Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Women an Underserved Community in the Information Technology Profession?

The role of women as an underserved community in the world of information technology (IT) is both unique and perplexing. In this paper we consider the question of women as an underserved community in IT, and in particular, their role as producers of IT. In order to better understand the ways in which women do and do not represent a coherent underserved community within the IT profession, the research question motivating this paper is: Do women vary with respect to the factors that help to explain the underrepresentation of women in the IT profession and, if so, how? In order to address this question, we draw on data from a multi-year qualitative investigation of the underrepresentation of women in the U.S. IT profession. In doing so, we investigate gender discourses with respect to: domestic responsibilities, career opportunities and IT as a masculine domain. We also demonstrate the range of responses to these discourses. This research contributes to an understanding of socio-cultural factors that serve as barriers to and facilitators of women’s recruitment and retention in the IT profession and to the factors that have enabled some women to overcome these barriers.
Thinking in systems: a primer

In the years following her role as the lead author of the international bestseller, Limits to Growth--the first book to show the consequences of unchecked growth on a finite planet-- Donella Meadows remained a pioneer of environmental and social analysis until her untimely death in 2001. Thinking in Systems, is a concise and crucial book offering insight for problem solving on scales ranging from the personal to the global. Edited by the Sustainability Institute's Diana Wright, this essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life. Some of the biggest problems facing the world--war, hunger, poverty, and environmental degradation--are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking. While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner. In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions.
Hybrid of Evolution and Reinforcement Learning for Othello Players

Although the reinforcement learning and evolutionary algorithm show good results in board evaluation optimization, the hybrid of both approaches is rarely addressed in the literature. In this paper, the evolutionary algorithm is boosted using resources from the reinforcement learning. 1) The initialization of initial population using solution optimized by temporal difference learning 2) Exploitation of domain knowledge extracted from reinforcement learning. Experiments on Othello game strategies show that the proposed methods can effectively search the solution space and improve the performance.
A taxonomy of market-based resource management systems for utility-driven cluster computing

In utility-driven cluster computing, cluster Resource Management Systems (RMSs) need to know the specific needs of different users in order to allocate resources according to their needs. This in turn is vital to achieve service-oriented Grid computing that harnesses resources distributed worldwide based on users’ objectives. Recently, numerous market-based RMSs have been proposed to make use of real-world market concepts and behavior to assign resources to users for various computing platforms. The aim of this paper is to develop a taxonomy that characterizes and classifies how market-based RMSs can support utility-driven cluster computing in practice. The taxonomy is then mapped to existing market-based RMSs designed for both cluster and other computing platforms to survey current research developments and identify outstanding issues. Copyright c © 2006 John Wiley & Sons, Ltd.
Building a QA System ( IID SQuAD track )

The task of this project is to build a question answering system that works well on SQuAD 2.0. The goal is to investigate and implement models to improve the performance of a baseline model, called Bidirectional Attention Flow (BiDAF). Specifically, the improvements mainly focus on 1) extending the baseline embedding layer implementation to include character-level word embeddings, 2) enhancing the baseline attention layer, 3) improving the output layer by conditioning the prediction of end position on the prediction of start position of the answer span. Experiment results show significant performance improvement with the additional character-level word embeddings, deep residual coattention, and a pointer network model in the output layer.
Buffett’s Alpha

Warren Buffett’s Berkshire Hathaway has realized a Sharpe ratio of 0.79 with significant alpha to traditional risk factors. The alpha became insignificant, however, when we controlled for exposure to the factors “betting against beta” and “quality minus junk. ” Furthermore, we estimate that Buffett’s leverage is about 1.7 to 1, on average. Therefore, Buffett’s returns appear to be neither luck nor magic but, rather, a reward for leveraging cheap, safe, high-quality stocks. Decomposing Berkshire’s portfolio into publicly traded stocks and wholly owned private companies, we found that the public stocks have performed the best, which suggests that Buffett’s returns are more the result of stock selection than of his effect on management. A practitioner's perspective on this article is provided in the In Practice piece "Demystifying Buffett’s Investment Success" by Keyur Patel. Disclosure: The authors are principals at AQR Capital Management, a global investment management firm, which may or may not apply investment techniques or methods of analysis similar to those described in this article. The views expressed here are those of the authors and not necessarily those of AQR. Editor’s Note Submitted 24 April 2018 Accepted 15 June 2018 by Stephen J. Brown
Super Resolution Image Reconstruction Based on Sparse Representation with Joint Dictionary Training and Compression

Recently, sparse representation has emerged as a powerful technique for solving various image restoration applications. In this paper, we investigate the application of sparse representation on single-image super-resolution problems. Super Resolution Image Reconstruction produces High Resolution images from one or more Low Resolution images. Sparse Representation technique obtain sparse coefficient of low resolution images and use these sparse coefficient in low resolution space to construct the high resolution image. For effectiveness, two dictionaries for low resolution & high resolution can be constructed and used to reconstruct the high resolution image. Hence Larger Dictionary increases execution time and decreases PSNR value. The Time complexity is increased due to searching for image patches and processing dictionary. Proposed work uses K-SVD for creating precise dictionary and Matching Pursuit algorithm for searching best image patches from dictionary. The Lloyd’s algorithm is used to compress the Dictionary to reduce Time Complexity. The Arithmetic coding is used to encode mean value, index value and atom number of image patches. The Proposed system improves the image quality and reduces time and PSNR.
Automatic Couplings With Mechanical Overload Protection for Modular Robots

This paper presents an automatic coupling mechanism with mechanical overload protection for use in modular robotic systems. The genderless coupling incorporates a novel safety mechanism that disengages two modules at a predefined torque threshold. Several key design aspects allow the minimalist coupling to contain all functionalities within a compact cylindrical space. A detailed analytical model of the overload protection mechanism is validated by testing two materials and fabrication methods. We further present a functional prototype of the coupling with a demonstration of two modular robots driving toward one another, automatically connecting and disconnecting, detecting the connection, communicating in the process, and sensing the angle between them. The coupling as well as the embedded technologies and mechanisms present essential steps toward enabling fully self-reconfigurable robots combining modular and origami features, while advancing the process of introducing modularity into a wide range of robotic systems.
Cyclic Test Time Augmentation with Entropy Weight Method (Supplementary material)

In this study, we use predefined transformations, similar to the baseline. However, in order to leverage the effect from the iterative transformation of cyclic mechanism, we change the magnitudes and establish a modified set of transformations as Figure 1. The 12 transformations include: identity, rotation, zooming, contrast adjustment, sharpness adjustment, and color saturation change. The transformations were performed using functions from torchvision and PIL libraries. For identity transformation no actual transformation of image is conducted for the image, leaving no changes. Rotation refers to clock/counter clock wise rotational transformation with magnitude of 20 or -20 degrees. Zoom transformation refers to resizing the image and cropping the central part of the image. Auto contrast transformation calculates the color histogram and stretches it to achieve uniform contrast level. Sharpness transformation refers to adjustment in sharpness, less than 1.0 meaning edges will be blurred and larger than 1.0 refers to sharpening of the edges. Color saturation changes the the saturation values of each pixel color values. For all the padding-requiring transformations, symmetrical padding was used.
Short-term population dynamics of tree species in tropical forests at Kodayar in the Western Ghats of Tamil Nadu, India

The population dynamics of tree species were studied in both deciduous and evergreen forests at Kodayar in the Western Ghats of Tamil Nadu. The mortality of trees was less than the number of new recruits, resulting in a net gain in population density and basal area. The increase in net population density and basal area of tree species could be because of their entry into the adult stage from the already existing sapling and seedling bank. Greater mortality of juveniles than that of adults could be due to intense competition for limited available resources at the juvenile stage. The present study concludes that to a larger extent, the forest ecosystems here are at building phase. Long-term studies are needed to understand the regeneration niche.
A Survey on Content-based Image Retrieval

The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. In this article, a survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work also includes publications that cover research aspects relevant to CBIR area. Namely, unsupervised and supervised learning and fusion techniques along with low-level image visual descriptors have been reported. Moreover, challenges and applications that emerged to support CBIR research have been discussed in this work.
An Enhanced Data Mining For Text Clustering

mining is based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within document only. Usually in text mining techniques the basic measures like term frequency of a term (word or phrase) is computed to compute the importance of the term in the document. But with statistical analysis, the original semantics of the term may not carry the exact meaning of the term. To overcome this problem, a new framework has been introduced which relies on concept based model approach. The proposed model can efficiently find significant matching and related concepts between documents according to concept based approaches.
Target Speech Detection and Separation for Humanoid Robots in Sparse Dialogue with Noisy Home Environments

In normal human communication, people face the speaker when listening and usually pay attention to the speaker’ face. Therefore, in robot audition, the recognition of the front talker is critical for smooth interactions. This paper presents an enhanced speech detection method for a humanoid robot that can separate and recognize speech signals originating from the front even in noisy home environments. The robot audition system consists of a new type of voice activity detection (VAD) based on the complex spectrum circle centroid (CSCC) method and a maximum signal-to-noise (Max-SNR) beamformer. This VAD based on CSCC can classify speech signals that are retrieved at the frontal region of two microphones embedded on the robot. The system works in real-time without needing training filter coefficients given in advance even in a noisy environment (SNR > 0 dB). It can cope with speech noise generated from televisions and audio devices that does not originate from the center. Experiments using a humanoid robot, SIG2, with two microphones showed that our system enhanced extracted target speech signals more than 12 dB (SNR) and the success rate of automatic speech recognition for Japanese words was increased about 17 points.
The evolution of the laws of software evolution

AbstractWith more than 40 years of history, software evolution should be nowadays a matureﬁeld. However, many questions still remain open, and controversial cases about thevalidity of the laws of software evolution are common. For the most of this period, thelaws have evolved to adapt to a changing environment, both in the state of the art ofresearch and in the practices common in the software industry. However during thelast 15 years, when most of the controversial cases have appeared in the literature, theformulation of the laws has remained unchanged. This paper presents an study, basedon a detailed literature review, on how the theory on software evolution has adapted inthe past to new software paradigms, standards and practices, and how it is perceived inthe current research environment. It also addresses the most likely developments andchallenges to occur in the future, such as the impact of the availability of large datasetson software development. In particular, it is shown how they are enabling the large-scale empirical analysis of software evolution using statistical techniques, as Lehmanhimself already suggested and attempted in the seventies of the past century, in a muchmore constrained environment.Keywords: software evolution, laws of software evolution1. IntroductionIn 1969, Meir M. Lehman performed an empirical study (originally conﬁdential,later published [1]) within IBM, with the goal of improving the programming e ec-tiveness of the company. That work received little attention in the company and hadno impact on its development practices. However it started a new and proliﬁc ﬁeld ofresearch: software evolution.The aim of Lehman’s research was to formulate a theory of software evolution. Inthis sense, a theory must be based on empirical results, and on the ﬁnding of conservedquantities and invariant properties. The found invariants were summarised as the lawsof software evolution [2].
Speaker Identification in Emotional Environments

The performance of speaker identification is almost perfect in the neutral environment. However, the performance is significantly deteriorated in emotional environments. In this work, three different and separate models have been used, tested and compared to identify speakers in each of the neutral and emotional environments (completely two separate environments). Our emotional environments in this work consist of five emotions. These emotions are: angry, sad, happy, disgust and fear. The three models are: Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s) and Suprasegmental Hidden Markov Models (SPHMMs). Our results show that the three models perform extremely well for speaker identification in the neutral environment. In emotional environments, the average speaker identification performance based on HMMs, CHMM2s and SPHMMs is 61.4%, 66.4% and 69.1%, respectively. Our results in this work are better than those obtained in subjective evaluation by human judges.
Team Knowledge and Coordination in Geographically Distributed Software Development

Coordinating software tasks across geographic locations is difficult because of factors like: limited opportunities for interaction, lack of contextual references, and leaner communication media. Team cognition research suggests that team knowledge helps members coordinate because they can anticipate task issues more accurately, communicate more efficiently and find expertise within the team, among other things. In this field study we investigate one large team of geographically distributed developers collaborating on a software task in a telecommunications firm to better understand how team knowledge influences coordination effectiveness and how this varies with geographic dispersion. Our findings reveal that: software developers have different types of coordination needs; coordination across sites is more challenging than within a site; team knowledge helps members coordinate, but more so when they are separated by geographic distance; and the effect of different types of team knowledge on coordination effectiveness differs between co-located and geographically dispersed collaborators.
Natural variations in the rhenium isotopic composition of meteorites

Rhenium is an important element with which to test hypotheses of isotope variation. Historically, it has been difficult to precisely correct the instrumental mass bias in thermal ionization mass spectrometry. We used W as an internal standard to correct mass bias on the MC‐ICP‐MS, and obtained the first precise δ187Re values (~±0.02‰, 2SE) for iron meteorites and chondritic metal. Relative to metal from H chondrites, IVB irons are systematically higher in δ187Re by ~0.14 ‰. δ187Re for other irons are similar to H chondritic metal, although some individual samples show significant isotope fractionation. Since 185Re has a high neutron capture cross section, the effect of galactic cosmic‐ray (GCR) irradiation on δ187Re was examined using correlations with Pt isotopes. The pre‐GCR irradiation δ187Re for IVB irons is lower, but the difference in δ187Re between IVB irons and other meteoritic metal remains. Nuclear volume‐dependent fractionation for Re is about the right magnitude near the melting point of iron, but because of the refractory and compatible character of Re, a compelling explanation in terms of mass‐dependent fractionation is elusive. The magnitude of a nucleosynthetic s‐process deficit for Re estimated from Mo and Ru isotopes is essentially unresolvable. Since thermal processing reduced nucleosynthetic effects in Pd, it is conceivable that Re isotopic variations larger than those in Mo and Ru may be present in IVBs since Re is more refractory than Mo and Ru. Thus, the Re isotopic difference between IVBs and other irons or chondritic metal remains unexplained.
SparRL: Graph Sparsification via Deep Reinforcement Learning

Graph sparsiﬁcation concerns data reduction where an edge-reduced graph of a similar structure is preferred. Existing methods are mostly sampling-based, which introduce high computation complexity in general and lack of ﬂexibility for a different reduction objective. We present SparRL, the ﬁrst general and effective reinforcement learning-based framework for graph sparsiﬁcation. SparRL can easily adapt to different reduction goals and promise graph-size-independent complexity. Extensive experiments show that SparRL outperforms all prevailing sparsiﬁcation methods in producing high-quality sparsiﬁed graphs concerning a variety of objectives
Parsing Discontinuous constituents in Dependency Grammar

Discontinuous constituents--for example, a noun and its modifying adjective separated by words unrelated to them--arc common in variable-word-order languages; Figure I shows examples. But phrase structure grammars, including ID /LP grammars, require each constituent to be a contiguous series of words. Insofar as standard parsing algorithms are based on phrase structure rules, they are inadequate for parsing such languagesJ The algorithm presented here, however, does not require constituents to be continuous, but merely prefers them so. It can therefore parse languages in which conventional parsing techniques do not work. At the same time, because of its preference for nearby attachments, it prefers to make constituents continuous when more than one analysis is possible. The new algorithm has been used successfully to parse Russian and Latin (Covington 1988, 1990). This algorithm uses dependency grammar. That is, instead of breaking the sentence into phrases and subphrases, it establishes links between individual words. Each link connects a word (the "head") with one of its "dependents" (an argument or modifier). Figure 2 shows how this works. The arrows point from head to dependent; a head can have many dependents, but each dependent can have only one head. Of course the same word can be the head in one link and the dependent in another. 2 Dependency grammar is equivalent to an X-bar theory with only one phrasal bar level (Figure 3) the dependents of a word are the heads of its sisters. Thus dependency grammar captures the increasingly recognized importance of headship in syntax. At the same time, the absence of phrasal nodes from the dependency representation streamlines the search process during parsing. The parser presupposes a grammar that specifies which words can depend on which. In the prototype, the grammar consists of unification-based dependency rules (called D-rules) such as:
Magic Quadrant for Cloud Infrastructure as a Service

Cloud computing is a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service using Internet technologies. Cloud infrastructure as a service (IaaS) is a type of cloud computing service; it parallels the infrastructure and data center initiatives of IT. Cloud compute IaaS constitutes the largest segment of this market (the broader IaaS market also includes cloud storage and cloud printing). Only cloud compute IaaS is evaluated in this Magic Quadrant; it does not cover cloud storage providers, platform as a service (PaaS) providers, software as a service (SaaS) providers, cloud services brokerages or any other type of cloud service provider, nor does it cover the hardware and software vendors that may be used to build cloud infrastructure. Furthermore, this Magic Quadrant is not an evaluation of the broad, generalized cloud computing strategies of the companies profiled.