LLukas22/scidocs huggingface dataset:
A robust RGB-D SLAM algorithm

Recently RGB-D sensors have become very popular in the area of Simultaneous Localisation and Mapping (SLAM). The major advantage of these sensors is that they provide a rich source of 3D information at relatively low cost. Unfortunately, these sensors in their current forms only have a range accuracy of up to 4 metres. Many techniques which perform SLAM using RGB-D cameras rely heavily on the depth and are restrained to office type and geometrically structured environments. In this paper, a switching based algorithm is proposed to heuristically choose between RGB-BA and RGBD-BA based local maps building. Furthermore, a low cost and consistent optimisation approach is used to join these maps. Thus the potential of both RGB and depth image information are exploited to perform robust SLAM in more general indoor cases. Validation of the proposed algorithm is performed by mapping a large scale indoor scene where traditional RGB-D mapping techniques are not possible.
Content Based Image Retrieval: Review of State of Art and Future Directions

In this paper we discuss the state of the art of the content based image retrieval highlighting the main components and reviewing various approaches employed at each stage, while enhancing the main challenges and key contributions. Along these lines, some analogy with text retrieval systems will be discussed.
A historical perspective of speech recognition

What do we know now that we did not know 40 years ago?
Understanding of a convolutional neural network

The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to surpass classical methods performance in different fields; especially in pattern recognition. One of the most popular deep neural networks is the Convolutional Neural Network (CNN). It take this name from mathematical linear operation between matrixes called convolution. CNN have multiple layers; including convolutional layer, non-linearity layer, pooling layer and fully-connected layer. The convolutional and fully-connected layers have parameters but pooling and non-linearity layers don't have parameters. The CNN has an excellent performance in machine learning problems. Specially the applications that deal with image data, such as largest image classification data set (Image Net), computer vision, and in natural language processing (NLP) and the results achieved were very amazing. In this paper we will explain and define all the elements and important issues related to CNN, and how these elements work. In addition, we will also state the parameters that effect CNN efficiency. This paper assumes that the readers have adequate knowledge about both machine learning and artificial neural network.
Cognitive outcome in children and adolescents treated for acute lymphoblastic leukaemia with chemotherapy only

OBJECTIVE
To examine cognitive outcome in children and adolescents with acute lymphoblastic leukaemia (ALL) in remission, treated with central nervous system prophylactic chemotherapy only.


METHOD
Thirty-five children and adolescents, age 8.4-15.3 years in long-term remission from ALL, 4.2-12.4 years post diagnosis, without relapse and no pre-diagnosis history of neurodevelopmental disorder were compared with 35 healthy controls matched for gender and age, on measures of intellectual functioning Wechsler Intelligence Scale for Children-Third Edition (WISC-III).


RESULTS
All but two of the ALL survivors treated by chemotherapy only obtained WISC-III Total Intelligence Quotient (IQ) scores in the normal range (M = 95.3), but their scores were significantly below levels for their matched controls and below normative standards for WISC-III. The difference between patients and controls was significant at the p < 0.001 level for the following measures: Total IQ, Verbal IQ, Verbal Comprehension Index, Freedom from Distraction Index and three verbal subtest scores.


CONCLUSION
The results indicate long-term sequelae in global cognitive functions, and indicate that verbal function, processing speed, attention and complex visual-spatial problem solving may be affected in the chemotherapy only group.
Applying design metrics to predict fault-proneness: a case study on a large-scale software system

The purpose of this study was to identify fault-prone functions that are likely to contain faults in a given software system. Five metrics were used: Di , an internal design metric which incorporates factors related to a function’s internal structure; De, an external design metric which focuses on a function’s external relationships to the rest of the software system;D(G), a composite design metric which is a linear combination of Di and De; and the union and intersection ofDi , De, and D(G). Since the system being considered was already developed, a very important aspect of our study was to extract the design information directly from the source code rather than from the corresponding design documentation which may not exist or, if it does exist, it may be incomplete, difficult to understand, or not updated. To make the analysis more accurate and efficient, a metric analysis tool ( χMetrics) was implemented. We conducted experiments usingχMetrics on part of a distributed software system, written in C, with a client–server architecture, and identified a small percentage of its functions as good candidates for fault-proneness. Files containing these functions were then validated by the real defect data collected between a recent major release and its subsequent release for their fault-proneness. The results indicate that our metrics are good indicators of fault-prone functions. Two extra experiments were also conducted to show that function size cannot replace any of our metrics; and where the function size was factored out our metrics performed better than the normalized metrics. The important benefit of our metrics is that they help project managers determine where additional testing effort should be spent and possibly which fault-prone functions should be assigned to more experienced programmers if modifications are required. Copyright  2000 John Wiley & Sons, Ltd.
Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise

Biometric fingerprint scanners are positioned to provide improved security in a great span of applications from government to private. However, one highly publicized vulnerability is that it is possible to spoof a variety of fingerprint scanners using artificial fingers made from Play-Doh, gelatin and silicone molds. Therefore, it is necessary to offer protection for fingerprint systems against these threats. In this paper, an anti-spoofing detection method is proposed which is based on ridge signal and valley noise analysis, to quantify perspiration patterns along ridges in live subjects and noise patterns along valleys in spoofs. The signals representing gray level patterns along ridges and valleys are explored in spatial, frequency and wavelet domains. Based on these features, separation (live/spoof) is performed using standard pattern classification tools including classification trees and neural networks. We test this method on a larger dataset than previously considered which contains 644 live fingerprints (81 subjects with 2 fingers for an average of 4 sessions) and 570 spoof fingerprints (made from Play-Doh, gelatin and silicone molds in multiple sessions) collected from the Identix fingerprint scanner. Results show that the performance can reach 99.1% correct classification overall. The proposed anti-spoofing method is purely software based and integration of this method can provide protection for fingerprint scanners against gelatin, Play-Doh and silicone spoof fingers. ∗Phone: (1)315-2686536 ∗∗Fax: (1)315-2687600 Email addresses: tanb@clarkson.edu, sschucke@clarkson.edu (Bozhao Tan, Stephanie Schuckers) Preprint submitted to Pattern Recognition November 12, 2009
Economic design of an inventory policy for non-instantaneous deteriorating items under permissible delay in payments

In this paper, Economic Order Quantity (EOQ) based model for non-instantaneous deteriorating items with permissible delay in payments is proposed. This model aids in minimizing the total inventory cost by finding an optimal replenishment policy. In this model shortages are allowed and partially backlogged. The backlogging rate is variable and dependent on the waiting time for the next replenishment. Some useful theorems have been framed to characterize the optimal solutions. The necessary and sufficient conditions of the existence and uniqueness of the optimal solutions are also provided. An algorithm is designed to find the optimal replenishment cycle time and order quantity under various circumstances. Numerical examples are given to demonstrate the theoretical results. Sensitivity analysis of the optimal solution with respect to major parameters of the system has been carried out and the implications are discussed in detail. In the discussions, suggestions are given to minimize the total cost of the inventory system. © 2009 Elsevier B.V. All rights reserved.
[European Association of Urology guidelines on assessment and nonsurgical management of urinary incontinence].

CONTEXT
The previous European Association of Urology (EAU) guidelines on urinary incontinence comprised a summary of sections of the 2009 International Consultation on Incontinence. A decision was made in 2010 to rewrite these guidelines based on an independent systematic review carried out by the EAU guidelines panel, using a sustainable methodology.


OBJECTIVE
We present a short version of the full guidelines on assessment, diagnosis, and nonsurgical treatment of urinary incontinence, with the aim of increasing their dissemination.


EVIDENCE ACQUISITION
Evidence appraisal included a pragmatic review of existing systematic reviews and independent new literature searches, based on Population, Intervention, Comparator, Outcome questions. Appraisal of papers was carried out by an international panel of experts, who also collaborated on a series of consensus discussions, to develop concise structured evidence summaries and action-based recommendations using a modified Oxford system.


EVIDENCE SUMMARY
The full version of the guidelines is available online (http://www.uroweb.org/guidelines/online-guidelines/). The guidelines include algorithms that refer the reader back to the supporting evidence, and they are more immediately useable in daily clinical practice.


CONCLUSIONS
These new guidelines present an up-to-date summary of the available evidence, together with clear clinical algorithms and action-based recommendations based on the best available evidence. Where such evidence does not exist, they present a consensus of expert opinion.
Compacting Transactional Data in Hybrid OLTP & OLAP Databases

Growing main memory sizes have facilitated database management systems that keep the entire database in main memory. The drastic performance improvements that came along with these in-memory systems have made it possible to reunite the two areas of online transaction processing (OLTP) and online analytical processing (OLAP): An emerging class of hybrid OLTP and OLAP database systems allows to process analytical queries directly on the transactional data. By offering arbitrarily current snapshots of the transactional data for OLAP, these systems enable real-time business intelligence. Despite memory sizes of several Terabytes in a single commodity server, RAM is still a precious resource: Since free memory can be used for intermediate results in query processing, the amount of memory determines query performance to a large extent. Consequently, we propose the compaction of memory-resident databases. Compaction consists of two tasks: First, separating the mutable working set from the immutable “frozen” data. Second, compressing the immutable data and optimizing it for efficient, memoryconsumption-friendly snapshotting. Our approach reorganizes and compresses transactional data online and yet hardly affects the mission-critical OLTP throughput. This is achieved by unburdening the OLTP threads from all additional processing and performing these tasks asynchronously.
Ask the Instructors: Motivations and Challenges of Teaching Massive Open Online Courses

Massive Open Online Courses (MOOCs) have experienced rapid growth and attracted significant attention within aca-demia. However, despite widespread acceptance of MOOCs as a unique opportunity to transform educational practices, many questions remain regarding their sustainability, given the high dropout rates and challenges related to collabora-tive learning support [12]. Recent research has attempted to address these concerns by analyzing studentsâ MOOC expe-riences and how MOOCs may fall short in meeting stu-dentsâ learning needs [41]. However, very little research has approached the problem from an instructor perspective. We report an interview study of 14 MOOC instructors in which we used grounded theory to uncover the complex processes, motivations, and challenges associated with teaching a MOOC. A key finding is that we should provide support through the whole instruction process. By enhancing sup-port for instructors and their MOOC collaborators, we may improve outcomes for all MOOC stakeholders, including students.
Structured Spreadsheet Modelling and Implementation with Multiple Dimensions - Part 1: Modelling

Dimensions are an integral part of many models we use every day. Without thinking about it, we frequently use the time dimension: many financial and accounting spreadsheets have columns representing months or years. Representing a second dimension is often done by repeating blocs of formulas in a worksheet of creating multiple worksheets with the same structure.
Process for the manufacture of glass with a high transmission characteristic in the visible spectral range and a high reflection behavior for heat radiation, and lenses made by the process.

MANUFACTURING PROCESS OF HIGH PERFORMANCE DISK DRIVE IN THE SPECTRUM VISIBLE AND HIGH PERFORMANCE FOR REFLECTION radiant heat as well as low surface resistance. MINERAL GLASS substrates GOES RIDING A layer system in the following order: LAYER1: OXIDE GROUP stannic oxide, silicon dioxide, aluminum oxide, tantalum oxide and zirconium oxide or oxides MIXTURES. Layer 2: ALLOY 80% by weight of nickel and 20% by weight of chromium. CAPA3: silver or a silver alloy with a silver content of 50% WEIGHT minimum. CAPA4: ALLOY 80% by weight of nickel and 20% by weight of chromium. CAPA5: OXIDE GROUP stannic oxide, silicon oxide, aluminum oxide, tantalum oxide and zirconium oxide or oxides MIXTURES. Then heats the substrate YET SET LAYER TO Softening CRYSTAL AND PERMANENTLY TO GIVE YOU THE DUAL.
His Other Half: Men Looking at Women Through Art

Divided selves mothers Degas' nudes Gissing's even-handed oddness Henry James and the battle of the sexes Hitchcock's couples Randall Jarrell's sinister beings Beaton's ladies the disembodied body of Marilyn Monroe Stanwyck closing the circle.
Sticking together or falling apart: Ingroup identification as a psychological determinant of group commitment versus individual mobility

Two experiments investigated how in-group identification, manipulated with a bogus pipeline technique affects group members' desire for individual mobility lo another group. In die first experiment (JV = 88), the in-group had low status, and group boundaries were either permeable or impermeable. Low identifiers perceived the group as less homogeneous, were less committed to their group, and more strongly desired individual mobility to a higher status group than did high identifiers. The structural possibility of mobility afforded by permeable group boundaries had no comparable effect. The second experiment (JV = 51) investigated whether in-group identification can produce similar effects when relative group status is unknown. Even in the absence of an identity threat, low identifiers were less likely to see the groups as homogeneous, felt less committed to their group, and more strongly desired individual mobility than did high identifiers. Results are discussed with reference to social identity and self-categorization theories.
Dose-related neurocognitive effects of marijuana use.

BACKGROUND
Although about 7 million people in the US population use marijuana at least weekly, there is a paucity of scientific data on persistent neurocognitive effects of marijuana use.


OBJECTIVE
To determine if neurocognitive deficits persist in 28-day abstinent heavy marijuana users and if these deficits are dose-related to the number of marijuana joints smoked per week.


METHODS
A battery of neurocognitive tests was given to 28-day abstinent heavy marijuana abusers.


RESULTS
As joints smoked per week increased, performance decreased on tests measuring memory, executive functioning, psychomotor speed, and manual dexterity. When dividing the group into light, middle, and heavy user groups, the heavy group performed significantly below the light group on 5 of 35 measures and the size of the effect ranged from 3.00 to 4.20 SD units. Duration of use had little effect on neurocognitive performance.


CONCLUSIONS
Very heavy use of marijuana is associated with persistent decrements in neurocognitive performance even after 28 days of abstinence. It is unclear if these decrements will resolve with continued abstinence or become progressively worse with continued heavy marijuana use.
Material Hardship in U.S. Families Raising Children with Disabilities

Researchers analyzed the 2002 wave of the National Survey of America’s Families, conducted by the Urban Institute and Child Trends, and examined material hardship in families raising children with disabilities. Measures of hardship included food insecurity, housing instability, health care access, and telephone disconnection. The research indicated that families of children with disabilities experienced significantly greater hardship than did other families. As family income rose above the federal poverty level, hardship declined sharply for families of children without disabilities but not for families raising children with disabilities. Thus, the U.S. federal poverty level was found to be a particularly poor predictor of hardship for families raising children with disabilities. Finally, among families of children with disabilities, single-mother and cohabitingpartner families particularly were at risk for experiencing severe hardship. This article also discusses policy and advocacy implications. Exceptional Children
Power Allocation and Time-Domain Artificial Noise Design for Wiretap OFDM with Discrete Inputs

Optimal power allocation for orthogonal frequency division multiplexing (OFDM) wiretap channels with Gaussian channel inputs has already been studied in some previous works from an information theoretical viewpoint. However, these results are not sufficient for practical system designs. One reason is that discrete channel inputs, such as quadrature amplitude modulation (QAM) signals, instead of Gaussian channel inputs, are deployed in current practical wireless systems to maintain moderate peak transmission power and receiver complexity. In this paper, we investigate the power allocation and artificial noise design for OFDM wiretap channels with discrete channel inputs. We first prove that the secrecy rate function for discrete channel inputs is nonconcave with respect to the transmission power. To resolve the corresponding nonconvex secrecy rate maximization problem, we develop a low-complexity power allocation algorithm, which yields a duality gap diminishing in the order of O(1/√N), where N is the number of subcarriers of OFDM. We then show that independent frequency-domain artificial noise cannot improve the secrecy rate of single-antenna wiretap channels. Towards this end, we propose a novel time-domain artificial noise design which exploits temporal degrees of freedom provided by the cyclic prefix of OFDM systems to jam the eavesdropper and boost the secrecy rate even with a single antenna at the transmitter. Numerical results are provided to illustrate the performance of the proposed design schemes.
THE FALL AND RISE OF STRATEGIC PLANNING1: A META-ANALYSIS OF THE ENVIRONMENT-PLANNING-OUTCOME LINK

Introduction In his Harvard Business Review article in 1994, Henry Mintzberg coined strategic planning as the mere programming, i.e. the calculation of a plan. As such “strategic planning isn’t strategic thinking”, he confined (p 107). Indeed, strategic planning may actually spoil any creative strategic thinking and, hence, the possibility to create a competitive advantage. He condemned that planning itself has decreased the commitment of executives and created a disadvantageous atmosphere spit with formalized instead of creative thinking. Now, two decades later, calculating plans has become in vogue again. Since Bigdata has entered the business world, it has been celebrated as a savior in an increasing complex economic environment. In this vein, Mintzberg’s accuse has become more relevant than ever. Especially because of the globalization and internationalization of markets, companies are increasingly facing challenging business environments. Today’s markets are characterized by fierce competition, rapid changes and increasing uncertainty and complexity where Bigdata is becoming a central determinant for executives to decide on their strategic plan. Therefore, we ask: Does planning actually create a competitive advantage? What are the social and operational outcomes of strategic planning? Which environment actually favors the planning of strategies?
Deep learning based large scale handwritten Devanagari character recognition

In this paper, we introduce a new public image dataset for Devanagari script: Devanagari Handwritten Character Dataset (DHCD). Our dataset consists of 92 thousand images of 46 different classes of characters of Devanagari script segmented from handwritten documents. We also explore the challenges in recognition of Devanagari characters. Along with the dataset, we also propose a deep learning architecture for recognition of those characters. Deep Convolutional Neural Network (CNN) have shown superior results to traditional shallow networks in many recognition tasks. Keeping distance with the regular approach of character recognition by Deep CNN, we focus the use of Dropout and dataset increment approach to improve test accuracy. By implementing these techniques in Deep CNN, we were able to increase test accuracy by nearly 1 percent. The proposed architecture scored highest test accuracy of 98.47% on our dataset.