and others introduced the ELM algorithm, and put forward the online sequence ELM algorithm.
FIRE SEVERITY ASSESSMENT USING NDVI DERIVED FROM LANDSAT TM/ETM IMAGES AND TERRAIN DATA

The forest fires influence on the plants and soil depends on the fire severity and time of exposure. Fire severity integrates physical, chemical and biological changes occurring in ecosystems on the area as a consequence of fire influence. The purpose of the current investigation was to examine the role of the forest fire severity on the vegetation cover of the area of Svilengrad Municipality, using NDVI (Normalized Difference Vegetation Index) before fire and after fire, derived from LANDSAT 8 TM/ETM images. The comparison of the data from NDVI and that observed on the terrain data was also targeted. The results show that NDVI are changed significantly in fire affected area depending on vegetation cover and type of fire. This index also is very sensitive to changes during time after fire occurrence. One year after fire occurrence the NDVI values increased to +0.305 (0.048) for whole studied area. Through dNDVI could be distinguish the recovery rates of the fire affected areas with different tree species.
Comparative Study of AODV, DSR, DSDV, and OLSR Routing Protocols in MANET

MANET is an infrastructure less network based on a multihop relaying concept. It is relay from source node to destination node. Because MANETS are working distributed manner and without operating any fixed infrastructure. The routing protocols are employed in MANET for efficient communication. It is used as to finding a new route and selecting a best route from source to correct destination through the intermediate node. Every intermediate node is act as a router. So, this paper is encompasses the routing protocols such as table driven routing protocol and on-demand routing protocol is to maintain up-to-date routing information from each node to every other nodes. On-demand routing protocol is creates route only when these routes are available. The aim of this paper proposes a comparative study of efficient particular routing protocols in MANET.
Empirically-Derived Analytic Models of Wide-Area TCP Connections: Extended Report

We analyze 2.5 million TCP connections that occurred during 14 wide-area traffic traces. The traces were gathered at five “stub” networks and two internetwork gateways, providing a diverse look at wide-area traffic. We derive analytic models describing the random variables associated with telnet, nntp, smtp, and ftp connections, and present a methodology for comparing the effectiveness of the analytic models with empirical models such as tcplib [DJ91]. Overall we find that the analytic models provide good descriptions, generally modeling the various distributions as well as empirical models and in some cases better.
WEIGHTING INDIVIDUAL OPTIMAL FEATURE SELECTION IN NAIVE BAYES FOR TEXT CLASSIFICATION

In this massive amount of data, data is too vast so that text categorization is important issue. With the help of previously organize set of documents and classes we can automatically classify data. The main aim of this paper is find out patterns which improve speed as well as accuracy of text mining in this digital era. For that we have to select better feature will improve performance classification and the illuminate the inter-pretention. For good organization here first use kullback Leibler Divergence and Jefferies Divergence as binomial representation that matter type I and type II error, So establish another measure i.e Jeffreys Multi Hypothesis Divergence for multiclass classification and found two method of feature selection first is maximum discrimination and second is maximum discrimination X 2 . Further to improvement the bias capacity develop feature selection algorithms by weighting each distinct feature.
Edinburgh Research Explorer Structure matters

Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of eﬀective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several eﬀorts underway to enable the automated classi-ﬁcation of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of online discussions for the classiﬁcation of “cognitive presence” – the central dimension of the Community of Inquiry framework focusing on the quality of students’ critical thinking within online learning communities. We implemented a Conditional Random Field classiﬁcation solution, which incorporates structural features, which may be useful in increasing classiﬁcation performance over other implementations. Our approach leads to an improvement in classiﬁcation accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.
A Survey on Security Issues and Primary User Emulation Attack Detection Techniques in Cognitive Radio Network

With the improvement in wireless technology and services, unlicensed, Industrial, Medical and Scientific (ISM) band is getting overloaded, which leads to spectrum shortage problem. On the other hand, several part of fixed allocated spectrum is underutilized. Cognitive Radio is new and intriguing technology that enables a more flexible and efficacious usage of the radio spectrum. Basically, this technology allows unlicensed users to use licensed spectrum, without interfering with the incumbent transmission. As Cognitive radio networks are wireless in nature, they suffer from all the classic threats present in traditional wireless networks. This paper focuses on an attack that poses a threat to spectrum sensing function of CR, known as Primary User Emulation Attack (PUEA). In this attack is a malicious secondary user mimics signal characteristics of a primary user to acquire channel resources without sharing with other secondary users, thus reducing spectrum usage probability and efficiency. The objective of this paper is to highlight various security issues related to dynamic spectrum access then discuss the PUEA with the existing countermeasures to mitigate it. In addition, future security challenges are addressed. General Terms Cognitive Radio Network, Primary User Emulation Attack, spectrum sensing, Primary User (PU), Secondary User (SU)
Performance Evaluation of Efficient Scheduling Algorithms for Achieving Optimal Flow Delay and to Enhance Throughput in Multichannel Wireless Networks

In this paper, we focus on designing a low complexity scheduling algorithms that can provide higher throughput along with low delay. By exploiting the features of essential conditions and appropriately combining policies from the classes of Oldest Packet First (OPF) and Maximum Weight First (MWF) policies, hybrid policies are designed, that are both delay-optimal and high throughput with a less complexity. There are 3 algorithms implemented in this paper; OPF, MWF & hybrid. Here, the maximum waiting time of the packet in each algorithm is measured and a comparison graph plotted for analysis of delay & throughput for different number of users. Network Simulator 2 (NS2) is used for design & analysis.
Aslib Proceedings A possible solution for digital preservation of e-government: A centralised repository within a cloud computing framework

Purpose – Based on a three-level digital preservation framework, the purpose of this paper is to suggest a solution for long-term digital preservation for the public administration sector, in the form of a centralised intermediate repository, on the basis of the concept of cloud computing. Design/methodology/approach – By means of a thorough review of the literature in this area and an analysis of the current state of the art, the paper investigates a three-level digital preservation framework with a focus on the public sector. It cross-links this framework with the cloud computing concept, in order to propose an appropriate solution. Findings – The mapping of six factors of digital preservation to three levels of digital preservation shows that using appropriate steps supported by suitable strategies and policies enables the public administration sector to take advantage of modern information technology and solve the demanding and critical problem of digital preservation. Practical implications – The paper suggests an organisational and technological solution in the form of a centralised digital preservation repository within a cloud computing framework, to aid both organisations of the public sector which deal with active life cycle document phases and archives and libraries which deal with passive phases of documents and records. Originality/value – The paper addresses the difﬁculties of digital preservation implementation in the public administration sector. The ﬁndings indicate that while developing and implementing digital preservation, the public administration sector should formulate common policies and solutions based on the suggested results of this paper and, in doing so, maximise the beneﬁts of current technologies.
Wlab of University of Szeged at ImageCLEF 2014 Plant Identification Task

Our aim is to implement a plant identification application that can run on smartphones, and this shared task includes it. After the plant identification task of 2013 we concluded that the most frequent trees (e. g. in Hungary) can be identified well by a leaf, when there is a white paper background behind it at the time of photographing. This is why we want to classify more accurately this kind of pictures. Our other important goal is to develop a system which can be trained online while smartphone users take more and more photos. In the previous year there was a separate shared task for the 'Sheet as Background' photos, how- ever this year the task is very complex, and supposes solutions for hard image processing problems, not just feature extraction and classification solutions.
Overleaf Example

Developing algorithms for extra-terrestrial robotic exploration has always been challenging. Along with the complexity associated with these environments, one of the main issues remains the evaluation of said algorithms. With the regained interest in lunar exploration, there is also a demand for quality simulators that will enable the development of lunar robots. In this paper, we propose Omniverse Lunar RoboticSim (OmniLRS) that is a photorealistic Lunar simulator based on Nvidia’s robotic simulator. This simulation provides fast procedural environment generation, multi-robot capabilities, along with synthetic data pipeline for machine-learning applications. It comes with ROS1 and ROS2 bindings to control not only the robots, but also the environments. This work also performs sim-to-real rock instance segmentation to show the effectiveness of our simulator for image-based perception. Trained on our synthetic data, a yolov8 model achieves performance close to a model trained on real-world data, with 5% performance gap. When finetuned with real data, the model achieves 14% higher average precision than the model trained on real-world data, demonstrating our simulator’s photorealism. The code is fully open-source, accessible here: https://github.com/AntoineRichard/LunarSim, and comes with demonstrations.
AN ELECTROMAGNETISM META-HEURISTIC FOR THE RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEM

Recently, a new heuristic algorithm for global optimization has been proposed by Birbil and Fang (2003), referred to as Electromagnetism (EM). This technique is based on the electromagnetism theory of physics, and simulates attraction and repulsion of sample points in order to move towards an optimal solution. In this paper, we have extended the EM methodology for combinatorial optimization problems. To that purpose, we use the EM framework for solving the well-known resource-constrained project scheduling problem (RCPSP) heuristically. We rely on problem-specific characteristics to adapt the original EM framework to the problem under study. We present computational experiments on a standard benchmark dataset, compare the results of the different modifications on the original EM framework with current state-of-the-art heuristics, and show that the procedure is capable of producing consistently good results for challenging instances of the resource-constrained project scheduling problem. We also give directions for future research in order to make the EM framework competitive with the current state-of-the-art heuristics.
Mining Repair Actions for Automated Program Fixing

Automated program fixing consists of generating source code in order to fix bugs in an automated manner. Our intuition is that automated program fixing can imitate human-based program fixing. Hence, we present a method to mine repair actions from software repositories. A repair action is a small semantic modification on code such as adding a method call. A repair model can be defined as a set of repair action. By applying our method on 14 repositories of Java software and 89993 versioning transactions, we present two repair models that are meant to be generalizable and reusable for automated program fixing. Hence, we then show how those repair actions can be used in an automated software repair process called MCRepair.
Mid-Piacenzian variability of Nordic Seas surface circulation linked to terrestrial climatic change in Norway

During the mid-Piacenzian, Nordic Seas sea surface temperatures (SSTs) were higher than today. While SSTs provide crucial climatic information, on their own they do not allow a reconstruction of potential underlying changes in water masses and currents. A new dinoflagellate cyst record for Ocean Drilling Program (ODP) Site 642 is presented to evaluate changes in northward heat transport via the Norwegian Atlantic Current (NwAC) between 3.320 and 3.137 Ma. The record is compared with vegetation and SST reconstructions from Site 642 and SSTs from ODP Site 907, Iceland Sea, to identify links between SSTs, ocean currents and vegetation changes. The dinocyst record shows strong Atlantic water influence via the NwAC corresponds to higher-than-present SSTs and cool temperate vegetation during Marine Isotope Stage (MIS) transition M2–M1 and KM5. Reduced Atlantic water inflow relative to the warm stages coincides with near-modern SSTs and boreal vegetation during MIS M2, KM6 and KM4–KM2. During most of the studied interval, a strong SST gradient between sites 642 and 907 indicates the presence of a proto-Arctic Front (AF). An absent gradient during the first half of MIS KM6, due to reduced Atlantic water influence at Site 642 and warm, presumably Atlantic water reaching Site 907, is indicative of a weakened NwAC and EGC. We conclude that repeated changes in Atlantic water influence directly affect terrestrial climate and that an active NwAC is needed for an AF to develop. Obliquity forcing may have played a role, but the correlation is not consistent.
Repositório ISCTE-IUL

Microblogs, such as Twitter, have become an important socio-political analysis tool. One of the most important tasks in such analysis is the detection of relevant actors within a given topic through data mining, i.e., identifying who are the most inﬂuential participants discussing the topic. Even if there is no gold standard for such task, the adequacy of graph based centrality tools such as PageRank and Katz is well documented. In this paper, we present a case study based on a “London Riots” Twitter database, where we show that Katz is not as adequate for the task of important actors detection since it fails to detect what we refer to as “indirect gloating”, the situation where an actor capitalizes on other actors referring to him.