Miracle-dz/UniFAR-FTUs huggingface dataset:
Semi-automatic labeling of coreferent named entities: an experimental study

In this paper, we investigate the problem of matching coreferent named entities extracted from text collections in a robust way: our long-term goal is to build similarity methods without (or with the minimum amount of) prior knowledge. In this framework, string similarity measures are the main tool at our disposal. Here we focus on the problem of evaluating such a task, especially in finding a methodology to label the data in a semi-automatic way.
Modified framework for sarcasm detection and classification in sentiment analysis

Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.
Parallel algorithms of timetable generation

Context: Most of the problem of generating timetable for a school belongs to the class of NP-hard problems. Complexity and practical value makes this kind of problem interesting for parallel computing. Objectives: This paper focuses on Class-Teacher problem with weighted time slots and proofs that it is NP-complete problem. Branch and bound scheme and two approaches to distribute the simulated annealing are proposed. Empirical evaluation of described methods is conducted in an elementary school computer laboratory. Methods: Simulated annealing implementation described in literature are adapted for the problem, and prepared for execution in distributed systems. Empirical evaluation is conducted with the real data from Polish elementary school. Results: Proposed branch and bound scheme scales nearly logarithmically with the number of nodes in computing cluster. Proposed parallel simulated annealing models tends to increase solution quality. Conclusions: Despite a significant increase in computing power, computer laboratories are still unprepared for heavy computation. Proposed branch and bound method is infeasible with the real instances. Parallel Moves approach tends to provide better solution at the beginning of execution, but the Multiple Independent Runs approach outruns it after some time.
Exploring the temporal dimension of forum participation in MOOCs

ABSTRACT Discussion forums are increasingly central to massive open online courses (MOOCs), and it is vital for learners to participate in associated forum activities. Active forum participation positively relates to learner achievement in that more posts yield better learner performance. However, this numerically aggregated measure overlooks the fact that longitudinal trajectories of forum participation temporally vary among learners with different motivations in taking MOOCs. To provide timely support for learners to stay engaged, it is important to understand the temporal variation of longitudinal forum participation and how different motivations account for the variance. Using educational data mining techniques, this research identified three clusters with different longitudinal participation trajectories and also indicated that intrinsically motivated learners outperformed others in their longitudinal forum engagement. Also, examining longitudinal forum participation more accurately differentiated learner performance than the numerically aggregated measure. Last, learners persistently engaging in forums were more likely to perform better in MOOCs.
Characteristics of space‐diversity branch using parallel dipole antennas in mobile radio communications

The configuration of two parallel half-wave dipoles is the basic space-diversity branch for space-diversity reception in mobile radio communications. This paper presents theoretical and experimental study on the antenna diversity branch connected to a 50-ohm receiver. Post-detection selection diversity is applied to determine the relation of the mean effective gain (MEG) and the correlation coefficient with the variation of angle of antenna inclination from the vertical direction, cross-polarization power ratio (XPR) of the propagation path, and the distribution of the incident wave. First, it is clarified theoretically that, if antenna spacing is made less than 0.2 wavelength, then efficiency is reduced due to the coupling between the antenna elements and MEG is greatly deteriorated. Moreover, minimum antenna spacing for better MEG characteristics is in the range of 0.3 to 0.4 wavelengths. Next, it is clarified that the correlation coefficient is less than 0.1 for variation of antenna inclination, XPR, and the distribution of incident wave when the antenna spacing ranges from 0.2 to 0.3 wavelengths. Thus, an optimum antenna spacing exists in the forementioned space-diversity branch, which theoretically is found to be about 0.3 wavelength. The validity of the theoretical results is confirmed by 900-MHz band indoor experiments.
An Accelerator based on the ?-VEX Processor: An Exploration using OpenCL

In recent years the use of co-processors to accelerate specific tasks is becoming more common. To simplify the use of these accelerators in software, the OpenCL framework has been developed. This framework provides programs a cross-platform interface for using accelerators. The ? -VEX processor is a run-time reconfigurable VLIW processor. It allows run-time switching of configurations, executing a large amount of contexts with low issue-width or a low amount of contexts with high issue-width. This thesis investigates if the ? -VEX processor can be competitively used as an accelerator using OpenCL. To answer this question, a design and implementation is made of such an accelerator. By measuring the speed of various components of this implementation, a model is created for the run-time of a kernel. Using this model, a projection is made of the execution time on an accelerator produced as an ASIC. For the implementation of the accelerator, the ? -VEX processor is instantiated on an FPGA and connected to the host using the PCI Express bus. A Linux kernel driver has been developed to provide interfaces for user space applications to communicate with the accelerator. These interfaces are used to implement a new device-layer for the pocl OpenCL framework. By modeling the execution time, three major contributing factors to the execution time were found: the data transfer throughput, the kernel compile time, and the kernel execution time. It is projected that an accelerator based on the ? -VEX processor, using similar production technologies and without architectural changes, can achieve 1.2 to 0.11 times the performance of a modern GPU.
Algorithmic Foundations for Business Strategy

I introduce algorithmic models for the study of strategic problem solving, aimed at illuminating the processes and procedures by which strategic managers and firms deal with complex problems. These models allow us to explore the relationship between the complexity of an environment, the sophistication of the problem solving processes and procedures used to optimally map problem statements into strategic actions, and the organizational structures that are best suited to the implementation of solutions. This approach allows us to distinguish among levels of sophistication in the strategic management of complexity, specifically among rational, irrational, quasi-rational and super-rational problem solving processes and responses of strategic managers and organizations. It highlights a set of dynamic search and adaptation capabilities that can be studied via the algorithmic and computational properties of the problems they are meant to solve and the efficiency and reliability by which they search a solution space. It points to several new components of competitive advantage that are linked to the complexity adaptation of a firm: ‘offline problem solving’ and ‘simulation advantage’ are key strategic differentiators for firms facing complex problems.
Emerging risks in the IoT ecosystem: Who's afraid of the big bad smart fridge?

Rapid technological innovations, including the emergence of the Internet of Things (IoT), introduce a range of uncertainties, opportunities, and risks. While it is not possible to accurately foresee IoT's myriad ramifications, futures and foresight methodologies allow for the exploration of plausible futures and their desirability. Drawing on the futures and foresight literature, the current paper employs a standardised expert elicitation approach to study emerging risk patterns in descriptions of IoT risk scenarios. We surveyed 19 IoT experts between January and February 2018 using an online questionnaire. The submitted scenarios provided expert's perception of evolving IoT risk trajectories and were evaluated using thematic analysis, a method used to identify and report patterns within data. Four common themes were extracted: physical safety; crime and exploitation; loss of control; and social norms and structures. These themes provide suitable analytical tools to contextualise emerging risks and help detecting gaps about security and privacy challenges in the IoT.
Impedance Control of Robots Using Voltage Control Strategy Revisited

In this note, we show that the impedance control st ategy proposed in the paper is not feasible from practical implementation point of view. Next, a rob ust impedance controller is proposed for electrically driven robots using Fourier series (FS ). The fact that robots' actuators have limited voltage is also considered in controller design pro cedure. In comparison with other impedance controllers using FS, the proposed controller resul ts in fewer numbers of FS and consequently less computational load. These superiorities become more d minant when the manipulator degrees of freedom are increased. Simulation results on a Puma 560 manipulator actuated by permanent magnet direct current electrical motors indicate the effic iency of proposed method.
Gateway Discovery Approaches Implementationand Performance Analysis in the Integrated MobileAd Hoc Network (MANET)-Internet Scenario

The connection of ad hoc network to the internet is established using gateways. To start the internet connection in MANET, first process is that the gateway is to be discovered by the mobile nodes. As the internet is the global service the user are to be satisfied by the integration of internet with the MANET. For integration of two networks the gateway is used as the bridge. In this paper the AODV reactive routing protocol is used for avoiding the scarcity of network resources in MANET. We have designed and analyzed the various gateway discovery approaches and the stimulation is carried out by NS2 which is used for all network scenarios. The performance is evaluated by three metricspacket delivery ratio (or) fraction, throughput and drops of packets or packet loss ratio.
Fast Deformation Part Model with CNN for Face Detection

This paper proposes a fast deformation part model (DPM) with neural network (FDPDPM) for face detection. It can make fully use of the advantage of high level feature and classifier with spatial location information. In fact, this paper uses a truncated the variant of VVGNET and a pyramid of multi-scale filter method to maintain the scale invariance. As a result, it not only improves the detection efficiency, but also makes a contribution to better detection effect on the face with small size and partial occlusion.
The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model

Abstract. The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.
Pixels Classification With Neurocomputer

This paper developes a new method for the pixel classification of an intensity image. A neural networks of non-linear analog neurons have been shown extremely effective. This problem is considered as an optimally classification of an image based on their original activation. Optimization is defined in terms of energy which is a function of neurons the output values which vary continuously. The neurons are modelled as amplifiers which have sigmoid monotonic input-output relations. A synapse between two neurons is defined by a conductance which connects the output of neuron to the input of another neuron. The net input current to any neuron is the sum of the currents flowing through the set of resistors connecting its input to the outputs of the other neurons. We have formulate the problems in terms of desired optima, subject to certain constraints.
Remote sensing spectrometry of a temperate deciduous forest-a modeling approach

This work evaluates the potential of remote sensing visible and near infrared spectrometry for studying the dynamics of a temperate deciduous forest. It is conducted with the airborne Reflective Optics Spectrometric Imaging System (ROSIS) in the frame of the European Multisensors Airborne Campaign (EMAC-94). A modeling approach, i.e. coupled canopy and leaf reflectance models, is used for assessing the sensitivity of forest parameters and associated relationships to experimental (illumination and viewing conditions) and biophysical (soil reflectance, forest structure) factors.
An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption

Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental results show two major improvements over the existing approaches for VM placement. First, our algorithm efficiently balances the utilization of multiple types of resource by minimizing the amount of physical servers used. Second, it reduces system cost compared with existing approaches in heterogeneous environment.
CoExist: Preserving Immediate Access to Source Code and Run-time Information of Previous Development States

Programmers make many changes to the program to eventually find a good solution for a given task. Every little change and the respective development state can be of value in future development situations when, for example, promising ideas suddenly turn out inappropriate or the interplay of objects turns out more complex than initially expected before making changes. Programmers would benefit from tool sup-port that provides immediate access to source code and run-time of previous development states of interest. We present IDE extensions, implemented for Squeak/Smalltalk, to preserve, retrieve, and work with this information. With such tool support, programmers can work without worries be-cause they can rely on tools that help them with whatever their explorations will reveal. They no longer have to follow certain best practices only to avoid undesired consequences of changing code.
Bug Patterns in Java

From the Publisher: Just as good programming skills involve the knowledge of many design patterns, which developers can combine and apply in various contexts, good debugging skills involve knowledge of bug patterns. Bug patterns are recurring correlations between signaled errors and underlying bugs in a program. This concept is not novel to programming. Medical doctors rely on similar types of correlations when diagnosing disease. They learn to do so by working closely with senior doctors during their internships. Their very education focuses on learning to make such diagnoses. In contrast, software engineers education focuses on design processes and algorithmic analysis. These skills are, of course, important, but little attention is paid to teaching the process of debugging. "Bug Patterns in Java" presents a methodology for diagnosing and debugging computer programs. The act of debugging will be presented as an ideal application of the scientific method. Skill in this area is entirely independent of other programming skills, such as designing for extensibility and reuse. Nevertheless, it is seldom taught explicitly. Eric Allen lays out a theory of debugging, and how it relates to the rest of the development cycle. In particular, he stresses the critical role of unit testing in effective debugging. At the same time, he argues that testing and debugging, while often conflated, are properly considered to be distinct tasks. Once laying this groundwork, he then discusses various "bug patterns" (recurring relationships between signaled errors and underlying bugs in a program) that occur frequently in computer programs. For each pattern, the book discusses how to identify them, how to treat them, and how to prevent them.
Fondements calculatoires de la locomotion anthropomorphe

Anthropomorphic locomotion is a complex process that involves a very large number of degrees of freedom, the human body having more than three hundred joints against thirty in humanoid robots. Taken as a whole, these degrees of freedom show a certain coherence making it possible to set the anthropomorphic system in motion and maintain its equilibrium, in order to avoid falling. This thesis highlights the computational foundations behind this orchestration. It introduces a unified mathematical framework allowing both the study of human locomotion and the generation of locomotive trajectories for humanoid robots. This framework consists of a reduction of the body-complete dynamics of the system to consider only its projection around the center of gravity, also called centroid dynamics. Although reduced, we show that this centroidal dynamics plays a central role in the understanding and formation of locomotive movements. To do this, we first establish the observability conditions of this dynamic, that is to say that we show to what extent this data can be apprehended from sensors commonly used in biomechanics and robotics. Based on these observability conditions, we propose an estimator able to reconstruct the unbiased position of the center of gravity. From this estimator and the acquisition of walking motions on various subjects, we highlight the presence of a cycloidal pattern of the center of gravity in the sagittal plane when the human is walking nominally, that is, to say without thinking. The presence of this motif suggests the existence of a motor synergy hitherto unknown, supporting the theory of a general coordination of movements during locomotion. The last contribution of this thesis is on multi-contact locomotion. Humans have remarkable agility to perform locomotive movements that require joint use of the arms and legs, such as when climbing a rock wall. How to equip humanoid robots with such capabilities? The difficulty is certainly not technological, since current robots are able to develop sufficient mechanical powers. Their performances, evaluated both in terms of quality of movement and computing time, remain very limited. In this thesis, we address the problem of generating multi-contact trajectories in the form of an optimal control problem. The interest of this formulation is to start from the reduced model of centroid dynamics while responding to equilibrium constraints. The original idea is to maximize the likelihood of this reduced dynamic with respect to body-complete dynamics. It is based on learning a measurement of occupation that reflects the kinematic and dynamic capabilities of the robot. It is effective: the resulting algorithmic is compatible with real-time applications. The approach has been successfully evaluated on the humanoid robot HRP-2, on several modes of locomotion, thus demonstrating its versatility.