Miracle-dz/UniFAR-FTUs huggingface dataset:
Judgment of Code Style

The Judgement of Code Style (JOCS) system is an automated tool that mimics the human grading of Java code style. Due to the emphasis on human-readable code in corporate and professional programming environments, students of computer science require comprehensive feedback on their code style. However, increases in enrollment in programming courses both online and oine make providing this feedback dicult. The JOCS system provides an automated, extensible solution that diers in scope from currently available tools. The JOCS system evaluates code for readability and style using metrics based on accepted stylistic guidelines such as consistency, code density, modularity, and documentation. Machine learning techniques are used to discern which metrics are appropriate for scoring a given corpus of code submissions, based on a history of human generated grades. The JOCS system is then used to generate style scores for future submissions. The results provide insight into which features of the code can be used as indicators of good or bad style.
Fuzzy Scheduling Design in Computer Control

In order to improve the control performance of systems and use the limited computing resource reasonably,it is necessary to consider the codesign problem between control and real-time scheduling in computer-controlled systems synthetically.In this paper,the real-time scheduling problem of multiple control tasks with fuzzy deadlines is studied.The concept of dedication index and the scheduling policy of largest dedication first(LDF) are proposed.In order to relieve the overhead caused by the thrashing among tasks,the threshold-based largest dedication first(TLDF) is presented.Finally,Two proposed scheduling policies LDF and TLDF are compared by simulation.The scheduling of control tasks with fuzzy deadlines is implemented,and the utilization of computing resource gets increased meanwhile the control performance cost gets decreased and tradeoff.
Research on prediction of the short-term traffic flow based on cloud model

In order to improve the prediction precision of the short-term traffic flow,a prediction method of short-term traffic flow based on cloud model is proposed by artificial intelligence with uncertainty.The traffic flow is fit by cloud model.The historical cloud and the present cloud are built by historical traffic flow and present traffic flow.The forecast cloud is produced by both clouds.Then,combining with the volume of the short-term traffic flow of an intersection in Guangzhou city,the model is calculated and simulated through programming.Max absolute error(MAE) and mean absolute percent error(MAPE) are used to estimate the effect of prediction.The simulation results indicate that this prediction method is effective and advanced.The change of the historical and real time traffic flow is taken into account in this method.Because the short-term traffic flow is dealt with as a whole,the error of prediction is avoided.The prediction precision and real-time prediction are satisfied.
Introduction to Bayesian item response modelling

This article provides a brief survey of developments in item response modelling from a Bayesian perspective. There is a description of the influential literature in the application of Markov chain Monte Carlo algorithms to fit IRT models. To give insight into current Bayesian work, we give overviews of recent papers on Bayesian multilevel modelling, IRT modelling using flexible asymmetric link functions, and detection of multidimensional structure using posterior predictive model checking. To show the recent advances in Bayesian software, we illustrate the use of an R package to fit a two-parameter IRT model by MCMC methods.
Automated Negotiation Decision Model Based on Classifier Fusion

Due to the confidentiality of information in e-commerce environment,negotiation participants can not get opponent's negotiation preferences,thereby affecting the negotiation performance.To solve this,an automated negotiation decision model based on classifier fusion was proposed.The model incorporates support vector machine and Bayesian classifier by combining the advantages of both,improving the effect of classification learning of negotiation preferences.Based on accurate estimation of opponent's negotiation preference,a particle swarm optimization algorithm was used to search the optimal counter proposal.The experimental data show that the new method is better than the single classifier,and can maintain a high total negotiation utility in the noisy small scale training set.
An improved knowledge evolution algorithm and its application to chemical process dynamic optimization

The intelligent optimization algorithms have been widely used in solving dynamic optimization problems for their ability to converge to global optimum at a specific probability without being trapped in local optimums.However,the practical industrial application fields of such random mechanism-based algorithms are restricted due to their demerits,such as slow convergence and low searching efficiency.Therefore,an improved knowledge-based evolutionary algorithm structure was presented for improving the efficiency of intelligent optimization algorithms which were used to solve dynamic optimization problems.The structure included a discretization method of candidate solutions in time and control domains,the evolution of the repository,population evolution steered by knowledge.When applied to dynamic optimization problems of four typical chemical processes,such as batch reactor,which owned distinguishing control feature,this algorithm demonstrated its competitive optimal searching ability,meanwhile verifying its satisfying convergence efficiency by using a smaller scale of population guided by knowledge and consuming less computational cost.
Towards real-time SVD based motion detection on GPU

The Singular Value Decomposition (SVD) can be eﬃciently used to detect motion in videos captured by a static camera. However, the SVD is computationally demanding when a large matrix - a spatio-temporal data window typically composed of ten to thirty frames - is repeatedly processed. Recently, a running (incremental) version [1] of the SVD has been proposed and applied to motion detection. Although much faster than the direct implementation, the CPU-based running SVD is still too slow for real-time processing of VGA or larger size videos. In this paper, we present a GPU implementation of the running SVD that is suitable for robust, close to real-time motion detection in challenging full-size, standard frame rate videos. The computational performances of diﬀerent hardware conﬁgurations are compared showing a signiﬁcant gain in processing speed due to the proposed solution.
Localization and Proximity Detection in the Internet of Things Based on an Augmented UHF RFID System

In the "Internet of Things" (IoT), the things will be able to sense, communicate, and interact. They will also exchange data, information and knowledge, and locate themselves and other things that surround them. In order to be able to interact, the things need to recognize that they are in proximity of other things. It is anticipated that the most widespread components of the IoT will be passive radio frequency identi cation (RFID) tags because they are inexpensive and provide automatic identi cation. However, passive RFID tags are not capable of performing complex operations, such as proximity detection and localization, which will be required in future networks. In this thesis, we describe existing problems with current RFID systems and survey potential solutions for localization and proximity detection. Then we present a new RFID device called "Sense-a-Tag" (ST) that can passively detect and decode backscattered signals from tags in its proximity. There have already been an attempt to use this device for tracking. However, detailed analysis of the performance of the ST especially for proximity detection has not been performed yet. We show that when STs are added to a standard RFID system, the problems of proximity detection and localization with RFID tags can readily be solved. Then we applied ST-based system for identifying people and object interactions. The potential uses of ST as an augmented device for IoT applications are discussed in this thesis. Advantages and limitations of an ST based RFID system have been investigated in details for each application. Results obtained from real experiments illustrate that an ST-based RFID system is feasible for proximity detection applications. In addition, a special software is developed in C# to process the data and run a localization algorithm based on proximity detection information. The same software has been used for tracking people's activity. Di erent scenarios have been considered in the experiments. We tried to consider majority of factors that might a ect the accuracy in the experiments including: angle and distance between the reader/ST and tags, timing in sending queries, presence of human body, etc. The simulations based on real experiments and results illustrates that an ST-based RFID system can be a realistic solution for proximity detection and localization for Location Positioning systems (LPS) and activity monitoring in future IoT.
The Research of Background-subtraction Based Moving Objects Detection Technology

A statistical method is used to obtain the background model, which is updated real time in order to adapt to illumination changes and scene changes. After the threshold operation, morphologic operation and connected region area measurement are introduced to solve the background disturbance problem. Finally, the shadow is detected using HSV color space information and gets exact moving objects. 
Research on the Web Crawler

The traditional search engines can not satisfy the demands of getting useful information with the blast developing of information resources on Internet, as the foundation and important part of search engine, the action of the Web Crawler appears especially important, the article introduces the concept of Web crawler, the trouble of crawling and the resolvent, also have a research on four kinds of familiar Web Crawler.
Automated System For Analyzing Sleep

We present an automated system that emulates the visual analysis of sleep records (EEG, EOG. and EMG) by detecting relevant waveforms (features) and evaluating the sleep stage according to the standard scoring manual. New algorithms for detection of K-Complexes, spindles, REMs, EMG bursts, delta, alpha, and mixed frequency waveforms based on morphological parameters that reflect visual recognition, perform in the 91 to 98 % correct range. The staging algorithms that were developed use bgic structures that emlate human decision-making. The somnogram obtained on the first full-night (8 hours) sleep record that was analyzed is in 92% agreement with the somnogram provided by an experienced sleep clinician of the Johns Hopkins Sleep Disorders Center. The automated system detects all the features on-line, and completes the staging In 5 minutes off-line.
Design and implementation of a new MANET simulator model for AODV simulation

Distributed systems, such as ad hoc networks, are composed of nodes communicating with each other using underlying wireless technologies. Communication protocols, connection technologies, traffic ows, and routing algorithms are very complex in the design process of ad hoc networks. Due to the challenges in current network simulators such as complexity, mobility, and scalability, studies on ad hoc networks are far beyond real life. For the purpose of overcoming the problems above, modeling and simulation tools are extremely signicant in ad hoc research. In this study, for studying complexity, scalability, and adaptability aspect problems of mobile systems, a MANET model and its simulator have been developed and called the MANET-DEVS simulator. For the purpose of showing the model's superiority and performance, various networks with different sizes and congurations have been created and simulated under various traffic loads. Furthermore, the performance of the AODV routing protocol as the most common MANET routing protocol has been investigated in the developed simulator. The developed model's validation and verication tests have been carried out to depict the model's condence. The results show that the MANET-DEVS simulator provides robust and easy simulations for MANET characteristics and facilitates the education of the AODV routing protocol.
Building A Sentiment Analysis Corpus With Multifaceted Hierarchical Annotation

A corpus is a collection of documents. An annotated corpus consists of documents or entities annotated with some task related labels such as part of speech tags, sentiment etc. While it is customary to annotate a document for a specific task, it is also possible to annotate it for multiple tasks, resulting in a multifaceted annotation scheme. These annotations can be organized in a hierarchical fashion, if such a scheme naturally occurred in the data, resulting in a hierarchical text categorization problem. We developed a multifaceted, multilingual corpus for hierarchical sentiment analysis. The different facets include hierarchical nominal sentiment labels, a numerical sentiment score, language, and the dialect. Our corpus consists of 191K reviews of hotels in Saudi Arabia. The reviews are divided into eleven different categories. Within each category, the reviews are further divided into two positive and negative categories. The corpus contains 1.8 million tokens. Reviews are mostly written in Arabic and English but there are instances of other languages too.
Design and Implementation of a Text-to Speech System using the Prosody and Duration Information

To produce more natural speech in a Text-to-Speech system, the processing of the prosody and duration must be processing in advance, and then extracted the prosody and duration information by means of trial-and-error experiments. In this paper, a method is proposed to improve the naturalness in a Text-to Speech system using this information. As the results, the Text-to-Speech system proposed and implemented in this paper showed more natural speech synthesis than the systems, which do not use this information, did.
A Novel Approach for Mining Stochastic Process Model from Workflow Logs

Workflow is a series of interrelated and automatic business activities or tasks, in which many participants can automatically exchange their documents, information and tasks according to the predefined rules and models, so that they can achieve some business goals. Techniques of workflow mining are now becoming more important and they have recently received remarkable attention for researchers. By workflow mining approach, the developers can avoid the design of a complex formal process model, and they only need to discover the corresponding models from workflow logs, which are collected and stored by most information systems. This paper proposes a novel approach for mining stochastic workflow model from the execution logs automatically. We first propose a statistical method to construct the original relations among tasks. Then, several algorithms are designed for identifying the different control structures among tasks. In this way, an original workflow net is constructed. Next, by using state equations, we verify and refine the constructed workflow net. Finally, the stochastic relations among tasks are also mined from the logs and incorporated into the workflow net. We also implement a mining tool called SPM_Miner to validate the effectiveness of our method.
Fast Concurrent Data-Structures Through Explicit Timestamping

Concurrent data-structures, such as stacks, queues and deques, often implicitly enforce a total order over elements with their underlying memory layout. However, linearizability only requires that elements are ordered if the inserting methods ran sequentially. We propose a new data-structure design which uses explicit timestamping to avoid unwanted ordering. Elements can be left unordered by associating them with unordered timestamps if their insert operations ran concurrently. In our approach, more concurrency translates into less ordering, and thus less-contended removal and ultimately higher performance and scalability. As a proof of concept, we realise our approach in a non-blocking doubleended queue. In experiments our deque outperforms and outscales the Michael-Scott queue by a factor of 4.2 and the Treiber stack by a factor of 2.8. It even outscales the elimination-backoff stack, the fastest concurrent stack of which we are aware, and the flat-combining queue, a fast queue more scalable than Michael-Scott.
DOM based Web information extraction

Due to the information on the Web being tremendous, dynamic and irregular, it is difficult to query information on the Web or to integrate information from the Web. This paper mainly discusses an approach of information extraction from Web documents of HTML format and presents a DOM-based method of fast information extraction. This method generates information extraction rules that is based on DOM path through attaching syntax information and sample-learning. It implements information extraction through traveling DOM tree. The method can be applied to Web query and wrapper generation in information integration system.
A generalized modeling framework in software reliability and related problems

Computers are bringing revolutionary changes to our life with their involvement in most human-made systems for sensing, communication, control, guidance, and decisionmaking. When the requirements for and dependencies on computers increase, the crises of computer failures also increase. The impact of hardware and software failures range from inconvenience (malfunctions of home appliances) and economic loss (interceptions of banking systems) to lifethreatening (failures of flight systems and medical software).
Research on the Classification and Recognition of Multi-channel EEG Signal Based on the RBF Kernel Support Vector Machine Classification

Nonstationary randomness signal (NRS) is difficult to classify and recognize. In order to improve the performance of the classifying technique of NRS, a novel technique for classifying multi-channel EEG signal is introduced in this paper. First of all, subjects in the states of one eye open and one eye closed with a single-channel EEG feature are extracted, then the characteristics of single-channel EEG signal with bad classifying results are selected and combined into multi-channel EEG characteristics. Finally, RBF Kernel Support Vector Machine classifier is used to classify the characteristics under different states. The results show that the correct classification rate is greatly improved.
Group Signature Scheme Based on Lattice

An attack is mounted on a group signature scheme based on lattice posted in Asia Crypt 2010.It shows the group signature scheme is vulnerable to trap attacks and a dishonest group manager can get all group members′ signing keys and then forge all group members′ valid signatures.Meanwhile,the scheme can′t increase or delete group members flexibly and efficiently which is not applicable to dynamic groups.Every time a new member joins,the system has to update the public key and all group members′ signing keys which are of huge calculation and low efficiency.Also,the scheme doesn′t provide an effective method to revoke group members.Using statistical zero-knowledge proofs and time parameters,an improved scheme based on the hardness of the closest vector problem is proposed.The improved scheme is anti-trap attacks and can dynamic increase or deletes group members more efficiently and applicably.
Proteomic profiles of Longissimus dorsi pig muscle

GENERAL INTRODUCTION. REFERENCES. CHAPTER I. ABSTRACT. RESUMO. Introduction. Materials and Methods. Animals and sampling. Extraction of proteins for the Liquid Nitrogen preservation method. Extraction of proteins for the RNAholder preservation method. Protein quantitation. Mono-dimensional gel electroporesis. First dimension of the 2-DE. Second dimension of the 2-DE. Image and data analysis. In-gel digestion of proteins. Protein identification. Results and Discussion. Conclusions. Acknowledgments. References. CHAPTER II. ABSTRACT. RESUMO. Introduction. Materials and Methods. Animals and sampling. Extraction of proteins . First dimension of the 2-DE. Second dimension of the 2-DE. Image and data analysis. In-gel digestion of proteins. Protein identification. Results and Discussion. Conclusions. Acknowledgments. References. GENERAL CONCLUSION. v
Reflectance and Shape Estimation for Cartoon Shaded Objects

Although many photorealistic relighting methods provide a way to change the illumination of objects in a digital photograph, it is currently difficult to relight a cartoon shading style in digital illustrations. The main difference between photorealistic and cartoon shading styles is that cartoon shading is characterized by soft color quantization and nonlinear color variations that cause noticeable reconstruction errors under a physical reflectance assumption such as Lambertian. To handle this non-photorealistic shading property, we focus on the shading analysis of the most fundamental cartoon shading technique. Based on its color map shading representation, we propose a simple method to decompose the input shading to a smooth shape with a nonlinear reflectance property. We have conducted simple ground-truth evaluations to compare our results to those obtained by other approaches.
Fabrication of Barium Titanate Single Crystals by Solid-State Grain Growth.

BaTiO3 single crystals were prepared by solid-state grain growth. The single crystals were obtained by seeding a poly-crystalline, TiO2-excess BaTiO3, which exhibited abnormal grain growth. The condition for single-crystal growth was essentially dependent on the grain growth behavior of the polycrystalline, sintered bodies. The annealing temperature suitable for the single-crystal growth was just below the critical temperature of abnormal grain growth in TiO2-excess BaTiO3, which is about 1300°C.
RESISTING STATISTICAL HITS IN OPEN NETS

Many works were suggested in several types of threat to achieve various benefits for search for example single keyword search, multi-keyword rated search, and so forth. Of individuals works, multi-keyword kinds of rated search is becoming more importance because of its realistic effectiveness. We submit a great search method which pulls round the tree above encoded cloud information, and additionally it manages multi-keyword search additionally to dynamic process on selection of documents. For acquiring of high search effectiveness, we produce a tree-based index structure and propose an formula while using the index tree. The forecasted plan's referred to as to provide multi-keyword query additionally to specific result ranking, furthermore dynamic update above document collections. Due to important structure of tree-based index, forecasted search system will effectively get sub-straight line search a serious amounts of manage the operation of deletion additionally to insertion of documents.