Miracle-dz/UniFAR-FTUs huggingface dataset:
On noise reduction for enhancement of speech in mobile telephony applications

Noise reduction for mobile telephony applications is considered. A power spectral density error analysis is applied to the method of power subtraction. A minimum power spectral density error subtraction factor is derived, where the resulting factor depends on introduced quality factors. The outcome of the analysis is compared with experimental work as well as standardized noise reduction schemes.
The Influence of Galactic Outflows on the Formation of Nearby Dwarf Galaxies

We show that the gas in growing density perturbations is vulnerable to the influence of winds outflowing from nearby collapsed galaxies that have already formed stars. This suggests that the formation of nearby galaxies with masses ≲109 M☉ is likely to be suppressed, irrespective of the details of galaxy formation. An impinging wind may shock-heat the gas of a nearby perturbation to above the virial temperature, thereby mechanically evaporating the gas, or the baryons may be stripped from the perturbation entirely if they are accelerated to above the escape velocity. We show that baryonic stripping is the most effective of these two processes, because shock-heated clouds that are too large to be stripped are able to radiatively cool within a sound crossing time, limiting evaporation. The intergalactic medium temperatures and star formation rates required for outflows to have a significant influence on the formation of low-mass galaxies are consistent with current observations, but may soon be examined directly via associated distortions in the cosmic microwave background and with near-infrared observations from the Next Generation Space Telescope, which may detect the supernovae from early-forming stars.
Detecting and recognizing numerical strings in Farsi document images

In this paper, we propose a new approach for detecting and recognizing numerical strings in Farsi/Arabic handwritten or machine-printed document images. We assign a label to each of the connected components as they belong to a numerical string or not. First, in order to differentiate between digit and non-digit connected components, some simple features are extracted from all connected components in each text line. Then, these features are classified with a fuzzy rule-based classifier to extract some candidate strings. After using a digit recognizer, syntax of the numerical strings are validated by a syntactic verifier. Experimental results show an acceptable detection rate with low false positive rate.
Height measurement as a session-based biometric for people matching across disjoint camera views

A surveillance session can be defined as the period between an individual’s entrance into a surveillance system, and their exit. Utilising this framework enables personal features, such as clothing and height estimates, to become invariant for the duration of the surveillance session. These can then be used as session-based biometrics for matching an individual uniquely as they move throughout the surveillance system, which may include significantly disjoint cameras. By utilising a hierarchy of biometrics to match individuals one can improve the speed of the overall matching. This eliminates unlikely matches quickly by using biometrics that are fast to calculate first. This paper proposes height estimation as an example of such a session-based biometric that would be used earlier in the hierarchy. It demonstrates how statistical height analysis can be quickly calculated as one of the session-based biometric components used to match the tracks of the same individual through image sequences taken by two disjoint camera views in the same surveillance session.
Toward a Flexible Ability Selection Mechanism for Personal Assistant Agent using Ontology Reasoning

In this paper, we present a flexible ability selection mechanism to realize cooperative task execution based on ontology reasoning techniques. We prepare an ability ontology as meta-data of agentsf behaviors, and also present a mechanism that agents can flexibly select behaviors from agentsf current abilities using inference-enabled SPARQL endpoints. Agentsf behaviors are managed by their own goals. Those abilities can be used to accomplish optional tasks as well as finding and utilizing alternative abilities using ontology reasoning.
On the interaction between time and frequency filtering of speech parameters for robust speech recognition

One of the great today's challenges in speech recognition is to ensure the robustness of the used speech representation. Usually, the recognition rate is strongly reduced when the speech is corrupted, e.g. by convolutional or additive noise, and the speech features are not designed to be robust. In this paper we study the effect of additive noise on the logarithmic filter-bank energy representation. We use time and frequency filtering techniques to emphasize the discriminative information and to reduce the mismatch between noisy and clean speech representation. A 2-D spectral representation is introduced to see the regions most affected by noise in the 2-D quefrency-modulation frequency domain and to help to design the frequency and time filter shapes. Experiments with one and two dynamic feature sets show the usefulness of the combination of time and frequency filtering for both, white and low-pass noise speech recognition. At the end the power time and frequency filtering technique is presented.
Institute for Research on Poverty Discussion Papers Estimating the Returns to a Secondary Education for Female Dropouts Estimating the Returns to a Secondary Education for Female Dropouts

Department of Health and Human Services. The opinions and conclusions expressed in this paper are those of the author and do not necessarily reflect the opinions or policy of the DHHS or the IRP. ABSTRACT Data from the National Longitudinal Survey of Youth is used to empirically estimate the impact on the earnings capacities of young female dropouts if they were to return to complete either a regular high school education or a General Educational Development (GED) degree. To reduce the potential upward bias on these estimated rates of return, dropouts are allowed to have lower levels of innate ability and lower rates of human capital accumulation in school. After controlling for the sample selection bias associated with the observation of wage rates among only employed women, the rates of return for the average dropout are estimated to be 10.2 percent for a high school diploma and 6.2 percent for a GED degree. After allowing for self-selection in the decision of whether or not to complete a secondary education, these estimated rates of return actually rise slightly to 10.9 percent and 6.5 percent, respectively. Because of differences in other productivity characteristics, this education would only eliminate up to one-third of the substantial gap that already exists between the earnings capacities of dropouts and secondary school completers. There is renewed optimism in this country over the efficacy of general educational attainment in alleviating poverty. ' Perhaps this is a by-product of recent concerns that the government has neglected basic education and that income and in-kind transfer programs have failed to substantially reduce poverty rates. The idea is that economic self-sufficiency can best be promoted by encouraging or requiring individuals who are at risk of welfare recipiency to increase their educational attainment. The Family Support Act of 1988 revised the national Aid to Families with Dependent Children (AFDC) program. To reduce long-term welfare dependence, states are now required to set up a Job Opportunities and Basic Skills (JOBS) program. Although states have a great deal of latitude in choosing the mix of services that will be provided to welfare recipients under their JOBS programs, some states have decided to include basic and remedial education in these packages. For example, a recent welfare reform proposal in Missouri would require AFDC recipients who have not completed their high school education and who are not exempt because of home responsibilities to work toward their high school …
CHARACTERIZATION OF A COOPERATIVE TARGET FOR GROUND-PENETRATING RADAR

A cooperative target (CT) has been developed to enhance the ground-penetrating radar (GPR) signal-toclutter ratio for buried man-made targets. Applications include tagging high-value buried structures and monitoring microtunnelling equipment. Results are presented for a time-domain CT, comprised of a dipole antenna connected to an unterminated delay line. Multiple GPR measurements made from separate positions, together with a priori knowledge of the dielectric properties of the intervening ground, allow good estimation of the position of the tagged target through triangulation. By using several independent time-domain cooperative targets, strategically arrayed about a target, the rotational aspect of the target can also be obtained. Finally, harmonic generation is demonstrated as a technique for a frequency-domain CT.
Filter design by using map information on Wireless-LAN location awareness system

Technologies to identify people and determine their positions are developing rapidly day by day. Many means exist to estimate a person's position. Mainly, wireless sensors are used. Nevertheless wireless sensors have problems in accuracy. Because of multipath problem, wireless sensors cannot actually measure one's position as the level of their specifications. This causes some fatal problems to estimate an area where person is. In this paper, we present a way to improve position information by filtering data which was obtained by a wireless location awareness system. We also show designed filter which is based on particle filter is effective by simulations and experiments.
Architectural Comparisons for a Quantum Monte Carlo Application

Recent technological advances have led to a number of emerging platforms such as multi-cores, reconfigurable computing, and graphics processing units. We present a comparative study of multi-cores, field-programmable gate arrays, and graphics processing units for a Quantum Monte Carlo chemistry application. The speedups of these implementations are measured relative to a multi-core implementation and the accuracy of each implementation compared against the double-precision CPU implementation. The Brook+ AMD implementation shows the best overall speedup and accuracy, with the mixed-precision NVIDIA CUDA implementation outperforming the Brook+ implementation for larger atomic clusters with a slightly lower accuracy than the Brook+ and FPGA implementations. KeywordsQuantum Monte Carlo, Field-Programmable Gate Arrays, Multi-core processors, Graphics Processing Units.
Successful Advanced Maternal Age Pregnancy with Mosaic Turner Syndrome Conceived after Ovulation Induction with Clomiphene Citrate: A Case Report

Turner women typically experience gonadal dysfunction that results in amenorrhea and sterility. We encountered a case of mosaic Turner syndrome where conception was possible after ovulation induction with clomiphene citrate (CC). The patient's ovaries were overresponsive to induction with CC. The challenges and successful outcome are reported.
An Agent-based Simulation Study for Exploring Organizational Adaptation

Agile enterprises with different organizational structures manifest different organizational behaviors when responding to environmental changes. In this paper, we use a computational model to examine organizational adaptation on four dimensions: Agility, Robustness, Resilience, and Survivability. We analyze the dynamics of organizational adaptation by a simulation study on the interaction between tasks and organization in a sales enterprise. The `what if' analyses in different scenarios show that more flexible communication between employees and less hierarchy level with the suitable centralization can improve organizational adaptation. The developed simulation model supports the exploration of the parametric space that defines alternative organizing processes for the optimal strategy given the specified environmental dynamics.
An improved design approach in spatial databases using frequent Association Rule Mining algorithm

Recently Negative Association Rule Mining (NARM) has become a focus in the field of spatial data mining. Negative association rules are useful in data analysis to identify objects that conflict with each other or that complement each other. Much effort has been devoted for developing algorithms for efficiently discovering relation between objects in space. All the traditional association rule mining algorithms were developed to find positive associations between objects. By positive correlation we refer to associations between frequently occurring objects in space such as a city is always located near a river and so on. Recently the problem of identifying negative associations (or “dissociations”) that is absence of objects has been explored and considered relevant. This paper presents an improved design approach for mining both positive and negative association rules in spatial databases. This approach extends traditional association rules to include negative association rules using a minimum support count. Experimental results show that this approach is efficient on simple and sparse datasets when minimum support is high to some degree, and it overcomes some limitations of the previous mining methods. The proposed form will extend related applications of negative association rules to a greater extent.
Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method.
High Proportion of HIV Serodiscordance among HIV-Affected Married Couples in Northern Vietnam

Introduction Little is known about the state of HIV transmission among married couples in Vietnam. This study aims to clarify HIV serostatus in this group and elucidate risk factors for intra-marital HIV transmission. Methods In 2012, we enrolled a group of HIV-positive married men registered at the HIV outpatient clinic of a referral hospital in northern Vietnam, along with their wives. Sociodemographic, behavioural and clinical data were collected from men and wives. HIV serodiscordant couples were followed until March 2014 to determine seroconversion rate. A phylogenetic analysis was performed based on env V3 sequence to detail cluster formation among men. Results Of the 163 HIV-positive men enrolled in the study, 101 (62.0%) had wives testing HIV-negative. Half of men reported injecting drug use (IDU) as a likely transmission route. Couples reported a high incidence of unprotected sexual intercourse prior to diagnosis; the median (inter quartile range) was 4 (4–8) times per month. Only 17 couples (10.4%) reported using condoms during at least half these instances. Multivariable analysis revealed IDU history among men was independently associated with HIV-negative wives (adjusted OR 0.31; 95% CI 0.10–0.95, p=0.041). Phylogenetic analysis of 80 samples indicated CRF01_AE. Of these, 69 (86.3%) clustered with IDU-associated viruses from Vietnam. No HIV seroconversion was identified during a follow-up of 61 serodiscordant couples, with 126.5 person-years of observation during which HIV-infected men were on antiretroviral drug therapy (ART). Conclusion High HIV serodiscordance was observed among HIV-affected married couples in northern Vietnam. A large number of at-risk wives therefore remain HIV-negative and can be protected with measures including proper use of ART if couples are made aware of the serodiscordance through screening.
Modelisation and representation of expression data in Ascidians: The virtual ascidian embryo project

Ascidians are marine invertebrate chordates, which display a larval body plan similar to that of vertebrates. Their embryos are composed of a small number of cells and develop with an invariant cell lineage. In addition, genomes for two species are available [1]. These characteristics make this system a unique opportunity to understand the developmental strategies of a chordate at the embryological, genetic and molecular level. While more and more data become available for this model, the need of an informatics system able to correlate different kind of information with an efficient knowledge representation system becomes essential. The ANISEED (Ascidian Network for In Situ Expression and Embryological Data) system has been designed to offer an advanced database able to relate expression data with other knowledge fields such as lineage, induction, cell fates, genomic information, or blastomere position and morphology in the developing embryo. Data can be accessed through a web portal, but ANISEED also provides a virtual 3D embryo to display the information in an interactive manner.
Hilti: an Abstract Execution Environment for High‐performance Network Traffic Analysis

Applications Analysis and Control ") and NSF‐0915667 (" A High‐Performance Abstract Machine for Network Intrusion Detection "). Abstract When building applications that process large volumes of network traffic—such as high‐ performance firewalls or intrusion detection systems—one faces a striking gap between the ease with which the desired analysis can often be described in high‐level terms, and the tremendous amount of low‐level implementation details one must still grapple with for coming to an efficient and robust system. We present a novel environment that provides a bridge between these two levels by offering to the application designer the high‐level abstractions required for effectively describing typical network analysis tasks, while still ensuring the performance necessary for monitoring Gbps networks in operational settings. This new middle‐ layer comprises two main pieces: an abstract machine model that is specifically tailored to the networking domain and directly supports the field's common abstractions and idioms in its instruction set; and a compilation strategy for turning programs written for the abstract machine into highly optimized, natively executable task‐parallel code for a given target platform. We present the design and an early prototype of the new environment and discuss opportunities for extensive compile‐time code optimizations that our approach enables by leveraging domain‐specific context. Such an environment holds promise for unleashing the community's potential to build libraries of efficient analysis functionality, reusable across a wide range of scenarios.
Selecting and Using Sample Papers in Holistic Evaluation.

Anyone experienced in holistic scoring understands the vital importance of sample papers. By general consensus they are, next to the chief reader, the single most important element in a successful scoring session. But if test developers or chief readers look to scholarship for help in selecting and using sample papers, they will not find much. In the literature on writing assess ment in general and holistic evaluation specifically, the selection and use of sample papers is an issue lost among larger concerns of reliability and validity (Charney) or among more "obvious" concerns like writing prompts (Ruth and Murphy). The variety of names for sample papers? anchor papers, prototypes, range-finders? suggests their multiple characteristics, manifold functions, and problematic nature. A review of the literature shows that sample papers are supposed to fit all of the following descriptions: papers with attributes that characterize each score level and exemplify various point scores (Cooper 41); papers which have "the desired normative distribution" (Miles Myers 32); papers that are representative, especially of the middle range of a single score (Northwest 65); papers "which illustrate the full range of performance on the assignment" (Odell and Cooper 36); papers that "virtually all readers" will agree upon (Spandel and Stiggins 20); and papers that illustrate "the dis tinctions in quality described by the scoring guide" (White, Teaching 160). While these are overlapping descriptions, the differences among them complicate the role of sample papers while indicating little about selecting and using them. Assessment literature often sidesteps the troubling nature of sample papers with a common refrain: "It all depends on the particular testing situation. " Despite the appropriateness of this qualification (and its useful ness will become evident as we continue), it cannot serve as the answer to all the critical questions which precede a reading. Our purpose here is to outline the series of decisions involved in selecting and then using sample papers. We want, in particular, to make test-developers aware that they have choices to make and that their choices will have serious consequences for student and evaluator alike.
Two-view structure-from-motion algorithm

Structure from motion (SfM) is an important task in computer vision. Two-view SfM, which means SfM from a pair of images, is the foundation of incremental SfM methods and is quite useful in some real applications. Based on the analysis of camera geometry, this paper proposes a two-view SfM algorithm. The proposed algorithm is able to use the rotation information naturally. Simulation and experimental results show that the proposed two-view algorithm is feasible.
Apriori Based Novel Frequent Itemset Mining Mechanism

Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. In computer science and data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. Other algorithms are designed for finding association rules in data having no transactions, or having no timestamps. Frequent itemset mining is not a real-time system, so the precise speed of execution is not especially important. What is important is the ability to process datasets that are otherwise simply too large from which to extract meaningful patterns. In this paper proposed an efficient frequent itemset mining using Priori algorithm which naturally lends it to sorting because, without any loss in efficiency, every step of it can be designed to either create or preserve sort order. This allows us to improve every step of the original algorithm. We believe that this work opens up many avenues for yet more pronounced improvement. Given the locality and independence of the data structures used, they can be partitioned quite easily.