Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Metric Thresholds Derivation for Code Smell Detection

Code smells are archetypes of design shortcomings in the code that can potentially cause problems during maintenance. One known approach for detecting code smells is via detection rules: a combination of different object-oriented metrics with pre-defined threshold values. The usage of inadequate thresholds when using this approach could lead to either having too few observations (too many false negatives) or too many observations (too many false positives). Furthermore, without a clear methodology for deriving thresholds, one is left with those suggested in literature (or by the tool vendors), which may not necessarily be suitable to the context of analysis. In this paper, we propose a data-driven (i.e., Benchmark-based) method to derive threshold values for code metrics, which can be used for implementing detection rules for code smells. Our method is transparent, repeatable and enables the extraction of thresholds that respect the statistical properties of the metric in question (such as scale and distribution). Thus, our approach enables the calibration of code smell detection rules by selecting relevant systems as benchmark data. To illustrate our approach, we generated a benchmark dataset based on 74 systems of the Qualitas Corpus, and extracted the thresholds for five smell detection rules.
Quantifying coordination in multiple DOF movement and its application to evaluating 6 DOF input devices

Study of computer input devices has primarily focused on trial completion time and target acquisition errors. To deepen our understanding of input devices, particularly those with high degrees of freedom (DOF), this paper explores device influence on the user’s ability to coordinate controlled movements in a 3D interface. After reviewing various existing methods, a new measure of quantifying coordination in multiple degrees of freedom, based on movement efficiency, is proposed and applied to the evaluation of two 6 DOF devices: a free-moving position-control device and a desk-top rate-controlled hand controller. Results showed that while the users of the free moving device had shorter completion time than the users of an elastic rate controller, their movement trajectories were less coordinated. These new findings should better inform system designers on development and selection of input devices. Issues such as mental rotation and isomorphism vs. tools operation as means of computer input are also discussed.
Sensorless estimation of global device-parameters based on Fmax testing

Post-fabrication performance compensation and adaptive delay testing are indispensable means for improving yield and reliability of LSIs, The global parameter estimations, such as of threshold voltages, play a key role in maximizing their effectiveness. This paper proposes a novel technique that realizes an accurate device-parameter estimation through Fmax testing framework. In the proposed method, statistical path delay distributions of sensitized paths in Fmax testing are utilized to calculate device-parameters, such that they most likely explain the measurements in the Fmax testing. Two estimation procedures are proposed: one utilizes discrete Bayesian estimation and the other uses maximum likelihood estimation. Numerical experiments demonstrate that both methods achieve 2.5mV accuracy in estimating threshold voltages.
Mobile Robot Path Planing Using Gauss Potential Functions and Neural Network

This work deals with the problem of potential field based mobile robot motion planning in unorganised environment. The new approach, using a combination of negative gradient and vortex field based on Gauss potential functions is proposed. Radial Basis Function Neural Network (RBF Neural Network) learns the dependence between Gauss function parameters and velocity of mobile robot (or relative velocity between robot and obstacle in dynamical environment) ensuring passage between two closely spaced obstacles and smooth path condition for different mobile robot initial conditions. This approach overcomes some standard problems in classical potential field methods like local minima avoidance, problems of no passage between closely spaced obstacles, strong variation of the repulsive force near the minimum distance, and avoidance of moving obstacles. The method is illustrated on the example of mobile robot navigation between several closely spaced obstacles and example of avoidance of moving obstacle.
PRELIMINARY AND INCOMPLETE: DO NOT CITE OR DISTRIBUTE WITHOUT AUTHOR'S PERMISSION Government Impact at Research Universities: Reactions by Alumni, Corporations, Foundations, and Others to Federal Research Expenditures

This paper examines the relationship between federal research funding and private donations at 221 private and public research and doctoral universities between 1973 and 1997. As with previous studies, there is a positive relationship between these two sources of funding for these universities. This paper extends previous research by examining the effect of federal research funding across five different types of private donors: alumni, corporations, foundations, other individuals, and donations given as bequests. The results suggest there is a positive correlation across all types of donors, but that the effect varies across the donors. After controlling for potential endogeneity and omitted variable bias, the effect of increasing federal research expenditures by one dollar ranges between 6 and 61 cents. The strongest effect is seen with respect to alumni, especially alumni from private universities. Although there are statistically significant differences in the reactions by alumni at private and public universities to changes in federal funding, I find no significant differences in the reactions by other types of donors across these types of universities.
Rhythmic component extraction for multi-channel EEG data analysis

A practical method for extracting and enhancing a rhythmic waveform appearing in multi-channel electroencephalogram (EEQ) data is proposed. In order to facilitate clinical diagnosis and/or implement so-called brain computer interface (BCI), detecting the rhythmic activity from EEQ data recorded in a noisy environment is crucial; however, classical signal processing techniques like linear filtering or the Fourier transform cannot detect such a rhythmic signal if the power of noise is so large. This paper presents a simple but practical method for extracting a rhythmic signal by fully exploiting the multi-variate nature of EEQ data. The rhythmic component of interest is estimated as the weighted sum of multi-channel signals, and the optimal weights are then derived so as to maximize the power of the component. After the derivation is illustrated, adaptive weights, which give a new time-frequency analysis, are introduced. Moreover, the application to recently developed empirical mode decomposition (EMD) is presented. Experimental results on real EEQ data support the analysis.
A Novel Strategy for TNF-Alpha Production by 2-APB Induced Downregulated SOCE and Upregulated HSP70 in O. tsutsugamushi-Infected Human Macrophages

Orientia (O.) tsutsugamushi-induced scrub typhus is endemic across many regions of Asia and the Western Pacific, where an estimated 1 million cases occur each year; the majority of patients infected with O. tsutsugamushi end up with a cytokine storm from a severe inflammatory response. Previous reports have indicated that blocking tumor necrosis factor (TNF)-α reduced cell injury from a cytokine storm. Since TNF-α production is known to be associated with intracellular Ca2+ elevation, we examined the effect of store-operated Ca2+ entry (SOCE) inhibitors on TNF-α production in O. tsutsugamushi-infected macrophages. We found that 2-aminoethoxydiphenyl borate (2-APB), but not SKF96365, facilitates the suppression of Ca2+ mobilization via the interruption of Orai1 expression in O. tsutsugamushi-infected macrophages. Due to the decrease of Ca2+ elevation, the expression of TNF-α and its release from macrophages was repressed by 2-APB. In addition, a novel role of 2-APB was found in macrophages that causes the upregulation of heat shock protein 70 (HSP70) expression associated with ERK activation; upregulated TNF-α production in the case of knockdown HSP70 was inhibited with 2-APB treatment. Furthermore, elevated HSP70 formation unexpectedly did not help the cell survival of O. tsutsugamushi-infected macrophages. In conclusion, the parallelism between downregulated Ca2+ mobilization via SOCE and upregulated HSP70 after treatment with 2-APB against TNF-α production was found to efficiently attenuate an O. tsutsugamushi-induced severe inflammatory response.
MULTIVARIATE DENSITY ESTIMATION BASED ON ADAPTIVE PARTITIONING: CONVERGENCE RATE, VARIABLE SELECTION AND SPATIAL ADAPTATION

Density estimation is one of the most fundamental problems in statistical inference. Here, we introduce a non-parametric approach to multivariate density estimation. The estimators are piecewise constant density functions supported by binary partitions. The partition of the sample space is learned by maximizing the likelihood of the corresponding histogram on that partition. We analyze the convergence rate of these maximum likelihood estimators and reach a conclusion that for a relatively rich class of density functions the rate does not directly depend on the dimension. This suggests that, under certain conditions, this method is immune to the curse of dimensionality. We also apply this method to variable selection and spatial adaptation cases, and calculate the explicit convergence rates respectively. These results help us further understand under what circumstances, and for which density classes, would this method perform well.
Bacterial Communities in Polluted Seabed Sediments: A Molecular Biology Assay in Leghorn Harbor

Seabed sediments of commercial ports are often characterized by high pollution levels. Differences in number and distribution of bacteria in such areas can be related to distribution of pollutants in the port and to sediment conditions. In this study, the bacterial communities of five sites from Leghorn Harbor seabed were characterized, and the main bacterial groups were identified. T-RFLP was used for all samples; two 16S rRNA libraries and in silico digestion of clones were used to identify fingerprint profiles. Library data, phylogenetic analysis, and T-RFLP coupled with in silico digestion of the obtained sequences evidenced the dominance of Proteobacteria and the high percentage of Bacteroidetes in all sites. The approach highlighted similar bacterial communities between samples coming from the five sites, suggesting a modest differentiation among bacterial communities of different harbor seabed sediments and hence the capacity of bacterial communities to adapt to different levels and types of pollution.
Yonsei/ETRI at TREC-10: Utilizing Web Document Properties

As our first TREC participation, four runs were submitted for the ad hoc task and two runs for the home page finding task in the web track. For the ad hoc task we experimented on the usefulness of anchor texts. However, no significant gain in retrieval effectiveness was observed. The substring relationship between URL's was found to be effective in the home page finding task.
FCFD: Teach the machine to accomplish face detection step by step

In this paper, a novel progressive strategy is proposed to teach the machine to accomplish face detection in the wild. Firstly, deep model named Fully-connected Face Classifier (FCFC) is built up. With the targeted training data, FCFC learns the knowledge corresponding to distinguish face in various pose, facial expression, occlusion proportion, and blur degree from background gradually. Secondly, FCFC is converted to Fully Convolutional Face Detector (FCFD) which is able to handle a face image in arbitrary size. Finally, FCFDs with different receptive fields and sliding strides are combined to detect faces in various sizes for a given image scale. Experiments on FDDB face detection benchmark dataset and social network selfies show that the proposed FCFD achieves promising results, especially in the detection of blurred or occluded face. Generalization of our FCFD to social network selfies is instructive to analyze and retrieve those photos, since more discriminative information of one person (e.g., gender, identity) can be extracted once his or her face is located.
A Morphological-Based License Plate Location

License plate location is an important phase in vehicle license plate recognition for intelligent transport systems. This paper presents a robust and real time method of license plate location. The proposed algorithm consists of some stages. In the first stage, we extract vertical edges of the input image using Sobel mask. In the next stage, histogram analysis is used for finding the candidate regions of license plate. Candidate regions are also verified by defined compact factor. In the last stage, we locate the license plate exactly with some morphological operators. Experiments have been conducted for this algorithm. 400 images taken from various scenes were employed, including diverse angles, different lightening conditions. The algorithm can quickly and correctly detect the region of license plate. The license plate detecting rate of success is 83.50%.
Internal Labour Markets in Japan

Japanese labour market practices have attracted considerable attention in the West for two reasons. Firstly, innovative human resource management (HRM) is responsible for the development of competitive industrial sectors. Secondly, inner flexibility of the labour market has produced low unemployment and wage flexibility. This study, originally published in 2000, provides a thorough investigation of the distinctive features of Japanese internal labour markets (ILM) and occupational labour markets (OLM), closely analyses important changes in ILM and considers future developments. It combines a mixture both of descriptive and of theoretical and econometric work and builds on the authors' wel- known previous research in this area. Also contains a detailed case study and the econometric analysis of HRM policies used by a large Japanese firm. Although the focus is on Japanese ILM, international comparisons are made throughout, mainly with reference to Europe and the United States.
Universität Des Saarlandes Fachrichtung 6.1 – Mathematik a Variational Multiscale Method for Turbulent Flow Simulation with Adaptive Large Scale Space a Variational Multiscale Method for Turbulent Flow Simulation with Adaptive Large Scale Space

In turbulent flows it is only feasible to simulate large flow structures. Variational multiscale (VMS) methods define these flow structures by projections into appropriate function spaces. This paper presents a finite element VMS method which defines the large scale projection space adaptively. The adaption controls the influence of an eddy viscosity model and it is based on the size of the so–called resolved small scales. The adaptive procedure is described in detail. Numerical studies at a turbulent channel flow and a turbulent flow around a cylinder are presented. It is shown that the method selects the large scale space in a correct way and that appropriately chosen parameters improve the results compared to the basic method, which uses the same local large scale space in the whole domain and for all times.
SyCERS: a SystemC Design Exploration Framework for SoC Reconfigurable Architecture

Reconfigurable devices, such as FPGAs, introduce into the design workflow of embedded systems a new degree of freedom: the designer can have the system autonomously modify the functionality carried out by the IP-Core according to the application’s changing needs while it runs. Increasing the complexity of the design provides to the designers much more flexibility in their decisions but imply that the time to market of the final solution is dramatically increasing to. This paper presents a design methodology for dynamically reconfigurable IP-core based architectures. The methodology is based on the SystemC class library and targets the design of reconfigurable architectures for embedded systems. The use of the SystemC library enables the designer to specify a system in its hardware and software parts with just one language.
Suporte à Edição de UML 2 no ambiente SEA

The current paper is intended to offer support to the UML 2 edition in the SEA software development environment, having as its final result a new version written in Java language of the environment with the building of new tools that reuse the edition features supplied for the OCEAN framework. Object-oriented frameworks and UML modeling are approached in order to introduce the conceptual aspects related to the developed prototype. A new specification structure that uses the current techniques of UML 2 modeling was built and incorporated to the SEA environment, aggregating concepts of the new diagrams.
An Effi cient CCA2-Secure Variant of the McEliece Cryptosystem in the Standard Model

Recently, a few chosen-ciphertext secure (CCA2-secure) variants of the McEliece public-key encryption (PKE) scheme in the standard model were introduced. All the proposed schemes are based on encryption repetition paradigm and use general transformation from CPA-secure scheme to a CCA2-secure one. Therefore, the resulting encryption scheme needs \textit{separate} encryption and has \textit{large} key size compared to the original scheme, which complex public key size problem in the code-based PKE schemes. Thus, the proposed schemes are not sufficiently efficient to be used in practice. In this work, we propose an efficient CCA2-secure variant of the McEliece PKE scheme in the standard model. The main novelty is that, unlike previous approaches, our approach is a generic conversion and can be applied to \textit{any} one-way trapdoor function (OW-TDF), the lowest-level security notion in the context of public-key cryptography, resolving a big fundamental and central problem that has remained unsolved in the past two decades.
Autoscanning for coupled scene reconstruction and proactive object analysis

Detailed scanning of indoor scenes is tedious for humans. We propose autonomous scene scanning by a robot to relieve humans from such a laborious task. In an autonomous setting, detailed scene acquisition is inevitably coupled with scene analysis at the required level of detail. We develop a framework for object-level scene reconstruction coupled with object-centric scene analysis. As a result, the autoscanning and reconstruction will be object-aware, guided by the object analysis. The analysis is, in turn, gradually improved with progressively increased object-wise data fidelity. In realizing such a framework, we drive the robot to execute an iterative analyze-and-validate algorithm which interleaves between object analysis and guided validations. The object analysis incorporates online learning into a robust graph-cut based segmentation framework, achieving a global update of object-level segmentation based on the knowledge gained from robot-operated local validation. Based on the current analysis, the robot performs proactive validation over the scene with physical push and scan refinement, aiming at reducing the uncertainty of both object-level segmentation and object-wise reconstruction. We propose a joint entropy to measure such uncertainty based on segmentation confidence and reconstruction quality, and formulate the selection of validation actions as a maximum information gain problem. The output of our system is a reconstructed scene with both object extraction and object-wise geometry fidelity.
A New Approach to Implement Discrete Wavelet Transform Using Collaboration of Reconfigurable Elements

The Discrete Wavelet Transform (DWT) is an important operation in applications of digital signal processing. In this paper, we review several traditional DWT implementation approaches, e.g., application-specific integrated circuits, field-programmable gate arrays, digital signal processors, general-purpose processors, and graphic processors, and discuss their limitations in terms of performance and flexibility. In order to provide both high-performance and flexibility, we propose a new approach, namely a parallel architecture exploiting the collaboration of reconfigurable processing elements in grid computing. Grid computing can exploit the task level parallelism to execute the 2D DWT. In addition, reconfigurable computing offers a flexible platform and can be used as hardware accelerators. We mapped the DWT in a grid. Our experimental results show that speedups of up to 4.1x can be achieved.
MODELING INTRA-OBJECT PARALLELISMINPARALLEL DISCRETE EVENT SIMULATIONFrancesco

In parallel discrete event simulation, a run in which events are executed at each object according to their timestamp is a correct run (i.e., it satisses causal-ity). In such a run, the execution order of events at any object corresponds to the total order imposed on them by timestamps. In this paper we introduce the notion of weak causality between simulation events. Such a relation deenes a partial order on the events occurring at an object based on both timestamp values and a notion of events' connict here introduced. Weak causality models the intra-object parallelism and allows to deene the largest set of runs of a simulation that are equivalent to the timestamp ordered run. More technically, all runs in which the execution order of events at each object is a linear extension of the partial order imposed by weak causality are shown to be equivalent to the timestamp ordered run. Finally , we describe an application of weak causality to optimistic synchronization (Time Warp) by introducing a synchronization protocol, namely weak causality based Time Warp, that allows out of timestamp order executions of events which are not weakly causally related. Compared to classical Time Warp synchronization , the introduced protocol has the potential to reduce the number of rollbacks and their extent. Finally, our analysis shows some similarity with the database se-rializability theory.
Correlation of measurable serum markers of inflammation with lung levels following bilateral femur fracture in a rat model

Introduction Evaluation of the systemic inflammatory status following major orthopedic trauma has become an important adjunct in basing post-injury clinical decisions. In the present study, we examined the correlation of serum and lung inflammatory marker levels following bilateral femur fracture. Materials and methods 45 Sprague Dawley rats underwent sham operation or bilateral femoral intramedullary pinning and mid-diaphyseal closed fracture via blunt guillotine. Animals were euthanized at specific time points after injury. Serum and lung tissue were collected, and 24 inflammatory markers were analyzed by immunoassay. Lung histology was evaluated by a blinded pathologist. Results Bilateral femur fracture significantly increased serum markers of inflammation including interleukin (IL)-2, IL-6, IL-10, GM-CSF, KC/GRO, MCP-1, and WBC. Femur fracture significantly increased serum and lung levels of IL-1a and KC/GRO at 6 hours. Lung levels of IL-6 demonstrated a trend towards significance. Histologic changes in pulmonary tissue after fracture included pulmonary edema and bone elements including cellular hematopoietic cells, bone fragments and marrow emboli. Discussion and conclusion Our results indicate that bilateral femur fracture with fixation in rats results in increases in serum markers of inflammation. Among the inflammatory markers measured, rise in the serum KC/GRO (CINC-1), a homolog to human IL-8, correlated with elevated levels of lung KC/GRO. Ultimately, analysis of serum levels of KC/GRO (CINC-1), or human IL-8, may be a useful adjunct to guide clinical decisions regarding surgical timing.