Miracle-dz/UniFAR-FTUs huggingface dataset:
A survey of types of text noise and techniques to handle noisy text

Often, in the real world noise is ubiquitous in text communications. Text produced by processing signals intended for human use are often noisy for automated computer processing. Automatic speech recognition, optical character recognition and machine translation all introduce processing noise. Also digital text produced in informal settings such as online chat, SMS, emails, message boards, newsgroups, blogs, wikis and web pages contain considerable noise. In this paper, we present a survey of the existing measures for noise in text. We also cover application areas that ingest this noisy text for various tasks like Information Retrieval and Information Extraction.
PELIPUTAN INVESTIGASI, PROFESIONALISME WARTAWAN INVESTIGASI DAN INTERPLAY ANTARA STRUKTUR DAN AGENCY (Studi Kasus Dalam Praktiknya di majalah Tempo)

Background of this study is the phenomenon of investigative reporting practice in relation to the journalist's professionalism. This research method is the case study. This research focuses oninvestigative reporting practice in Tempo magazine; journalist's professionalism in investigating in perspective of competency standards of reporter; and interplay between structure and agency. The conclusion is journalist have competency to conduct investigative reporting on the basis of good experience. In the implementation of journalist-competency standard, detail process of investigation can not be conducted according to working procedure. The fact shows that investigative reporting ofTempo is earlier in situation anticipation. In the process of investigative reporting,the role of structure and agency gives freedom to each other for journalists. Investigation team's rule of Tempo enables and constrains according to rule and resources. But, Tempo investigative reporting team has its own standards. In certain cases of investigative reporting, Tempo reporters implement higher level of competency than journalist-competency standard. Related to Structuration theory between structure and agency structure in reality, structure do not overburden team performance.The structure does not hold a decisive role. Interplay does not depend on structure. Other researchers should conduct observation research in media which apply competency test for reporters from other agencies.
Performance Optimization and FPGA Implementation of Real-Time Tone Mapping

This brief analyzes the performance of the hardware-based tone-mapping operators for compression of high-dynamic-range images. The bottlenecks of a tone-mapping system are determined, and a high-performance field-programmable gate array (FPGA) implementation of an operator is introduced. The operator utilizes a polynomial mapping technique, which is adaptive to the pixel values, thus preserving high-contrast areas. The technique is further optimized for the presented resource-efficient FPGA implementation. We show that the timing optimization does not reduce the image quality by obtaining a high peak signal-to-noise ratio of the resulting images. The timing comparison to the similar implementations shows 2.5 times increase in the achieved throughput, irrespective of the hardware platform.
Implementing and Sustaining Empowerment

This article explores the experiences of two organizations, one nonprofit and the other forprofit, seeking to create a culture of empowerment. Both organizations make a strong start and achieve significant change that is reflected in qualitative observations, quantitative measures of empowerment, and organizational performance results. The for-profit organization manages to sustain ongoing change in the direction of greater empowerment; however, the nonprofit loses focus and eventually drops the change effort. The article compares the two cases paying particular attention to contextual factors to better understand what may have lead to the different outcomes and then closes with ten lessons for sustaining empowerment efforts.
Phylogeography, population structure and dispersal patterns of the beluga whale Delphinapterus leucas in the western Nearctic revealed by mitochondrial DNA

The recent evolutionary history, population structure and movement patterns of beluga whales in the western Nearctic were inferred from an analysis of mitochondrial DNA control region sequence variation of 324 whales from 32 locations representing five summer concentration areas in Alaska and north‐west Canada. Phylogenetic relationships among haplotypes were inferred from parsimonious networks, and genetic subdivision was examined using haplotypic frequency‐based indices and an analysis of variance method modified for use with interhaplotypic distance data. MtDNA relationships were characterized by a series of star‐like phylogenies which, when viewed in conjunction with information on haplotype frequency and distribution, suggested a rapid radiation of beluga whales into the western Nearctic following the Pleistocene, and an early divergence of the Beaufort Sea from the Chukchi and Bering Seas subpopulations. Overall nucleotide diversity was low (0.51%) yet all major summering concentrations were significantly differentiated (ΦST= 0.33) from one another. Stratification of samples by gender and age from the three northernmost subpopulations suggested that female cohorts from neighbouring subpopulations were more differentiated than males. Further stratification of adult animals by age revealed that older adults were substantially less subdivided among locations than younger adults, particularly for males, suggesting that dispersal, although limited, is biased toward older adult males. Overall, the patterns of mtDNA variation in beluga whales indicated that the summering concentrations are demographically, if not phyletically distinct. Population structure appears to be maintained primarily by natal homing behaviour, while asymmetries in dispersal may be associated with the type of mating system.
Providing Efficient Privacy-Aware Incentives for Mobile Sensing

Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Existing work on privacy-aware incentive is limited to special scenario of mobile sensing where each sensing task needs only one data report from each user, and thus not appropriate for generic scenarios in which sensing tasks may require multiple reports from each user (e.g., in environmental monitoring applications). In this paper, we propose a privacy-aware incentive scheme for general mobile sensing, which allows each sensing task to collect one or multiple reports from each user as needed. Besides being more flexible in task management, our scheme has much lower computation and communication cost compared to the existing solution. Evaluations show that, when each node only contributes data for a small fraction of sensing tasks (e.g, due to the incapability or disqualification to generate sensing data for other tasks), our scheme runs at least one order of magnitude faster.
Fighting experience alters brain androgen receptor expression dependent on testosterone status

Contest decisions are influenced by the outcomes of recent fights (winner–loser effects). Steroid hormones and serotonin are closely associated with aggression and therefore probably also play important roles in mediating winner–loser effects. In mangrove rivulus fish, Kryptolebias marmoratus, individuals with higher testosterone (T), 11-ketotestosterone and cortisol levels are more capable of winning, but titres of these hormones do not directly mediate winner–loser effects. In this study, we investigated the effects of winning/losing experiences on brain expression levels of the receptor genes for androgen (AR), oestrogen α/β (ERα/β), glucocorticoid (GR) and serotonin (5-HT1AR). The effect of contest experience on AR gene expression depended on T levels: repeated losses decreased, whereas repeated wins increased AR gene expression in individuals with low T but not in individuals with medium or high T levels. These results lend strong support for AR being involved in mediating winner–loser effects, which, in previous studies, were more detectable in individuals with lower T. Furthermore, the expression levels of ERα/β, 5-HT1AR and GR genes were higher in individuals that initiated contests against larger opponents than in those that did not. Overall, contest experience, underlying endocrine state and hormone and serotonin receptor expression patterns interacted to modulate contest decisions jointly.
Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data

Name of an identity is strongly influenced by his/her cultural background such as gender and ethnicity, both vital attributes for user profiling, attribute-based retrieval, etc. Typically, the associations between names and attributes (e.g., people named "Amy" are mostly females) are annotated manually or provided by the census data of governments. We propose to associate a name and its likely demographic attributes by exploiting click-throughs between name queries and images with automatically detected facial attributes. This is the first work attempting to translate an abstract name to demographic attributes in visual-data-driven manner, and it is adaptive to incremental data, more countries and even unseen names (the names out of click-through data) without additional manual labels. In the experiments, the automatic name-attribute associations can help gender inference with competitive accuracy by using manual labeling. It also benefits profiling social media users and keyword-based face image retrieval, especially for contributing 12% relative improvement of accuracy in adapting to unseen names.
Comparative study of DCT and discrete Legendre transform for image compression

-The discrete Legendre transform is compared to the discrete cosine transform (DCT), which is based on Chebyshev polynomials, in terms of image compression efficiency. Using standard test images in various image compression configurations, the DCT is found to perform marginally better than the discrete Legendre transform in all cases examined. A simplified fundamental matrix theory for constructing discrete orthogonal transforms is introduced, and some simple Chebyshev and Legendre matrices are calculated.
Carbon dynamics in the future forest: the importance of long‐term successional legacy and climate–fire interactions

Understanding how climate change may influence forest carbon (C) budgets requires knowledge of forest growth relationships with regional climate, long‐term forest succession, and past and future disturbances, such as wildfires and timber harvesting events. We used a landscape‐scale model of forest succession, wildfire, and C dynamics (LANDIS‐II) to evaluate the effects of a changing climate (A2 and B1 IPCC emissions; Geophysical Fluid Dynamics Laboratory General Circulation Models) on total forest C, tree species composition, and wildfire dynamics in the Lake Tahoe Basin, California, and Nevada. The independent effects of temperature and precipitation were assessed within and among climate models. Results highlight the importance of modeling forest succession and stand development processes at the landscape scale for understanding the C cycle. Due primarily to landscape legacy effects of historic logging of the Comstock Era in the late 1880s, C sequestration may continue throughout the current century, and the forest will remain a C sink (Net Ecosystem Carbon Balance > 0), regardless of climate regime. Climate change caused increases in temperatures limited simulated C sequestration potential because of augmented fire activity and reduced establishment ability of subalpine and upper montane trees. Higher temperatures influenced forest response more than reduced precipitation. As the forest reached its potential steady state, the forest could become C neutral or a C source, and climate change could accelerate this transition. The future of forest ecosystem C cycling in many forested systems worldwide may depend more on major disturbances and landscape legacies related to land use than on projected climate change alone.
An efficient collaborative filtering algorithm using SVD-free latent Semantic indexing and particle swarm optimization

The amount of accessible information in the Internet increases every day and it becomes greatly difficult to deal with such a huge source of information. Consequently, Recommender Systems (RS) which are considered as powerful tools for Information Retrieval (IR), can access these available information efficiently. Unfortunately, the recommendations accuracy is seriously affected by the problems of data sparsity and scalability. Additionally, the time of recommendations is very essential in the Recommender Systems. Therefore, we propose a proficient dimensionality reduction-based Collaborative Filtering (CF) Recommender System. In this technique, the Singular Value Decomposition-free (SVD-free) Latent Semantic Indexing (LSI) is utilized to obtain a reduced data representation solving the sparsity and scalability limitations. Also, the SVD-free extremely reduce the time and memory usage required for dimensionality reduction employing the partial symmetric Eigenproblem. Moreover, to estimate the optimal number of reduced dimensions which greatly influences the system accuracy, the Particle Swarm Optimization (PSO) algorithm is utilized to automatically obtain it. As a result, the proposed technique enormously increases the recommendations prediction quality and speed. In additions, it decreases the memory requirements. To show the efficiency of the proposed technique, we employed it to the MovieLens dataset and the results was very promising.
ON THE ALLEGED INADEQUACY OF PSYCHOLOGICAL EXPLANATIONS OF RACISM

In this article, we examine and rebut some of the most important arguments in support of the claim that psychological explanations are doomed to inadequacy or worse, given the agendas of those concerned with race and racism. We focus on three arguments made by sociologist David Wellman, and we show that the worries Wellman articulates, and we suspect some philosophers also hold, are unfounded. There is no reason to think that psychological explanations of race are inadequate, obstructive, or corrosive. Philosophers and other researchers interested in race have often ignored the explanations of racism developed by psychologists (for some recent exceptions, see Glasgow 2009; Blum forthcoming). We have argued elsewhere that this disregard of psychology has impoverished the arguments and considerations philosophers have relied upon (Faucher and Machery 2009; Kelly et al. in press). However, not everyone agrees with our assessment: Other researchers dismiss psychology from philosophical discussions about race, explicitly claiming that psychological explanations are doomed to inadequacy or worse, given the agendas of those concerned with race and racism. Our goal in this article is to examine and rebut some of the most important arguments in support of this cluster of claims. We will mainly discuss sociologist David Wellman’s (2007) work. We focus on Wellman’s arguments because contrary to other philosophers or social scientists, many of whom simply toss psychology aside as irrelevant or naïve, he engages with, and is admirably explicit about what he sees as the shortcomings of, psychological explanations of racism. Accordingly, Section 1 will review Wellman’s claims and arguments against psychological explanations of racism. Each remaining section, then, focuses on one claim in turn, and lays out our reasons for rejecting it. In Section 2, we focus on the claim that psychology is badly equipped
In translation: a visualization of language

In Translation is an interactive application that takes an alternative visual approach to translating languages using colors and shapes. The visual output reveals aspects of everyday language that most of the time go unnoticed but are intrinsic to language and meaning. In Translation combines concepts from both traditional InfoViz research and the Visual Arts to create an audience-focused, interactive learning experience. It is a computer-based application that viewers can use to generate and compare visualizations of chosen texts from 9 different languages that use the Roman alphabet.
Potential speedup using decimal floating-point hardware

This paper addresses the potential speedup achieved by using decimal floating-point hardware, instead of software routines, on a high-performance superscalar architecture. Software routines were written to perform decimal addition, subtraction, multiplication, and division. Cycle counts were then measured for each instruction using the Simplescalar simulator. After this, new hardware algorithms were developed, existing hardware algorithms were analyzed, and cycle counts were estimated for the same set of instructions using specialized decimal floating-point hardware. This data was then used to show the potential speedup obtained for programs with different instruction mixes and a previously developed benchmark.
An audio watermarking scheme robust against stereo attacks

In this paper, a watermarking scheme for both monophonic and stereophonic audio files is presented. The suggested method uses MPEG 1 Layer 3 compression to determine where and how the embedded mark must be introduced, combined with an error correcting code and a majority voting scheme. The scheme is shown to achieve high robustness against malicious attacks while maintaining a reasonable imperceptibility. The mark is embedded by modifying the magnitude of the spectrum at certain frequencies which have been chosen according to their relevance. The results show that the suggested watermarking scheme is robust against the attacks of the audio StirMark benchmark and compression attacks using the Sound Quality Assessment Material as the experimental corpus set.
Tree-Structured Diagram Drawing System Using CLP

This support system for drawing tree-structured diagrams, which is implemented in Prolog and a commercial constraint package, enables a user to nd a layout condition adequate to the drawing purpose. Using meta-programming, it automatically produces a corresponding layout program that generates a constraint logic program (CLP) for deriving a layout solution under the selected layout condition and then solves the generated CLP. The layout program produced by our system can be included and easily used by any other application program for drawing tree-structured diagrams.
Learning to Coordinate Efficiently: A Model-based Approach

In common-interest stochastic games all players receive an identical payoff. Players participating in such games must learn to coordinate with each other in order to receive the highest-possible value. A number of reinforcement learning algorithms have been proposed for this problem, and some have been shown to converge to good solutions in the limit. In this paper we show that using very simple model-based algorithms, much better (i.e., polynomial) convergence rates can be attained. Moreover, our model-based algorithms are guaranteed to converge to the optimal value, unlike many of the existing algorithms.
A Dynamic Energy Balance Method for Wireless Sensor Networks with Small World Characteristics

Wireless sensor networks are often used in harsh environments, such as military fields, the node energy is difficult to be added. How to design an energy efficient network model to achieve the balanced utilization of node energy and prolong the lifetime is an important challenge for the research of wireless sensor networks. Small world networks have short average path length and large clustering coefficient, which present the characteristics of high global data transmission efficiency and good local node connectivity. So the characteristics of small world networks provide a theoretical basis for building an energy efficient network. In this paper, the small world construction methods for wireless sensor networks are summarized. Wireless sensor networks with small world characteristics are constructed based on the hybrid mode, which can be divided into the stage of topology optimization, small world model construction and energy dynamic balance. The MSW-WSNs-A3 and MSW-WSNs-A3 Cov are formed based on hybrid model of small world wireless sensor networks. Based on Java platform, the new network modes are simulated and analyzed. The results show that MSW-WSNs-A3 and MSW-WSNs-A3 Cov have longer lifetime and better effectiveness.
Performance analysis of global software development teams using a structured stochastic modeling formalism

Measuring productivity in globally distributed projects is crucial to improve team performance. These measures often display information on whether a given project is moving forward or starts to demonstrate undesired behaviors. In this paper we are interested in showing how analytical models could be derived in order to represent a distributed software collaboration project. We present a model comprising a distributed reality and we show that it is possible, for instance, to determine the level of coordination required due to project requirements. We focus our attention to the level of interaction among project participants and its close relation with team’s productivity. The models are parametrized for different sets of member where each member reacts according to the demands of a central team. We change the resource availability levels, team’s expertise and support levels to see each metric impact on the overall project performance. Finally, we present our derived performance indices for all scenarios ending our analysis with a comprehensive discussion on how to extend the models to represent more intricate behaviors and communication patterns that are usually present in globally distributed software projects.
An Effective Genetic Algorithm for Outlier Detection

The main objective the outlier detection is to find the data that are exceptional from other data in the data set. Detection of such exceptional data’s is an important issue in many fields like fraud detection, Intrusion detection and Medicine . In this paper we are proposing an algorithm to detect outliers using genetic algorithm. The proposed method was exceptionally accurate in identifying the outliers the datasets that we have tested. The result analysis is done on some standard dataset to view accuracy of the algorithm.
Multiple-Time-Series Clinical Data Processing for Classification: A Review

Data mining is a multidisciplinary subfield of computer science. It is used in various fields such as medical research, financial, telecommunication, scientific application. Classification is a method used in data mining. Data mining includes wide varieties of data such as clinical, scientific, biological, remote sensing etc. Clinical data can be used for clinical data mining. Clinical data mining helps the clinicians for diagnosis, therapy and prognosis of various diseases. Most popular primary liver cancer is hepatocellular carcinoma (HCC). It is the fifth most common tumour in the world. HCC can be treated by using Radiofrequency ablation (RFA). Recurrence prediction of hepatocellular carcinoma (HCC) after RFA treatment is an important task. This problem can be solved by using a classification technique that classifies persons into two groups: 1) HCC recurrence and 2) no evidence of recurrence of HCC. In this paper a review is being carried out in various techniques used in HCC recurrence prediction are discussed.