nils-herrmann/clean_scirepeval huggingface dataset:
The content validity index: are you sure you know what's being reported? Critique and recommendations.

Scale developers often provide evidence of content validity by computing a content validity index (CVI), using ratings of item relevance by content experts. We analyzed how nurse researchers have defined and calculated the CVI, and found considerable consistency for item-level CVIs (I-CVIs). However, there are two alternative, but unacknowledged, methods of computing the scale-level index (S-CVI). One method requires universal agreement among experts, but a less conservative method averages the item-level CVIs. Using backward inference with a purposive sample of scale development studies, we found that both methods are being used by nurse researchers, although it was not always possible to infer the calculation method. The two approaches can lead to different values, making it risky to draw conclusions about content validity. Scale developers should indicate which method was used to provide readers with interpretable content validity information.
The language of mental health problems in social media

Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.
Virtual device passthrough for high speed VM networking

Supporting network I/O at high packet rates in virtual machines is fundamental for the deployment of Cloud data centers and Network Function Virtualization. Historically, SR-IOV and hardware passthrough were thought as the only viable solution to reduce the high cost of virtualization. In previous work we showed how even plain device emulation can achieve VM-to-VM speeds of millions of packets per second (Mpps), though still at least 3 times slower than bare metal. In this paper, to fill this gap, we present ptnetmap, a virtual passthrough network device (based on the netmap framework). ptnetmap allows VMs to connect to any netmap port (physical devices, software switches, netmap pipes), conserving the speed and isolation of the native netmap system, and removing the constraints of hardware pass through. Our work includes two key features not present in previous proposals: we provide a high speed path also to untrusted VMs, and do not require dedicated polling cores/threads, which is fundamental to achieve an efficient use of resources. Besides these features, our speed is also beyond previously published values. Running on top of ptnetmap, VMs can saturate a 10 Gbit link at 14.88 Mpps, talk at over 20 Mpps to untrusted VMs, and over 70 Mpps to trusted VMs.ptnetmap extends the netmap framework, and currently supports Linux and FreeBSD guests, and QEMU/KVM host. Support for bhyve/FreeBSDhost is under development.
FlowFence: Practical Data Protection for Emerging IoT Application Frameworks

Emerging IoT programming frameworks enable building apps that compute on sensitive data produced by smart homes and wearables. However, these frameworks only support permission-based access control on sensitive data, which is ineffective at controlling how apps use data once they gain access. To address this limitation, we present FlowFence, a system that requires consumers of sensitive data to declare their intended data flow patterns, which it enforces with low overhead, while blocking all other undeclared flows. FlowFence achieves this by explicitly embedding data flows and the related control flows within app structure. Developers use FlowFence support to split their apps into two components: (1) A set of Quarantined Modules that operate on sensitive data in sandboxes, and (2) Code that does not operate on sensitive data but orchestrates execution by chaining Quarantined Modules together via taint-tracked opaque handles—references to data that can only be dereferenced inside sandboxes. We studied three existing IoT frameworks to derive key functionality goals for FlowFence, and we then ported three existing IoT apps. Securing these apps using FlowFence resulted in an average increase in size from 232 lines to 332 lines of source code. Performance results on ported apps indicate that FlowFence is practical: A face-recognition based doorcontroller app incurred a 4.9% latency overhead to recognize a face and unlock a door.
Weather classification with deep convolutional neural networks

In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.
Causes and mechanisms of common coccydynia: role of body mass index and coccygeal trauma.

A total of 208 consecutive coccydynia patients were examined with the same clinical and radiologic protocol.



To study radiographic coccygeal lesions in the sitting position, to elucidate the influence of body mass index on the different lesions, and to establish the effect of coccygeal trauma.



A protocol comparing standing radiographs and radiographs subsequently taken in the painful sitting position in coccydynia patients and in controls has shown two culprit lesions: posterior luxation and hypermobility. Obesity and a history of trauma have been identified as risk factors for luxation.



Dynamic radiographs were obtained. The body mass index was compared with the coccygeal angle of incidence, sagittal rotation of the pelvis when sitting down, and the presence and time of previous trauma. The patients with the newly described lesions were examined after an anesthetic block under fluoroscopic guidance.



Two new coccygeal lesions are described (anterior luxation and spicules). Obesity was found to be a risk factor. The body mass index determines the way a subject sits down, and lesion patterns were different in obese, normal-weight, and thin patients (posterior luxation: 51%, 15.2%, 3.7%; hypermobility: 26.5%, 30.3%, 14.8%; spicules: 2%, 15.9%, 29.6%; normal: 16.3%, 32.6%, 48.1%, respectively; P < 0.0001). Trauma affected the type of lesion only if it was recent (<1 month before the onset of coccydynia), in which case the instability rate increased from 55.6% to 77.1%. Backward-moving coccyges were at greatest risk of trauma.



This protocol allows identification of the culprit lesion in 69.2% of cases. The body mass index determines the causative lesion, as does trauma sustained within the month preceding the onset of the pain.
Loading of the knee joint during activities of daily living measured in vivo in five subjects.

Detailed knowledge about loading of the knee joint is essential for preclinical testing of implants, validation of musculoskeletal models and biomechanical understanding of the knee joint. The contact forces and moments acting on the tibial component were therefore measured in 5 subjects in vivo by an instrumented knee implant during various activities of daily living. Average peak resultant forces, in percent of body weight, were highest during stair descending (346% BW), followed by stair ascending (316% BW), level walking (261% BW), one legged stance (259% BW), knee bending (253% BW), standing up (246% BW), sitting down (225% BW) and two legged stance (107% BW). Peak shear forces were about 10-20 times smaller than the axial force. Resultant forces acted almost vertically on the tibial plateau even during high flexion. Highest moments acted in the frontal plane with a typical peak to peak range -2.91% BWm (adduction moment) to 1.61% BWm (abduction moment) throughout all activities. Peak flexion/extension moments ranged between -0.44% BWm (extension moment) and 3.16% BWm (flexion moment). Peak external/internal torques lay between -1.1% BWm (internal torque) and 0.53% BWm (external torque). The knee joint is highly loaded during daily life. In general, resultant contact forces during dynamic activities were lower than the ones predicted by many mathematical models, but lay in a similar range as measured in vivo by others. Some of the observed load components were much higher than those currently applied when testing knee implants.
Model-Based IT Governance Maturity Assessments with Cobit

IT governance is an executive level concern in many enterprises today, but a method for credible, reliable, and cost-efficient IT governance maturity assessment has been lacking. Control Objectives for Information and related Technology (Cobit) is best practice in the area, but the method requires an experienced analyst to perform the assessment and the provided analysis framework is vague and ambiguous. This paper presents a Cobit based method designed to overcome these featured problems. It comprises a modeling language for IT governance based on Cobit, and a transparent analysis framework which enables aggregation of single metrics into comprehensive maturity scores. The applicability was tested in a small case study. Results demonstrate that the method can be used to conduct time-efficient, valid and reliable IT governance maturity assessments without the help of an experienced analyst.
VCDB: A Large-Scale Database for Partial Copy Detection in Videos

The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large dataset. Since collecting and annotating large datasets of real partial copies are extremely time-consuming, previous video copy detection research used either small-scale datasets or large datasets with simulated partial copies by imposing several pre-defined transformations (e.g., photometric or geometric changes). While the simulated datasets were useful for research, it is unknown how well the techniques developed on such data work on real copies, which are often too complex to be simulated. In this paper, we introduce a large-scale video copy database (VCDB) with over 100,000 Web videos, containing more than 9,000 copied segment pairs found through careful manual annotation. We further benchmark a baseline system on VCDB, which has demonstrated state-of-the-art results in recent copy detection research. Our evaluation suggests that existing techniques—which have shown near-perfect results on the simulated benchmarks—are far from satisfactory in detecting complex real copies. We believe that the release of VCDB will largely advance the research around this challenging problem.
A Study of Genetic Algorithms to Solve the School Timetabling Problem

This paper examines the use of genetic algorithms (GAs) to solve the school timetabling problem. The school timetabling problem falls into the category of NP-hard problems. Instances of this problem vary drastically from school to school and country to country. Previous work in this area has used genetic algorithms to solve a particular school timetabling problem and has not evaluated the performance of a GA on different problems. Furthermore, GAs have not previously been applied to solving the South African primary or high school timetabling problem. The paper presents a two-phased genetic algorithm approach to solving the school timetabling problem and provides an analysis of the effect of different low-level construction heuristics, selection methods and genetic operators on the success of the GA approach in solving these problems with respect to feasibility and timetable quality. The GA approach is tested on a benchmark set of “hard” school timetabling problems, the Greek high school timetabling problem and a South African primary and high school timetabling problem. The performance of the GA approach was found to be comparable to other methods applied to the same problems. This study has also revealed that different combinations of low-level construction heuristics, selection methods and genetic operators are needed to produce feasible timetables of good quality for the different school timetabling problems. Future work will investigate methods for the automatic configuration of GA architectures of both phases.
LONGITUDINAL MULTIPLE SCLEROSIS LESION SEGMENTATION USING 3 D CONVOLUTIONAL NEURAL NETWORKS

We present our entry for the Longitudinal Multiple Sclerosis Challenge 2015 using 3D convolutional neural networks (CNN). We model a voxel-wise classifier using multi-channel 3D patches of MRI volumes as input. For each ground truth, a CNN is trained and the final segmentation is obtained by combining the probability outputs of these CNNs. Efficient training is achieved by using sub-sampling methods and sparse convolutions. We obtain accurate results with dice scores comparable to the inter-rater variability.
Review spotlight: a user interface for summarizing user-generated reviews using adjective-noun word pairs

Many people read online reviews written by other users to learn more about a product or venue. However, the overwhelming amount of user-generated reviews and variance in length, detail and quality across the reviews make it difficult to glean useful information. In this paper, we present the iterative design of our system, called Review Spotlight. It provides a brief overview of reviews using adjective-noun word pairs, and allows the user to quickly explore the reviews in greater detail. Through a laboratory user study which required participants to perform decision making tasks, we showed that participants could form detailed impressions about restaurants and decide between two options significantly faster with Review Spotlight than with traditional review webpages.
Spatial as Deep: Spatial CNN for Traffic Scene Understanding

Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.
Brain development during childhood and adolescence: a longitudinal MRI study

Pediatric neuroimaging studies, up to now exclusively cross sectional, identify linear decreases in cortical gray matter and increases in white matter across ages 4 to 20. In this large-scale longitudinal pediatric neuroimaging study, we confirmed linear increases in white matter, but demonstrated nonlinear changes in cortical gray matter, with a preadolescent increase followed by a postadolescent decrease. These changes in cortical gray matter were regionally specific, with developmental curves for the frontal and parietal lobe peaking at about age 12 and for the temporal lobe at about age 16, whereas cortical gray matter continued to increase in the occipital lobe through age 20.
A General Divide and Conquer Approach for Process Mining

Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESEor passage-based decomposition). Moreover, the generic divide-andconquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligences. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrates results toward this goal: developing a machine's ability to recognize human aaective state given four physiological signals. We describe diicult issues unique to obtaining reliable aaective data, and collect a large set of data from a subject trying to elicit and experience each of eight emotional states, daily, over multiple weeks. This paper presents and compares multiple algorithms for feature-based recognition of emotional state from this data. We analyze four physiological signals that exhibit problematic day-today variations: the features of diierent emotions on the same day tend to cluster more tightly than do the features of the same emotion on diierent days. To handle the daily variations, we propose new features and algorithms and compare their performance. We nd that the technique of seeding a Fisher Projection with the results of Sequential Floating Forward Search improves the performance of the Fisher Projection, and provides the highest recognition rates reported to date for classiication of aaect from physiology: 81% recognition accuracy on eight classes of emotion, including neutral.
e-Learning, Online Learning, and Distance Learning Environments: Are They the Same?

a r t i c l e i n f o It is not uncommon that researchers face difficulties when performing meaningful cross-study comparisons for research. Research associated with the distance learning realm can be even more difficult to use as there are different environments with a variety of characteristics. We implemented a mixed-method analysis of research articles to find out how they define the learning environment. In addition, we surveyed 43 persons and discovered that there was inconsistent use of terminology for different types of delivery modes. The results reveal that there are different expectations and perceptions of learning environment labels: distance learning, e-Learning, and online learning. Distance education has a history that spans almost two centuries (Spector, Merrill, Merrienboer, & Driscoll, 2008), and this time period represents significant changes in how learning occurs and is communicated. From basic correspondence through postal service to the wide variety of tools available through the Internet, society has embraced new forms of communication through the years. One such form, online learning, is known to have a history of access beginning in the 1980's whereas another term, referred to as e-Learning, does not have its origins fully disclosed (Harasim, 2000). As researchers and designers utilized these emerging technologies, we find that a relaxed use of the terminology makes it difficult to design and evaluate similar learning environments without understanding the specific characteristics (Phipps & Merisotis, 1999). The design of different types of learning environments can depend on the learning objective, target audience, access (physical, virtual and/or both), and type of content. It is important to know how the learning environment is used, and the influences of the tools and techniques that distinguish the differences in learning outcomes as the technology evolves. 2. Literature definitions As learning technology and its associated fields continue to evolve, practitioners and researchers have yet to agree on common definitions and terminologies (Lowenthal & Wilson, 2010; Volery & Lord, 2000). As a result, it is difficult for researchers to perform meaningful cross-study comparisons and build on the outcomes from the previous studies. This contributes to conflicting findings about distance learning, e-Learning, and online learning environments. In addition, terms are often interchanged without meaningful definitions. As an initial step, we reviewed the relevant literature to determine how these learning environments were defined. Distance education is the most renowned descriptor used when referencing distance learning. It often describes the effort of …
Neuroplasticity in the context of motor rehabilitation after stroke

Approximately one-third of patients with stroke exhibit persistent disability after the initial cerebrovascular episode, with motor impairments accounting for most poststroke disability. Exercise and training have long been used to restore motor function after stroke. Better training strategies and therapies to enhance the effects of these rehabilitative protocols are currently being developed for poststroke disability. The advancement of our understanding of the neuroplastic changes associated with poststroke motor impairment and the innate mechanisms of repair is crucial to this endeavor. Pharmaceutical, biological and electrophysiological treatments that augment neuroplasticity are being explored to further extend the boundaries of poststroke rehabilitation. Potential motor rehabilitation therapies, such as stem cell therapy, exogenous tissue engineering and brain–computer interface technologies, could be integral in helping patients with stroke regain motor control. As the methods for providing motor rehabilitation change, the primary goals of poststroke rehabilitation will be driven by the activity and quality of life needs of individual patients. This Review aims to provide a focused overview of neuroplasticity associated with poststroke motor impairment, and the latest experimental interventions being developed to manipulate neuroplasticity to enhance motor rehabilitation.