nils-herrmann/clean_scirepeval huggingface dataset:
Language Model Based Grammatical Error Correction without Annotated Training Data

Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), research into language model (LM) based approaches to GEC has largely stagnated. In this paper, we re-examine LMs in GEC and show that it is entirely possible to build a simple system that not only requires minimal annotated data (∼1000 sentences), but is also fairly competitive with several state-of-the-art systems. This approach should be of particular interest for languages where very little annotated training data exists, although we also hope to use it as a baseline to motivate future research.
Cognitive Architecture for a Connected World

We propose a unified architecture for next generation cognitive, low cost, mobile internet. The end user platform is able to scale as per the application and network requirements. It takes computing out of the data center and into end user platform. Internet enables open standards, accessible computing and applications programmability on a commodity platform. The architecture is a super-set to present day infrastructure web computing. The Java virtual machine (JVM) derives from the stack architecture. Applications can be developed and deployed on a multitude of host platforms. O(1)→ O(N). Computing and the internet today are more accessible and available to the larger community. Machine learning has made extensive advances with the availability of modern computing. It is used widely in NLP, Computer Vision, Deep learning and AI. A prototype device for mobile could contain N compute and N MB of memory.  mobile, server, internet
Validating an innovative real-time Delphi approach-A methodological comparison between real-time and conventional Delphi studies

Article history: Received 29 October 2010 Received in revised form 28 March 2011  A novel and innovative real-time Delphi technique is introduced in order to address previously identified weaknesses of the conventional Delphi method, such as complicated facilitator tasks, lack of real-time presentation of results, and difficulties in tracking progress over time. We demonstrate how the real-time (computer-based) method increases the efficiency of the process, accommodates expert availability, and reduces drop-out-rates. Modifications in the Delphi procedure (e.g. change of iteration principle) not only increase efficiency but also change the nature and process of the survey technique itself. By identifying and analysing three individual effects (initial condition effect, feedback effect, and iteration effect) we examine whether the modifications in the survey process cause deviations to the survey results. Empirical data obtained from both conventional as well as real-time Delphi studies is analysed based onmultiple statistical analyses. The research findings indicate that significant differences between the two Delphi survey formats do not exist and final survey results are not affected by changes in the survey procedure.
Comparison of fuzzy numbers using a fuzzy distance measure

A new approach for ranking fuzzy numbers based on a distance measure is introduced. A new class of distance measures for interval numbers that takes into account all the points in both intervals is developed -rst, and then it is used to formulate the distance measure for fuzzy numbers. The approach is illustrated by numerical examples, showing that it overcomes several shortcomings such as the indiscriminative and counterintuitive behavior of several existing fuzzy ranking approaches.
Query Intent Detection using Convolutional Neural Networks

Understanding query intent helps modern search engines to improve search results as well as to display instant answers to the user. In this work, we introduce an accurate query classification method to detect the intent of a user search query. We propose using convolutional neural networks (CNN) to extract query vector representations as the features for the query classification. In this model, queries are represented as vectors so that semantically similar queries can be captured by embedding them into a vector space. Experimental results show that the proposed method can effectively detect intents of queries with higher precision and recall compared to current methods.
Automatic Coupling of Answer Extraction and Information Retrieval

Information Retrieval (IR) and Answer Extraction are often designed as isolated or loosely connected components in Question Answering (QA), with repeated overengineering on IR, and not necessarily performance gain for QA. We propose to tightly integrate them by coupling automatically learned features for answer extraction to a shallow-structured IR model. Our method is very quick to implement, and significantly improves IR for QA (measured in Mean Average Precision and Mean Reciprocal Rank) by 10%-20% against an uncoupled retrieval baseline in both document and passage retrieval, which further leads to a downstream 20% improvement in QA F1.
Layer Normalization

Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to that neuron on each training case. This significantly reduces the training time in feedforward neural networks. However, the effect of batch normalization is dependent on the mini-batch size and it is not obvious how to apply it to recurrent neural networks. In this paper, we transpose batch normalization into layer normalization by computing the mean and variance used for normalization from all of the summed inputs to the neurons in a layer on a single training case. Like batch normalization, we also give each neuron its own adaptive bias and gain which are applied after the normalization but before the non-linearity. Unlike batch normalization, layer normalization performs exactly the same computation at training and test times. It is also straightforward to apply to recurrent neural networks by computing the normalization statistics separately at each time step. Layer normalization is very effective at stabilizing the hidden state dynamics in recurrent networks. Empirically, we show that layer normalization can substantially reduce the training time compared with previously published techniques.
Fuzzy C-Means Clustering With Local Information and Kernel Metric For Image Segmentation

Image segmentation is one of the key techniques in image understanding and computer vision. The task of image segmentation is to divide an image into a number of non overlapping regions, which have same characteristics such as gray level, color, tone, texture, etc. A lot of clustering based methods have been proposed for image segmentation. we define a new trade-off weighted fuzzy factor to adaptively control the local neighbor relationship. This factor depends on space distance of all neighbor pixels and their gray level discrepancy simultaneously, Image processing operations can be roughly divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. It involves reducing the amount of memory needed to store a digital image. Image defects which could be caused by the digitization process or by faults in the imaging setup (for example, bad lighting) can be corrected using Image Enhancement techniques. Once the image is in good condition, the Measurement Extraction operations can be used to obtain useful information from the image. Some examples of Image Enhancement and Measurement Extraction are given below. The examples shown all operate on 256 grey-scale images. This means that each pixel in the image is stored as a number between 0 to 255, where 0 represents a black pixel, 255 represents a white pixel and values in-between represent shades of grey. These operations can be extended to operate on colour images. The examples below represent only a few of the many techniques available for operating on images. Details about the inner workings of the operations have not been given, but some references to books containing this information are given at the end for the interested reader.
A novel POWERPACK for robotic application, integrated torque sensor, harmonic drive and motor

Force-sensing system represents one of the vital components of robotic systems for physical interaction performances. This system is facilitated by the force controllable actuators. However, to make a force controllable actuator, it is still a challenging subject for the majority of the robotic system. This paper proposes a novel POWERPACK unit integrated a torque sensor, a harmonic drive and a motor for enabling the force control. The torque sensing element is based on the capacitance sensing to achieve a compact and a simple structure. In this research, reveals the practical details of the POWERPACK and evaluates the performance of the actuator unit.
Beyond citation analysis: a model for assessment of research impact.

Is there a means of assessing research impact beyond citation analysis?



The case study took place at the Washington University School of Medicine Becker Medical Library.



This case study analyzed the research study process to identify indicators beyond citation count that demonstrate research impact.



The authors discovered a number of indicators that can be documented for assessment of research impact, as well as resources to locate evidence of impact. As a result of the project, the authors developed a model for assessment of research impact, the Becker Medical Library Model for Assessment of Research.



Assessment of research impact using traditional citation analysis alone is not a sufficient tool for assessing the impact of research findings, and it is not predictive of subsequent clinical applications resulting in meaningful health outcomes. The Becker Model can be used by both researchers and librarians to document research impact to supplement citation analysis.
Visualizing the Loss Landscape of Neural Nets

Neural network training relies on our ability to find “good” minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and wellchosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple “filter normalization” method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization

We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays. Deep neural networks have shown great success in a plethora of visual recognition tasks such as image classification and object detection by stacking multiple layers of convolutional neural networks (CNN) in a feed-forward manner. However, the performance gain by going deeper has reached bottlenecks as a result of the trade-off between model complexity and discrimination power. We address this problem by utilizing the recently developed routing-by agreement mechanism in our architecture. A novel characteristic of our network structure is that it extends routing to two types of layer connections (1) connection between feature maps in dense layers, (2) connection between primary capsules and prediction capsules in final classification layer. We show that our networks achieve comparable results with much fewer layers in the measurement of AUC score. We further show the combined benefits of model interpretability by generating Gradient-weighted Class Activation Mapping (Grad-CAM) for localization. We demonstrate our results on the NIH chestX-ray14 dataset that consists of 112,120 images on 30,805 unique patients including 14 kinds of lung diseases.
Motor training of the lumbar paraspinal muscles induces immediate changes in motor coordination in patients with recurrent low back pain.

Recurrent low back pain (LBP) is associated with altered motor coordination of the lumbar paraspinal muscles. Whether these changes can be modified with motor training remains unclear. Twenty volunteers with unilateral LBP were randomly assigned to cognitively activate the lumbar multifidus independently from other back muscles (skilled training) or to activate all paraspinal muscles with no attention to any specific muscles (extension training). Electromyographic (EMG) activity of deep (DM) and superficial multifidus (SM) muscles were recorded bilaterally using intramuscular fine-wire electrodes and that of superficial abdominal and back muscles using surface electrodes. Motor coordination was assessed before and immediately after training as onsets of trunk muscle EMG during rapid arm movements, and as EMG amplitude at the mid-point of slow trunk flexion-extension movements. Despite different intentions of the training tasks, the pattern of activity was similar for both. After both training tasks, activation of the DM and SM muscles was earlier during rapid arm movements. However, during slow trunk movements, DM and SM activity was increased, and EMG activity of the superficial trunk muscles was reduced only after skilled training. These findings show the potential to alter motor coordination with motor training of the lumbar paraspinal muscles in recurrent LBP.



Changes in motor coordination differed between skilled and extension training during slows trunk movements. As identical patterns of muscle activity were observed between training protocols, the results suggest that training-induced changes in motor coordination are not simply related to the muscle activation, but appear to be related to the task.
Mobile Marketing : From Marketing Strategy to Mobile Marketing Campaign Implementation

Over the past few years, mobile marketing has generated an increasing interest among academics and practitioners. While numerous studies have provided important insights into the mobile marketing, our understanding of this topic of growing interest and importance remains deficient. Therefore, the objective of this article is to provide a comprehensive framework intended to guide research efforts focusing on mobile media as well as to aid practitioners in their quest to achieve mobile marketing success. The framework builds on the literature from mobile commerce and integrated marketing communications (IMC) and provides a broad delineation as to how mobile marketing should be integrated into the firm’s overall marketing communications strategy. It also outlines the mobile marketing from marketing communications mix (also called promotion mix) perspective and provides a comprehensive overview of divergent mobile marketing activities. The article concludes with a detailed description of mobile marketing campaign planning and implementation.
Efficient human action recognition using histograms of motion gradients and VLAD with descriptor shape information

Feature extraction and encoding represent two of the most crucial steps in an action recognition system. For building a powerful action recognition pipeline it is important that both steps are efficient and in the same time provide reliable performance. This work proposes a new approach for feature extraction and encoding that allows us to obtain real-time frame rate processing for an action recognition system. The motion information represents an important source of information within the video. The common approach to extract the motion information is to compute the optical flow. However, the estimation of optical flow is very demanding in terms of computational cost, in many cases being the most significant processing step within the overall pipeline of the target video analysis application. In this work we propose an efficient approach to capture the motion information within the video. Our proposed descriptor, Histograms of Motion Gradients (HMG), is based on a simple temporal and spatial derivation, which captures the changes between two consecutive frames. For the encoding step a widely adopted method is the Vector of Locally Aggregated Descriptors (VLAD), which is an efficient encoding method, however, it considers only the difference between local descriptors and their centroids. In this work we propose Shape Difference VLAD (SD-VLAD), an encoding method which brings complementary information by using the shape information within the encoding process. We validated our proposed pipeline for action recognition on three challenging datasets UCF50, UCF101 and HMDB51, and we propose also a real-time framework for action recognition.
Best Topic Word Selection for Topic Labelling

This paper presents the novel task of best topic word selection, that is the selection of the topic word that is the best label for a given topic, as a means of enhancing the interpretation and visualisation of topic models. We propose a number of features intended to capture the best topic word, and show that, in combination as inputs to a reranking model, we are able to consistently achieve results above the baseline of simply selecting the highest-ranked topic word. This is the case both when training in-domain over other labelled topics for that topic model, and cross-domain, using only labellings from independent topic models learned over document collections from different domains and genres.
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics

The ability to associate images with natural language sentences that describe what is depicted in them is a hallmark of image understanding, and a prerequisite for applications such as sentence-based image search. In analogy to image search, we propose to frame sentence-based image annotation as the task of ranking a given pool of captions. We introduce a new benchmark collection for sentence-based image description and search, consisting of 8,000 images that are each paired with five different captions which provide clear descriptions of the salient entities and events. We introduce a number of systems that perform quite well on this task, even though they are only based on features that can be obtained with minimal supervision. Our results clearly indicate the importance of training on multiple captions per image, and of capturing syntactic (word order-based) and semantic features of these captions. We also perform an in-depth comparison of human and automatic evaluation metrics for this task, and propose strategies for collecting human judgments cheaply and on a very large scale, allowing us to augment our collection with additional relevance judgments of which captions describe which image. Our analysis shows that metrics that consider the ranked list of results for each query image or sentence are significantly more robust than metrics that are based on a single response per query. Moreover, our study suggests that the evaluation of ranking-based image description systems may be fully automated.
Algorithm evolution for face recognition: what makes a picture difficult

One of the classic problems in computer vision is the face recognition problem. In general this problem can take on a wide variety of forms, but the most common face recognition problem is “Who is this a picture of?” Evolutionarycomputation has, in the past, been applied indirectlyto this problem through techniques like learning Neural Networks. This paper introduces a Genetic Programming style approach to learning algorithms that directly investigate face images and are coordinated into a face recognition system. Through a series of experiments, we will show that evolved algorithms can accomplish the face recognition task. We will also highlight several pitfalls and misconceptions surrounding face recognition as a learning problem.
Mining Twitter Data to Improve Detection of Schizophrenia

Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models. Our support vector machine model achieved the best performance with 92% precision and 71% recall on the held-out test set. Additionally, we built a web application that dynamically displays summary statistics between cohorts. This enables outreach to undiagnosed individuals, improved physician diagnoses, and destigmatization of schizophrenia.
Rapid stress reduction and anxiolysis among distressed women as a consequence of a three-month intensive yoga program.

Emotional distress is an increasing public health problem and Hatha yoga has been claimed to induce stress reduction and empowerment in practicing subjects. We aimed to evaluate potential effects of Iyengar Hatha yoga on perceived stress and associated psychological outcomes in mentally distressed women.



A controlled prospective non-randomized study was conducted in 24 self-referred female subjects (mean age 37.9+/-7.3 years) who perceived themselves as emotionally distressed. Subjects were offered participation in one of two subsequential 3-months yoga programs. Group 1 (n=16) participated in the first class, group 2 (n=8) served as a waiting list control. During the yoga course, subjects attended two-weekly 90-min Iyengar yoga classes. Outcome was assessed on entry and after 3 months by Cohen Perceived Stress Scale, State-Trait Anxiety Inventory, Profile of Mood States, CESD-Depression Scale, Bf-S/Bf-S' Well-Being Scales, Freiburg Complaint List and ratings of physical well-being. Salivary cortisol levels were measured before and after an evening yoga class in a second sample.



Compared to waiting-list, women who participated in the yoga-training demonstrated pronounced and significant improvements in perceived stress (P<0.02), State and Trait Anxiety (P<0.02 and P<0.01, respectively), well-being (P<0.01), vigor (P<0.02), fatigue (P<0.02) and depression (P<0.05). Physical well-being also increased (P<0.01), and those subjects suffering from headache or back pain reported marked pain relief. Salivary cortisol decreased significantly after participation in a yoga class (P<0.05).



Women suffering from mental distress participating in a 3-month Iyengar yoga class show significant improvements on measures of stress and psychological outcomes. Further investigation of yoga with respect to prevention and treatment of stress-related disease and of underlying mechanism is warranted.