Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering, reporting, and fixing performance bugs

Software performance is critical for how users perceive the quality of software products. Performance bugs - programming errors that cause significant performance degradation - lead to poor user experience and low system throughput. Designing effective techniques to address performance bugs requires a deep understanding of how performance bugs are discovered, reported, and fixed. In this paper, we study how performance bugs are discovered, reported to developers, and fixed by developers, and compare the results with those for non-performance bugs. We study performance and non-performance bugs from three popular code bases: Eclipse JDT, Eclipse SWT, and Mozilla. First, we find little evidence that fixing performance bugs has a higher chance to introduce new functional bugs than fixing non-performance bugs, which implies that developers may not need to be over-concerned about fixing performance bugs. Second, although fixing performance bugs is about as error-prone as fixing nonperformance bugs, fixing performance bugs is more difficult than fixing non-performance bugs, indicating that developers need better tool support for fixing performance bugs and testing performance bug patches. Third, unlike many non-performance bugs, a large percentage of performance bugs are discovered through code reasoning, not through users observing the negative effects of the bugs (e.g., performance degradation) or through profiling. The result suggests that techniques to help developers reason about performance, better test oracles, and better profiling techniques are needed for discovering performance bugs.
Neurotensin negatively modulates Akt activity in neurotensin receptor‐1‐transfected AV12 cells

Neurotensin (NT) regulates a variety of biological processes primarily through interaction with neurotensin receptor‐1 (NTR1), a heterotrimeric G‐protein‐coupled receptor (GPCR). Stimulation of NTR1 has been linked to activation of multiple signaling transduction pathways via specific coupling to Gq, Gi/o, or Gs, in various cell systems. However, the function of NT/NTR1 in the regulation of the Akt pathway remains unknown. Here, we report that activation of NTR1 by NT inhibits Akt activity as determined by the dephosphorylation of Akt at both Ser473 and Thr308 in AV12 cells constitutively expressing human NTR1 (NTR1/AV12). The inactivation of Akt by NT was rapid and dose‐dependent. This effect of NT was completely blocked by the specific NTR1 antagonist, (S)‐(+)‐[1‐(7‐chloro‐4‐quinolinyl)‐5‐(2,6‐dimethoxyphenyl)pyrazol‐3‐yl)‐carbonylamino] cyclohexylacetic acid (SR 48527), but unaffected by the less active enantiomer ((R)‐(−)‐[1‐(7‐chloro‐4‐quinolinyl)‐5‐(2,6‐dimethoxyphenyl)pyrazol‐3‐yl)‐carbonylamino] cyclohexylacetic acid (SR 49711)), indicating the stereospecificity of NTR1 in the negative regulation of Akt. In addition, NT prevented insulin‐ and epidermal growth factor (EGF)‐mediated Akt activation. Our results provide insight into the role of NT in the modulation of Akt signaling and the potential physiological significance of Akt regulation by NT. © 2004 Wiley‐Liss, Inc.
Administrative Leadership and Situational Antecedents in the Romanian Public Administration

Leadership is a complex phenomenon that is hard to study and understand by looking solely at leaders (be it traits, behavior or competencies). Previous researches (House et. al., 2004) indicate significant variance in leadership depending on situational and cultural factors. In this research we explore the antecedent conditions that lead to unique administrative leadership styles observed in the Romanian public administration, by making a distinction between cultural, political and administrative antecedents. The study population consists of civil servants occupying a managerial position. Initial results indicate that cultural values are similar to the ECC cluster from the GLOBE study, a preference for NWS (Neo Weberian State) type of organization, low trust between politicians and civil servants and predominantly transformational leadership behaviours. Low response rate (approx. 7%) led to a rather small sample (N=188), which negatively influences the possibility of generalizing the findings.
The Use of Pigmentation and Shading Information in Recognising the Sex and Identities of Faces

An investigation of what can be learned about representational processes in face recognition from the independent and combined effects of inverting and negating facial images is reported. In experiment 1, independent effects of inversion and negation were observed in a task of identifying famous faces. In experiments 2 through 4 the question of whether effects of negation were still obtained when effects due to the reversal of pigmentation in negative images were eliminated was examined. By the use of images of the 3-D surfaces of faces measured by laser, and displays as smooth surfaces devoid of pigmentation, only effects of inversion were obtained reliably, suggesting that the effects observed in experiment 1 arose largely through the inversion of pigmentation values in normal images of faces. The results of experiment 5 suggested that the difference was not due to the different task demands of experiments 2–4 compared with those of experiment 1. When normally pigmented face images were used in a task making similar demands to that of experiment 4, independent effects of inversion and negation were again observed. When a task of sex classification was used in experiments 6 and 7, clear effects of negation as well as inversion were observed on latencies, though not accuracies, of responding. The results are interpreted in terms of the information content of pigmentation relative to shape from shading in different face-classification tasks. The results also reinforce other recent evidence demonstrating the importance of image intensity as well as spatial layout of face ‘features’.
Entrepreneurial Intent and Commercialization ofApplications on a Technology Platform

Third-party providers as entrepreneurs boost technology platforms. Yet, despite increasing interest in technological platforms, existing research offers little predictive insight into how firms can identify individuals who are likely to become entrepreneurs. We take the strategic perspective of a platform owning company, asking how to pinpoint those individuals who transition into entrepreneurship in the near future. We base our analysis on automatically registered behavioral data, such as the complete sales history of all applications related to a technological platform, and the complete history of communications in communities related to the platform. We employ logistic regression models to predict: (a) the transition from registered platform user to third-party developer (i.e. entrepreneurial intent) and (b) the launch of a first platform application (i.e. commercialization). We control for individuals’ social network positions, their communication behaviors, exposure to input from other entrepreneurs .
Maximising profits for an EPQ model with unreliable machine and rework of random defective items

This article deals with an economic production quantity (EPQ) model in an imperfect production system. The production system may undergo in ‘out-of-control’ state from ‘in-control’ state, after a certain time that follows a probability density function. The density function varies with reliability of the machinery system that may be controlled by new technologies, investing more costs. The defective items produced in ‘out-of-control’ state are reworked at a cost just after the regular production time. Occurrence of the ‘out-of-control’ state during or after regular production-run time is analysed and also graphically illustrated separately. Finally, an expected profit function regarding the inventory cost, unit production cost and selling price is maximised analytically. Sensitivity analysis of the model with respect to key parameters of the system is carried out. Two numerical examples are considered to test the model and one of them is illustrated graphically.
How Are Discussions Associated with Bug Reworking?: An Empirical Study on Open Source Projects

Background: Bug fixing is one major activity in software maintenance to solve unexpected errors or crashes of software systems. However, a bug fix can also be incomplete and even introduce new bugs. In such cases, extra effort is needed to rework the bug fix. The reworking requires to inspect the problem again, and perform the code change and verification when necessary. Discussions throughout the bug fixing process are important to clarify the reported problem and reach a solution. Aims: In this paper, we explore how discussions during the initial bug fix period (i.e., before the bug reworking occurs) associate with future bug reworking. We focus on two types of "reworked bug fixes": 1) the initial bug fix made in a re-opened bug report; and 2) the initially submitted patch if multiple patches are submitted for a single bug report. Method: We perform a case study using five open source projects (i.e., Linux, Firefox, PDE, Ant and HTTP). The discussions are studied from six perspectives (i.e., duration, number of comments, dispersion, frequency, number of developers and experience of developers). Furthermore, we extract topics of discussions using Latent Dirichlet Allocation (LDA). Results: We find that the occurrence of bug reworking is associated with various perspectives of discussions. Moreover, discussions on some topics (e.g., code inspection and code testing) can decrease the frequency of bug reworking. Conclusions: The discussions during the initial bug fix period may serve as an early indicator of what bug fixes are more likely to be reworked.
Automatic extraction and representation of geographic entities in eGovernment

In this paper we present a system that automatically extracts and geocodes named entities from unstructured, natural language textual documents. The system uses the Geo-Net-PT ontology and Google maps as auxiliary data sources. This type of system is particularly useful to automate the geocoding of existing information in e-government applications, which usually requires human intervention. Within the paper we introduce the relevant human language technologies, describe the system that was developed, present and discuss the preliminary results and draw the relevant conclusions and future work.
Business Value of Enterprise Micro-blogging: Empirical Study from Weibo.com in Sina

The increasing use of micro-blogging as a marketing tool has increased research attention on usage and business value of enterprise micro-blogging. Based on research on information system (IS) usage and resource-based view (RBV) theory, this study develops a model to reveal the mechanism of business value creation of enterprise micro-blogging. The model consists of metrics on micro-blogging usage, micro-blogging operational performance, marketing capability, and firm performance. Questionnaires were distributed to firms that use micro-blogging. This study collects 241 valid responses for empirical analysis. The results suggest that the use of enterprise micro-blogging improves operational performance of enterprise micro-blogging directly and indirectly by increasing marketing capability, while operational performance of enterprise micro-blogging significantly affects firm performance. This study extends the stream of research that combines IS usage and RBV theory.
Improved Ensemble Classification Method of Thyroid Disease Based on Random Forest

The thyroid disease has already been the second largest in the field of endocrine, and the classification of disease is the primary problem in clinical treatment. In computer-aided diagnosis (CAD), machine learning techniques have been widely used to assist the medical experts in decision making. This paper proposed a new method for thyroid disease classification based on random forest. Principal Component Analysis is used to preserve the variability in the data. Rotation Transformation can enlarge the discrepancy of the base classifiers and improve the accuracy of the ensemble classifier. Our method performs much better than Bagging, Random forest and AdaBoost, and can solve the accuracy-diversity dilemma. Experimental results show that the classification accuracy of this method can reach to 95.63% on the dataset from UCI machine learning repository. In order to verify the effectiveness of the method furthermore, this paper also chooses the real clinical medical data set. It is more complex than the UCI standard dataset in data quantity and dimension. Compared with other methods, the classification accuracy of our method reaches to 96.16%.
Corticofugally induced c-fos expression, synchronized oscillation and its propagation in the thalamus

A variety of fast and slow rhythmic brain oscillations have been revealed to occur spontaneously during different behavioral states. Fast rhythms are associated with cognition, and slow rhythms (e.g. spindle, 7-15 Hz; delta, 1-4 Hz; slow oscillation, 0.3-1 Hz) define the slow-wave sleep. However, the precise cellular and synaptic mechanisms underlying these different rhythms remain unclear and much controversial despite intensive investigations in the past two decades. Even little evidence is available in the auditory system. The present study investigated the mechanisms underlying the generation, synchronization and propagation of the spontaneous slow rhythms (< 15 Hz) in the corticothalamic networks within a given modality and across different modalities in animals under anesthesia by using multi-channel extracellular recordings and immunohistochemical method. In an attempt to study the contribution of the corticofugal projection to thalamic neurons in the ascending auditory circuitry by using Fos as an activity marker, we ascertained for the first time, the presence of dense Fos positive neurons in the ventral division (MGv) of medial geniculate body (MGB) after the auditory cortex (AC) was activated by injection of bicuculline methobromide (BIM), a GABAA receptor antagonist. We further investigated the relationship between c-fos expression in the MGB and corticofugal activation, as well as its pathway
Simultaneous Wireless Information and Power Transfer Based on Joint Subcarrier and Power Allocation in OFDM Systems

Energy harvesting (EH) is a prominent method to extend the operation time of energy-limited wireless networks. By integrating EH into wireless communications, the same spectrum is able to be used by simultaneous wireless information and power transfer (SWIPT) without affecting the quality of service. In this paper, we propose a joint subcarrier and power allocation-based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A fraction of the received subcarriers are allocated to form one group, which is used for information decoding (ID), and the remaining subcarriers form the other group, which is used for energy harvesting. Thus, no splitter is needed at the receiver. A joint subcarrier and power allocation problem is formulated to maximize the harvested energy, subject to the ID constraint. By using the dual decomposition method, an efficient algorithm is proposed to solve this joint resource allocation problem.
Robust Video/Ultrasonic Fusion-Based Estimation for Automotive Applications

In this paper, we use recently developed robust estimation ideas to improve object tracking by a stationary or nonstationary camera. Large uncertainties are always present in vision-based systems, particularly, in relation to the estimation of the initial state as well as the measurement of object motion. The robustness of these systems can be significantly improved by employing a robust extended Kalman filter (REKF). The system performance can also be enhanced by increasing the spatial diversity in measurements via employing additional cameras for video capture. We compare the performances of various image segmentation techniques in moving-object localization and show that normal-flow-based segmentation yields comparable results to, but requires significantly less time than, optical-flow-based segmentation. We also demonstrate with simulations that dynamic system modeling coupled with the application of an REKF significantly improves the estimation system performance, particularly, when subjected to large uncertainties.
Estimation Model of Core Loss Under DC Bias

The current and loss of a transformer increase significantly under DC bias, which cause overheating and insulation aging. Depended on the Jiles-Atherton (JA) hysteresis theory, an estimation model of the core loss under DC bias is proposed in this paper. First, based on the iron loss separate theory, the origin JA model without DC bias is extended to JA dynamic hysteresis model considering the frequency-dependent eddy current loss and the anomalous loss. Second, a DC bias experiment is built by using Epstein frame, from which the hysteresis loss and the dynamic loss coefficients of the core are obtained. Finally, the relationship between DC bias and the dynamic loss coefficient is analyzed. The dynamic loss coefficient is normalized and fitted, and JA dynamic hysteresis model is revised by using the fitted results. The calculation results are compared with the measurement from the Epstein frame, which demonstrates the effectiveness of the method.
MMG-based classification of muscle activity for prosthesis control

We have previously proposed the use of "muscle sounds" or mechanomyography (MMG) as a reliable alternative measure of muscle activity with the main objective of facilitating the use of more comfortable and functional soft silicone sockets with below-elbow externally powered prosthesis. This work describes an integrated strategy where data and sensor fusion algorithms are combined to provide MMG-based detection, estimation and classification of muscle activity. The proposed strategy represents the first ever attempt to generate multiple output signals for practical prosthesis control using a MMG multisensor array embedded distally within a silicon soft socket. This multisensor fusion strategy consists of two stages. The first is the detection stage which determines the presence or absence of muscle contractions in the acquired signals. Upon detection of a contraction, the second stage, that of classification, specifies the nature of the contraction and determines the corresponding control output. Tests with real amputees indicate that with the simple detection and classification algorithms proposed, MMG is indeed comparable to and may exceed EMG functionally.
Adaptive Coherence Granularity for Multi-Socket Systems

The emerging large-scale multi-socket systems make the need for more sophisticated large-scale coherence management of necessity. Directory-based coherence has been an ad hoc solution and a clear candidate for large-scale shared-memory systems. A vanilla directory design, however, suffers from inefficient use of storage to keep coherence metadata, resulting in a high storage overhead for large-scale systems. In this paper, we propose a dynamic multi-grain directory for large multi-socket systems. The idea is to track coherence of regions of different sizes which requires storing much less information in the directory than having a directory entry per each data block. It dynamically refines granularity according to the application phase and therefore tracks coherence information for regions of varying sizes. The results show that the proposal allows to reduce the directory storage by an order of magnitude, while the loss of precision does not cause performance penalty. The paper demonstrates that different applications and different application phases have different requirements for the region size. Performance results are compared against two state-of-the-art multi-grain directories and it is the one that obtains the better results.
Automatic Generation of Self-Scheduling Programs

Techniques are described for the automatic generation of self-scheduling parallel programs. Both scheduling algorithms and the concurrent components of applications are expressed in a high-level concurrent language. Partitioning and data dependency information are expressed by simple control statements, which may be generated either automatically or manually. A self-scheduling compiler, implemented as a source-to-source transformation, takes application code, control statements, and scheduling routines and generates a new program that can schedule its own execution on a parallel computer. The approach has several advantages compared to previous proposals. It generates programs that are portable over a wide range of parallel computers. There is no need to embed special control structures in application programs. The use of a high-level language to express applications and scheduling algorithms facilitates the development, modification, and reuse of parallel programs. >
Protection from Clostridium difficile toxin B-catalysed Rac1/Cdc42 glucosylation by tauroursodeoxycholic acid-induced Rac1/Cdc42 phosphorylation

Abstract Toxin A (TcdA) and toxin B (TcdB) are the major virulence factors of Clostridium difficile-associated diarrhoea (CDAD). TcdA and TcdB mono-glucosylate small GTPases of the Rho family, thereby causing actin re-organisation in colonocytes, resulting in the loss of colonic barrier function. The hydrophilic bile acid tauroursodeoxycholic acid (TUDCA) is an approved drug for the treatment of cholestasis and biliary cirrhosis. In this study, TUDCA-induced activation of Akt1 is presented to increase cellular levels of pS71-Rac1/Cdc42 in human hepatocarcinoma (HepG2) cells, showing for the first time that bile acid signalling affects the activity of Rho proteins. Rac1/Cdc42 phosphorylation, in turn, protects Rac1/Cdc42 from TcdB-catalysed glucosylation and reduces the TcdB-induced cytopathic effects in HepG2 cells. The results of this study indicate that TUDCA may prove useful as a therapeutic agent for the treatment of CDAD.
COMPETITION FOR COMPOSITION: LESSONS FROM NECTAR-FEEDING ANT COMMUNITIES

Studies of the relationship between the composition of nectar and its consumers often focus on single or very few species, thus ignoring dynamics in diverse assemblages. Conversely, most documented patterns of nectarivore communities have not been linked to nectar quality measures. In a study of nectar-foraging ant communities in an Australian rain forest, we found that nectar source partitioning between consumers may be driven by two factors: (1) variation in nectar composition preferences mediated by taste and physiological requirements, and (2) severe asymmetrical competitive interactions within the community. Ant communities are strongly shaped by competitive hierarchies. When foraging for extrafloral and floral nectar sources, wound sap, and homopteran honeydew, competitively superior weaver-ants (Oecophylla smaragdina) showed a significant preference for nectar composition, whereas most other common community members were nonselective. Nectars frequently used by O. smaragdina were characterized by s.
Robust control for robotic manipulators with non-smooth strategy

In this paper, a novel non-smooth robust control approach is presented for robotic manipulators. By using decimal power rule in Lyapunov redesign methods, the conventional robust control for robot is improved. Our approach can achieve higher control precision with faster convergence speed. The formulations of estimating residual set and settling time have been initially established. The practical stability property is analysed. An illustrative example is bench tested to validate the effectiveness of the proposed approach.