Miracle-dz/UniFAR-FTUs huggingface dataset:
The depth-inversion problem in shallow water 1

Two Depth Inversion Algorithms (DIAs) were developed and validated using on results of computations for the shoaling of periodic waves over mild slopes, in a twodimensional numerical wave tank, based on fully nonlinear potential flow theory. The first algorithm, DIA1, uses sets of values of wave celerity c, heightH , and spatial wavelengths Lc and Lt, simultaneously measured at a number of locations xi (i = 1; :::; N) in the direction of wave propagation (e.g., using remote sensing techniques), to predict the depth variation h(xi). The second algorithm, DIA2, uses spatial wave asymmetry s2=s1 calculated from wave phase, instead of H . Results indicate that state-of-the-art depth inversion methods based on the linear dispersion relation may lead to large errors (5070%) for the depth prediction in very shallow water, whereas the present methods are 3-10 times more accurate.
A novel wearable biometric capture system

In the present paper we introduce a novel wireless system capable of simultaneously track and monitor the motion of the body and the associated muscular stress. The device provides with an array of four inertial and magnetic measurement units combined with up to 32 independent high sensitivity ADC channels. An embedded computing system does allow the high speed (4 KHz) EMG acquisition and filtering, combined with (100Hz) motion reconstruction and relevant features (20Hz) detection. The device connects to any receiving host using a portable server architecture and an extensible computing system which uses a web interface and a plugin architecture based on shared library. The device has been conceived to monitor body activities in application on-the-go, where the use of wired devices is not suitable and the need for a strict synchronizations among the motion reconstruction and the myo-electric signals is fundamental. In what follows we will describe the general architecture of the system, the embedded algorithms for motion and EMG reconstruction and the software architecture of the host system.
Through-wall tracking with radio tomography networks using foreground detection

This paper presents a novel method for tracking a moving person or object through walls using wireless networks. The method takes advantage of the motion-induced variation of received signal strength (RSS) measurements in a radio tomography network. Based on real measurements of a deployed network, we show that the RSS distribution on a wireless link can be modeled as a mixture of Gaussians. An online learning algorithm is then proposed to update the model and detect whether the link is affected by the motion. Using spatial locations of the affected links, we apply the sequential Monte Carlo (SMC) methods to track the coordinates of a moving target. Experimental results show that the proposed method achieves high tracking accuracy in time-varying environment without the need for offline training.
A Novel Adaptive Crosstalk Cancellation using Psychoacoustic Model for 3D Audio

In rendering a virtual sound over two loudspeakers, adaptive inverse filtering is required for crosstalk cancellation. Although various adaptive algorithms have been proposed for crosstalk cancellation, few have been effective especially in time-varying environments where fast convergence rate is required. Until now, the least-mean square (LMS) algorithm known for its simplicity and robustness has been the predominant algorithm used, but its convergence rate is considered slow for colored inputs. In this paper, human perceptual characteristics which have never been incorporated in an LMS algorithm is introduced. In our experiment, the proposed algorithm achieved higher perceptual accuracy and faster convergence rate than the conventional LMS algorithm.
Pattern Recognition Society Visual Surveillance for Airport Monitoring Applications

This paper presents the Object Tracking component of a complete multi-camera surveillance system that was developed as part of the AVITRACK project. The aim of the project is to automatically recognise activities around a parked aircraft in an airport apron area to improve the efficiency, safety and security of the servicing operation. The overall Object Tracking component comprises three main modules: Motion detection, frame to frame object tracking and object categorisation.
Licensed under Creative Commons Attribution Cc by Keyword Extraction Using Clustering and Semantic Analysis

Keywords are list of significant words or terms that best present the document context in brief and relate to the textual context. Extraction models are categorized into either statistical, linguistic, machine learning or a combination of these approaches. This paper introduces a model for extracting keywords by making words pairs and clustering these pairs based on the Semantic similarity that will be provided by using lesk algorithm and (WordNet), a lexical database for the English language. The model also used a statistical method to ensure clusters cohesion and provide more reliable result, because the final keywords will be selected from these clusters. This paper also show three other basic approaches to extract keywords, these approaches will be used to measure the efficient of the main approach. The proposed model showed enhanced over the three other approaches in both precision and recall.
Measuring Anonymity of Pseudonymized Data After Probabilistic Background Attacks

There is clear demand among organizations for sharing their data for mining and other purposes without compromising the privacy of individual objects contained in the data. Pseudonymization is a simple, yet widely employed technique for sanitizing such data prior to its release; it replaces identifying names in the data by pseudonyms. Well-known metrics already exist in the literature for measuring the amount of anonymity still contained in some pseudonymized data in the aftermath of an infeasibility background attack. While the need for a metric for the much wider and more realistic class of probabilistic background attacks has also been well identified, currently no such metric exists. We fulfill that long identified need by presenting two metrics, an approximate and a more exact one, for measuring anonymity in pseudonymized data in the wake of a probabilistic attack. These metrics are rather intractable, thus impractical to employ in real-life situations. Therefore, we also develop an efficient heuristic for our superior metric, and show the remarkable accuracy of our heuristic. Our metrics and heuristic assist a data owner in evaluating the safety level of pseudonymized data against probabilistic attacks before making a decision on its release.
AMCIS2004 Submission to Text and Data Mining for Decision Support 1

The rapid growth of online investing and virtual investing-related communities (VICs) has a wideraging impact on research, practice and policy. In this context, this research addresses how information is generated, discussed, and diffused within and across VICs, and how such activities impact market efficiency. Regulators are particularly interested given the potential for fraud and spreading of false rumors. However, understanding information processing in VIC is a challenge given enormity of posted messages. Automated analysis of these messages is primarily complicated by three factors: (a) the amount of irrelevant messages or "noise" messages (e.g., spam, insults), (b) the highly unstructured nature of the text (e.g., abbreviations), and finally, and (c) the wide variation in what is considered relevant information for a given company. We have developed a mechanism relying on commonly occurring terms and a set of classifying criteria to identify: (1)"noisy" messages that bear no relevance to the topic at hand, (2) messages that have relevance to the topic at hand, but do not express an opinion as to the quality of the investment, and (3) messages that are both relevant and express a sentiment about the quality of the investment. To test our mechanism we have collected approximately 3 million messages related to 46 stocks over a 2-year period. Preliminary results show sufficient promise to classify messages and how participants react. Preliminary results show the classifier a classification accuracy of 54% .
Contested landscapes: territorial conflicts and the production of different ruralities in Brazil

ABSTRACT From the perspective of Brazilian agrarian geography, the conflicts generated by land tenure disputes have as protagonists the families of the Landless Workers’ Movement (Movimento dos Trabalhadores Rurais Sem Terra), traditional landowners, and agribusiness corporations. In this article, we present a reflection on land conflicts and the spatial and territorial production of latifundia, peasant smallholders, and large-scale agribusiness through studies of the landscapes of two municipalities in the state of São Paulo. Among the various tools for analysing territorial disputes, Google Earth can be used for local, regional, and international comparative studies of how landscapes have been transformed. Informed by traditional research, the article analyses a diversity of photographs, from satellite to field shots, as evidence of landscapes that express conflicting social relations in disputes over different models of social and territorial development.
Ring ORAM: Closing the Gap Between Small and Large Client Storage Oblivious RAM

We present Ring ORAM, a simple and low-latency ORAM construction that can be parameterized for either small or large client storage. Simply by tuning parameters, Ring ORAM matches or exceeds the performance of the best-known small and large client storage schemes and can achieve a constant factor online bandwidth overhead over insecure systems. We evaluate Ring ORAM in theory and in practice. On the theory side, we prove that Ring ORAM matches the asymptotic bandwidth and client storage of Path ORAM, the prior-art scheme for small client storage. Tuning parameters for small client storage, Ring ORAM reduces overall bandwidth relative to Path ORAM by a factor of 2.7× and reduces online bandwidth to constant (a 57× improvement over Path ORAM given realistic parameters). Tuning parameters for large client storage, Ring ORAM outperforms Path ORAM (which is given equal storage) by 4.5× and SSS ORAM, the prior-art scheme for large client storage, by 16-33%. Using secure processors as a case study for small client storage, Ring ORAM on average reduces ORAM response time by nearly 5× and improves workload completion time by 2.75×, relative to Path ORAM. In the large storage setting, running an enterprise file system trace with bursty requests, Ring ORAM adds negligible overhead to response time given a > 100 Mbps network bandwidth. By comparison, Burst ORAM incurs large overheads in response time unless network bandwidth > 350 Mbps. These results suggest that Ring ORAM is a compelling construction in both large client storage (e.g., file server) and small client storage (e.g., remote secure processor) settings.
Decoupling Policy from Practice: The Case of Stock Repurchase Programs

This study examines firms' decoupling of informal practices from formally adopted policies through analysis of the implementation of stock repurchase programs by large U.S. corporations in the late 1980s and early 1990s, when firms were experiencing external pressures to adopt policies that demonstrate corporate control over managerial behavior. We develop theory to explain variation in the responses of firms to such pressures, i.e., why some firms acquiesce by actually implementing stock repurchase programs, while others decouple formally adopted repurchase programs from actual corporate investments, so that the plans remain more symbolic than substantive. Results of a longitudinal study of stock repurchase programs over a six-year time period show that decoupling is more likely to occur when top executives have power over boards to avoid institutional pressures for change and when social structural or experiential factors enhance awareness among powerful actors of the potential for organizational decoupling. The study has implications for future research on decoupling, organizational learning, and corporate governance.
Licensed under Creative Commons Attribution Cc by Supporting Privacy Protection in Personalized Web Search with Secured User Profile

Web search engines (e.g. Google, Yahoo, Microsoft Live Search, etc.) are widely used to find certain data among a huge amount of information in a minimal amount of time. These useful tools also pose a privacy threat to the users: web search engines profile their users by storing and analyzing past searches submitted by them. For improving better search quality the String Similarity Match Algorithm (SSM Algorithm) can be implemented with the proposed. Current solutions propose new mechanisms that introduce a high cost in terms of computation and communication, to address this privacy threat. Personalized search is a promising way to improve the accuracy of web search, also it is attracting much attention recently. Effective personalized search requires collecting and aggregating user information, which often raises serious concerns of privacy infringement for many users. These concerns have become one of the main barriers for deploying personalized search applications, and privacy-preserving personalization is a great challenge. Adversaries are tried to resist in proposed system with the help of broader background knowledge (i.e. richer relationship among topics). Richer relationship means we generalize the user profile results by using the background knowledge which is going to store in history. Through this we can hide the user search results. With the help of this mechanism, privacy can be achieved. 1. Introduction Novel protocol is proposed specially designed to protect the users' privacy in front of web search profiling. Adversaries are tried to resist in proposed system with the help of broader background knowledge i.e. knowing richer relationship among topics. Richer relationship means we generalize the user profile results by using the background knowledge which is going to store in history. Through this we can hide the user search results. In the existing System, Greedy DP and, Greedy IL algorithm it takes large computational and communication time. For generalize the retrieved data by using the background knowledge [1], [5], [3], [7] through this adversaries can be avoided. Privacy protection in publishing transaction data is an important problem. A key feature of data transaction is the extreme scarcity, which renders any single technique ineffective in anonymizing such data. Among recent works, some suffer from performance drawbacks, some incur high information loss and some result in data hard to interpret. This approach proposes to integrate generalization and compression to reduce information loss. However, the integration is non-trivial. Novel techniques are proposed to address the efficiency and scalability …
Ultrafast and selective reduction of sidewall roughness in silicon waveguides using self-perfection by liquefaction

We use a novel technique, self-perfection by liquefaction (SPEL), to smooth the rough sidewalls of Si waveguides. An XeCl excimer laser with 308 nm wavelength and 20 ns pulse duration is used to selectively melt the surface layer of the waveguide. This molten layer flows under surface tension and this results in smooth sidewalls upon resolidification. Our experimental results show that this technique reduces the average sidewall roughness (1σ) from 13 to 3 nm. Our calculations show that the waveguide transmission loss due to sidewall roughness in these waveguides would be reduced from 53 to 3 dB cm−1, an improvement with light transmission five orders of magnitude greater. Due to a low viscosity of molten Si (below water), SPEL can be achieved on a Si surface within ∼100 ns. This short time, together with SPEL’s material selectivity, makes it possible to repair defective components on a chip without damaging surrounding components and materials, making SPEL a promising candidate for defect repair in integrated optics and nanophotonics.
An Investigation into the Characteristics of Merged Code Clones during Software Evolution

SUMMARY Although code clones (i.e. code fragments that have similar or identical code fragments in the source code) are regarded as a factor that increases the complexity of software maintenance, tools for supporting clone refactoring (i.e. merging a set of code clones into a single method or function) are not commonly used. To promote the development of refactoring tools that can be more widely utilized, we present an investigation of clone refactoring carried out in the development of open source software systems. In the investigation, we identified the most frequently used refactoring patterns and discovered how merged code clone token sequences and
Pml 39 , a novel protein of the nuclear periphery required for nuclear retention of improper mRNPs

A novel pre-mRNP retention factor at NPC Abstract Using a genetic screen, we have identified a previously uncharacterized S. cerevisiae ORF (renamed PML39) that displays a specific interaction with nucleoporins of the Nup84 complex. Localization of a Pml39-GFP fusion and two-hybrid studies revealed that Pml39 is mainly docked to a subset of nuclear pore complexes opposite to the nucleolus through interactions with Mlp1 and Mlp2. The absence of Pml39 leads to a specific leakage of unspliced mRNAs that is not enhanced upon MLP1 deletion. In addition, overexpression of PML39-GFP induces a specific trapping of mRNAs transcribed from an intron-containing reporter and of the hnRNP Nab2 within discrete nuclear domains. In a nup60∆ mutant, Pml39 is mislocalized together with Mlp1 and Mlp2 in intranuclear foci that also recruit Nab2. Moreover, pml39∆ partially rescues the thermosensitive phenotypes of mRNP assembly mutants, indicating that PML39 deletion also bypasses the requirement for normally assembled mRNPs. Altogether, these data indicate that Pml39 is an upstream effector of the Mlps, involved in the retention of improper mRNPs in the nucleus prior to their export.
Inhibition of Neuropathic Pain by Selective Ablation of Brainstem Medullary Cells Expressing the μ-Opioid Receptor

Neurons in the rostroventromedial medulla (RVM) project to spinal loci where the neurons inhibit or facilitate pain transmission. Abnormal activity of facilitatory processes may thus represent a mechanism of chronic pain. This possibility and the phenotype of RVM cells that might underlie experimental neuropathic pain were investigated. Cells expressing μ-opioid receptors were targeted with a single microinjection of saporin conjugated to the μ-opioid agonist dermorphin; unconjugated saporin and dermorphin were used as controls. RVM dermorphin–saporin, but not dermorphin or saporin, significantly decreased cells expressing μ-opioid receptor transcript. RVM dermorphin, saporin, or dermorphin–saporin did not change baseline hindpaw sensitivity to non-noxious or noxious stimuli. Spinal nerve ligation (SNL) injury in rats pretreated with RVM dermorphin–saporin failed to elicit the expected increase in sensitivity to non-noxious mechanical or noxious thermal stimuli applied to the paw. RVM dermorphin or saporin did not alter SNL-induced experimental pain, and no pretreatment affected the responses of sham-operated groups. This protective effect of dermorphin–saporin against SNL-induced pain was blocked by β-funaltrexamine, a selective μ-opioid receptor antagonist, indicating specific interaction of dermorphin–saporin with the μ-opioid receptor. RVM microinjection of dermorphin–saporin, but not of dermorphin or saporin, in animals previously undergoing SNL showed a time-related reversal of the SNL-induced experimental pain to preinjury baseline levels. Thus, loss of RVM μ receptor-expressing cells both prevents and reverses experimental neuropathic pain. The data support the hypothesis that inappropriate tonic-descending facilitation may underlie some chronic pain states and offer new possibilities for the design of therapeutic strategies.
1 Visual Prosthesis 2 Microelectronic implants that provide identification of simple objects and motion 3 detection for blind patients have been tested and evaluated; further development is 4 needed for face recognition and reading implants.

Electronic visual prostheses have demonstrated the ability to restore a rudimentary sense of vision to blind individuals. This review paper will highlight past and recent progress in this field as well as some technical challenges to further advancement. Retinal implants have now been tested in humans by four independent groups. Optic nerve and cortical implants have been also been evaluated in humans. The first implants have achieved remarkable results, including detection of motion and distinguishing objects from a set. To improve on these results, a number of research groups have performed simulations that predict up to 1000 individual pixels may be needed to restore significant functions such as face recognition and reading. In order to achieve a device that can stimulate the visual system in this many locations, issues of power con- sumption and electronic packaging must be resolved.
Robust structure selection of radial basis function networks for nonlinear system identification

This paper proposed a robust structure selection method of radial basis function (RBF) networks for nonlinear system identification problems. A greedy algorithm is first employed by combining information criteria with the forward stepwise selection to choose the RBF network structures. Then, a robust selection procedure, which can select a concise and generalized network structure, is developed based on the forward stepwise selection and the subsampling method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method by using the disturbance storm time index data.
Eliminators and classifiers

Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with K possibilities a decision support system may still be useful in such cases if instead of classification elimination of improbable classes is done. Eliminators may be constructed using classifiers assigning new cases to a pool of several classes instead of just one winning class. Elimination may be done with the help of several classifiers using modified error functions. A real life medical example is presented illustrating the usefulness of elimination.
RFI shielding and mitigation techniques for a sensitive search for the 327 MHz line of Deuterium

An array of 24 stations, each with 24 crossed-dipoles, has been built at the Haystack Observatory in Westford MA. This array has been designed to make a sensitive search for the 327 MHz spectral line of Deuterium. Since the deuterium line is expected to be about 50 dB weaker than the 1420 MHz hydrogen line the amelioration of RFI is the major challenge for the "Deuterium array". Locally generated RFI both from the array and from nearby sites has been reduced by extensive shielding and in some cases the removal of consumer electronics, like certain digital answering machines, which emit strong signals in the 327.3 to 327.5 MHz band of prime importance for the search. Since almost all the RFI comes from the horizon the station array has parasitic directors added to the dipoles to reduce the response at the horizon. A RFI monitor with 12 active Yagi antennas pointed every 30 degrees in azimuth provides a means of determining the direction of the RFI and information of frequencies and time spans with need to be excised from the array data. We present details of the array design, the RFI excision algorithms, levels of spectral and continuum RFI measured at the observatory and the performance of the array from initial long integrations from a subset of the full array.