Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovering heap anomalies in the wild

Programmers increasingly rely on managed languages (e.g. Java and C#) to develop applications faster and with fewer bugs. Managed languages encourage allocating objects in the heap and rely on automatic memory management (garbage collection) to reclaim objects the program can no longer access. With more objects in the heap, the heap encodes more program state than ever before and offers new opportunities for optimization and analysis. This dissertation shows how to efficiently leverage the managed runtime to perform dynamic heap analysis. Previous heap analysis approaches significantly slow down programs, require special hardware, and/or increase memory consumption by 75% or more. We presents two synergistic techniques—dynamic object sampling (DOS) and heap summarization (HSG)—that mine program state embedded in the heap efficiently enough to use in production and effectively enough to improve performance, find bugs, and increase program understanding. We use these techniques to address three problems: (1) Performance of managed language. Because some objects live for a long time, they incur disproportionate collection costs. We optimize these costs with dynamic pretenuring . Dynamic pretenuring uses DOS to accurately predict allocation site survival rates and uses these predictions to improve performance. (2)Finding bugs. Memory leaks in managed languages occur when a program inadvertently maintains references to objects that it no longer needs. Along with degrading performance and resulting in program crashes, memory leaks cause systematic heap growth. We introduce Cork which uses the simplest type of HSG, a class points-from summary graph (CPFG), to detect systematic heap growth. Cork quickly identifies growing data structures observed in three popular benchmarks (fop, jess, and jbb2000) while adding an average of only 2.3% to total time. Additionally, we use Cork to debug a reported memory leak in Eclipse. (3) Program understanding. For a long time, static analysis has sought to statically summarize the shape of dynamic data structures to aid in program verification and understanding. Unfortunately, it only works on small programs. We introduce ShapeUp which instead characterizes recursive data structures dynamically by discovering data structure shape and degree invariants at runtime. ShapeUp uses DOS and a class field-wise summary graph (CFSG) to track in- and out-degree invariants of data structure nodes. We show how ShapeUp automatically identifies recursive data structures and likely shape invariants. Finally, we monitor discovered shape invariants to detect when a data structure becomes malformed. In summary, this dissertation is the first to leverage the managed runtime to perform dynamic heap analysis both accurately and efficiently. Our results show that the heap contains an enormous amount of program state and that there is much potential for dynamically mining heap characteristics for optimization, debugging, and program understanding.
PARALLEL HIERARCHICAL MODEL OF VISUALISATION COMPUTING

The aim of this paper is to dedicate some informations about problem parallel hierarchical computing model for rendering in visualisation subsystem of virtual reality system. In first part are described problems about modeling, traversal and visualisation of virtual world. Next part contains solution of visualisation in cluster environment. Parallel implementation using three/four level nodes hierarchy. Is described here too. Last part described implemenetation in virtual reality system PROLAND, which is developed on DCI FEI TU Kosice. Keywords: virtual reality, parallel computing, cluster, visualisation
Cooperate and compete! A hybrid solving strategy for task-parallel SAT solving on Peer-to-Peer Desktop Grids

The presented work is part of our long-term research goal to develop parallel SAT solving methods for large scale Peer-to-Peer Desktop Grids, which aggregate globally distributed resources. In such a parallel environment, appropriate strategies for dealing with massive parallelism are necessary. In particular, efficiency and at the same time robustness of the parallel methods must be ensured. In this paper, we report on a hybrid strategy for parallel SAT Solving, which combines exploratory and competition parallelism in an adaptive way to meet these goals.
English Sentence Analysis: An introductory course

English Sentence Analysis: An introductory course is designed as a 10-week course for students of English Language and Literature, Linguistics, or other language related fields. In 10 weeks the student will be proficient in English analysis at sentence, clause and phrase level and have a solid understanding of the traditional terms and concepts of English syntax. This introduction prepares for practical courses in grammar and writing skills and for theoretical courses in syntactic argumentation. The Course Book provides  sentence structures in clear graphics;  logically structured chapters with Introductions and Summaries;  exercises with quotations and excerpts from English, American and Australian literature and pop songs. English Sentence Analysis: An introductory course has been classroom tested at various universities. The students seem to enjoy the ‘dreaded’ syntax course and pass rates have gone up significantly from 50 to 70%. Originally, this book was accompanied by a CD-rom with a Practice Program for Windows. The Practice Program on CD-rom is not updated anymore by its creators and as a result is no longer compatible with current Windows versions. For this reason, we have ceased to include it with the book.
Morphophonological and conceptual effects on Dutch subject–verb agreement

Language production theories differ in their assumptions about the information flow between levels. Serial models hypothesise that different types of information, such as conceptual factors and morphophonological make up, would have an effect at different points during the implementation of agreement and would, therefore, not interact. Constraint-based models, on the other hand, entail an interplay of these two types of factors. Here, we present data from an experiment designed to test whether a conceptual factor (notional number) interacts with a morphophonological factor (determiner number ambiguity) resulting in an increased number of subject–verb agreement errors in Dutch. Analyses showed main effects of both factors but no interaction. We also carried out simulations of one specific serial model for production of subject–verb number agreement, the Marking and Morphing model. Our simulations in Dutch yielded an excellent fit between the model and these data (as well as other previously collected data). In conclusion, our results argue in favour of independent processing of these two types of information during agreement production and, more specifically, offer support for the Marking and Morphing model.
Human-managed, hierarchical control of multiple mobile agents

In this paper we describe MASC, a multiple agent supervisory control system which will provide a human supervisor with the capabilities to interact at various hierarchical levels with a multiple mobile agent system. This hierarchy of mediation allows the agents of the system to request assistance from the human when necessary. It also provides the supervisor with the ability to assume control of the agents when they enter a state from which they are incapable of self correction. We describe the levels of the mediation hierarchy, their implementations, and their system control implications. Our goal is to create a semi-autonomous system which successfully executes all feasible tasks. <<ETX>>
A discrimination system using neural network for EMG-controlled prostheses

The electromyographic (EMG) signal from active muscle is observed on the surface of the living body, and considered for controlling an externally powered upper extremity prosthesis. However, the EMG signal depends on physical condition, the state of mind and so on. So it is difficult that the original EMG signal could be used as a command for controlling an externally powered upper extremity prosthesis directly. In this paper, focusing on excellent functions of neural network of learning and processing, a discrimination system using a neural network for generating commands to control EMG-controlled externally powered upper extremity prosthesis is proposed. The neural network is used in this system to learn the relation between the power spectrum of the EMG signal analyzed by fast Fourier transform method and the performance desired by the handicapped. The neural network has three layers, that is, the input layer, the middle layer and the output layer. It was cleared that the discrimination system with the neural network could discriminate 7 performance from the EMG signals with the probability of 0.81. <<ETX>>
Multilevel linear models analysis using generalized maximum entropy

En This paper introduces the general multilevel models and discusses the generalized maximum entropy (GME) estimation method (Golan et al 1996) that may be used to fit such models. The proposed procedure is applied to two-level data generated in a simulation study. The GME estimates are compared with Goldstein’s generalized least squares estimates. The comparisons are made by two criteria, bias and efficiency. We find that the estimates of the fixed effects and variance components are substantially and significantly biased using Goldstein’s generalized Least Squares approach. However, the GME estimates are unbiased and consistent; we conclude that the GME approach is a recommended procedure to fit multilevel models.
Performance of M-ary/SSMA using soft decision Viterbi decoding

In this paper, we discuss the performance of M-ary/SSMA and M-ary/SSMA systems using convolutional code and co-channel interference (CCI) cancellation techniques. First, we show a proposed system model and describe a soft decision method of M-ary/SS which can operate soft decision Viterbi decoding. Next, we evaluate the system performance in an AWGN environment and a Rayleigh fading environment by computer simulations. As a result, spectral efficiency of the system using convolutional code is improved in both environments, though its effects depend on the coding rate. Finally, we discuss the systems using CCI cancellation and convolutional code. As a result, when the spreading factor becomes large, its performance becomes inferior to that of the system which applies no convolutional code in an AWGN environment. However, under a Rayleigh fading environment, a considerable improvement of the performance can be obtained. © 2002 Scripta Technica, Electron Comm Jpn Pt 3, 85(6): 11–20, 2002
Enanching the consistency between requirements and test cases through the definition of a Controlled Natural Language

Requirements and testing engineering build up the solid base for developing successful software projects. In particular along the development process, testing follows and relies on requirements engineering: an incoherent specification of one of the two can affect the correctness of a project resulting in delays, failures, unhappy customers and other consequences on the project delivery.Especially nowadays software companies are competing in fast changing markets where the delivery time of the products is the most crucial aspect and it really affects the quality and the success of the product. Given the semantic gap between requirements (typically written in natural language) and test specifications, it is not rare that requirements are misunderstood leading to erroneous tests. This risk is even more relevant when trying to perform early validation, since testing is mainly based on requirements definition.This thesis work introduces an investigation to close the gap between requirements specification and test cases by providing automatic test case generation. Requirements are written in Natural Language, their subsequent restructuring in a more formal controlled natural language, and the final automatic translation derives test cases. The soundness of the concept is demonstrated through a practical implementation tailored to a previous real project developed at MDH. The implementation not only demonstrates the feasibility of the idea, but also shows interesting results in terms of generated test cases against the ones obtained for the project by hand.
Automatic Acquisition of Tactical Go Rules

Gogol is a rule-based computer Go program. It uses a lot of reliable tactical rules. Tactical rules are rules about simple goals such as connecting and making an eye. Gogol uses a simplified game theory to represent the degree of achievement of the goals. The automatic acquisition of tactical rules follows a three step process : Pattern generation, Game evaluation, Generalisation. Gogol has metainformation about the correct use of the rules in global Go positions.
Parallel Models of Associative Memory

Contents: G.E. Hinton, J.A. Anderson, Introduction to the Updated Edition. D.E. Rumelhart, D.A. Norman, Introduction. J.A. Anderson, G.E. Hinton, Models of Information Processing in the Brain. J.A. Feldman, A Connectionist Model of Visual Memory. D. Willshaw, Holography, Associative Memory, and Inductive Generalization. T. Kohonen, E. Oja, P. Lehtio, Storage and Processing of Information in Distributed Associative Memory Systems. S.E. Fahlman, Representing Implicit Knowledge. G.E. Hinton, Implementing Semantic Networks in Parallel Hardware. T.J. Sejnowski, Skeleton Filters in the Brain. J.A. Anderson, M.C. Mozer, Categorization and Selective Neurons. S. Geman, Notes on a Self-Organizing Machine. R. Ratcliff, Parallel-Processing Mechanisms and Processing of Organized Information in Human Memory.
Malware Analysis Platform Based on Secondary Development of Xen

The API calls reflect the functional levels of a program, analysis of the API calls would lead to an understanding of the behavior of the malware. Malware analysis environment has been widely used, but some malware already have the anti-virtual, anti-debugging and anti-tracking ability with the evolution of the malware. These analysis environments use a combination of API hooking and/or API virtualization, which are detectable by malware running at the same privilege level. In this work, we develop the fully automated platform to trace the native API calls based on secondary development of Xen and have obtained the most transparent and similar system to a Windows OS as possible in order to obtain an execution trace of a program as if it was run in an environment with no tracer present. In contrast to other approaches, the hardware-assisted nature of our approach implicitly avoids many shortcomings that arise from incomplete or inaccurate system emulation.
Answering Why-Not Questions

With the increasing amount of available data and transformations manipulating the data, it has become essential to analyze and debug data transformations. A sub-problem of data transformation analysis is to understand why some data are not part of the result of a relational query. One possibility to explain the lack of data in a query result is to identify where in the query data pertinent to the expected, but missing output is lost during query processing. A first approach to this so called why-not provenance has been recently proposed, but we show that this first approach has some shortcomings. To overcome these shortcomings, we propose an algorithm to explain non-existing data in a query result. This algorithm allows to compute the why-not provenance for rela- tional queries involving selection, projection, join and union. After providing necessary definitions, this paper contributes a detailed description of the algorithm. A comparative evaluation shows that it is both more efficient and effective than the state-of-the-art ap- proach.
Error-correcting coding for CDMA systems

The preselected title of this paper highlights the misconception that most engineers, sometimes even communication specialists, still harbor concerning the central role of channel coding in communication system performance. More properly descriptive terms are "error-preventing", "error-protecting", or the more common "error control. " The reason is that in modern communication systems, designed according to the norms of information theory, coding is an integral part of the modulation process and the optimum modulator and demodulator combine the coding and modulation, as well as the decoding and demodulation, processes into one function at both the transmitter and the receiver. This viewpoint has become commonplace for modulation systems which operate in additive Gaussian noise, with relatively high signal-to-noise ratios, at bit rates considerably greater than their transmission bandwidth. For such systems and channels, the evolution and general acceptance of trellis coded modulation (TCM) has demonstrated how modulation and channel coding can be integrated to provide superior performance. <<ETX>>
Impact of arrhythmias on heart rate variability-strategies to deal with imperfect clinical data

By introducing differing degrees and quality of ectopic beats into clinical data files, the considerable impact of arrhythmias on heart rate variability is demonstrated. Arrhythmias can be removed by either of three approaches proposed, each having variable fidelity with respect to restoring time and frequency domain indices of heart rate variability. Methods devised for the correction of the RR-interval time series substitute the ectopy associated RR-intervals by either a constant RR-interval, identical to the last normal value prior to the ectopic beat (Method I), by the mean value of the total sum of ectopy-associated RR-intervals (Method II) or by values generated by cubic splining and interpolation over the duration of the arrhythmia (Method III). It is shown that splining with interpolation is the overall best technique of overcoming undesired segments in data files with complex arrhythmias. The other techniques, however, have their own merits, particularly when only little ectopy is present. <<ETX>>
Speech Enhancement Based on Feature Compensation for Independently Applying to Different Types of Speech Recognition Systems

본 논문에서는 이기종 음성 인식 시스템에 독립적으로 적용할 수 있는 음성 향상 기법을 제안한다. 잡음 환경 음성 인식에 효과적인 것으로 알려져 있는 특징 보상 기법이 효과적으로 적용되기 위해서는 특징 추출 기법와 음향 모델이 음성 인식 시스템과 일치해야 한다. 상용화된 음성 인식 시스템에 부가적으로 전처리 기법을 적용하는 상황과 같이, 음성 인식 시스템에 대한 정보가 알려져 있지 않은 상황에서는 기존의 특징 보상 기법을 적용하기가 어렵다. 본 논문에서는 기존의 PCGMM 기반의 특징 보상 기법에서 얻어지는 이득을 이용하는 음성 향상 기술을 제안한다. 실험 결과에서는 본 논문에서 제안하는 기법이 미지의 (Unknown) 음성 인식 시스템 적용 환경에서 기존의 전처리 기법에 비해 다양한 잡음 및 SNR 조건에서 월등한 인식 성능을 나타내는 것을 확인한다.
Description of individual data items and codes in CRIB

______________________________ Introduction ___________________________ Individual data items _______________ Index list of data items _______________ Code lists ____________________________ A. Record type ___________________ B. Status of exploration or development __ C. CRIB country code list ______________ D. State codes for United States and Province codes for Canada ___________ E. Commodity codes ________________ F. Deposit types (examples) ____________ G. Accuracy ________________________ Page Bl 1 2 19 22 22 22 23
An artificial co-stimulation classifier for malicious API calls classification in portable executable malwares / Saman Mirza Abdulla

Recently, most researchers have employed behaviour based detection systems to classify Portable Executable (PE) malwares. They usually tried to identify malicious Application Programming Interface (API) calls among the sequence of calls that made by a suspected application. They depended mostly on measuring the similarity or the distance between the suspected API calls with a set of predefined calls that collected from normal and malware applications. However, malwares always tried to keep their normality through hiding their malicious activities. Within such behaviours, calls that made by PE malwares become more similar to normal, which in turn, challenging most distinguishing models. Even such similarity puts the accuracy of most classifier models in a very critical situation as many misclassified and doubtful results will be recorded. Therefore, this work has addressed the accuracy problem of the API call behaviour classifier models. To achieve that, the work has proposed a biological model that defined as Artificial Costimulation Classifier (ACC). The model can mimic the Costimulation phenomenon that occurred inside the Human Immune Systems (HIS) to control errors and to avoid self-cell attacking. Moreover, Costimulation can work as safety and balance processes inside the Artificial Immune System (AIS). To build the ACC model, this work has employed the Feed forward Back-Propagation Neural Network (FFBP-NN) with Euclidean Distance. The work also used the K-fold cross validation method to validate the dataset. The results of our work showed the ability of the ACC model to improve the accuracy of malicious API call classification up to 90.23%. The results of the ACC model have been compared with four types of classifier models and it shows its outperformance.
Analysis and Synthesis of Computer Systems

Basic Tools of Probabilistic Modelling The Queue with Server of Walking Type and Its Applications to Computer System Modelling Queueing Network Models Queueing Networks with Multiple Classes of Positive and Negative Customers and Product Form Solution Markov-Modulated Queues Diffusion Approximation Methods for General Queueing Networks Approximate Decomposition and Iterative Techniques for Closed Model Solution Synthesis Problems in Single-Resource Systems: Characterisation and Control of Achievable Performance Control of Performance in Mutliple-Resource Systems A Queue with Server of Walking Type.
Soft Switched Multi-Output Flyback Converter with Voltage Doubler

A novel multi-output voltage doubler circuit with resonant switching technique is proposed in this paper. The resonant topology in the primary side of the flyback transformer switches the device either at zero voltage or current thus optimizing the switching devices by mitigating the losses. The voltage doubler circuit introduced in the load side increases the voltage by twice the value thereby increasing the load power and density. The proposed Multi-output Isolated Converter removes the need for mutiple SMPS units for a particular application. This reduces the size and weight of the converters considerably leading to a greater payload. This paper aims at optimizing the proposed converter with some design changes. The results obtained from the hardware prototype are given in a comprehensive manner for a 3.5W converter operating at output voltages of 5V and 3.3V at 50 kHz switching frequency. The converter output is regulated with the PI controller designed with SG3523 IC. The effects of load and line regulation for ±20% variations are analyzed in detail.
Bradio: a wireless infrastructure for pervasive computing environments

The ability to identify the locations and identities of objects is important to research in pervasive computing. While several commercial systems exist for tracking inventory and identifying objects (RFID tags, barcodes, etc.), none of these systems provide the programmable flexibility required to support research in pervasive environments. The goal of pervasive systems research is to investigate how a computationally rich environment can support human-computer interactions. More specifically, we are building infrastructure to connect sensors, actuators and displays in these environments. To aid in this effort, we have developed a flexible platform for experimenting with wireless communication. The goal of this effort is to create an infrastructure to support identification and communication wirelessly. Our secondary goal is to enable this communication in a small, low-power package. The end result is a system we call Bradio. We describe the motivation for building the system, the system architecture, the different components in the architecture, and an example application.