Miracle-dz/UniFAR-FTUs huggingface dataset:
NetAuth: Supporting User-Based Network Services

In User-Based Network Services (UBNS), the process servicing requests from user U runs under U's ID. This enables (operating system) access controls to tailor service authorization to U. Like privilege separation, UBNS partitions applications into processes in such a way that each process' permission is minimized. However, because UBNS fundamentally affects the structure of an application, it is best performed early in the design process. UBNS depends on other security mechanisms, most notably authentication and cryptographic protections. These seemingly straightforward needs add considerable complexity to application programming. To avoid this complexity, programmers regularly ignore security issues at the start of program construction. However, after the application is constructed, UBNS is difficult to apply since it would require significant structural changes to the application code. This paper describes easy-to-use security mechanisms supporting UBNS, and thus significantly reducing the complexity of building UBNS applications. This simplification enables much earlier (and hence more effective) use of UBNS. It focuses the application developer's attention on the key security task in application development, partitioning applications so that least privilege can be effectively applied. It removes vulnerabilities due to poor application implementation or selection of security mechanisms. Finally, it enables significant control to be externally exerted on the application, increasing the ability of system administrators to control, understand, and secure such services.
In situ observations and measurements during solidification of CuNi weld pools

A two-scale in situ observation set-up has been used to investigate the physical phenomena around the solid/liquid interface in welding. A spot gas tungsten arc welding process is used to observe weld pool solidification phenomenon after arc extinction. The solidification front and the fluid flow in the weld pool have been observed at a microscale by a high speed camera. The whole weld pool and the surrounding base metal have been filmed at a macroscale by an infrared camera to estimate temperature fields around the weld pool and bead shape. A qualitative and quantitative dataset was extracted from these observations that involve useful results for further improvements of the understanding of weld solidification mechanisms and to enhance solidification models.
Nonlinear controller design based on polynomial and non-polynomial representation

In this paper, to synthesize stabilizing controllers for nonlinear systems we develop nonlinear controller design methods based on polynomial representation and nonpolynomial representation using particle swarm optimization( PSO). To do this we derive the stability conditions of polynomial systems and non-polynomial systems for estimating domain of attraction(DOA) with input magnitude constraints. From these conditions stabilizing controllers of polynomial systems and non-polynomial systems are synthesized by using sum of squares (SOS) and PSO techniques respectively. Finally, obtained controllers are applied to Furuta pendulum and validity of these controllers is demonstrated by numerical simulations.
Pathogenesis analysis of pituitary adenoma based on gene expression profiling

The aim of the current study was to investigate the pathogenesis of pituitary adenoma through screening of the differentially-expressed genes (DEGs) and proteins in normal pituitary and pituitary adenoma tissues, and analyzing the interactions among them. Following the acquisition of gene expression profiling data from a public functional genomics data repository, Gene Expression Omnibus, DEGs were screened in normal pituitary and pituitary adenoma tissues. Upregulated and downregulated DEGs were further identified through gene ontology functional enrichment analysis. Subsequently, the DEGs were mapped to the Search Tool for the Retrieval of Interacting Genes database, and the protein-protein interaction (PPI) networks of the upregulated and downregulated DEGs were constructed. Finally, the functional modules of the PPI network of the downregulated DEGs were analyzed. In total, 211 upregulated and 413 downregulated DEGs were screened between the normal pituitary and pituitary adenoma samples. Downregulated DEGs were associated with certain functions, including the immune response, hormone regulation and cell proliferation. Upregulated genes were associated with cation transport functions. Five modules were acquired from the PPI network of the downregulated DEGs. Transcription factors, including signal transducer and activator of transcription 3 (STAT3), interleukin 6 (IL-6), B-cell lymphoma 6 protein, early growth response 1, POU1F1, jun B proto-oncogene and FOS were the core nodes in the functional modules. In summary, the DEGs and proteins were identified through screening gene expression profiling and PPI networks. The results of the present study indicated that low expression levels of hormone- and immune-related genes facilitated the occurrence of pituitary adenoma. Low expression levels of IL-6 and STAT3 were significant in the dysimmunity of pituitary adenoma. Furthermore, the low expression level of POU1F1 contributed to the reduction in pituitary hormone secretion.
A Dynamic Approach to Visualize the Impacts for Support Selective Testing on Regression Testing

up with the advancement in hardware technology, the size and complexity of software systems are increasing at a rapid rate, thus, making them difficult to maintain, expand, and evolve. To alleviate such difficulties, change impact analysis (CIA) and its implementations has been the subject of research for several years. Generally, CIA facilitates regression testing. Specifically, CIA helps to estimate the potential consequences of a software change, including the affected module(s) and their data dependencies, re-testing needs, as well as the required resource planning. Historically, many CIA implementations use static analysis and traditional text-based impact reporting. Although useful, static based CIA implementations often cited as time- and effort-intensive (e.g. requiring extensive documentation/design search). Dynamic slicing is an option to address the aforementioned issues. However, the volume of analyzable data potentially impedes understanding. Visualization can be a good leverage for improving analyzability and understanding of impact analysis from dynamic slicing.In line with such a prospect, this paper offers a dynamic approach to visualize the impacts for support selective testing on regression testing.
Asymptotic analysis of small defects near a singular point in antiplane elasticity, with an application to the nucleation of a crack at a notch

We use matching asymptotic expansions to treat the antiplane elastic problem associated with a small defect located at the tip of a notch. In a first part, we develop the asymptotic method for any type of defect and present the sequential procedure which allows us to calculate the different terms of the inner and outer expansions at any order. This requires in particular separating in each term its singular part from its regular part. In a second part, the asymptotic method is applied to the case of a crack of variable length located at the tip of a given notch. We show that the first two nontrivial terms of the expansion of the energy release rate are sufficient to well approximate the dependence of the energy release rate on the crack length in the range of values of the length which are sufficient to treat the problem of nucleation. This problem is considered in the last part where we compare the nucleation and the propagation of a crack predicted by two different models: the classical Griffith law and the Francfort–Marigo law based on an energy minimization principle. Several numerical results illustrate the interest of the method.
Deep Neural Network Approach for the Dialog State Tracking Challenge

While belief tracking is known to be important in allowing statistical dialog systems to manage dialogs in a highly robust manner, until recently little attention has been given to analysing the behaviour of belief tracking techniques. The Dialogue State Tracking Challenge has allowed for such an analysis, comparing multiple belief tracking approaches on a shared task. Recent success in using deep learning for speech research motivates the Deep Neural Network approach presented here. The model parameters can be learnt by directly maximising the likelihood of the training data. The paper explores some aspects of the training, and the resulting tracker is found to perform competitively, particularly on a corpus of dialogs from a system not found in the training.
Buffer control at video-streaming proxy

Proxy servers can be used to stream video stored at a geographically separate location. This separation between the proxy server and the storage introduces a non-negligible delay in retrieving video frames in real time. We develop an effective scheme to achieve consistent, high streaming quality under such delays. Central to the scheme is the control of buffer occupancy at the proxy server. We model the buffer as a bilinear dynamical system disturbed by a point process with stochastic state-dependent intensity, reflecting the behavior of an additive-increase/multiplicative-decrease transport protocol. Using the buffer model, we construct two controllers based on prediction of future system states, taking into account both the delay and the state-dependent disturbance. Our empirical study illustrates the effectiveness of the scheme and shows that the controllers exploiting the buffer model perform well in overcoming the adverse impact of the retrieval delay.
xploring and characterizing the folding processes of Lys-and Arg-containing la-based peptides : A molecular dynamics study

In this study, molecular dynamics simulations were carried out on Lysand Arg-containing Ala-based peptides (i.e. Ace-(AAAAK)nA-NH2 and Ace-(AAAAR)nA-NH2, where n = 1–4), in order to explore and characterize their folding processes. For the oligopeptides, the evolution of -helical structure with regard to the whole conformation, as well as to each residue was investigated, and the helix-forming propensities were characterized. On the basis of the helicity curves, representing the alteration of average helicity as a function of time, the typical time values describing the folding processes and subprocesses were
Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task

Recent research has suggested that differences between intelligent tu tor lessons predict a large amount of the variance in the prevalence of gaming the system (4). Within this paper, we investigate whether such differences also predict how much students choose to go off-task, and if so, which differences predict how much off-task behavior will occur. We utilize an enumeration of the differences between intelligent tutor lessons, the Cognitive Tutor Lesson Variation Space 1.1 (CTLVS1.1), to identify 79 differences between tutor lessons, within 20 lessons from an intelligent tutoring system for Algebra. We utilize a machine-learned detector of off-task behavior to predict 58 students' off-task behavior within that tutor, in each lesson. Surprisingly, the best model predicting off-task behavior from lesson features contains only one feature: lessons that involve equation-solving. We discuss possible explanations for this finding, and further studies that could shed light on this relationship.
Detecting Internet Worms Using Data Mining Techniques

Internet worms pose a serious threat to computer security. Traditional approaches using signatures to detect worms pose little danger to the zero day attacks. The focus of malware research is shifting from using signature patterns to identifying the malicious behavior displayed by the malwares. This paper presents a novel idea of extracting variable length instruction sequences that can identify worms from clean programs using data mining techniques. The analysis is facilitated by the program control ﬂow information contained in the instruction sequences. Based upon general statistics gathered from these instruction sequences we formulated the problem as a binary classi-ﬁcation problem and built tree based classiﬁers including decision tree, bagging and random forest. Our approach showed 95.6% detection rate on novel worms whose data was not used in the model building process.
Fuzzy System in Multivehicle Swarming

A large-scale multivehicle model is presented. Based on individual local observed information of the swarm, a fuzzy logical controller (FLC) of the attraction/repulsion function is built. Combining with the rate consensus algorithm, the swarming of the separation, cohesion and alignment is achieved. By applying the connected network from the individual local perceived information, the aggregation of the multi-agent systems can swarm following a whole consensus in the computer simulation.
Big Batch SGD: Automated Inference using Adaptive Batch Sizes

Classical stochastic gradient methods for optimization rely on noisy gradient approximations that become progressively less accurate as iterates approach a solution. The large noise and small signal in the resulting gradients makes it difficult to use them for adaptive stepsize selection and automatic stopping. We propose alternative "big batch" SGD schemes that adaptively grow the batch size over time to maintain a nearly constant signal-to-noise ratio in the gradient approximation. The resulting methods have similar convergence rates to classical SGD, and do not require convexity of the objective. The high fidelity gradients enable automated learning rate selection and do not require stepsize decay. Big batch methods are thus easily automated and can run with little or no oversight.
Active learning accelerated automatic heuristic construction for parallel program mapping

Building effective optimization heuristics is a challenging task which often takes developers several months if not years to complete. Predictive modelling has recently emerged as a promising solution, automatically constructing heuristics from training data, however, obtaining this data can take months per platform. This is becoming an ever more critical problem as the pace of change in architecture increases. Indeed, if no solution is found we shall be left with out of date heuristics which cannot extract the best performance from modern machines. In this work, we present a low-cost predictive modelling approach for automatic heuristic construction which significantly reduces this training overhead. Typically in supervised learning the training instances are randomly selected to evaluate regardless of how much useful information they carry, but this wastes effort on parts of the space that contribute little to the quality of the produced heuristic. Our approach, on the other hand, uses active learning to select and only focus on the most useful training examples and thus reduces the training overhead. We demonstrate this technique by automatically creating a model to determine on which device to execute four parallel programs at differing problem dimensions for a representative Cpu-Gpu based system. Our methodology is remarkably simple and yet effective, making it a strong candidate for wide adoption. At high levels of classification accuracy the average learning speed-up is 3×, as compared to the state-of-the-art.
Regulation of RON Tyrosine Kinase-mediated Invasion of Breast Cancer Cells*

RON (recepteur d'origine nantais), a tyrosine kinase receptor for macrophage-stimulating protein (MSP) was implicated in tumor progression. However, it was not investigated how this important oncogene is regulated. We show that MSP promotes invasion of MDA MB 231 and MDA MB 468 but not MCF-7 breast cancer cells. Reverse transcription-PCR and Western analysis indicated the expression of RON message and protein, respectively, in MDA MB 231 and MDA MB 468 cells but not in MCF-7 cells. RON expression correlated with Sp1 expression. Initial analysis of a 1.2-kb and 400-bp RON promoter in MDA MB 231 and MDA MB 468 cells suggested the presence of all the necessary regulatory elements within 400 bp from the transcription start site. Site-directed mutagenesis of the 400-bp RON promoter revealed that the overlapping Sp1 sites at–94 (Sp1-3/4) and Sp1 site at –113 (Sp1-5) are essential for RON gene transcription. Electrophoretic mobility shift assays and chromatin immunoprecipitation analysis indicated that Sp1 binding to these sites is required for RON promoter activity. Ectopic Sp1 expression in Sp1 null SL2 cells confirmed the involvement of these Sp1 sites in the regulation of oncogenic RON tyrosine kinase. Treatment of MDA MB 231 cells with mithramycin A, an inhibitor of Sp1 binding, or siRNA knock-down of Sp1 blocked RON gene expression and MSP-mediated invasion of MDA MB 231 cells. This is the first report demonstrating a clear link between Sp1-dependent RON tyrosine kinase expression and invasion of breast carcinoma cells.
The Goslon Yardstick in Patients with Unilateral Cleft Lip and Palate: Review of a Vienna Sample

Objective: To compare a Vienna unilateral cleft lip and palate (UCLP) patient sample with the Eurocleft samples using the GOSLON score, to determine the intra- and interrater agreement between several raters and ratings, and to establish whether training with the original GOSLON models enhances accuracy. Patients and Methods: One hundred twenty-three plaster casts of UCLP patients born between 1970 and 1997, with an average age of 9.2 years and all treated with the same regimen, were rated according to the GOSLON score. Results: Of the patients, 71.5% were ranked GOSLON 1 or 2. Only 8.9% were rated GOSLON 4 or 5. There were no significant differences between the different raters and the ratings. Training with the original GOSLON models increased kappa from 0.57 before training to 0.84 after training. Conclusion: The “Vienna concept” was found to be a good regimen for treating UCLP patients in regard to maxillary growth. Personal training on the original GOSLON models appears to improve the accuracy of rating.
Unsupervised speaker adaptation of BLSTM-RNN for LVCSR based on speaker code

Recently, the speaker code based adaptation has been successfully expanded to recurrent neural networks using bidirectional Long Short-Term Memory (BLSTM-RNN) [1]. Experiments on the small-scale TIMIT task have demonstrated that the speaker code based adaptation is also valid for BLSTM-RNN. In this paper, we evaluate this method on large-scale task and introduce an error normalization method to balance the back-propagation errors derived from different layers for speaker codes. Meanwhile, we use singular value decomposition (SVD) method to conduct model compression. Results show that the speaker code based adaptation with SVD shows better recognition performance than the i-vector based speaker adaptation of the same dimension. Experimental results on Switchboard task show that the speaker code based adaptation on the hybrid BLSTM-DNN topology can achieve more than 9% relative reduction in word error rate (WER) compared to the speaker independent (SI) baseline.
IFAU – Does early intervention help ? 1 Does early intervention help the unemployed youth ?

This paper evaluates a measure targeted at unemployed individuals aged 20-24. The main purpose of the measure is to prevent long-term unemployment by guaranteeing an assignment to some labor market program within 100 days of unemployment. Municipalities voluntarily agree to offer the guarantee. To identify the effect of the measure, we use three conditions: The guarantee covers individuals aged 24 but not 25, one fifth of the municipalities does not provide the guarantee, and the guarantee existed in 1998 but not in 1997. We find no evidence that the measure did significantly improve the future labor market situation of the youth, which suggests that early intervention in the unemployment spell is not important.
Truthful multi-attribute auction with discriminatory pricing in cognitive radio networks

In this paper, we design a market-based channel allocation scheme for cognitive radio networks by exploiting multi-attribute channel-aware auctions to consider channel diversity in frequency, time, and space domains. Different from existing research, our objective is to maximize the winning SUs' service satisfaction degree while enhancing the utilities of winning PUs and SUs, which can effectively encourage them to join the auction and improve the sustainability of the spectrum market. Based on an elaborately devised preference function, we allocate channels to SUs satisfying their demands while considering spatial and temporal channel reuse to enhance channel utilization. Moreover, we propose a discriminatory pricing method to enhance the utilities of winning PUs and SUs. A comprehensive analysis indicates that our multi-attribute auction is individually-rational, ex-post budget balanced, value-truthful, and attribute-truthful. Our simulation results indicate that the proposed multi- attribute auction can significantly increase the winners' utilities and ensure SUs' service satisfaction.
Dimensional emotion driven facial expression synthesis based on the multi-stream DBN model

This paper proposes a dynamic Bayesian network (DBN) based MPEG-4 compliant 3D facial animation synthesis method driven by the (Evaluation, Activation) values in the continuous emotion space. For each emotion, a state synchronous DBN model (SS_DBN) is firstly trained using the Cohn-Kanade (CK) database with two streams of inputs: (i) the annotated (Evaluation, Activation) values, and (ii) the extracted Facial Action Parameters (FAPs) of the face image sequences. Then given an input (Evaluation, Activation) sequence, the optimal FAP sequence is estimated via the maximum likelihood estimation (MLE) criterion, and then used to construct the MPEG-4 compliant 3D facial animation. Compared with the state-of-the-art approaches where the mapping between the emotional space and the FAPs has been made empirically, in our approach the mapping is learned and optimized using DBN to fit the input (Evaluation, Activation) sequence. Emotion recognition results on the constructed facial animations, as well as subjective evaluations, show that the proposed method obtains natural facial animations representing well the dynamic process of the emotions from neutral to exaggerate.
An Efficient Sport Video Segmentation System

Digital sport video segmentation is an active area of research. And such an efficient sport video segmentation system is necessary. The system employs the feature-based motion estimator to estimate the video motion model parameters. Thus, we can use the result to compose the panorama. Project frames with motion parameters, the corresponding frames were connected and then stitched into a panoramic image according to infer the frames on a 2D manifold. For accurate alignment, we iterate between the motion estimator and topology determination to optimized parameters. Accuracy panorama composition is implemented to redress the error accumulation and achieve the pixel combination. After that, for the purpose of removing the foreground objects, we use the panorama estimation process. At last, we determine the sportsman segmentation masks that are the final output of the segmentation system. The test results showed that the proposed system is efficient.