nils-herrmann/clean_scirepeval huggingface dataset:
vercoming overconfidence in learning from video-recorded lectures : mplications of interpolated testing for online education arl

The video-recorded lecture represents a central feature of most online learning platforms. Nonetheless, little is known about how to best structure video-recorded lectures in order to optimize learning. Here, we focused on the tendency for high school and college students to be overconfident in their learning from video-recorded modules, and demonstrated that testing could be used to effectively improve the calibration between predicted and actual performance. Notably, interpolating a lecture with repeated
Discovering Models of Software Processes from Event-Based Data

Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be difficult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are first captured from an on-going process and then used to generate a formal model of the behavior of that process. In this article we describe a Markov method that we developed specifically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study.
Structural inpainting

Scene-agnostic visual inpainting remains very challenging despite progress in patch-based methods. Recently, Pathak et al. [26] have introduced convolutional "context encoders'' (CEs) for unsupervised feature learning through image completion tasks. With the additional help of adversarial training, CEs turned out to be a promising tool to complete complex structures in real inpainting problems. In the present paper we propose to push further this key ability by relying on perceptual reconstruction losses at training time. We show on a wide variety of visual scenes the merit of the approach forstructural inpainting, and confirm it through a user study. Combined with the optimization-based refinement of [32] with neural patches, our context encoder opens up new opportunities for prior-free visual inpainting.
1 Governance Structures , Voluntary Disclosures and Public Accountability : The Case of UK Higher Education Institutions

We investigate the extent of voluntary disclosures in UK higher education institutions’ (HEIs) annual reports and examine whether internal governance structures influence disclosure in the period following major reform and funding constraints.  We adopt a modified version of Coy and Dixon’s (2004) public accountability index, referred to in this paper as a public accountability and transparency index (PATI), to measure the extent of voluntary disclosures in 130 UK HEIs’ annual reports. Informed by a multitheoretical framework drawn from public accountability, legitimacy, resource dependence and stakeholder perspectives, we propose that the characteristics of governing and executive structures in UK universities influence the extent of their voluntary disclosures.  We find a large degree of variability in the level of voluntary disclosures by universities and an overall relatively low level of PATI (44%), particularly with regards to the disclosure of teaching/research outcomes. We also find that audit committee quality, governing board diversity, governor independence, and the presence of a governance committee are associated with the level of disclosure. Finally, we find that the interaction between executive team characteristics and governance variables enhances the level of voluntary disclosures, thereby providing support for the continued relevance of a ‘shared’ leadership in the HEIs’ sector towards enhancing accountability and transparency in HEIs.  In spite of significant funding cuts, regulatory reforms and competitive challenges, the level of voluntary disclosure by UK HEIs remains low. Whilst the role of selected governance mechanisms and ‘shared leadership’ in improving disclosure, is asserted, the varying level and selective basis of the disclosures across the surveyed HEIs suggest that the public accountability motive is weaker relative to the other motives underpinned by stakeholder, legitimacy and resource dependence perspectives.  This is the first study which explores the association between HEI governance structures, managerial characteristics and the level of disclosure in UK HEIs.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation

Given two consecutive frames, video interpolation aims at generating intermediate frame(s) to form both spatially and temporally coherent video sequences. While most existing methods focus on single-frame interpolation, we propose an end-to-end convolutional neural network for variable-length multi-frame video interpolation, where the motion interpretation and occlusion reasoning are jointly modeled. We start by computing bi-directional optical flow between the input images using a U-Net architecture. These flows are then linearly combined at each time step to approximate the intermediate bi-directional optical flows. These approximate flows, however, only work well in locally smooth regions and produce artifacts around motion boundaries. To address this shortcoming, we employ another U-Net to refine the approximated flow and also predict soft visibility maps. Finally, the two input images are warped and linearly fused to form each intermediate frame. By applying the visibility maps to the warped images before fusion, we exclude the contribution of occluded pixels to the interpolated intermediate frame to avoid artifacts. Since none of our learned network parameters are time-dependent, our approach is able to produce as many intermediate frames as needed. To train our network, we use 1,132 240-fps video clips, containing 300K individual video frames. Experimental results on several datasets, predicting different numbers of interpolated frames, demonstrate that our approach performs consistently better than existing methods.
Social Welfare Program Administration and Evaluation and Policy Analysis Using Knowledge Discovery and Data Mining (KDD) on Administrative Data

New technology in knowledge discovery and data mining (KDD) make it possible to extract valuable information from operational data. Private businesses already use the technology for better management, planning, and marketing. Social welfare government agencies have a wealth of information about the experiences of families and individuals that are the most needy in our society in their administrative databases. These data too can be mined and analyzed with proper application of KDD technology. Such social science research could be priceless for better welfare program administration, program evaluation, and policy analysis. In this paper, we discuss a successful case study involving research in computer science as well as social welfare. In a long standing collaboration between the North Carolina DHHS and the University of North Carolina, we have (1) successfully built a longitudinal information system that tracks the experiences of families and individuals on welfare in NC since 1995 (2) developed a dynamic website reporting on the various aspects of the welfare program at the county level in order to assist county staff in the administration of the welfare program and (3) developed a new method to analyze sequential data, which can detect common patterns of welfare services given over time.
"Sandwich" bone augmentation technique: rationale and report of pilot cases.

The aim of this article is to present a new technique for augmentation of deficient alveolar ridges and/or correction of osseous defects around dental implants. Current knowledge regarding bone augmentation for treatment of osseous defects prior to and in combination with dental implant placement is critically appraised. The "sandwich" bone augmentation technique is demonstrated step by step. Five pilot cases with implant dehiscence defects averaging 10.5 mm were treated with the technique. At 6 months, the sites were uncovered, and complete defect fill was noted in all cases. Results from this pilot case study indicated that the sandwich bone augmentation technique appears to enhance the outcomes of bone augmentation by using the positive properties of each applied material (autograft, DFDBA, hydroxyapatite, and collagen membrane). Future clinical trials for comparison of this approach with other bone augmentation techniques and histologic evaluation of the outcomes are needed to validate these findings.
Multidimensional Data Modeling for Complex Data

Systems for On-Line Analytical Processing (OLAP) consider ably ease the process of analyzing business data and have become widely used in industry. OLAP syste ms primarily employ multidimensional data models to structure their data. However, current multi dimensional data models fall short in their ability to model the complex data found in some real-world ap plication domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real-world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and d imensions, built-in support for handling change and time, and support for uncertainty as well as diffe rent levels of granularity in the data. The paper defines an extended multidimensional data model, whic h addresses all nine requirements. Along with the model, we present an associated algebra, and outlin e how to implement the model using relational databases.
The H.264/AVC Advanced Video Coding standard: overview and introduction to the fidelity range extensions

H.264/MPEG-4 AVC is the latest international video coding standard. It was jointly developed by the Video Coding Experts Group (VCEG) of the ITU-T and the Moving Picture Experts Group (MPEG) of ISO/IEC. It uses state-of-the-art coding tools and provides enhanced coding efficiency for a wide range of applications, including video telephony, video conferencing, TV, storage (DVD and/or hard disk based, especially high-definition DVD), streaming video, digital video authoring, digital cinema, and many others. The work on a new set of extensions to this standard has recently been completed. These extensions, known as the Fidelity Range Extensions (FRExt), provide a number of enhanced capabilities relative to the base specification as approved in the Spring of 2003. In this paper, an overview of this standard is provided, including the highlights of the capabilities of the new FRExt features. Some comparisons with the existing MPEG-2 and MPEG-4 Part 2 standards are also provided.
The effect of cilia and the mucociliary clearance on successful drug delivery.

Nasal mucociliary clearance is one of the most important factors affecting nasal delivery of drugs and vaccines. This is also the most important physiological defense mechanism inside the nasal cavity. It removes inhaled (and delivered) particles, microbes and substances trapped in the mucus. Almost all inhaled particles are trapped in the mucus carpet and transported with a rate of 8-10 mm/h toward the pharynx. This transport is conducted by the ciliated cells, which contain about 100-250 motile cellular appendages called cilia, 0.3 µm wide and 5 µm in length that beat about 1000 times every minute or 12-15 Hz. For efficient mucociliary clearance, the interaction between the cilia and the nasal mucus needs to be well structured, where the mucus layer is a tri-layer: an upper gel layer that floats on the lower, more aqueous solution, called the periciliary liquid layer and a third layer of surfactants between these two main layers. Pharmacokinetic calculations of the mucociliary clearance show that this mechanism may account for a substantial difference in bioavailability following nasal delivery. If the formulation irritates the nasal mucosa, this mechanism will cause the irritant to be rapidly diluted, followed by increased clearance, and swallowed. The result is a much shorter duration inside the nasal cavity and therefore less nasal bioavailability.
Comprehensive control of human papillomavirus infections and related diseases.

Infection with human papillomavirus (HPV) is recognized as one of the major causes of infection-related cancer worldwide, as well as the causal factor in other diseases. Strong evidence for a causal etiology with HPV has been stated by the International Agency for Research on Cancer for cancers of the cervix uteri, penis, vulva, vagina, anus and oropharynx (including base of the tongue and tonsils). Of the estimated 12.7 million new cancers occurring in 2008 worldwide, 4.8% were attributable to HPV infection, with substantially higher incidence and mortality rates seen in developing versus developed countries. In recent years, we have gained tremendous knowledge about HPVs and their interactions with host cells, tissues and the immune system; have validated and implemented strategies for safe and efficacious prophylactic vaccination against HPV infections; have developed increasingly sensitive and specific molecular diagnostic tools for HPV detection for use in cervical cancer screening; and have substantially increased global awareness of HPV and its many associated diseases in women, men, and children. While these achievements exemplify the success of biomedical research in generating important public health interventions, they also generate new and daunting challenges: costs of HPV prevention and medical care, the implementation of what is technically possible, socio-political resistance to prevention opportunities, and the very wide ranges of national economic capabilities and health care systems. Gains and challenges faced in the quest for comprehensive control of HPV infection and HPV-related cancers and other disease are summarized in this review. The information presented may be viewed in terms of a reframed paradigm of prevention of cervical cancer and other HPV-related diseases that will include strategic combinations of at least four major components: 1) routine introduction of HPV vaccines to women in all countries, 2) extension and simplification of existing screening programs using HPV-based technology, 3) extension of adapted screening programs to developing populations, and 4) consideration of the broader spectrum of cancers and other diseases preventable by HPV vaccination in women, as well as in men. Despite the huge advances already achieved, there must be ongoing efforts including international advocacy to achieve widespread-optimally universal-implementation of HPV prevention strategies in both developed and developing countries. This article summarizes information from the chapters presented in a special ICO Monograph 'Comprehensive Control of HPV Infections and Related Diseases' Vaccine Volume 30,  2012. Additional details on each subtopic and full information regarding the supporting literature references may be found in the original chapters.
A typology of reviews: an analysis of 14 review types and associated methodologies.

The expansion of evidence-based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains.



Following scoping searches, an examination was made of the vocabulary associated with the literature of review and synthesis (literary warrant). A simple analytical framework -- Search, AppraisaL, Synthesis and Analysis (SALSA) -- was used to examine the main review types.



Fourteen review types and associated methodologies were analysed against the SALSA framework, illustrating the inputs and processes of each review type. A description of the key characteristics is given, together with perceived strengths and weaknesses. A limited number of review types are currently utilized within the health information domain.



Few review types possess prescribed and explicit methodologies and many fall short of being mutually exclusive. Notwithstanding such limitations, this typology provides a valuable reference point for those commissioning, conducting, supporting or interpreting reviews, both within health information and the wider health care domain.
Bipedal Walking Robot using Deep Deterministic Policy Gradient

Machine learning algorithms have found several applications in the field of robotics and control systems. The control systems community has started to show interest towards several machine learning algorithms from the sub-domains such as supervised learning, imitation learning and reinforcement learning to achieve autonomous control and intelligent decision making. Amongst many complex control problems, stable bipedal walking has been the most challenging problem. In this paper, we present an architecture to design and simulate a planar bipedal walking robot(BWR) using a realistic robotics simulator, Gazebo. The robot demonstrates successful walking behaviour by learning through several of its trial and errors, without any prior knowledge of itself or the world dynamics. The autonomous walking of the BWR is achieved using reinforcement learning algorithm called Deep Deterministic Policy Gradient(DDPG). DDPG is one of the algorithms for learning controls in continuous action spaces. After training the model in simulation, it was observed that, with a proper shaped reward function, the robot achieved faster walking or even rendered a running gait with an average speed of 0.83 m/s. The gait pattern of the bipedal walker was compared with the actual human walking pattern. The results show that the bipedal walking pattern had similar characteristics to that of a human walking pattern.
Battery management system in the Bayesian paradigm: Part I: SOC estimation

Accurate State-of-Charge (SOC) estimation of Li-ion batteries is essential for effective battery control and energy management of electric and hybrid electric vehicles. To this end, first, the battery is modelled by an OCV-R-RC equivalent circuit. Then, a dual Bayesian estimation scheme is developed-The battery model parameters are identified online and fed to the SOC estimator, the output of which is then fed back to the parameter identifier. Both parameter identification and SOC estimation are treated in a Bayesian framework. The square-root recursive least-squares estimator and the extended Kalman-Bucy filter are systematically paired up for the first time in the battery management literature to tackle the SOC estimation problem. The proposed method is finally compared with the convectional Coulomb counting method. The results indicate that the proposed method significantly outperforms the Coulomb counting method in terms of accuracy and robustness.
No easy compromise: sustainability and the dilemmas and dynamics of change

Sustainable HCI grapples with how to use technology design to make social change. This is made difficult by recurring dilemmas about how to truly make change, like how to increase the scale and duration of design impact. In this essay, we reflect on our own journey to better understand design for social change by taking inspiration from two groups that have long engaged in making change towards sustainability -- simple living and organic farm families. We describe 5 key dilemmas that both the families and HCI designers struggle with and reflect on how we can learn from families' practices to negotiate these dilemmas. We contribute a deepened understanding of the dilemmas of and opportunities for making change for sustainable HCI.
SysML-based and Prolog-supported FMEA

Failure Mode and Effects Analysis (FMEA) is a well-known technique for evaluating the effects of potential failure modes of components of a system. It is a crucial reliability and safety engineering activity for critical systems requiring systematic inductive reasoning from postulated component failures. We present an approach based on SysML and Prolog to support the tasks of an FMEA analyst. SysML block diagrams of the system under analysis are annotated with valid and error states of components and of their input flows, as well as with the logical conditions that may determine erroneous outputs. From the annotated model, a Prolog knowledge base is automatically built, transparently to the analyst. This can then be queried, e.g., to obtain the flows' and blocks' states that lead to system failures, or to trace the propagation of faults. The approach is suited for integration in modern model-driven system design processes. We describe a proof-of-concept implementation based on the Papyrus modeling tool under Eclipse, and show a demo example.
A DCM for resting state fMRI

This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity--as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.
Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts

This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76% and the accuracy of 95.30% for character identification.
Decrypting SSL/TLS traffic for hidden threats detection

The paper presents an analysis of the main mechanisms of decryption of SSL/TLS traffic. Methods and technologies for detecting malicious activity in encrypted traffic that are used by leading companies are also considered. Also, the approach for intercepting and decrypting traffic transmitted over SSL/TLS is developed, tested and proposed. The developed approach has been automated and can be used for remote listening of the network, which will allow to decrypt transmitted data in a mode close to real time.