nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets

Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA) contest. The small amount of available datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsupervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. Moreover, it can be used efficiently as feature extractor and classifier for unsupervised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our automatically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80%. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.
Melanocyte-keratinocyte interaction induces calcium signalling and melanin transfer to keratinocytes.

Physical contact between melanocytes and keratinocytes is a prerequisite for melanosome transfer to occur, but cellular signals induced during or after contact are not fully understood. Herein, it is shown that interactions between melanocyte and keratinocyte plasma membranes induced a transient intracellular calcium signal in keratinocytes that was required for pigment transfer. This intracellular calcium signal occurred due to release of calcium from intracellular stores. Pigment transfer observed in melanocyte-keratinocyte co-cultures was inhibited when intracellular calcium in keratinocytes was chelated. We propose that a 'ligand-receptor' type interaction exists between melanocytes and keratinocytes that triggers intracellular calcium signalling in keratinocytes and mediates melanin transfer.
A Literacy Approach to the Digital Divide

Unequal access to new technology is often referred to as the "digital divide." But the notion of a digital divide is unclear. This paper explores the concept by attention to prior research on information access. It considers three forms of access, to a device, to an ongoing conduit, and to new social practices, with the latter being the most encompassing and valuable. Earlier research on literacy provides a useful framework for an interpretation of the digital divide based on practices, rather than merely devices or conduits. Both literacy and technology access are multiple, context-dependent, stratified along continua, tied closely for their benefits to particular functions, and dependent on not only education and culture but also power. They also both entail new forms of semiotic interpretation and production. Research in schools illuminates the importance of a precise understanding of the digital divide. Educational reform efforts that place emphasis on a device, such as the One Laptop per Child program, have proven unsuccessful, while those that support new forms of meaning-making and social engagement bring more significant benefits.
Beyond the Lambertian assumption: A generative model for Apparent BRDF fields of faces using anti-symmetric tensor splines

Human faces are neither exactly Lambertian nor entirely convex and hence most models in literature which make the Lambertian assumption, fall short when dealing with specularities and cast shadows. In this paper, we present a novel anti-symmetric tensor spline (a spline for tensor-valued functions) based method for the estimation of the Apparent BRDF (ABRDF) field for human faces that seamlessly accounts for specularities and cast shadows. Furthermore, unlike other methods, it does not require any 3D information to build the model and can work with as few as 9 images. In order to validate the accuracy of our anti-symmetric tensor spline model, we present a novel approximation of the ABRDF using a continuous mixture of single-lobed spherical functions. We demonstrate the effectiveness of our anti-symmetric tensor-spline model in comparison to other popular models in the literature, by presenting extensive results for face relighting and face recognition using the Extended Yale B database.
Efficient Data Management for GPU Databases

General purpose GPUs are a new and powerful hardware device with a number of applications in the realm of relational databases. We describe a database framework designed to allow both CPU and GPU execution of queries. Through use of our novel data structure design and method of using GPUmapped memory with efficient caching, we demonstrate that GPU query acceleration is possible for data sets much larger than the size of GPU memory. We also argue that the use of an opcode model of query execution combined with a simple virtual machine provides capabilities that are impossible with the parallel primitives used for most GPU database research. By implementing a single database framework that is efficient for both the CPU and GPU, we are able to make a fair comparison of performance for a filter operation and observe speedup on the GPU. This work is intended to provide a clearer picture of handling very abstract data operations efficiently on heterogeneous systems in anticipation of further application of GPU hardware in the relational database domain. Speedups of 4x and 8x over multicore CPU execution are observed for arbitrary data sizes and GPU-cacheable data sizes, respectively.
INVESTIGATING THE DIFFUSION OF 4 D BIM INNOVATION

UK Government regularly applies challenging strategic targets to the construction industry, chief amongst these are requirements for more rapid project delivery processes and consistent improvements to the time predictability aspects of on-site construction delivery periods. Latest industry KPI data has revealed a recent increase across measures of time predictability, however more than half of UK construction projects continue to exceed agreed time schedules. The aim of this research was to investigate the diffusion of 4D BIM innovation as adoption of this innovation is seen as a potential solution in response to these targets of construction time predictability. Through purposive sampling, a quantitative survey was undertaken using an online questionnaire that measured 4D BIM innovation adoption using accepted diffusion research methods. These included an exploration of several perceived attributes including compatibility, complexity, observability and the relative advantages of 4D BIM innovation in comparison against conventional functions of construction planning and against stages of the construction planning processes. Descriptive and inferential analysis of the data addresses how the benefits are being realised and explore reasons for adoption or rejection decisions of this innovation. Results indicate an increasing rate of 4D BIM innovation adoption and reveal the typical time lag between awareness and first use.
Identity-Aware Textual-Visual Matching with Latent Co-attention

Textual-visual matching aims at measuring similarities between sentence descriptions and images. Most existing methods tackle this problem without effectively utilizing identity-level annotations. In this paper, we propose an identity-aware two-stage framework for the textual-visual matching problem. Our stage-1 CNN-LSTM network learns to embed cross-modal features with a novel Cross-Modal Cross-Entropy (CMCE) loss. The stage-1 network is able to efficiently screen easy incorrect matchings and also provide initial training point for the stage-2 training. The stage-2 CNN-LSTM network refines the matching results with a latent co-attention mechanism. The spatial attention relates each word with corresponding image regions while the latent semantic attention aligns different sentence structures to make the matching results more robust to sentence structure variations. Extensive experiments on three datasets with identity-level annotations show that our framework outperforms state-of-the-art approaches by large margins.
Hormetic Response to Low-Dose Radiation: Focus on the Immune System and Its Clinical Implications

The interrelationship between ionizing radiation and the immune system is complex, multifactorial, and dependent on radiation dose/quality and immune cell type. High-dose radiation usually results in immune suppression. On the contrary, low-dose radiation (LDR) modulates a variety of immune responses that have exhibited the properties of immune hormesis. Although the underlying molecular mechanism is not fully understood yet, LDR has been used clinically for the treatment of autoimmune diseases and malignant tumors. These advancements in preclinical and clinical studies suggest that LDR-mediated immune modulation is a well-orchestrated phenomenon with clinical potential. We summarize recent developments in the understanding of LDR-mediated immune modulation, with an emphasis on its potential clinical applications.
Surgical Anatomy of the Eyelids.

Slight alterations in the intricate anatomy of the upper and lower eyelid or their underlying structures can have pronounced consequences for ocular esthetics and function. The understanding of periorbital structures and their interrelationships continues to evolve and requires consideration when performing complex eyelid interventions. Maintaining a detailed appreciation of this region is critical to successful cosmetic or reconstructive surgery. This article presents a current review of the anatomy of the upper and lower eyelid with a focus on surgical implications.
An Overview of Autonomous Mobile Robot Path Planning Algorithms

Determination of a collision free path for a robot between start and goal positions through obstacles cluttered in a workspace is central to the design of an autonomous robot path planning. This paper presents an overview of autonomous mobile robot path planning focusing on algorithms that produce an optimal path for a robot to navigate in an environment. To complete the navigation task, the algorithms will read the map of the environment or workspace and subsequently attempts to create free paths for the robot to traverse in the workspace without colliding with objects and obstacles. Appropriate or correct and suitable algorithms will fulfill its function fast enough, that is, to find an optimal path for the robot to traverse in, even if there are a large number of obstacles cluttered in a complex environment. To achieve this, various approaches in the design of algorithms used to develop an ideal path planning system for autonomous mobile robots have been proposed by many researchers. Simulation and experimental results from previous research shows that algorithms play an important role to produce an optimal path (short, smooth and robust) for autonomous robot navigation and simultaneously it prove that appropriate algorithms can run fast enough to be used practically without time-consuming problem. This paper presents an overview and discusses the strength and weakness of path planning algorithms developed and used by previous and current researchers.
Should empiric antibiotic therapy be withheld when aetiology of preterm birth is non-infectious? A protocol for a systematic review

Preterm birth (PTB) at <37 weeks of gestation is the leading cause of perinatal morbidity and mortality in developed countries. The traditional approach has been based on the assumption that PTB is primarily a result of intrauterine infection, which triggers preterm labour and puts the newborn at risk of early onset sepsis (EOS). We are currently experiencing a rise in prematurity that results from maternal and fetal diseases unrelated to infection. We have designed a systematic review to assess whether chemoprophylaxis should be withheld when the aetiology of preterm birth is non-infectious.



Our study will focus on studies evaluating EOS in preterm infants. We will conduct a comprehensive search of literature available up to 28 February 2018. An information specialist will search for eligible studies in Medline (Ovid interface, 1948 and onwards), Embase (Ovid interface, 1980 onwards) and the Cochrane Central Register of Controlled Trials (Wiley interface, current issue). We will search databases and registries including records of ongoing research, conference proceedings and thesis (clinical trials,  Two authors will independently extract data from eligible studies and assess risk of bias. For continuous outcomes, which follow discrete distribution, mean difference will be calculated. Dichotomous data will be presented using risk ratios, while count data will be expressed using rate ratios. Time-to-event outcomes will be reported as HRs. All estimates will be presented together with 95% CI. Studies comparable with respect to methodology and reporting the same outcomes will be combined in a meta-analysis.


ETHICS AND DISSEMINATION
Our systematic review does not require approval from the research and ethics board. We will use the findings to prepare a future multicentre randomised control trial in order to establish safe and adequate antibiotics policies for preterm infants, based on the aetiology of PTB.
Video segmentation by tracing discontinuities in a trajectory embedding

Our goal is to segment a video sequence into moving objects and the world scene. In recent work, spectral embedding of point trajectories based on 2D motion cues accumulated from their lifespans, has shown to outperform factorization and per frame segmentation methods for video segmentation. The scale and kinematic nature of the moving objects and the background scene determine how close or far apart trajectories are placed in the spectral embedding. Such density variations may confuse clustering algorithms, causing over-fragmentation of object interiors. Therefore, instead of clustering in the spectral embedding, we propose detecting discontinuities of embedding density between spatially neighboring trajectories. Detected discontinuities are strong indicators of object boundaries and thus valuable for video segmentation. We propose a novel embedding discretization process that recovers from over-fragmentations by merging clusters according to discontinuity evidence along inter-cluster boundaries. For segmenting articulated objects, we combine motion grouping cues with a center-surround saliency operation, resulting in “context-aware”, spatially coherent, saliency maps. Figure-ground segmentation obtained from saliency thresholding, provides object connectedness constraints that alter motion based trajectory affinities, by keeping articulated parts together and separating disconnected in time objects. Finally, we introduce Gabriel graphs as effective per frame superpixel maps for converting trajectory clustering to dense image segmentation. Gabriel edges bridge large contour gaps via geometric reasoning without over-segmenting coherent image regions. We present experimental results of our method that outperform the state-of-the-art in challenging motion segmentation datasets.
Ontology-Based Natural Language Query Interfaces for Data Exploration

Enterprises are creating domain-specific knowledge bases by curating and integrating all their business data, structured, unstructured and semi-structured, and using them in enterprise applications to derive better business decisions. One distinct characteristic of these enterprise knowledge bases, compared to the open-domain general purpose knowledge bases like DBpedia [16] and Freebase , is their deep domain specialization. This deep domain understanding empowers many applications in various domains, such as health care and finance. Exploring such knowledge bases, and operational data stores requires different querying capabilities. In addition to search, these databases also require very precise structured queries, including aggregations, as well as complex graph queries to understand the various relationships between various entities of the domain. For example, in a financial knowledge base, users may want to find out “which startups raised the most VC funding in the first quarter of 2017”; a very precise query that is best expressed in SQL. The users may also want to find all possible relationships between two specific board members of these startups, a query which is naturally expressed as an all-paths graph query. It is important to note that general purpose knowledge bases could also benefit from different query capabilities, but in this paper we focus on domain-specific knowledge graphs and their query needs. Instead of learning and using many complex query languages, one natural way to query the data in these cases is using natural language interfaces to explore the data. In fact, human interaction with technology through conversational services is making big strides in many application domains in recent years [13]. Such interfaces are very desirable because they do not require the users to learn a complex query language, such as SQL, and the users do not need to know the exact schema of the data, or how it is stored. There are several challenges in building a natural language interface to query data sets. The most difficult task is understanding the semantics of the query, hence the user intent. Early systems [3, 30] allowed only a set of keywords, which had very limited expressive power. There have been works to interpret the semantics of a full-blown English language query. These works in general try to disambiguate among the potentially multiple meanings of the words and their relationships. Some of these are machine-learning based [5, 24, 29] that require good training sets, which are hard to obtain. Others require user feedback [14, 17, 18]. However, excessive user interaction to resolve ambiguities can be detrimental to user experience. In this paper, we describe a unique end-to-end ontology-based system for natural language querying over complex data sets. The system uses domain ontologies, which describe the semantic entities and their relationships, to reason about and capture user intent. To support multiple query types, the system provides a poly store
An Analysis of the Cryptocurrency Industry

The cryptocurrency market has evolved erratically and at unprecedented speed over the course of its short lifespan. Since the release of the pioneer anarchic cryptocurrency, Bitcoin, to the public in January 2009, more than 550 cryptocurrencies have been developed, the majority with only a modicum of success . Research on the industry is still scarce. The majority of it is singularly focused on Bitcoin rather than a more diverse spread of cryptocurrencies and is steadily being outpaced by fluid industry developments, including new coins, technological progression, and increasing government regulation of the markets. Though the fluidity of the industry does, admittedly, present a challenge to research, a thorough evaluation of the cryptocurrency industry writ large is necessary. This paper seeks to provide a concise yet comprehensive analysis of the cryptocurrency industry with particular analysis of Bitcoin, the first decentralized cryptocurrency. Particular attention will be given to examining theoretical economic differences between existing coins. Section 1 of this paper provides an overview of the industry. Section 1.1 provides a brief history of digital currencies, which segues into a discussion of Bitcoin in section 1.2. Section 2 of this paper provides an in-depth analysis of coin economics, partitioning the major currencies by their network security protocol mechanisms, and discussing the long-term theoretical implications that these classes entail. Section 2.1 will discuss network security protocol. The mechanisms will be discussed in the order that follows. Section 2.2 will discuss the proof-of-work (PoW) mechanism used in the Bitcoin protocol and various altcoins. Section 2.3 will discuss the proof-of-stake (PoS) protocol scheme first introduced by Peercoin in 2011, which relies on a less energy intensive security mechanism than PoW. Section 2.4 will discuss a hybrid PoW/PoS mechanism. Section 2.5 will discuss the Byzantine Consensus mechanism. Section 2.6 presents the results of a systematic review of 21 cryptocurrencies. Section 3 provides an overview of factors affecting industry growth, focusing heavily on the regulatory environment in section 3.1. Section 3.2 discusses public perception and acceptance of cryptocurrency as a payment system in the current retail environment. Section 4 concludes the analysis. A note on sources: Because the cryptocurrency industry is still young and factors that impact it are changing on a daily basis, few comprehensive or fully updated academic sources exist on the topic. While academic work was of course consulted for this project, the majority of the information that informs this paper was derived from …
Game theoretic study on blockchain based secure edge networks

Blockchain has been applied to study data privacy and network security recently. In this paper, we propose a punishment scheme based on the action record on the blockchain to suppress the attack motivation of the edge servers and the mobile devices in the edge network. The interactions between a mobile device and an edge server are formulated as a blockchain security game, in which the mobile device sends a request to the server to obtain real-time service or launches attacks against the server for illegal security gains, and the server chooses to perform the request from the device or attack it. The Nash equilibria (NEs) of the game are derived and the conditions that each NE exists are provided to disclose how the punishment scheme impacts the adversary behaviors of the mobile device and the edge server.
Ocular dermoids: 116 consecutive cases.

The aim was to describe the demographic and clinical data of 116 consecutive cases of ocular dermoids.



This was a retrospective case series and a review of clinical records of all the patients diagnosed with ocular dermoids. Both demographic and clinical data were recorded. Statistical analysis was performed with SPSS v. 18. Descriptive statistics are reported.



The study included 116 consecutive patients with diagnosis consistent with ocular dermoids: corneal 18% (21), dermolipomas 38% (44), and orbital 44% (51). Sixty-five percent (71) were female, and 46% (54) were detected at birth. Secondary manifestations: amblyopia was present in 14% (3), and strabismus was detected in 6.8% (8). The Goldenhar syndrome was the most frequent syndromic entity in 7.5% (12) of the patients. Surgical resection was required on 49% (25) of orbital dermoids, 24% (5) of corneal dermoids, and 13% (6) of dermolipomas.



Orbital dermoids were the most frequent variety, followed by conjunctival and corneal. In contrast to other reports, corneal dermoids were significantly more prevalent in women. Goldenhar syndrome was the most frequent syndromatic entity.