nils-herrmann/clean_scirepeval huggingface dataset:
Physical Description of Quasi-Saturation and Impact-Ionization Effects in High-Voltage Drain-Extended MOSFETs

This paper presents a physical description of two specific aspects in drain-extended MOS transistors, i.e., quasi-saturation and impact-ionization effects. The 2-D device simulator Medici provides the physical insights, and both the unique features are originally attributed to the Kirk effect. The transistor dc model is derived from regional analysis of carrier transport in the intrinsic MOS and the drift region. The substrate-current equations, considering extra impact-ionization factors in the drift region, are also rigorously derived. The proposed model is primarily validated by MATLAB program and exhibits excellent scalability for various transistor dimensions, drift-region doping concentration, and voltage-handling capability.
Predicting Age Range of Users over Microblog Dataset

In this paper, we present the idea and methodologies on predicting the age span of users over microblog dataset. Given a user’s personal information such as user tags, job, education, self-description, and gender, as well as the content of his/her microblogs, we automatically classify the user’s age into one of four predefined ranges. Particularly, we extract a set of features from the given information about the user, and employ a statistic-based framework to solve this problem. The measurement shows that our proposed method incorporating selected features has an accuracy of around 71% on average over the training dataset.
Biochemistry and molecular cell biology of diabetic complications

Diabetes-specific microvascular disease is a leading cause of blindness, renal failure and nerve damage, and diabetes-accelerated atherosclerosis leads to increased risk of myocardial infarction, stroke and limb amputation. Four main molecular mechanisms have been implicated in glucose-mediated vascular damage. All seem to reflect a single hyperglycaemia-induced process of overproduction of superoxide by the mitochondrial electron-transport chain. This integrating paradigm provides a new conceptual framework for future research and drug discovery.
Review and Evaluation of Mindfulness-Based iPhone Apps

There is growing evidence for the positive impact of mindfulness on wellbeing. Mindfulness-based mobile apps may have potential as an alternative delivery medium for training. While there are hundreds of such apps, there is little information on their quality.



This study aimed to conduct a systematic review of mindfulness-based iPhone mobile apps and to evaluate their quality using a recently-developed expert rating scale, the Mobile Application Rating Scale (MARS). It also aimed to describe features of selected high-quality mindfulness apps.



A search for "mindfulness" was conducted in iTunes and Google Apps Marketplace. Apps that provided mindfulness training and education were included. Those containing only reminders, timers or guided meditation tracks were excluded. An expert rater reviewed and rated app quality using the MARS engagement, functionality, visual aesthetics, information quality and subjective quality subscales. A second rater provided MARS ratings on 30% of the apps for inter-rater reliability purposes.



The "mindfulness" search identified 700 apps. However, 94 were duplicates, 6 were not accessible and 40 were not in English. Of the remaining 560, 23 apps met inclusion criteria and were reviewed. The median MARS score was 3.2 (out of 5.0), which exceeded the minimum acceptable score (3.0). The Headspace app had the highest average score (4.0), followed by Smiling Mind (3.7), iMindfulness (3.5) and Mindfulness Daily (3.5). There was a high level of inter-rater reliability between the two MARS raters.



Though many apps claim to be mindfulness-related, most were guided meditation apps, timers, or reminders. Very few had high ratings on the MARS subscales of visual aesthetics, engagement, functionality or information quality. Little evidence is available on the efficacy of the apps in developing mindfulness.
Challenges, Designs, and Performances of Large-Scale Open-P2SP Content Distribution

Content distribution on today's Internet operates primarily in two modes: server-based and peer-to-peer (P2P). To leverage the advantages of both modes while circumventing their key limitations, a third mode: peer-to-server/peer (P2SP) has emerged in recent years. Although P2SP can provide efficient hybrid server-P2P content distribution, P2SP generally works in a closed manner by only utilizing its private owned servers to accelerate its private organized peer swarms. Consequently, P2SP still has its limitations in both content abundance and server bandwidth. To this end, the fourth mode (or says a generalized mode of P2SP) has appeared as "open-P2SP" that integrates various third-party servers, contents, and data transfer protocols all over the Internet into a large, open, and federated P2SP platform. In this paper, based on a large-scale commercial open-P2SP system named "QQXuanfeng" , we investigate the key challenging problems, practical designs and real-world performances of open-P2SP. Such "white-box" study of open-P2SP provides solid experiences and helpful heuristics to the designers of similar systems.
Essential oil of the leaves of Eugenia uniflora L.: antioxidant and antimicrobial properties.

Essential oil (EO) of the leaves of Eugenia uniflora L. (Brazilian cherry tree) was evaluated for its antioxidant, antibacterial and antifungal properties. The acute toxicity of the EO administered by oral route was also evaluated in mice. The EO exhibited antioxidant activity in the DPPH, ABTS and FRAP assays and reduced lipid peroxidation in the kidney of mice. The EO also showed antimicrobial activity against two important pathogenic bacteria, Staphylococcus aureus and Listeria monocytogenes, and against two fungi of the Candida species, C. lipolytica and C. guilliermondii. Acute administration of the EO by the oral route did not cause lethality or toxicological effects in mice. These findings suggest that the EO of the leaves of E. uniflora may have the potential for use in the pharmaceutical industry.
A Genome-Wide Analysis of Small Regulatory RNAs in the Human Pathogen Group A Streptococcus

The coordinated regulation of gene expression is essential for pathogens to infect and cause disease. A recently appreciated mechanism of regulation is that afforded by small regulatory RNA (sRNA) molecules. Here, we set out to assess the prevalence of sRNAs in the human bacterial pathogen group A Streptococcus (GAS). Genome-wide identification of candidate GAS sRNAs was performed through a tiling Affymetrix microarray approach and identified 40 candidate sRNAs within the M1T1 GAS strain MGAS2221. Together with a previous bioinformatic approach this brings the number of novel candidate sRNAs in GAS to 75, a number that approximates the number of GAS transcription factors. Transcripts were confirmed by Northern blot analysis for 16 of 32 candidate sRNAs tested, and the abundance of several of these sRNAs were shown to be temporally regulated. Six sRNAs were selected for further study and the promoter, transcriptional start site, and Rho-independent terminator identified for each. Significant variation was observed between the six sRNAs with respect to their stability during growth, and with respect to their inter- and/or intra-serotype-specific levels of abundance. To start to assess the contribution of sRNAs to gene regulation in M1T1 GAS we deleted the previously described sRNA PEL from four clinical isolates. Data from genome-wide expression microarray, quantitative RT-PCR, and Western blot analyses are consistent with PEL having no regulatory function in M1T1 GAS. The finding that candidate sRNA molecules are prevalent throughout the GAS genome provides significant impetus to the study of this fundamental gene-regulatory mechanism in an important human pathogen.
User Modeling, Adaptation, and Personalization

Interactive technologies pervade every aspect of modern life. Web sites, mobile devices, household gadgets, automotive controls, aircraft flight decks; everywhere you look, people are interacting with technologies. This trend is set to continue as we move towards a world comprising Smart Cities built around the Internet of Things. Unfortunately, much of the rhetoric surrounding this dawning age of ubiquitous and embedded computing fails to appropriately consider the people at the centre of it. These people are embodied social agents with motivations, emotions, capabilities, capacities, proclivities and predilections. Technological imaginings around the Internet of Things are often steeped in generalities or idealised scenarios of use. Such imaginings typically forget that design is always about meeting particular peoples’ needs in particular contexts. From concept to ideation to prototype and evaluation, the design of interactive technologies and systems that are intended for people should start with some understanding of who the users will be, what tasks and experiences they are aiming for, and what the circumstances, conditions or context(s) are at play. In this talk, I will discuss a simple people-centric framework devised with my colleagues and coauthors to inform the way we think about design, the ABCS of designing interactive systems. A descriptive guide rather than a prescriptive checklist, the framework draws on basic research in ergonomics, psychology and user modeling. It is intended to focus design thinking about people as the users of interactive, computational systems. It is intended to support us as the designers of interactive technologies as we scope, draft and iterate on the design space of imagined interactive experiences. Using examples from my own work, I will illustrate how this framework has been explicitly and/or tacitly applied in the design, development and evaluation of interactive, multimedia systems. In particular, I will consider how this framework is currently being applied to rethinking the concept of personalization.
A Reliable Multicast Framework for Light-Weight Sessions and Application Level Framing

This paper describes SRM (Scalable Reliable Multicast), a reliable multicast framework for application level framing and light-weight sessions. The algorithms of this framework are efficient, robust, and scale well to both very large networks and very large sessions. The framework has been prototyped in wb, a distributed whiteboard application, and has been extensively tested on a global scale with sessions ranging from a few to more than 1000 participants. The paper describes the principles that have guided our design, including the IP multicast group delivery model, an end-to-end, receiver-based model of reliability, and the application level framing protocol model. As with unicast communications, the performance of a reliable multicast delivery algorithm depends on the underlying topology and operational environment. We investigate that dependence via analysis and simulation, and demonstrate an adaptive algorithm that uses the results of previous loss recovery events to adapt the control parameters used for future loss recovery. With the adaptive algorithm, our reliable multicast delivery algorithm provides good performance over a wide range of underlying topologies.
A probabilistic framework for semi-supervised clustering

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn the distance measure. We propose a probabilistic model for semi-supervised clustering based on Hidden Markov Random Fields (HMRFs) that provides a principled framework for incorporating supervision into prototype-based clustering. The model generalizes a previous approach that combines constraints and Euclidean distance learning, and allows the use of a broad range of clustering distortion measures, including Bregman divergences (e.g., Euclidean distance and I-divergence) and directional similarity measures (e.g., cosine similarity). We present an algorithm that performs partitional semi-supervised clustering of data by minimizing an objective function derived from the posterior energy of the HMRF model. Experimental results on several text data sets demonstrate the advantages of the proposed framework.
Extractive multi-document summarization using population-based multicriteria optimization

Multi-document summarization is the process of extracting salient information from a set of source texts and present that information to the user in a condensed form. In this paper, we propose a multi-document summarization system which generates an extractive generic summary with maximum relevance and minimum redundancy by representing each sentence of the input document as a vector of words in Proper Noun, Noun, Verb and Adjective set. Five features, such as TF ISF, Aggregate Cross Sentence Similarity, Title Similarity, Proper Noun and Sentence Length associated with the sentences, are extracted, and scores are assigned to sentences based on these features. Weights that can be assigned to different features may vary depending upon the nature of the document, and it is hard to discover the most appropriate weight for each feature, and this makes generation of a good summary a very tough task without human intelligence. Multi-document summarization problem is having large number of decision parameters and number of possible solutions from which most optimal summary is to be generated. Summary generated may not guarantee the essential quality and may be far from the ideal human generated summary. To address this issue, we propose a population-based multicriteria optimization method with multiple objective functions. Three objective functions are selected to determine an optimal summary, with maximum relevance, diversity, and nov∗Ansamma John Email addresses: ansamma.john@gmail.com (Ansamma John), premjith1190@gmail.com (Premjith P.S), wilsyphilipose@hotmail.com (Wilscy M)  2017
Cloud Computing: Security Issues and Research Challenges

Cloud computing is an architecture for providing computing service via the internet on demand and pay per use access to a pool of shared resources namely networks, storage, servers, services and applications, without physically acquiring them. So it saves managing cost and time for organizations. Many industries, such as banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on the resources such as processing power used, transactions carried out, bandwidth consumed, data transferred, or storage space occupied etc. Cloud computing is a completely internet dependent technology where client data is stored and maintain in the data center of a cloud provider like Google, Amazon, Salesforce.som and Microsoft etc. Limited control over the data may incur various security issues and threats which include data leakage, insecure interface, sharing of resources, data availability and inside attacks. There are various research challenges also there for adopting cloud computing such as well managed service level agreement (SLA), privacy, interoperability and reliability. This research paper outlines what cloud computing is, the various cloud models and the main security risks and issues that are currently present within the cloud computing industry. This research paper also analyzes the key research and challenges that presents in cloud computing and offers best practices to service providers as well as enterprises hoping to leverage cloud service to improve their bottom line in this severe economic climate.
Study on Unconstrained Facial Recognition Using the Boston Marathon Bombings Suspects

The investigation surrounding the Boston Marathon bombings was a missed opportunity for automated facial recognition to assist law enforcement in identifying suspects. We simulate the identification scenario presented by the investigation using three state-of-the-art commercial face recognition systems, and evaluate the maturity of face recognition technology in matching low quality face images of uncooperative subjects. Our experimental results show one instance where a commercial face matcher returns a rank-one hit for suspect Dzhokhar Tsarnaev against a one million mugshot background database. Though issues surrounding pose, occlusion, and resolution continue to confound matchers, there have been significant advances made in face recognition technology to assist law enforcement agencies in their investigations.
The incidence of coring with blunt versus sharp needles.

With the advent of safety needles to prevent inadvertent needle sticks in the operating room (OR), a potentially new issue has arisen. These needles may result in coring, or the shaving off of fragments of the rubber stopper, when the needle is pierced through the rubber stopper of the medication vial. These fragments may be left in the vial and then drawn up with the medication and possibly injected into patients. The current study prospectively evaluated the incidence of coring when blunt and sharp needles were used to pierce rubber topped vials. We also evaluated the incidence of coring in empty medication vials with rubber tops. The rubber caps were then pierced with either an18-gauge sharp hypodermic needle or a blunt plastic (safety) needle. Coring occurred in 102 of 250 (40.8%) vials when a blunt needle was used versus 9 of 215 (4.2%) vials with a sharp needle (P < 0.0001). A significant incidence of coring was demonstrated when a blunt plastic safety needle was used. This situation is potentially a patient safety hazard and methods to eliminate this problem are needed.
Probabilistic Topic Models

In this article, we review probabilistic topic models: graphical models that can be used to summarize a large collection of documents with a smaller number of distributions over words. Those distributions are called "topics" because, when fit to data, they capture the salient themes that run through the collection. We describe both finite-dimensional parametric topic models and their Bayesian nonparametric counterparts, which are based on the hierarchical Dirichlet process (HDP). We discuss two extensions of topic models to time-series data-one that lets the topics slowly change over time and one that lets the assumed prevalence of the topics change. Finally, we illustrate the application of topic models to nontext data, summarizing some recent research results in image analysis.
How to resolve the caudal septal deviation? Clinical outcomes after septoplasty with bony batten grafting.

Caudal septal deviation interrupts normal nasal breathing, due to the narrowing of the external valve area and nasal valve angle. In this study, we found a different approach for correction of caudal septal deviation with no associated deformity of the external nose.



Individual case-control study.



The 39 patients completed questionnaires by interviews postoperatively for assessment of nasal obstruction. In addition, patients assessed the severity of their nasal symptoms (i.e., mouth breathing, mouth dryness, hyposmia, rhinorrhea, epistaxis, snoring, postnasal drip, and headache) preoperatively and postoperatively using a visual analog scale (VAS). Improvement in the treatment of nasal obstruction using a VAS and a questionnaire for subjective satisfaction were evaluated 3 months after septoplasty. To evaluate outcomes objectively, endoscopic photographs of the nasal cavity and acoustic rhinometry before and after surgery were evaluated. For comparison between preoperative and postoperative status, the Wilcoxon signed ranks test was used.



Patients reported a significant decrease in the VAS severity of all nasal symptoms. The minimal cross-sectional area (MCA1) of the convex side after vascular constriction using acoustic rhinometry showed significant widening. Patients were divided into a turbinoplasty group and a nonturbinoplasty group, and the turbinoplasty group showed a significant increase in both the convex side and concave side in MCA1 and in the convex side in the anterior portion of the inferior turbinate.



Endonasal septoplasty using bony batten grafting for caudal septal deviation resulted in an improvement of nasal obstruction symptoms and acoustic rhinometry components.
Proton++: a customizable declarative multitouch framework

Proton++ is a declarative multitouch framework that allows developers to describe multitouch gestures as regular expressions of touch event symbols. It builds on the Proton framework by allowing developers to incorporate custom touch attributes directly into the gesture description. These custom attributes increase the expressivity of the gestures, while preserving the benefits of Proton: automatic gesture matching, static analysis of conflict detection, and graphical gesture creation. We demonstrate Proton++'s flexibility with several examples: a direction attribute for describing trajectory, a pinch attribute for detecting when touches move towards one another, a touch area attribute for simulating pressure, an orientation attribute for selecting menu items, and a screen location attribute for simulating hand ID. We also use screen location to simulate user ID and enable simultaneous recognition of gestures by multiple users. In addition, we show how to incorporate timing into Proton++ gestures by reporting touch events at a regular time interval. Finally, we present a user study that suggests that users are roughly four times faster at interpreting gestures written using Proton++ than those written in procedural event-handling code commonly used today.
Can mobile usage predict illiteracy in a developing country?

The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users’ financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geographical mapping of illiteracy is crucial to know where the illiterate people are, and where to put in resources. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than $1 trillion dollars each year . These results potentially enable costeffective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale.
An overview of depth cameras and range scanners based on time-of-flight technologies

Time-of-flight (TOF) cameras are sensors that can measure the depths of scene points, by illuminating the scene with a controlled laser or LED source and then analyzing the reflected light. In this paper, we will first describe the underlying measurement principles of time-of-flight cameras, including: (1) pulsed-light cameras, which measure directly the time taken for a light pulse to travel from the device to the object and back again, and (2) continuous-wave-modulated light cameras, which measure the phase difference between the emitted and received signals, and hence obtain the travel time indirectly. We review the main existing designs, including prototypes as well as commercially available devices. We also review the relevant camera calibration principles, and how they are applied to TOF devices. Finally, we discuss the benefits and challenges of combined TOF and color camera systems.