nils-herrmann/clean_scirepeval huggingface dataset:
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

In this work, we present a hybrid learning method for training task-oriented dialogue systems through online user interactions. Popular methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pretraining models. Efficiency of such learning method may suffer from the mismatch of dialogue state distribution between offline training and online interactive learning stages. To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback. We design a neural network based task-oriented dialogue agent that can be optimized end-toend with the proposed learning method. Experimental results show that our end-to-end dialogue agent can learn effectively from the mistake it makes via imitation learning from user teaching. Applying reinforcement learning with user feedback after the imitation learning stage further improves the agent’s capability in successfully completing a task.
Recognition of Isolated Words using Features based on LPC, MFCC, ZCR and STE, with Neural Network Classifiers

This paper proposes an approach to recognize English words corresponding to digits Zero to Nine spoken in an isolated way by different male and female speakers. A set of features consisting of a combination of Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), Zero Crossing Rate (ZCR), and Short Time Energy (STE) of the audio signal, is used to generate a 63-element feature vector, which is subsequently used for discrimination. Classification is done using artificial neural networks (ANN) with feedforward back-propagation architectures. An accuracy of 85% is obtained by the combination of features, when the proposed approach is tested using a dataset of 280 speech samples, which is more than those obtained by using the features singly.
JOB-SHOP SCHEDULING USING NEURAL NETWORKS 1

Complete enumeration of all the sequences to establish global optimality is not feasible as the search space, for a general job-shop scheduling problem, ΠG has an upper bound of (n!). Since the early fifties a great deal of research attention has been focused on solving ΠG, resulting in a wide variety of approaches such as Branch and Bound, Simulated Annealing, Tabu Search, etc. However limited success has been achieved by these methods due to the shear intractability of this generic scheduling problem. Recently, much effort has been concentrated on using neural networks to solve ΠG as they are capable of adapting to new environments with little human intervention and can mimic thought processes. Major contributions in solving ΠG using a Hopfield neural network, as well as applications of back-error propagation to general scheduling problems are presented. To overcome the deficiencies in these applications a modified back-error propagation model, a simple yet powerful parallel architecture which can be successfully simulated on a personal computer, is applied to solve ΠG.
Drift and Cost Comparison of Different Structural Systems for Tall Buildings

The race towards new heights and architecture has not been without challenges. Tall structures have continued to climb higher and higher facing strange loading effects and very high loading values due to dominating lateral loads. The design criteria for tall buildings are strength, serviceability, stability and human comfort. But the factors govern the design of tall and slender buildings all the times are serviceability and human comfort against lateral loads. As a result, lateral stiffness is a major consideration in the design of tall buildings. The first parameter that is used to estimate the lateral stiffness of a tall building is drift index. Different lateral load resisting structural subsystems can be used to impart stiffness and reduce drift in the building. Lateral load resisting subsystems can take many forms depending upon the orientation, integration and addition of the various structural components. In this research, sixteen different lateral load resisting structural subsystems are used to design a tall building and finally the most economical structural system is selected amongst these. For this purpose a hundred and five storey square shaped prismatic steel building uniform through the height is selected, analyzed and designed for gravity and wind loads. Analysis and design of selected lateral load resisting structural subsystems reveals that, for the building configuration selected, the structural system containing composite super columns with portals subsystem is most efficient.
Finger Vein Recognition Using Local Line Binary Pattern

In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).
Proposal of Improved Winding Method for VR Resolver

The variable reluctance (VR) resolver is generally used instead of an optical encoder as a position sensor on motors for hybrid electric vehicles or electric vehicles owing to its reliability, low cost, and ease of installation. The commonly used conventional winding method for the VR resolver has disadvantages, such as complicated winding and unsuitability for mass production. This paper proposes an improved winding method that leads to simpler winding and better suitability for mass production than the conventional method. In this paper, through the design and finite element analysis for two types of output winding methods, the advantages and disadvantages of each method are presented, and the validity of the proposed winding method is verified. In addition, experiments with the VR resolver using the proposed winding method have been performed to verify its performance.
Graph Analytics Through Fine-Grained Parallelism

Large graphs are getting increasingly popular and even indispensable in many applications, for example, in social media data, large networks, and knowledge bases. Efficient graph analytics thus becomes an important subject of study. To increase efficiency and scalability, in-memory computation and parallelism have been explored extensively to speed up various graph analytical workloads. In many graph analytical engines (e.g., Pregel, Neo4j, GraphLab), parallelism is achieved via one of the three concurrency control models, namely, bulk synchronization processing (BSP), asynchronous processing, and synchronous processing. Among them, synchronous processing has the potential to achieve the best performance due to fine-grained parallelism, while ensuring the correctness and the convergence of the computation, if an effective concurrency control scheme is used. This paper explores the topological properties of the underlying graph to design and implement a highly effective concurrency control scheme for efficient synchronous processing in an in-memory graph analytical engine. Our design uses a novel hybrid approach that combines 2PL (two-phase locking) with OCC (optimistic concurrency control), for high degree and low degree vertices in a graph respectively. Our results show that the proposed hybrid synchronous scheduler has significantly outperformed other synchronous schedulers in existing graph analytical engines, as well as BSP and asynchronous schedulers.
The Study of the Impact of Perceived Quality and Value of Social Enterprises on Customer Satisfaction and RePurchase Intention

Previous studies on social enterprises reported that unlike private enterprise consumers, social enterprise consumers appreciate the social value of the enterprise products and that social value affects customer satisfaction and repurchase intention. However, previous literature also pointed out that focusing only on social value as the factor affecting purchase behavior does not reflect the change in the situation of social enterprises. We expect that not only social but also a variety of other value consumers perceive from the products of social enterprises influence consumer satisfaction and repurchase intention. The purpose of this study is as follows. First, we intend to find the customer value for the products and services of social enterprises. Second, we intend to examine whether the positive relationships between quality and value of products/services reported in numerous previous studies applies to social enterprises. Third, we would like to find out whether satisfaction from social enterprise products and services affect the actual repurchase intention. Finally, in order to find dynamic interaction among the variables, this study models the key flow of the factors influencing the social enterprise consumers’ repurchase intention: perceived quality  perceived value  customer satisfaction  repurchase intention. The results show that there are positive relationships between the consumer perception of quality and that of functional, emotional and social value. We also find positive relationships between the perception of functional, emotional and social value and customer satisfaction. Our findings show that the consumers of social enterprises perceive social value, along with the functional and emotional value, through the quality of products and services. The perceived value has positive effects on customer satisfaction and repurchase intention in the future. This study shows that the positive relationships between quality and value and customer satisfaction and repurchase intention found in numerous previous studies also exist in the context of social enterprises. In the last section, we discuss the practical implications for social enterprises based on the findings of our study and present the directions for future studies.
Assessing the influence of risk factors on rates and dynamics of peripheral vein phlebitis: an observational cohort study.

To assess the influence of risk factors on the rates and kinetics of peripheral vein phlebitis (PVP) development and its theoretical influence in absolute PVP reduction after catheter replacement.



All peripheral short intravenous catheters inserted during one month were included (1201 catheters and 967 patients). PVP risk factors were assessed by a Cox proportional hazard model. Cumulative probability, conditional failure of PVP and theoretical estimation of the benefit from replacement at different intervals were performed.



Female gender, catheter insertion at the emergency or medical-surgical wards, forearm site, amoxicillin-clavulamate or aminoglycosides were independent predictors of PVP with hazard ratios (95 confidence interval) of 1.46 (1.09-2.15), 1.94 (1.01-3.73), 2.51 (1.29-4.88), 1.93 (1.20-3.01), 2.15 (1.45-3.20) and 2.10 (1.01-4.63), respectively. Maximum phlebitis incidence was reached sooner in patients with ≥2 risk factors (days 3-4) than in those with <2 (days 4-5). Conditional failure increased from 0.08 phlebitis/one catheter-day for devices with ≤1 risk factors to 0.26 for those with ≥3. The greatest benefit of routine catheter exchange was obtained by replacement every 60h. However, this benefit differed according to the number of risk factors: 24.8% reduction with ≥3, 13.1% with 2, and 9.2% with ≤1.



PVP dynamics is highly influenced by identifiable risk factors which may be used to refine the strategy of catheter management. Routine replacement every 72h seems to be strictly necessary only in high-risk catheters.
Comparative studies on 3 G , 4 G and 5 G wireless technology

The fifth generation wireless 5G development initiative is based upon 4G, which at present is struggling to meet its performance goals. The comparison between 3G and 4G wireless communication systems in relation to its architecture, speed, frequency band, switching design basis and forward error correction is studied, and were discovered that their performances are still unable to solve the unending problems of poor coverage, bad interconnectivity, poor quality of service and flexibility. An ideal 5G model to accommodate the challenges and shortfalls of 3G and 4G deployments is discussed as well as the significant system improvements on the earlier wireless technologies. The radio channel propagation characteristics for 4G and 5G systems is discussed. Major advantages of 5G network in providing myriads of services to end users personalization, terminal and network heterogeneity, intelligence networking and network convergence among other benefits are highlighted.The significance of the study is evaluated for a fast and effective connection and communication of devices like mobile phones and computers, including the capability of supporting and allowing a highly flexible network connectivity.
Direct causation in the linguistic coding and individuation of causal events

This research proposes a new theory of direct causation and examines how this concept plays a key role in the linguistic coding and individuation of causal events. According to the no-intervening-cause hypothesis, a causal chain can be described by a single-clause sentence and construed as a single event if there are no intervening causers between the initial causer and the final causee. Consistent with this hypothesis, participants used single-clause sentences (lexical causatives) more often than two-clause sentences (e.g. periphrastic causatives) for causal chains in which (1) the causer and causee touched (Experiments 1 and 2), and (2) an intervening entity could be construed as an enabling condition rather than another cause (Experiments 2-4). In addition, event judgments paralleled linguistic descriptions: chains that could be described with single-clause expressions were more often construed as single events than chains that could not (Experiments 1-3). Implications for languages other than English, for the linguistic coding of accidental outcomes and for the relationship between cognition and language in general are discussed.
Improved Stochastic Gradient Descent Algorithm for SVM

In order to improve the efficiency and classification ability of Support vector machines (SVM) based on stochastic gradient descent algorithm, three algorithms of improved stochastic gradient descent (SGD) are used to solve support vector machine, which are Momentum, Nesterov accelerated gradient (NAG), RMSprop. The experimental results show that the algorithm based on RMSprop for solving the linear support vector machine has faster convergence speed and higher testing precision on five datasets (Alpha, Gamma, Delta, Mnist, Usps).
A Survey of Human Activity Recognition Using WiFi CSI

In this article, we present a survey of recent advances in passive human behaviour recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. Movement of human body causes a change in the wireless signal reflections, which results in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behaviour can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performances, however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural network (RNN), and show the improved performance. We also discuss about different challenges such as environment change, frame rate selection, and multi-user scenario, and suggest possible directions for future work.
Gami fi cation and Mobile Marketing Effectiveness

A variety of business sectors have been buffeted by the diffusion of mobile technology, a trend that presents a variety of difficult challenges but interesting opportunities to marketers. One such opportunity is gamification, which, one hopes, will enhance appeal to mobile consumers. Our sense from both personal experience and the literature is that the gamified mobile apps currently offered by firms mostly miss the mark. We provide a systematic overview of game design and note how principles derived from that field are highly applicable to gamification in mobile marketing settings. We are aided by the work of Schell (2008), whose Elemental Game Tetrad Model allows us to offer a coherent look at how gamification should affect mobile marketing outcomes.
Artifact-Centric Process Mining ( preprint 1 )

Artifact-centric process mining is an extension of classical process mining (van der Aalst 2016) that allows to analyze event data with more than one case identifier in its entirety. It allows to analyze the dynamic behavior of (business) processes that create, read, update, and delete multiple data objects that are related to each other in relationships with one-to-one, one-to-many, and many-to-many cardinalities. Such event data is typically stored in relational databases of, for example, Enterprise Resource Planning (ERP) systems (Lu et al 2015). Artifact-centric process mining comprises artifact-centric process discovery, conformance checking, and enhancement. The outcomes of artifact-centric process mining can be used for documentation of the actual data flow in an organization, and for analyzing deviations in the data flow for performance and conformance analysis. The input to artifact-centric process discovery is either an event log where events carry information about the data objects and their changes, or a relational database also containing records about data creation, change, and deletion events. The output of artifact-centric process discovery are a data model of the objects (each defining its own case identifier) and relations between objects, and an artifact-centric pro-
Efficient Deep Web Crawling Using Reinforcement Learning

Deep web refers to the hidden part of the Web that remains unavailable for standard Web crawlers. To obtain content of Deep Web is challenging and has been acknowledged as a significant gap in the coverage of search engines. To this end, the paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and selects an action (query) to submit to the environment according to Q-value. The framework not only enables crawlers to learn a promising crawling strategy from its own experience, but also allows for utilizing diverse features of query keywords. Experimental results show that the method outperforms the state of art methods in terms of crawling capability and breaks through the assumption of full-text search implied by existing methods.
On the challenges of learning with inference networks on sparse, high-dimensional data

We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such models using inference (or recognition) networks; we identify a crucial problem when modeling large, sparse, highdimensional datasets – underfitting. We study the extent of underfitting, highlighting that its severity increases with the sparsity of the data. We propose methods to tackle it via iterative optimization inspired by stochastic variational inference (Hoffman et al. , 2013) and improvements in the sparse data representation used for inference. The proposed techniques drastically improve the ability of these powerful models to fit sparse data, achieving state-of-the-art results on a benchmark textcount dataset and excellent results on the task of top-N recommendation.
Big Healthcare Data Analytics : Challenges and Applications

Increasing demand and costs for healthcare, exacerbated by ageing populations and a great shortage of doctors, are serious concerns worldwide. Consequently, this has generated a great amount of motivation in providing better healthcare through smarter healthcare systems. Management and processing of healthcare data are challenging due to various factors that are inherent in the data itself such as high-dimensionality, irregularity and sparsity. A long stream of research has been proposed to address these problems and provide more efficient and scalable healthcare systems and solutions. In this chapter, we shall examine the challenges in designing algorithms and systems for healthcare analytics and applications, followed by a survey on various relevant solutions. We shall also discuss next-generation healthcare applications, services and systems, that are related to big healthcare data analytics.
Reconstruction for Feature Disentanglement in Pose-invariant Face Recognition

Deep neural networks (DNNs) trained on large-scale datasets have recently achieved impressive improvements in face recognition. But a persistent challenge remains to develop methods capable of handling large pose variations that are relatively under-represented in training data. This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data. We first propose to use a synthesis network for generating non-frontal views from a single frontal image, in order to increase the diversity of training data while preserving accurate facial details that are critical for identity discrimination. Our next contribution is a multi-source multi-task DNN that seeks a rich embedding representing identity information, as well as information such as pose and landmark locations. Finally, we propose a Siamese network to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features obtained from two images of the same subject. Experiments on face datasets in both controlled and wild scenarios, such as MultiPIE, LFW and 300WLP, show that our method consistently outperforms the state-of-the-art, especially on images with large head pose variations.
Hardware accelerated convolutional neural networks for synthetic vision systems

In this paper we present a scalable hardware architecture to implement large-scale convolutional neural networks and state-of-the-art multi-layered artificial vision systems. This system is fully digital and is a modular vision engine with the goal of performing real-time detection, recognition and segmentation of mega-pixel images. We present a performance comparison between a software, FPGA and ASIC implementation that shows a speed up in custom hardware implementations.
RUNNING HEAD : GUILT PRONENESS Guilt Proneness and Moral Character

Guilt proneness is a personality trait indicative of a predisposition to experience negative feelings about personal wrongdoing, even when the wrongdoing is private. It is characterized by the anticipation of feeling bad about committing transgressions rather than by guilty feelings in a particular moment or generalized guilty feelings that occur without an eliciting event. Our research has revealed that guilt proneness is an important character trait because knowing a person’s level of guilt proneness helps us to predict the likelihood that they will behave unethically. For example, online studies of adults across the U.S. have shown that people who score high in guilt proneness (compared to low scorers) make fewer unethical business decisions, commit fewer delinquent behaviors, and behave more honestly when they make economic decisions. In the workplace, guilt-prone employees are less likely to engage in counterproductive behaviors that harm their organization.