Miracle-dz/UniFAR-FTUs huggingface dataset:
ALBU: An approximate Loopy Belief message passing algorithm for LDA to improve performance on small data sets

Variational Bayes (VB) applied to latent Dirichlet allocation (LDA) has become the most popular algorithm for aspect modeling. While sufficiently successful in text topic extraction from large corpora, VB is less successful in identifying aspects in the presence of limited data. We present a novel variational message passing algorithm as applied to Latent Dirichlet Allocation (LDA) and compare it with the gold standard VB and collapsed Gibbs sampling. In situations where marginalisation leads to non-conjugate messages, we use ideas from sampling to derive approximate update equations. In cases where conjugacy holds, Loopy Belief update (LBU) (also known as Lauritzen-Spiegelhalter) is used. Our algorithm, ALBU (approximate LBU), has strong similarities with Variational Message Passing (VMP) (which is the message passing variant of VB). To compare the performance of the algorithms in the presence of limited data, we use data sets consisting of tweets and news groups. Additionally, to perform more fine grained evaluations and comparisons, we use simulations that enable comparisons with the ground truth via Kullback-Leibler divergence (KLD). Using coherence measures for the text corpora and KLD with the simulations we show that ALBU learns latent distributions more accurately than does VB, especially for smaller data sets.
Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification

Cervical cancer is the fourth most common category of cancer, affecting more than 500,000 women annually, owing to the slow detection procedure. Early diagnosis can help in treating and even curing cancer, but the tedious, time-consuming testing process makes it impossible to conduct population-wise screening. To aid the pathologists in efficient and reliable detection, in this paper, we propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer. The main concern in developing an automatic detection tool for biomedical image classification is the low availability of publicly accessible data. Ensemble Learning is a popular approach for image classification, but simplistic approaches that leverage pre-determined weights to classifiers fail to perform satisfactorily. In this research, we use the Sugeno Fuzzy Integral to ensemble the decision scores from three popular pretrained deep learning models, namely, Inception v3, DenseNet-161 and ResNet-34. The proposed Fuzzy fusion is capable of taking into consideration the confidence scores of the classifiers for each sample, and thus adaptively changing the importance given to each classifier, capturing the complementary information supplied by each, thus leading to superior classification performance. We evaluated the proposed method on three publicly available datasets, the Mendeley Liquid Based Cytology (LBC) dataset, the SIPaKMeD Whole Slide Image (WSI) dataset, and the SIPaKMeD Single Cell Image (SCI) dataset, and the results thus yielded are promising. Analysis of the approach using GradCAM-based visual representations and statistical tests, and comparison of the method with existing and baseline models in literature justify the efficacy of the approach.
Habitat Characteristic of Pongo pygmaeus morio in Prevab Area, Kutai National Park, Borneo, Indonesia

The population of Bornean Orangutan now isolated in many habitat fragments. Prevab area in the Kutai National Park (Prevab KNP) is one of the few important natural habitats of Pongo pygmaeus morio remaining in East Kalimantan. Botanical assessments such as floristic composition, forest structure, and food sources availability studies are essential in view of their value in understanding the extent of the forest as an orangutan habitat and ecosystems. In this study, we investigate tree species composition, structure of forest stand, and distribution of orangutan food trees in the Prevab KNP. This study was carried out in the Prevab area (Orangutan Research Station in the Kutai National Park), East Kutai, East Kalimantan from October 2013 to September 2014. The structure data collection used the botanical plot of 0.6 ha and 1 ha for tree species composition. We were determined the number, total height, diameter of breast high, height of clear bole, the height of maximum crown width, and the crown width of all tree species. -----------------------------------------------------------------------* Corresponding author. International Journal of Sciences: Basic and Applied Research (IJSBAR)(2016) Volume 30, No 3, pp 8-20 9 Food trees were determined based on the direct observation, the information of community and staff of national parks, observed the after-eating signs of the orangutan, and the works of literature study. Important Value Index is the pattern of calculation used to decide the dominant vegetation ecologically in the study area. The structure of forest stand was described in the form of architecture profile (horizontal and vertical). The food trees describe in the form of distribution map. Tree species (dbh ≥5 cm) found in the botanical plot represented at least 148 species, 85 genera, and 43 families. The study plots are dominated by Cananga odorata. The trees density with ≥5 cm dbh was 864 trees/ha, the average dbh of ±16 cm, the total basal area of ±28.68 m/ha. They vary greatly in species and dimension. There were 5 layers of the canopy in Prevab KNP. The food trees density was ±376 trees/ha, at least 376 food trees/ha, the number of trees wich producing fruit or flower every month less than 6%. 65% of the total number of trees with dbh ≥10 cm is the potential food resources for the orangutan. The Prevab KNP is the good habitat for orangutan if seen from the aspect of either structure, vegetation composition, or food resources. The combination of these three factors may have increased the habitat quality for orangutans in the Prevab KNP.
Performance Instrumentation and Compiler Optimizations for MPI / OpenMP Applications ⋆

This article describes how the integration of the OpenUH OpenMP compiler with the KOJAK performance analysis tool can assist developers of OpenMP and hybrid codes in optimizing their applications with as little user intervention as possible. In particular, we (i) describe how the compiler’s ability to automatically instrument user code down to the flow-graph level can improve the location of performance problems and (ii) outline how the performance feedback provided by KOJAK will direct the compiler’s optimization decisions in the future. To demonstrate our methodology, we present experimental results showing how reasons for the performance slow down of the ASPCG benchmark could be identified.
Emerging antiplatelet agents, differential pharmacology, and clinical utility

The aspirin–clopidogrel combination is the current gold standard antiplatelet regimen following percutaneous coronary intervention and for the treatment of acute coronary syndrome. Despite the clinical benefit of this combination, patients continue to have vascular events. Another purinergic (P2Y12) receptor antagonist, prasugrel, became available last year. Although prasugrel is superior to clopidogrel in reducing clinical endpoints, a higher bleeding rate has been identified particularly in high-risk patients. Ticagrelor, a reversible P2Y12 receptor antagonist currently being evaluated for approval, is also more potent than clopidogrel but has a similar bleeding risk. Two additional P2Y12 antagonists are being investigated that will be available as an intravenous formulation. Apart from the P2Y12 receptor antagonists, multiple other agents are being developed with unique mechanisms of platelet inhibition. These agents are being studied as an alternative to or in combination with clopidogrel. The antiplatelet agents currently under development include: thrombin receptor antagonists, phosphodiesterase inhibitors, a thromboxane–prostaglandin receptor antagonist, a serotonin receptor blocker, a platelet adhesion antagonist, nitric oxide-releasing aspirin, a glycoprotein VI antagonist, and a cyclooxygenase inhibitor. The purpose of this review is to describe the efficacy and safety profiles of the emerging antiplatelet agents and their role in the treatment of atherosclerotic cardiovascular diseases.
Novel Verification Techniques for ARM A15 Multi-core Subsystem Using IEEE 1647

— Modern designs are becoming bigger and more complex, yielding revolutionary and evolutionary products in the user space. This trend manifests in longer DV cycles and tougher challenges. Specman/e based eRM provides an elegant solution for handling these issues efficiently. Here we present our testbench methodology and the application of Specman/e in a ARM® Cortex® -A15 multi-core subsystems verification effort.
Sensitivity of Barnes Ice Cap, Baffin Island, Canada, to climate state and internal dynamics

Barnes Ice Cap is a remnant of the Laurentide Ice Sheet, which covered much of northern North America during the Last Glacial Maximum. Barnes reached a quasi‐equilibrium state ~2000 years ago and has remained similar in size since then, with a small increase during the Little Ice Age. In this study, we combine historical observations (1960–1980) with more recent satellite and airborne data (1995–2010) to drive a mass balance model coupled to a transient thermomechanical model with an adaptive mesh geometry. The model is used to characterize the current state of the ice cap and to investigate its stability as a function of climate and its own internal dynamics. On millennial time scales we show that ice flow is influenced by adjustment of an unsteady shape, by gently sloping bedrock, and by contrasting viscosities between the Pleistocene and Holocene ice. On shorter time scales, Barnes is affected by surge activity. Sensitivity tests reveal that Barnes experienced climate conditions which enabled its stability 2000 to 3000 years ago but will disappear under current climate conditions in the next millennium.
International Journal of Advanced Research in Computer Science and Software Engineering

— Cloud computing can be defined as a new style of computing in which dynamically scalable and often virtualized resources are provided as services over the internet. With cloud computing technology, users use a variety of devices, including PCs, laptops, smart phones, and PDAs to access programs, storage, and application-development platforms over the Internet, via services offered by cloud computing providers. Advantages of cloud computing technology include cost savings, high availability, and easy scalability. Cloud computing moves the application software and data bases to the large data centres, where administration of the data and services may not be fully trustworthy. This unique aspect, however, poses many new security challenges which have not been well understood. In this article, we focus on cloud storage security, which has always been important feature of quality of service. To ensure the correctness of users data in the cloud, we suggest an effective and flexible technique for managing the data storage technology in the secure way. By utilizing the PMAR homorphic encryption algorithm with dispersed authentication of erasure coded data , our scheme achieves the incorporation of storage correctness insurance and data error localization. i.e, the identification of misbehaving server.
Efficient image-based methods for rendering soft shadows

We present two efficient imaged-based approaches for computation and display of high-quality soft shadows from area light sources. Our methods are related to shadow maps and provide the associated benefits. The computation time and memory requirements for adding soft shadows to an image depend on image size and the number of lights, not geometric scene complexity. We also show that because area light sources are localized in space, soft shadow computations are particularly well suited to imaged-based rendering techniques. Our first approach—layered attenuation maps—achieves interactive rendering rates, but limits sampling flexibility, while our second method—coherence-based raytracing of depth images—is not interactive, but removes the limitations on sampling and yields high quality images at a fraction of the cost of conventional raytracers. Combining the two algorithms allows for rapid previewing followed by efficient high-quality rendering.
Constant-Delay Enumeration for Nondeterministic Document Spanners

One of the classical tasks in information extraction is to extract subparts of texts through regular expressions. In the database theory literature, this approach has been generalized and formalized as document spanners. In this model, extraction is performed by evaluating a particular kind of automata, called a sequential variable-set automaton (VA). The efficiency of this task is then measured in the context of enumeration algorithms: we first run a preprocessing phase computing a compact representation of the answers, and second we produce the results one after the other with a short time between consecutive answers, called the delay of the enumeration. Our goal is to have an algorithm that is tractable in combined complexity, i.e., in the sizes of the input document and the VA, while ensuring the best possible data complexity bounds in the input document size, i.e., a constant delay that does not depend on the document. We present such an algorithm for a variant of VAs called extended sequential VAs and give an experimental evaluation of this algorithm.
Transduction with Con(cid:12)dence and Credibility

In this paper we follow the same general ideology as in [Gammerman et al., 1998], and describe a new transductive learning algorithm using Support Vector Machines. The algorithm presented provides con(cid:12)dence values for its predicted classi(cid:12)cations of new examples. We also obtain a measure of \credibility" which serves as an indicator of the reliability of the data upon which we make our prediction. Experiments compare the new algorithm to a standard Support Vector Machine and other trans-ductive methods which use Support Vector Machines, such as Vapnik's margin transduction. Empirical results show that the new algorithm not only produces con(cid:12)dence and credibility measures, but is comparable to, and some-times exceeds the performance of the other algorithms.
Increasing autonomy of UAVs

This article has presented a tightly integrated systems architecture for a decentralized CSAT mission management algorithm and demonstrated successful implementation in actual hardware flight tests. This CSAT architecture allows each UAV to accomplish a combined search and track mission by conceptualizing the searching aspect as a spare time strategy to be executed optimally over a short time horizon when the agents are not actively tracking a vehicle. This presented a balance between the two conflicting search and track modes and allowed the mission to achieve more than simply searching or tracking alone.
Toward Hypermedia Support for Information Relationship Management

The overall research goal of this project is to provide hypermedia functionality to information systems (ISs) not enhanced with hypermedia with minimal or no changes to the ISs. ISs dynamically generate their contents and thus require some mapping mechanism to automatically map the generated contents to hypermedia constructs (nodes, links and link markers) instead of hypermedia links being hardcoded over static contents. No systematic approach exists, however, for identifying information relationships and building mapping rules to infer useful links that give users direct access to the ISs’ primary functionality, give access to meta-information about IS objects, give access to relationships among information objects and enable annotation and ad hoc (user-declared) linking. This paper contributes a procedure for analyzing ISs and building mapping rules that supplement ISs with hypermedia support, which results in new ways to view and manage the IS’s knowledge and information relationships.
Artificial intelligence opportunities and an end-do-end data-driven solution for predicting hardware failures

@ 2016 Mario Orozco Gabriel. All rights reserved. The author herby grants MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in w oleor-in part in any medium now or hereafter created. Signature redacted Signature of Author . re acted Department of M nical Engineering and MIT Sloan School of Management May 6, 2016 C tifiA b Signature redacted e r e y . Kalyan Veeramachaneni, Thesis Supervisor Principal Research Scientist in Laboratory for Information and Decision Systems Signature redacted C e rtifie d b y . ,. I . I. Tauhid Zaman, Thesis Supervisor KDD Career Development Professor in Communications and Technology Signature redacted I . John J. Leonard, Mechanical Engineering Reader Samuel C. Collins Professor of Mechanical and Ocean Engineering Uigntu re redac UtedA ccep ted b y . Rohan Abeyaratne, Chairman of the Committee of Graduate Students Department / Mechanical Engineering Accepted by . Signature redacted . MASSACHUSES INSTITUTE Maura Herson, Director of MIT Sloan MBA Program I OFTECHNOLOGY . MIT Sloan School of Management
Learning through network interaction : the potential of ego-networks

This study deals with online personal social networks (i.e., ego-networks) of youth 12-18 years old, in the Netherlands and investigates if and how these networks operate with respect to learning. The online ego-networks of youth, and the potential these networks have for learning, are largely unexplored. What kinds of resources do youth have access to through their networks? With whom do they connect? How can we characterize these relations in terms of the frequency they meet online and offline, emotional closeness, topics of conversation, and geographical dispersion of contacts? What kinds of networks provide learning experiences? How can we predict these networks? This study describes in detail the characteristics of these ego-networks. Furthermore, we tested the claim that learning in online networks is a likely result of frequent network activity. Particularly we questioned if popular social network activities such as sharing links, giving feedback and editing or creating artefacts together online would be related to the discovery of new information. With a multi-level analysis model we were able to differentiate the individual influences and the influence of their egonetworks on the frequency of discovering new information and overall network activity. The results showed that these network activities strongly and positively predicted discovery of new information. With respect to the people with whom youth construct their networked communities, the study show that youth connects online primarily with contacts who are similar, who live close by and who are emotionally close. In contrast to claims in the literature in which innovation and learning is associated with heterogeneous contacts, these results show that youth chooses homogeneous, emotionally close and locally based online relationships to explore their interests, to relate to and to discover new information together. A possible explanation may be that in this age group, youth are still fostering the ties to their immediate community and that being accepted and being similar may allow for a safer exploring of the world. These results suggests that rather than stating how a particular kind of tie or network predicts innovation, or is likely to provide new information, these relations need to be contextualised and understood from their local, specific settings and social dynamics.
Design of Mixed-Signal Systems on Chip

The electronics industry is increasingly focused on the consumer marketplace, which requires low-cost high-volume products to be developed very rapidly. This, combined with advances in deep sub-micron technology have resulted in the ability and the need to put entire systems on a single chip. As more of the system is included on a single chip, it is increasingly likely that the chip will contain both analog and digital sections. Developing these mixed-signal systems-on-chip presents enormous challenges both to the designers of the chips and to the developers of the CAD systems that are used during the design process. This paper presents many of the issues that act to complicate the development of large single-chip mixed-signal systems and how CAD systems are expected to develop to overcome these issues. Index Terms —Design methodology, design automation, mixed analog-digital integrated circuits, hardware design languages, simulation, integrated circuit layout, integrated circuit modeling, and testing.
AEGIS: Exposing Backdoors in Robust Machine Learning Models

—The introduction of robust optimisation has pushed the state-of-the-art in defending against adversarial attacks. However, the behaviour of such optimisation has not been studied in the light of a fundamentally different class of attacks called backdoors. In this paper, we demonstrate that adversarially robust models are susceptible to backdoor attacks. Subsequently, we observe that backdoors are reﬂected in the feature representation of such models. Then, this observation is leveraged to detect backdoor-infected models via a detection technique called AEGIS. Speciﬁcally, AEGIS uses feature clustering to effectively detect backdoor-infected robust Deep Neural Networks (DNNs). In our evaluation of several visible and hidden backdoor triggers on major classiﬁcation tasks using CIFAR-10, MNIST and FMNIST datasets, AEGIS effectively detects robust DNNs infected with backdoors. AEGIS detects a backdoor-infected model with 91.6% accuracy, without any false positives. Furthermore, AEGIS detects the targeted class in the backdoor-infected model with a reasonably low (11.1%) false positive rate. Our investigation reveals that salient features of adversarially robust DNNs break the stealthy nature of backdoor attacks.
Accurate Fiducial Mapping for Pose Estimation Using Manifold Optimization

The accurate pose estimation for moving objects within a given workspace is one of the most fundamental tasks for many applications including augmented reality, robotics' control, planning and navigation. The information of objects' pose is often given by motion capture systems and global positioning systems indoor and outdoor respectively. However, motion capture systems are costly and limited in workspace, while global positioning systems degrade severely in clustering environments. In this paper, we propose an approach to build a map of fiducial markers based on manifold optimization and then extend the fiducial map for pose estimation. The fiducial map based pose estimation system is cost-effective, lightweight and can work both indoor and outdoor. The proposed method starts by fiducial detection and pose estimation for collected images in order to establish an initial graph which stacks measurements of markers' relative poses. Then for each relative pose, multiple measurements are fused using manifold optimization for an optimal estimation. To deal with pose ambiguity problem, inlier poses are selected using the random sample consensus algorithm. Finally, a global pose optimization is done on manifold to minimize per frame reprojection errors. Mapping experiments with synthetic and real data demonstrated the accuracy and consistency of the proposed approach. The accuracy of pose estimation using prebuilt fiducial map was evaluated by benchmark tests with motion capture system.