Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing user's privacy control through flexible Web bug detection

People usually provide personal information when visiting Web sites, even though they are not aware of this fact. In some cases, the collected data is misused, resulting on user privacy violation. The existing tools which aim at guaranteeing user privacy usually restrict access to personalized services. In this work, we propose the Web bug detector. Upon detecting and informing users about browsing tracking mechanisms which invisibly collect their personal information when visiting sites, it represents an alternative that provides a better control over privacy while allowing personalization. Through experimental results, we demonstrate the applicability of our strategy by applying the detector to a real workload. We found that about 5.37% of user's requests were being tracked by third-party sites.
Probabilistic unit life status estimation (PULSE)

Abstract : We present here the Probabilistic Unit Life Status Estimation (PULSE) methodology and software architecture. The PULSE approach leverages Temporal Belief Networks (TBNs) to model the subject's fundamental physiological dynamics (e.g. shock response) over time, to perform life status estimation in a robust manner that accounts for sensor hardware malfunctions. We developed a limited-scope prototype using our in-house TBN engine to estimate clinical status, which served as a framework to demonstrate the PULSE approach for two separate but related clinical assessment tasks. In the first task, the PULSE prototype provides a life status assessment of a simulated unit, allowing for the principled introduction of noise to demonstrate the system's ability to detect sensor failure. In the second task, the PULSE prototype provides a clinical assessment of a unit's degree of acclimatization, using pre-recorded data from studies of soldiers at altitude.
A novel technique for assessment of neuromuscular coupling in pathological tremor

We describe a novel technique for identification of cortical centres that have electrical activity coupled to the activity of skeletal muscles in pathological tremor. This technique utilises concurrent recordings of highdensity surface electromyograms (EMG) and electroencephalograms (EEG). First, the discharge patterns of individual motor units are identified from surface EMG signals by previously introduced Convolution Kernel Compensation technique. Next, spike-triggered averaging is applied to the EEG signals, using the identified motor unit discharges as triggering spikes. As a result, the cortical activities coupled to the discharges of a selected skeletal muscle are identified. The extracted EEG averages are then inserted into Statistical Parametric Mapping (SPM) tool and used to identify the cortical sources contributing to the observed EEG averages. When tested on 5 Parkinsonian and 5 essential tremor patients, this methodology consistently identified two cortical centres, one in the contralateral sensorimotor cortex, and the other in the contralateral prefrontal/premotor cortex.
Accelerating Optimization under Uncertainty via Online Convex Optimization

In this paper, we consider two paradigms that are developed to account for uncertainty in optimization models: robust optimization (RO) and joint estimation-optimization (JEO). We examine recent developments on efficient and scalable iterative first-order methods for these problems, and show that these iterative methods can be viewed through the lens of online convex optimization (OCO). The standard OCO framework has seen much success for its ability to handle decision-making in dynamic, uncertain, and even adversarial environments. Nevertheless, our applications of interest present further flexibility in OCO via three simple modifications to standard OCO assumptions: we introduce two new concepts of weighted regret and online saddle point problems and study the possibility of making lookahead (anticipatory) decisions. We demonstrate how these new flexibilities are instrumental in exploiting structural properties of functions which results in improved convergence rates in our flexible OCO framework. In particular, relaxing the usual OCO assumption of uniform weights (non-anticipatory decisions) allows us to utilize algorithms that can significantly speed up the convergence and go beyond the established lower bounds on regret for strongly convex (smooth) loss functions. We then apply these OCO tools to RO and JEO. Our results improve the convergence guarantees of first-order methods in the context of RO and JEO under certain structural assumptions, and in certain cases, they match the best known or optimal rates in the corresponding problem classes without data uncertainty.
A Viable Approach for Measuring the Risk-Return Relationship of IT Investments

The importance of managing the risk-return balance of information technology (IT) investments has become clearer than ever. Yet, quantitative assessment of IT investment risk and return based on financial measures remains a major challenge. Recently scholars have used event study analysis to measure the value created via IT investment, by examining the abnormal changes in shareholder wealth around the time a specific IT investment is announced. The abnormal return on equity due to such an event is considered a good proxy for the economic value of that event. In the same spirit, this research proposes estimating several forms of IT investment risk, by combining event study analysis with the use of arbitrage pricing theory. In so doing, this research contributes towards the development of an integrated approach for quantifying the risk-return relationship for IT investment so that practitioners can make more informed investment decisions.
Implementation of a Vision System for a Landmine Detecting Robot Using Artificial Neural Network

Landmines, specifically anti-tank mines, cluster bombs, and unexploded ordnance form a serious problem in many countries. Several landmine sweeping techniques are use d for minesweeping. This paper presents the design and the implementation of the vision system of an autonomous robot for landmines localization. The proposed work develops state -of-the-art techniques in digital image processing for pre -processing captured images of the contaminated area. After enhancement, Artificial Neural Network (ANN) is used in order to identify, recognize and classify the landmines’ make and model. The Back-Propagation algorithm is used for training the network. The proposed work prov ed to be able to identify and classify different types of landmines under various conditions (rotated landmine, partially covered landmine) with a success rate of up to 90%.
Robust Characterizations of Polynomials with Applications to Program Testing

The study of self-testing and self-correcting programs leads to the search for robust characterizations of functions. Here the authors make this notion precise and show such a characterization for polynomials. From this characterization, the authors get the following applications. Simple and efficient self-testers for polynomial functions are constructed. The characterizations provide results in the area of coding theory by giving extremely fast and efficient error-detecting schemes for some well-known codes. This error-detection scheme plays a crucial role in subsequent results on the hardness of approximating some NP-optimization problems.
On the Relationship Between Delaying Operators and Language-Level Semantics

The notion of “atomicity” implies that it is safe to rearrange memory accesses within a transaction. In this paper, we sketch a mechanism for postponing contentious transactional operations until commit time, where they become impervious to aborts. We then contemplate the interplay between such a mechanism and languagelevel semantics. Though preliminary, our algorithms and recommendations should prove useful to designers of transactional compilers and languages.
Exploring Perceptrons in Branch Prediction

Branch prediction has become a more important component of the modern processor, due to longer pipelines and the desire to exploit more instruction-level parallelism. Perceptrons, well-understood tools in machine learning, have recently been applied to this area of research with excellent results. We use this investigation to show tracking local and global branch histories can improve perceptrons' performance. We also explore the effects of using various caching mechanisms to combat aliasing. 1 Introduction Over the past few years, branch prediction has become an increasingly important feature in the modern microprocessor. This is largely due to the ever-growing emphasis on processor speed: as pipeline lengths increase, more CPU time is wasted on a branch mispredict. There is also the desire to exploit i nstruction-level parallelism: if a branch predictor can tell the CPU its confidence level for a particular prediction, the CPU can better decide if it should speculatively execute both branch outcomes (low confidence), or simply execute the predicted outcome, freeing functional units for use on other tasks. There are two main types of branch prediction. Static prediction issues its decision based on some stationary set of criteria. A particular instruction placed at a particular memory address will always generate the same guess under static prediction. Common examples are " always-taken " (all branches are assumed to be always taken), " backwards-taken " (backward-pointing branches are assumed to be always taken), and other strategies where some instructions are assumed always taken, and other instructions are assumed always not taken. Dynamic prediction often produces superior results to static prediction by using simple statistical methods to decide if a branch is likely to be taken. Examples are " same-as-last " (branch is predicted taken if and only if it was taken last time), gshare and other SUD counter-based approaches, and perceptrons. The improved performance over static prediction is due to this class's ability to adapt to the particular quirks of the code that is being executed. However, the increased performance also comes at a cost of hardware complexity. It has been recently shown that perceptrons can be applied to the branch prediction problem with excellent results. In past reports, perceptrons had only been used with global history of branch outcomes. In this investigation, we explore how perceptrons fare when taking both the global history as input, several bits of local history, and several bits of regional history. We will also examine the …
Neuroinflammation by Cytotoxic T-Lymphocytes Impairs Retrograde Axonal Transport in an Oligodendrocyte Mutant Mouse

Mice overexpressing proteolipid protein (PLP) develop a leukodystrophy-like disease involving cytotoxic, CD8+ T-lymphocytes. Here we show that these cytotoxic T-lymphocytes perturb retrograde axonal transport. Using fluorogold stereotactically injected into the colliculus superior, we found that PLP overexpression in oligodendrocytes led to significantly reduced retrograde axonal transport in retina ganglion cell axons. We also observed an accumulation of mitochondria in the juxtaparanodal axonal swellings, indicative for a disturbed axonal transport. PLP overexpression in the absence of T-lymphocytes rescued retrograde axonal transport defects and abolished axonal swellings. Bone marrow transfer from wildtype mice, but not from perforin- or granzyme B-deficient mutants, into lymphocyte-deficient PLP mutant mice led again to impaired axonal transport and the formation of axonal swellings, which are predominantly located at the juxtaparanodal region. This demonstrates that the adaptive immune system, including cytotoxic T-lymphocytes which release perforin and granzyme B, are necessary to perturb axonal integrity in the PLP-transgenic disease model. Based on our observations, so far not attended molecular and cellular players belonging to the immune system should be considered to understand pathogenesis in inherited myelin disorders with progressive axonal damage.
An Efficient Indexing for Content-based Video Search on Peer-to-Peer Networks

In the past few years, videos become an ordinary communication mean for both personal and business activities. Not only the keyword search that have been utilized widely, but also the video content-based search, i.e. given a query video, the similar video sequences can be retrieved. Meanwhile, the increasing of the video utilization has posed both the computational and communication problem since the quality of service should be considered. In this paper, we address the efficiency issue of content-based video indexing over Peer-to-Peer (P2P) networks. The traditional video index is improved to suit with the P2P computational model. The algorithms to perform the video query based on the content similarity, increase the efficiency when new P2P nodes are to be joined with the network, manage the departure of nodes, and insert the videos, are proposed based on a well-known P2P overlay network. From the experiment results, our proposed approach outperforms a naive approach, which directly applies the P2P model with fully-replication, when a number of P2P nodes to be joined, as well as a number of videos to be inserted, is increased. Meanwhile, the efficiency of our approach in terms of the query answering is bounded by linear complexity.
Privatizing the Welfare State: Nonprofit Community-Based Organizations as Political Actors

This paper examines a form of state social provision that has been neglected by current sociological theory: publicly funded supportive services. Federal policies of privatization and devolution, embraced since the Reagan years, have made private, nonprofit organizations the primary deliverers of these services. Public supportive services are distributed via competitive state- and local-level allocative processes that send government contracts to specific nonprofit community-based organizations (CBOs), which in turn serve specific neighborhoods and individuals. I describe a model by which CBOs generate greater contract revenues by adding electoral politics to their more traditional roles of providing services and building communities. This model produces a new kind of CBO: the machine politics CBO. By reciprocally distributing services to residents and binding residents to the organization, machine politics CBOs create reliable voting constituencies for local elected officials. These officials trade these constituencies at higher levels of the governmental system and steer government human service contracts to favored CBOs. Through this process, nonprofit CBOs can influence the allocation of service-based social provision in cities and therefore impact individuals' ability to access these services.
L'inventaire français du développement communicatif chez le nourrisson de huit à trente mois : essai d'élaboration d'un manuel technique à l'usage des parents et création d'un guide d'utilisation pour orthophonistes dans une perspective de guidance, d'accompagnement parental

L'Inventaire Francais du Developpement Conununicatif ( IDC ) chez le nourrisson âge de huit a trente mois consiste en des listes d'items reunis sous des rubriques se referant tantot aux capacites linguistiques, tantot aux capacites cognitives du nourrisson. C'est aux parents eux-memes qu'il revient de cocher ou non ces items. En effet, l'IDC est un compte-rendu parental : il a ete concu specialement pour les parents. Alors, comment un tel outil pourrait-il etre utilise dans une demarche orthophonique ? Une telle problematique souleve deux questions distinctes : premierement, celle de l'utilisation de l'IDC par les parents, puis deuxiemement, celle de l'utilisation de l'IDC par les orthophonistes. Ce qui nous a conduits a envisager la creation d'un manuel technique pour les parents et d'un guide d'utilisation pour les orthophonistes. Le manuel technique preconise des modalites d'utilisation de l'IDC specifiques a un usage parental. Ces criteres d'utilisation ont ete mis en evidence grâce a un questionnaire soumis aux parents. Mais, compte tenu de la difficulte a standardiser des normes d'utilisation identiques pour tous les parents, il s'agit plus exactement d'un essai d'elaboration d'un manuel technique. Ce choix est d'autant plus motive par le fait que les parents ne se sentent pas en difficultes d'un point de vue technique, pratique lors de l'utilisation de l'IDC. Leurs difficultes viennent en revanche d'un manque de connaissances sur le developpement communicatif de leur enfant. Ce qui justifie pleinement la creation d'un guide d'utilisation. Ce Guide d'utilisation est destine aux orthophonistes ; il a pour but de permettre aux orthophonistes de faire de la guidance, de l'accompagnement parental pour aider les parents a mieux connaitre leur enfant. C'est dans cette optique que l'IDC peut etre integre et exploite dans une demarche orthophonique.
An information network overlay architecture for the NSDL

We describe the underlying data model and implementation of a new architecture for the National Science Digital Library (NSDL) by the Core Integration Team (CI). The architecture is based on the notion of an information network overlay. This network, implemented as a graph of digital objects in a Fedora repository, allows the representation of multiple information entities and their relationships. The architecture provides the framework for contextualization and reuse of resources, which we argue is essential for the utility of the NSDL as a tool for teaching and learning
A novel role for p73 in the regulation of Akt-Foxo1a-Bim signaling and apoptosis induced by the plant lectin, Concanavalin A.

Virtually all human cancers encounter disruption of the "p53 network. " From a therapeutic point of view, it is important to devise strategies that eliminate cancer cells, which are often defective in functional p53 and protect p53-expressing normal cells. By comparing the response of a pair of isogenic cell lines, we identify a plant-derived compound, Concanavalin A (Con A), which differentially kills p53-null cells. Further, we find that p53 family member, p73, plays a critical role that is unmasked in the absence of p53. Con A treatment leads to induction of p73 and several others that are important mediators of apoptosis and act downstream, such as p21, Bax, Foxo1a, and Bim. Inactivation of p73 reverses the expression of these proteins and apoptosis. Inhibition of Akt activation sensitizes otherwise resistant cells. These observations thus reveal a novel role for p73 in the regulation of Akt-Foxo1a-Bim signaling and apoptosis especially when p53 is absent.
Multi-scale Science: Supporting Emerging Practice with Semantically Derived Provenance

Scientific progress is becoming increasingly dependent of our ability to study phenomena at multiple scales and from multiple perspectives. The ability to recontextualize third party data within the semantic and syntactic framework of a given research project is increasingly seen as a primary barrier in multi-scale science. Within the Collaboratory for Multiscale Chemical Science (CMCS) project, we are developing a general-purpose informatics-based approach that emphasizes ''on-demand'' metadata creation, configurable data translations, and semantic mapping to support the rapidly increasing and continually evolving requirements for managing data, metadata, and data relationships in such projects. A concrete example of this approach is the design of the CMCS provenance subsystem. The concept of provenance varies across communities, and multiple independent applications contribute to and use provenance. In CMCS, we have developed generic tools for viewing provenance relationships and for using them to, for example, scope notifications and searches. These tools rely on a configurable concept of provenance defined in terms of other relationships. The result is a very flexible mechanism capable of tracking data provenance across many disciplines and supporting multiple uses of provenance information.
Automatic Learning of Language Model Structure

Statistical language modeling remains a challenging task, in particular for morphologically rich languages. Recently, new approaches based on factored language models have been developed to address this problem. These models provide principled ways of including additional conditioning variables other than the preceding words, such as morphological or syntactic features. However, the number of possible choices for model parameters creates a large space of models that cannot be searched exhaustively. This paper presents an entirely data-driven model selection procedure based on genetic search, which is shown to outperform both knowledge-based and random selection procedures on two different language modeling tasks (Arabic and Turkish).
Privacy and Utility of Inference Control Mechanisms for Social Computing Applications

Modern social computing platforms (e.g., Facebook) are extensible. Third-party developers deploy extensions (e.g., Facebook applications) that augment the functionalities of the underlying platforms. Previous work demonstrated that permission-based protection mechanisms, adopted to control access to users' personal information, fail to control inference - the inference of private information from public information. We envision an alternative protection model in which user profiles undergo sanitizing transformations before being released to third-party applications. Each transformation specifies an alternative view of the user profile. Unlike permission-based protection, this framework addresses the need for inference control. This work lays the theoretical foundation for view-based protection in three ways. First, existing work in privacy- preserving data publishing focuses on structured data (e.g., tables), but user profiles are semi-structured (e.g., trees). In information-theoretic terms, we define privacy and utility goals that can be applied to semi-structured data. Our notions of privacy and utility are highly targeted, mirroring the set up of social computing platforms, in which users specify their privacy preferences and third-party applications focus their accesses on selected components of the user profile. Second, we define an algebra of trees in which sanitizing transformations previously designed for structured data (e.g., generalization, noise introduction, etc) are now formulated for semi-structured data in terms of tree operations. Third, we evaluate the usefulness of our model by illustrating how the privacy enhancement and utility preservation effects of a view (a sanitizing transformation) can be formally and quantitatively assessed in our model. To the best of our knowledge, ours is the first work to articulate precise privacy and utility goals of inference control mechanisms for third-party applications in social computing platforms.
SLD Eligibility: A Users' Guide to the New Regulations.

© 2006 National Research Center on Learning Disabilities Students with specific learning disability (“SLD”) account for approximately half of all students that districts have determined to be eligible for special education under the Individuals with Disabilities Education Act (“IDEA”). Since the late 970s, the IDEA regulations have required states to use severe discrepancy—based on a comparison of the child’s academic achievement, typically based on a standardized test, with the child’s intellectual ability, typically based on an IQ test—as the primary but not exclusive criterion for determining SLD. The new regulations, issued in August 2006 to implement the 2004 amendments to the IDEA, provide significant changes in the determination of SLD eligibility. One of the new provisions concerns responsiveness to intervention (“RTI”), which is an alternate approach that starts with scientific, research-based instruction in general education and offers increasing levels of intervention based on continuous progress monitoring.