Miracle-dz/UniFAR-FTUs huggingface dataset:
VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data

We present VisIRR, an interactive visual information retrieval and recommendation system for large-scale document data. Starting with a query, VisIRR visualizes the retrieved documents in a scatter plot along with their topic summary. Next, based on interactive personalized preference feedback on the documents, VisIRR collects and visualizes potentially relevant documents out of the entire corpus so that an integrated analysis of both retrieved and recommended documents can be performed seamlessly.
Towards A General Method for Building Predictive Models of Learner Success using Educational Time Series Data

This paper presents a pedagogical and instructional-technology general method for building predictive models for education from time series log data. While it is common for models of learner achievement to include cognitive features, we instead are data mining only resource accesses in the learning environment. This has benefits in that the approach is inherently scalable to new contexts due to its data driven nature. While we have only just begun to apply these methods to our institutional Massive Open Online Course (MOOC) data, it shows promise as both a descriptive modeling technique as well as an engine for creating predictive early alerts.
A linear programming approach to network utility maximization

This paper describes a linear programming (LP) approach for solving the network utility maximization problem. The developed approach is inspired by a convex relaxation technique from non-convex polynomial optimization methods. In contrast to most of the existing results where concavity of the network's utility function is often assumed, the proposed LP approach may still be used to solve the NUM problem even in the absence of such a concavity assumption. Although the presented LP approach is originally formulated to compute upper bounds for the global optima of the NUM problem, we illustrate through simulation examples that the obtained bounds often correspond to the exact global optima.
A retrieval technique for similar shapes

We propose an organization for aclatabase of objects that permits the efficient retrieval of all objects in the database with a shape similar to a search template. The retrieval technique wepropse isrobust indtepresence of noise, and can handle several different notions of similarity inchrding changes in scale, position, and even relative sizes of components. We can thus have the computer reproduce, on a huge database of images, the process performed by a human in ‘ ‘riffling” through a book, using an index structure to retrieve likely candidates quickly.
Structural Graphical Lasso for Learning Mouse Brain Connectivity

Investigations into brain connectivity aim to recover networks of brain regions connected by anatomical tracts or by functional associations. The inference of brain networks has recently attracted much interest due to the increasing availability of high-resolution brain imaging data. Sparse inverse covariance estimation with lasso and group lasso penalty has been demonstrated to be a powerful approach to discover brain networks. Motivated by the hierarchical structure of the brain networks, we consider the problem of estimating a graphical model with tree-structural regularization in this paper. The regularization encourages the graphical model to exhibit a brain-like structure. Specifically, in this hierarchical structure, hundreds of thousands of voxels serve as the leaf nodes of the tree. A node in the intermediate layer represents a region formed by voxels in the subtree rooted at that node. The whole brain is considered as the root of the tree. We propose to apply the tree-structural regularized graphical model to estimate the mouse brain network. However, the dimensionality of whole-brain data, usually on the order of hundreds of thousands, poses significant computational challenges. Efficient algorithms that are capable of estimating networks from high-dimensional data are highly desired. To address the computational challenge, we develop a screening rule which can quickly identify many zero blocks in the estimated graphical model, thereby dramatically reducing the computational cost of solving the proposed model. It is based on a novel insight on the relationship between screening and the so-called proximal operator that we first establish in this paper. We perform experiments on both synthetic data and real data from the Allen Developing Mouse Brain Atlas; results demonstrate the effectiveness and efficiency of the proposed approach.
Isolation and production of bacteriocin by marine Lactobacillus fermentum SBS001

Bacteriocins are polypeptides, with bactericidal or bacteriostatic activity, against those bacteria which are closely related to the producer strain (Klaenhammer, 1998 and Tagg et al., 1976). Bacteriocins are highly specific antibacterial proteins produced by strains of bacteria active mainly against some other strains of same or related species (Gaur et al., 2004). The bacteriocins produced by gram positive bacteria, in particular, the lactic acid bacteria display fairly broad inhibitory spectra with food preservative (Galvez et al., 2008) and therapeutic (Jack et al., 1995) potentials. Bacterial strains with antimicrobial activity play an important role in the food industry, agriculture and pharmaceutical industry. Different bacteriocin exhibits ISSN: 2319-7706 Volume 2 Number 4 (2013) pp. 67-73 http://www.ijcmas. com
Interactive virtual angioscopy

Virtual angioscopy is a non invasive medical procedure for exploring parts of the human vascular system. We have developed an interactive tool that takes as input, data acquired with standard medical imaging modalities and regards it as a virtual environment to be interactively inspected. The system supports real time navigation with stereoscopic direct volume rendering and dynamic endoscopic camera control, interactive tissue classification, and interactive point picking for morphological feature measurement. We provide an overview of the system, discuss the techniques used in our prototype, and present experimental results on human data sets.
Critical-PMOS-aware clock tree design methodology for anti-aging zero skew clock gating

Due to clock gating, the PMOS transistors in the clock tree often have different active probabilities, which lead to different NBTI delay degradations. To ensure that the clock skew is always zero, there is a demand to eliminate the degradation difference. In this paper, we present a critical-PMOS-aware clock tree design methodology to deal with this problem. First, we prove that, under the same tree topology, the NAND-type-matching clock tree has the minimum number of critical PMOS transistors. Then, we propose a 0–1 ILP (integer linear programming) approach to minimize the power consumption overhead while eliminating the degradation difference. Benchmark data consistently show that our design methodology can achieve very good results in terms of both the clock skew (due to the degradation difference) and the power consumption overhead.
The Entropy Power Inequality and Mrs. Gerber's Lemma for Abelian Groups of Order 2^n

Shannon's Entropy Power Inequality can be viewed as characterizing the minimum differential entropy achievable by the sum of two independent random variables with fixed differential entropies. The entropy power inequality has played a key role in resolving a number of problems in information theory. It is therefore interesting to examine the existence of a similar inequality for discrete random variables. In this paper we obtain an entropy power inequality for random variables taking values in an abelian group of order 2^n, i.e. for such a group G we explicitly characterize the function f_G(x,y) giving the minimum entropy of the sum of two independent G-valued random variables with respective entropies x and y. Random variables achieving the extremum in this inequality are thus the analogs of Gaussians in this case, and these are also determined. It turns out that f_G(x,y) is convex in x for fixed y and, by symmetry, convex in y for fixed x. This is a generalization to abelian groups of order 2^n of the result known as Mrs. Gerber's Lemma.
DAPA-V10: Discovery and Analysis of Patterns and Anomalies in Volatile Time-Evolving Networks

We address the problems of finding patterns and detecting anomalous activities in volatile time-evolving networks such as communication networks (as opposed to slowly evolving networks like co-authorship graphs). Our approach, DAPA-V10, utilizes a simple compact graph representation that assigns weights to edges in a way that captures the frequency, duration, and recency of edges. Given this weighted “cumulative” graph, DAPA-V10 finds persistent patterns by extracting connected components of regularly-occurring edges. These persistent patterns provide a basis for expected normal behavior in the network over time, which are then utilized to detect anomalous behavior on both local and global scales. In particular, DAPA-V10 uses a scalable approach based on the Product Rule for the Central Limit Theorem to measure the likelihood of events and flag anomalies. Figure 1 provides an overview of DAPA-V10. Experiments on the Enron email dataset illustrate the effectiveness of our approach.
Recognising suicidal messages in Dutch social media

Early detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the automatic recognition of suicidal messages in social media. We present experiments for automatically detecting relevant messages (with suicide-related content), and those containing suicide threats. A sample of 1357 texts was annotated in a corpus of 2674 blog posts and forum messages from Netlog, indicating relevance, origin, severity of suicide threat and risks as well as protective factors. For the classification experiments, Naive Bayes, SVM and KNN algorithms are combined with shallow features, i.e. bag-of-words of word, lemma and character ngrams, and post length. The best relevance classification is achieved by using SVM with post length, lemma and character ngrams, resulting in an F-score of 85.6% (78.7% precision and 93.8% recall). For the second task (threat detection), a cascaded setup which first filters out irrelevant messages with SVM and then predicts the severity with KNN, performs best: 59.2% F-score (69.5% precision and 51.6% recall).
Detecting Web Pages with Events

The semantic web is a concept being promoted by the World Wide Web Consortium, with the vision of allowing machines to “understand” web‐pages, without maintaining scrapers of human‐visible HTML. This structured markup in web‐pages have recently been powering features in Google Search, such as Rich Snippets. The project purpose is detecting web pages that contain information about events, based on the main text in the page. Thus making it possible to reach the websites’ owners, suggesting they add structured data for the events in their site.
Efficient opacity specification based on feature visibilities in direct volume rendering

Due to 3D occlusion, the specification of proper opacities in direct volume rendering is a time‐consuming and unintuitive process. The visibility histograms introduced by Correa and Ma reflect the effect of occlusion by measuring the influence of each sample in the histogram to the rendered image. However, the visibility is defined on individual samples, while volume exploration focuses on conveying the spatial relationships between features. Moreover, the high computational cost and large memory requirement limits its application in multi‐dimensional transfer function design.
Coupled simulations of solid rocket motors

We describe simulations of solid rocket motors that involve coupling between the core fluid flow, the structural response of the propellant and case, and the combustion of the propellant. A partitioned predictorcorrector algorithm is employed to treat the fluidstructure interaction. The combustion rate of the propellant is coupled to the fluid flow via an empirical power law relationship. Our algorithm couples the physical processes involved using a partitioned approach, enabling us to use existing codes to perform the bulk of our simulations. We give special consideration to the jump conditions that hold at the fluid-structure-combustion interface, and specialize them for the early burn phase. The interface between the eroding solid and the fluid is treated using an ALE formulation, which provides a consistent technique for handling the eroding solid. Data are presented that demonstrate the parallel performance of our code on a variety of architectures. Results from simulations of the space shuttle solid rocket motor demonstrate the applicability of our approach. Future extensions of the simulation capability to include thermal effects, turbulence and material failure will be discussed.
KEEP SLS: An Enhanced ePortfolio System Supporting Constructive Social Learning Activities

This paper provided a new approach of bridging Web 2.0 technologies and traditional ePortfolio systems to enhance educational benefits to individual users as well as learning community. Reasons for promoting constructive social learning in education were analyzed. Related design problems in current elearning systems were discussed. The design and development processes for a social learning enhanced ePortfolio system, i.e., KEEP Social Learning Suite (SLS), were illustrated.
Image Restoration with Compound Regularization Using a Bregman Iterative Algorithm

Some imaging inverse problems may require the solution to simultaneously exhibit properties that are not enforceable by a single regularizer. One way to attain this goal is to use a linear combinations of regularizers, thus encouraging the solution to simultaneously exhibit the characteristics enforced by each individual regularizer. In this paper, we address the optimization problem resulting from this type of compound regularization using the split Bregman iterative method. The resulting algorithm only requires the ability to efficiently compute the denoising operator associated to each involved regularizer. Convergence is guaranteed by the theory behind the Bregman iterative approach to solving constrained optimization problems. In experiments with images that are simultaneously sparse and piece-wise smooth, the proposed algorithm successfully solves the deconvolution problem with a compound regularizer that is the linear combination of the `1 and total variation (TV) regularizers. The lowest MSE obtained with the (`1+TV) regularizer is lower than that obtained with TV or `1 alone, for any value of the corresponding regularization parameters.
Scalable, Fuzzy, Multiclassifier System

This paper deals with the design of scalable, fuzzy adaptive, decision making systems constructed from sets of heterogeneous classiflers. We propose a multiclassifler architecture formed by sparsely connected coalitions of classiflers. Coalitions are deflned by fuzzy integral operators on small, but not necessarily disjoint, subsets of classiflers. The small size constraint on individual coalitions is intended for both the interpretability of fuzzy measures and low complexity of fuzzy integral operators. In addition, the sparse connection constraint guarantees realizable good independent coalitions amenable to further information fusion stages. Simple rules regarding the number of coalitions, the number of classiflers per coalition and the number of coalitions where each classifler should participate are presented. Experimental results show the feasibility of our proposal.
Toward Globalization: Construct Validation of Global Identity Scale in a Nigerian Sample

Two studies examined the construct validity of the 10-item Global Identity Scale (GIS-10) developed by Turken & Rudmin (2013) in a Nigerian sample. The first study utilizes exploratory factor analysis (EFA), internal consistency analysis (Cronbach’s alpha), divergent validity, and confirmatory factor analysis (CFA) to answer the research questions about the factorial validity and utility of the scale in a Nigerian sample (n = 300 undergraduates). The second study provided additional evidence for the internal consistency, concurrent, divergent, and predictive validity of the scale (n = 90 undergraduates). The results of Study 1 showed that two major factors emerged from factor loading (openness to culture and non-nationalism). The Cronbach’s alpha(s) (total and sub factors) reached acceptable range and the CFA parameters: Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI) and Non-Normed Fit Index (NNFI) also reached acceptable ranges. Study 2 results provided evidence for concurrent, divergent, and predictive validity of the GIS-10 when correlated with openness to experience and pro-social personality. Discussion of the findings, includes their limitations, suggestions for further studies and implications.
Fuzzified neural network based human condition monitoring using a small flexible monitoring device

For maintaining our daily healthcare, we need to understand our own physical condition. In understanding such data, additional information such as what the subject is doing at that time is needed. For example, let us assume that we have a record of a certain heart rate 90. If such value is observed when the subject person was sleeping, that value is high and the subject may have some trouble on his/her health. On the other hand, when the subject was running, the subject has no problem on his/her health. In this paper, we propose a combined system for maintaining our healthcare. Our proposed system consists of both systems; (1) a fuzzified neural network based unusual condition detection and (2) a standard neural network based action estimation. The proposed system can handle multiple kinds of sensors' data. In this paper, the following three kinds of sensors were handled; (1) three-axis acceleration data, (2) heart rate, and (3) breathing rate. From experimental results, the effectiveness of our proposed system is shown for understanding our conditions.
Modeling Cohesiveness and Enmity for Signed Social Communities

Social interactions can exhibit either positive or negative linkages, resulting in the abstraction of “signed graphs” or “signed social networks. ” The social balance theory implies that a signed social network tends to converge to a balanced state where all individuals are divided into two cohesive groups that are antagonistic to each other. In this paper, we take this implication as a premise, and model the cohesiveness within each community and the enmity between two communities. We evaluate our model using synthetic signed networks, online signed social networks, and the U.S. Senate Voting network, and demonstrate its effectiveness in modeling the cohesiveness and enmity.
A Self-Organizing Lookup Service for Dynamic Ambient Services

The provisioning of ambient services is gaining importance as users become more and more embedded in environments that are saturated with electronic devices. In our previous work, we have proposed the ad hoc service grid (ASG) approach as a means for deliberately setting up an infrastructure for providing ambient services at medium-sized locations like shopping malls. In this paper, we introduce a self-repairing lookup service architecture that is able to handle dynamic services in an AGS. These services can autonomously replicate and migrate within an ASG to optimize resource usage and message latency. Our lookup service is able to cope with the inconsistencies caused by service migrations efficiently by applying a lazy, request-driven update protocol. We discuss the architecture and the protocols employed by the lookup service. Moreover, we provide experimental results that show the efficiency and effectiveness of our approach
Content based social behavior prediction: a multi-task learning approach

Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a "microeconomics" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r. t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.
Continuous wearable health monitoring using novel PPG optical sensor and device

We are presenting a novel photoplethysmographic (PPG) optical sensor and device with ambient optical, electrical and electromagnetic noises cancellation, thus allowing only the useful optical signals to be received by the health monitoring device. We are also disclosing a new processing technique for canceling the ambient noises contributed by optical, electrical and electromagnetic artifacts in the measured PPG signals. Such a device and method allow the enhancement of the performance of the PPG sensors compared to conventional apparatus and methods. The presented sensor and methodology have been integrated into a prototype device for noninvasive, continuous, wearable, remote and mobile monitoring of human vital signs, such as heart rate, oxygen saturation, blood pressure, respiration rate, etc. This small device allows the user to read, store, process and transmit all the measurements made using the PPG optical sensor and the electronic unit to a remote location.