nils-herrmann/clean_scirepeval huggingface dataset:
Toward Natural Turn-Taking in a Virtual Human Negotiation Agent

In this paper we assess our progress toward creating a virtual human negotiation agent with fluid turn-taking skills. To facilitate the design of this agent, we have collected a corpus of human-human negotiation roleplays as well as a corpus of Wizard-controlled human-agent negotiations in the same roleplay scenario. We compare the natural turn-taking behavior in our human-human corpus with that achieved in our Wizard-of-Oz corpus, and quantify our virtual human’s turn-taking skills using a combination of subjective and objective metrics. We also discuss our design for a Wizard user interface to support real-time control of the virtual human’s turntaking and dialogue behavior, and analyze our wizard’s usage of this interface.
An overview of end-to-end language understanding and dialog management for personal digital assistants

Spoken language understanding and dialog management have emerged as key technologies in interacting with personal digital assistants (PDAs). The coverage, complexity, and the scale of PDAs are much larger than previous conversational understanding systems. As such, new problems arise. In this paper, we provide an overview of the language understanding and dialog management capabilities of PDAs, focusing particularly on Cortana, Microsoft's PDA. We explain the system architecture for language understanding and dialog management for our PDA, indicate how it differs with prior state-of-the-art systems, and describe key components. We also report a set of experiments detailing system performance on a variety of scenarios and tasks. We describe how the quality of user experiences are measured end-to-end and also discuss open issues.
Rhizosphere interactions: root exudates, microbes, and microbial communities 1

The study of the interactions between plants and their microbial communities in the rhizosphere is important for developing sustainable management practices and agricultural products such as biofertilizers and biopesticides. Plant roots release a broad variety of chemical compounds to attract and select microorganisms in the rhizosphere. In turn, these plantassociated microorganisms, via different mechanisms, influence plant health and growth. In this review, we summarize recent progress made in unraveling the interactions between plants and rhizosphere microbes through plant root exudates, focusing on how root exudate compounds mediate rhizospheric interactions both at the plant–microbe and plant–microbiome levels. We also discuss the potential of root exudates for harnessing rhizospheric interactions with microbes that could lead to sustainable agricultural practices.
A Class of Submodular Functions for Document Summarization

We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and the other which positively rewards diversity. Critically, our functions are monotone nondecreasing and submodular, which means that an efficient scalable greedy optimization scheme has a constant factor guarantee of optimality. When evaluated on DUC 2004-2007 corpora, we obtain better than existing state-of-art results in both generic and query-focused document summarization. Lastly, we show that several well-established methods for document summarization correspond, in fact, to submodular function optimization, adding further evidence that submodular functions are a natural fit for document summarization.
Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval

As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank, retrieval systems can learn directly from implicit feedback inferred from user interactions. In such an online setting, algorithms must obtain feedback for effective learning while simultaneously utilizing what has already been learned to produce high quality results. We formulate this challenge as an exploration–exploitation dilemma and propose two methods for addressing it. By adding mechanisms for balancing exploration and exploitation during learning, each method extends a state-of-the-art learning to rank method, one based on listwise learning and the other on pairwise learning. Using a recently developed simulation framework that allows assessment of online performance, we empirically evaluate both methods. Our results show that balancing exploration and exploitation can substantially and significantly improve the online retrieval performance of both listwise and pairwise approaches. In addition, the results demonstrate that such a balance affects the two approaches in different ways, especially when user feedback is noisy, yielding new insights relevant to making online learning to rank effective in practice.
Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis

Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedia. We use machine learning techniques to explicitly represent the meaning of any text as a weighted vector of Wikipedia-based concepts. Assessing the relatedness of texts in this space amounts to comparing the corresponding vectors using conventional metrics (e.g., cosine). Compared with the previous state of the art, using ESA results in substantial improvements in correlation of computed relatedness scores with human judgments: from r = 0.56 to 0.75 for individual words and from r = 0.60 to 0.72 for texts. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.
ACS Surgery : Principles and Practice 8 MINIMALLY INVASIVE ESOPHAGEAL PROCEDURES — 2 Esophageal Manometry

During the 1970s and the 1980s, operations for benign esophageal disorders were often withheld or delayed in favor of less effective forms of treatment in an effort to prevent the postoperative discomfort, the long hospital stay, and the recovery time associated with open surgical procedures. For instance, pneumatic dilatation became first-line therapy for achalasia, even though surgical management had been shown to be clearly superior.1 In the first part of the 1990s, it became clear that treatment of benign esophageal disorders with minimally invasive procedures yielded results comparable to those of treatment with traditional operations while causing minimal postoperative discomfort, reducing the duration of hospitalization, shortening recovery time, and permitting earlier return to work.2,3 Consequently, minimally invasive surgery was increasingly considered as first-line treatment for achalasia, and laparoscopic fundoplication was considered more readily and at an earlier stage in the management of gastroesophageal reflux disease (GERD). Since then, minimally invasive esophageal procedures have continued to evolve, thanks to better instrumentation and improved surgical expertise. In addition, with greater experience and longer follow-up periods, it has become possible to analyze techniques and their results more rigorously. For instance, whereas a few years ago a left thoracoscopic Heller myotomy was considered the procedure of choice for achalasia, the current procedure of choice is a laparoscopic Heller myotomy with partial fundoplication, which has proved to be better at relieving dysphagia and controlling postoperative reflux.4-7 Similarly, whereas total fundoplication and partial fundoplication were initially considered equally effective in treating GERD,8 total fundoplication is now viewed as clearly superior for this purpose and should be used whenever feasible.9 In this chapter, I focus on minimally invasive approaches to the treatment of abnormal gastroesophageal reflux and esophageal motility disorders.The standard open counterparts of these operations are described elsewhere [see 4:7 Open Esophageal Procedures].
Ecological responses to recent climate change

There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible.
Local Intensity Order Pattern for feature description

This paper presents a novel method for feature description based on intensity order. Specifically, a Local Intensity Order Pattern(LIOP) is proposed to encode the local ordinal information of each pixel and the overall ordinal information is used to divide the local patch into subregions which are used for accumulating the LIOPs respectively. Therefore, both local and overall intensity ordinal information of the local patch are captured by the proposed LIOP descriptor so as to make it a highly discriminative descriptor. It is shown that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation but also robust to many other geometric and photometric transformations such as viewpoint change, image blur and JEPG compression. The proposed descriptor has been evaluated on the standard Oxford dataset and four additional image pairs with complex illumination changes. The experimental results show that the proposed descriptor obtains a significant improvement over the existing state-of-the-art descriptors.
A Taxonomy of Blockchain-Based Systems for Architecture Design

Blockchain is an emerging technology for decentralised and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where agreement on shared states can be established without trusting a central integration point. A major difficulty for architects designing applications based on blockchain is that thetechnology has many configurations and variants. Since blockchains are at an early stage, there is little product data or reliable technology evaluation available to compare different blockchains. In this paper, we propose how to classify and compare blockchains and blockchain-based systems to assist with the design and assessment of their impact on software architectures. Our taxonomy captures major architectural characteristics of blockchains and the impact of their principal design decisions. This taxonomy is intended to help with important architectural considerations about the performance and quality attributes of blockchain-based systems.
Detecting Rumors from Microblogs with Recurrent Neural Networks

Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over time. Experimental results on datasets from two real-world microblog platforms demonstrate that (1) the RNN method outperforms state-of-the-art rumor detection models that use hand-crafted features; (2) performance of the RNN-based algorithm is further improved via sophisticated recurrent units and extra hidden layers; (3) RNN-based method detects rumors more quickly and accurately than existing techniques, including the leading online rumor debunking services.
Models of occupational therapy service provision in the school system.

Occupational therapy is a related service when provided within the public schools, which means that services must enhance or support educational goals. Three service provision models have been described for school therapists: direct service, monitoring, and consultation. Direct service addresses individualized needs that require specialized intervention strategies which can safely be performed only by the occupational therapist. Monitoring, which is sometimes referred to as integrated programming, uses therapeutic expertise within functional tasks to maximize opportunities for practice and generalization. Consultation addresses problems by enabling others to work more effectively on the educational goals they have set for the students. Evidence is accumulating to demonstrate that each model is effective when chosen and applied appropriately. This paper reviews each model, provides examples of studies that have been conducted, and suggests directions for future research.
IMAGE STEGANOGRAPHY IN YUV COLOR SPACE

Presently there is a rapid development of the internet and the telecommunication techniques. Importance of information security is increasing. Cryptography and steganography are the major areas which work on information hiding and security. In this paper a method is used to embed a color secret image inside a color cover image. A 2-3-3 LSB insertion method has been used for image steganography. The important quality of a steganographic system is to be less distortive while increasing the size of the secret image. Use of cryptography along with steganography increases the security. Arnold CATMAP encryption technique is used for encrypting the secret image. Color space plays an important role in increasing network bandwidth efficiency. YUV color space provides reduced bandwidth for chrominance components. This paper demonstrates that YUV color space can also be used for security purposes.  Watermarking, Haar Wavelet, DWT, PSNR
Translating Embeddings for Modeling Multi-relational Data

We consider the problem of embedding entities and relationships of multirelational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.
Churn analysis using deep convolutional neural networks and autoencoders

Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0.743 on the test dataset using no more than 12 temporal features for each customer. Unsupervised learning was conducted using autoencoders to better understand the reasons for customer churn. Images that maximally activate the hidden units of an autoencoder trained with churned customers reveal ample opportunities for action to be taken to prevent churn among strong data, no voice users.
End-to-End Neural Entity Linking

Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition

Motion representation plays a vital role in human action recognition in videos. In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach. The OFF is derived from the definition of optical flow and is orthogonal to the optical flow. The derivation also provides theoretical support for using the difference between two frames. By directly calculating pixel-wise spatio-temporal gradients of the deep feature maps, the OFF could be embedded in any existing CNN based video action recognition framework with only a slight additional cost. It enables the CNN to extract spatiotemporal information, especially the temporal information between frames simultaneously. This simple but powerful idea is validated by experimental results. The network with OFF fed only by RGB inputs achieves a competitive accuracy of 93.3% on UCF-101, which is comparable with the result obtained by two streams (RGB and optical flow), but is 15 times faster in speed. Experimental results also show that OFF is complementary to other motion modalities such as optical flow. When the proposed method is plugged into the state-of-the-art video action recognition framework, it has 96.0% and 74.2% accuracy on UCF-101 and HMDB-51 respectively. The code for this project is available at: https://github.com/kevin-ssy/Optical-Flow-Guided-Feature
Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach

In this paper, we hypothesize that morphological properties of nuclei are crucial for classifying dysplastic changes. Therefore, we propose to represent a whole histopathology slide as a collection of smaller images containing patches of nuclei and adjacent tissue. For this purpose, we use a deep multiple instance learning approach. Within this framework we first embed patches in a low-dimensional space using convolutional and fully-connected layers. Next, we combine the lowdimensional embeddings using a multiple instance learning pooling operator and eventually we use fully-connected layers to provide a classification. We evaluate our approach on esophagus cancer histopathology dataset.
Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition

In this work we present a framework for the recognition of natural scene text. Our framework does not require any human-labelled data, and performs word recognition on the whole image holistically, departing from the character based recognition systems of the past. The deep neural network models at the centre of this framework are trained solely on data produced by a synthetic text generation engine – synthetic data that is highly realistic and sufficient to replace real data, giving us infinite amounts of training data. This excess of data exposes new possibilities for word recognition models, and here we consider three models, each one “reading” words in a different way: via 90k-way dictionary encoding, character sequence encoding, and bag-of-N-grams encoding. In the scenarios of language based and completely unconstrained text recognition we greatly improve upon state-of-the-art performance on standard datasets, using our fast, simple machinery and requiring zero data-acquisition costs.
Few-shot Object Detection

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named “few-shot object detection”. The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC’07 and ILSVRC’13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets

In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a message (term-level task). Among submissions from 44 teams in a competition, our submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. We also generated two large word–sentiment association lexicons, one from tweets with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated using freely available
Comparing Hybrid Peer-to-Peer Systems

“Peer-to-peer” systems like Napster and Gnutella have recently become popular for sharing information. In this paper, we study the relevant issues and tradeoffs in designing a scalable P2P system. We focus on a subset of P2P systems, known as “hybrid” P2P, where some functionality is still centralized. (In Napster, for example, indexing is centralized, and file exchange is distributed.) We model a file-sharing application, developing a probabilistic model to describe query behavior and expected query result sizes. We also develop an analytic model to describe system performance. Using experimental data collected from a running, publicly available hybrid P2P system, we validate both models. We then present several hybrid P2P system architectures and evaluate them using our model. We discuss the tradeoffs between the architectures and highlight the effects of key parameter values on system performance.