Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirical study of a model for program error prediction

Accuracy in program error prediction is a major problem in quality control of a large-scale software system. This paper presents a model to estimate the number of errors remaining in a program at the beginning of the testing phase of development. Ten hypothesized environmental factors are statistically analyzed and the model is then derived by using the factors significantly identified in the analysis. This empirical study was done with data collected during the development of large-scale software systems. Results of the study indicate that factors such as frequency of program specification change, programmer's skill, and volume of program design document are significant and that the model based on these factors is more reliable than conventional error prediction methods based on program size alone.
Towards behavior elaboration and synthesis using modes

Early system requirements are often captured by declarative and property-based artifacts, such as scenarios and goals. While such artifacts are intuitive and useful, they are partial and typically lack an overarching structure to allow systematic elaboration of the fragmented behaviors they denote. I aim to develop a design technique for structuring the partial specifications by partitioning the state-space based on Parnas' notions of 'modes' and 'mode-classes'. A mode is set of states, characterized by a predicate. A mode-class is a set of disjoint modes completely covering the state space. The structuring framework supports early elaboration of partial specification, and facilitates improved synthesis of integrated system behavioral prototype.
Application Research of Cross Docking Logistics in Food Cold-Chain Logistics

CDL (Cross Docking Logistics), as an advanced logistic distribution strategy and a mode of operation, has the advantages of cross docking and features the distinct nature of information-based management model. As a result, CDL would bring its supply chain a high benefit on time, space and cost. This paper examines the CDL strategic application in food sale logistics based on cold-chain. The requirements and values for CDL application in food cold-chain are analyzed in detail. The bases for CDL new strategy design, together with a novel CDL process and model based on food cold-chain are also presented. Finally, benefits that can be expected with the implementation of CDL strategy are described.
Exact and approximate algorithms for the extension of embedded processor instruction sets

In embedded computing, cost, power, and performance constraints call for the design of specialized processors, rather than for the use of the existing off-the-shelf solutions. While the design of these application-specific CPUs could be tackled from scratch, a cheaper and more effective option is that of extending the existing processors and toolchains. Extensibility is indeed a feature now offered in real designs, e.g., by processors such as Tensilica Xtensa [T. R. Halfhill, Microprocess Rep., 2003], ARC ARCtangent [T. R. Halfhill, Microprocess Rep., 2000], STMicroelectronics ST200 [P. Faraboschi, G. Brown, J. A. Fisher, G. Desoli, and F. Homewood, Proc. 27th Annu. Int. Symp. Computer Architecture, 2000, p. 203], and MIPS CorExtend [T. R. Halfhill, Microprocess Rep., 2003]. While all these processors provide development environments with simulation capabilities for evaluating efficiently hand-crafted solutions, the tools to identify automatically the best processor configuration for a given application are less common. In particular, solutions to choose specialized instruction-set extensions (ISEs) have been investigated in the past years but are still seldom part of commercial toolchains. This paper provides a formal methodology and a set of algorithms that help address the problem. It proposes exact algorithms to derive optimal ISEs; exact identification of a single ISE is applicable to basic blocks of up to 1500 assembler-like instructions. This paper also introduces approximate methods that can process basic blocks of larger size. Results show that the described algorithms find solutions close to those that a designer would obtain by a detailed study of the application code. Both heuristic and exact algorithms find ISEs able to speed up unextended processors up to 5.0x. State-of-the-art comparisons show that the presented algorithms outperform existing ones by up to 2.6x
Selective Oxidation of Raw Glycerol Using Supported AuPd Nanoparticles

Bimetallic AuPd supported on different carbonaceous materials and TiO2 was tested in the liquid phase oxidation of commercial grade and raw glycerol. The latter was directly obtained from the base-catalyzed transesterification of edible rapeseed oil using KOH. The best catalytic results were obtained using activated carbon and nitrogen-functionalized carbon nanofibers as supports. In fact, the catalysts were more active using pure glycerol instead of the one obtained from rapeseed, where strong deactivation phenomena were present. Fourier transform infrared (FT-IR) and TEM were utilized to investigate the possible reasons for the observed loss of activity.
Multiplier-based, robust H8 estimation with application to robust fault detection

This paper uses the Popov-Tsypkin multiplier (which has intimate connections to mixed structured singular value theory) to design robust H∞ estimators for uncertain, linear discrete-time systems and considers the application of robust H∞ estimators to robust fault detection. The key to estimator-based, robust fault detection is to generate residuals which are robust against plant uncertainties and external disturbance inputs, which in turn requires the design of robust estimators. The robust H∞ estimation problem is formulated as a Riccati equation feasibility problem in which a cost function is minimized subject to a Riccati equation constraint. A continuation algorithm that uses quasi-Newton (BFGS) corrections is developed to solve the minimization problem. The algorithm is initialized with an H∞ estimator designed for the nominal system. The initializing multiplier matrices are obtained by solving a linear matrix inequality. The robust H∞ estimator framework is then applied to the robust fault detection of dynamic systems. The results are applied to a simplified longitudinal flight control system. It is shown that the robust fault detection procedure based on the robust H∞ estimation methodology proposed in this paper can reduce the false alarm rate.
Accessing Rarity of System Call Patterns for Anomaly Detection

Today computer security is important field research because more and more security threats arising in daily basis. Many researches focus on use intrusion detection system so as to prevent these intrusions. But most of the intrusion detection system suffers from high FPR and TPR. This is because of incomplete training and strategy used by them for detecting suspicious behaviour. This paper presents a host based intrusion detection that uses natural language concepts and rarity index to find anomalous system call trace. Also proposed system uses approximate pattern matching over mismatched sequence to defend against incomplete training. Evaluation of proposed is carried using publicly available UNM intrusion detection dataset. The results support the proposed system.
Hand Gestures and Posture Recognition to Control Television Using Haar-Like Features

Earlier time, control of television was only using a specific key or the remote. The development of everchanging technology led to changes in a way to control the television. Changes in the way should be happen so people can interact with interactive television and user friendly. Because of the demands of time, now it no longer just a television with remote control only. But it can be also done by doing hand gesture. The hand gestures that are performed by humans will be recorded by a camera and then turned it into a command code which commands the television to do something. In this paper, we have two model structures. The first structure use computer, camera, and television. In this structure, the overall process will be performed by the computer. The second structure uses a television which has been embedded with a camera. The overall process will be performed by the television itself. In the recognition posture process, we use Haar-Like Features algorithm. This article is a proposal of idea to make a better communication between human and
Understanding Data Characteristics and Access Patterns in a Cloud Storage System

Understanding the inherent system characteristics is crucial to the design and optimization of cloud storage system, and few studies have systematically investigated its data characteristics and access patterns. This paper presents an analysis of file system snapshot and five-month access trace of a campus cloud storage system that has been deployed on Tsinghua campus for three years. The system provides online storage and data sharing services for more than 19,000 students and 500 student groups. We report several data characteristics including file size and file type, as well as some access patterns, including read/write ratio, read-write dependency and daily traffic. We find that there are many differences between cloud storage system and traditional file systems: our cloud storage system has larger file sizes, lower read/write ratio, and smaller set of active files than those of a typical traditional file system. With a trace-driven simulation, we find that the cache efficiency can be improved by 5 times using the guidance from our observations.
Analysis of simulation output by resampling

Bootstrap resampling is an extremely practical and effective way of studying the distributional properties of simulation output when this is subject to random variation. The purpose of this paper is to show that many problems of sensitivity analysis (SA) and validation can be very simply handled by this method. We consider classical non-parametric version of the bootstrap and show that it can be applied in a natural way in simulation studies without becoming immersed in complicated statistical methodology. The main message of the paper is that the bootstrap method should be the statistical method of first resort in SA and validation studies.
High bandwidth multi-variable combined resonant and integral resonant controller for fast image scanning of atomic force microscope

This paper presents the design and implementation of a multi-variable combined resonant and integral resonant controller for the piezoelectric tube scanner (PTS) of an atomic force microscope (AFM) to damp the resonant mode of the scanner, reduce the cross coupling effect between the axes of the scanner, increase the bandwidth of the overall closed-loop system, and improve the high speed imaging performance of the AFM. The lateral and longitudinal positioning system of the PTS is treated as a two-input two-output system and the system is identified using measured open-loop data. The value of each parameter of the proposed controller is determined by minimizing H2 norm of the difference between the desired and the actual closed-loop transfer function. The performance improvement achieved by the proposed controller is presented by comparing scanned images obtained by implementing the proposed controller and the built-in proportional-integral (PI) controller of the AFM.
On the effectiveness of statistical hypothesis testing in infrared-based face recognition in heterogeneous environments

In this work, our objective is to study the impact of statistical hypothesis tests for the purpose of improving heterogeneous face recognition (FR). A series of tests are conducted to find the most suitable type of statistical analysis test (parametric vs. non-parametric). To conduct the experiments, we used a multi-spectral face database (visible and Near-IR) collected under challenging conditions, i.e. at night time and at four different standoff distances, namely 30, 60,90 and 120 meters. Next, the selected statistical analysis test is used to find the statistical significance of; (i) image restoration, (ii) fusion of scores. First, Gabor Wavelets, Histogram of gradients (HOG) and Local binary patterns (LBP) feature descriptors are empirically selected. Then the statistical analysis reveals which descriptors result in higher recognition performance. Finally, statistical hypothesis tests are performed to explore the impact of data stratification (grouping of gallery and probe sets) in terms of ethnicity, gender. A set of face identification studies are performed. Experimental results suggest that our proposed image restoration approach, fusion schemes and the usage of stratification result in a significantly better performance results than the baseline, e.g. the rank-one score is improved from 50% to 71% when using image restoration, to 73% when using fusion of scores and to 75% (i.e. in the case of testing FR accuracy only on the female Asian class) when employing database stratification.
Effects of Exercise Alone on Insulin Sensitivity and Glucose Tolerance in Obese Youth

As with the dramatic increases in childhood obesity over the past decades, the incidence of type 2 diabetes has increased among children and adolescents in the United States. Insulin resistance is a common feature of childhood obesity and increases the risk of type 2 diabetes, metabolic syndrome, and atherogenic lipoprotein profile in obese youth. Although cross-sectional studies report beneficial effects of physical activity or cardiorespiratory fitness on insulin sensitivity, the role of regular exercise alone (e.g., no calorie restriction) as a strategy to reduce the risk of type 2 diabetes is unclear in obese children and adolescents. In this mini review, we examined the independent effects of various exercise on glucose tolerance and insulin sensitivity in obese youth.
Adaptive gain tuning in nonlinear control systems designed via singular perturbation technique

The paper treats a question of adaptive proportional-integral (PI) and adaptive proportional-integral-derivative (PID) controller design for nonlinear systems as well as the design of a universal adaptive controller which is an extension of the adaptive PI (PID) control scheme. The presented design methodology guarantees desired output transient performance indices by inducing of two-time-scale motions in the closed-loop system where the controller dynamics is a singular perturbation with respect to the system dynamics. Stability conditions imposed on the fast and slow modes and sufficiently large mode separation rate between fast and slow modes can ensure that the full-order closed-loop nonlinear system achieves the desired properties in such a way that the output transient performances are desired and insensitive to external disturbances and plant's parameter variations. The novelty in the paper is that the high-frequency-gain online identification and adaptive gain tuning are incorporated in the control system in order to maintain the two-time-scale structure in the closed-loop system trajectories and stability of fast-motion transients for a large range of plant's parameter variations. The singular perturbation method is used through-out the paper in order to get explicit expressions for evaluation of the controller parameters. Numerical example and simulation results are presented.
An Algorithm for Optimizing Multidimensional K-NN Queries in Wireless Sensor Networks

This paper presents a filter-based algorithm called PREDICTOR for optimizing multidimensional K-NN queries in WSN. A filter installed at each sensor node is a node value distribution range. It is used to prevent the node from sending the data that belongs to the covering range of the filter, and so the node's energy is saved. The server keeps the historical sample data of all the nodes, and determines filters for them according to the query requirement and the samples. Three optimization strategies are proposed: (1) the method for dynamically adjusting the assignment strategies of the filter covering ranges so that a node with little chance to contribute to the final result is assigned with a larger covering range; (2) the method for sharing the filter among nodes so that the nodes with similar historical data are assigned with the same filter; (3) the method for transmitting filters in a compressed way to reduce the cost of filter updating for different K-NN queries. Evaluation experiment results prove the efficiency of the algorithm PREDICTOR in energy saving. Compared with the naive method, this approach can reduce the transmission volume dramatically.
Final Report: Structure in Reinforcement Learning

As planned, in the beginning of the project I’ve been concentrating on the topic of online aggregation for undiscounted reinforcement learning in Markov decision processes (MDPs). I’ve started research on online aggregation already back in Austria, so that I could quickly conclude work by proving regret bounds for a modified UCRL2 algorithm [2], which employs confidence intervals for calculating an aggregation of the estimated model MDP before computing an optimistic policy. More precisely, given an unknown MDP the proposed algorithm UCAgg maintains confidence intervals for rewards and transition probabilities in order to define a set of plausible MDPs just like UCRL2. However, before selecting an optimistic plausible model and a respective optimal policy maximizing the optimal average reward, UCAgg tries to aggregate states for which the confidence intervals for rewards and transition probabilities intersect and are of comparable length. (This latter condition can be shown to be necessary.) That way, policy computation can be performed on a smaller MDP and therefore more efficiently. Indeed, the proposed algorithm has also been tested in experiments and showed improved performance when compared to UCRL2. Concerning theoretical guarantees, it was possible to derive bounds on the regret the UCAgg algorithm suffers with respect to an optimal policy. These regret bounds depend on the quality of the employed aggregation algorithm, expressed by the connectivity of the aggregation returned. Intuitively, if the connectivity is low, then the aggregation algorithm aggregates only very similar states, while the higher the connectivity the more dissimilar states will be aggregated. As a simple aggregation algorithm a modification of the approximate aggregation algorithm of [1] can be used whose connectivity is √ S (where S is the number of states in the MDP). Thus, when combining this aggregation algorithm with UCAgg one obtains regret bounds as for UCRL2 [2], just with an additional factor √ S. Since these results neither depend on an improved algorithm for state aggregation nor on more general bounds for the error caused
A Lane Detection Vision Module for Driver Assistance

A driver support system should provide assistance and security to the driver. For navigaton tasks it is neccessary to determine position of the ego vehicle relative to the road. One of the principal approaches is to detect road boundaries and lanes using a vision system in the vehicle. Within the European research project "Secure Propulsion using Advanced Redundant Control (SPARC)" different approaches of lane detection are developed to meet the needs of real traffic situations. The vision module presented here is based on several image filters that provide diverse information about the environment. A set of hypotheses about the state of the system is generated by a probabilistic particle filter. Assuming a predefined model of the road the particles are tested according to image filters to infere the best belief vehicle position. Emphasis was placed on extracting relevant information from the scene and efficient testing. In particular, a new testing module based on Canny edge filter and Hough transform increased the accuracy and robustness of estimation. Perfomance of the vision module was tested under various real-road conditions.
The complexity of multiway cuts (extended abstract)

In the Multiway Cut problem we are given an edge-weighted graph and a subset of the vertices called terminals, and asked for a minimum weight set of edges that separates each terminal from all the others. When the number <italic>k</italic> of terminals is two, this is simply the min-cut, max-flow problem, and can be solved in polynomial time. We show that the problem becomes NP-hard as soon as <italic>k</italic> = 3, but can be solved in polynomial time for planar graphs for any fixed <italic>k</italic>. The planar problem is NP-hard, however, if <italic>k</italic> is not fixed. We also describe a simple approximation algorithm for arbitrary graphs that is guaranteed to come within a factor of 2–2/<italic>k</italic> of the optimal cut weight.
Effects of a dairy supplement and resistance training on lean mass and insulin-like growth factor in women.

PURPOSE To examine the effect of yogurt supplementation pre- and postexercise on changes in body composition in overweight women engaged in a resistance-training program. METHODS Participants (age = 36.8 ± 4.8 yr) with a body-mass index of 29.1 ± 2.1 kg/m2 were randomized to yogurt supplement (YOG; n = 15) or isoenergetic sucrose beverage (CONT; n = 14) consumed before and after exercise for 16 wk. Participants were also instructed to reduce energy intake daily (-1,046 kJ) during the study. Body composition was assessed by dual-energy X-ray absorptiometry, waist circumference, and sagittal diameter. Strength was measured with 1-repetition maximum. Dietary recalls were obtained by a multipass approach using Nutrition Data System software. Insulin-like growth factor-1 and insulin-like growth-factor-binding protein-3 were measured with ELISA. RESULTS Significant weight losses of 2.6 ± 4.5 kg (YOG) and 1.2 ± 2.5 kg (CONT) were observed. Total lean weight increased significantly over time in both YOG (0.8 ± 1.2 kg) and CONT (1.1 ± 0.9 kg). Significant reductions in total fat (YOG = 3.4 ± 4.1 kg vs. CONT = 2.3 ± 2.4 kg) were observed over time. Waist circumference, sagittal diameter, and trunk fat decreased significantly over time without group differences. Both groups significantly decreased energy intake while maintaining protein intake. Strength significantly increased over time in both groups. No changes over time or between groups were observed in hormone levels. CONCLUSIONS These data suggest that yogurt supplementation offered no added benefit for increasing lean mass when combined with resistance training and modest energy restriction.
Material adaptation and optimisation of passive Ka-band multilayer circuit designs using an efficient modular design strategy

A modular method for the efficient and flexible design of multilayer microwave modules is investigated. In order to minimise the computational efforts for the layout and optimisation process of passive microwave circuitry, a module data base and a decomposition and cascading strategy are put forward. To verify the efficiency of this approach in the context of a demanding application, low temperature co-fired ceramics (LTCC) structures developed for one specific substrate material have been adapted to another substrate material and subsequently optimised for operation in the Ka-band frequency range. The promising results encourage further investigations, to exploit the potential of this technique for future microwave design issues.
An Approach for Sentiment Analysis on Social Networking Sites

Identifying the sentiment of the text has recently gained a lot of popularity probably due to availability of huge datasets, especially on social networking sites of internet. The social networking sites like twitter and facebook provides to people in general the effective platform for expression of their thoughts and ideas. These thoughts can be harnessed for extraction of sentiments of people related to various issues. But since expression of the verbal thoughts differs individually, identifying the right sentiment from bulk of data becomes the real challenge. In this paper we suggest an approach to analyze the sentiment of the text available on social media.