nils-herrmann/clean_scirepeval huggingface dataset:
A Secure Sketch-based Authentication Scheme for Telecare Medicine Information Systems

The Telecare Medicine Information System (TMIS) has brought us a lot of conveniences. However, it may also reveal patients’ privacies and other important information. So the security of TMIS can be paid much attention to, in which identity authentication plays a very important role in protecting TMIS from being illegally used. To improve the situation, TMIS needs a more secure and more efficient authentication scheme. Recently, Yan and Li et al. have proposed a secure authentication scheme for the TMIS based on biometrics, claiming that it can withstand various attacks. In this paper, we present several security problems in their scheme as follows: (a) it cannot really achieve three-factor authentication; (b) it has design flaws at the password change phase; (c) users’ biometric may be locked out; (d) it fails to achieve users’ anonymous identity. To solve these problems, a new scheme using the theory of Secure Sketch is proposed. The thorough analysis shows that our scheme can provide a stronger security than Yan-Li’s protocol, despite the little higher computation cost at client. What’s more, the proposed scheme not only can achieve anonymity preserving but also can achieve session key agreement.
Soil health and sustainability: managing the biotic component of soil quality

Soil health is the capacity of soil to function as a vital living system, within ecosystem and land-use boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and promote plant and animal health. Anthropogenic reductions in soil health, and of individual components of soil quality, are a pressing ecological concern. A conference entitled ‘Soil Health: Managing the Biological Component of Soil Quality’ was held was held in the USA in November 1998 to help increase awareness of the importance and utility of soil organisms as indicators of soil quality and determinants of soil health. To evaluate sustainability of agricultural practices, assessment of soil health using various indicators of soil quality is needed. Soil organism and biotic parameters (e.g. abundance, diversity, food web structure, or community stability) meet most of the five criteria for useful indicators of soil quality. Soil organisms respond sensitively to land management practices and climate. They are well correlated with beneficial soil and ecosystem functions including water storage, decomposition and nutrient cycling, detoxification of toxicants, and suppression of noxious and pathogenic organisms. Soil organisms also illustrate the chain of cause and effect that links land management decisions to ultimate productivity and health of plants and animals. Indicators must be comprehensible and useful to land managers, who are the ultimate stewards of soil quality and soil health. Visible organisms such as earthworms, insects, and molds have historically met this criterion. Finally, indicators must be easy and inexpensive to measure, but the need for knowledge of taxonomy complicates the measurement of soil organisms. Several farmer-participatory programs for managing soil quality and health have incorporated abiotic and simple biotic indicators. The challenge for the future is to develop sustainable management systems which are the vanguard of soil health; soil quality indicators are merely a means towards this end.
The Fleet Size and Mix Vehicle Routing Problem with Backhauls : Formulation and Set Partitioning-based Heuristics

In this paper we present a new variant of the classical Vehicle Routing Problem – th Fleet Size and Mix Vehicle Routing Problem with Backhauls (FSMVRPB). An ILP formulation of the FSMVRPB is presented. Optimal solutions for small size instances are produced and upper and lower bounds are generated for larger ones. In this paper we also propose a S t Partitioning Problem (SPP) based heuristic. Three frameworks are developed and tested on a set of new FSMVRPB data instances which we generated. Computational results are presented which can be used for future benchmarking.
Modeling and monitoring of cardiovascular dynamics for patients in critical care

In modern intensive care units (ICUs) a vast and varied amount of physiological data is measured and collected, with the intent of providing clinicians with detailed information about the physiological state of each patient. The data include measurements from the bedside monitors of heavily instrumented patients, imaging studies, laboratory test results, and clinical observations. The clinician’s task of integrating and interpreting the data, however, is complicated by the sheer volume of information and the challenges of organizing it appropriately. This task is made even more difficult by ICU patients’ frequently-changing physiological state. Although the extensive clinical information collected in ICUs presents a challenge, it also opens up several opportunities. In particular, we believe that physiologically-based computational models and model-based estimation methods can be harnessed to better understand and track patient state. These methods would integrate a patient’s hemodynamic data streams by analyzing and interpreting the available information, and presenting resultant pathophysiological hypotheses to the clinical staff in an efficient manner. In this thesis, such a possibility is developed in the context of cardiovascular dynamics. The central results of this thesis concern averaged models of cardiovascular dynamics and a novel estimation method for continuously tracking cardiac output and total peripheral resistance. This method exploits both intra-beat and inter-beat dynamics of arterial blood pressure, and incorporates a parametrized model of arterial compliance. We validated our method with animal data from laboratory experiments and ICU patient data. The resulting root-mean-square-normalized errors – at most 15% depending on the data set – are quite low and clinically acceptable. In addition, we describe a novel estimation scheme for continuously monitoring left ventricular ejection fraction and left ventricular end-diastolic volume. We validated this method on an animal data set. Again, the resulting root-mean-square-normalized errors were quite low – at most 13%. By continuously monitoring cardiac output, total peripheral resistance, left ventricular ejection fraction, left ventricular end-diastolic volume, and arterial blood pressure, one has the basis for distinguishing between cardiogenic, hypovolemic, and septic shock. We hope that the results in this thesis will contribute to the development of a next-generation patient monitoring system. Thesis Supervisor: Professor George C. Verghese Title: Professor of Electrical Engineering Thesis Supervisor: Dr. Thomas Heldt Title: Postdoctoral Associate
Grid-Voltage-Oriented Sliding Mode Control for DFIG Under Balanced and Unbalanced Grid Faults

Doubly fed induction generators (DFIGs) are widely used in variable-speed wind turbines. Despite the well-accepted performance of DFIGs, these generators are highly sensible to grid faults. Hence, the presence of grid faults must be considered in the design of any control system to be deployed on DFIGs. Sliding mode control (SMC) is a useful alternative for electric machinery control since SMC offers fast dynamic response and less sensitivity to parameter variations and disturbances. Additionally, the natural outputs of SMC are discontinuous signals allowing direct switching of power electronic devices. In this paper, a grid-voltage-oriented SMC is proposed and tested under low voltage grid faults. Unlike other nonmodulated techniques such as direct torque control, there is not a necessity of modifying the controller structure for withstanding low depth voltage dips. For stator natural flux cancelation, the torque and reactive power references are modified to inject a demagnetizing current. Simulation results demonstrate the demagnetization of the natural flux component as well as a robust tracking control under balanced and unbalanced voltage dips.
Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners' needs. Study results show that ANFIS has been successfully implemented for learning content adaptation within different learning context scenarios. The performance of the ANFIS model was evaluated using standard error measurements which revealed the optimal setting necessary for better predictability. The MATLAB simulation results indicate that the performance of the ANFIS approach is valuable and easy to implement. The study results are based on analysis of different model settings; they confirm that the m-learning application is functional. However, it should be noted that an increase in the number of inputs being considered by the model will increase the system response time, and hence the delay for the mobile learner.
Validation of the Regicor Short Physical Activity Questionnaire for the Adult Population

To develop and validate a short questionnaire to estimate physical activity (PA) practice and sedentary behavior for the adult population.



The short questionnaire was developed using data from a cross-sectional population-based survey (n = 6352) that included the Minnesota leisure-time PA questionnaire. Activities that explained a significant proportion of the variability of population PA practice were identified. Validation of the short questionnaire included a cross-sectional component to assess validity with respect to the data collected by accelerometers and a longitudinal component to assess reliability and sensitivity to detect changes (n = 114, aged 35 to 74 years).



Six types of activities that accounted for 87% of population variability in PA estimated with the Minnesota questionnaire were selected. The short questionnaire estimates energy expenditure in total PA and by intensity (light, moderate, vigorous), and includes 2 questions about sedentary behavior and a question about occupational PA. The short questionnaire showed high reliability, with intraclass correlation coefficients ranging between 0.79 to 0.95. The Spearman correlation coefficients between estimated energy expenditure obtained with the questionnaire and the number of steps detected by the accelerometer were as follows: 0.36 for total PA, 0.40 for moderate intensity, and 0.26 for vigorous intensity. The questionnaire was sensitive to detect changes in moderate and vigorous PA (correlation coefficients ranging from 0.26 to 0.34).



The REGICOR short questionnaire is reliable, valid, and sensitive to detect changes in moderate and vigorous PA. This questionnaire could be used in daily clinical practice and epidemiological studies.
Region Representation Learning via Mobility Flow

Increasing amount of urban data are being accumulated and released to public; this enables us to study the urban dynamics and address urban issues such as crime, traffic, and quality of living. In this paper, we are interested in learning vector representations for regions using the large-scale taxi flow data. These representations could help us better measure the relationship strengths between regions, and the relationships can be used to better model the region properties. Different from existing studies, we propose to consider both temporal dynamics and multi-hop transitions in learning the region representations. We propose to jointly learn the representations from a flow graph and a spatial graph. Such a combined graph could simulate individual movements and also addresses the data sparsity issue. We demonstrate the effectiveness of our method using three different real datasets.
Survey of Security and Privacy Issues of Internet of Things

----------------------------------------------------------------------ABSTRACT----------------------------------------------------------This paper is a general survey of all the security issues existing in the Internet of Things (IoT) along with an analysis of the privacy issues that an end-user may face as a consequence of the spread of IoT. The majority of the survey is focused on the security loopholes arising out of the information exchange technologies used in Internet of Things. No countermeasure to the security drawbacks has been analyzed in the paper.
Antimicrobial activity of natural products against Clostridium difficile in vitro.

To investigate the antimicrobial activity of various natural products against Clostridium difficile in vitro.



The antibacterial activity of 20 natural products was determined by the agar well diffusion and broth microdilution assays against four C. difficile strains, three comparator organisms and four gastrointestinal commensal organisms. Of the raw natural products, garlic juice had the highest activity. The most active processed products were peppermint oil and the four pure compounds trans-cinnamaldehyde, allicin, menthol and zingerone. Furthermore, Bacteroides species had similar susceptibility to C. difficile to most natural products; however, Lactobacillus casei was less susceptible. The combined effect of natural products with vancomycin or metronidazole was determined using the conventional checkerboard titration method and the fractional inhibitory concentration index was calculated. The results showed a possible synergism between trans-cinnamaldehyde and vancomycin and partial synergy between trans-cinnamaldehyde and metronidazole.



The study indicates a range of antimicrobial activity of natural products against C. difficile and suggests that they may be useful as alternative or complementary treatments for C. difficile infection (CDI), particularly as most are able to be given orally.



This study encourages further investigation of natural products for treatment of CDI.
Audio-based emotion estimation for interactive robotic therapy for children with autism spectrum disorder

Recently, efforts in the development of speech recognition systems and robots have come to fruition with an overflow of applications in our daily lives. However, we are still far from achieving natural interaction between humans and robots, given that robots do not take into account the emotional state of speakers. The objective of this research is to create an automatic emotion classifier integrated with a robot, such that the robot can understand the emotional state of a human user by analyzing the speech signals from the user. This becomes particularly relevant in the realm of using assistive robotics to tailor therapeutic techniques towards assisting children with autism spectrum disorder (ASD). Over the past two decades, the number of children being diagnosed with ASD has been rapidly increasing, yet the clinical and societal support have not been enough to cope with the needs. Therefore, finding alternative, affordable, and accessible means of therapy and assistance has become more of a concern. Improving audio-based emotion prediction for children with ASD will allow for the robotic system to properly assess the engagement level of the child and modify its responses to maximize the quality of interaction between the robot and the child and sustain an interactive learning environment.
The Principle of Maximum Causal Entropy for Estimating Interacting Processes

The principle of maximum entropy provides a powerful framework for estimating joint, conditional, and marginal probability distributions. However, there are many important distributions with elements of interaction and feedback where its applicability has not been established. This paper presents the principle of maximum causal entropy-an approach based on directed information theory for estimating an unknown process based on its interactions with a known process. We demonstrate the breadth of the approach using two applications: a predictive solution for inverse optimal control in decision processes and computing equilibrium strategies in sequential games.
Vascular Ehlers-Danlos Syndrome Without the Characteristic Facial Features

As a type of Ehlers-Danlos syndrome (EDS), vascular EDs (vEDS) is typified by a number of characteristic facial features (eg, large eyes, small chin, sunken cheeks, thin nose and lips, lobeless ears). However, vEDs does not typically display hypermobility of the large joints and skin hyperextensibility, which are features typical of the more common forms of EDS. Thus, colonic perforation or aneurysm rupture may be the first presentation of the disease. Because both complications are associated with a reduced life expectancy for individuals with this condition, an awareness of the clinical features of vEDS is important. Here, we describe the treatment of vEDS lacking the characteristic facial attributes in a 24-year-old healthy man who presented to the emergency room with abdominal pain. Enhanced computed tomography revealed diverticula and perforation in the sigmoid colon. The lesion of the sigmoid colon perforation was removed, and Hartmann procedure was performed. During the surgery, the control of bleeding was required because of vascular fragility. Subsequent molecular and genetic analysis was performed based on the suspected diagnosis of vEDS. These analyses revealed reduced type III collagen synthesis in cultured skin fibroblasts and identified a previously undocumented mutation in the gene for a1 type III collagen, confirming the diagnosis of vEDS. After eliciting a detailed medical profile, we learned his mother had a history of extensive bruising since childhood and idiopathic hematothorax. Both were prescribed oral celiprolol. One year after admission, the patient was free of recurrent perforation. This case illustrates an awareness of the clinical characteristics of vEDS and the family history is important because of the high mortality from this condition even in young people. Importantly, genetic assays could help in determining the surgical procedure and offer benefits to relatives since this condition is inherited in an autosomal dominant manner.
DNA-Based Storage: Trends and Methods

We provide an overview of current approaches to DNA-based storage system design and of accompanying synthesis, sequencing and editing methods. We also introduce and analyze a suite of new constrained coding schemes for both archival and random access DNA storage channels. The analytic contribution of our work is the construction and design of sequences over discrete alphabets that avoid pre-specified address patterns, have balanced base content, and exhibit other relevant substring constraints. These schemes adapt the stored signals to the DNA medium and thereby reduce the inherent error-rate of the system.
Classification of packed executables for accurate computer virus detection

Executable packing is the most common technique used by computer virus writers to obfuscate malicious code and evade detection by anti-virus software. Universal unpackers have been proposed that can detect and extract encrypted code from packed executables, therefore potentially revealing hidden viruses that can then be detected by traditional signature-based anti-virus software. However, universal unpackers are computationally expensive and scanning large collections of executables looking for virus infections may take several hours or even days. In this paper we apply pattern recognition techniques for fast detection of packed executables. The objective is to efficiently and accurately distinguish between packed and non-packed executables, so that only executables detected as packed will be sent to an universal unpacker, thus saving a significant amount of processing time. We show that our system achieves very high detection accuracy of packed executables with a low average processing time.
Oxidation resistance of graphene-coated Cu and Cu/Ni alloy.

The ability to protect refined metals from reactive environments is vital to many industrial and academic applications. Current solutions, however, typically introduce several negative effects, including increased thickness and changes in the metal physical properties. In this paper, we demonstrate for the first time the ability of graphene films grown by chemical vapor deposition to protect the surface of the metallic growth substrates of Cu and Cu/Ni alloy from air oxidation. In particular, graphene prevents the formation of any oxide on the protected metal surfaces, thus allowing pure metal surfaces only one atom away from reactive environments. SEM, Raman spectroscopy, and XPS studies show that the metal surface is well protected from oxidation even after heating at 200 °C in air for up to 4 h. Our work further shows that graphene provides effective resistance against hydrogen peroxide. This protection method offers significant advantages and can be used on any metal that catalyzes graphene growth.
An open platform for rapid-prototyping protection and control schemes with IEC 61850

Communications is becoming increasingly important to the operation of protection and control schemes. Although offering many benefits, using standards-based communications, particularly IEC 61850, in the course of the research and development of novel schemes can be complex. This paper describes an open source platform which enables the rapid-prototyping of communications-enhanced schemes. The platform automatically generates the data model and communications code required for an Intelligent Electronic Device (IED) to implement publisher-subscriber Generic Object-Oriented Substation Event (GOOSE) and Sampled Value (SV) messaging. The generated code is tailored to a particular System Configuration Description (SCD) file, and is therefore extremely efficient at run-time. It is shown how a model-centric tool, such as the open source Eclipse Modeling Framework, can be used to manage the complexity of the IEC 61850 standard, by providing a framework for validating SCD files and by automating parts of the code generation process.
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba

Recommender systems (RSs) have been the most important technology for increasing the business in Taobao, the largest online consumer-to-consumer (C2C) platform in China. There are three major challenges facing RS in Taobao: scalability, sparsity and cold start. In this paper, we present our technical solutions to address these three challenges. The methods are based on a well-known graph embedding framework. We first construct an item graph from users' behavior history, and learn the embeddings of all items in the graph. The item embeddings are employed to compute pairwise similarities between all items, which are then used in the recommendation process. To alleviate the sparsity and cold start problems, side information is incorporated into the graph embedding framework. We propose two aggregation methods to integrate the embeddings of items and the corresponding side information. Experimental results from offline experiments show that methods incorporating side information are superior to those that do not. Further, we describe the platform upon which the embedding methods are deployed and the workflow to process the billion-scale data in Taobao. Using A/B test, we show that the online Click-Through-Rates (CTRs) are improved comparing to the previous collaborative filtering based methods widely used in Taobao, further demonstrating the effectiveness and feasibility of our proposed methods in Taobao's live production environment.