Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Discovery in Telecommunication Services Data Using Bayesian Network Models

Fraud and uncollectible debt are multi-billion dollar problems in the telecommunications industry. Because it is difficult to know which accounts will go bad, we are faced with the difficult knowledge-discovery task of characterizing a rare binary outcome using large amounts of noisy, high-dimensional data. Binary characterizations may be of interest but will not be especially useful in this domain. Instead, proposing an action requires an estimate of the probability that a customer or a call is uncollectible. This paper addresses the discovery of predictive knowledge bearing on fraud and uncollectible debt using a supervised machine learning method that constructs Bayesian network models. The new method is able to predict rare event outcomes and cope with the quirks and copious amounts of input data. The Bayesian network models it produces serve as an input module to a normative decision-support system and suggest ways to reinforce or redirect existing efforts in the problem area. We compare the performance of several conditionally independent models with the conditionally dependent models discovered by the new learning system using real-world datasets of 4-6 million records and 600-800 million bytes.
The Research of A-Priori Algorithm Candidates Based on Support Counts

Apriori algorithm is one of the most standard, popular way among associational rule mining algorithms. Its famous character ‘all the sub-frequent itemsets are frequent itemsets, and no one itemset is frequent if its sub-frequent is infrequent itemsets’ has been reduction the number of candidate itemsets, but the number of candidate itemsets is still large. Moreover, the ratio is so small between frequent itemsets and candidate itemsets that the largest time is wasted in checking the candidate itemsets. According to the character of Apriori, this paper sums up three regularities to improve the ratio of useful candidates. At the same time the independent support count is cited to prune the frequent itemsets who are independent their father itemsets. The optimize algorithm has been proved and realized by one database. This paper is arranged as the following: section 2 presents some key definitions on these rules such as support count, attenuation to support count, constant support count on fixed itemsets and three rules will be introduced shortly. Demonstrations and applications on these rules will be detailed in section 3. Section 4 introduces the algorithm of rules and an example is introduced to test the efficiency of these rules. The last section draws some conclusions and future works.
Sentence-based relevance flow analysis for high accuracy retrieval

Traditional ranking models for information retrieval lack the ability to make a clear distinction between relevant and nonrelevant documents at top ranks if both have similar bag-of-words representations with regard to a user query. We aim to go beyond the bag-of-words approach to document ranking in a new perspective, by representing each document as a sequence of sentences. We begin with an assumption that relevant documents are distinguishable from nonrelevant ones by sequential patterns of relevance degrees of sentences to a query. We introduce the notion of relevance flow, which refers to a stream of sentence-query relevance within a document. We then present a framework to learn a function for ranking documents effectively based on various features extracted from their relevance flows and leverage the output to enhance existing retrieval models. We validate the effectiveness of our approach by performing a number of retrieval experiments on three standard test collections, each comprising a different type of document: news articles, medical references, and blog posts. Experimental results demonstrate that the proposed approach can improve the retrieval performance at the top ranks significantly as compared with the state-of-the-art retrieval models regardless of document type. © 2011 Wiley Periodicals, Inc.
Bisexual Rhapsody: On the everyday sexual identity negotiations of bisexual people in Rotterdam and Amsterdam and the productions of bisexual spaces

Since the 1990s, geographies of sexualities have evolved into a body of work which is able to provide an overview of everyday life experiences of sexual minorities, especially of gay men and lesbians. A review of the literature, however, observes that bisexuality is often neglected. I argue that this is the result of an approach to sexualised space that immediately links the sexual coding of space with the dominant sexual identity. This paper aims to theorise bisexual spaces as a result of bisexual practices, which are derived from the Klein Sexual Orientation Grid. I will also stress the importance of linguistic practices in practicing (or doing) bisexuality. This paper concludes with a call to investigate bisexual geographies in the mundane, everyday realities of bisexual citizens. 2.
Realtime view-dependent isosurface visualization for regular volume data

The problem of the interactive visualization of isosurfaces in volume data arises in medical applications, visualization of mathematical simulation results and many other areas. We propose an adaptive approach for interactive extraction and visualization of isosurfaces that employs hierarchical representation for the volume data and view-dependent isosurface reconstruction at the different levels of detail. To speedup extraction process an isosurface is constructed only in the visible part of the dataset and its updates are performed incrementally as observer moves. Possible cracks between isosurface parts constructed at different hierarchy levels are eliminated by proposed stitching procedure. Due to low dataset preprocessing costs our approach has been proven to handle dynamical updates in volume data.
Performance analysis of mobile WiMAX communication using directional antenna over fading channel

Worldwide interoperability for microwave access (WiMAX) is an IEEE802.16 standard that provides low cost, high-speed and long-range wireless communications, where the 802.16m is the latest version of WiMAX standard fully optimized for fixed and pedestrian speeds. Due to dynamic multipath fading and high Doppler shift, there is service degradation on quality of service (QoS) and data rate. In this paper, an outage probability expression is derived based on analytical model for WiMAX communications at high vehicular speeds over Nakagami m<;1 fading channel. Moreover, the new derived outage probably and an existing BER are analytically evaluated using a directional antenna on mobile receiver instead of Omni-directional antenna that shows significant enhancement on performance.
Default timed concurrent constraint programming

We extend the model of [SJG94b] to express strong time-outs (and pre-emption): if an event <italic>A</italic> does not happen through time <italic>t</italic>, cause event <italic>B</italic> to happen at time <italic>t</italic>. Such constructs arise naturally in practice (e.g. in modeling transistors) and are supported in languages such as ESTEREL (through instantaneous watchdogs) and LUSTRE (through the “current” operator). The fundamental conceptual difficulty posed by these operators is that they are non-monotonic. We provide a simple compositional semantics to the non-monotonic version of concurrent constraint programming (CCP) obtained by changing the underlying logic from intuitionist logic to Reiter's default logic. This allows us to use the same construction (uniform extension through time) to develop Default Timed CCP (<bold>Default tcc</bold>) as we had used to develop Timed CCP (<bold>tcc</bold>) from CCP. Indeed the smooth embedding of CCP processes into <bold>Default cc</bold> processes lifts to a smooth embedding of <bold>tcc</bold> processes into <bold>Default tcc</bold> processes. Interesting <bold>tcc</bold> properties such as determinacy, multiform time, a uniform pre-emption construct (“clock”), full-abstraction, and compositional compilation into automata are preserved. <bold>Default tcc</bold> thus provides a simple and natural (denotational) model capable of representing the full range of pre-emption constructs supported in ESTEREL, LUSTRE and other synchronous programming languages.
Judging the Other: The Intersection of Race, Gender, and Class in Family Court

This critical ethnographic study of Family Court child maltreatment proceedings describes and illuminates the ways in which racial, gender and class disadvantages can manifest on the ground as judges, attorneys, social service workers, and parents – joined often by gender but split by race and class – adjudicate cases. The findings suggest that intersectionality worked in ways that exponentially marginalized poor mothers of color in the courtroom, both through the rules of the adversarial process, which silenced their voices and through the construction of narratives which emphasized individual weakness over structural obstacles and personal irresponsibility over expressions of maternal care and concern. Standard due process courtroom practices also communicated bias or social exclusion, especially in a courtroom split by race and class.
Automatic Deployment of Distributed Teams of Robots From Temporal Logic Motion Specifications

We present a computational framework for automatic synthesis of decentralized communication and control strategies for a robotic team from global specifications, which are given as temporal and logic statements about visiting regions of interest in a partitioned environment. We consider a purely discrete scenario, where the robots move among the vertices of a graph. However, by employing recent results on invariance and facet reachability for dynamical systems in environments with polyhedral partitions, the framework from this paper can be directly implemented for robots with continuous dynamics. While allowing for a rich specification language and guaranteeing the correctness of the solution, our approach is conservative in the sense that we might not find a solution, even if one exists. The overall amount of required computation is large. However, most of it is performed offline before the deployment. Illustrative simulations and experimental results are included.
On the Performance of TDMA Protocols Under Self-Similar Traffic: A Simulation Study

Time division multiple-access (TDMA) protocols have been designed to operate primarily under uniform traffic patterns. However, real traffic measurements have proven to be rather self-similar. In this article, the authors examine the performance and behavior of TDMA family protocols (TDMA, RTDMA, LTDMA) under self-similar traffic. The comparison of the simulation results of the above protocols showed the superiority of LTDMA, which exhibits remarkably high throughput-delay performance.
Growth Hormone Protects the Intestine Preserving Radiotherapy Efficacy on Tumors: A Short-Term Study

The efficacy of radiotherapy on tumors is hampered by its devastating adverse effects on healthy tissue, particularly that of the gastrointestinal tract. These effects cause acute symptoms that are so disruptive to patients that they can lead to interruption of the radiotherapy program. These adverse effects could limit the intensity of radiation received by the patient, resulting in a sublethal dose to the tumor, thus increasing the risk of tumor resistance. The lack of an effective treatment to protect the bowel during radiation therapy to allow higher radiation doses that are lethal to the tumor has become a barrier to implementing effective therapy. In this study, we present a comparative analysis of both intestinal and tumor tissue in regard to the efficacy and the preventive impact of a short-term growth hormone (GH) treatment in tumor-bearing rats as a protective agent during radiotherapy. Our data show that the exogenous administration of GH improved intestinal recovery after radiation treatment while preserving the therapeutic effect against the tumor. GH significantly increased proliferation in the irradiated intestine but not in the irradiated tumors, as assessed by Positron Emission Tomography and the proliferative markers Ki67, cyclin D3, and Proliferating Cell Nuclear Antigen. This proliferative effect was consistent with a significant increase in irradiated intestinal villi and crypt length. Furthermore, GH significantly decreased caspase-3 activity in the intestine, whereas GH did not produce this effect in the irradiated tumors. In conclusion, short-term GH treatment protects the bowel, inducing proliferation while reducing apoptosis in healthy intestinal tissue and preserving radiotherapy efficacy on tumors.
Detection of reactive jamming in sensor networks

An integral part of most security- and safety-critical applications is a dependable and timely alarm notification. However, owing to the resource constraints of wireless sensor nodes (i.e., their limited power and spectral diversity), ensuring a timely and jamming-resistant delivery of alarm messages in applications that rely on wireless sensor networks is a challenging task. With current alarm forwarding schemes, blocking of an alarm by jamming is straightforward and jamming is very likely to remain unnoticed. In this work, we propose a novel jamming detection scheme as a solution to this problem. Our scheme is able to identify the cause of bit errors for individual packets by looking at the received signal strength during the reception of these bits and is well-suited for the protection of reactive alarm systems with very low network traffic. We present three different techniques for the identification of bit errors based on: predetermined knowledge, error correcting codes, and limited node wiring. We perform a detailed evaluation of the proposed solution and validate our findings experimentally with Chipcon CC1000 radios. The results show that our solution effectively detects sophisticated jamming attacks that cannot be detected with existing techniques and enables the formation of robust sensor networks for dependable delivery of alarm notifications. Our scheme also meets the high demands on the energy efficiency of reactive surveillance applications as it can operate without introducing additional wireless network traffic.
Speeding Up SIFT Algorithm by Multi-core Processor Supporting SIMD Instruction Sets

Scale Invariant Feature Transform (SIFT) method plays a critical role in a wide variety of vision applications. But it is now facing the real-time computational challenge. Parallel computing is one of the most promising solutions to overcome the computational challenge. In this paper, we target at parallelizing SIFT by multi-core architecture with per-core SIMD support. We focus on the SIMDization of data parallel parts of SIFT to fully utilize per-core computing power. At Orientation Assignment and Key point Descriptor stages, we observe that load balance is an important factor. We also implement the optimized algorithm on multi-core system with SIMD support from Tianhe-2 Supercomputer and make comparison with the State-of-the-Art parallel SIFT algorithms.
Methanolic Extract of Ganoderma lucidum Induces Autophagy of AGS Human Gastric Tumor Cells

Ganoderma lucidum is one of the most widely studied mushroom species, particularly in what concerns its medicinal properties. Previous studies (including those from some of us) have shown some evidence that the methanolic extract of G. lucidum affects cellular autophagy. However, it was not known if it induces autophagy or decreases the autophagic flux. The treatment of a gastric adenocarcinoma cell line (AGS) with the mushroom extract increased the formation of autophagosomes (vacuoles typical from autophagy). Moreover, the cellular levels of LC3-II were also increased, and the cellular levels of p62 decreased, confirming that the extract affects cellular autophagy. Treating the cells with the extract together with lysossomal protease inhibitors, the cellular levels of LC3-II and p62 increased. The results obtained proved that, in AGS cells, the methanolic extract of G. lucidum causes an induction of autophagy, rather than a reduction in the autophagic flux. To our knowledge, this is the first study proving that statement.
Automated quantification of apoptosis after neoadjuvant chemotherapy for breast cancer: early assessment predicts clinical response.

PURPOSE A large body of evidence implicates apoptosis in the effects of cancer chemotherapeutic agents on tumor cells in vitro and tumor xenografts in vivo, but the predictive value of apoptosis as an early marker for clinical response in cancer patients remains unclear. EXPERIMENTAL DESIGN We developed an automated, laser scanning cytometer-based method to quantify the percentage of tumor cells containing DNA fragmentation characteristic of apoptosis in tumor sections. We measured levels of apoptosis in a panel of 15 matched, 18-gauge core breast cancer biopsies obtained before and 48 h after neoadjuvant therapy with docetaxel plus doxorubicin or paclitaxel as part of two prospective clinical trials. RESULTS The results revealed a strongly significant (P = 0.0023) association between chemotherapy-induced apoptosis and pathological response. CONCLUSIONS If the results can be validated in a larger patient cohort, the method could be used to "tailor" therapy to optimize benefit in a patient-specific fashion.
Employment Outcomes and Job Quality of Vocational Rehabilitation Consumers With Deaf-Blindness

The purpose of this study was to investigate employment outcomes for vocational rehabilitation (VR) consumers with deaf-blindness, a population that has received no attention in the literature. The sample was obtained from Rehabilitation Services Administration Case Service Report (RSA-911) data and included 1,382 consumers with deaf-blindness identified as their primary or secondary disability whose cases were closed during fiscal years 2013, 2014, and 2015. Independent variables consisted of consumer personal characteristics and VR service-related variables. Two measures of employment outcomes were used: obtainment of competitive employment and a composite measure of job quality. Overall, the results indicate that several VR service-related factors are associated with whether deaf-blind consumers obtain competitive employment, but consumers’ personal characteristics are much more important in determining job quality. Implications for improving employment outcomes for consumers who are deaf-blind include providing job-related services, supporting educational advancement, and providing counseling and guidance. Results also support the importance of accounting for employment status at application in RSA-911 analyses and the efficacy of service provision by separate agencies for the blind.
Codes and Formulation of Appropriate Constraints via Entropy Measures

In present communication, we have developed the suitable constraints for the given the mean codeword length and the measures of entropy. This development has proved that Renyi’s entropy gives the minimum value of the log of the harmonic mean and the log of power mean. We have also developed an important relation between best 1:1 code and the uniquely decipherable code by using different measures of entropy. Keywords—Codeword, Instantaneous code, Prefix code, Uniquely decipherable code, Best one-one code, Mean codeword length
The design of an anthropomorphic dexterous humanoid foot

The human foot serves three main functions; adapts to contours of ground, absorbs shock impacts and stores and releases energy. Despite significant development of humanoid robots the foot has seen little research and as a result most bipeds do not walk in a humanlike manner. In this work a study of the human foot is conducted to determine how each of these is achieved. The paper then describes the design, construction and testing of a fully articulated humanoid foot which has the same functionality as the human foot.
QoS buffer management of multimedia networking with GREEN algorithm

The provision of QoS control is a key of the successful deployment of multimidia networks. Buffer management plays an important role in QoS control. Therefore, this paper proposes a novel QoS buffer management algorithm named GREEN (Global Random Early Estimation for Nipping), which extends the concepts of ERD (early random drop) and RED (random early detection). Specifically, GREEN enhances the concept of “Random” to “Global Random” by globally considering the random probability function. It also enhances the concept of “Early” to “Early Estimation” by early estimating the network status. For performance evaluation, except compared with RED, extensive simulation cases are performed to probe the characteristics of GREEN.
Prostate cancer: evaluation of vascular characteristics with dynamic contrast-enhanced T1-weighted MR imaging--initial experience.

PURPOSE To use contrast material-enhanced magnetic resonance (MR) imaging and a distributed-parameter tracer kinetics model for prospectively evaluating the vascular characteristics of prostate cancer. MATERIALS AND METHODS Twenty-two patients between 57 and 76 years of age (mean age, 67 years) with histologically proved adenocarcinoma of the prostate were examined by using three-dimensional dynamic contrast-enhanced T1-weighted MR imaging at 1.5 T. The local research ethics committee approved this study, and written consent was obtained from all patients. Data from regions of interest drawn in tumor, normal-appearing peripheral zone tissue, and muscle were analyzed to provide estimates of perfusion, blood volume, interstitial volume, and microvascular permeability-surface area product. These estimates were compared by using the nonparametric Wilcoxon signed rank test. RESULTS Mean blood flow was significantly (P < .001) higher in 22 prostate tumors than in 20 contralateral peripheral zones (66 vs 32 mL/100 mL/min). Similarly, the interstitial distribution volume in tumors was enlarged compared with the interstitial distribution volume in normal peripheral zones (42 vs 27 mL/100 mL). Blood volume and microvascular permeability-surface area product values in tumors (1.0 mL/100 mL and 22 mL/100 mL/min, respectively) were similar to estimated values in peripheral zone tissue (1.5 mL/100 mL and 21 mL/100 mL/min, respectively). CONCLUSION These findings show considerable promise for isolating vascular characteristics of prostate cancer.
Statistical Relational Learning for Natural Language Information Extraction

1.1 Introduction Understanding natural language presents many challenging problems that lend themselves to statistical relational learning (SRL). Historically, both logical and probabilistic methods have found wide application in natural language processing (NLP). NLP inevitably involves reasoning about an arbitrary number of entities (people, places, and things) that have an unbounded set of complex relationships between them. Representing and reasoning about unbounded sets of entities and relations has generally been considered a strength of predicate logic. However, NLP also requires integrating uncertain evidence from a variety of sources in order to resolve numerous syntactic and semantic ambiguities. Effectively integrating multiple sources of uncertain evidence has generally been considered a strength of Bayesian probabilistic methods and graphical models. Consequently, NLP problems are particularly suited for SRL methods that combine the strengths of first-order predicate logic and probabilistic graphical models. In this article, we review our recent work (Bunescu and Mooney [2004]) on using Relational Markov Networks