nils-herrmann/clean_scirepeval huggingface dataset:
Patent Analysis of Key Technologies for Smart Retailing and their Projected Economic Impact
Venue: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2018

With the growing population of digital users gaining online consumption power, retail industries are facing evolutionary changes in attracting and maintaining online and offline customers. The concepts and implementation of smart malls and retail shops are proposed by retailers or solution providers to satisfy new customer demands. The foundation of smart retailing includes information integration, customer experience, and e transactions. This research investigates the economic impact of smart retailing and the growth of related intellectual properties (IPs) specifically, the retail domain online related patents. The technological aspect of smart retailing is organized by constructing a patent ontology map to define the domain boundary. The economic impact of technological transformations is studied by measuring the economic growth of the leading regions and countries and the representative patent assignees. Consumption preferences and labor structure changes in relation to the retail industry are analyzed to verify the economic impact of corresponding intellectual properties. The goal of the research is to develop regional development strategies for smart retailing using global patent and economic impact analyses.
Robust active learning for the diagnosis of parasites
Venue: Pattern Recognit.
Year: 2015

We have developed an automated system for the diagnosis of intestinal parasites from optical microscopy images. The objects (species of parasites and impurities) segmented from these images form a large dataset. We are interested in the active learning problem of selecting a reasonably small number of objects to be labeled under an expert's supervision for use in training a pattern classifier. However, impurities are very numerous, constitute several clusters in the feature space, and can be quite similar to some species of parasites, leading to a significant challenge for active learning methods. We propose a technique that pre organizes the data and then properly balances the selection of samples from all classes and uncertain samples for training. Early data organization avoids reprocessing of the large dataset at each learning iteration, enabling the halting of sample selection after a desired number of samples per iteration, yielding interactive response time. We validate our method by comparing it with state of the art approaches, using a previously labeled dataset of almost 6000 objects. Moreover, we report results from experiments on a very realistic scenario, consisting of a dataset with over 140,000 unlabeled objects, under unbalanced classes, the absence of some classes, and the presence of a very large set of impurities. HighlightsA robust active learning method, called RDS, based on a priori data organization.RDS properly balances sample diversity and uncertainty for useful sample selection.It provides high classification accuracy for the automated diagnosis of parasites.Comparisons with different clustering, classification and other literature methods.RDS was evaluated by an experienced expert in parasitology using a realistic scenario.
Mental Health Effects of COVID 19 Pandemic on Healthcare Workers
Year: 2021

Healthcare workers (HCWs) have been adapting to the changing needs of people worldwide in response to the COVID 19 pandemic. The purpose of this descriptive study was to evaluate how HCWs are managing dietary self care needs during the increased stress load of the COVID 19 pandemic and determine if nutritional changes are being made that could place them at higher risk for mental health problems. A cross sectional epidemiological nonprobability snowball sampling design was used to expand access to participants. Occupational stress is a major concern among HCWs in general; however, increased chronic levels of stress during the COVID 19 pandemic had not been evaluated in terms of stress and dietary behavior as risk factors for mental health problems in this population. The results from this survey indicate that HCWs treating patients during the COVID 19 pandemic are experiencing chronically increased stress loads, and the increased stress load is negatively affecting dietary behaviors. Dietary behavior changes included perceived consumption of a less healthy diet overall, increased consumption of sweets and alcoholic drinks, and decreased consumption of fresh fruits and vegetables since the pandemic began. Unhealthy dietary behaviors were associated in a predictive manner with negative mood states in HCWs. In addition, this study uncovered evidence of exhaustion, symptoms of post traumatic stress disorder and suicidal thinking in this normally resilient population.
Strategic agility in international business: A conceptual framework for "agile" multinationals
Year: 2020

Strategic agility is a fuzzy concept that may be counter intuitive as well as confounding to some scholars in terms of the agile strategies' contextual issues. At the same time, the need to be agile is crucial for firms, especially for Multinational Enterprises (MNEs) that operate in culturally different host countries. Thus, a deep understanding of strategic agility is very intriguing for both academics and executives, as several gaps are apparent in the extant literature. In this paper, we review mainstream studies on agility in the international business context, discussing its relevance and proposing main aspects of strategic agility to clarify further this indistinct concept. Moreover, we provide a novel conceptual framework based on the integration of agility in different operational areas (e.g. Information Technology, supply chain and production) that organizations should foster to become an "agile multinational" Our synthesis represents an innovative strategic direction for MNEs to understand better strategic agility, which clearly extends the concept of flexibility, while managing stakeholder relationships in order to develop key dynamic capabilities. Finally, we also discuss the main contributions of the other articles included in this special issue, thus providing specific examples of agility in well debated IB contexts (e.g. emerging markets) We also suggest some future research areas for this complex and ambiguous concept.
TLS for generating multi LOD of 3D building model
Year: 2014

The popularity of Terrestrial Laser Scanners (TLS) to capture three dimensional (3D) objects has been used widely for various applications. Development in 3D models has also led people to visualize the environment in 3D. Visualization of objects in a city environment in 3D can be useful for many applications. However, different applications require different kind of 3D models. Since a building is an important object, CityGML has defined a standard for 3D building models at four different levels of detail (LOD) In this research, the advantages of TLS for capturing buildings and the modelling process of the point cloud can be explored. TLS will be used to capture all the building details to generate multi LOD. This task, in previous works, involves usually the integration of several sensors. However, in this research, point cloud from TLS will be processed to generate the LOD3 model. LOD2 and LOD1 will then be generalized from the resulting LOD3 model. Result from this research is a guiding process to generate the multi LOD of 3D building starting from LOD3 using TLS. Lastly, the visualization for multi LOD model will also be shown.
One of the rare reason of abdominal pain: abdominal wall endometriosis.
Venue: Turkish journal of surgery
Year: 2021

Endometriosis is defined as the presence of normal endometrial mucosa abnormally implanted in locations other than the uterine cavity. It is most commonly located in the pelvis but it is also rarely observed in the gastrointestinal tract, lung, liver, kidneys, central nervous system and abdominal wall. Abdominal wall endometriosis (AWE) commonly occurs following a caesarean section or pelvic surgery. The patients consult the physician mostly with complaints of cyclic abdominal pain and a palpable mass in the abdomen. The basic methods in diagnosing AWE are anamnesis and physical examination but ultrasound, computerized tomography, and sometimes magnetic resonance imaging of the abdomen are also used.  In our study, we retrospectively analyzed 9 patients who underwent surgery at Avcilar State Hospital General Surgery Service between January 2015 and December 2018 with a preliminary diagnosis of AWE and confirmation through pathology results.  Median age of the patients was 32 4.66 and median body mass index (BMI) was 24.6 1.15. Every patient except 1 had a history of cesarean section history. One patient was operated because of recurrence. Patients consulted the hospital with complaints of pain during menstruation and abdominal swelling. The start of the complaints was 4.1 years following C section. Mostly ultrasound was used for imaging. For treatment, they all received en bloc mass excision and their pathological diagnosis were compliant with endometriosis. Average surgery time was 40 minutes and average endometriosis lesion dimension was 3.4 cm. It was observed that the lesion extended to the anterior abdominal fascia in 6 of the patients, and 2 patients underwent fascia repair with propylene mesh because of the excessive defect size. No postoperative complication occured in any patient and no recurrence is observed.  In patients with periodic abdominal pain and swelling on the abdominal wall, AWE could be suspected and early diagnosis can be realized by carefully taking medical history and following physical examination, and appropriate radiological examinations and necessary surgical intervention can be performed. The method of diagnosis and treatment is to remove the lesion through wide excision.
Constraints of List Based Knowledge Interaction on an Android App for Emergency Medicine
Year: 2013

The "AGN Notfallfibel Medikamente Richtwerte in der Notfallmedizin" is a standard quick reference guide for emergency doctors and paramedics in the German speaking area. It has been sold approximately 60,000 times. The paper based booklet has been subsequently developed, tested in the field and constantly improved for 20 years by Dr. med. Ralf Muller, emergency doctor at Graz LKH University Hospital and is practically in the pocket of every emergency and family doctor and paramedics in the German speaking area. Due to the enormous success, availability and performance of Smartphones, the time was ripe to develop an electronic version providing faster access and better search facilities.  We designed and development an electronic version and hypothesized that the electronic version of the handbook allows more efficient and more reliable work than the printed handbook. However, certain design aspects which make sense on printed paper must be revised for mobile applications and adapted to the mobile app paradigm. In this paper we report on the challenges and lessons learned during the development and the tests.  The application's design emphasizes extensibility and modularity, which is supported by Android's Activity paradigm in which single activity classes, usually representing one view or "window" are only loosely connected via so called Intents. The final implementation of the working app consists of more than 100 custom Java classes. All parts of the application were tested, either manually or using automated tools. Unit tests were created for certain parts of the implementation, however, not all parts of the application are easily testable using unit tests or using tools performing black box testing. Robotium for Android, a powerful JUnit based testing framework for user interfaces on Android was used for some user interface tests. The tests were partly executed on emulator devices running different Android versions and on three different physical devices (Samsung Nexus S, ASUS Nexus 7, and Samsung Galaxy Xcover)  The current version of the application is a result of continuous improvement of the back and frontend. During the development of the app new requirements for the XML data language arose, as well as for the user interface. During the tests we realized that list based interactions with medical data sets have serious limitations. This is vital when using such applications within a medical context, especially in the context of emergency medicine, since list based interactions do not make use of the power that modern multi touch devices offer.
Natural Gradient Shared Control
Venue: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO MAN)
Year: 2020

We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control authority while allowing the robot to support the user. This can be done by enforcing constraints or acting optimally when the intent is clear. Our proposed solution relies on natural gradients emerging from the divergence constraint between the robot and the shared policy. We approximate the Fisher information by sampling a learned robot policy and computing the local gradient to augment the user control when necessary. A user study performed on a manipulation task demonstrates that our approach allows for more efficient task completion while keeping control authority against a number of baseline methods.
Occupational exposure to blood and body fluids among health care workers in a general hospital, China.
Venue: American journal of industrial medicine
Year: 2009

To understand current status of occupational exposure to blood and body fluids (BBF) and awareness of knowledge about occupational bloodborne pathogen exposures and universal precaution among hospital based health care workers (HCWs)  A cross sectional study was conducted during April to May 2004 to study incidence of occupational exposure to BBF among 1,144 hospital based HCWs.  The total incidence and the average number of episodes exposure to BBF was 66.3/100 HCWs per year and 7.5 per person per year in the past year, respectively. The incidence (per 100/HCWs per year) and the average number of episodes (per HCW per year) of percutaneous injury (PCI) mucous membrane exposure (MME) and exposure to BBF by damaged skin was 50.3 and 1.8; 34.4 and 1.7; and 37.9 and 4.0, respectively. The leading incidence and the average number of episodes of PCI occurred in delivery room (82.6 and 1.8) The highest percentage of PCI's that occurred during the previous 2 weeks occurred during a surgical operation (22.8% Of all sharp instruments, the suture needle contributed the highest percentage of PCI's (24.7% among HCWs in the last 2 weeks. Over two thirds (68.3% of respondents were immunized with Hepatitis B vaccine; less than one half (47% of HCWs wore gloves while doing procedures on patients. The respondents demonstrated a lack of knowledge regarding transmission of bloodborne diseases and universal precautions.  Risk for potential exposure to BBF appears high in HCWs, and almost all of episodes are not reported. It is urgent to establish the Guideline for Prevention and Control of Occupational Exposure to Bloodborne Pathogens among HCWs.  2009.
Effect of pore fluid on the cyclic behavior of laterally loaded offshore piles modelled in centrifuge
Year: 2017

The common practice in centrifuge modelling of dynamic processes is to use high viscosity pore fluids to unify the time scaling factors for the generation and dissipation of pore pressures. This paper focuses on the effects of the density and viscosity of the pore fluid on the behaviour of an offshore rigid pile subjected to cyclic and monotonic lateral loads. The soil used in these tests is dense sand and it was saturated with three different fluids: 1) water, 2) mixture of glycerine and water, 3) a fibre based viscous fluid with a density very close to that of water. The results of the tests in terms of the changes in the pore pressures at the interface of the pile and soil as well as monotonic lateral bearing capacity of the pile are compared. The tests illustrate that the behaviour of the model with water is very similar to that of the dry model, implying a fully drained behaviour. Moreover, it was observed that the density of the pore viscous fluid plays a major role in the evolution of the pore pressures. Therefore, it affects directly the stress state of the soil in the model.
Ecdysone Receptors of Pest Insects Molecular Cloning, Characterisation, and a Ligand Binding Domain Based Fluorescence Polarization Screen
Year: 2009

EcR and USP encoding cDNAs of four pest insects (Lucilia cuprina, Myzus persicae, Bemisia tabaci, Helicoverpa armigera) were cloned from high quality lambda cDNA libraries and sequenced. Cognate EcR USP cDNA pairs were shown to express functional ecdysone receptors in transfected cells. The amino acid sequences of the EcR ligand binding domains (LBDs) were employed in conjunc tion with those known for other arthropods to construct a phylogenetic tree. Affinity tagged EcR USP LBD heterodimers were co expressed efficiently in insect cells using a baculovirus vector. The recombinant EcR and USP DE/F segments from each species associated spontaneously to form heterodimers that bound ecdysteroids with high affinity.  also asso ciated spontaneously to form a functional heterodimer, but one with ligand binding affinities several times lower than its DE/F counterpart. A fluorescein inokosterone conjugate was synthesized and used to develop a novel ligand binding assay based on fluorescence polarization. This assay can be used in place of the classical 3 H) ponasterone A binding assay, and is ideally suited to high throughput screen ing. The ligand binding data obtained in vitro using recombinant LBD heterodim ers reflect the ability of agonists to induce ecdysone receptor controlled transgene expression in recombinant mammalian cells; in vitro binding data can also reflect the potency of ligands to act as insecticides.
Wampum, sequoyan, and story: Decolonizing the digital archive
Year: 2013

or some years, the Cherokee Nation has hosted a digital archive that includes stories in the Cherokee language, available to anyone who registers for the Nation's free online language classes or requests them to be sent in DVD format. On the tsalagi dikanohesda dikanogida /tsalagi dikanohesda dikanogida/ Cherokee Stories and Songs DVD for instance, users see an opening interface that offers selections of digital stories, kids shows, songs, and animated books. All feature the eighty five character Cherokee syllabary.2 The Cherokee Songs and Stories DVD was created to support the curriculum of the K 6 Cherokee immersion school in Tahlequah, Oklahoma, as one facet of larger language perseverance efforts, which take advantage of the affordances of digital archiving to reproduce often told Cherokee stories in the Cherokee language. The DVD presents a particularly rich illustration of the promise of digital language preservation materials for one tribe and for scholars who hope to engage in historiographical inquiry using digital spaces like these. Digital archives, such as the Cherokee Stories and Songs DVD, have captured the imagination of humanities scholars and rhetoric and composition scholars alike for good reason. Digital archives provide opportunities for cross disciplinary collaborations around collections (Bulger et al. Methodologically, they can provide scholars with a means to gather and analyze literacy narratives, as the Digital Archive of Literacy Narratives (DALN) does (Selfe) DALN provides models for one of the most common first year writing assignments and also serves as a repository of data
Game of Missuggestions: Semantic Analysis of Search Autocomplete Manipulations
Venue: NDSS
Year: 2018

As a new type of blackhat Search Engine Optimization (SEO) autocomplete manipulations are increasingly utilized by miscreants and promotion companies alike to advertise desired suggestion terms when related trigger terms are entered by the user into a search engine. Like other illicit SEO, such activities game the search engine, mislead the querier, and in some cases, spread harmful content. However, little has been done to understand this new threat, in terms of its scope, impact and techniques, not to mention any serious effort to detect such manipulated terms on a large scale. Systematic analysis of autocomplete manipulation is challenging, due to the scale of the problem (tens or even hundreds of millions suggestion terms and their search results) and the heavy burdens it puts on the search engines. In this paper, we report the first technique that addresses these challenges, making a step toward better understanding and ultimately eliminating this new threat. Our technique, called Sacabuche, takes a semantics based, two step approach to minimize its performance impact: it utilizes Natural Language Processing (NLP) to analyze a large number of trigger and suggestion combinations, without querying search engines, to filter out the vast majority of legitimate suggestion terms; only a small set of suspicious suggestions are run against the search engines to get query results for identifying truly abused terms. This approach achieves a 96.23% precision and 95.63% recall, and its scalability enables us to perform a measurement study on 114 millions of suggestion terms, an unprecedented scale for this type of studies. The findings of the study bring to light the magnitude of the threat (0.48% Google suggestion terms we collected manipulated) and its significant security implications never reported before (e.g. exceedingly long lifetime of campaigns, sophisticated techniques and channels for spreading malware and phishing content)
Dimensionality Reduction by Learning an Invariant Mapping
Venue: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
Year: 2006

Dimensionality reduction involves mapping a set of high dimensional input points onto a low dimensional manifold so that 'similar" points in input space are mapped to nearby points on the manifold. We present a method called Dimensionality Reduction by Learning an Invariant Mapping (DrLIM) for learning a globally coherent nonlinear function that maps the data evenly to the output manifold. The learning relies solely on neighborhood relationships and does not require any distancemeasure in the input space. The method can learn mappings that are invariant to certain transformations of the inputs, as is demonstrated with a number of experiments. Comparisons are made to other techniques, in particular LLE.
Detecting Abusive Language on Online Platforms: A Critical Analysis
Venue: ArXiv
Year: 2021

Abusive language on online platforms is a major societal problem, often leading to important societal problems such as the marginalisation of underrepresented minorities. There are many different forms of abusive language such as hate speech, profanity, and cyber bullying, and online platforms seek to moderate it in order to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Within the field of Natural Language Processing, researchers have developed different methods for automatically detecting abusive language, often focusing on specific subproblems or on narrow communities, as what is considered abusive language very much differs by context. We argue that there is currently a dichotomy between what types of abusive language online platforms seek to curb, and what research efforts there are to automatically detect abusive language. We thus survey existing methods as well as content moderation policies by online platforms in this light, and we suggest directions for future work.