nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised named entity recognition: learning to recognize 100 entity types with little supervision

Named Entity Recognition (NER) aims to extract and to classify rigid designators in text such as proper names, biological species, and temporal expressions. There has been growing interest in this field of research since the early 1990s. In this thesis, we document a trend moving away from handcrafted rules, and towards machine learning approaches. Still, recent machine learning approaches have a problem with annotated data availability, which is a serious shortcoming in building and maintaining large-scale NER systems. In this thesis, we present an NER system built with very little supervision. Human supervision is indeed limited to listing a few examples of each named entity (NE) type. First, we introduce a proof-of-concept semi-supervised system that can recognize four NE types. Then, we expand its capacities by improving key technologies, and we apply the system to an entire hierarchy comprised of 100 NE types. Our work makes the following contributions: the creation of a proof-of-concept semisupervised NER system; the demonstration of an innovative noise filtering technique for generating NE lists; the validation of a strategy for learning disambiguation rules using automatically identified, unambiguous NEs; and finally, the development of an acronym detection algorithm, thus solving a rare but very difficult problem in alias resolution. We believe semi-supervised learning techniques are about to break new ground in the machine learning community. In this thesis, we show that limited supervision can build complete NER systems. On standard evaluation corpora, we report performances that compare to baseline supervised systems in the task of annotating NEs in texts.
From Compressed Sensing to Compressed Bit-Streams: Practical Encoders, Tractable Decoders

Compressed sensing is now established as an effective method for dimension reduction when the underlying signals are sparse or compressible with respect to some suitable basis or frame. One important, yet under-addressed problem regarding the compressive acquisition of analog signals is how to perform quantization. This is directly related to the important issues of how “compressed” compressed sensing is (in terms of the total number of bits one ends up using after acquiring the signal) and ultimately whether compressed sensing can be used to obtain compressed representations of suitable signals. In this paper, we propose a concrete and practicable method for performing “analog-to-information conversion”. Following a compressive signal acquisition stage, the proposed method consists of a quantization stage, based on  $ \Sigma \Delta $  (sigma-delta) quantization, and a subsequent encoding (compression) stage that fits within the framework of compressed sensing seamlessly. We prove that, using this method, we can convert analog compressive samples to compressed digital bitstreams and decode using tractable algorithms based on convex optimization. We prove that the proposed analog-to-information converter (AIC) provides a nearly optimal encoding of sparse and compressible signals. Finally, we present numerical experiments illustrating the effectiveness of the proposed AIC.
Neural Architectures for Multilingual Semantic Parsing

In this paper, we address semantic parsing in a multilingual context. We train one multilingual model that is capable of parsing natural language sentences from multiple different languages into their corresponding formal semantic representations. We extend an existing sequence-to-tree model to a multi-task learning framework which shares the decoder for generating semantic representations. We report evaluation results on the multilingual GeoQuery corpus and introduce a new multilingual version of the ATIS corpus.
Instagram at the museum: communicating the museum experience through social photo sharing

The everyday use of smartphones with high quality built-in cameras has lead to an increase in museum visitors' use of these devices to document and share their museum experiences. In this paper, we investigate how one particular photo sharing application, Instagram, is used to communicate visitors' experiences while visiting a museum of natural history. Based on an analysis of 222 instagrams created in the museum, as well as 14 interviews with the visitors who created them, we unpack the compositional resources and concerns contributing to the creation of instagrams in this particular context. By re-categorizing and re-configuring the museum environment, instagrammers work to construct their own narratives from their visits. These findings are then used to discuss what emerging multimedia practices imply for the visitors' engagement with and documentation of museum exhibits. Drawing upon these practices, we discuss the connection between online social media dialogue and the museum site.
Challenges of Implementing E-Learning in Kenya : A Case of Kenyan Public Universities

In this paper, we discuss the challenges experienced by Kenyan public universities in implementation of e-learning and recommend possible solutions towards its successful implementation. In the last few years, most Kenyan public universities have adopted e-learning as a new approach to teaching and learning. However, the implementation challenges faced by these universities have continued to impact negatively on its effective utilization. This paper presents the findings from a survey of 148 staff of three Kenyan public universities who are currently using e-learning in blended mode approach. The purpose of this study was to investigate the challenges hindering the implementation of e-learning in Kenyan public universities. Data was collected through questionnaires, in-depth interviews and document analysis. The findings reveal that elearning comes with some challenges that must be addressed by Kenyan public universities before successful implementation can be realized. However, the benefits and opportunities presented by e-learning far outweigh the challenges. The paper finally recommends some possible solutions that public universities could embrace towards successful implementation of e-learning.
Problems Related to the Translation of Political Texts

This study deals with the problems related to the translation of political texts in the theoretical framework elaborated by the researchers working in the field of translation studies and reflects on the terminological peculiarities of the special language used for this text type . Consideration of the theoretical framework is followed by the analysis of a specific text spoken then written in English and translated into Hungarian and Romanian. The conclusions are intended to highlight the fact that there are no recipes for translating a political speech, because translation is not only a technical process that uses translation procedures and applies transfer operations, but also a matter of understanding cultural, historical and political situations and their significance.
Proper complex random processes with applications to information theory

The “covariance” of complex random variables and processes, when defined consistently with the corresponding notion for real random variables, is shown to be determined by the usual (complex) covariance together with a quantity called the pseudo-covariance. A characterization of uncorrelatedness and wide-sense stationarity in terms of covariance and pseudocovariance is given. Complex random variables and processes with a vanishing pseudo-covariance are called proper. It is shown that properness is preserved under affine transformations and that the complex-multivariate Gaussian density assumes a natural form only for proper random variables. The maximum-entropy theorem is generalized to the complex-multivariate case. The differential entropy of a complex random vector with a fixed correlation matrix is shown to be maximum, if and only if the random vector is proper, Gaussian and zero-mean. The notion of circular stutionarity is introduced. For the class of proper complex random processes, a discrete Fourier transform correspondence is derived relating circular stationarity in the time domain to uncorrelatedness in the frequency domain. As an application of the theory, the capacity of a discrete-time channel with complex inputs, proper complex additive white Gaussian noise, and a finite complex unit-sample response is determined. This derivation is considerably simpler than an earlier derivation for the real discrete-time Gaussian channel with intersymbol interference, whose capacity is obtained as a by-product of the results for the complex channel. Proper complex random processes, circular stationarity, intersymbol interference, capacity.
MAC: Mining Activity Concepts for Language-Based Temporal Localization

We address the problem of language-based temporal localization in untrimmed videos. Compared to temporal localization with fixed categories, this problem is more challenging as the language-based queries not only have no pre-defined activity list but also may contain complex descriptions. Previous methods address the problem by considering features from video sliding windows and language queries and learning a subspace to encode their correlation, which ignore rich semantic cues about activities in videos and queries. We propose to mine activity concepts from both video and language modalities by applying the actionness score enhanced Activity Concepts based Localizer (ACL). Specifically, the novel ACL encodes the semantic concepts from verb-obj pairs in language queries and leverages activity classifiers' prediction scores to encode visual concepts. Besides, ACL also has the capability to regress sliding windows as localization results. Experiments show that ACL significantly outperforms state-of-the-arts under the widely used metric, with more than 5% increase on both Charades-STA and TACoS datasets.
Classification using Machine Learning Techniques

Large amount of information are available online on web.The discussion forum, review sites, blogs are some of the opinion rich resources where review or posted articles is their sentiment, or overall opinion towards the subject matter. The opinions obtained from those can be classified in to positive or negative which can be used by customer to make product choice and by businessmen for finding customer satisfaction .This paper studies online movie reviews using sentiment analysis approaches. In this study, sentiment classification techniques were applied to movie reviews. Specifically, we compared two supervised machine learning approaches SVM, Navie Bayes for Sentiment Classification of Reviews. Results states that Naïve Bayes approach outperformed the svm. If the training dataset had a large number of reviews, Naive bayes approach reached high accuracies as compare to other.
Influence of formulation excipients and physical characteristics of inhalation dry powders on their aerosolization performance.

The objective of this study was to determine the effects of formulation excipients and physical characteristics of inhalation particles on their in vitro aerosolization performance, and thereby to maximize their respirable fraction. Dry powders were produced by spray-drying using excipients that are FDA-approved for inhalation as lactose, materials that are endogenous to the lungs as albumin and dipalmitoylphosphatidylcholine (DPPC); and/or protein stabilizers as trehalose or mannitol. Dry powders suitable for deep lung deposition, i.e. with an aerodynamic diameter of individual particles <3 microm, were prepared. They presented 0.04--0.25 g/cm(3) bulk tap densities, 3--5 microm geometric particle sizes, up to 90% emitted doses and 50% respirable fractions in the Andersen cascade impactor using a Spinhaler inhaler device. The incorporation of lactose, albumin and DPPC in the formulation all improved the aerosolization properties, in contrast to trehalose and the mannitol which decreased powder flowability. The relative proportion of the excipients affected aerosol performance as well. The lower the bulk powder tap density, the higher the respirable fraction. Optimization of in vitro aerosolization properties of inhalation dry powders can be achieved by appropriately selecting composition and physical characteristics of the particles.
Main human factors affecting information system security

In this research relevant areas that are important in Information System Security have been reviewed based on the health care industry of Malaysia. Some concepts such as definition of Information System Security, System Security Goals, System Security Threats and human error have been studied. The Human factors that are effective on Information System Security have been highlighted and also some relevant models have been introduced. Reviewing the pervious factors helped to find out the Health Information System factors. Finally, the effective human factors on Health Information System have been identified and the structure of Healthcare industry has been studied. Moreover, these factors are categorized in three new groups: Organizational Factors, Motivational Factors and Learning. This information will help to design a framework in Health Information System.  With less concern for people and organizational issues, a major part of information systems security strategies are technical in nature. As a consequence, since most information systems security strategies are of importance as they concentrate on technical oriented solutions, for instance checklists, risk analysis and assessment techniques, there is a necessity to investigate other ways of managing information systems security as they tend to disregard the social factors of risks and the informal structures of organizations. This investigation concentrates chiefly on human and organizational factors within the computer and information security system. The impact on security can be drastic if human and organizational factors influence their employment and use, irrespective of the power of technical controls (Bishop, 2002). In this aspect, the juncture for computer and information security vulnerabilities may be set by vulnerable computer and information security protection (e.g., weak passwords or poor usability) and malicious intentions may appear. The results of blemished organizational policies and individual practices whose origins are deeply rooted within early design presumptions or managerial choices causes susceptibilities (Besnard and Arief, 2004). Health Information System (HIS) has been implemented in Malaysia since late 1990s. HIS is an integration of several hospitals' information system to manage administration works, patients and clinical records. Because it is easy to access HIS data through the internet so its vulnerability to misuses, data lost and attacks will increase. Health data is very sensitive, therefore they require high protection and information security must be carefully watched as it plays an important role to protect the data from being stolen or harmed. Despite the vast research in information security, the human factor has been neglected …
Object Detection using the Canny Edge Detector

Object detection is one of the applications in computer vision where an image is searched for the presence of an object. An object can be described by its features like its shape or its texture. The shape of a 2-D object is made up of its edges. Many algorithms are currently in use in image processing for detecting the edges in an image. This edge information extracted from the image can be used for object detection. This paper presents an approach for object detection using the Canny edge detector. The approach proceeds by obtaining the edges from the query image which is the image which must be tested for the occurrence of the object and the image of the object to be detected and then employing a template matching procedure.
Maxillary gap at 11-13 weeks' gestation: marker of cleft lip and palate.

To describe a new sign of cleft lip and palate (CLP), the maxillary gap, which is visible in the mid-sagittal plane of the fetal face used routinely for measurement of nuchal translucency thickness.



This was a retrospective study of stored images of the mid-sagittal view of the fetal face at 11-13 weeks' gestation in 86 cases of CLP and 86 normal controls. The images were examined to determine if a maxillary gap was present, in which case its size was measured.



In 37 (43.0%) cases of CLP the defect was isolated and in 49 (57.0%) there were additional fetal defects. In the isolated CLP group, the diagnosis of facial cleft was made in the first trimester in nine (24.3%) cases and in the second trimester in 28 (75.7%). In the group with additional defects, the diagnosis of facial cleft was made in the first trimester in 46 (93.9%) cases and in the second trimester in three (6.1%). A maxillary gap was observed in 96% of cases of CLP with additional defects, in 65% of those with isolated CLP and in 7% of normal fetuses. There was a large gap (>1.5 mm) or complete absence of signals from the maxilla in the midline in 69% of cases of CLP with additional defects, in 35% of those with isolated CLP and in none of the normal controls.



The maxillary gap is a new simple marker of possible CLP, which could increase the detection rate of CLP, especially in isolated cases.
Quality Metrics for Information Visualization

The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization’s quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multiand high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.
Depression, diet and exercise.

Unhealthy lifestyle behaviour is driving an increase in the burden of chronic non-communicable diseases worldwide. Recent evidence suggests that poor diet and a lack of exercise contribute to the genesis and course of depression. While studies examining dietary improvement as a treatment strategy in depression are lacking, epidemiological evidence clearly points to diet quality being of importance to the risk of depression. Exercise has been shown to be an effective treatment strategy for depression, but this is not reflected in treatment guidelines, and increased physical activity is not routinely encouraged when managing depression in clinical practice. Recommendations regarding dietary improvement, increases in physical activity and smoking cessation should be routinely given to patients with depression. Specialised and detailed advice may not be necessary. Recommendations should focus on following national guidelines for healthy eating and physical activity.
Ultra-Reliable Low-Latency Communication (URLLC): Principles and Building Blocks

Ultra-reliable low latency communication (URLLC) is an important new feature brought by 5G, with a potential to support a vast set of applications that rely on mission-critical links. In this article, we first discuss the principles for supporting URLLC from the perspective of the traditional assumptions and models applied in communication/information theory. We then discuss how these principles are applied in various elements of the system design, such as use of various diversity sources, design of packets and access protocols. The important messages are that there is a need to optimize the transmission of signaling information, as well as a need for a lean use of various sources
Defining Emotion Concepts

This article demonstrates that emotion concepts-including the so-called basic ones, such as anger or sadness-can be defined in terms of universal semantic primitives such as ‘good’, ‘bad’, ‘do’, ‘happen’, ‘know’, and ‘want’, in terms of which all areas of meaning, in all languages, can be rigorously and revealingly portrayed. The definitions proposed here take the form of certain prototypical scripts or scenarios, formulated in terms of thoughts, wants, and feelings. These scripts, however, can be seen as formulas providing rigorous specifications of necessary and sufficient conditions (not for emotions as such, but for emotion concepts), and they do not support the idea that boundaries between emotion concepts are “fuzzy.” On the contrary, the small set of universal semantic primitives employed here (which has emerged from two decades of empirical investigations by the author and colleagues) demonstrates that even apparent synonyms such as sad and unhappy embody different-and fully specifiable-conceptual structures.
Stress effects on memory: An update and integration

It is well known that stressful experiences may affect learning and memory processes. Less clear is the exact nature of these stress effects on memory: both enhancing and impairing effects have been reported. These opposite effects may be explained if the different time courses of stress hormone, in particular catecholamine and glucocorticoid, actions are taken into account. Integrating two popular models, we argue here that rapid catecholamine and non-genomic glucocorticoid actions interact in the basolateral amygdala to shift the organism into a 'memory formation mode' that facilitates the consolidation of stressful experiences into long-term memory. The undisturbed consolidation of these experiences is then promoted by genomic glucocorticoid actions that induce a 'memory storage mode', which suppresses competing cognitive processes and thus reduces interference by unrelated material. Highlighting some current trends in the field, we further argue that stress affects learning and memory processes beyond the basolateral amygdala and hippocampus and that stress may pre-program subsequent memory performance when it is experienced during critical periods of brain development.
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM

Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding. In this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges of both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge. We augment the long short-term memory (LSTM) network with a hierarchical attention mechanism consisting of a targetlevel attention and a sentence-level attention. Commonsense knowledge of sentiment-related concepts is incorporated into the end-to-end training of a deep neural network for sentiment classification. In order to tightly integrate the commonsense knowledge into the recurrent encoder, we propose an extension of LSTM, termed Sentic LSTM. We conduct experiments on two publicly released datasets, which show that the combination of the proposed attention architecture and Sentic LSTM can outperform state-of-the-art methods in targeted aspect sentiment tasks.