nils-herrmann/clean_scirepeval huggingface dataset:
Development of a cloud-based RTAB-map service for robots

With the recent advances in wireless communication and cloud computing technology, conventional robots can enhance their capabilities on the fly. A number of functionalities can be offered to robots for which they were not initially designed for such as heavy computation offloading, navigation, map building, etc. In this paper, we have offered a cloud-enabled framework for robots where along with offloading their computation they can also use navigation, map building, path planning, etc. as a service. In order to validate the working of our framework, a RTAB-map (Real Time Appearance Based Mapping) service is built for mobile robots. In this, we have incorporated SOA (Service Oriented Architecture) design to standardized the RTAB-map service, i.e., service listing are defined via WSDL (Web Service Definition Language) and communicated over SOAP (Simple Object Access Protocol) protocol respectively. Some simulation results of service execution and cloud usage analysis are also presented to support the proposed system.
Fault Detection and Autoline Distribution System with GSM Module

Transmission lines is the important factor of the power system. Transmission and distribution lines has good contribution in the generating unit and consumers to obtain the continuity of electric supply. To economically transfer high power between systems and from control generating field. Transmission line run over hundreds of kilometers to supply electrical power to the consumers. It is a required for industries to detect the faults in the power system as early as possible. “Fault Detection and Auto Line Distribution System With GSM Module” is a automation technique used for fault detection in AC supply and auto sharing of power. The significance undetectable faults is that they represent a serious public safety hazard as well as a risk of arcing ignition of fires. This paper represents under voltage and over current fault detection. It is useful in technology to provide many applications like home, industry etc..
CD44: A Multifunctional Cell Surface Adhesion Receptor Is a Regulator of Progression and Metastasis of Cancer Cells

CD44 is a cell surface adhesion receptor that is highly expressed in many cancers and regulates metastasis via recruitment of CD44 to the cell surface. Its interaction with appropriate extracellular matrix ligands promotes the migration and invasion processes involved in metastases. It was originally identified as a receptor for hyaluronan or hyaluronic acid and later to several other ligands including, osteopontin (OPN), collagens, and matrix metalloproteinases. CD44 has also been identified as a marker for stem cells of several types. Beside standard CD44 (sCD44), variant (vCD44) isoforms of CD44 have been shown to be created by alternate splicing of the mRNA in several cancer. Addition of new exons into the extracellular domain near the transmembrane of sCD44 increases the tendency for expressing larger size vCD44 isoforms. Expression of certain vCD44 isoforms was linked with progression and metastasis of cancer cells as well as patient prognosis. The expression of CD44 isoforms can be correlated with tumor subtypes and be a marker of cancer stem cells. CD44 cleavage, shedding, and elevated levels of soluble CD44 in the serum of patients is a marker of tumor burden and metastasis in several cancers including colon and gastric cancer. Recent observations have shown that CD44 intracellular domain (CD44-ICD) is related to the metastatic potential of breast cancer cells. However, the underlying mechanisms need further elucidation.
Altered structural connectivity in neonates at genetic risk for schizophrenia: A combined study using morphological and white matter networks

Recently, an increasing body of evidence suggests that developmental abnormalities related to schizophrenia may occur as early as the neonatal stage. Impairments of brain gray matter and wiring problems of axonal fibers are commonly suspected to be responsible for the disconnection hypothesis in schizophrenia adults, but significantly less is known in neonates. In this study, we investigated 26 neonates who were at genetic risk for schizophrenia and 26 demographically matched healthy neonates using both morphological and white matter networks to examine possible brain connectivity abnormalities. The results showed that both populations exhibited small-world network topology. Morphological network analysis indicated that the brain structural associations of the high-risk neonates tended to have globally lower efficiency, longer connection distance, and less number of hub nodes and edges with relatively higher betweenness. Subgroup analysis showed that male neonates were significantly disease-affected, while the female neonates were not. White matter network analysis, however, showed that the fiber networks were globally unaffected, although several subcortical-cortical connections had significantly less number of fibers in high-risk neonates. This study provides new lines of evidence in support of the disconnection hypothesis, reinforcing the notion that the genetic risk of schizophrenia induces alterations in both gray matter structural associations and white matter connectivity.
Chemical Organization Theory as a modeling framework for self-organization , autopoiesis and resilience

Chemical Organization Theory (COT) is a recently developed formalism inspired by chemical reactions. Because of its simplicity, generality and power, COT seems able to tackle a wide variety of problems in the analysis of complex, self-organizing systems across multiple disciplines. The elements of the formalism are resources and reactions, where a reaction (which has the form a + b + ... → c + d +...) maps a combination of resources onto a new combination. The resources on the input side are “consumed” by the reaction, which “produces” the resources on the output side. Thus, a reaction represents an elementary process that transforms resources into new resources. Reaction networks tend to self-organize into invariant subnetworks, called “organizations”, which are attractors of their dynamics. These are characterized by closure (no new resources are added) and self-maintenance (no existing resources are lost). Thus, they provide a simple model of autopoiesis: the organization persistently recreates its own components. Organizations can be more or less resilient in the face of perturbations, depending on properties such as the size of their basin of attraction or the redundancy of their reaction pathways. Concrete applications of organizations can be found in autocatalytic cycles, metabolic or genetic regulatory networks, ecosystems, sustainable development, and social systems.
Patient-Centered Care Creating a Patient Complaint Capture and Resolution Process to Incorporate Best Practices for Patient-Centered Representation Article-ata-Glance

Volume 40 Number 11 November 2014 484 Health care organizations today are finding that simply providing a “good” health care experience is insufficient to meet patient expectations. Organizations must train staff to provide excellent customer service to all patients. Many patients have become savvy health care “consumers” and consider customer service when they evaluate the quality of care they receive. The challenge for health care organizations is that patients want and expect not only outstanding clinical interventions but also excellent customer service—on every single visit.1(p. 25) A growing body of evidence suggests that patient (including family) feedback can provide compelling opportunities for developing risk management and quality improvement strategies, as well as improving customer satisfaction.2–5 Research links patient dissatisfaction with malpractice claims and unnecessary expenses.6–10 Cold food, rude behavior, long waiting periods, and quality of care concerns should be taken seriously by hospital leadership not only because such attention is addressed in Joint Commission accreditation standards or required by the Centers for Medicare & Medicaid Services (CMS) but because it is the right thing to do. The Joint Commission standards speak to the collection of, response to, and documentation of complaints from hospital patients and their families,*11 and CMS deems a time frame of 7 days appropriate for resolution for most complaints, with 21 days for complex complaints.†12 In addition, in July 2008 Joint Commission Sentinel Event Alert 40 stated that disruptive and intimidating physician behavior toward patients and colleagues may lead to medical errors, poor patient satisfaction, preventable adverse outcomes Patient-Centered Care
The Delphi list: a criteria list for quality assessment of randomized clinical trials for conducting systematic reviews developed by Delphi consensus.

Most systematic reviews rely substantially on the assessment of the methodological quality of the individual trials. The aim of this study was to obtain consensus among experts about a set of generic core items for quality assessment of randomized clinical trials (RCTs). The invited participants were experts in the field of quality assessment of RCTs. The initial item pool contained all items from existing criteria lists. Subsequently, we reduced the number of items by using the Delphi consensus technique. Each Delphi round comprised a questionnaire, an analysis, and a feedback report. The feedback report included staff team decisions made on the basis of the analysis and their justification. A total of 33 international experts agreed to participate, of whom 21 completed all questionnaires. The initial item pool of 206 items was reduced to 9 items in three Delphi rounds. The final criteria list (the Delphi list) was satisfactory to all participants. It is a starting point on the way to a minimum reference standard for RCTs on many different research topics. This list is not intended to replace, but rather to be used alongside, existing criteria lists.
How language production shapes language form and comprehension

Language production processes can provide insight into how language comprehension works and language typology-why languages tend to have certain characteristics more often than others. Drawing on work in memory retrieval, motor planning, and serial order in action planning, the Production-Distribution-Comprehension (PDC) account links work in the fields of language production, typology, and comprehension: (1) faced with substantial computational burdens of planning and producing utterances, language producers implicitly follow three biases in utterance planning that promote word order choices that reduce these burdens, thereby improving production fluency. (2) These choices, repeated over many utterances and individuals, shape the distributions of utterance forms in language. The claim that language form stems in large degree from producers' attempts to mitigate utterance planning difficulty is contrasted with alternative accounts in which form is driven by language use more broadly, language acquisition processes, or producers' attempts to create language forms that are easily understood by comprehenders. (3) Language perceivers implicitly learn the statistical regularities in their linguistic input, and they use this prior experience to guide comprehension of subsequent language. In particular, they learn to predict the sequential structure of linguistic signals, based on the statistics of previously-encountered input. Thus, key aspects of comprehension behavior are tied to lexico-syntactic statistics in the language, which in turn derive from utterance planning biases promoting production of comparatively easy utterance forms over more difficult ones. This approach contrasts with classic theories in which comprehension behaviors are attributed to innate design features of the language comprehension system and associated working memory. The PDC instead links basic features of comprehension to a different source: production processes that shape language form.
Improving the IATA delay data coding system for enhanced data analytics

Aviation delays inconvenience travelers and result in financial losses for stakeholders. Without complex data pre-processing, delay data collected by the existing IATA delay coding system are inadequate to support advanced delay analytics, e.g. large-scale delay propagation tracing in an airline network. Consequently, we developed three new coding schemes aiming at improving the current IATA system. These schemes were tested with specific analysis tasks using simulated delay data and were benchmarked against the IATA system. It was found that a coding scheme with a well-designed reporting style can facilitate automated data analytics and data mining, and an improved grouping of delay codes can minimise potential confusion at the data entry and recording stages.
Error Correlation Prediction in Lockstep Processors for Safety-Critical Systems

This paper presents a new phenomenon called error correlation prediction for lockstep processors. Lockstep processors run the same copy of a program, and their outputs are compared at every cycle to detect divergence, and have been popular in safety-critical systems. When the lockstep error checker detects an error, it alerts the safety-critical system by putting the lockstep processor in a safe state in order to prevent hazards. This is done by running the online diagnostics to identify the cause of the error because the lockstep processor has no knowledge of whether the error is caused by a transient or permanent fault. The online diagnostics can be avoided if the error is caused by a transient fault, and the lockstep processor can recover from it. If, however, it is caused by a permanent fault, having prior knowledge about error's likely location(s) within the CPU speeds up the diagnostics process. We discover that the error's type and likely location(s) inside CPUs from which the fault may have originated can be predicted by analyzing the output signals of the CPU(s) when the error is detected. We design a simple static predictor exploiting this phenomenon and show that system availability can be increased by 42-64% with an overhead of less than 2% in silicon area and power.
Filling the Blanks (hint: plural noun) for Mad Libs Humor

Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib R ©, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny.
Brain Computer Interfaces for Communication and Control

Brain Computer Interfaces (BCI) is an area of research that is rapidly growing in the neuroscience and bioengineering fields. One popular approach to the generation of a BCI system consist in the recognition by a computer of the patterns of electrical activity on the scalp gathered from a series of electrodes. One of the problems related to the use of surface EEG is the blurring effect due to the smearing of the skull on the transmission of the potential distribution from the cerebral cortex toward the scalp electrodes. This happens since the skull has a very low electric conductivity when compared with the scalp or the brain ones. The blurring effect makes the EEG data gathered from the scalp electrodes rather correlated, a problem not observed in the cortical EEG data recorded from the invasive implants in monkeys and man. Such correlation makes problematic the work of the classifiers, since the features extracted from the different scalp electrodes tends to be rather similar and this correlation is hard to be disentangled with blind methods like Principal Component Analysis. In the last decade, high-resolution EEG technologies have been developed to enhance the spatial information content of EEG activity. Furthermore, since the ultimate goal of any EEG recording is to provide useful information about the brain activity, a body of mathematical techniques, known as inverse procedures, has been developed to estimate the cortical activity from the raw EEG recordings. Examples of these inverse procedures are the dipole localization, the distributed source and the cortical imaging techniques. Inverse procedures could use linear and non linear techniques to localize putative cortical sources from EEG data, by using mathematical models of the head as volume conductor. More recently, it has been suggested that with the use of the modern high resolution EEG technologies it could be possible to estimate the cortical activity associated to the mental imagery of the upper limbs movements in humans better than with the scalp electrodes. In this presentation we will review main achievements in the field of the Brain Computer Interfaces and we will demonstrate how it is possible run a BCI system able to drive and control several electronic and robotic devices in a house environment. In particular, we first describe a BCI system used on a group of normal subjects in which the technology of the estimation of the cortical activity is illustrated. Then, we used the BCI system for the command of several electronic devices within a three-room environment employed for the neurorehabilitation.
Sematch: Semantic similarity framework for Knowledge Graphs

Sematch is an integrated framework for the development, evaluation and application of semantic similarity for Knowledge Graphs. The framework provides a number of similarity tools and datasets, and allows users to compute semantic similarity scores of concepts, words, and entities, as well as to interact with Knowledge Graphs through SPARQL queries. Sematch focuses on knowledge-based semantic similarity that relies on structural knowledge in a given taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents. Researchers can use Sematch to develop and evaluate semantic similarity metrics and exploit these metrics in applications.
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention

In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i.e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem. We generate NL explanations comprising of the evidence to support the answer to a question asked to an image using two sources of information: (a) annotations of entities in an image (e.g., object labels, region descriptions, relation phrases) generated from the scene graph of the image, and (b) the attention map generated by a VQA model when answering the question. We show how combining the visual attention map with the NL representation of relevant scene graph entities, carefully selected using a language model, can give reasonable textual explanations without the need of any additional collected data (explanation captions, etc). We run our algorithms on the Visual Genome (VG) dataset and conduct internal user-studies to demonstrate the efficacy of our approach over a strong baseline. We have also released a live web demo showcasing our VQA and textual explanation generation using scene graphs and visual
An Effectiveness Study on Trajectory Similarity Measures

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a sheer volume of trajectory data representing the motion history of moving objects. Measuring similarity between trajectories is undoubtedly one of the most important tasks in trajectory data management since it serves as the foundation of many advanced analyses such as similarity search, clustering, and classification. In this light, tremendous efforts have been spent on this topic, which results in a large number of trajectory similarity measures. Generally, each individual work introducing a new distance measure has made specific claims on the superiority of their proposal. However, for most works, the experimental study was focused on demonstrating the efficiency of the search algorithms, leaving the effectiveness aspect unverified empirically. In this paper, we conduct a comparative experimental study on the effectiveness of six widely used trajectory similarity measures based on a real taxi trajectory dataset. By applying a variety of transformations we designed for each original trajectory, our experimental observations demonstrate the advantages and drawbacks of these similarity measures in different circumstances.
WSES Jerusalem guidelines for diagnosis and treatment of acute appendicitis

Acute appendicitis (AA) is among the most common cause of acute abdominal pain. Diagnosis of AA is challenging; a variable combination of clinical signs and symptoms has been used together with laboratory findings in several scoring systems proposed for suggesting the probability of AA and the possible subsequent management pathway. The role of imaging in the diagnosis of AA is still debated, with variable use of US, CT and MRI in different settings worldwide. Up to date, comprehensive clinical guidelines for diagnosis and management of AA have never been issued. In July 2015, during the 3rd World Congress of the WSES, held in Jerusalem (Israel), a panel of experts including an Organizational Committee and Scientific Committee and Scientific Secretariat, participated to a Consensus Conference where eight panelists presented a number of statements developed for each of the eight main questions about diagnosis and management of AA. The statements were then voted, eventually modified and finally approved by the participants to The Consensus Conference and lately by the board of co-authors. The current paper is reporting the definitive Guidelines Statements on each of the following topics: 1) Diagnostic efficiency of clinical scoring systems, 2) Role of Imaging, 3) Non-operative treatment for uncomplicated appendicitis, 4) Timing of appendectomy and in-hospital delay, 5) Surgical treatment 6) Scoring systems for intra-operative grading of appendicitis and their clinical usefulness 7) Non-surgical treatment for complicated appendicitis: abscess or phlegmon 8) Pre-operative and post-operative antibiotics.
A Case Report of Autonucleation and Psychosis

Autoenucleation is considered the most catastrophic of self-inflicted eye injuries. This is a case of a patient with unspecified psychosis and a history of self-enucleation of one eye. Autoenucleation is considered major self-mutilation [MSM] and a rare but devastating complication of severe mental illness. Acute psychosis, in particular firstepisode schizophrenia, appears to be the primary cause of MSM. There are no set guidelines on how to prevent MSM, conclusive evidence on risk for future MSM, or how to prevent future MSM.
Cofabrication of Vacuum Field Emission Transistor (VFET) and MOSFET

Co-fabrication of a nanoscale vacuum field emission transistor (VFET) and a metal-oxide-semiconductor field effect transistor (MOSFET) is demonstrated on a silicon-on-insulator wafer. The insulated-gate VFET with a gap distance of 100 nm is achieved by using a conventional 0.18-μm process technology and subsequent photoresist ashing process. The VFET shows a turn-on voltage of 2 V at a cell current of 2 nA and a cell current of 3 μA at the operation voltage of 10 V with an ON/OFF current ratio of 104. The gap distance between the cathode and anode in the VFET is defined to be less than the mean free path of electrons in air, and consequently, the operation voltage is reduced to be less than the ionization potential of air molecules. This allows the relaxation of the vacuum requirement. The present integration scheme can be useful as it combines the advantages of both structures on the same chip.