Miracle-dz/UniFAR-FTUs huggingface dataset:
Word Size Removal (WSR) method for Bit Parallel String Matching

Bit Parallel String Matching algorithms are the most efficient and latest class of String Matching Algorithms. These algorithms use the intrinsic property of computer known as Bit Parallelism for performing bit operations in parallel with-in single computer word. BNDM, TNDM, BNDMq are the popular single pattern bit parallel string matching algorithms whereas Shift-OR and Shift-OR with Q-grams algorithms are popular in multiple pattern string matching. All these algorithms are restricted by the limitation of pattern size. These all algorithms are not working on patterns whose sizes are longer than computer word. In this paper, we proposed the generic method named WSR (Word Size Removal) for bit parallel string matching. With the inclusion of WSR these bit parallel string matching algorithms can work on longer than computer word size patterns. This paper presented WSR method and all modified algorithms.
Analysis of reservation algorithms for cooperative planning at intersections

Intersections concentrate a too important number of accidents. It is quite obvious they are dangerous places because it is where cars potentially collide due to intersecting trajectories, as opposed to “normal” roads where they are parallel. This paper present a framework designed initially for cybercars (fully automated cars) but that could also be applied — though with major differences — to human driven cars. It is a world where vehicles have to reserve pieces of roads to cross a junction. This work is an enhancement of a previous work that demonstrated the feasibility of such a reservation algorithm.
Growth Models of Developmental Language Disorders

The prior quote is from a recent issue of Science with a special section on the topic of developmental timing. The temporal events that guide development are key parts of the puzzle of contemporary molecular genetics. Genes are known to turn on at certain times in development; microRNAs show temporaland tissuespecific patterns of gene expression (cf. Carrington & Ambros, 2003); and genes activate to trigger downstream developmental events removed in time from the triggering event (cf. Gehring & Ikeo, 1999; Marcus & Fisher, 2003). Timing mechanisms are fundamental to the way that inherited elements direct an individual’s growth, whether at the cellular or organism level. The fact that language acquisition is a developmental phenomenon that emerges in the early childhood period is a commonplace observation that is known to parents and scholars alike. The literature is full of debates about the extent to which growth in language early on is attributable to inborn, language-specialized mechanisms; inborn, generalized learning mechanisms that are heavily influenced by environmental input; or fundamental learning 10
Reconfigurable FEC codes for software-defined optical transceivers

Flexible Optical Networks can optimize the network resources by offering increased spectral utilization. However, these networks require the use of novel software-defined Optical Transceivers that can adapt their features to the channel's requirements. This paper presents a novel optical transceiver architecture using a reconfigurable FEC coding scheme for flexible optical networks. The proposed approach can dynamically support different versions of FEC codes (with different overheads, code lengths, and LLR-widths) based on the network's Quality-of-Service requirements, whereas it can also be applied for the dynamic reconfiguration of the DSP units and the realization of dynamic coded-modulation schemes. The proposed approach has been implemented into an FPGA platform. The performance evaluation shows that the reconfiguration time is always less than 12 ms, which means that the proposed scheme could be utilized in many optical networks, where the reconfiguration time (e.g. for the optical switches or the WSS) is in the order of a few ms.
Automated Detection of Cholesterol Presence using Iris Recognition Algorithm

Arcus senilis is a grayish or whitish bow shaped or ringshaped deposit in the cornea. It is associated with coronary heart disease (CHD). It is also recognized as a sign of hyperlipidemia. Iridology is an alternative medicine to detect diseases using iris‟s pattern observation. Iridologists believe that the grayish or whitish deposit on the iris is sign of presence of cholesterol or Arcus senilis disease. The simple and non-invasive automation system is developed to detect cholesterol presence using iris recognition algorithm in image processing. This study applies iris recognition method to segment out the iris area, normalization process and lastly determines the cholesterol presence using OTSU‟s thresholding method and histogram to determine the optimum threshold value. The result showed that the presence of cholesterol was high when the eigenvalue exceeds an optimum threshold value.
ICFHR 2012 Competition on Recognition of On-Line Mathematical Expressions (CROHME 2012)

This paper presents an overview of the second Competition on Recognition of Online Handwritten Mathematical Expressions, CROHME 2012. The objective of the contest is to identify current advances in mathematical expression recognition using common evaluation performance measures and datasets. This paper describes the contest details including the evaluation measures used as well as the performance of the 7 submitted systems along with a short description of each system. Progress as compared to the 1st version of CROHME is also documented.
Optimal Sample-Based Estimates of the Expectation of the Empirical Minimizer

We study sample-based estimates of the expectation of the function produced by the empirical minimization algorithm. We investigate the extent to which one can estimate the rate of convergence of the empirical minimizer in a data dependent manner. We establish three main results. First, we provide an algorithm that upper bounds the expectation of the empirical minimizer in a completely data-dependent manner. This bound is based on a structural result in [3], which relates expectations to sample averages. Second, we show that these structural
Susceptibility to Tinnitus Revealed at 2 kHz Range by Bilateral Lower DPOAEs in Normal Hearing Subjects with Noise Exposure

We investigated potential markers of susceptibility to tinnitus in a group of normal hearing young pilots aged 25–35 years and with 8 ± 5 years of aircraft noise exposure. 316 pilots were interviewed about their tinnitus status and were tested for hearing thresholds (audiograms) and distortion products otoacoustic emissions (DPOAE-grams). There was no subject with permanent tinnitus. 23% reported having occasionally perceived tinnitus after flight missions and 77% reported never having experienced tinnitus after flight missions. General discomfort in the ears to noise was higher in the occasional tinnitus group (15 vs. 6%). The major finding was that difference of susceptibility to tinnitus in normal hearing subjects exposed to noise on a daily basis seemed to be clearly related to lower DPOAEs, bilaterally, in the 1500- to 2800-kHz range. However, no difference could be observed between groups on audiograms at the 2-kHz frequency range. This study provided evidence of outer hair cell dysfunctions in normal hearing subjects exposed to noise and susceptible to tinnitus. Hypersensitivity to noise and decreased DPOAEs in a non-noise-specific frequency range support the idea of another alteration mechanism than noise itself. This point was discussed in the light of recent publications.
A component architecture for FPGA-based, DSP system design

Introducing FPGA components into DSP system implementations creates an assortment of challenges across system architecture and logic design. Recognizing that some of the greatest challenges occur in the integration of the various components, we have developed a component architecture and an associated set of software tools, collectively called the Logic Foundry. Using the Logic Foundry, an FPGA-based DSP system can be easily constructed from pre-built components and implemented on a variety of back-end FPGA platforms. The resulting implementation can then be encapsulated and integrated into a variety of front-end software application environments. This paper develops the component architecture and integration capabilities of the Logic Foundry, and examines a number of application case studies that we have experimented with using the Logic Foundry.
NRSSPrioritize: Associating Protein Complex and Disease Similarity Information to Prioritize Disease Candidate Genes

The identification of disease-associated genes has recently gathered much attention for uncovering disease complex mechanisms that could lead to new insights into the treatment of diseases. For exploring disease-susceptible genes, not only experimental approaches such as genome-wide association studies (GWAS) have been used, but also computational methods. Since experimental approaches are both time-consuming and expensive, numerous studies have utilized computational techniques to explore disease genes. These methods use various biological data sources and known disease genes to prioritize disease candidate genes. In this paper, we propose a gene prioritization method (NRSSPrioritize), which benefits from both local and global measures of a protein-protein interaction (PPI) network and also from disease similarity knowledge to suggest candidate genes for colorectal cancer (CRC) susceptibility. Network Propagation, Random Walk with Restart, and Shortest Paths are three network analysis tools that are applied to a PPI network for the purpose of scoring candidate genes. Also, by looking through diseases with similar symptoms to CRC and obtaining their causing genes, candidate genes are scored in a different way. Finally, to integrate these four different scoring schemes, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Network Process (ANP) methods are applied to obtain appropriate weights for the above four quantified measures and the weighted summation of these measures are used to calculate the final score of each candidate gene. NRSSPrioritize was validated by cross-validation analysis and its results were compared with other prioritization tools, which gave the best performance when using our proposed method.
Semi-Supervised Novelty Detection

A common setting for novelty detection assumes that labeled examples from the nominal class are available, but that labeled examples of novelties are unavailable. The standard (inductive) approach is to declare novelties where the nominal density is low, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated sample is also available at learning time. We argue that novelty detection in this semi-supervised setting is naturally solved by a general reduction to a binary classification problem. In particular, a detector with a desired false positive rate can be achieved through a reduction to Neyman-Pearson classification. Unlike the inductive approach, semi-supervised novelty detection (SSND) yields detectors that are optimal (e.g., statistically consistent) regardless of the distribution on novelties. Therefore, in novelty detection, unlabeled data have a substantial impact on the theoretical properties of the decision rule. We validate the practical utility of SSND with an extensive experimental study. We also show that SSND provides distribution-free, learning-theoretic solutions to two well known problems in hypothesis testing. First, our results provide a general solution to the general two-sample problem, that is, the problem of determining whether two random samples arise from the same distribution. Second, a specialization of SSND coincides with the standard p-value approach to multiple testing under the so-called random effects model. Unlike standard rejection regions based on thresholded p-values, the general SSND framework allows for adaptation to arbitrary alternative distributions in multiple dimensions.
Does buprenorphine maintenance improve the quality of life of opioid users?

Background & objectives: The quality of life (QOL) of substance abusers is known to be severely impaired. Information on impact of opioid maintenance treatment on the QOL of opioid dependent subjects though available from the developed countries, is lacking from India. This study was carried out to assess the impact of buprenorphine maintenance treatment on the quality of life (QOL) of opioid dependent subjects at nine months follow up. Methods: Based on specified inclusion criteria a total of 231 subjects were recruited from five participating centres across India. They received sublingual buprenorphine as a directly observed therapy along with brief psychosocial intervention (provided in groups of 8-10 subjects) after intake in to the study. The WHOQOL-BREF scale domain scores obtained at baseline were compared to domain scores at nine months follow up. Results: At nine months follow up, among the 64.1 per cent retained in buprenorphine maintenance, there was a significant (P<0.001) decline in opioid use from 24.9 ± 10.1 days at baseline to 1.7 ± 4.7 days at nine months follow up and improvements in score of the four WHOQOL-BREF domains (Physical, Psychological, Social relationships and Environment). Interpretation & conclusions: The results showed the beneficial effects of buprenorphine maintenance treatment in improving the QOL of opioid-dependent subjects at nine month follow up. These results point towards the need for an expanded nation-wide provision of buprenorphine maintenance treatment as a harm reduction strategy for the opioid dependent population.
An IoT based monitoring framework for software defined 5G mobile networks

This paper presents a new approach for comprehensive monitoring of software defined 5G mobile network by using IoT (Internet of Things) based framework. The proposed framework gives much easier implementation of monitoring system for mobile network operators. Moreover, the framework uses a unified IoT protocol for telemetry transport, which is light, data-agnostic, and interoperable.
Sound-based proximity detection with mobile phones

We present a method for proximity detection with mobile phones that is based on a combination of Bluetooth communication (for the detection of coarse proximity) and sound beacons in an inaudible spectrum around 18kHz for a finer spatial resolution. The system performs a real-time recognition of personal encounters in two common situations: standing together and walking by each other. We evaluate our approach in a variety of settings ranging from office corridor, through a busy street to a shopping mall.
Video object tracing based on particle filter with ant colony optimization

Classical particle filter needs large numbers of samples to properly approximate the posterior density of the state evolution. Furthermore, sample impoverishment is an inevitable problem, which is a key issue in the performance of a particle filter. In this paper, a particle filtering algorithm based on ant colony optimization (ACO) was proposed to enhance the performance of particle filter with small sample set. ACO algorithm optimized the sample set before re-sampling step. Target state estimation was computed according to the optimized samples. Ant colony algorithm can effectively eliminate particle degeneration and enhance its robustness. Experiment results demonstrate that the proposed algorithm effectively improved the efficiency of video object tracking system.
Automated support for framework selection and customization

This work proposes a new technique for automated support of selection and customization of application frameworks, by using labeled transition systems (LTSs) together with metrics technique. We model the behavior of the frameworks and the system behavior specified in the requirements specification by using two LTSs respectively. We attach some measures to the LTS of the framework, such as the number of the hot spots to be filled. These measures are used to estimate developer's efforts in filling the hot spots for each implementation. The result of estimating the efforts guides the developers to select the implementation, and the structure of the application-specific codes to be filled in can be automatically generated from the selected implementation. Furthermore, we discuss case studies in the area of Web application, where Struts and Turbine can be used.
On the Optimal Robot Routing Problem in Wireless Sensor Networks

Given a set of sparsely distributed sensors in the Euclidean plane, a mobile robot is required to visit all sensors to download the data and finally return to its base. The effective range of each sensor is specified by a disk, and the robot must at least reach the boundary to start communication. The primary goal of optimization in this scenario is to minimize the traveling distance by the robot. This problem can be regarded as a special case of the traveling salesman problem with neighborhoods (TSPN), which is known to be NP-hard. In this paper, we present a novel TSPN algorithm for this class of TSPN, which can yield significantly improved results compared to the latest approximation algorithm.
Stuck at home: A portrayal of educational work in community spaces

Abstract This paper considers the positioning of work in community learning spaces through insights drawn from a particular case of adult community education in Victoria, Australia. Despite the rhetorical location of adult community education in Victoria as a legitimate and important sector of post compulsory education and as part of the platform on which the government's lifelong learning agenda is realised, perceptions held of it continue to devalue the significant educational work that is done within it and the outcomes it achieves. The paper draws on an analysis of a sample of qualitative interview data collected in the course of related research projects (Clemans, 2005; Billett et al., 2005; Seddon et al. 2008) to highlight the ways in which work in a community learning space is positioned, perceived and (mis)understood. These perceptions stem from those who work both within and are located outside of the community-based adult education sector. The findings demonstrate the centrality of the notion of the ‘domestic’ or home space in the ways that educational work in community settings is constructed and perceived. They suggest that a consistent emphasis on care overlays notions of unpaid, private and domestic work onto those who work in the community learning space. The paper invites readers to view the community as a spatial construction, drawing on ideas founded in feminist geographies and those within adult education. It argues that these scholarly resources are helpful in understanding the ways in which work within the adult community education space is distinctively positioned, yet consistently devalued, despite its significant impact on ‘second chance’ learners.
Distributed average consensus with deterministic quantization: An ADMM approach

This paper develops efficient algorithms for distributed average consensus with quantized communication using the alternating direction method of multipliers (ADMM). When rounding quantization is employed, a distributed ADMM algorithm is shown to converge to a consensus within 3 + ⌈log1+δ Ω⌉ iterations where δ > 0 depends on the network topology and Ω is a polynomial of the quantization resolution, the agents' data and the network topology. A tight upper bound on the consensus error is also obtained, which depends only on the quantization resolution and the average degree of the graph. This bound is much preferred in large scale networks over existing algorithms whose consensus errors are increasing in the range of agents' data, the quantization resolution, and the number of agents. To minimize the consensus error, our final algorithm uses dithered quantization to obtain a good starting point and then adopts rounding quantization to reach a consensus. Simulations show that the consensus error of this algorithm is typically less than one quantization resolution for all connected networks with agents' data of arbitrary magnitudes.
Annotation of Grammatemes in the Prague Dependency Treebank 2 . 0

In this paper we report our work on the system of grammatemes (mostly semantically-oriented counterparts of morphological categories such as number, degree of comparison, or tense), the concept of which was introduced in Functional Generative Description, and has been recently further elaborated in the layered annotation scenario of the Prague Dependency Treebank 2.0. We present also a hierarchical typology of tectogrammatical nodes, which is used as a formal means for ensuring presence or absence of respective grammatemes.
‘Joy is resistance’: cross-platform resilience and (re)invention of Black oral culture online

ABSTRACT Existing research has affirmed that Black people historically mastered oral communication strategies to resist subjugation and oppression by dominant groups, and have emerged as leaders in technological innovation. This article takes seriously Black users’ social media engagement and focuses particularly on Black joy online. We analyze a rich collection of discourse spanning both Twitter and Vine through which Black users utilize the affordances of both platforms to challenge dominant narratives that demean and dehumanize Black people. We argue that Black users seize upon the interplay of the applications to not only express and foster joy, but to extend historic legacies of Black oral culture and further cultivate contemporary strategies that leverage – but also transcend – the affordances of each platform.
On a keyword-lifecycle model for real-time event detection in social network data

Social networks like Twitter and Facebook have gained a significant popularity with people from all parts of the society in the past decade, providing a new kind of data source for novel social-aware applications. A great majority of the users are online all the time, posting real-time information on various topics including unpredicted events. An accident or a natural disaster is often posted on social networks hours before appearing in traditional news. In this paper, we outline a framework for real-time event detection in Twitter data. In contrast to prior works where the absolute or relative changes in the frequencies of some predefined keywords are taken into account, we introduce a lifecycle for each keyword to be observed, expressing their average behavior (e.g. average frequency changes) over time. As a motivation, we show that some keywords exhibit periodic behavior that can be handled by our model. The proposed lifecycle model enables us to define novel temporal features used by our framework in real-time event detection.
Oscillation Theorems for Certain Even Order Delay Differential Equations Involving General Means

Abstract By using the general means, we establish some oscillation theorems for the even order delay differential equation (𝑟(𝑡)|𝑥(𝑛–1)(𝑡)| α–1𝑥(𝑛–1)(𝑡))′ + 𝐹(𝑡, 𝑥[𝑔(𝑡)]) = 0, where α > 0 is a constant, , and 𝑔 ∈ 𝐶([𝑡0, ∞), ℝ). The results obtained extend and improve some results known in the literature.
Exploring Document Collections with Topic Frames

Topics automatically derived by topic models are not always easy and clearly interpretable by humans. The most probable top words of a topic may leave room for ambiguous interpretations, especially when the top words are exclusively nouns. We demonstrate how part-of-speech (POS) tagging and co-location analysis of terms can be used to derive linguistic frames that yield more interpretable topic representations. The so-called topic frames are demonstrated as feature of the TopicExplorer system that allows to explore document collections using topic models, visualizations and key word search. Demo versions of TopicExplorer are available at http://topicexplorer.informatik. uni-halle.de/ .