nils-herrmann/clean_scirepeval huggingface dataset:
Inter-rater Reliability of the Dysphagia Outcome and Severity Scale (DOSS): Effects of Clinical Experience, Audio-Recording and Training

The Dysphagia Outcome and Severity Scale (DOSS) is widely used to measure dysphagia severity based on videofluoroscopy (VFSS). This study investigated inter-rater reliability (IRR) of the DOSS. It also determined the effect of clinical experience, VFSS audio-recording and training on DOSS IRR. A quantitative prospective research design was used. Seventeen speech and language pathologists (SLPs) were recruited from an acute teaching hospital, Dublin (> 3 years’ VFSS experience, n = 10) and from a postgraduate dysphagia programme in a university setting (< 3 years’ VFSS experience; n = 7). During testing, participants viewed eight VFSS clips (5 with audio-recording). Each VFSS clip was independently rated using the DOSS scale. Four weeks later, the less experienced group attended a 1-h training session on DOSS rating after which DOSS IRR was re-tested. Cohen’s kappa co-efficient was used to establish IRR. IRR of the DOSS presented only fair agreement (κ = 0.36, p < 0.05). DOSS IRR was significantly higher (κ = 0.342) within the more experienced SLP group, compared to the less experienced SLP group (κ = 0.298) (p < 0.05). DOSS IRR was significantly higher in VFSS clips with audio-recording (κ = 0.287) compared to VFSS clips without audio-recording (κ = − 0.0395) (p < 0.05). IRR of the DOSS pre-training (κ = 0.328) was significantly better comparing to post-training (κ = 0.218) (p < 0.05). Findings raise concerns as the DOSS is frequently used in clinical practice to capture dysphagia severity and to monitor changes.
On GitHub's Programming Languages

GitHub is the most widely used social, distributed version control system. It has around 10 million registered users and hosts over 16 million public repositories. Its user base is also very active as GitHub ranks in the top 100 Alexa most popular websites. In this study, we collect GitHub’s state in its entirety. Doing so, allows us to study new aspects of the ecosystem. Although GitHub is the home to millions of users and repositories, the analysis of users’ activity time-series reveals that only around 10% of them can be considered active. The collected dataset allows us to investigate the popularity of programming languages and existence of pattens in the relations between users, repositories, and programming languages. By, applying a k-means clustering method to the usersrepositories commits matrix, we find that two clear clusters of programming languages separate from the remaining. One cluster forms for “web programming” languages (Java Script, Ruby, PHP, CSS), and a second for “system oriented programming” languages (C, C++, Python). Further classification, allow us to build a phylogenetic tree of the use of programming languages in GitHub. Additionally, we study the main and the auxiliary programming languages of the top 1000 repositories in more detail. We provide a ranking of these auxiliary programming languages using various metrics, such as percentage of lines of code, and PageRank.
MicroRNAs in liver disease.

MicroRNAs are small noncoding RNA molecules that regulate gene expression posttranscriptionally through complementary base pairing with thousands of messenger RNAs. They regulate diverse physiological, developmental, and pathophysiological processes. Recent studies have uncovered the contribution of microRNAs to the pathogenesis of many human diseases, including liver diseases. Moreover, microRNAs have been identified as biomarkers that can often be detected in the systemic circulation. We review the role of microRNAs in liver physiology and pathophysiology, focusing on viral hepatitis, liver fibrosis, and cancer. We also discuss microRNAs as diagnostic and prognostic markers and microRNA-based therapeutic approaches for liver disease.
Voiceless speech recognition using dynamic visual speech features

This paper describes a voiceless speech recognition technique that utilizes dynamic visual features to represent the facial movements during phonation. The dynamic features extracted from the mouth video are used to classify utterances without using the acoustic data. The audio signals of consonants are more confusing than vowels and the facial movements involved in pronunciation of consonants are more discernible. Thus, this paper focuses on identifying consonants using visual information. This paper adopts a visual speech model that categorizes utterances into sequences of smallest visually distinguishable units known as visemes. The viseme model used is based on the viseme model of Moving Picture Experts Group 4 (MPEG-4) standard. The facial movements are segmented from the video data using motion history images (MHI). MHI is a spatio-temporal template (grayscale image) generated from the video data using accumulative image subtraction technique. The proposed approach combines discrete stationary wavelet transform (SWT) and Zernike moments to extract rotation invariant features from the MHI. A feedforward multilayer perceptron (MLP) neural network is used to classify the features based on the patterns of visible facial movements. The preliminary experimental results indicate that the proposed technique is suitable for recognition of English consonants.
Digital Forensics: Validation and Verification in a Dynamic Work Environment

Many forensic computing practitioners work in a high workload and low resource environment. With the move by the discipline to seek ISO 17025 laboratory accreditation, practitioners are finding it difficult to meet the demands of validation and verification of their tools and still meet the demands of the accreditation framework. Many agencies are ill-equipped to reproduce tests conducted by organizations such as NIST since they cannot verify the results with their equipment and in many cases rely solely on an independent validation study of other peoples' equipment. This creates the issue of tools in reality never being tested. Studies have shown that independent validation and verification of complex forensic tools is expensive and time consuming, and many practitioners also use tools that were not originally designed for forensic purposes. This paper explores the issues of validation and verification in the accreditation environment and proposes a paradigm that will reduce the time and expense required to validate and verify forensic software tools
Closure of elective abdominal incisions with monofilament, non-absorbable suture material versus polyfilament absorbable suture material.

Closure of abdominal incisions with different suture materials has been a matter of great controversy. Polypropylene and Polylactide with Polyglycolide are among the commonest suture materials used for closure of abdominal incisions. Objectives of this study were to assess optimal suture material used for closure of elective abdominal incisions and to see complications associated with these suture materials.



This prospective, comparative, randomised study was conducted at public and private sector hospitals of Nawabshah and Hyderabad from 1st Jan 2005 to 31st October 2009. All patients who underwent abdominal surgery under the investigators' supervision were included in the study. Patients were divided into 2 groups on even or odd numbers. In group-A patients' incision was closed with monofilament, non-absorbable Polypropylene (Prolene) No. 1 suture material and in group-B incision was closed with Polyfilament, absorbable, co-polymer of Polylactide with Polyglycolide (Vicryle) No. 1.



A total 274 patients were finally analysed for closure of elective abdominal incisions, with 138 (50.4%) patients in Group-A and 136 (49.6%) patients in Group-B. Vicryle was found superior in knot security and suture handling. Superficial wound infection was found in 5.79% patients of Group-A and 6.61% of Group-B. Discharging sinus was found in 3.62% of Group-A vs 0.73% of Group-B. Burst abdomen was seen in 2.17% patients in Group-A and 1.47% in Group-B. Incisional hernia was present in 4.34% of Group-A and 0.73% patients of Group-B. No patient in Group-B developed persistent pain at incisional site while it was found in 8.69% patients of Group-A.



Polylactide is an optimal suture material in closure of elective abdominal incisions.
Dynamic portfolio management with transaction costs

We develop a recurrent reinforcement learning (RRL) system that directly induces portfolio management policies from time series of asset pri ces and indicators, while accounting for transaction costs. The RRL approach le arns a direct mapping from indicator series to portfolio weights, bypassing the need to explicitly model the time series of price returns. The resulting polici es dynamically optimize the portfolio Sharpe ratio, while incorporating changing c onditions and transaction costs. A key problem with many portfolio optimization m ethods, including Markowitz, is discovering ”corner solutions” with weight c oncentrated on just a few assets. In a dynamic context, naive portfolio algorithm s can exhibit switching behavior, particularly when transaction costs are ignored . In this work, we extend the RRL approach to produce better diversified portfoli os and smoother asset allocations over time. The solutions we propose are to inclu de realistic transaction costs and to shrink portfolio weights toward the prior p tfolio. The methods are assessed on a global asset allocation problem consistin g of the Pacific, North America and Europe MSCI International Equity Indices.
Working in a danger zone: A qualitative study of Taiwanese nurses' work experiences in a negative pressure isolation ward.

Hospital nurses are frontline health care workers in controlling the spread of infectious diseases. It is not known if nurses working in negative pressure isolation wards (NPIWs) are better prepared than before to safely care for patients with common infectious diseases.



For this qualitative descriptive study, 10 nurses were interviewed in depth about their experiences caring for patients in an NPIW. Tape recordings were transcribed verbatim and analyzed by qualitative content analysis.



The following 5 themes were identified: (1) complexity of patient care, (2) dissatisfaction with the quantity and quality of protective equipment, (3) shortage of nursing staff, (4) continued worries about being infected, and (5) sensitivity to self-protection. Our participants' anxiety and uncertainty about being infected in the NPIW were increased by the complexity of patients' health problems and organizational factors. To protect themselves against infection before and during patient care, participants also developed sensitivity to, concepts about, and strategies to improve self-protection.



NPIW administrators should pay more attention to nurses' concerns about improving the NPIW working environment, supply good quality protective equipment, and provide appropriate psychologic support and ongoing education to ensure that nurses feel safe while working. This ongoing education should refresh and update nurses' knowledge about disease transmission, therefore decreasing unnecessary anxiety based on misunderstandings about becoming infected.
Geographical information system parallelization for spatial big data processing: a review

With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.
Soft, smart contact lenses with integrations of wireless circuits, glucose sensors, and displays

Recent advances in wearable electronics combined with wireless communications are essential to the realization of medical applications through health monitoring technologies. For example, a smart contact lens, which is capable of monitoring the physiological information of the eye and tear fluid, could provide real-time, noninvasive medical diagnostics. However, previous reports concerning the smart contact lens have indicated that opaque and brittle components have been used to enable the operation of the electronic device, and this could block the user’s vision and potentially damage the eye. In addition, the use of expensive and bulky equipment to measure signals from the contact lens sensors could interfere with the user’s external activities. Thus, we report an unconventional approach for the fabrication of a soft, smart contact lens in which glucose sensors, wireless power transfer circuits, and display pixels to visualize sensing signals in real time are fully integrated using transparent and stretchable nanostructures. The integration of this display into the smart lens eliminates the need for additional, bulky measurement equipment. This soft, smart contact lens can be transparent, providing a clear view by matching the refractive indices of its locally patterned areas. The resulting soft, smart contact lens provides real-time, wireless operation, and there are in vivo tests to monitor the glucose concentration in tears (suitable for determining the fasting glucose level in the tears of diabetic patients) and, simultaneously, to provide sensing results through the contact lens display.
[Prenatal diagnosis and evolution of patent urachus].

Prenatal ultrasonographic features of patent urachus consist in anechoic proximal cord cyst communicating with the bladder associated with large umbilicus. Distinction should be made with omphalocele and bladder extrophy. Spontaneous evolution leads to rupture during mid-trimester and bladder protrusion. Karyotyping is not mandatory in isolated typical cases. Early postnatal surgery is usually required.
Functional outcome after repair of distal biceps tendon ruptures using the endobutton technique.

The purpose of this study is to report the functional outcome of the repair of a distal biceps tendon rupture by the use of the endobutton technique. We hypothesized that the endobutton provides excellent strength and clinical results after repair of distal biceps tendon rupture.



Twenty-six patients underwent repair of biceps tendon ruptures by use of an endobutton for fixation of the biceps tendon stump to the radial tuberosity. There were 20 men and three women. The mean age was 52 years (39-75). The dominant side was involved in 11 patients. A partial rupture of the biceps tendon was found in four patients. The average delay in diagnosis was 16 days, with four patients presenting at six weeks or more after trauma.



At an average follow-up of 16 months (6-48), 23 of 26 patients were available for follow-up and were examined clinically, radiologically, and by isokinetic testing. The average postoperative Mayo Elbow Performance Score (MEPS) was 94 points. The average Visual Analogue Scale (VAS) for pain was 1.5. Patients regained an almost full range of motion. Average flexion strength recovery was 80% and corresponding recovery of supination strength was 91%. Two patients developed asymptomatic heterotopic ossification seen on standard radiographs. In three patients, the endobutton had apparently disengaged without important difference in functional outcome. In one case, the endobutton had to be removed. There were no neurological complications.



This study shows that a distal biceps tendon can be safely reattached to the radius by using the endobutton technique, yielding excellent and reproducible results.


 Retrospective case series, no control group.
Attribute-Driven Community Search

Recently, community search over graphs has attracted significant attention and many algorithms have been developed for finding dense subgraphs from large graphs that contain given query nodes. In applications such as analysis of protein protein interaction (PPI) networks, citation graphs, and collaboration networks, nodes tend to have attributes. Unfortunately, most previously developed community search algorithms ignore these attributes and result in communities with poor cohesion w.r.t. their node attributes. In this paper, we study the problem of attribute-driven community search, that is, given an undirected graph G where nodes are associated with attributes, and an input query Q consisting of nodes Vq and attributes Wq , find the communities containing Vq , in which most community members are densely inter-connected and have similar attributes. We formulate our problem of finding attributed truss communities (ATC), as finding all connected and close k-truss subgraphs containing Vq, that are locally maximal and have the largest attribute relevance score among such subgraphs. We design a novel attribute relevance score function and establish its desirable properties. The problem is shown to be NP-hard. However, we develop an efficient greedy algorithmic framework, which finds a maximal k-truss containing Vq, and then iteratively removes the nodes with the least popular attributes and shrinks the graph so as to satisfy community constraints. We also build an elegant index to maintain the known k-truss structure and attribute information, and propose efficient query processing algorithms. Extensive experiments on large real-world networks with ground-truth communities shows the efficiency and effectiveness of our proposed methods.
A survey of open source tools for machine learning with big data in the Hadoop ecosystem

With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The world’s data is growing rapidly, and traditional tools for machine learning are becoming insufficient as we move towards distributed and real-time processing. This paper is intended to aid the researcher or professional who understands machine learning but is inexperienced with big data. In order to evaluate tools, one should have a thorough understanding of what to look for. To that end, this paper provides a list of criteria for making selections along with an analysis of the advantages and drawbacks of each. We do this by starting from the beginning, and looking at what exactly the term “big data” means. From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together. We discuss the advantages and disadvantages of three different processing paradigms along with a comparison of engines that implement them, including MapReduce, Spark, Flink, Storm, and H2O. We then look at machine learning libraries and frameworks including Mahout, MLlib, SAMOA, and evaluate them based on criteria such as scalability, ease of use, and extensibility. There is no single toolkit that truly embodies a one-size-fits-all solution, so this paper aims to help make decisions smoother by providing as much information as possible and quantifying what the tradeoffs will be. Additionally, throughout this paper, we review recent research in the field using these tools and talk about possible future directions for toolkit-based learning.
Dynamic Network Embeddings: From Random Walks to Temporal Random Walks

Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, the majority of research in network representation learning has focused on static snapshots of the graph and has largely ignored the temporal dynamics of the network. In this work, we describe a general framework for incorporating temporal information into network embedding methods. The framework gives rise to methods for learning time-respecting embeddings from continuous-time dynamic networks. Overall, the experiments demonstrate the effectiveness of the proposed framework and dynamic network embedding approach as it achieves an average gain of 11.9% across all methods and graphs. The results indicate that modeling temporal dependencies in graphs is important for learning appropriate and meaningful network representations.
Wi-Fi evolution for future dense networks: Does sensing threshold adaptation help?

Emerging Wi-Fi technologies are expected to cope with large amounts of traffic in dense networks. Consequently, proposals for the future IEEE 802.11ax Wi-Fi amendment include sensing threshold and transmit power adaptation, in order to improve spatial reuse. However, it is not yet understood to which extent such adaptive approaches — and which variant — would achieve a better balance between spatial reuse and the level of interference, in order to improve the network performance. Moreover, it is not clear how legacy Wi-Fi devices would be affected by new-generation Wi-Fi implementing these adaptive design parameters. In this paper we present a thorough comparative study in ns-3 for four major proposed adaptation algorithms and we compare their performance against legacy non-adaptive Wi-Fi. Additionally, we consider mixed populations where both legacy non-adaptive and new-generation adaptive populations coexist. We assume a dense indoor residential deployment and different numbers of available channels in the 5 GHz band, relevant for future IEEE 802.11ax. Our results show that for the dense scenarios considered, the algorithms do not significantly improve the overall network performance compared to the legacy baseline, as they increase the throughput of some nodes, while decreasing the throughput of others. For mixed populations in dense deployments, adaptation algorithms that improve the performance of new-generation nodes degrade the performance of legacy nodes and vice versa. This suggests that to support Wi-Fi evolution for dense deployments and consistently increase the throughput throughout the network, more sophisticated algorithms are needed, e.g. considering combinations of input parameters in current variants.
Software visualization

The field of software visualization (SV) investigates approaches and techniques for static and dynamic graphical representations of algorithms, programs (code), and processed data. SV is concerned primarily with the analysis of programs and their development. The goal is to improve our understanding of inherently invisible and intangible software, particularly when dealing with large information spaces that characterize domains like software maintenance, reverse engineering, and collaborative development. The main challenge is to find effective mappings from different software aspects to graphical representations using visual metaphors. This paper provides an overview of the SV research, describes current research directions, and includes an extensive list of recommended readings.