nils-herrmann/clean_scirepeval huggingface dataset:
Ant colony optimization : Introduction and recent trends

Ant colony optimization is a technique for optimization that was introduced in the early 1990’s. The inspiring source colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important pro telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optim algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete optimization. Then, we ant colony optimization in more general terms in the context of discrete optimization, and present some of the nowad performing ant colony optimization variants. After summarizing some important theoretical results, we demonstrate how a optimization can be applied to continuous optimization problems. Finally, we provide examples of an interesting recent direction: The hybridization with more classical techniques from artificial intelligence and operations research.
Combining Big Data and Thick Data Analyses for Understanding Youth Learning Trajectories in a Summer Coding Camp

In this paper we explore how to assess novice youths' learning of programming in an open-ended, project-based learning environment. Our goal is to combine analysis of frequent, automated snapshots of programming (e.g., "big" data) within the "thick" social context of kids? learning for deeper insights into their programming trajectories. This paper focuses on the first stage of this endeavor: the development of exploratory quantitative measures of youths? learning of computer science concepts. Analyses focus on kids? learning in a series of three Scratch Camps where 64 campers aged 10-13 used Scratch 2.0 to make a series of creative projects over 30 hours in five days. In the discussion we consider the highlights of the insights-and blind spots-of each data source with regard to youths' learning.
Financial cost of social exclusion: follow up study of antisocial children into adulthood.

To compare the cumulative costs of public services used through to adulthood by individuals with three levels of antisocial behaviour in childhood.



Costs applied to data of 10 year old children from the inner London longitudinal study selectively followed up to adulthood.



Inner London borough.



142 individuals divided into three groups in childhood: no problems, conduct problems, and conduct disorder.



Costs in 1998 prices for public services (excluding private, voluntary agency, indirect, and personal costs) used over and above basic universal provision.



By age 28, costs for individuals with conduct disorder were 10.0 times higher than for those with no problems (95% confidence interval of bootstrap ratio 3.6 to 20.9) and 3.5 times higher than for those with conduct problems (1.7 to 6.2). Mean individual total costs were 70 019 pounds sterling for the conduct disorder group (bootstrap mean difference from no problem group 62 pound sterling; 898 pound sterling 22 692 pound sterling to 117 pound sterling) and 24 324 pound sterling (16 707 pound sterling; 6594 pound sterling to 28 149 pound sterling) for the conduct problem group, compared with 7423 pound sterling for the no problem group. In all groups crime incurred the greatest cost, followed by extra educational provision, foster and residential care, and state benefits; health costs were smaller. Parental social class had a relatively small effect on antisocial behaviour, and although substantial independent contributions came from being male, having a low reading age, and attending more than two primary schools, conduct disorder still predicted the greatest cost.



Antisocial behaviour in childhood is a major predictor of how much an individual will cost society. The cost is large and falls on many agencies, yet few agencies contribute to prevention, which could be cost effective.
An Integrated Model of Information Systems Adoption in Small Businesses

Based on theories from the technological innovation literature, this study develops an integrated model of information systems (IS) adoption in small businesses. The model specifies contextual variables such as decision-maker characteristics , IS characteristics, organizational characteristics, and environmental characteristics as primary determinants of IS adoption in small businesses. A questionnaire survey was conducted in 166 small businesses. Data analysis shows that small businesses with certain CEO characteristics (innovativeness and level of IS knowledge), innovation characteristics (relative advantage, compatibility, and complexity of IS), and organizational characteristics (business size and level of employees' IS knowledge) are more likely to adopt IS. While CEO and innovation characteristics are important determinants of the decision to adopt, they do not affect the extent of IS adoption. The extent of IS adoption is mainly determined by organizational characteristics. Finally, the environmental characteristic of competition has no direct effect on small business adoption ofIS. THE ADOPTION OF TECHNOLOGICAL INNOVATIONS MAYBE DEPICTED as a three-stage sequence of initiation, adoption, and implementation [69, 82]. The initiation stage is concerned with gathering and evaluating information about the technological innovation. This is followed by the adoption stage in which a decision is made about adopting the technological innovation. When the decision is to go ahead with adoption, the implementation stage involves implementing the technological innovation in the business. An important and fast growing technological innovation during this century is computer-based information systems (IS). IS provide an opportunity for businesses to
Compressive Sequential Learning for Action Similarity Labeling

Human action recognition in videos has been extensively studied in recent years due to its wide range of applications. Instead of classifying video sequences into a number of action categories, in this paper, we focus on a particular problem of action similarity labeling (ASLAN), which aims at verifying whether a pair of videos contain the same type of action or not. To address this challenge, a novel approach called compressive sequential learning (CSL) is proposed by leveraging the compressive sensing theory and sequential learning. We first project data points to a low-dimensional space by effectively exploring an important property in compressive sensing: the restricted isometry property. In particular, a very sparse measurement matrix is adopted to reduce the dimensionality efficiently. We then learn an ensemble classifier for measuring similarities between pairwise videos by iteratively minimizing its empirical risk with the AdaBoost strategy on the training set. Unlike conventional AdaBoost, the weak learner for each iteration is not explicitly defined and its parameters are learned through greedy optimization. Furthermore, an alternative of CSL named compressive sequential encoding is developed as an encoding technique and followed by a linear classifier to address the similarity-labeling problem. Our method has been systematically evaluated on four action data sets: ASLAN, KTH, HMDB51, and Hollywood2, and the results show the effectiveness and superiority of our method for ASLAN.
Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation

Out-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources. OOV paraphrasing that augments the translation model for the OOV words by using the translation knowledge of their paraphrases has been proposed to address the OOV problem. In this paper, we propose using word embeddings and semantic lexicons for OOV paraphrasing. Experiments conducted on a low resource setting of the OLYMPICS task of IWSLT 2012 verify the effectiveness of our proposed method.
OCNet: Object Context Network for Scene Parsing

In this paper, we address the problem of scene parsing with deep learning and focus on the context aggregation strategy for robust segmentation. Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we introduce an object context pooling (OCP) scheme, which represents each pixel by exploiting the set of pixels that belong to the same object category with such a pixel, and we call the set of pixels as object context. Our implementation, inspired by the self-attention approach, consists of two steps: (i) compute the similarities between each pixel and all the pixels, forming a socalled object context map for each pixel served as a surrogate for the true object context, and (ii) represent the pixel by aggregating the features of all the pixels weighted by the similarities. The resulting representation is more robust compared to existing context aggregation schemes, e.g., pyramid pooling modules (PPM) in PSPNet and atrous spatial pyramid pooling (ASPP), which do not differentiate the context pixels belonging to the same object category or not, making the reliability of contextually aggregated representations limited. We empirically demonstrate our approach and two pyramid extensions with state-ofthe-art performance on three semantic segmentation benchmarks: Cityscapes, ADE20K and LIP. Code has been made available at: https://github.com/PkuRainBow/ OCNet.pytorch.
Systematic review of the research evidence examining the effectiveness of interventions using a sensory integrative approach for children.

Twenty-seven studies were systematically reviewed to identify, evaluate, and synthesize the research literature on the effectiveness of sensory integration (SI) intervention on the ability of children with difficulty processing and integrating sensory information to engage in desired occupations and to apply these findings to occupational therapy practice. Results suggest the SI approach may result in positive outcomes in sensorimotor skills and motor planning; socialization, attention, and behavioral regulation; reading-related skills; participation in active play; and achievement of individualized goals. Gross motor skills, self-esteem, and reading gains may be sustained from 3 mo to 2 yr. Findings may be limited by Type II error because of small sample sizes, variable intervention dosage, lack of fidelity to intervention, and selection of outcomes that may not be meaningful to clients and families or may not change with amount of treatment provided. Replication of findings with methodologically and theoretically sound studies is needed to support current findings.
A framework for 3D visualisation and manipulation in an immersive space using an untethered bimanual gestural interface

Immersive Environments offer users the experience of being submerged in a virtual space, effectively transcending the boundary between the real and virtual world. We present a framework for visualization and manipulation of 3D virtual environments in which users need not resort to the awkward command vocabulary of traditional keyboard-and-mouse interaction. We have adapted the transparent toolglass paradigm as a gestural interface widget for a spatially immersive environment. To serve that purpose, we have implemented a bimanual gesture interpreter to recognize and translate a user's actions into commands for control of these widgets. In order to satisfy a primary design goal of keeping the user completely untethered, we use purely video-based tracking techniques.
Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task

The Transformer architecture has become the state-of-the-art in Machine Translation. This model, which relies on attention-based mechanisms, has outperformed previous neural machine translation architectures in several tasks. In this system description paper, we report details of training neural machine translation with multi-source Romance languages with the Transformer model and in the evaluation frame of the biomedical WMT 2018 task. Using multi-source languages from the same family allows improvements of over 6 BLEU points.
Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor

We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints—common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.
Indoor location sensing using geo-magnetism

We present an indoor positioning system that measures location using disturbances of the Earth's magnetic field caused by structural steel elements in a building. The presence of these large steel members warps the geomagnetic field in a way that is spatially varying but temporally stable. To localize, we measure the magnetic field using an array of e-compasses and compare the measurement with a previously obtained magnetic map. We demonstrate accuracy within 1 meter 88% of the time in experiments in two buildings and across multiple floors within the buildings. We discuss several constraint techniques that can maintain accuracy as the sample space increases.
Geometric control and differential flatness of a quadrotor UAV with a cable-suspended load

A quadrotor with a cable-suspended load with eight degrees of freedom and four degrees underactuation is considered and a coordinate-free dynamic model, defined on the configuration space SE(3)×S2, is obtained by taking variations on manifolds. The quadrotor-load system is established to be a differentially-flat hybrid system with the load position and the quadrotor yaw serving as the flat outputs. A nonlinear geometric control design is developed, that enables tracking of outputs defined by (a) quadrotor attitude, (b) load attitude, and (c) position of the load. In each case, the closed-loop system exhibits almost-global properties. Stability proofs for the controller design, as well as simulations of the proposed controller are presented.
Evaluating a PID, pure pursuit, and weighted steering controller for an autonomous land vehicle

Wright Laboratory, at Tyndall AFB, Florida, has contracted the University ofFlorida to develop autonomous navigation systems for a variety ofrobotic vehicles, capable ofperforming tasks associated with the location and removal of bombs and mines. One ofthe tasks involves surveying closed target ranges for unexploded buried munitions. Accuracy in path following is critical to the task. There are hundreds of acres that currently require surveying. The sites are typically divided into regions, where each mission can take up to 4.5 hours. These sites are usually surveyed along parallel rows. By improving the accuracy ofpath following, the distance betweenthe rows can be increased to nearly the detection width ofthe ground penetrating sensors, resulting in increased acreage surveyed per mission. This paper evaluates a high-level PID and a pure pursuit steering controller. The controllers were combined into a weighted solution so that the desirable features of each controller is preserved. This strategy was demonstrated in simulation and implemented on a Navigation Test Vehicle (NTV). For a test path ofvarying curvature, the average lateral control error was 2 cm at a vehicle speed of 1.34 mIs.
Mmdenselstm: An Efficient Combination of Convolutional and Recurrent Neural Networks for Audio Source Separation

Deep neural networks have become an indispensable technique for audio source separation (SS). It was recently reported that a variant of CNN architecture called MM-DenseNet was successfully employed to solve the SS problem of estimating source amplitudes, and state-of-the-art results were obtained for DSD 100 dataset. To further enhance MMDenseNet, here we propose a novel architecture that integrates long short-term memory (LSTM) in multiple scales with skip connections to efficiently model long-term structures within an audio context. The experimental results show that the proposed method outperforms MMDenseNet, LSTM and a blend of the two networks. The number of parameters and processing time of the proposed model are significantly less than those for simple blending. Furthermore, the proposed method yields better results than those obtained using ideal binary masks for a singing voice separation task.
CREDIT RISK MANAGEMENT AND FINANCIAL PERFORMANCE OF MICROFINANCE INSTITUTIONS IN KAMPALA , UGANDA

The objective of this study was to evaluate whether relationship exist between credit risk management techniques and financial performance of microfinance institutions in Kampala, Uganda. Specifically, the study examined whether there is a relationship between credit risk identification, credit risk appraisal, credit risk monitoring, credit risk mitigation and financial performance of microfinance institutions in Kampala using sample of 60 members of staff in finance and credit departments of three licensed microfinance institutions in Kampala, Uganda namely Finca Uganda Ltd, Pride Microfinance Ltd, UGAFODE Microfinance Ltd. Primary data was collected using questionnaires and it comprised of closed ended questions. Secondary data was collected from the microfinance institutions (MDI’s) annual reports (2011 2015). Frequencies and descriptive statistics were used to analyse the population. Pearson linear correlation coefficient was adopted to examine relationship between credit risk management techniques and financial performance.
Creating people-aware IoT applications by combining design thinking and user-centered design methods

This article presents a methodology based on design thinking and user experience design methods for creating what we call `people-aware' IoT applications, where user needs, not technological opportunities, drive the development. This methodology is divided into 7 steps: discovery, capturing, research, design, prototype, evaluate and refine. The tools used include conventional user experience procedures such as problem identification, group brainstorming, surveys, or interviews, mixed with more IoT-specific design specificities. The results of the methodology include well-described and user-oriented scenarios meeting user's needs and also a complete toolbox to assist the implementation and the testing of abovementioned scenarios in an IoT perspective. The article describes the methodology in detail with the help of a use case conducted in a business environment available for the project that leads to the identification and partial design of concrete people-aware IoT applications in the context of a smart meeting room.
Methods for LiDAR point cloud classification using local neighborhood statistics

LiDAR data are available in a variety of publicly-accessible forums, providing high-resolution, accurate 3dimensional information about objects at the Earth’s surface. Automatic extraction of information from LiDAR point clouds, however, remains a challenging problem. The focus of this research is to develop methods for point cloud classification and object detection which can be customized for specific applications. The methods presented rely on analysis of statistics of local neighborhoods of LiDAR points. A multi-dimensional vector composed of these statistics can be classified using traditional data classification routines. Local neighborhood statistics are defined, and examples are given of the methods for specific applications such as building extraction and vegetation classification. Results indicate the feasibility of the local neighborhood statistics approach and provide a framework for the design of customized classification or object detection routines for LiDAR point clouds.
Mind's eye: A recurrent visual representation for image caption generation

In this paper we explore the bi-directional mapping between images and their sentence-based descriptions. Critical to our approach is a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. The representation automatically learns to remember long-term visual concepts. Our model is capable of both generating novel captions given an image, and reconstructing visual features given an image description. We evaluate our approach on several tasks. These include sentence generation, sentence retrieval and image retrieval. State-of-the-art results are shown for the task of generating novel image descriptions. When compared to human generated captions, our automatically generated captions are equal to or preferred by humans 21.0% of the time. Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.