Miracle-dz/UniFAR-FTUs huggingface dataset:
From code understanding needs to reverse engineering tool capabilities

A major portion of the software maintenance effort is spent on the reverse engineering activity of understanding existing software. If one can learn more about how programmers understand code successfully, one can build better tools to support the understanding process. This contributes to higher quality and improved efficiency of maintenance tasks. An integrated code comprehension model and experiences with it in an industrial setting are presented. Audio-taped, think-aloud reports were used to investigate how well the integrated code comprehension model works during industrial maintenance activities that range from code fixes to enhancements, code leverage, and reuse. The tapes were analyzed for information needs during maintenance activities, and tool capabilities were derived accordingly. The results are presented and discussed.
A learning method for returning ball in robotic table tennis

A learning method of the point for a robot to hit a coming ball in table tennis is proposed in this paper. The learning is performed based on the artificial neural network. In order to learn the effects of the rotational velocity and the air resistance, the inputs and outputs are defined as the variations of the measured data and the hitting point from those produced by a simple model, which consists of the equations of motion without the air resistance and the Newton's rebound model without friction. The learning and verification are performed using the simulation and experimental data, where the simulation is executed with the aerodynamics model and the table rebound model, and the ball trajectories in the experiment are measured when two humans play table tennis.
The Iceberg Theory of Campaign Contributions: Political Threats and Interest Group Behavior

This paper presents a model of campaign contributions where a special interest group can condition its contributions not only on the receiving candidate's support but also on that of her opponent. This allows the interest group to obtain support both from contributions as well as from the implicit threat of contributing to the opponent. These out-of-equilibrium contributions can help explain the "missing money" puzzle in the empirical literature. Our framework contradicts standard models in predicting that interest groups do not give to both sides of a same race. It also predicts that stronger candidates get more money from special interest groups primarily because more contributors give to lop-sided winners, not because more money is given per contribution. Both of these predictions are strongly supported in FEC data for U.S. House Elections from 1984-2004. Our theory also predicts that special interest groups will mainly target lop-sided winners whereas general (partisan) interest groups will contribute mainly to candidates in close races. This is also verified empirically. Finally, our framework implies that stricter campaign finance rules will always lower special interest influence but may lead to an increase in equilibrium contributions, making the latter a poor measure of effectiveness.
Cross-lingual Pronoun Prediction for English, French and German with Maximum Entropy Classification

We present our submission to the crosslingual pronoun prediction (CLPP) shared task for English-German and EnglishFrench at the First Conference on Machine Translation (WMT16). We trained a Maximum Entropy (MaxEnt) classifier based on features from Wetzel et al. (2015), that we adapted to the new task and applied to a new language pair. Additional features such as n-grams of the pronoun context and prediction of NULLtranslations proved helpful to a varying degree. Experiments with a sequence classifier over pronoun sequences did not show any improvements. Our submission is among the top three systems for English-French (61.62% macro-averaged recall) and in the middle range for EnglishGerman (48.72%) out of nine submissions.
Angle vertex and bisector geometric model for triangular road sign detection

We present a new transformation for angle vertex and bisector detection. The Vertex and Bisector Transformation (VBT) takes the image gradient as input and outputs two arrays, accumulating evidence of respectively angle vertex and angle bisector. A geometric model of the gradient orientation is implemented using a pair-wise voting scheme: normal vectors of two adjacent sides of a triangle have a specific relationship depending on the corresponding vertex angle. Our approach is able to accurately detect vertices and bisectors of a triangular road sign in a 360×270 image in about 50 ms with no particular optimization. We tested our approach on a 48 images database containing 40 triangular signs with different colors and orientations (red intersection and give way warnings, blue pedestrian crossing): 33 are correctly detected (82%) and 7 are missed with 2 false positives.
Peptide Aptamers with Binding Specificity for the Intracellular Domain of the ErbB2 Receptor Interfere with AKT Signaling and Sensitize Breast Cancer Cells to Taxol

The ErbB2 receptor tyrosine kinase is overexpressed in ∼30% of breast tumor cases and its overexpression correlates with an unfavorable prognosis. A major contributor for this course of the disease is the insensitivity of these tumors toward chemotherapy. Monoclonal antibodies, inhibiting the ligand-induced activation of the receptor and tyrosine kinase inhibitors acting on the intrinsic enzymatic activity of the intracellular domain, have been developed as targeted drugs. Both have been shown to be beneficial for breast cancer patients. We targeted a third aspect of receptor function: its association with intracellular signaling components. For this purpose, we selected peptide aptamers, which specifically interact with defined domains of the intracellular part of the receptor. The peptide aptamers were selected from a random peptide library using a yeast two-hybrid system with the intracellular tyrosine kinase domain of ErbB2 as a bait construct. The peptide aptamer AII-7 interacts with high specificity with the ErbB2 receptor in vitro and in vivo. The aptamers colocalized with the intracellular domain of ErbB2 within cells. We investigated the functional consequences of the aptamer interaction with the ErbB2 receptor within tumor cells. The aptamer sequences were either expressed intracellularly or introduced into the cells as recombinant aptamer proteins. The phosphorylation of p42/44 mitogen-activated protein kinase was nearly unaffected and the activation of signal transducers and activators of transcription-3 was only modestly reduced. In contrast, they strongly inhibited the induction of AKT kinase in MCF7 breast cancer cells treated with heregulin, whereas AKT activation downstream of insulin-like growth factor I or epidermal growth factor receptor was not or only slightly affected. High AKT activity is responsible for the enhanced resistance of ErbB2-overexpressing cancer cells toward chemotherapeutic agents. Peptide aptamer interference with AKT activation resulted in the restoration of regular sensitivity of breast cancer cells toward Taxol. (Mol Cancer Res 2006;4(12):983–98)
A 13.56 MHz on/off delay-compensated fully-integrated active rectifier for biomedical wireless power transfer systems

A 13.56 MHz 64.8 mW fully-integrated CMOS active rectifier for biomedical wireless power transfer system is presented in this summary. It employs an adaptive on/off delay compensation technique to accurately compensate for the circuit delays despite PVT variations and mismatches. With an AC input that ranges from 1.8 V to 3.6 V, the measured voltage conversion ratio is higher than 90% and the measured power conversion efficiency is higher than 89.1% for a load resistor of 500 Ω.
A hierarchical motion planning strategy for a uniform self-reconfigurable modular robotic system

Describes a multi-layered hierarchical motion planning strategy for a class of self-reconfigurable modular robotic systems, I-Cubes. The approach is based on the synthesis of motion on the basis of metacubes, which have a particular structure possessing 8 Cubes and 16 Links. The developed strategy organizes the metacube motions and the corresponding cube-level motions. At the lowest level, link motions are generated. The resulting system is demonstrated to be capable of performing a pre-specified task of moving from one position/shape to another. The paper describes the latest results of our planning strategy through some experimentally justified examples.
Higher-Order Flexibilities in Multi-Valued Networks

Computation of complete flexibility (CF) of a node in the non-deterministic MV network is introduced in [10]. CF describes the freedom to modify a node without violating the functionality of the network. It is defined for a node assuming a fixed representation of other nodes. CF is a first-order flexibility because it allows for optimization of one node at a time. In this paper, we extend the concept of CF to a group of nodes. The higher-order flexibility expresses the freedom to modify several nodes simultaneously without violating the functionality of the network. We show how this flexibility can be used to solve optimization problems arising in binary and multi-valued logic synthesis. In particular, it leads to a more efficient merging algorithm, an improved full_simplify procedure, and certain advantages in decomposition. A preliminary experimental evaluation in the environment of MVSIS [12] suggests that the computation and use of higher-order flexibilities is feasible for a variety of benchmarks.
The Regime Shift Associated with the 2004–2008 US Housing Market Bubble

The Subprime Bubble preceding the Subprime Crisis of 2008 was fueled by risky lending practices, manifesting in the form of a large abrupt increase in the proportion of subprime mortgages issued in the US. This event also coincided with critical slowing down signals associated with instability, which served as evidence of a regime shift or phase transition in the US housing market. Here, we show that the US housing market underwent a regime shift between alternate stable states consistent with the observed critical slowing down signals. We modeled this regime shift on a universal transition path and validated the model by estimating when the bubble burst. Additionally, this model reveals loose monetary policy to be a plausible cause of the phase transition, implying that the bubble might have been deflatable by a timely tightening of monetary policy.
A Review on Deconstruction and Criticism

ABSTRACT This essay is a review on Deconstruction and Criticism. In this book, Bloom explores how strong poets, through misreading, conquer the influence of anxiety from the precursors so as to achieve their uniqueness; Paul de Man discusses how the rhetoric of language governed by trope subverts its fulfillment of a unified meaning; Derrida discusses the problems of the boundary of text, describing the way the text overruns the limits assigned to it. In this way, he redefines the text and extends the boundary of the text; Hartman mainly focuses on the reading experience. He advocates that the writer is also a reader and we should not only talk of reader intrinsically but also historically; Miller’s essay is on the parasitical relationship between deconstructionist reading and “obvious” reading, nihilism and metaphysics, as well as poem and earlier poems. Those critics have their different views on deconstruction and literary criticism; they, however, share some common ideas on the issues of text interpretation, intertextuality as well as the boundary of text.
Learning from halophytes: physiological basis and strategies to improve abiotic stress tolerance in crops.

BACKGROUND Global annual losses in agricultural production from salt-affected land are in excess of US$12 billion and rising. At the same time, a significant amount of arable land is becoming lost to urban sprawl, forcing agricultural production into marginal areas. Consequently, there is a need for a major breakthrough in crop breeding for salinity tolerance. Given the limited range of genetic diversity in this trait within traditional crops, stress tolerance genes and mechanisms must be identified in extremophiles and then introduced into traditional crops. SCOPE AND CONCLUSIONS This review argues that learning from halophytes may be a promising way of achieving this goal. The paper is focused around two central questions: what are the key physiological mechanisms conferring salinity tolerance in halophytes that can be introduced into non-halophyte crop species to improve their performance under saline conditions and what specific genes need to be targeted to achieve this goal? The specific traits that are discussed and advocated include: manipulation of trichome shape, size and density to enable their use for external Na(+) sequestration; increasing the efficiency of internal Na(+) sequestration in vacuoles by the orchestrated regulation of tonoplast NHX exchangers and slow and fast vacuolar channels, combined with greater cytosolic K(+) retention; controlling stomata aperture and optimizing water use efficiency by reducing stomatal density; and efficient control of xylem ion loading, enabling rapid shoot osmotic adjustment while preventing prolonged Na(+) transport to the shoot.
Transfer Learning Based Diagnosis for Configuration Troubleshooting in Self-Organizing Femtocell Networks

Diagnosis for configuration troubleshooting in femtocell networks is extremely important for end users and network operators. However, because the small-size femtocell only serves several users, the historical data are very scarce. The data scarcity makes traditional cellular troubleshooting solutions which require a large amount of historical data not applicable. In this paper, we propose a new framework based on transfer learning technology to address the data scarcity so as to enhance the accuracy of the diagnosis model. The proposed framework extracts additional diagnosis knowledge by transferring data information from other femtocells. Based on this framework, we design a Cell-Aware Transfer scheme (CAT), which splits data for each femtocell to further enhance the diagnosis accuracy. Extensive evaluations show that our approach can achieve higher accuracy than traditional methods in self-organizing femtocell network scenarios.
The Ejaki Project: A Quality of Service Regulator for Citizens

What is the meaning of "place"? How to provide a citizen with the right information anywhere, anytime? How to stimulate the growth of a user community, thus enriching the system's potential and the quality of the services provided? In this paper, we propose to contribute to these issues according to the artificial intelligence (AI), ubiquitous systems and community dynamics perspectives. The way we envisaged to achieve these goals was by a system which consists of a rated POI (point of interest) sharing platform accessible by several kind of mobile devices that we describe here.
Simultaneous Localization and Planning for Physical Mobile Robots via Enabling Dynamic Replanning in Belief Space

Simultaneous planning while localizing is a crucial ability for an autonomous robot operating under uncertainty. This paper addresses this problem by designing methods to dynamically replan while the localization uncertainty or environment map is updated. In particular, relying on sampling-based methods, the proposed online planning scheme can cope with challenging situations, including when the localization update or changes in the environment alter the homotopy class of trajectories, in which the optimal plan resides. The proposed algorithm eliminates the need for stabilization in the state-of-the-art FIRM (Feedback-based Information RoadMap) method, and outperforms its performance and success probability. Applying belief space planning to physical systems brings with it a plethora of challenges. Thus, designing computationally tractable algorithms, a key focus and contribution of this paper is to implement the proposed planner on a physical robot and show the SLAP (simultaneous localization and planning) performance under uncertainty, in changing environments, and in the presence of large disturbances such as kidnapped robot situation.
Automated fuzzy model generation through weight and fuzzification parameters’ optimization

In this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy modelpsilas parameters. Based on this methodology, the generated fuzzy model includes a set of parameters, which are all the parameters included in the sigmoid functions. In addition, local, global and class weights are included, thus the fuzzy model is optimized with respect to both sigmoid function parameters and weights. The class weight introduction, which is a novel approach, grants to the fuzzy model the ability to identify the individual importance of each class and thus more accurately reflect the underlying properties of the classes under examination, in the domain of application. The above described methodology is applied to five known classification problems, obtained from the UCI machine learning repository, and the obtained classification accuracy is high.
Development of Holistic Creative Learning Skills for Elderly People in Rural Area of Thailand: Career Dimension

Thailand is an aging society because the elderly population is increasing rapidly and the elderly, especially those living in rural areas, still have insufficient income and learning skills for their careers. They need care and learning skills in today’s rapidly changing society. This article presents the results of the study on the situation of the elderly people’s learning in rural Thailand concerning their careers, which are an aspect of the study of the elderly on the aspects of physical health, mental health, socio-culture, and career. As a result of this study, the researchers developed the SPACE OF Happiness Model for improving learning skills in elderly people living in rural areas.
Physiological and Clinical Consequences of Relief of Right Ventricular Outflow Tract Obstruction Late After Repair of Congenital Heart Defects

Background— Right ventricular outflow tract obstruction (RVOTO) is a common problem after repair of congenital heart disease. Percutaneous pulmonary valve implantation (PPVI) can treat this condition without consequent pulmonary regurgitation or cardiopulmonary bypass. Our aim was to investigate the clinical and physiological response to relieving RVOTO. Methods and Results— We studied 18 patients who underwent PPVI for RVOTO (72% male, median age 20 years) from a total of 93 who had this procedure for various indications. All had a right ventricular outflow tract (RVOT) gradient >50 mm Hg on echocardiography without important pulmonary regurgitation (less than mild or regurgitant fraction <10% on magnetic resonance imaging [MRI]). Cardiopulmonary exercise testing, tissue Doppler echocardiography, and MRI were performed before and within 50 days of PPVI. PPVI reduced RVOT gradient (51.4 to 21.7 mm Hg, P<0.001) and right ventricular systolic pressure (72.8 to 47.3 mm Hg, P<0.001) at catheterization. Symptoms and aerobic (25.7 to 28.9 mL · kg−1 · min−1, P=0.002) and anaerobic (14.4 to 16.2 mL · kg−1 · min−1, P=0.002) exercise capacity improved. Myocardial systolic velocity improved acutely (tricuspid 4.8 to 5.3 cm/s, P=0.05; mitral 4.7 to 5.5 cm/s, P=0.01), whereas isovolumic acceleration was unchanged. The tricuspid annular velocity was not maintained on intermediate follow-up. Right ventricular end-diastolic volume (99.9 to 89.7 mL/m2, P<0.001) fell, whereas effective stroke volume (43.7 to 48.3 mL/m2, P=0.06) and ejection fraction (48.0% to 56.8%, P=0.01) increased. Left ventricular end-diastolic volume (72.5 to 77.4 mL/m2, P=0.145), stroke volume (45.3 to 50.6 mL/m2, P=0.02), and ejection fraction (62.6% to 65.8%, P=0.03) increased. Conclusions— PPVI relieves RVOTO, which leads to an early improvement in biventricular performance. Furthermore, it reduces symptoms and improves exercise tolerance. These findings have important implications for the management of this increasingly common condition.
Improved Particle Swarm Optimization and its Application into Optimal preparing Process

Aiming at the premature convergence problem of particle swarm optimization algorithm (PSO), an improved PSO is proposed, which is based on the effects of inertia weight on the convergence performance. In the improved PSO, the inertia weight nonlinearly decreases with iteration time increasing, and its changing curve is just like the shape of S, moreover the curvature of the curve can be adaptively adjusted according to current searching state of the particle swarm. This algorithm is applied to raw mix slurry optimal preparing of alumina process. The calculated results show that it not only has convergence property obviously prior to traditional PSO and genetic algorithm, but also can avoid the premature convergence problem effectively.
Knowledge Aggregation in Human Flesh Search

With the pervasive participation from the Chinese netizens in the cyberspace, the Human Flesh Search (HFS) is becoming a phenomenon profoundly affecting the public life in China. While hot debate and discussion arise, it is still not clear how the knowledge fragments are gathered by the netizens, and eventually merge to an answer in a HFS. This paper presents a first study to the knowledge aggregation in HFS. We formally present a normative knowledge aggregation model of the HFS. Simulation experiments have been conducted on several network topologies, with which the convergence feature and the gap between the elites and the crowd in a HFS are reconstructed.
Bandwidth Enhancement of RF Resonators Using Duffing Nonlinear Resonance for Wireless Power Applications

This paper presents a nonlinear resonator circuit based on the Duffing equation to enhance the bandwidth of wireless power harvesting and wireless power transfer systems. The Duffing resonator circuit can be implemented with passive components. In this paper, analysis of an electrical Duffing resonator is presented. The detailed discussion of the Duffing resonator's properties is provided. As a proof of principle, a wireless power harvester employing such a nonlinear resonator has been designed and tested. It is shown that the Duffing resonator circuit enhances the bandwidth of the harvester by 2.7 times compared with a harvester using a linear resonator with the same quality factor, while achieving a similar amplitude level near the resonance frequency. Furthermore, the bandwidth of the nonlinear resonator is independent of its quality factor (Q).
Social network analysis in epidemiology: Current trends and perspectives

The increasing development of world trade has promoted the spread of diseases. Recent events, like the H1N1 outbreak and spread, have confirmed this tendency. Everyone could observe that this kind of infectious disease often has the ability to cross countries borders and spread rapidly. Preventing, monitoring and controlling the outbreak of these diseases have appeared to be a public health problem of primary importance for years. However, although numerous works have been conducted in order to understand and study epidemics, this subject remains a topical issue because of the complexity of the phenomena. In this paper, we present new opportunities offered by the field of social networks for understanding the spread of infectious diseases. This work focuses both on techniques already used in epidemiology, and very recent works in social network analysis that open new perspectives. We give an overview of methods currently used to model and study the spread of diseases and future directions that the social network approach allows to glimpse.