nils-herrmann/clean_scirepeval huggingface dataset:
Photometric stereo for outdoor webcams

We present a photometric stereo technique that operates on time-lapse sequences captured by static outdoor webcams over the course of several months. Outdoor webcams produce a large set of uncontrolled images subject to varying lighting and weather conditions. We first automatically select a suitable subset of the captured frames for further processing, reducing the dataset size by several orders of magnitude. A camera calibration step is applied to recover the camera response function, the absolute camera orientation, and to compute the light directions for each image. Finally, we describe a new photometric stereo technique for non-Lambertian scenes and unknown light source intensities to recover normal maps and spatially varying materials of the scene.
Privacy, Trust and Identity in Pervasive Computing: A Review of Technical Challenges and Future Research Directions

Developments in pervasive computing introduced a new world of computing where networked processors embedded and distributed in everyday objects communicating with each other over wireless links. Computers in such environments work in the background while establishing connections among them dynamically and hence will be less visible and intrusive. Such a vision raises questions about how to manage issues like privacy, trust and identity in those environments. In this paper, we review the technical challenges that face pervasive computing environments in relation to each of these issues. We then present a number of security related considerations and use them as a basis for comparison between pervasive and traditional computing. We will argue that these considerations pose particular concerns and challenges to the design and implementation of pervasive environments which are different to those usually found in traditional computing environments. To address these concerns and challenges, further research is needed. We will present a number of directions and topics for possible future research with respect to each of the three issues.
Self-Adapting Index Compilation

Creating or modifying a primary index is a time-consuming process, as the index typically needs to be rebuilt from scratch. In this paper, we explore a more graceful “just-in-time” approach to index reorganization, where small changes are dynamically applied in the background. To enable this type of reorganization, we formalize a composable organizational grammar, expressive enough to capture instances of not only existing index structures, but arbitrary hybrids as well. We introduce an algebra of rewrite rules for such structures, and a framework for defining and optimizing policies for just-in-time rewriting. Our experimental analysis shows that the resulting index structure is flexible enough to adapt to a variety of performance goals, while also remaining competitive with existing structures like the C++ standard template library map.
Wearable Sensors and Systems

It is now more than 50 years since the time when clinical monitoring of individuals in the home and community settings was first envisioned. Until recently, technologies to enable such vision were lacking. However, wearable sensors and systems developed over the past decade have provided the tools to finally implement and deploy technology with the capabilities required by researchers in the field of patients' home monitoring. As discussed, potential applications of these technologies include the early diagnosis of diseases such as congestive heart failure, the prevention of chronic conditions such as diabetes, improved clinical management of neurodegenerative conditions such as Parkinson's disease, and the ability to promptly respond to emergency situations such as seizures in patients with epilepsy and cardiac arrest in subjects undergoing cardiovascular monitoring. Current research efforts are now focused on the development of more complex systems for home monitoring of individuals with a variety of preclinical and clinical conditions. Recent research on the clinical assessment of wearable technology promises to deliver methodologies that are expected to lead to clinical adoption within the next five to ten years. In particular, combining home robots and wearable technology is likely to be a key step toward achieving the goal of effectively monitoring patients in the home. These efforts to merge home robots and wearable technology are expected to enable a new generation of complex systems with the ability to monitor subjects' status, facilitate the administration of interventions, and provide an invaluable tool to respond to emergency situations.
Bucket elimination: A unifying framework for probabilistic inference

Probabilistic inference algorithms for find­ ing the most probable explanation, the max­ imum aposteriori hypothesis, and the maxi­ mum expected utility and for updating belief are reformulated as an elimination-type al­ gorithm called bucket elimination. This em­ phasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining condition­ ing and elimination within this framework. Bounds on complexity are given for all the al­ gorithms as a function of the problem's struc­ ture.
Design of a CMOS Comparator using 0 . 18 μ m Technology

In Analog to digital convertor design converter, high speed comparator influences the overall performance of Flash/Pipeline Analog to Digital Converter (ADC) directly. This paper presents the schematic design of a CMOS comparator with high speed, low noise and low power dissipation. A schematic design of this comparator is given with 0.18μm TSMC Technology and simulated in cadence environment. Simulation results are presented and it shows that this design can work under high speed clock frequency 100MHz. The design has a low offset voltage 280.7mv, low power dissipation 0.37 mw and low noise 6.21μV.
Webis: An Ensemble for Twitter Sentiment Detection

We reproduce four Twitter sentiment classification approaches that participated in previous SemEval editions with diverse feature sets. The reproduced approaches are combined in an ensemble, averaging the individual classifiers’ confidence scores for the three classes (positive, neutral, negative) and deciding sentiment polarity based on these averages. The experimental evaluation on SemEval data shows our re-implementations to slightly outperform their respective originals. Moreover, not too surprisingly, the ensemble of the reproduced approaches serves as a strong baseline in the current edition where it is top-ranked on the 2015 test set.
Generating Performance Distributions via Probabilistic Symbolic Execution

Analyzing performance and understanding the potential best-case, worst-case and distribution of program execution times are very important software engineering tasks. There have been model-based and program analysis-based approaches for performance analysis. Model-based approaches rely on analytical or design models derived from mathematical theories or software architecture abstraction, which are typically coarse-grained and could be imprecise. Program analysis-based approaches collect program profiles to identify performance bottlenecks, which often fail to capture the overall program performance. In this paper, we propose a performance analysis framework PerfPlotter. It takes the program source code and usage profile as inputs and generates a performance distribution that captures the input probability distribution over execution times for the program. It heuristically explores high-probability and low-probability paths through probabilistic symbolic execution. Once a path is explored, it generates and runs a set of test inputs to model the performance of the path. Finally, it constructs the performance distribution for the program. We have implemented PerfPlotter based on the Symbolic PathFinder infrastructure, and experimentally demonstrated that PerfPlotter could accurately capture the best-case, worst-case and distribution of program execution times. We also show that performance distributions can be applied to various important tasks such as performance understanding, bug validation, and algorithm selection.
A MODEL FOR STEADY-STATE , BALLISTIC CHARGE TRANSPORT THROUGH QUANTUM DOT LAYER SUPERLATTICES

We derive a model for one-dimensional charge-transport through two-terminal semiconducture heterostructure nano-devices comprising stacked layers of quantum dots with transverse Bravais lattice layout. All dots in a layer are assumed to be identical. The stacked layers are assumed to be perfectly vertically-aligned so that the entire device has a well-defined transverse unit-cell. We allow for an arbitrary sequence of quantum dot layers and intervening spacer and wetting layers between two heavily-doped, ohmic contacts. The model naturally accounts for longrange, inter-dot correlations. We rigorously prove that the device behaves as a diffraction grating which distributes incident wavefunction-phases into specific patterns of transmitted phases. This establishes several mathematical properties that allow efficient decomposition of the problem and permit feasible strategies for computational implementation. We motivate the study of this family of devices citing experimental developments.
Low back pain: prevalence and associated risk factors among hospital staff.

This paper is a report of a study conducted to describe the prevalence and risk factors for lower back pain amongst a variety of Turkish hospital workers including nurses, physicians, physical therapists, technicians, secretaries and hospital aides.



Hospital workers experience more low back pain than many other groups, the incidence varies among countries. Work activities involving bending, twisting, frequent heavy lifting, awkward static posture and psychological stress are regarded as causal factors for many back injuries.



A 44-item questionnaire was completed by 1600 employees in six hospitals associated with one Turkish university using a cross-sectional survey design. Data were collected over nine months from December 2005 to August 2006 and analysed using Chi square and multivariate logistic regression techniques.



Most respondents (65.8%) had experienced low back pain, with 61.3% reporting an occurrence within the last 12 months. The highest prevalence was reported by nurses (77.1%) and the lowest amongst secretaries (54.1%) and hospital aides (53.5%). In the majority of cases (78.3%), low back pain began after respondents started working in the hospital, 33.3% of respondents seeking medical care for 'moderate' low back pain while 53.8% (n = 143) had been diagnosed with a herniated lumbar disc. Age, female gender, smoking, occupation, perceived work stress and heavy lifting were statistically significant risk-factors when multivariate logistic regression techniques were conducted (P < 0.05).



Preventive measures should be taken to reduce the risk of lower back pain, such as arranging proper rest periods, educational programmes to teach the proper use of body mechanics and smoking cessation programmes.
Chayamaritia (Gesneriaceae: Didymocarpoideae), a new genus from Southeast Asia

Based on a phylogenetic analysis of Asian Gesneriaceae with the most comprehensive coverage at the genus level to date, the new genus Chayamaritia is established and described in subfamily Didymocarpoideae, tribe Trichosporeae, subtribe Didymocarpinae. It contains two species, of which one, Chayamaritia smitinandii (B.L.Burtt) D.J.Middleton, was formerly placed in the genera Chirita and Henckelia. The other, Chayamaritia banksiae D.J.Middleton, is newly described. The exclusion of Chayamaritia smitinandii from Henckelia further clarifies the taxonomic and biogeographic limits of Henckelia following its considerable recircumscription during the recent remodelling and synonymisation of Chirita.
LBP features for breast cancer detection

Cancer is nowadays considered as one of the most dangerous diseases in the world. Especially, breast cancer represents for women the second most common type of cancer and is a main cause of cancer dead. This paper presents a novel method for breast cancer detection from mammographic images based on Local Binary Patterns (LBP). This approach successfully uses LBP based features with a classifier and thresholding. The proposed method is evaluated on a set composed of images extracted from MIAS and DDSM databases. We have experimentally shown that the proposed method is efficient and effective because the achieved accuracy is about 84%.
Oxidative Stress as a Critical Factor in Nonalcoholic Fatty Liver Disease Pathogenesis.

Nonalcoholic fatty liver disease (NAFLD), characterized by liver triacylglycerol build-up, has been growing in the global world in concert with the raised prevalence of cardiometabolic disorders, including obesity, diabetes, and hyperlipemia. Redox imbalance has been suggested to be highly relevant to NAFLD pathogenesis.  As a major health problem, NAFLD progresses to the more severe nonalcoholic steatohepatitis (NASH) condition and predisposes susceptible individuals to liver and cardiovascular disease. Although NAFLD represents the predominant cause of chronic liver disorders, the mechanisms of its development and progression remain incompletely understood, even if various scientific groups ascribed them to the occurrence of insulin resistance, dyslipidemia, inflammation, and apoptosis. Nevertheless, oxidative stress (OxS) more and more appears as the most important pathological event during NAFLD development and the hallmark between simple steatosis and NASH manifestation.


CRITICAL ISSUES
The purpose of this article is to summarize recent developments in the understanding of NAFLD, essentially focusing on OxS as a major pathogenetic mechanism. Various attempts to translate reactive oxygen species (ROS) scavenging by antioxidants into experimental and clinical studies have yielded mostly encouraging results.



Although augmented concentrations of ROS and faulty antioxidant defense have been associated to NAFLD and related complications, mechanisms of action and proofs of principle should be highlighted to support the causative role of OxS and to translate its concept into the clinic.
Applying Kotter’s 8-Step Process for Leading Change to the Digital Transformation of an Orthopedic Surgical Practice Group in Toronto, Canada

This research involves the digital transformation of an orthopedic surgical practice office housing three community orthopedic surgeons and a physical therapy treatment clinic in Toronto, Ontario. All three surgeons engage in both a private community orthopaedic surgery practice and hold surgical privileges at a local community hospital which serves a catchment area of more than 850,000 people in the northwest Greater Toronto Area. The clinic employs two full time physical therapists and one office manager for therapy services as well as four administrative assistants who manage the surgeon’s practices.
A fresh look at tumor immunosurveillance and immunotherapy

Despite major advances in our understanding of adaptive immunity and dendritic cells, consistent and durable responses to cancer vaccines remain elusive and active immunotherapy is still not an established treatment modality. The key to developing an effective anti-tumor response is understanding why, initially, the immune system is unable to detect transformed cells and is subsequently tolerant of tumor growth and metastasis. Ineffective antigen presentation limits the adaptive immune response; however, we are now learning that the host's innate immune system may first fail to recognize the tumor as posing a danger. Recent descriptions of stress-induced ligands on tumor cells recognized by innate effector cells, new subsets of T cells that regulate tumor tolerance and the development of spontaneous tumors in mice that lack immune effector molecules, beckon a reflection on our current perspectives on the interaction of transformed cells with the immune system and offer new hope of stimulating therapeutic immunity to cancer.
The battle against phishing: Dynamic Security Skins

Phishing is a model problem for illustrating usability concerns of privacy and security because both system designers and attackers battle using user interfaces to guide (or misguide) users.We propose a new scheme, Dynamic Security Skins, that allows a remote web server to prove its identity in a way that is easy for a human user to verify and hard for an attacker to spoof. We describe the design of an extension to the Mozilla Firefox browser that implements this scheme.We present two novel interaction techniques to prevent spoofing. First, our browser extension provides a trusted window in the browser dedicated to username and password entry. We use a photographic image to create a trusted path between the user and this window to prevent spoofing of the window and of the text entry fields.Second, our scheme allows the remote server to generate a unique abstract image for each user and each transaction. This image creates a "skin" that automatically customizes the browser window or the user interface elements in the content of a remote web page. Our extension allows the user's browser to independently compute the image that it expects to receive from the server. To authenticate content from the server, the user can visually verify that the images match.We contrast our work with existing anti-phishing proposals. In contrast to other proposals, our scheme places a very low burden on the user in terms of effort, memory and time. To authenticate himself, the user has to recognize only one image and remember one low entropy password, no matter how many servers he wishes to interact with. To authenticate content from an authenticated server, the user only needs to perform one visual matching operation to compare two images. Furthermore, it places a high burden of effort on an attacker to spoof customized security indicators.
A Quantitative Evaluation Approach of Subjective Trust for E-Commerce

For E-commerce, such as online trade and interactions on the Internet are on the rise, a key issue is how to use simple and effective evaluation methods to accomplish trust decision-making for customers. It is well known subjective trust holds uncertainty like randomness and fuzziness. However, existing approaches commonly based on probability or fuzzy set theory cannot attach enough importance to uncertainty. To remedy this problem, a new quantificational subjective trust evaluation approach is proposed based on the cloud model. The subjective trust may be modeled with cloud model, and expected value and hyper-entropy of subjective cloud is used to evaluate the reputation of trust objects. Our experimental data shows that the method can effectively support subjective trust decision, which provides a helpful exploitation for the subjective trust evaluation.
A Web-Based Intelligent Tutoring System for Computer Programming

Web Intelligence is a direction for scientific research that explores practical applications of Artificial Intelligence to the next generation of Web-empowered systems. In this paper, we present a Web-based intelligent tutoring system for computer programming. The decision making process conducted in our intelligent system is guided by Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.
A Review : A Digital Image Steganography

Now a days, a lot of applications are Internet based and in some cases it is desired that the communication be made secret, digital communication has become an essential part of infrastructure. Information hiding has an important research field to resolve the problems in network security, quality of service control & secure communication through public & private channels. Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video file. Steganography has various useful applications. Steganography’s ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review of existing methods of steganography in digital images.
Effective Top-down Clustering Algorithms for Location Database Systems

Recent technological advances in mobile communication systems have made explosive growth in the number of mobile device users worldwide. One of the most important issues in designing a mobile computing system is location management of users. The hierarchical systems had been proposed to solve the scalability problem in the location management. The scalability problem occurs when there are too many users for a mobile system to handle thus, the system is likely to react slow or even get down due to the late updates of the location databases. In this paper, we first propose a top-down clustering algorithm for hierarchical location database systems in a wireless network. A hierarchical location database system employs a tree structure. The proposed algorithm uses a top-down approach and utilizes the number of visits to each cell made by the users along with the movement information between a pair of adjacent cells. We then present a modified algorithm by incorporating the exhaustive method when there remain a few levels of the tree to be processed. The experimental results show that the modified top-down algorithm reduces the average update cost by 19.0% and the update time by about 61.0% over the previous work.
Unpleasant and pleasant memories of intensive care in adult mechanically ventilated patients--findings from 250 interviews.

In order to improve the patients' comfort and well-being during and after a stay in the intensive care unit (ICU), the patients' perspective on the intensive care experience in terms of memories is essential. The aim of this study was to describe unpleasant and pleasant memories of the ICU stay in adult mechanically ventilated patients.



Mechanically ventilated adults admitted for more than 24hours from two Swedish general ICUs were included and interviewed 5 days after ICU discharge using two open-ended questions. The data were analysed exploring the manifest content.



Of the 250 patients interviewed, 81% remembered the ICU stay, 71% described unpleasant memories and 59% pleasant. Ten categories emerged from the content analyses (five from unpleasant and five from pleasant memories), contrasting with each other: physical distress and relief of physical distress, emotional distress and emotional well-being, perceptual distress and perceptual well-being, environmental distress and environmental comfort, and stress-inducing care and caring service.



Most critical care patients have both unpleasant and pleasant memories of their ICU stay. Pleasant memories such as support and caring service are important to relief the stress and may balance the impact of the distressing memories of the ICU stay.