nils-herrmann/clean_scirepeval huggingface dataset:
Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models

An important aspect for the task of grammatical error correction (GEC) that has not yet been adequately explored is adaptation based on the native language (L1) of writers, despite the marked influences of L1 on second language (L2) writing. In this paper, we adapt a neural network joint model (NNJM) using L1-specific learner text and integrate it into a statistical machine translation (SMT) based GEC system. Specifically, we train an NNJM on general learner text (not L1-specific) and subsequently train on L1-specific data using a Kullback-Leibler divergence regularized objective function in order to preserve generalization of the model. We incorporate this adapted NNJM as a feature in an SMT-based English GEC system and show that adaptation achieves significant F0.5 score gains on English texts written by L1 Chinese, Russian, and Spanish writers.
Emotion Analysis of Twitter Data That Use Emoticons and Emoji Ideograms

Twitter is an online social networking service on which users worldwide publish their opinions on a variety of topics, discuss current issues, complain, and express many kinds of emotions. Therefore, Twitter is a rich source of data for opinion mining, sentiment and emotion analysis. This paper focuses on this issue by analysing symbols called emotion tokens, including emotion symbols (e.g. emoticons and emoji ideograms). According to observations, emotion tokens are commonly used in many tweets. They directly express one’s emotions regardless of his/her language, hence they have become a useful signal for sentiment analysis in multilingual tweets. The paper describes the approach to extending existing binary sentiment classification approaches using a multi-way emotions classification.
Social Media : A TOOL FOR OPEN INNOVATION

Despite the exponential rise of social media use in external stakeholder engagement, academic research and managerial practice have paid little attention to how it can be used for open innovation across the entire innovation funnel, spanning ideation, R&D, and commercialization. As a result, there is little understanding of how companies can organize for and implement social media for open innovation. Utilizing a multiple case study design, this article examines its application across the entire innovation process. It proposes a range of organizational and technological adaptations that managers can implement to ensure they realize the innovative benefits of social media application.
SupportNet: solving catastrophic forgetting in class incremental learning with support data

A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting. Here we propose a novel method, SupportNet, to efficiently and effectively solve the catastrophic forgetting problem in the class incremental learning scenario. SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information. Two powerful consolidation regularizers are applied to stabilize the learned representation and ensure the robustness of the learned model. We validate our method with comprehensive experiments on various tasks, which show that SupportNet drastically outperforms the state-of-theart incremental learning methods and even reaches similar performance as the deep learning model trained from scratch on both old and new data. Our program is accessible at: https://github.com/lykaust15/SupportNet
Machine learning in cell biology - teaching computers to recognize phenotypes.

Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.
MetaAnchor: Learning to Detect Objects with Customized Anchors

We propose a novel and flexible anchor mechanism named MetaAnchor for object detection frameworks. Unlike many previous detectors model anchors via a predefined manner, in MetaAnchor anchor functions could be dynamically generated from the arbitrary customized prior boxes. Taking advantage of weight prediction, MetaAnchor is able to work with most of the anchor-based object detection systems such as RetinaNet. Compared with the predefined anchor scheme, we empirically find that MetaAnchor is more robust to anchor settings and bounding box distributions; in addition, it also shows the potential on transfer tasks. Our experiment on COCO detection task shows that MetaAnchor consistently outperforms the counterparts in various scenarios.
Integrated Modular Avionics: Managing the Allocation of Shared Intersystem Resources

The integrated modular avionics (IMA) architectures have ushered in a new wave of thought regarding avionics integration. IMA architectures utilize shared, configurable computing, communication, and I/O resources. These architectures allow avionics system integrators to benefit from increased system scalability, as well as from a form of platform management that reduces the workload for aircraft-level avionics integration activities. In order to realize these architectural benefits, the avionics suppliers must engage in new philosophies for sharing a set of system-level resources that are managed a level higher than each individual avionics system. The mechanisms for configuring and managing these shared intersystem resources are integral to managing the increased level of avionics integration that is inherent to the IMA architectures. This paper provides guidance for developing the methodology and tools to efficiently manage the set of shared intersystem resources. This guidance is based upon the author's experience in developing the Genesis IMA architecture at Smiths Aerospace. The Genesis IMA architecture was implemented on the Boeing 787 Dreamliner as the common core system (CCS)
A novel approach for medical assistance using trained chatbot

There are lot of treatments that are available for various diseases. No human can possibly know about all the medicines and the diseases. So, the problem is that there isn't any place where anyone can have the details of the diseases or the medicines. What if there is a place where you can find your health problem just by entering symptoms or just scanning an ECG or you can check whether the prescribed medicine is supposed to be used the way you are told to. Then it will help us to deduce the problem and to verify the solution. The proposed idea is to create a system with artificial intelligence that can meet the requirements. The AI can predict the diseases based on the symptoms and give the list of available treatments. The System can also give the composition of the medicines and their prescribed uses. It helps them to take the correct treatment. Hence the people can have an idea about their health and can have the right protection.
Disorders of the Cerebellum : Ataxia , Dysmetria of Thought , and the Cerebellar Cognitive Affective Syndrome

From the Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Address correspondence to Dr. Schmahmann, Department of Neurology, VBK 915, Massachusetts General Hospital, Fruit St., Boston, MA 02114; jschmahmann@partners.org (E-mail).  Disorders of the Cerebellum: Ataxia, Dysmetria of Thought, and the Cerebellar Cognitive Affective Syndrome
Encoding Module ( you ) tell vivian be quietto ROOT ( imperative )

Spoken language understanding (SLU) is a core component of a spoken dialogue system, which involves intent prediction and slot filling and also called semantic frame parsing. Recently recurrent neural networks (RNN) obtained strong results on SLU due to their superior ability of preserving sequential information over time. Traditionally, the SLU component parses semantic frames for utterances considering their flat structures, as the underlying RNN structure is a linear chain. However, natural language exhibits linguistic properties that provide rich, structured information for better understanding. This paper proposes to apply knowledge-guided structural attention networks (K-SAN), which additionally incorporate non-flat network topologies guided by prior knowledge, to a language understanding task. The model can effectively figure out the salient substructures that are essential to parse the given utterance into its semantic frame with an attention mechanism, where two types of knowledge, syntax and semantics, are utilized. The experiments on the benchmark Air Travel Information System (ATIS) data and the conversational assistant Cortana data show that 1) the proposed K-SAN models with syntax or semantics outperform the state-of-the-art neural network based results, and 2) the improvement for joint semantic frame parsing is more significant, because the structured information provides rich cues for sentence-level understanding, where intent prediction and slot filling can be mutually improved.
A high-resolution spontaneous 3D dynamic facial expression database

Facial expression is central to human experience. Its efficient and valid measurement is a challenge that automated facial image analysis seeks to address. Most publically available databases are limited to 2D static images or video of posed facial behavior. Because posed and un-posed (aka “spontaneous”) facial expressions differ along several dimensions including complexity and timing, well-annotated video of un-posed facial behavior is needed. Moreover, because the face is a three-dimensional deformable object, 2D video may be insufficient, and therefore 3D video archives are needed. We present a newly developed 3D video database of spontaneous facial expressions in a diverse group of young adults. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System. Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action.
Defending Web Servers with Feints, Distraction and Obfuscation

In this paper we investigate deceptive defense strategies for web servers. Web servers are widely exploited resources in the modern cyber threat landscape. Often these servers are exposed in the Internet and accessible for a broad range of valid as well as malicious users. Common security strategies like firewalls are not sufficient to protect web servers. Deception based Information Security enables a large set of counter measures to decrease the efficiency of intrusions. In this work we depict several techniques out of the reconnaissance process of an attacker. We match these with deceptive counter measures. All proposed measures are implemented in an experimental web server with deceptive counter measure abilities. We also conducted an experiment with honeytokens and evaluated delay strategies against automated scanner tools.
Downlink and Uplink Cell Association With Traditional Macrocells and Millimeter Wave Small Cells

Millimeter wave (mmWave) links will offer high capacity but are poor at penetrating into or diffracting around solid objects. Thus, we consider a hybrid cellular network with traditional sub-6 GHz macrocells coexisting with denser mmWave small cells, where a mobile user can connect to either opportunistically. We develop a general analytical model to characterize and derive the uplink and downlink cell association in the view of the signal-to-interference-and-noise-ratio and rate coverage probabilities in such a mixed deployment. We offer extensive validation of these analytical results (which rely on several simplifying assumptions) with simulation results. Using the analytical results, different decoupled uplink and downlink cell association strategies are investigated and their superiority is shown compared with the traditional coupled approach. Finally, small cell biasing in mmWave is studied, and we show that unprecedented biasing values are desirable due to the wide bandwidth.
An Automatic Classification of Brain Tumors through MRI Using Support Vector Machine

Brain tumor is a life threatening disease. It is any mass that outcomes from abnormal growths of cells in the brain. In this paper a brain tumor diagnostic system is developed. The system determines the type of the tumor which is benign or malignant using the Magnetic Resonance Imaging (MRI) images which are in the Digital Imaging and Communications in Medicine (DICOM) standard format. The system is assessed based on a series of brain tumor images. Experimental results demonstrate that the proposed system has a classification accuracy of 98.9%.
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning

Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of oversmoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals.
EdgeCons: Achieving Efficient Consensus in Edge Computing Networks

Fast event ordering is critical for delay-sensitive edge computing applications that serve massive geographically distributed clients. Using a centralized cloud to determine the event order suffers from unsatisfactory latency. Naive edge-centric solutions, which designate one edge node to order all the events, have scalability and single point of failure issues. To address these problems, we propose EdgeCons, a novel consensus algorithm optimized for edge computing networks. EdgeCons achieves fast consensus by running a sequence of Paxos instances among the edge nodes and dynamically distributing their leadership based on the recent running history. It also guarantees progressiveness by incorporating a reliable, backend cloud. A preliminary evaluation shows that EdgeCons works more efficiently than the state-of-theart consensus algorithms, in the context of achieving fast event ordering in edge computing networks.
Human-robot interaction in rescue robotics

Rescue robotics has been suggested by a recent DARPA/NSF study as an application domain for the research in human-robot integration (HRI). This paper provides a short tutorial on how robots are currently used in urban search and rescue (USAR) and discusses the HRI issues encountered over the past eight years. A domain theory of the search activity is formulated. The domain theory consists of two parts: 1) a workflow model identifying the major tasks, actions, and roles in robot-assisted search (e.g., a workflow model) and 2) a general information flow model of how data from the robot is fused by various team members into information and knowledge. The information flow model also captures the types of situation awareness needed by each agent in the rescue robot system. The article presents a synopsis of the major HRI issues in reducing the number of humans it takes to control a robot, maintaining performance with geographically distributed teams with intermittent communications, and encouraging acceptance within the existing social structure.
Artificial intelligence in medicine.

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
Randomized, Placebo-Controlled Trial of Platelet Glycoprotein IIb/IIIa Blockade With Primary Angioplasty for Acute Myocardial Infarction

The benefit of catheter-based reperfusion for acute myocardial infarction (MI) is limited by a 5% to 15% incidence of in-hospital major ischemic events, usually caused by infarct artery reocclusion, and a 20% to 40% need for repeat percutaneous or surgical revascularization. Platelets play a key role in the process of early infarct artery reocclusion, but inhibition of aggregation via the glycoprotein IIb/IIIa receptor has not been prospectively evaluated in the setting of acute MI. Patients with acute MI of,12 hours’ duration were randomized, on a double-blind basis, to placebo or abciximab if they were deemed candidates for primary PTCA. The primary efficacy end point was death, reinfarction, or any (urgent or elective) target vessel revascularization (TVR) at 6 months by intention-to-treat (ITT) analysis. Other key prespecified end points were early (7 and 30 days) death, reinfarction, or urgent TVR. The baseline clinical and angiographic variables of the 483 (242 placebo and 241 abciximab) patients were balanced. There was no difference in the incidence of the primary 6-month end point (ITT analysis) in the 2 groups (28.1% and 28.2%, P50.97, of the placebo and abciximab patients, respectively). However, abciximab significantly reduced the incidence of death, reinfarction, or urgent TVR at all time points assessed (9.9% versus 3.3%, P50.003, at 7 days; 11.2% versus 5.8%, P50.03, at 30 days; and 17.8% versus 11.6%, P50.05, at 6 months). Analysis by actual treatment with PTCA and study drug demonstrated a considerable effect of abciximab with respect to death or reinfarction: 4.7% versus 1.4%, P50.047, at 7 days; 5.8% versus 3.2%, P50.20, at 30 days; and 12.0% versus 6.9%, P50.07, at 6 months. The need for unplanned, “bail-out” stenting was reduced by 42% in the abciximab group (20.4% versus 11.9%, P50.008). Major bleeding occurred significantly more frequently in the abciximab group (16.6% versus 9.5%, P 0.02), mostly at the arterial access site. There was no intracranial hemorrhage in either group. Aggressive platelet inhibition with abciximab during primary PTCA for acute MI yielded a substantial reduction in the acute (30-day) phase for death, reinfarction, and urgent target vessel revascularization. However, the bleeding rates were excessive, and the 6-month primary end point, which included elective revascularization, was not favorably affected.(Circulation. 1998;98:734-741.)
Conversion of Lignocellulosic Biomass to Nanocellulose: Structure and Chemical Process

Lignocellulosic biomass is a complex biopolymer that is primary composed of cellulose, hemicellulose, and lignin. The presence of cellulose in biomass is able to depolymerise into nanodimension biomaterial, with exceptional mechanical properties for biocomposites, pharmaceutical carriers, and electronic substrate's application. However, the entangled biomass ultrastructure consists of inherent properties, such as strong lignin layers, low cellulose accessibility to chemicals, and high cellulose crystallinity, which inhibit the digestibility of the biomass for cellulose extraction. This situation offers both challenges and promises for the biomass biorefinery development to utilize the cellulose from lignocellulosic biomass. Thus, multistep biorefinery processes are necessary to ensure the deconstruction of noncellulosic content in lignocellulosic biomass, while maintaining cellulose product for further hydrolysis into nanocellulose material. In this review, we discuss the molecular structure basis for biomass recalcitrance, reengineering process of lignocellulosic biomass into nanocellulose via chemical, and novel catalytic approaches. Furthermore, review on catalyst design to overcome key barriers regarding the natural resistance of biomass will be presented herein.