nils-herrmann/clean_scirepeval huggingface dataset:
Sentiment Embeddings with Applications to Sentiment Analysis

We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore the sentiment of texts. It is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarity, such as good and bad, are mapped to neighboring word vectors. We address this issue by encoding sentiment information of texts (e.g., sentences and words) together with contexts of words in sentiment embeddings. By combining context and sentiment level evidences, the nearest neighbors in sentiment embedding space are semantically similar and it favors words with the same sentiment polarity. In order to learn sentiment embeddings effectively, we develop a number of neural networks with tailoring loss functions, and collect massive texts automatically with sentiment signals like emoticons as the training data. Sentiment embeddings can be naturally used as word features for a variety of sentiment analysis tasks without feature engineering. We apply sentiment embeddings to word-level sentiment analysis, sentence level sentiment classification, and building sentiment lexicons. Experimental results show that sentiment embeddings consistently outperform context-based embeddings on several benchmark datasets of these tasks. This work provides insights on the design of neural networks for learning task-specific word embeddings in other natural language processing tasks.
Effective Organizational Communication : a Key to Employee Motivation and Performance

Organisational Communication, in today’s organizations has not only become far more complex and varied but has become an important factor for overall organizational functioning and success. The way the organization communicates with its employees is reflected in morale, motivation and performance of the employees. The objective of the present paper is to explore the interrelationship between communication and motivation and its overall impact on employee performance. The paper focuses on the fact that communication in the workplace can take many forms and has a lasting effect on employee motivation. If employees feel that communication from management is effective, it can lead to feelings of job satisfaction, commitment to the organisation and increased trust in the workplace. This study was conducted through a comprehensive review and critical analysis of the research and literature focused upon the objectives of the paper. It also enumerates the results of a study of organizational communication and motivational practices followed at a large manufacturing company, Vanaz Engineers Ltd., based at Pune, to support the hypothesis propounded in the paper.
Traffic sign recognition based on the NVIDIA Jetson TX1 embedded system using convolutional neural networks

Traffic sign recognition is an important step for integrating smart vehicles into existing road transportation systems. In this paper, an NVIDIA Jetson TX1-based traffic sign recognition system is introduced for driver assistance applications. The system incorporates two major operations, traffic sign detection and recognition. Image color and shape based detection is used to locate potential signs in each frame. A pre-trained convolutional neural network performs classification on these potential sign candidates. The proposed system is implemented on NVIDIA Jetson TX1 board with web-camera. Based on a well-known benchmark suite, 96% detection accuracy is achieved while executing at 1.6 frames per seconds.
Mapping sentence form onto meaning: The syntax–semantic interface

The understanding of sentences involves not only the retrieval of the meaning of single words, but the identification of the relation between a verb and its arguments. The way the brain manages to process word meaning and syntactic relations during language comprehension on-line still is a matter of debate. Here we review the different views discussed in the literature and report data from crucial experiments investigating the temporal and neurotopological parameters of different information types encoded in verbs, i.e. word category information, the verb's argument structure information, the verb's selectional restriction and the morphosyntactic information encoded in the verb's inflection. The neurophysiological indices of the processes dealing with these different information types suggest an initial independence of the processing of word category information from other information types as the basis of local phrase structure building, and a later processing stage during which different information types interact. The relative ordering of the subprocesses appears to be universal, whereas the absolute timing of when during later phrases interaction takes places varies as a function of when the relevant information becomes available. Moreover, the neurophysiological indices for non-local dependency relations vary as a function of the morphological richness of the language.
Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions. In the present work, we extend the usage of LRP to recurrent neural networks. We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs. We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.
Detecting Contradictions in News Quotations

Detecting contradicting statements is a fundamental and challenging natural language processing and machine learning task, with numerous applications in information extraction and retrieval. For instance, contradictions need to be recognized by question answering systems or multi-document summarization systems. In terms of machine learning, it requires the ability, through supervised learning, to accurately estimate and capture the subtle differences between contradictions and for instance, paraphrases. In terms of natural language processing, it demands a pipeline approach with distinct phases in order to extract as much knowledge as possible from sentences. Previous state-of-the-art systems rely often on semantics and alignment relations. In this work, I move away from the commonly setup used in this domain, and address the problem of detecting contradictions as a classification task. I argue that for such classification, one can heavily rely on features based on those used for detecting paraphrases and recognizing textual entailment, alongside with numeric and string based features. This M.Sc. dissertation provides a system capable of detecting contradictions from a pair of affirmations published across newspapers with both a F1-score and Accuracy of 71%. Furthermore, this M.Sc. dissertation provides an assessment of what are the most informative features for detecting contradictions and paraphrases and infer if exists a correlation between contradiction detection and paraphrase identification.
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e., requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol.
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

Consider two data providers, each maintaining private records of different feature sets about common entities. They aim to learn a linear model jointly in a federated setting, namely, data is local and a shared model is trained from locally computed updates. In contrast with most work on distributed learning, in this scenario (i) data is split vertically, i.e. by features, (ii) only one data provider knows the target variable and (iii) entities are not linked across the data providers. Hence, to the challenge of private learning, we add the potentially negative consequences of mistakes in entity resolution. Our contribution is twofold. First, we describe a three-party end-to-end solution in two phases—privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme—, secure against a honest-but-curious adversary. The system allows learning without either exposing data in the clear or sharing which entities the data providers have in common. Our implementation is as accurate as a naive non-private solution that brings all data in one place, and scales to problems with millions of entities with hundreds of features. Second, we provide what is to our knowledge the first formal analysis of the impact of entity resolution’s mistakes on learning, with results on how optimal classifiers, empirical losses, margins and generalisation abilities are affected. Our results bring a clear and strong support for federated learning: under reasonable assumptions on the number and magnitude of entity resolution’s mistakes, it can be extremely beneficial to carry out federated learning in the setting where each peer’s data provides a significant uplift to the other. ∗All authors contributed equally. Richard Nock is jointly with the Australian National University & the University of Sydney. Giorgio Patrini is now at the University of Amsterdam. 1 ar X iv :1 71 1. 10 67 7v 1 [ cs .L G ] 2 9 N ov 2 01 7
Environmental Impact Assessment of a Proposed for Solid Waste Management Facility in Al-Wihda Sub District , Baghdad Governorate

Over the past decades, uncontrolled population growth and rapid urbanization and industrialization have resulted in environmental problems. One of the most important problems is inadequate solid waste management. Nowadays, increasing public awareness of the environment compels local authorities to define and to adopt new solutions for waste management. Although there are some efforts to reduce and recover the waste, disposal in landfills is still the most common method for waste destination. Therefore, the recycle facility should be selected carefully by considering both regulations and constraints on other sources. In this study, candidate site for an appropriate solid waste facility area in Al-Wihda sub district in Baghdad city is determined by using The GPS coordinates (Latitude 33 ̊ 07.674N Longitude 44 ̊42.420 E), according to the EIA legal (Category B) in article 24, 48 for landfill and solid waste management facility number (3) 2011 based on law number (27) 2009. The selected site serves a population of Al-Madain districts (461107 capita), and covered area about 53.64 km. Area method has been choosing in the proposed design because the level of groundwater in this reign is so high, also the amount of predict solid waste has been calculated for next ten years 2371174.7 ton. A sample of solid waste was taken to identify the quality of the waste shows that it contents (steel, aluminum, plastic, glass, paper, carton and temper) which can be recycled. A set of experimental tests have been done to evaluate and monitor the environmental impact of pollutant on the surrounding area of the proposed project at the present and future time. From which was mentioned above, the selected location is suitable for constriction the solid waste facility according to the EIA legal (Category B) in article 24, 48 for landfill and solid waste management facility number (3) 2011 based on law number (27) 2009, therefore we recommend implementing this necessary project.
Accelerating Human Learning with Deep Reinforcement Learning

Guiding a student through a sequence of lessons and helping them retain knowledge is one of the central challenges in education. Online learning platforms like Khan Academy and Duolingo tackle this problem in part by using interaction data to estimate student proficiency and recommend content. While the literature proposes a variety of algorithms for modeling student learning, there is relatively little work on principled methods for sequentially choosing items for the student to review in order to maximize learning. We study this decision problem as an instance of reinforcement learning, and draw on recent advances in training deep neural networks to learn flexible and scalable teaching policies that select the next item to review. Our primary contribution is an analysis of a model-free review scheduling algorithm for spaced repetition systems that does not explicitly model the student, and instead learns a policy that directly operates on raw observations of the study history. As a preliminary study, we train and evaluate this method using a student simulator based on cognitive models of human memory. Results show that modelfree scheduling is competitive against widely-used heuristics like SuperMemo and the Leitner system on various learning objectives and student models.
Public-private partnerships in Canadian health care : A case study of the Brampton Civic Hospital by

This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. In recent years, the cost of delivering health care in developed and developing countries has been rising exponentially. Governments around the world are searching for alternative mechanisms to reduce costs while increasing the capacity of social programmes with significant investments in infrastructure. A number of jurisdictions have begun to utilise public-private partnerships (PPPs) as a means of achieving these objectives. The use of PPPs in the Canadian health system is a relatively new phenomenon. Generally, the success of PPP projects is evaluated on the basis of the qualitative outcomes of the project, most commonly in a value-for-money analysis. In this article, we explore whether quantitative elements are sufficient to measure PPPs in politically sensitive areas of public policy, such as health care. We propose that the best way to evaluate the outcomes of PPPs in public health system projects requires both quantitative and qualitative criteria. We use a framework developed from neo-institutional economics that contextualises outcomes through a balance of quantitative and qualitative assessment criteria. We apply this evaluation framework to a specific Canadian case study in order to determine key success factors for future PPP health infrastructure projects. The analysis concludes that, given the complex and politically sensitive nature of health care, particular attention must be paid to communications and public relations and to design and post-construction planning in order to deliver a successful PPP. 2 PPP relationships differ in a fundamental way from conventional procurement contracting. In conventional procurement, risks are assumed to be contained in a contract focused on a short-term infrastructure deliverable, such as construction of a road, airport, water and sewer facility, or hospital. In PPPs, developing risk-sharing mechanisms is key to enhancing the returns to both the public and private sector. PPPs are based upon a stewardship model in which the private sector takes a more aggressive role in aspects of the project from which it had previously been excluded in the conventional procurement approach, such as design, financing, operations and maintenance. The hypothesis is that when the private sector assumes greater responsibility in the project, there will be incentives to ensure a steady stream of revenue for the private sector over the life of the project. …
A 10 nW–1 µW Power Management IC With Integrated Battery Management and Self-Startup for Energy Harvesting Applications

This paper presents a 10 nW-1 μW power management IC with 3.2 nW quiescent power consumption for solar energy harvesting applications. The chip integrates a switch matrix that can be configured as a buck or a boost dc-dc converter using a single inductor as well as output voltage regulation logic, battery management block, and self-startup. The control circuit of the converter is designed in an asynchronous fashion that scales the effective switching frequency of the converter with the level of power transferred. The on-time of the converter switches adapts dynamically to the input and output voltages for peak-current control and zero-current switching. For input power of 500 nW, the proposed chip achieves an efficiency of 82%, including the control circuit overhead, while charging the energy storage device at 3 V from 0.5 V input. In buck mode, it achieves a peak efficiency of 87% and maintains efficiency greater than 80% for output power of 50 nW-1 μW with input voltage of 3 V and output voltage of 1 V.
Li-ion battery SOC estimation using particle filter based on an equivalent circuit model

In this paper, a method for Li-ion battery state of charge (SOC) estimation using particle filter (PF) is proposed. The equivalent circuit model for Li-ion battery is established based on the available battery block in MATLAB/Simulink. To improve the model's accuracy, the circuit parameters are represented by functions of SOC. Then, the PF algorithm is utilized to do SOC estimation for the battery model. From simulation it reveals that PF provides accurate SOC estimation. It is demonstrated that the proposed method is effective on Li-ion battery SOC estimation.
A Comprehensive LVRT Control Strategy for DFIG Wind Turbines With Enhanced Reactive Power Support

The paper presents a new control strategy to enhance the ability of reactive power support of a doubly fed induction generator (DFIG) based wind turbine during serious voltage dips. The proposed strategy is an advanced low voltage ride through (LVRT) control scheme, with which a part of the captured wind energy during grid faults is stored temporarily in the rotor's inertia energy and the remaining energy is available to the grid while the DC-link voltage and rotor current are kept below the dangerous levels. After grid fault clearance, the control strategy ensures smooth release of the rotor's excessive inertia energy into the grid. Based on these designs, the DFIG's reactive power capacity on the stator and the grid side converter is handled carefully to satisfy the new grid code requirements strictly. Simulation studies are presented and discussed.
A computer algorithm for reconstructing a scene from two projections

A simple algorithm for computing the three-dimensional structure of a scene from a correlated pair of perspective projections is described here, when the spatial relationship between the two projections is unknown. This problem is relevant not only to photographic surveying1 but also to binocular vision2, where the non-visual information available to the observer about the orientation and focal length of each eye is much less accurate than the optical information supplied by the retinal images themselves. The problem also arises in monocular perception of motion3, where the two projections represent views which are separated in time as well as space. As Marr and Poggio4 have noted, the fusing of two images to produce a three-dimensional percept involves two distinct processes: the establishment of a 1:1 correspondence between image points in the two views—the ‘correspondence problem’—and the use of the associated disparities for determining the distances of visible elements in the scene. I shall assume that the correspondence problem has been solved; the problem of reconstructing the scene then reduces to that of finding the relative orientation of the two viewpoints.
Overview of State-of-the-Art Algorithms for Stack-Based High-Dynamic Range (HDR) Imaging

Modern digital cameras have very limited dynamic range, which makes them unable to capture the full range of illumination in natural scenes. Since this prevents them from accurately photographing visible detail, researchers have spent the last two decades developing algorithms for high-dynamic range (HDR) imaging which can capture a wider range of illumination and therefore allow us to reconstruct richer images of natural scenes. The most practical of these methods are stack-based approaches which take a set of images at different exposure levels and then merge them together to form the final HDR result. However, these algorithms produce ghost-like artifacts when the scene has motion or the camera is not perfectly static. In this paper, we present an overview of state-of-the-art deghosting algorithms for stackbased HDR imaging and discuss some of the tradeoffs of each.
VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation

We present a novel algorithm for point cloud segmentation using group convolutions. Our approach uses a radial basis function (RBF) based variational autoencoder (VAE) network. We transform unstructured point clouds into regular voxel grids and use subvoxels within each voxel to encode the local geometry using a VAE architecture. In order to handle sparse distribution of points within each voxel, we use RBF to compute a local, continuous representation within each subvoxel. We extend group equivariant convolutions to 3D point cloud processing and increase the expressive capacity of the neural network. The combination of RBF and VAE results in a good volumetric representation that can handle noisy point cloud datasets and is more robust for learning. We highlight the performance on standard benchmarks and compare with prior methods. In practice, our approach outperforms state-of-the-art segmentation algorithms on the ShapeNet and S3DIS datasets.