Miracle-dz/UniFAR-FTUs huggingface dataset:
Securing concurrent programs with dynamic information-flow control

The work presented in this thesis focusses on dealing with timing covert channels in dynamic information-flow control systems, particularly for the LIO library in Haskell. Timing channels are dangerous in the presence of concurrency. Therefore, we start with the design, formalisation and implementation of a concurrent version of LIO which is secure against them. More specifically, we remove leaks due to non-terminating behaviour of programs (termination covert channel) and leaks produced by forcing certain interleavings of threads, as a result of affecting their timing behaviour (internal timing covert channel). The key insight is to decouple computations so that threads observing the timing or termination behaviour of other threads are required to be at the same confidentiality level. This work only deals with internal timing that can be exploited through language-level operations. We also mitigate leaks that result from the precise measurement of the timing of observable events (external timing covert channel), e.g. by using a stopwatch. Timing channels can also be exploited through hardware-based shared resources, such as the processor cache. This thesis presents a cache-based attack on LIO that relies on timing perturbations to leak sensitive information through internal timing. To address this problem, we modify the Haskell runtime to support instruction-based scheduling, a scheduling strategy that is indifferent to such perturbations from underlying hardware components, such as the cache, TLB, and CPU buses. We show this scheduler is secure against cache-based internal timing attacks for applications using a single CPU. Additionally, we provide a purely language-based implementation of the instruction-based strategy for LIO, by means of a library. We leverage the notion of resumptions, a restricted form of continuations, to control the interleaving of threads, forcing each thread to yield after every LIO operation. Due to the flexibility of this approach, we are able to support parallel computation in the library, a novel feature in information-flow control tools. Finally, we present a new manifestation of internal timing in Haskell, by exploiting lazy evaluation to encode sensitive information as timing perturbations. We illustrate our claim with a concrete attack on LIO that relies on memoisation of shared thunks to leak information. We also propose a countermeasure based on restricting the implicit sharing of values.
Neural networks approach v/s Algorithmic approach : A study through pattern recognition

There is a great scope of expansion in the field of Neural Network, as it can be viewed as massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections. NN models attempt to use some organizational principles in a weighted directed graphs in which nodes are artificial neurons and directed edges are connections between neuron outputs and neuron inputs. The main characteristic of neural network is that they have the ability to learn complex nonlinear input output relationships. A single artificial neuron is a simulation of a neuron (basic human brain cell) and scientists have tried to emulate the neuron in a form of artificial neuron called perceptron. Pattern recognition is one of the areas where the neural approach has been successfully tried. This study is concerned to see the journey of pattern recognition from algorithmic approach to neural network approach.
A Kendama learning robot based on a dynamic optimization theory

A general theory of movement pattern perception based on a dynamic optimization theory can be used for motion capture and learning by watching in robotics. We exemplify our methods for the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has exactly the same kinematic structure as a human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients were (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution.
Klasifikasi Fragmen Metagenom menggunakan Principal Component Analysis dan K-Nearest Neighbor

VICTORIA FEBRINA ROMAULI SIMANGUNSONG. Fragments Metagenome Classification using Principal Component Analysis and K-Nearest Neighbor. Supervised by WISNU ANANTA KUSUMA. Metagenomics is a study of metagenom analysis which its genetic materials is obtained directly from environmental samples. The process of metagenome sequencing produce fragments from mixture organisms. Thus, assembling fragments directly will generate chimeric contigs. Furthermore, a bining process is required to classify these fragments into a particular taxonomic level. In this study, the classification of metagenome fragment were extracted using n-mers, reduced its dimension using principal component analysis and classified using knearest neighbor. The experiments were conducted from in the various fragment length from 0.5 Kbp to 10 Kbp. The best results were obtained using KNN with k=7 and implementing 4-mers frequency. The accuracies of classifying known organisms obtained using PCA 95% were ranged from 91.6% to 99.9%. Moreover, the accuracies were slightly decreased when classifying unknown organisms, from 89.64% to 99.32%.
Fast multisite optical measurement of membrane potential: three examples

This paper describes three kinds of measurements from nervous systems using a microscope camera with unusual characteristics. This camera has poor spatial resolution (24324 pixels), but its temporal resolution (1 ms) and dynamic range (100 dB) are close to ideal. For each kind of measurement we present arguments suggesting that the limited spatial resolution of the camera is not actually limiting. In all of these experiments, the source of the optical signals are voltage-sensitive dyes. These are dye molecules that bind to membranes and change their spectral properties (absorption and/or fluorescence) in response the changes in membrane potential (for reviews see refs 1–5). The dyes that we have used are both linear and fast (6, 7); they respond to changes in membrane potential with a time constant of ,10 ms. The first kind of measurement uses the optical signal of membrane potential to determine the spread of potential in the processes of an individual neuron. The second kind of measurement uses the dyes to monitor the spike activity in many individual cell bodies in an invertebrate ganglion while that ganglion is generating a behavior. The third kind of measurement records the population signals representing the average change in membrane potential from many cells and processes in a vertebrate central nervous system (CNS). All three kinds of recordings have provided information about the function of the nervous system that was previously unobtainable. The measurements were made with a 464 element silicon photodiode array system (24324 grid with corner detectors omitted) with parallel readout (NeuroPlex; RedShirt Imaging, LLC, Fairfield, Conn.) placed in the image plane formed by a microscope objective. With this many detectors it is easy to sample a complete frame every millisecond. The parallel readout allows the measurement of a large number of photons (up to 10/ms), which yields a dynamic range of up to 100 dB. (For additional details about the apparatus and measurements see refs 8, 9). RESULTS
Learning Motor Primitives with Reinforcement Learning

One of the major challenges in action generation for robotics and in the understanding of human motor control is to learn the "building blocks of move- ment generation," or more precisely, motor primitives. Recently, Ijspeert et al. [1, 2] suggested a novel framework how to use nonlinear dynamical systems as motor primitives. While a lot of progress has been made in teaching these mo- tor primitives using supervised or imitation learning, the self-improvement by interaction of the system with the environment remains a challenging problem. In this poster, we evaluate different reinforcement learning approaches can be used in order to improve the performance of motor primitives. For pursuing this goal, we highlight the difficulties with current reinforcement learning methods, and line out how these lead to a novel algorithm which is based on natural policy gradients [3]. We compare this algorithm to previous reinforcement learning algorithms in the context of dynamic motor primitive learning, and show that it outperforms these by at least an order of magnitude. We demonstrate the efficiency of the resulting reinforcement learning method for creating complex behaviors for automous robotics. The studied behaviors will include both discrete, finite tasks such as baseball swings, as well as complex rhythmic patterns as they occur in biped locomotion.
Fifteenth mathematical and statistical modeling workshop for graduate students

We analyzed air pollution, exposure estimates, and mortality to compare relative rates of mortality associated with airborne particulate matter smaller than 10 microns (PM10) to those of the Air Pollution Exposure (APEX) simulated exposure estimates to PM10. We estimated the form of the relationship between PM10 concentration and mortality and APEX exposure estimates and mortality. To estimate the relative risk associated with PM10 and estimated exposure for each city, we built generalized additive models that included adjustments for weather variables, long-term trends, and the presence of other pollutants. We completed a case study on data sets from Cook County, IL (Chicago) and Cuyahoga County, OH (Cleveland). A simulation study was conducted to compare the predictive abilities of the APEX estimates on mortality to those of ambient concentrations. Conclusions were discussed based on the results of the case study and simulation study.
Fragment-based drug design : tools, practical approaches, and examples

There are numerous excellent reviews on fragment-based drug discovery (FBDD), but there are to date no hand-holding guides or protocols with which one can embark on this orthogonal approach to complement traditional high throughput screening methodologies. This Methods in Enzymology volume offers the tools, practical approaches, and hit-to-lead examples on how to conduct FBDD screens. The chapters in this volume cover methods that have proven to be successful in generating leads from fragments, including chapters on how to apply computational techniques, nuclear magnetic resonance, surface plasma resonance, thermal shift and binding assays, protein crystallography, and medicinal chemistry in FBDD. Also elaborated by experienced researchers in FBDD are sample preparations of fragments, proteins, and GPCR as well as examples of how to generate leads from hits. Offers the tools, practical approaches, and hit-to-lead examples on how to conduct FBDD screens The chapters in this volume cover methods that have proven to be successful in generating leads from fragments, including chapters on how to apply computational techniques, nuclear magnetic resonance, surface plasma resonance, thermal shift and binding assays, protein crystallography, and medicinal chemistry in FBDD
Extensive Framework for Query-Dependent Local Landmark Scheme

In recent days the graph size is continuously increasing because of this it is necessary to shortest path between two nodes in computer engineering. This kind of approach is effectively used in various applications such as social network, communication network, and internet. Varity of algorithm have already been developed by different authors such as Dijkshtra's algorithm, A* search algorithm. In query-independent global landmark selection introduce large relative error for nearby nodes that are distant from global landmark to overcome this inaccuracy the local landmark scheme has been developed to find a local landmark for a particular pair of nodes after that it find the distance among two query nodes as the sum of their shortest distances to local landmark which is closer than the global one. In a social network with a huge amount of nodes, the technology among the whole SPT re-computation by traditional static algorithms is extremely ineffective. It will take tremendous computation time. Therefore we developed Extensive Framework for Query-Dependent Local Landmark Scheme to extend previously presented robust method with improved SPT approach. In this paper, we propose an improved algorithm for the dynamic SPT update to solve the above problems. The proposed algorithm is based on the understanding of the dynamic update process to reduce redundancy.
Particle Tracing Based Validation for CFD Models of Stirred Reactors

Modeling the flow field in a stirred tank is always a challenging task because multiple processes (e.g. heat and component transport processes, reaction) must be considered to build an adequate model. Nowadays Computational Fluid Dynamics (CFD) models can be used to examine the different physical phenomena in a stirred tank. However the validation of these models is not an easy task. In this work particle tracing methods were used to analyze the flow patterns in a laboratory glass reactor. The particle movements were recorded from two different angles to follow the trajectories of the particles in three dimensions. Beside the physical experiments, the geometry and the momentum balance model were implemented in CFD software. Based on the experimental results the CFD models were validated. A statistical analysis was applied in the model validation. MATLAB/Simulink was applied for video processing and COMSOL Multiphysics for CFD model implementation and simulation studies. In our experience the proposed method can significantly simplify the validation of CFD models.
Compiler techniques for matlab programs

This thesis addresses the issues of translating an interactive array language, such as MATLAB$\sp1$, into a traditional compiled language, such as Fortran, in order to achieve better performance. It describes the main techniques, developed for our MATLAB-to-Fortran 90 compiler, to extract information from the high-level semantics of MATLAB for the generation of high-performance code. To perform the translation, an inference mechanism is necessary to generate the declarations for a typed language, to select the appropriate functions and operations, and to allocate the necessary space. The inference mechanism developed for this MATLAB-to-Fortran 90 compiler combines static and dynamic inference methods for intrinsic type, shape, rank, and structural inference. This inference is enhanced with value propagation and symbolic-dimension propagation analyses. The experimental results, which compare compiled generated programs with the corresponding interpreted MATLAB execution, show that the compiler can generate code that performs more than 1000 times faster than MATLAB on an SGI Power Challenge, and as fast as the corresponding hand-written Fortran 90 programs. When compared with the performance of C-MEX files generated by the MathWorks MATLAB compiler, we observed that, for our tests, the Fortran 90 programs ran faster than the corresponding C-MEX programs on an SGI Power Challenge and on a Sun SPARCstation 10. This better performance is mainly attributed to our enhanced inference mechanism. ftn$\sp1$MATLAB is a trademark of The MathWorks, Inc.
An Improve Performance , Discovery and Interruption of Sybil Attack in MANET

Mobile ad-hoc network (MANET) is an independent network which consists of many nodes and these nodes used wireless links to communicate with each other. Mobility can be used to enhance security. An advantage of such a network is that no fixed infrastructure is required. It is used to without loss of data and to enhance security. Sybil attack, in which an attacker manages and control to more than one identity on a single device. It is strongly desirable to detect and prevent a Sybil attack and eliminated them from the network. In this paper, Sybil’s attack can detect and prevent from the attackers. It can detect and prevent the information from sender to receiver without loss of information using the neighbor discovers distance (NDD) algorithms. An NDD algorithm detects neighbor node and act as a server to send information to destination. In this method, passive adhoc identity method and key, distributions are used. Detection can be done by a single node or multiple nodes can join to improve the accuracy of detection. It can be used to secure and avoid attacking the system on the network.
Error MatrixCompleted Low − rank Matrix ? Observed Sparse Matrix Training Items Testing Items Incomplete LabelsNoisy Features

The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (entity pairs) in a sparse matrix that concatenates training and testing textual features with training labels. Our algorithmic framework is based on the assumption that the rank of item-by-feature and item-by-label joint matrix is low. We apply two optimization models to recover the underlying low-rank matrix leveraging the sparsity of featurelabel matrix. The matrix completion problem is then solved by the fixed point continuation (FPC) algorithm, which can find the global optimum. Experiments on two widely used datasets with different dimensions of textual features demonstrate that our low-rank matrix completion approach significantly outperforms the baseline and the state-of-the-art methods.
Asymmetric Rent-seeking Contests with Multiple Agents of Two Types ∗

We consider a complete-information rent-seeking contest with multiple agents of two different ability levels. A single winner is selected based on a modified lottery contest success function with constant returns to scale. Two features of variation among players are considered: type heterogeneity, measured by the lobbying effectiveness ratio between the two types; and group composition, represented by the numbers of the two types of players. We first solve for the unique Nash equilibrium of the contest game, then examine how rent dissipation ratios and total effort level depend on agents’ type heterogeneity and group composition. Based on the equilibrium analysis, we further consider whether adding a preliminary stage can benefit a contest organizer who aims at maximizing total expected effort from all agents. The dominant contest structure is fully characterized by conditions on agents’ type heterogeneity and group composition. JEL Codes: C72, D72
Generating Plans in Concurrent, Probabilistic, Over-Subscribed Domains

My thesis topic is plan generation in temporal, parallel, probabilistic domains with oversubscribed goals. I have defined a framework that includes two novel extensions. First, the plans can include parallel steps that serve the same goal and increase the probability of success in addition to parallel steps that serve different goals and decrease execution time. Second, already executing plan steps can be terminated if doing so saves resources to achieve more goals. My algorithm called CPOAO* (Concurrent, Probabilistic, Oversubscription AO*) can deal with these extensions. In this paper, I summarize the design and implementation of CPOAO* and its associated heuristics, and propose a plan of research.
What Determines Entrepreneurial Intention ?-A Comparative Study of Nigerian and Chinese College Students

What should be done in order to promote the conscious choice of entrepreneurship among the growing population of young people across developing countries? This study seeks to examine the role of perception of venture feasibility, perception of venture opportunity, and entrepreneurial selfefficacy in the formation of entrepreneurial intentions among young people, in the context of developing countries. Questionnaires were administered among selected samples of college students in two different countries, namely, Nigeria and China. Based on the choice of logistic regressions and ordinary least squares (OLS) techniques, the findings show that perception of venture feasibility is a significant predictor of entrepreneurial intentions among the Chinese students, while entrepreneurial self-efficacy is a significant predictor of entrepreneurial intentions among the Nigerian students. In addition, interviews were conducted and the findings, further, reveal the differences in the role of perception of venture feasibility and entrepreneurial self-efficacy between the two samples. These demonstrate that perceptions of entrepreneurship are prone to contextual differences. Hence, this study proposes three cardinal points that are necessary to boost progressive perceptions of entrepreneurship among young people.
IMPROVEMENT OF X-CBF HYSTERESIS BEHAVIOR BY INTRODUCTION OF MCS

An analysis of specificity in the hysteresis behavior of X-CBFs is presented. Attached is a brief overview of the undesirable modes of brittle fracture in the diagonals of such structures, often observed in the past earthquakes. A new approach for composition of frames with cross braces is developed by introduction of horizontal strut and by modification of the middle cross section of the diagonal. The aim of the proposal is to achieve predictable behavior and avoid the local buckling within the diagonal even in the stage of large inelastic strains. An experimental study of the proposed frame configuration and analysis of the hysteresis behavior was done. Based on the cyclic tests the frame response was studied and dissipated energy and behavior factor were obtained. Some conclusions and results of the study are presented.
Space and Behaviour : Study on Spatial Use of the Low-Cost Housing and its Residents Sri

This research provides empirical evidence on behavior, daily activities, use of space of residents living in low-cost housing in Jakarta. This study is focused on the contemporary housing units’ issue which were designed with little regard for users’ behaviour activities and which, by neglecting some of their fundamental needs (Heimsath 1977) and further leads to a range of both residents’ positive and negative perceptions toward their space and conducting a coping behaviours. The primary data, collected through semi-structured interviews with 21-sqm unit residents of low-cost housing in Jakarta, were analysed and interpreted by qualitative-quantitative methods using the Statistical Package for Social Science (SPSS) and results were further presented in descriptive form. This study revealed that the design strategies for the 21-sqm units inadequately meet the residents needs of daily activities and spatial use which was proven from residents’ negative perceptions of space and the rooms’ functions which is being utilized inappropriately and ineffective ways. The residents were enforced to adopt various coping-strategies and modes of spatial-adjustment to compensate for both physical and behavioural limitations of their living space and extended areas within housing-complex. The implications of findings suggest that architects should develop design ideas based on a more profound knowledge of potential users’ behaviour and daily activities, particularly in designing lowcost housing in which the users are mostly not involved in the design process.