nils-herrmann/clean_scirepeval huggingface dataset:
An organizational decision support system for effective R&D project selection

Research and development (R&D) project selection is an important task for organizations with R&D project management. It is a complicated multi-stage decision-making process, which involves groups of decision makers. Current research on R&D project selection mainly focuses on mathematical decision models and their applications, but ignores the organizational aspect of the decision-making process. This paper proposes an organizational decision support system (ODSS) for R&D project selection. Object-oriented method is used to design the architecture of the ODSS. An organizational decision support system has also been developed and used to facilitate the selection of project proposals in the National Natural Science Foundation of China (NSFC). The proposed system supports the R&D project selection process at the organizational level. It provides useful information for decision-making tasks in the R&D project selection process. D 2004 Elsevier B.V. All rights reserved.
Data driven image models through continuous joint alignment

This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar as possible by removing variability along the known axes of variation. This technique can be used to eliminate "nuisance" variables such as affine deformations from handwritten digits or unwanted bias fields from magnetic resonance images. In addition to separating and modeling the latent images - i.e., the images without the nuisance variables - we can model the nuisance variables themselves, leading to factorized generative image models. When nuisance variable distributions are shared between classes, one can share the knowledge learned in one task with another task, leading to efficient learning. We demonstrate this process by building a handwritten digit classifier from just a single example of each class. In addition to applications in handwritten character recognition, we describe in detail the application of bias removal from magnetic resonance images. Unlike previous methods, we use a separate, nonparametric model for the intensity values at each pixel. This allows us to leverage the data from the MR images of different patients to remove bias from each other. Only very weak assumptions are made about the distributions of intensity values in the images. In addition to the digit and MR applications, we discuss a number of other uses of congealing and describe experiments about the robustness and consistency of the method.
Providing Streaming Joins as a Service at Facebook

Stream processing applications reduce the latency of batch data pipelines and enable engineers to quickly identify production issues. Many times, a service can log data to distinct streams, even if they relate to the same real-world event (e.g., a search on Facebook’s search bar). Furthermore, the logging of related events can appear on the server side with different delay, causing one stream to be significantly behind the other in terms of logged event times for a given log entry. To be able to stitch this information together with low latency, we need to be able to join two different streams where each stream may have its own characteristics regarding the degree in which its data is out-of-order. Doing so in a streaming fashion is challenging as a join operator consumes lots of memory, especially with significant data volumes. This paper describes an end-to-end streaming join service that addresses the challenges above through a streaming join operator that uses an adaptive stream synchronization algorithm that is able to handle the different distributions we observe in real-world streams regarding their event times. This synchronization scheme paces the parsing of new data and reduces overall operator memory footprint while still providing high accuracy. We have integrated this into a streaming SQL system and have successfully reduced the latency of several batch pipelines using this approach. PVLDB Reference Format: G. Jacques-Silva, R. Lei, L. Cheng, G. J. Chen, K. Ching, T. Hu, Y. Mei, K. Wilfong, R. Shetty, S. Yilmaz, A. Banerjee, B. Heintz, S. Iyer, A. Jaiswal. Providing Streaming Joins as a Service at Facebook. PVLDB, 11 (12): 1809-1821, 2018.  https://doi.org/10.14778/3229863.3229869
MultiPoint: Comparing laser and manual pointing as remote input in large display interactions

We present MultiPoint, a set of perspective-based remote pointing techniques that allows users to perform bimanual and multi-finger remote manipulation of graphical objects on large displays. We conducted two empirical studies that compared remote pointing techniques performed using fingers and laser pointers, in single and multi-finger pointing interactions. We explored three types of manual selection gestures: squeeze, breach and trigger. The fastest and most preferred technique was the trigger gesture in the single point experiment and the unimanual breach gesture in the multi-finger pointing study. The laser pointer obtained mixed results: it is fast, but inaccurate in single point, and it obtained the lowest ranking and performance in the multipoint experiment. Our results suggest MultiPoint interaction techniques are superior in performance and accuracy to traditional laser pointers for interacting with graphical objects on a large display from a distance. & 2012 Elsevier Ltd. All rights reserved.
Feature Extraction of X-ray Fracture Image and Fracture Classification

X-ray images are the essential aiding means all along in clinical diagnosis of fracture. So the processing and analysis of X-ray fracture images is particularly important. Extracting the features of X-ray images is a very important process in classifying fracture images according to the principle of AO classification of fractures. A proposed algorithm is used in this paper. First, use marker-controlled watershed transform based on gradient and homotopy modification to segment X-ray fracture images. Then the features consisted of region number, region area, region centroid and protuberant polygon of fracture image are extracted by marker processing and regionprops function. Next we use Hough transform to detect and extract lines in the protuberant polygon of X-ray fracture image. The lines are consisted of fracture line and parallel lines of centerline. Through the parallel lines of centerline, we obtain centerline over centroid and perpendicular line of centerline over centroid. Finally compute the angle between fracture line and perpendicular line of centerline. This angle can be used to classify femur backbone fracture.
Trajectory Privacy Preservation Based on a Fog Structure for Cloud Location Services

The development of mobile cloud computing technology has made location-based service (LBS) increasingly more popular. Given the continuous requests to cloud LBS servers, the amounts of location and trajectory information collected by LBS servers are continuously increasing. Privacy awareness for LBS has been extensively studied in recent years. Among the privacy concerns about LBS, trajectory privacy preservation is particularly important. Based on privacy preservation models, previous work have mainly focused on peer-to-peer and centralized architectures. However, the burden on users is heavy in peer-to-peer architectures, because user devices need to communicate with LBS servers directly. In centralized architectures, a trusted third party (TTP) is introduced, and acts as a bridge between users and the LBS server. Anonymity technologies, such as k-anonymity, mix-zone, and dummy technologies, are usually implemented by the TTP to ensure safety. There are certain drawbacks in TTP architectures: Users have no physical control of the TTP. Moreover, the TTP is more attractive to adversaries, because substantially more sensitive information is stored by the TTP. To solve the above-mentioned problems, in this paper, we propose a fog structure to store partial important data with the dummy anonymity technology to ensure physical control, which can be considered as absolutely trust. Compared with cloud computing, fog computing is a promising technique that extends the cloud computing to the edge of a network. Moreover, fog computing provides local computation and storage abilities, wide geo-distribution, and support for mobility. Therefore, mobile users’ partial important information can be stored on a fog server to ensure better management. We take the principles of similarity, intersection, practicability, and correlation into consideration and design a dummy rotation algorithm with several properties. The effectiveness of the proposed method is validated through extensive simulations, which show that the proposed method can provide enhanced privacy preservation.
Can embodied interaction and virtual peer customization in a virtual programming environment enhance computational thinking?

To address the problem of underexposure, underrepresentation, and underproduction of diverse professionals in the field of computing, we target middle school education using an idea that combines computational thinking with dance and movement choreography. This lightning talk delves into a virtual reality education and entertainment application named Virtual Environment Interactions (VEnvI). Our in vivo study examines how VEnvI can be used to teach fundamental computer science concepts such as sequences, loops, variables, conditionals, functions, and parallel programming. We aim to reach younger students through a fun and intuitive interface for choreographing dance movements with a virtual character. Our study contrasts the highly immersive and embodied virtual reality metaphor of using VEnvI with a non-immersive desktop metaphor. Additionally, we examine the effects of user attachment by comparing the learning results gained with customizable virtual characters in contrast with character presets. By analyzing qualitative and quantitative user responses measuring cognition, presence, usability, and satisfaction, we hope to find how virtual reality can enhance interest in the field of computer science among middle school students.
Peptic ulcer disease in liver cirrhosis and chronic hepatitis: impact of portal hypertension.

The pathophysiology of peptic ulcer disease (PUD) in liver cirrhosis (LC) and chronic hepatitis has not been established. The aim of this study was to assess the role of portal hypertension from PUD in patients with LC and chronic hepatitis.



We analyzed the medical records of 455 hepatic vein pressure gradient (HVPG) and esophagogastroduodenoscopy patients who had LC or chronic hepatitis in a single tertiary hospital. The association of PUD with LC and chronic hepatitis was assessed by univariate and multivariate analysis.



A total of 72 PUD cases were detected. PUD was associated with LC more than with chronic hepatitis (odds ratio [OR]: 4.13, p = 0.03). In the univariate analysis, taking an ulcerogenic medication was associated with PUD in patients with LC (OR: 4.34, p = 0.04) and smoking was associated with PUD in patients with chronic hepatitis (OR: 3.61, p = 0.04). In the multivariate analysis, taking an ulcerogenic medication was associated with PUD in patients with LC (OR: 2.93, p = 0.04). However, HVPG was not related to PUD in patients with LC or chronic hepatitis.



According to the present study, patients with LC have a higher risk of PUD than those with chronic hepatitis. The risk factor was taking ulcerogenic medication. However, HVPG reflecting portal hypertension was not associated with PUD in LC or chronic hepatitis (Clinicaltrial number NCT01944878).
Bus detection system for blind people using RFID

This paper presents a bus detection system using RFID technology that aims to ease the traveling and movement of blind people. The proposed system consists of two detection subsystems; one on the buses and the other on the bus stations, database system and a website. In the bus detection subsystem, the nearby stations will be easily detected and then announced through a voice message inside the bus. Moreover, any existing blind person in the surrounding area of the station will be detected by the bus subsystem to alert the bus driver about the number of blind persons. In the bus station subsystem, the coming buses will be detected and then announced in the station in order to alert the blind people. A complete system prototype has been constructed and tested to validate the proposed system. The results show that the system performance is promising in terms of system functionality, safety, and cost.
Introduction to Ultra Reliable and Low Latency Communications in 5G

New wave of the technology revolution, often referred to as the fourth industrial revolution, is changing the way we live, work, and communicate with each other. These days, we are witnessing the emergence of unprecedented services and applications requiring lower latency, better reliability massive connection density, and improved energy efficiency. In accordance with this trend and change, international telecommunication union (ITU) defined three representative service categories, viz., enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low latency communication (uRLLC). Among three service categories, physical-layer design of the uRLLC service is arguably the most challenging and problematic. This is mainly because uRLLC should satisfy two conflicting requirements: low latency and ultra-high reliability. In this article, we provide the stateof-the-art overview of uRLLC communications with an emphasis on technical challenges and solutions. We highlight key requirements of uRLLC service and then discuss the physical-layer issues and enabling technologies including packet and frame structure, multiplexing schemes, and reliability improvement
Lung Cancer Concept Annotation from Spanish Clinical Narratives

Recent rapid increase in the generation of clinical data and rapid development of computational science make us able to extract new insights from massive datasets in healthcare industry. Oncological clinical notes are creating rich databases for documenting patient’s history and they potentially contain lots of patterns that could help in better management of the disease. However, these patterns are locked within free text (unstructured) portions of clinical documents and consequence in limiting health professionals to extract useful information from them and to finally perform Query and Answering (Q&A) process in an accurate way. The Information Extraction (IE) process requires Natural Language Processing (NLP) techniques to assign semantics to these patterns. Therefore, in this paper, we analyze the design of annotators for specific lung cancer concepts that can be integrated over Apache Unstructured Information Management Architecture (UIMA) framework. In addition, we explain the details of generation and storage of annotation outcomes.
A CNN Regression Approach for Real-Time 2D/3D Registration

In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. An automatic feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.
Off-line programming of robotic system based on DXF files of 3D models

For the convenience of complex trajectory programming of robot and in order to enrich robotic off-line programming (OLP) types, this paper presents an efficient robotic OLP method based on DXF files of 3D modeling software. By decoding DXF files, the 3D position information of object graphic elements such as points, lines, arcs and splines are first extracted, all the graphic elements are then converted into point elements. A trajectory planning algorithm is proposed to make these points linked into robot trajectories according to the drawing order in CAD software. Except for the position information of the trajectory, the orientation of the trajectory is also planned by an easy and efficient method. Coordinate transformation is fulfilled by calibration,The planned trajectory is verified with OpenGL simulation, the executable program is then generated and downloaded into the robot controlle. Experiment results of the 5-DoFs modular robot show that the presented OLP system is effective and can be used in practice.
Spectrum of mucocutaneous manifestations in 277 patients with joint hypermobility syndrome/Ehlers-Danlos syndrome, hypermobility type.

Cutaneous manifestations are a diagnostic criterion of Ehlers-Danlos syndrome, hypermobility type (EDS-HT) and joint hypermobility syndrome (JHS). These two conditions, originally considered different disorders, are now accepted as clinically indistinguishable and often segregate as a single-familial trait. EDS-HT and JHS are still exclusion diagnoses not supported by any specific laboratory test. Accuracy of clinical diagnosis is, therefore, crucial for appropriate patients' classification and management, but it is actually hampered by the low consistency of many applied criteria including the cutaneous one. We report on mucocutaneous findings in 277 patients with JHS/EDS-HT with both sexes and various ages. Sixteen objective and five anamnestic items were selected and ascertained in two specialized outpatient clinics. Feature rates were compared by sex and age by a series of statistical tools. Data were also used for a multivariate correspondence analysis with the attempt to identify non-causal associations of features depicting recognizable phenotypic clusters. Our findings identified a few differences between sexes and thus indicated an attenuated sexual dimorphism for mucocutaneous features in JHS/EDS-HT. Ten features showed significantly distinct rates at different ages and this evidence corroborated the concept of an evolving phenotype in JHS/EDS-HT also affecting the skin. Multivariate correspondence analysis identified three relatively discrete phenotypic profiles, which may represent the cutaneous counterparts of the three disease phases previously proposed for JHS/EDS-HT. These findings could be used for revising the cutaneous criterion in a future consensus for the clinical diagnosis of JHS/EDS-HT.
A multitask deep learning model for real-time deployment in embedded systems

We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems. We develop a multitask model for both Object Detection and Semantic Segmentation and analyze the challenges that appear during its training. Our multitask network is 1.6x faster, lighter and uses less memory than deploying the single-task models in parallel. We conclude that MTL has the potential to give superior performance in exchange of a more complex training process that introduces challenges not present in single-task models.
Compact wideband Vivaldi antenna array for microwave imaging applications

In the paper, we describe analysis of Vivaldi antenna array aimed for microwave image application and SAR application operating at Ka band. The antenna array is fed by a SIW feed network for its low insertion loss and broadband performances in millimeter wave range. In our proposal we have replaced the large feed network by a simple relatively broadband network of compact size to reduce the losses in substrate integrated waveguide (SIW) and save space on PCB. The feed network is power 8-way divider fed by a wideband SIW-GCPW transition and directly connected to the antenna elements. The final antenna array will be designed, fabricated and obtained measured results will be compared with numerical ones.
Transfer Learning for Brain-Computer Interfaces: An Euclidean Space Data Alignment Approach

Almost all EEG-based brain-computer interfaces (BCIs) need some labeled subject-specific data to calibrate a new subject, as neural responses are different across subjects to even the same stimulus. So, a major challenge in developing high-performance and user-friendly BCIs is to cope with such individual differences so that the calibration can be reduced or even completely eliminated. This paper focuses on the latter. More specifically, we consider an offline application scenario, in which we have unlabeled EEG trials from a new subject, and would like to accurately label them by leveraging auxiliary labeled EEG trials from other subjects in the same task. To accommodate the individual differences, we propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent. It has three desirable properties: 1) the aligned trial lie in the Euclidean space, which can be used by any Euclidean space signal processing and machine learning approach; 2) it can be computed very efficiently; and, 3) it does not need any labeled trials from the new subject. Experiments on motor imagery and event-related potentials demonstrated the effectiveness and efficiency of our approach.
Pigmented lesion of the left nipple in a 49-year-old woman.

Microscopic examination revealed polymorphic cells with abundant cytoplasm and large nuclei within the acanthotic epidermis . There were aggregated melanin granules in the epidermis, as well as a subepidermal lymphocytic infiltrate. The atypical cells were positive for CK7 . A few scattered cells were positive with the Melan-A stain . Pigmented lesion of the left nipple in a 49-year-old woman Case for Diagnosis
A Core Quantitative Coeffect Calculus

Linear logic is well known for its resource-awareness, which has inspired the design of several resource management mechanisms in programming language design. Its resource-awareness arises from the distinction between linear, single-use data and non-linear, reusable data. The latter is marked by the so-called exponential modality, which, from the categorical viewpoint, is a (monoidal) comonad. Monadic notions of computation are well-established mechanisms used to express effects in pure functional languages. Less well-established is the notion of comonadic computation. However, recent works have shown the usefulness of comonads to structure context dependent computations. In this work, we present a language `RPCF inspired by a generalized interpretation of the exponential modality. In `RPCF the exponential modality carries a label—an element of a semiring R—that provides additional information on how a program uses its context. This additional structure is used to express comonadic type analysis.