Miracle-dz/UniFAR-FTUs huggingface dataset:
GAS ABSORPTION IN THE KH 15D SYSTEM: FURTHER EVIDENCE FOR DUST SETTLING IN THE CIRCUMBINARY DISK

Na i D lines in the spectrum of the young binary KH 15D have been analyzed in detail. We find an excess absorption component that may be attributed to foreground interstellar absorption, and to gas possibly associated with the solids in the circumbinary disk. The derived column density is log = 12.5 cm−2, centered on a radial velocity that is consistent with the systemic velocity. Subtracting the likely contribution of the interstellar medium leaves log 12.3 cm−2. There is no detectable change in the gas column density across the “knife edge” formed by the opaque grain disk, indicating that the gas and solids have very different scale heights, with the solids being highly settled. Our data support a picture of this circumbinary disk as being composed of a very thin particulate grain layer composed of millimeter-sized or larger objects that are settled within whatever remaining gas may be present. This phase of disk evolution has been hypothesized to exist as a prelude to the formation of planetesimals through gravitational fragmentation, and is expected to be short-lived if much gas were still present in such a disk. Our analysis also reveals the presence of excess Na i emission relative to the comparison spectrum at the radial velocity of the currently visible star that plausibly arises within the magnetosphere of this still-accreting young star.
Object categorization with SVM: kernels for local features

In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.
An improved building detection in complex sites using the LIDAR height variation and point density

In this paper, the height variation in LIDAR (Light Detection And Ranging) point cloud data and point density are analyzed to remove the false building detection in highly vegetation and hilly sites. In general, the LIDAR points in a tree area have higher height variations than those in a building area. Moreover, the density of points having similar height values is lower in a tree area than in a building area. The proposed method uses such information as an improvement to a current state-of-the-art building detection method. The qualitative and object-based quantitative analyzes have been performed to verify the effectiveness of the proposed building detection method as compared with a current method. The analysis shows that proposed building detection method successfully reduces false building detection (i.e. trees in high complex sites of Australia and Germany), and the average correctness and quality have been improved by 6.36% and 6.16% respectively.
A Survey on Various Data Mining Techniques for ECG Meta Analysis

Data Mining is the process of examining the information from different point of view and compressing it for the relevant data. This data can also be utilized to build the incomes. Data Mining is also known as Data or Knowledge Discovery. The basic purpose of data mining is to search patterns which have minimal user inputs and efforts. Data Mining plays a very crucial role in the various fields. There are various data mining procedures which can be connected in different fields of innovation. By using data mining techniques, it is observed that less time is taken for the prediction of any disease with more accuracy. In this paper we would review various data mining techniques which are categorized under classification, regression and clustering and apply these algorithms over an ECG dataset. The purpose of this work is to determine the most suitable data mining technique and use it to improve the accuracy of analyzing ECG data for better decision making.
Calibration of LiDAR device using infrared images

We present a new approach for the calibration of a LiDAR system to improve its accuracy in the acquisition of 3D data. The proposed method establishes laser-pixel correspondences between a LiDAR device and an infrared camera according to the captured laser trajectories in infrared images. The established correspondences are used to perform optimization calculations for the intrinsic and extrinsic parameters of the LiDAR system. Two error functions are combined by a linear weighting function in the optimization process, namely the reprojection error function and the flatness error function. Experimental results have been provided to demonstrate that the proposed approach is able to provide more suitable calibration parameters for the LiDAR system, thus reducing errors caused by the usage of unsuitable calibration parameters and improving the accuracy of the acquired range data.
Enhancing Data Secrecy with Segmentation Based Isolation

Software memory disclosure attacks, such as buffer over-read, often work quietly and may cause leakage of secrets. The well-known OpenSSL Heartbleed vulnerability leaked out millions of servers' private keys, and caused most of Internet services insecure during that time. Existing solutions are either hard to apply to large code bases, or too heavyweight (e.g. by involving a hypervisor software or a modified operating system kernel). We propose SecSeg, an easy-to-use and lightweight system which leverages the traditional x86 segmentation mechanism to isolate the secrets from the remaining data. Software developers can prevent the secrets from being leaked out by simply declaring the secret variables with secure keyword. And our customized compiler will automatically separate the secrets from the remaining ones with an isolated hardware segment. Any legal instructions that have to visit the secrets will be automatically instrumented to special machine instructions which have access to the isolated segment. We have implemented an early SecSeg prototype with an open source compiler framework - the LLVM Compiler Infrastructure. The prototype proves that SecSeg is both secure and efficient.
Size Eect Zdeneï K P. Bazï Ant*

This paper surveys the available results on the size eect on the nominal strength of structures Ð a fundamental problem of considerable importance to concrete structures, geotechnical structures, geomechanics, arctic ice engineering, composite materials, etc., with applications ranging from structural engineering to the design of ships and aircraft. The history of the ideas on the size eect is brie ̄y outlined and recent research directions are emphasized. First, the classical statistical theory of size eect due to randomness of strength, completed by Weibull, is reviewed and its limitations pointed out. Subsequently, the energetic size eect, caused by stress redistributions due to large fractures, is discussed. Attention is then focused on the bridging between the theory of plasticity, which implies no size eect and is applicable for quasibrittle materials only on a suciently small scale, and the theory of linear elastic fracture mechanics, which exhibits the strongest possible deterministic size eect and is applicable for these materials on suciently large scales. The main ideas of the recently developed theory for the size eect in the bridging range are sketched. Only selected references to the vast amount of work that has recently been appearing in the literature are given. # 1999 Published by Elsevier Science Ltd. All rights reserved.
Modeling breach of contract risk through bundled options

In this paper, in order to model breach of contract risk, we design and value a bundled option that is composed of contract abandonment and price renegotiation. We show numerically that the bundled option is more valuable for the contract than either of the options, ie, contract abandonment and price renegotiation, in isolation. This value increases monotonically as the spot price becomes more volatile. The value of the bundled option is less than the sum of the individual option values, hence showing the subadditive property. We demonstrate that in the presence of high spotprice volatility, the bundled option is more valuable when the renegotiation date is selected to be closer to the half-life of the contract. We also show that early contract abandonment probability goes down in the presence of a renegotiation option. We conclude that the commodity supplier should negotiate with the buyer a supply chain contract with flexible options at the design stage, obtaining contract abandonment and price renegotiation options ‐ as a bundled option ‐ in order to enhance the supply contract value and reduce the breach of contract risk.
UB.dmirg: A Syntactic Lexical System for Recognizing Textual Entailments

This paper reports on our Recognizing Textual Entailment (RTE) system developed for participation in the Text Analysis Conference RTE 2009 competition. The development of the system is based on the lexical entailment between two text excerpts, namely the hypothesis and the text. To extract atomic parts of hypotheses and texts, we carry out syntactic parsing on the sentences. We then utilize WordNet and FrameNet lexical resources for estimating lexical coverage of the text on the hypothesis. Using a failure analysis process, we show that the main difficulty of our RTE system relates to the underlying difficulty of syntactic analysis of sentences.
A quality-threshold data summarization algorithm

As database sizes increase, semantic data summarization techniques have been developed, so that data mining algorithms can be run on the summarized set for the sake of efficiency. Clustering algorithms such as K-Means have popularly been used as semantic summarization methods where cluster centers become the summarized set. The goal of semantic summarization is to provide a summarized view of the original dataset such that the summarization ratio is maximized while the error (i.e., information loss) is minimized. This paper presents a new clustering-based data summarization algorithm, in which the quality of the summarized set can be controlled. The algorithm partitions a dataset into a number of clusters until the distortion of each cluster is less than a given threshold, thus guaranteeing the summarized set has less than a fixed amount of information loss. Based on the threshold, the number of clusters is automatically determined. The proposed algorithm, unlike traditional K-Means, adjusts initial centers based on the information about the data space discovered so far, thus significantly alleviating the local optimum effect. Our experiments show that our algorithm generates higher quality clusters than K-Means does and it also guarantees an error bound, an essential criterion for data summarization.
The Open University ’ s repository of research publications and other research outputs A taxonomy of asymmetric requirements aspects

The early aspects community has received increasing attention among researchers and practitioners, and has grown a set of meaningful terminology and concepts in recent years, including the notion of requirements aspects. Aspects at the requirements level present stakeholder concerns that crosscut the problem domain, with the potential for a broad impact on questions of scoping, prioritization, and architectural design. Although many existing requirements engineering approaches advocate and advertise an integral support of early aspects analysis, one challenge is that the notion of a requirements aspect is not yet well established to efficaciously serve the community. Instead of defining the term once and for all in a normally arduous and unproductive conceptual unification stage, we present a preliminary taxonomy based on the literature survey to show the different features of an asymmetric requirements aspect. Existing approaches that handle requirements aspects are compared and classified according to the proposed taxonomy. In addition, we study crosscutting security requirements to exemplify the taxonomy’s use, substantiate its value, and explore its future directions.
Kaposi’s Sarcoma-Associated Herpesvirus (KSHV) Induces the Oncogenic miR-17-92 Cluster and Down-Regulates TGF-β Signaling

KSHV is a DNA tumor virus that causes Kaposi’s sarcoma. Upon KSHV infection, only a limited number of latent genes are expressed. We know that KSHV infection regulates host gene expression, and hypothesized that latent genes also modulate the expression of host miRNAs. Aberrant miRNA expression contributes to the development of many types of cancer. Array-based miRNA profiling revealed that all six miRNAs of the oncogenic miR-17-92 cluster are up-regulated in KSHV infected endothelial cells. Among candidate KSHV latent genes, we found that vFLIP and vCyclin were shown to activate the miR-17-92 promoter, using luciferase assay and western blot analysis. The miR-17-92 cluster was previously shown to target TGF-β signaling. We demonstrate that vFLIP and vCyclin induce the expression of the miR-17-92 cluster to strongly inhibit the TGF-β signaling pathway by down-regulating SMAD2. Moreover, TGF-β activity and SMAD2 expression were fully restored when antagomirs (inhibitors) of miR-17-92 cluster were transfected into cells expressing either vFLIP or vCyclin. In addition, we utilized viral genetics to produce vFLIP or vCyclin knock-out viruses, and studied the effects in infected TIVE cells. Infection with wildtype KSHV abolished expression of SMAD2 protein in these endothelial cells. While single-knockout mutants still showed a marked reduction in SMAD2 expression, TIVE cells infected by a double-knockout mutant virus were fully restored for SMAD2 expression, compared to non-infected TIVE cells. Expression of either vFLIP or vCycIin was sufficient to downregulate SMAD2. In summary, our data demonstrate that vFLIP and vCyclin induce the oncogenic miR-17-92 cluster in endothelial cells and thereby interfere with the TGF-β signaling pathway. Manipulation of the TGF-β pathway via host miRNAs represents a novel mechanism that may be important for KSHV tumorigenesis and angiogenesis, a hallmark of KS.
Inverted Treaps

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using similar space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression, we represent the treap topology using different alternative compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. We also show how to extend this representation to support incremental updates over the index. Results show that, under the tf-idf scoring scheme, our index uses about the same space as state-of-the-art compact representations, while performing up to 2--20 times faster on ranked single-word, union, or intersection queries. Under the BM25 scoring scheme, our index may use up to 40% more space than the others and outperforms them less frequently but still reaches improvement factors of 2--20 in the best cases. The index supporting incremental updates poses an overhead of 50%--100% over the static variants in terms of space, construction, and query time.
Scalable Transactions for Scalable Distributed Database Systems

With the advent of the Internet and Internet-connected devices, modern applications can experience very rapid growth of users from all parts of the world. A growing user base leads to greater usage and large data sizes, so scalable database systems capable of handling the great demands are critical for applications. With the emergence of cloud computing, a major movement in the industry, modern applications depend on distributed data stores for their scalable data management solutions. Many large-scale applications utilize NoSQL systems, such as distributed key-value stores, for their scalability and availability properties over traditional relational database systems. By simplifying the design and interface, NoSQL systems can provide high scalability and performance for large data sets and high volume workloads. However, to provide such benefits, NoSQL systems sacrifice traditional consistency models and support for transactions typically available in database systems. Without transaction semantics, it is harder for developers to reason about the correctness of the interactions with the data. Therefore, it is important to support transactions for distributed database systems without sacrificing scalability.In this thesis, I present new techniques for scalable transactions for scalable database systems. Distributed data stores need scalable transactions to take advantage of cloud computing, and to meet the demands of modern applications. Traditional techniques for transactions may not be appropriate in a large, distributed environment, so in this thesis, I describe new techniques for distributed transactions, without having to sacrifice traditional semantics or scalability.I discuss three facets to improving transaction scalability and support in distributed database systems. First, I describe a new transaction commit protocol that reduces the response times for distributed transactions. Second, I propose a new transaction programming model that allows developers to better deal with the unexpected behavior of distributed transactions. Lastly, I present a new scalable view maintenance algorithm for convergent join views. Together, the new techniques in this thesis contribute to providing scalable transactions for modern, distributed database systems.
Why is German Dependency Parsing More Reliable than Constituent Parsing

In recent years, research in parsing has extended in several new directions. One of these directions is concerned with parsing languages other than English. Treebanks have become available for many European languages, but also for Arabic, Chinese, or Japanese. However, it was shown that parsing results on these treebanks depend on the types of treebank annotations used [ , ]. Another direction in parsing research is the development of dependency parsers. Dependency parsing profits from the non-hierarchical nature of dependency relations, thus lexical information can be included in the parsing process in a much more natural way. Especially machine learning based approaches are very successful (cf. e.g. [12, 13]). The results achieved by these dependency parsers are very competitive although comparisons are difficult because of the differences in annotation. For English, the Penn Treebank [11] has been converted to dependencies. For this version, Nivre et al. [14] report an accuracy rate of 86.3%, as compared to an F-score of 2.1 for Charniak’s parser [1]. The Penn Chinese Treebank [1 ] is also available in a constituent and a dependency representations. The best results reported for parsing experiments with this treebank give an F-score of 81.8 for the constituent version [2] and . % accuracy for the dependency version [14]. The general trend in comparisons between constituent and dependency parsers is that the dependency parser performs slightly worse than the constituent parser. The only exception occurs for German, where F-scores for constituent plus grammatical function parses range between 51.4 and 5.3, depending on the treebank, NEGRA [1 ] or TuBa-D/Z [1 ]. The dependency parser based on a converted version of Tuba-D/Z, in contrast, reached an accuracy of 3.4% [14], i.e. 12 percent points better than the best constituent analysis including grammatical functions.
Techniques to reduce the IEEE 802.11b handoff time

We analyze the link-layer handoff process in wireless LANs based on the IEEE 802.11b standard and suggests how to reduce its duration. Firstly, we divide the process into three phases: detection, search and execution. Our performance measurements indicate that the detection and search phases are the main contributors to the handoff time. We show that the link-layer detection time can be reduced to three consecutive lost frames. We also show that the search time can be reduced at least by 20% using active scanning with the two timers that control its duration set to 1 ms and 10.24 ms. Several simulations illustrate the achieved reduction in handoff time.
Coupled software transformations revisited

We revisit the notion of coupled software transformations (CX) which is concerned with keeping collections of software artifacts consistent in response to changes of individual artifacts. We model scenarios of CX while we abstract from technological spaces and application domains. Our objective is to mediate between universal consistency properties of CX and test-driven validation of concrete (illustrative) CX implementations. To this end, we leverage an emerging megamodeling language LAL which is based on many- and order-sorted predicate logic with support for reuse by inlining modulo substitution. We provide a simple translation semantics for LAL so that formulae can be rendered as test cases on appropriate interpretations of the megamodel elements. Our approach has been implemented and validated in logic programming; this includes the executable language definition of LAL and test-case execution on top of illustrative CX implementations.
SIFT and tensor based object classification in images using Deep Neural Networks

Object classification is an important task within the field of computer vision. It is the process of labeling objects into predefined and semantically meaningful categories using trained datasets. A classification is made using a segment of image which is actually a single pixel or a group of pixels which is called a classification unit. Many researchers are working in this area to improve the accuracy of object classification focusing on different features and classifiers. As SIFT feature dominates in current object classification problem, we incorporate Tensor features with SIFT to improve the accuracy of the problem. Moreover we use Deep Neural Network for classification which is a type of artificial intelligence that could solve complex perceptual problems as fast as human brain. Simulation results obtained illustrate that the proposed classifier model produces more accurate results than the existing methods which combines both SIFT and tensor features for feature extraction and DNN for classification.
Glimpses of dialogue: transitional practices in digitalised classrooms

ABSTRACT This socio-culturally informed qualitative study examines digitalised classrooms in Norwegian secondary schools, with a focus on the relationship between information and communication technology (ICT) and dialogic aspects of literacy practices. In the article, we foreground two cases: one on the use of digital mind maps and one on a writing process with online response. These cases display productive results of the tensions between old practices and new technology in that they open up spaces for dialogic interaction. This experience calls for a deeper historical contextualisation, and in the article we refer to different time scales: First, the restricted time scale of practices observed in the local school contexts over an academic year; second, the somewhat wider perspective of 20–30 years of educational research addressing technological innovation; and third, the extensive time scale of cultural history, with an analogy to the slow move from orality to literacy in ancient Greece. On this basis we suggest the term ‘transitional practices’ as an appropriate reference to all of these three time scales. Against this background, the glimpses of dialogue observed are seen as promising precursors of future development, but also as vulnerable plant shoots that may very well shrivel and die if they are not supported.