Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach of Harris Corner Detection of Noisy Images using Adaptive Wavelet Thresholding Technique

In this paper we propose a method of corner detection for obtaining features which is required to track and recognize objects within a noisy image. Corner detection of noisy images is a challenging task in image processing. Natural images often get corrupted by noise during acquisition and transmission. Though Corner detection of these noisy images does not provide desired results, hence de-noising is required. Adaptive wavelet thresholding approach is applied for the same. I. Introduction A corner is a point for which there are two dominant and different edge directions in the vicinity of the point. In simpler terms, a corner can be defined as the intersection of two edges, where an edge is a sharp change in image brightness. Generally termed as interest point detection, corner detection is a methodology used within computer vision systems to obtain certain kinds of features from a given image. The initial operator concept of "points of interest" in an image, which could be used to locate matching regions in different images, was developed by Hans P. Moravec in 1977. The Moravec operator is considered to be a corner detector because it defines interest points as points where there are large intensity variations in all directions. For a human, it is easier to identify a "corner", but a mathematical detection is required in case of algorithms. Chris Harris and Mike Stephens in 1988 improved upon Moravec's corner detector by taking into account the differential of the corner score with respect to direction directly, instead of using shifted patches. Moravec only considered shifts in discrete 45 degree angles whereas Harris considered all directions. Harris detector has proved to be more accurate in distinguishing between edges and corners. He used a circular Gaussian window to reduce noise. Still in cases of noisy Images, it's difficult to find out the exact number of corners. One of the most conventional ways of image de-noising is using linear filters like Wiener filter. In the presence of additive noise the resultant noisy image, through linear filters, gets blurred and smoothed with poor feature localization and incomplete noise suppression. To overcome these limitations, nonlinear filters have been proposed like adaptive wavelet thresholding approach. Adaptive wavelet thresholding approach gives a very good result for the same. Wavelet Transformation has its own excellent space-frequency localization property and thresholding removes coefficients that are inconsiderably relative to some adaptive data-driven threshold.
Reference Values for Peripheral Blood Lymphocyte Subsets in a Healthy Korean Population

Flow cytometric immunophenotyping of peripheral blood lymphocyte subsets is a powerful tool for evaluating cellular immunity and monitoring immune-mediated diseases. The numbers and proportions of blood lymphocyte subsets are influenced by factors such as gender, age, ethnicity, and lifestyle. This study aimed to establish reference ranges for peripheral blood lymphocyte subsets in a healthy Korean population. Blood samples from 294 healthy adults were collected. Lymphocyte subsets were analyzed using a single-platform method with a flow cytometer; white blood cells and lymphocytes were analyzed using an automated hematology analyzer. The mean value of the white blood cell count was 5,665 cells/µl, and the mean values of the subtype counts (percentages) were as follows: lymphocytes, 1,928 cells/µl (35.08%); CD3+ cells, 1,305 cells/µl (67.53%); CD3+CD4+ cells, 787 cells/µl (40.55%); CD3+CD8+ cells, 479 cells/µl (25.23%); CD3-CD19+ cells, 203 cells/µl (10.43%); and CD3-CD56+ cells, 300 cells/µl (15.63%). Additionally, the CD4+/CD8+ ratio was 1.81. In this study, gender and age significantly influenced blood lymphocyte subsets. Our results demonstrate that, as with other populations, a healthy Korean population has its own, region-specific, lymphocyte subset reference ranges.
A Novel Grayscale Watermarking Algorithm

In this paper,a novel grayscale watermarking algorithm based on Two-Level Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) is proposed. The watermark signal is (32×32×8) bit gray image.First,The original image is divided into blocks according to the size of the watermark;each block corresponds to each pixel value of watermark.Second,the DCT is applied in each block twice and form new blocks.Then,perform SVD on each new block to get matrices U,S and V for each block.The pixel value of watermark is embedded into the largest singular value of S matrix of each new block.And the watermark can be detected with the original image.The experimental results show that the algorithm can satisfy the transparence and robustness of the watermarking system very well. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks.
Demonstrating Ambient Search: Implicit Document Retrieval for Speech Streams

In this demonstration paper we describe Ambient Search, a system that displays and retrieves documents in real time based on speech input. The system operates continuously in ambient mode, i.e. it generates speech transcriptions and identifies main keywords and keyphrases, while also querying its index to display relevant documents without explicit query. Without user intervention, the results are dynamically updated; users can choose to interact with the system at any time, employing a conversation protocol that is enriched with the ambient information gathered continuously. Our evaluation shows that Ambient Search outperforms another implicit speech-based information retrieval system. Ambient search is available as open source software.
Query Expansion with Naive Bayes for Searching Distributed Collections

The proliferation of online information resources increases the importance of effective and efficient distributed searching. However, the problem of word mismatch seriously hurts the effectiveness of distributed information retrieval. Automatic query expansion has been suggested as a technique for dealing with the fundamental issue of word mismatch. In this paper, we propose a method - query expansion with Naive Bayes to address the problem, discuss its implementation in IISS system, and present experimental results demonstrating its effectiveness. Such technique not only enhances the discriminatory power of typical queries for choosing the right collections but also hence significantly improves retrieval results.
“Blaming The Victim” Under Memory Load

When presented with negative outcomes, people often engage in counterfactual thinking, imagining various ways that events might have been different. This appears to be a spontaneous behavior, with considerable adaptive value. Nevertheless, counterfactual thinking may also engender systematic biases in various judgment tasks, such as allocating blame for a mishap, or deciding on the appropriate compensation to a victim. Thus, counterfactuals sometimes require thought suppression or discounting, potentially resource-demanding tasks. In this study, participants made mock-jury decisions about control and counterfactual versions of simple stories. The judgments of two groups of participants, differing in their respective levels of working memory capacity, were compared. In addition, all participants held memory loads during various stages of the primary task. Lower-span individuals were especially susceptible to bias associated with the counterfactual manipulation, but only when holding memory loads during judgment. The results suggest that counterfactual thoughts arise automatically, and may later require effortful, capacity-demanding suppression.
CONSISTENCY OF A RECURSIVE ESTIMATE OF MIXING DISTRIBUTIONS

Mixture models have received considerable attention recently and Newton [Sankhyā Ser. A 64 (2002) 306-322] proposed a fast recursive algorithm for estimating a mixing distribution. We prove almost sure consistency of this recursive estimate in the weak topology under mild conditions on the family of densities being mixed. This recursive estimate depends on the data ordering and a permutation-invariant modification is proposed, which is an average of the original over permutations of the data sequence. A Rao-Blackwell argument is used to prove consistency in probability of this alternative estimate. Several simulations are presented, comparing the finite-sample performance of the recursive estimate and a Monte Carlo approximation to the permutation-invariant alternative along with that of the nonparametric maximum likelihood estimate and a nonparametric Bayes estimate.
Gender, growth, and poverty reduction

This note focuses on the core findings, and recommendations of the 1998 status report on poverty in Sub-Saharan Africa (SSA), prepared for the Special Program for Assistance for Africa (SPA), a thematic examination of the linkages between gender, growth, and poverty reduction in SSA. Primarily focused on agriculture, and the rural sector, the report argues that one of the factors constraining growth, and poverty in SSA is gender inequality in the access to, and control of a diverse range of assets. The note reviews the determinants of growth, and the interdependence of the market, and household economies, where much of women's productive work is unrecorded, (in Kenya, about sixty percent of female activities are unaccounted for, compared with only twenty four percent of male activities). Furthermore, micro-level analyses portray a consistent picture of gender-based asset inequality, pointing at patterns of disadvantage faced by women, in accessing the basic assets, and resources required for a full participation in SSA's growth potential. In education, although girls have made rapid strides in completing primary education, lowering the gender gap, differentials persist due to social, and cultural factors; and, in health, an enormous gender differential in the region's sexual, and reproductive burden of disease, is observed, as measured by deaths, and disability-adjusted life years. Recommendations include women's budget initiatives, sustained investments in education/health, and, raising the visibility of domestic work in national statistics.
Research on Scan-GMTI technology of airborne MIMO radar based on STAP

Through successive azimuth scanning with narrow beam steered to different angles, Scan-GMTI technology provides a significant way to implement wide-area surveillance and track a particular target simultaneously. Applying MIMO mode to Scan-GMTI, abundance channel and frequency resources provide significant advantages for STAP algorithm to implement. The space-time-frequency joint processing will provide an effective approach for improvement of detection of slowly moving targets, reduction of Doppler ambiguities and blind velocities.
Hidden Markov model-based packet loss concealment for voice over IP

As voice over IP proliferates, packet loss concealment (PLC) at the receiver has emerged as an important factor in determining voice quality of service. Through the use of heuristic variations of signal and parameter repetition and overlap-add interpolation to handle packet loss, conventional PLC systems largely ignore the dynamics of the statistical evolution of the speech signal, possibly leading to perceptually annoying artifacts. To address this problem, we propose the use of hidden Markov models for PLC. With a hidden Markov model (HMM) tracking the evolution of speech signal parameters, we demonstrate how PLC is performed within a statistical signal processing framework. Moreover, we show how the HMM is used to index a specially designed PLC module for the particular signal context, leading to signal-contingent PLC. Simulation examples, objective tests, and subjective listening tests are provided showing the ability of an HMM-based PLC built with a sinusoidal analysis/synthesis model to provide better loss concealment than a conventional PLC based on the same sinusoidal model for all types of speech signals, including onsets and signal transitions
Novel Insights into Guide RNA 5′-Nucleoside/Tide Binding by Human Argonaute 2

The human Argonaute 2 (hAgo2) protein is a key player of RNA interference (RNAi). Upon complex formation with small non-coding RNAs, the protein initially interacts with the 5′-end of a given guide RNA through multiple interactions within the MID domain. This interaction has been reported to show a strong bias for U and A over C and G at the 5′-position. Performing molecular dynamics simulations of binary hAgo2/OH–guide–RNA complexes, we show that hAgo2 is a highly flexible protein capable of binding to guide strands with all four possible 5′-bases. Especially, in the case of C and G this is associated with rather large individual conformational rearrangements affecting the MID, PAZ and even the N-terminal domains to different degrees. Moreover, a 5′-G induces domain motions in the protein, which trigger a previously unreported interaction between the 5′-base and the L2 linker domain. Combining our in silico analyses with biochemical studies of recombinant hAgo2, we find that, contrary to previous observations, hAgo2 is capable of functionally accommodating guide strands regardless of the 5′-base.
Decoupled Software Pipelining in LLVM 15-745 Final Project

Decoupled software pipelining [5] presents an easy way to automatically extract thread-level parallelism for general loops in any program. The compiler does this by examining the dependences of the loops in a given program, splitting the instructions of those loops into multiple smaller loops that execute in independent threads, and inserting dependence communication where necessary between these threads so that they remain synchronized. The full process is described below in detail.
Availability and Distribution of Emergency Obstetric Care Services in Karnataka State, South India: Access and Equity Considerations

Background As part of efforts to reduce maternal deaths in Karnataka state, India, there has been a concerted effort to increase institutional deliveries. However, little is known about the quality of care in these healthcare facilities. We investigated the availability and distribution of emergency obstetric care (EmOC) services in eight northern districts of Karnataka state in south India. Methods & Findings We undertook a cross-sectional study of 444 government and 422 private health facilities, functional 24-hours-a-day 7-days-a-week. EmOC availability and distribution were evaluated for 8 districts and 42 taluks (sub-districts) during the year 2010, based on a combination of self-reporting, record review and direct observation. Overall, the availability of EmOC services at the sub-state level [EmOC = 5.9/500,000; comprehensive EmOC (CEmOC) = 4.5/500,000 and basic EmOC (BEmOC) = 1.4/500,000] was seen to meet the benchmark. These services however were largely located in the private sector (90% of CEmOC and 70% of BemOC facilities). Thirty six percent of private facilities and six percent of government facilities were EmOC centres. Although half of eight districts had a sufficient number of EmOC facilities and all eight districts had a sufficient number of CEmOC facilities, only two-fifths of the 42 taluks had a sufficient number of EmOC facilities. With the private facilities being largely located in select towns only, the ‘non-headquarter’ taluks and ‘backward’ taluks suffered from a marked lack of coverage of these services. Spatial mapping further helped identify the clustering of a large number of contiguous taluks without adequate government EmOC facilities in northeastern Karnataka. Conclusions In conclusion, disaggregating information on emergency obstetric care service availability at district and subdistrict levels is critical for health policy and planning in the Indian setting. Reducing maternal deaths will require greater attention by the government in addressing inequities in the distribution of emergency obstetric care services.
Collaborative Exploration with Dynamically Configurable Sensing Agents

We present a novel approach that provides the necessary localization and mapping capabilities to allow a dynamic placement of independent sensing agents. The presented approach allows to build com- plex sensor configurations using simple agents that carry basic sensors, like e.g. low-resolution cameras, which can be combined to high preci- sion and high accuracy systems through fusion of their information and through optimized sensor placement. We discuss the mathematical background and verify the resulting sys- tem on simulated and real world data. We compare our system to other similar systems in the field of collaborative localization and mapping.
Distributed power control in ad-hoc wireless networks

Mobile ad-hoc networking involves peer-to-peer communication in a network with a dynamically changing topology. Achieving energy efficient communication in such a network is more challenging than in cellular networks since there is no centralized arbiter such as a base station that can administer power management. We propose and evaluate a power control loop, similar to those commonly found in cellular CDMA networks, for ad-hoc wireless networks. We use a comprehensive simulation infrastructure consisting of group mobility, group communication and terrain blockage models. A major focus of research in ad-hoc wireless networking is to reduce energy consumption because the wireless devices are envisioned to have small batteries and be incapable of energy scavenging. We show that this power control loop reduces energy consumption per transmitted byte by 10-20%. Furthermore, we show that it increases overall throughput by 15%.
Sparse representation for optic disk detection

Automatic detection of optic disc (OD) is a crucial step in automated eye image processing. In this paper we present a novel method that utilizes the fact that ODs exhibit similar characteristics like circular shape and serves as the region where blood vessels and nerves converge. A dictionary with training patterns, built out of sub-images, with OD at the center, is utilized. For a given test image, the method examines all subimages of the pre-determined size, and expresses them as linear combinations of basis images in the dictionary, minimizing l1-norm of the solution. The underlying assumption is that all sub-images with OD at the center, lie on a single sub-space. The proposed method was evaluated on the publicly-available database DIARETDB1 and DRIVE, with disjoint dictionary and testing images. Of the 89 images, the OD-center was detected within 2% of normalized error in 88 images. On DRIVE images we obtain accuracy of 85%.
on the Recognition of Speech, with

This paper describes a number of objective experiments on recognition, concerning particularly the relation between the messages received by the two ears. Rather than use steady tones or clicks (frequency or time-point signals) continuous speech is used, and the results interpreted in the main statistically. Two types of test are reported: (a) the behavior of a listener when presented with two speech signals simultaneously (statistical filtering problem) and (b) behavior when different speech signals are presented to his two ears.
A Generalized and Adaptive Method for Community Detection

Complex networks represent interactions between entities. They appear in various contexts such as sociology, biology, etc., and they generally contain highly connected subgroups called communities. Community detection is a well-studied problem and most of the algorithms aim to maximize the Newman-Girvan modularity function, the most popular being the Louvain method (it is well-suited on very large graphs). However, the classical modularity has many drawbacks: we can find partitions of high quality in graphs without community structure, e.g., on random graphs; it promotes large communities. Then, we have adapted the Louvain method to other quality functions. In this paper, we describe a generic version of the Louvain method. In particular, we give a sufficient condition to plug a quality function into it. We also show that global performance of this new version is similar to the classical Louvain algorithm, that promotes it to the best rank of the community detection algorithms.
Normal Vector Based Fault-Tolerant Event Boundary Detection in Wireless Sensor Networks

This paper presented a distributed fault-tolerant event boundary detection scheme for wireless sensor networks. In this paper, the node coordinate and its reading were mapped on a 3D mesh surface, By comparing the node normal vector got by Normal Vector Based Fault-Tolerant Event Boundary Detection (NVBD) with a predefined threshold, the event boundary nodes are found. This approach was composed of two major tasks: (1) detecting the faulty node and then replacing its detection value with its neighbors' weighted median value, (2) computing node normal vector using its neighbors' detection values. Then we compared it with a threshold to judge if it is a boundary node. The results of numerical simulations show that the NVBD scheme can achieve a substantial improvement on detection performance.
Semantic document engineering with WordNet and PageRank

This paper describes Natural Language Processing techniques for document engineering in combination with graph algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, we describe applications to word sense disambiguation and evaluate their accuracy on standard benchmarks.
A meta-analysis of association between glutathione S-transferase M1 gene polymorphism and Parkinson’s disease susceptibility

Abstract The aim of this meta-analysis was to evaluate whether there was an association between glutathione S-transferase M1(GSTM1)gene polymorphism and Parkinson’s disease (PD) susceptibility by pooling published data. We performed comprehensive electronic database search for articles published between February12,2015 and April30 2016. The published case-control or cohort studies related to GSTM1 gene polymorphism and Parkinson’s disease susceptibility were screened, reviewed, and included in this meta-analysis. The correlation between GSTM1 gene polymorphism and PD susceptibility was expressed by odds ratio (OR) and its corresponding 95% confidence interval (95%CI). Publication bias was evaluated by Begg’s funnel plot and Egger’s line regression test. All analysis was done by stata11.0 software. After searching the PubMed, EMBASE, and CNKI databases, seventeen case-control studies with 3,538 PD and 5,180 controls were included in the final meta-analysis. The data was pooled by a fixed-effect model for lack of statistical heterogeneity across the studies; the results showed GSTM1 null expression can significant increase the susceptibility of PD (OR=1.11, 95% CI:1.01-1.21, P<0.05). Subgroup analysis indicated GSTM1 gene polymorphism was associated with PD susceptibility in the Caucasian ethnic group (OR=1.15, 95% CI:1.05-1.27, P<0.05) but not in the Asian ethnic group (OR=0.89, 95% CI:0.70-1.12, P>0.05). Begg’s funnel plot and Egger’s line regression test showed no significant publication bias. Based on the present evidence, GSTM1 null expression can significant increase the susceptibility of PD in persons of Caucasian ethnicity.
Null hypothesis significance testing in simulation

Several papers have recently criticized the use of null hypothesis significance testing (NHST) in scientific applications of stochastic computer simulation. Their criticism can be underpinned by numerous articles from statistical methodologists. They have argued that focusing on p-values is not conducive to science, and that NHST is often dangerously misunderstood. A critical reflection of the arguments contra NHST shows, however, that although NHST is indeed ill-suited for many simulation applications and objectives it is by no means superfluous, neither in general, nor in particular for simulation.