Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 1 Parallel Classification on Shared-Memory Systems

An important task of data mining can be thought of as the proce ss of assigning things to predefined categories or classes – a process called Classification. Since the classes are predefined this is also known as Supervised Induction. The input for the classification system consists of a set of e xample records, called a training set , where each record consists of several fields or attributes. Attributes are either continuous, coming from an ordered domain, or categorical , coming from an unordered domain. One of the attributes, cal led theclassifying attribute, indicates the classor label to which each example belongs. The induced model con sists of patterns that are useful in class discrimination. Once indu ce , the model can help in the automatic prediction of new unclassified data. Classification has been identified a s an important problem in the emerging field of data mining (Agrawal, Imielinski, & Swami 1993). It has im portant applications in diverse domains like retail target marketing, customer retention, fraud detect ion and medical diagnosis (Michie, Spiegelhalter, & Taylor 1994). Classification is a well-studied problem (see (Weiss & Kulik owski 1991; Michie, Spiegelhalter, & Taylor 1994) for excellent overviews) and several models have been proposed over the years, which include neural networks (Lippmann 1987), statistical models like line ar/quadratic discriminants (James 1985), decision trees (Breimanet al. 1984; Quinlan 1993) and genetic algorithms (Goldberg 1989) . Among these models, decision trees are particularly suited for data mining (Agr awal, Imielinski, & Swami 1993; Mehta, Agrawal, & Rissanen 1996). Decision trees can be constructed relativ ly fast compared to other methods. Another advantage is that decision tree models are simple and easy to understand (Quinlan 1993). Moreover, trees can be easily converted into SQL statements that can be used t o access databases efficiently (Agrawal et al. 1992). Finally, decision tree classifiers obtain similar, a nd sometimes better, accuracy when compared with other classification methods (Michie, Spiegelhalter, & Tay lor 1994). We have therefore focused on building scalable and parallel decision-tree classifiers. While there has been a lot of research in classification in the past, the focus had been on memoryresident data, thus limiting their suitability for mining o ver large databases. Recent work has targeted the
Automated design of pin-constrained digital microfluidic arrays for lab-on-a-chip applications

Microfluidics-based biochips, also referred to as lab-on-a-chip (LoC), are devices that integrate fluid-handling functions such as sample preparation, analysis, separation, and detection. This emerging technology combines electronics with biology to open new application areas such as point-of-care diagnosis, on-chip DNA analysis, and automated drug discovery. We propose a design automation method for pin-constrained LoCs that manipulate nanoliter volumes of discrete droplets on a microfluidic array. In contrast to the direct-addressing scheme that has been studied thus far in the literature, we assign a small number of independent control pins to a large number of electrodes in the LoC, thereby reducing design complexity and product cost. We apply the proposed method to a microfluidic array for a set of multiplexed bioassays
Analysis of Mutant SOD1 Electrophoretic Mobility by Blue Native Gel Electrophoresis; Evidence for Soluble Multimeric Assemblies

Mutations in superoxide dismutase 1 (SOD1) cause familial forms of amyotrophic lateral sclerosis (fALS). Disease causing mutations have diverse consequences on the activity and half-life of the protein, ranging from complete inactivity and short half-life to full activity and long-half-life. Uniformly, disease causing mutations induce the protein to misfold and aggregate and such aggregation tendencies are readily visualized by over-expression of the proteins in cultured cells. In the present study we have investigated the potential of using immunoblotting of proteins separated by Blue-Native gel electrophoresis (BNGE) as a means to identify soluble multimeric forms of mutant protein. We find that over-expressed wild-type human SOD1 (hSOD1) is generally not prone to form soluble high molecular weight entities that can be separated by BNGE. For ALS mutant SOD1, we observe that for all mutants examined (A4V, G37R, G85R, G93A, and L126Z), immunoblots of BN-gels separating protein solubilized by digitonin demonstrated varied amounts of high molecular weight immunoreactive entities. These entities lacked reactivity to ubiquitin and were partially dissociated by reducing agents. With the exception of the G93A mutant, these entities were not reactive to the C4F6 conformational antibody. Collectively, these data demonstrate that BNGE can be used to assess the formation of soluble multimeric assemblies of mutant SOD1.
Context-Aware News Recommender in Mobile Hybrid P2P Network

With the emergence of pervasive environment, mobile recommender needs to make use of user in-time contextual information to provide personalized recommendation. In this paper, a proactive context-aware news recommender in mobile hybrid P2P network is designed and implemented. We develop a general Analytic Hierarchy Process (AHP) model through empirical studies. We discuss how the relative weight of each AHP criteria can be computed via user assignment and user history. We combine both Contend-based filtering and Collaborative filtering approach to predict user interest using Bayesian Network. The experiments show the system can recommend real time news stories that satisfy the user.
Self‐managing work teams and their external leadership: A primer for library administrators

Purpose – This paper seeks to review the history, development and utilization of self‐managing work teams (SMWTs) in business and library work environments.Design/methodology/approach – Based on a review of both the relevant management and library literature, this paper broadens the library literature related to SMWTs with the objective of finding relevant insights applicable to the management of SMWTs in the library environment.Findings – The paper finds that the use of appropriately designed and implemented SMWTs has the potential to increase employee productivity and workplace satisfaction while reducing absenteeism and employee turnover. Organizational democracy and managerial support are the primary drivers of successful SMWTs. However, the use of SMWTs by library administrators appears to be underutilized.Research limitations/implications – While the organizational and administrative benefits associated with the use of SMWTs are clear, the reasons why library administrators have not used this tool m.
Robust feature detection and local classification for surfaces based on moment analysis

The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.
Marital quality and occurrence of the metabolic syndrome in women.

BACKGROUND Marital status is associated with the early stages and progression of cardiovascular disease, an association that may stem in part from the influence of marital quality on metabolic factors. The objective of this study was to examine whether women reporting marital satisfaction are at reduced risk of developing the metabolic syndrome compared with other women. METHODS Four hundred thirteen middle-aged women from the Pittsburgh Healthy Women Study completed measures of marital status and marital satisfaction at baseline and 3 years later. Metabolic syndrome (diagnosed according to the criteria of the National Cholesterol Education Program) was assessed at baseline and at the last follow-up visit (an average of 11.5 years later). RESULTS Compared with maritally satisfied women, maritally dissatisfied (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.46-6.24), divorced (OR, 2.47; 95% CI, 1.02-5.97), and widowed (OR, 5.82; 95% CI, 1.88-18.03) women were significantly more likely to have the metabolic syndrome at follow-up. The differences between maritally satisfied women and dissatisfied (OR, 3.18; 95% CI, 1.42-7.15) and widowed (OR, 5.69; 95% CI, 1.70-9.04) women remained significant in the full multivariate model. The difference between maritally satisfied women and divorced women (OR, 2.35; 95% CI, 0.89-6.18) was reduced to marginal significance in the full multivariate model. Single (OR, 2.84; 95% CI, 0.84-9.64) and moderately satisfied (OR, 1.06; 95% CI, 0.35-3.21) women did not differ significantly from maritally satisfied women. CONCLUSIONS Women in high-quality marriages are at lower risk of developing the metabolic syndrome. Social histories of patients should include assessment of marital quality.
Approximate Data Collection in Sensor Networks using Probabilistic Models

Wireless sensor networks are proving to be useful in a variety of settings. A core challenge in these networks is to minimize energy consumption. Prior database research has proposed to achieve this by pushing data-reducing operators like aggregation and selection down into the network. This approach has proven unpopular with early adopters of sensor network technology, who typically want to extract complete "dumps" of the sensor readings, i.e., to run "SELECT *" queries. Unfortunately, because these queries do no data reduction, they consume significant energy in current sensornet query processors. In this paper we attack the "SELECT " problem for sensor networks. We propose a robust approximate technique called Ken that uses replicated dynamic probabilistic models to minimize communication from sensor nodes to the network’s PC base station. In addition to data collection, we show that Ken is well suited to anomaly- and event-detection applications. A key challenge in this work is to intelligently exploit spatial correlations across sensor nodes without imposing undue sensor-to-sensor communication burdens to maintain the models. Using traces from two real-world sensor network deployments, we demonstrate that relatively simple models can provide significant communication (and hence energy) savings without undue sacrifice in result quality or frequency. Choosing optimally among even our simple models is NPhard, but our experiments show that a greedy heuristic performs nearly as well as an exhaustive algorithm.
New group key computation technique for secure group communication

With the growth of Internet, many applications are based on a group communication model where members of the group would like to communicate among themselves over a public channel securely. To carry out the group communication confidentially over a public channel, a group key is required. In this paper we propose a new group key computation technique for secure group communication. The scheme is based on contributory key agreement approach, allows the users to verify the consistency of the shares used for computing the group key, and is applicable to dynamic groups. The paper also addresses periodic group rekeying.
Partial Planning for Situated Agents based on Active Logic

This paper presents an investigation of rational agents tha have limited computational resources and intentionally interact with t heir environments. We present an example logical formalism, based on Active Log ic and Situation Calculus, that can be employed in order to satisfy the requirements arising due to being situated in a dynamic universe. We analy se how such agents can combine, in a time-aware fashion, inductive lear ning from experience and deductive reasoning using domain knowledge. In p articular, we consider how partial plans are created and reasoned about, f oc sing on what new information can be provided as a result of action executi on.
Collusion-resistant outsourcing of private set intersection

Set intersection is a building block for many data analysis techniques, e.g. in data mining. Private set intersection enables to compute the set intersection without revealing the non-matching items. The advent of cloud computing drives the desire to outsource such computations, but without the need to trust the service provider. Homomorphic encryption enables secure, outsourced computations, but in case of multiple clients cannot prevent collusion. In this paper we present non-interactive, encrypted computation of the set intersection using an untrusted service provider. Two or more clients submit their encrypted sets to the service provider which facilitates the computation of their intersection. The service provider either learns the intersection or remains completely obvious to both input and output - including the intersection's size. We prove our protocols secure in the random oracle model and under the RSA assumption. Our prototypical implementation shows the difference between the protocols using different cryptographic techniques and that even a fully untrusted service provider can be practically feasible.
Dynamic detection and removal of inactive clauses in SAT with application in image computation

In this paper, we present a new technique for the efficient dynamic detection and removal of inactive clauses, i.e. clauses that do not affect the solutions of interest of a Boolean satisfiability (SAT) problem. The algorithm is based on the extraction of gate connectivity information during generation of the Boolean formula from the circuit, and its use in the inner loop of a branch-and-bound SAT algorithm. The motivation for this optimization is to exploit the circuit structure information, which can be used to find unobservable gates at circuit outputs under dynamic conditions. It has the potential to speed up all applications of SAT in which the SAT formula is derived from a logic circuit. In particular, we find that it has considerable impact on an image computation algorithm based on SAT. We present practical results for benchmark circuits which show that the use of this optimization consistently improves the performance for reachability analysis, in some cases enabling the prototype tool to reach more states than otherwise possible.
Convolutional Neural Networks for Climate Downscaling

A key challenge in climate modeling is the assessment of the impact of global climate variables on regional weather measurements such as temperature and precipitation. This assessment is usually done by downscaling the output of (coarse resolution) global climate models to regional (high resolution) predictions. There are two independent downscaling pathways: dynamic and statistical. Dynamic downscaling uses high resolution physical models of regional climate in conjunction with global models to make regional climate predictions. Although dynamic downscaling methods account for regional geographic variations, they are computationally expensive even on modern supercomputers. Statistical downscaling algorithms, on the other hand, establish correlations between global model outputs and historical (decadal) weather observations, developing statistical models that translate global model output into regional weather projections. In recent times, many machine learning (ML) algorithms and statistical methods have been used for statistical downscaling, e.g., Bayesian frameworks, artificial neural networks, support vector machines, multilayer neural networks [4, 5, 7, 9] and ensemble methods (e.g., boosting, bagging and stacking). However, climate downscaling poses some open challenges such as: incomplete representation (and non-linearity) of physical processes in global models, non-stationarity in the global models and (hence) the relationships with regional observations, and computational complexity of processing large Petabyte-scale datasets. Many existing ML algorithms are limited in their ability to model complex functions with many variations that represent deep relationships between input and output variables [1]. Research in ML shows that architectures designed to model complex functions need to be of sufficient depth (i.e., maximum length of path from any input to any output) to prevent poor generalization [2]. Many ML algorithms do not correspond to deep architectures, e.g., under certain assumptions, decision trees have two levels, logistic regression has one, kernel machines have two levels and ensemble methods add one level to the base learner [1]. Even in multilayered neural networks, learning algorithms perform poorly when complex functions require (error) gradients to be propagated across many levels. Another limitation of many ML algorithms is the use of local estimators, i.e., input space is partitioned into regions and the degrees of freedom in the underlying model are used to account for variations of the target function in these regions. As a result, algorithms such as decision-trees and local kernel machines (e.g., Gaussian processes and manifold learning algorithms) may not generalize well for complex functions with many variations [3].
Number-theoretic constructions of efficient pseudo-random functions

We describe efficient constructions for various cryptographic primitives (both in private-key and in public-key cryptography). We show these constructions to be at least as secure as the decisional version of the Diffie-Hellman assumption or as the assumption that factoring is hard. Our major result is a new construction of pseudo-random functions such that computing their value at any given point involves two multiple products. This is much more efficient than previous proposals. Furthermore, these functions have the advantage of being in TC/sup 0/ (the class of functions computable by constant depth circuits consisting of a polynomial number of threshold gates) which has several interesting applications. The simple algebraic structure of the functions implies additional features. In particular, we show a zero-knowledge proof for statements of the form "y=f/sub s/(x)" and "y/spl ne/f(x)" given a commitment to a key s of a pseudo-random function f/sub s/.
How Useful are the Strategic Tools We Teach in Business Schools?

Strategic tools are indispensible for business and competitive analysis. Yet we know very little about managers' internal logic as they put these tools into practical use. We situate our study in a business school context using action learning prior to the manifestation of practice to complement our understanding of practice. Using Personal Construct Theory and Repertory Grids, our mid-range theorizing showed that, contrary to current thinking about strategic tools, managers think in dualities (often paradoxically) and have a preference for multiple-tools-in-use, tools that provide different perspectives, peripheral vision, connected thinking, simultaneously help differentiate and integrate complex issues, and guide the thinking process. These findings are important for designing better tools and the nurturing of critical managerial competencies needed for a complicated world. Our study's focus also has wider implications for scholars as we see our own material evaluated by those who will put these lessons into practice.
Recovering IT in a Disaster: Lessons from Hurricane Katrina

On August 29, 2005, Hurricane Katrina destroyed a data center and much of the communications infrastructure at the Pascagoula and Gulfport, Mississippi, operations of the Ship Systems sector of Northrop Grumman Corporation. Simultaneously, the hurricane put a second data center out of commission in a shipyard near New Orleans. The storm disrupted the lives of the sector’s 20,000 employees and their dependents located in the Gulf Coast, caused over US$1 billion in damage for the company, and, for two weeks, brought two of the nation’s largest shipyards to a standstill.2
De Jong’s Sphere Model Test for a Human Community Based Genetic Algorithm Model (HCBGA)

A new structured population approach for genetic algorithm, based on the custom, behavior and pattern of human community is provided. This model is named the Human Community Based Genetic Algorithm (HCBGA) model. It includes gender, age, generation, marriage, birth and death. Using the De Jong’s first function 1, “The Sphere Model” comparisons between values and results concerning the averages and best fits of both, the Simple Standard Genetic Algorithm (SGA), and the Human Community Based Genetic Algorithm (HCBGA) model are obtained. These results are encouraging in that the Human Community Based Genetic Algorithm (HCBGA) model performs better in finding best fit solutions of generations in different populations than the Simple Standard Genetic Algorithm. The HCBGA model is an evolution of the simple Genetic Algorithm (SGA). The result of this paper is an extended of the result concerning algorithm in [6].
An Experimental Study to Investigate the Use of Additional Classifiers to Improve Information Extraction Accuracy

In this paper, we present an information extraction system and investigate the use of additional classifiers to help improve information extraction performance. We propose a simple idea of training an additional classifier using the same feature configurations on another corpus and then using this new classifier to classify the original dataset. The classification result of this new classifier is then used as a feature to the original classifier. We tested this approach on the CMU seminar announcements and the Austin job posting datasets and obtained results better than all previously reported systems.
The prospect of using thin oxides for silicon nanotransistors

We scale down transistors in each successive technology generation to increase circuit speed and to improve packing density. However, as scaling continues beyond the 0.1 /spl mu/m technology generation, concerns about three issues, reliability, leakage and mobility degradation, must be addressed. This paper summarizes the current understanding regarding these issues, and identifies areas on which device scaling research could focus.
ATLANTIS Trial: Results for Patients Treated Within 3 Hours of Stroke Onset

Background and Purpose— Only a single study has demonstrated beneficial effects of intravenous tissue plasminogen activator (tPA) in stroke patients. Methods— We evaluated the clinical outcomes of the 61 patients enrolled in the Alteplase Thrombolysis for Acute Noninterventional Therapy in Ischemic Stroke (ATLANTIS) study who were randomized to receive intravenous tPA or placebo within 3 hours of symptom onset. Results— Despite a significant increase in the rate of symptomatic intracranial hemorrhage, tPA-treated patients were more likely to have a very favorable outcome (score of ≤1) on the National Institutes of Health Stroke Scale at 90 days (P =0.01). Conclusions— These data support current recommendations to administer intravenous tPA to eligible ischemic stroke patients who can be treated within 3 hours of symptom onset.