nils-herrmann/clean_scirepeval huggingface dataset:
A privacy and security threat assessment framework for consumer health wearables

Health data is important as it provides an individual with knowledge of the factors needed to be improved for oneself. The development of fitness trackers and their associated software aid consumers to understand the manner in which they may improve their physical wellness. These devices are capable of collecting health data for a consumer such sleeping patterns, heart rate readings or the number of steps taken by an individual. Although, this information is very beneficial to guide a consumer to a better healthier state, it has been identified that they have privacy and security concerns. Privacy and Security are of great concern for fitness trackers and their associated applications as protecting health data is of critical importance. This is so, as health data is one of the highly sort after information by cyber criminals. Fitness trackers and their associated applications have been identified to contain privacy and security concerns that places the health data of consumers at risk to intruders. As the study of Consumer Health continues to grow it is vital to understand the elements that are needed to better protect the health information of a consumer. This research paper therefore provides a conceptual threat assessment framework that can be used to identify the elements needed to better secure Consumer Health Wearables. These elements consist of six core elements from the CIA triad and Microsoft STRIDE framework. Fourteen vulnerabilities were further discovered that were classified within these six core elements. Through this, better guidance can be achieved to improve the privacy and security of Consumer Health Wearables.
Accurate 3D measurement using a structured light system

This paper discusses a method for obtaining accurate 3D measurements using a temporally encoded structured light system. An objective of the work was to have a balance in the accuracy of all components in the system. This was achieved by including lens distortion in the models for both the camera and projector which comprise the structured light system. In addition, substripe estimation was used to estimate projector stripe values as a complement to subpixel estimators used for locating image features. Experimental evaluation shows that it is important to use substripe estimation and incorporate lens distortion in the projector model.
Safety and anti-inflammatory activity of curcumin: a component of tumeric (Curcuma longa).

Tumeric is a spice that comes from the root Curcuma longa, a member of the ginger family, Zingaberaceae. In Ayurveda (Indian traditional medicine), tumeric has been used for its medicinal properties for various indications and through different routes of administration, including topically, orally, and by inhalation. Curcuminoids are components of tumeric, which include mainly curcumin (diferuloyl methane), demethoxycurcumin, and bisdemethoxycurcmin.



The goal of this systematic review of the literature was to summarize the literature on the safety and anti-inflammatory activity of curcumin.



A search of the computerized database MEDLINE (1966 to January 2002), a manual search of bibliographies of papers identified through MEDLINE, and an Internet search using multiple search engines for references on this topic was conducted. The PDR for Herbal Medicines, and four textbooks on herbal medicine and their bibliographies were also searched.



A large number of studies on curcumin were identified. These included studies on the antioxidant, anti-inflammatory, antiviral, and antifungal properties of curcuminoids. Studies on the toxicity and anti-inflammatory properties of curcumin have included in vitro, animal, and human studies. A phase 1 human trial with 25 subjects using up to 8000 mg of curcumin per day for 3 months found no toxicity from curcumin. Five other human trials using 1125-2500 mg of curcumin per day have also found it to be safe. These human studies have found some evidence of anti-inflammatory activity of curcumin. The laboratory studies have identified a number of different molecules involved in inflammation that are inhibited by curcumin including phospholipase, lipooxygenase, cyclooxygenase 2, leukotrienes, thromboxane, prostaglandins, nitric oxide, collagenase, elastase, hyaluronidase, monocyte chemoattractant protein-1 (MCP-1), interferon-inducible protein, tumor necrosis factor (TNF), and interleukin-12 (IL-12).



Curcumin has been demonstrated to be safe in six human trials and has demonstrated anti-inflammatory activity. It may exert its anti-inflammatory activity by inhibition of a number of different molecules that play a role in inflammation.
Chlorophyll fluorescence for quantification of fungal foliar infection and assessment of the effectiveness of an induced systemic resistance activator

Chlorophyll fluorescence imaging was used to follow infections of Nicotiana benthamiana with the hemibiotrophic fungus, Colletotrichum orbiculare. Based on Fv/Fm images, infected leaves were divided into: healthy tissue with values similar to non-inoculated leaves; water-soaked/necrotic tissue with values near zero; and non-necrotic disease-affected tissue with intermediate values, which preceded or surrounded water-soaked/necrotic tissue. Quantification of Fv/Fm images showed that there were no changes until late in the biotrophic phase when spots of intermediate Fv/Fm appeared in visibly normal tissue. Those became water-soaked approx. 24 h later and then turned necrotic. Later in the necrotrophic phase, there was a rapid increase in affected and necrotic tissue followed by a slower increase as necrotic areas merged. Treatment with the induced systemic resistance activator, 2R, 3R-butanediol, delayed affected and necrotic tissue development by approx. 24 h. Also, the halo of affected tissue was narrower indicating that plant cells retained a higher photosystem II efficiency longer prior to death. While chlorophyll fluorescence imaging can reveal much about the physiology of infected plants, this study demonstrates that it is also a practical tool for quantifying hemibiotrophic fungal infections, including affected tissue that is appears normal visually but is damaged by infection.
Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

Knowledge Management (KM) is a key enabler for achieving quality in a lifecycle approach for production of biopharmaceuticals. Due to the important role that it plays towards successful implementation of Quality by Design (QbD), an analysis of KM solutions is needed. This work provides a comprehensive review of the interface between KM and QbD-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. A comprehensive set of 356 publications addressing the applications of KM tools to QbD-related tasks were screened and a query to gather industrial inputs from 17 major biopharmaceutical organizations was performed. Three KM tool classes were identified as having high relevance for biopharmaceutical production systems and have been further explored: knowledge indicators, ontologies, and process modeling. A proposed categorization of 16 distinct KM tool classes allowed for the identification of holistic technologies supporting QbD. In addition, the classification allowed for addressing the disparity between industrial and academic expectations regarding the application of KM methodologies. This is a first of a kind attempt and thus we think that this paper would be of considerable interest to those in academia and industry that are engaged in accelerating development and commercialization of biopharmaceuticals.
Insect exocrine glands.

Fig. 1. Noirot and Quennedey's original classification of insect exocrine glands, based on a rhinotermitid sternal gland. The asterisk indicates a subcuticular space.  C, cuticle; D, duct cells; G1, secretory cells class 1; G2, secretory cells class 2; G3, secretory cells class 3; S, campaniform sensilla (modified after Noirot and Quennedey, 1974). ‘Describe the differences between endocrine and exocrine glands’, it sounds a typical exam question from a general biology course during our time at high school. Because of their secretory products being released to the outside world, exocrine glands definitely add flavour to our lives. Everybody is familiar with their secretions, from the salty and perhaps unpleasantly smelling secretions from mammalian sweat glands to the sweet exudates of the honey glands used by some caterpillars to attract ants, from the most painful venoms of bullet ants and scorpions to the precious wax that honeybees use to make their nest combs. Besides these functions, exocrine glands are especially known for the elaboration of a broad spectrum of pheromonal substances, and can also be involved in the production of antibiotics, lubricants, and digestive enzymes. Modern research in insect exocrinology started with the classical works of Charles Janet, who introduced a histological approach to the insect world (Billen and Wilson, 2007). The French school of insect anatomy remained strong since then, and the commonly used classification of insect exocrine glands generally follows the pioneer paper of Charles Noirot and Andr e Quennedey (1974). These authors were leading termite researchers using their extraordinary knowledge on termite glands to understand related phenomena, such as foraging and reproductive behaviour. They distinguish between class 1 with secretory cells adjoining directly to the cuticle, and class 3 with bicellular units made up of a large secretory cell and its accompanying duct cell that carries the secretion to the exterior . The original classification included also class 2 secretory cells, but these are very rare and are only found in sternal and tergal glands of a cockroach and many termites (and also in the novel nasus gland described in this issue!). This classification became universally used, with the rather strange consequence that the vast majority of insect glands is illogically made up of class 1 and class 3 cells. In a follow-up paper, the uncommon class 2 cells were re-considered as oenocyte homologues (Noirot and Quennedey, 1991). Irrespectively of these objections, their 1974 pioneer paper is a cornerstone of modern works dealing with insect exocrine glands, as is also obvious in the majority of the papers in this special issue. This paper already received 545 citations at Web of Science and 588 at Google Scholar (both on 24 Aug 2015), so one can easily say that all researchers working on insect glands consider this work truly fundamental. Exocrine glands are organs of cardinal importance in all insects. The more common ones include mandibular and labial
The MetaCrawler architecture for resource aggregation on the Web

The MetaCrawler Softbot is a parallel Web search service that has been available at the University of Washington since June of 1995. It provides users with a single interface with which they can query popular general-purpose Web search services, such as Lycos[6] and AltaVista[1], and has some sophisticated features that allow it to obtain results of much higher quality than simply regurgitating the output from each search service. In this article, we briefly outline the motivation for MetaCrawler and highlight previous work, and then discuss the architecture of MetaCrawler and how it enables MetaCrawler to perform well and to scale and adapt to a dynamic Internet.
Analyzing Computer Security - A Threat / Vulnerability / Countermeasure Approach

analyzing computer security a threat or vulnerability or countermeasure approach What to say and what to do when mostly your friends love reading? Are you the one that don't have such hobby? So, it's important for you to start having that hobby. You know, reading is not the force. We're sure that reading will lead you to join in better concept of life. Reading will be a positive activity to do every time. And do you know our friends become fans of analyzing computer security a threat or vulnerability or countermeasure approach as the best book to read? Yeah, it's neither an obligation nor order. It is the referred book that will not make you feel disappointed.
$\mathtt {Deepr}$: A Convolutional Net for Medical Records

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive <italic>regular clinical motifs</italic> from <italic> irregular episodic records</italic>. We present <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math> </inline-formula> (short for <italic>Deep</italic> <italic>r</italic>ecord), a new <italic>end-to-end</italic> deep learning system that learns to extract features from medical records and predicts future risk automatically. <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math></inline-formula> transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math></inline-formula> permits transparent inspection and visualization of its inner working. We validate <inline-formula><tex-math notation="LaTeX">$\mathtt {Deepr}$ </tex-math></inline-formula> on hospital data to predict unplanned readmission after discharge. <inline-formula> <tex-math notation="LaTeX">$\mathtt {Deepr}$</tex-math></inline-formula> achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.
Development of instructional package with video clips on problem solving steps in mathematics of sixth-grade students in Bhutan

This study developed an instructional package with video clips on problem solving steps in mathematics for sixth-grade students in Bhutan. The instructional package composed of four components: content, lesson process, video, and evaluation. The purposive sampling technique was used for selecting 28 sixth-grade students of Khamdang Lower Secondary School, Trashi Yangtse, eastern Bhutan to study Unit 4: measurement. The success of the instructional package depended on the students' academic achievement and their satisfaction. To measure these, achievement tests were administered before (pre-test) and after (post-test) the experimental phase, and a satisfaction questionnaire was administered one time, after the experimental phase. The achievement test was analyzed using the paired sample t-test, and it revealed that achievement test was statistically significant p(0.000)<0.05. Student satisfaction questionnaire data were analyzed using descriptive statistics (mean and standard deviation) and showed that their satisfaction level was very high. Thus the current study concluded that the instructional package was successful and can be a reliable resource.
Probabilistic models for music

This thesis proposes to analyse symbolic musical data under a statistical viewpoint, using state-of-the-art machine learning techniques. Our main argument is to show that it is possible to design generative models that are able to predict and to generate music given arbitrary contexts in a genre similar to a training corpus, using a minimal amount of data. For instance, a carefully designed generative model could guess what would be a good accompaniment for a given melody. Conversely, we propose generative models in this thesis that can be sampled to generate realistic melodies given harmonic context. Most computer music research has been devoted so far to the direct modeling of audio data. However, most of the music models today do not consider the musical structure at all. We argue that reliable symbolic music models such a the ones presented in this thesis could dramatically improve the performance of audio algorithms applied in more general contexts. Hence, our main contributions in this thesis are three-fold: • We have shown empirically that long term dependencies are present in music data and we provide quantitative measures of such dependencies; • We have shown empirically that using domain knowledge allows to capture long term dependencies in music signal better than with standard statistical models for temporal data. We describe many probabilistic models aimed to capture various aspects of symbolic polyphonic music. Such models can be used for music prediction. Moreover, these models can be sampled to generate realistic music sequences; • We designed various representations for music that could be used as observations by the proposed probabilistic models.
Managing Infusion-Related Reactions for Patients With Chronic Lymphocytic Leukemia Receiving Obinutuzumab.

In patients with previously untreated chronic lymphocytic leukemia (CLL) and comorbidities, treatment with the glycoengineered, type II anti-CD20 monoclonal antibody obinutuzumab (Gazyva®) (GA101) plus chlorambucil (Leukeran®) was associated with superior outcomes to rituximab (Rituxan®) plus chlorambucil, with a similar safety profile. However, a higher occurrence of infusion-related reactions (IRRs) was reported with obinutuzumab. These reactions typically require additional management.



The focus of this article is to provide oncology nurses and physicians with advice for obinutuzumab IRR management based on clinical trial data and nursing experience.



The authors reviewed the published management strategies for IRRs with obinutuzumab that were identified during the phase III CLL11 trial and an expanded access phase IIb study (ML28979). Practical advice for obinutuzumab IRR management was developed based on available clinical trial information and nursing experience.



IRRs with obinutuzumab are generally manageable. Most IRRs (all grades), and all grade 3-4 IRRs, occurred during the first infusion. Therefore, IRR management could be improved substantially with extra vigilance at this early stage.
Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories

As the quality of natural language generated by artificial intelligence systems improves, writing interfaces can support interventions beyond grammar-checking and spell-checking, such as suggesting content to spark new ideas. To explore the possibility of machine-in-the-loop creative writing, we performed two case studies using two system prototypes, one for short story writing and one for slogan writing. Participants in our studies were asked to write with a machine in the loop or alone (control condition). They assessed their writing and experience through surveys and an open-ended interview. We collected additional assessments of the writing from Amazon Mechanical Turk crowdworkers. Our findings indicate that participants found the process fun and helpful and could envision use cases for future systems. At the same time, machine suggestions do not necessarily lead to better written artifacts. We therefore suggest novel natural language models and design choices that may better support creative writing.
Strategies to guide the antibody affinity maturation process.

Antibodies with protective activity are critical for vaccine efficacy. Affinity maturation increases antibody activity through multiple rounds of somatic hypermutation and selection in the germinal center. Identification of HIV-1 specific and influenza-specific antibody developmental pathways, as well as characterization of B cell and virus co-evolution in patients, has informed our understanding of antibody development. In order to counteract HIV-1 and influenza viral diversity, broadly neutralizing antibodies precisely target specific sites of vulnerability and require high levels of affinity maturation. We present immunization strategies that attempt to recapitulate these natural processes and guide the affinity maturation process.
The Digital Divide: Current and Future Research Directions

The digital divide refers to the separation between those who have access to digital information and communications technology (ICT) and those who do not. Many believe that universal access to ICT would bring about a global community of interaction, commerce, and learning resulting in higher standards of living and improved social welfare. However, the digital divide threatens this outcome, leading many public policy makers to debate the best way to bridge the divide. Much of the research on the digital divide focuses on first order effects regarding who has access to the technology, but some work addresses the second order effects of inequality in the ability to use the technology among those who do have access. In this paper, we examine both first and second order effects of the digital divide at three levels of analysis  the individual level, the organizational level, and the global level. At each level, we survey the existing research noting the theoretical perspective taken in the work, the research methodology employed, and the key results that were obtained. We then suggest a series of research questions at each level of analysis to guide researchers seeking to further examine the digital divide and how it impacts citizens, managers, and economies.
Comparing Deep Learning and Classical Machine Learning Approaches for Predicting Inpatient Violence Incidents from Clinical Text

Machine learning techniques are increasingly being applied to clinical text that is already captured in the Electronic Health Record for the sake of delivering quality care. Applications for example include predicting patient outcomes, assessing risks, or performing diagnosis. In the past, good results have been obtained using classical techniques, such as bag-of-words features, in combination with statistical models. Recently however Deep Learning techniques, such as Word Embeddings and Recurrent Neural Networks, have shown to possibly have even greater potential. In this work, we apply several Deep Learning and classical machine learning techniques to the task of predicting violence incidents during psychiatric admission using clinical text that is already registered at the start of admission. For this purpose, we use a novel and previously unexplored dataset from the Psychiatry Department of the University Medical Center Utrecht in The Netherlands. Results show that predicting violence incidents with state-of-the-art performance is possible, and that using Deep Learning techniques provides a relatively small but consistent improvement in performance. We finally discuss the potential implication of our findings for the psychiatric practice.
OptaSense: Fibre Optic Distributed Acoustic Sensing for Border Monitoring

The OptaSense® Distributed Acoustic Sensing (DAS) system is an acoustic and seismic sensing capability that uses simple fibre optic communications cables as the sensor. Using existing or new cables, it can provide low-cost and high-reliability surface crossing and tunnel construction detection, with power and communications services needed only every 80-100 km. The technology has been proven in worldwide security operations at over one hundred locations in a variety of industries including oil and gas pipelines, railways, and high-value facility perimeters - a total of 100,000,000 kilometre-hours of linear asset protection. The system reliably detects a variety of border threats with very few nuisance alarms. It can work in concert with existing border surveillance technologies to provide security personnel a new value proposition for fighting trans-border crime. Its ability to detect, classify and locate activity over hundreds of kilometres and provide information in an accurate and actionable way has proven OptaSense to be a cost-effective solution for monitoring long borders. It has been scaled to cover 1500 km controlled by a single central monitoring station in pipeline applications.