nils-herrmann/clean_scirepeval huggingface dataset:
FELICS - Fair Evaluation of Lightweight Cryptographic Systems

In this paper we introduce FELICS, a free and open-source benchmarking framework designed for fair and consistent evaluation of software implementations of lightweight cryptographic primitives for embedded devices. The framework is very flexible thanks to its modular structure, which allows for an easy integration of new metrics, target devices and evaluation scenarios. It consists of two modules that can currently asses the performance of lightweight block and stream ciphers on three widely used microcontrollers: 8-bit AVR, 16-bit MSP and 32-bit ARM. The metrics extracted are execution time, RAM consumption and binary code size. FELICS has a simple user interface and is intended to be used by cipher designers to compare new primitives with the state of the art. The extracted metrics are very detailed and assist embedded software engineers in selecting the best cipher to match the requirements of a particular application. The tool aims to increase the transparency and trust in benchmarking results of lightweight primitives and facilitates a fair comparison between different primitives using the same evaluation conditions.
Testing predictions from personality neuroscience. Brain structure and the big five.

We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstrate the potential of personality neuroscience (i.e., the systematic study of individual differences in personality using neuroscience methods) as a discipline.
Autopilot: automatic data center management

Microsoft is rapidly increasing the number of large-scale web services that it operates. Services such as Windows Live Search and Windows Live Mail operate from data centers that contain tens or hundreds of thousands of computers, and it is essential that these data centers function reliably with minimal human intervention. This paper describes the first version of Autopilot, the automatic data center management infrastructure developed within Microsoft over the last few years. Autopilot is responsible for automating software provisioning and deployment; system monitoring; and carrying out repair actions to deal with faulty software and hardware. A key assumption underlying Autopilot is that the services built on it must be designed to be manageable. We also therefore outline the best practices adopted by applications that run on Autopilot.
A discussion of cybersickness in virtual environments

An important and troublesome problem with current virtual environment (VE) technology is the tendency for some users to exhibit symptoms that parallel symptoms of classical motion sickness both during and after the VE experience. This type of sickness, cybersickness, is distinct from motion sickness in that the user is often stationary but has a compelling sense of self motion through moving visual imagery. Unfortunately, there are many factors that can cause cybersickness and there is no foolproof method for eliminating the problem. In this paper, I discuss a number of the primary factors that contribute to the cause of cybersickness, describe three conflicting cybersickness theories that have been postulated, and discuss some possible methods for reducing cybersickness in VEs.
Dynamic Malware Detection Using API Similarity

Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.
Parental vaccine safety concerns in 2009.

Vaccine safety concerns can diminish parents' willingness to vaccinate their children. The objective of this study was to characterize the current prevalence of parental vaccine refusal and specific vaccine safety concerns and to determine whether such concerns were more common in specific population groups.



In January 2009, as part of a larger study of parents and nonparents, 2521 online surveys were sent to a nationally representative sample of parents of children who were aged </=17 years. The main outcome measures were parental opinions on vaccine safety and whether the parent had ever refused a vaccine that a doctor recommended for his or her child.



The response rate was 62%. Most parents agreed that vaccines protect their child(ren) from diseases; however, more than half of the respondents also expressed concerns regarding serious adverse effects. Overall, 11.5% of the parents had refused at least 1 recommended vaccine. Women were more likely to be concerned about serious adverse effects, to believe that some vaccines cause autism, and to have ever refused a vaccine for their child(ren). Hispanic parents were more likely than white or black parents to report that they generally follow their doctor's recommendations about vaccines for their children and less likely to have ever refused a vaccine. Hispanic parents were also more likely to be concerned about serious adverse effects of vaccines and to believe that some vaccines cause autism.



Although parents overwhelmingly share the belief that vaccines are a good way to protect their children from disease, these same parents express concerns regarding the potential adverse effects and especially seem to question the safety of newer vaccines. Although information is available to address many vaccine safety concerns, such information is not reaching many parents in an effective or convincing manner.
Active SLAM using Model Predictive Control and Attractor based Exploration

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone
Enterprise Risk Management Integrated framework for Cloud Computing

Enterprise Risk Management Integrated framework for Cloud Computing Dr. M. Suresh Babu Professor & Head, Department of Computer Applications, Madanapalle Institute of Technology & Science, Madanapalle, AP, India – 517325. A. Mahesh babu Research Scholar, Department of Computer Science & Technology, Sri Krishnadevaraya University, Anantapur – 515003, AP, India.  These technological and commercial changes have an impact on current working practices. Businesses need to understand the impact of the new combinations of technology layers, and how they work together. A crucial part of this is analyzing and assessing the risks involved. In the evolution of computing technology, information processing has moved from mainframes to personal computers to server-centric computing to the Web. Today, many organizations are seriously considering adopting cloud computing, the next major milestone in technology and business collaboration. A supercharged version of delivering hosted services over the Internet, cloud computing potentially enables organizations to increase their business model capabilities and their ability to meet computing resource demands while avoiding significant investments in infrastructure, training, personnel, and software. In fall 2010, a Google executive testified before a U.S. congressional subcommittee that more than three million businesses worldwide were customers of its cloud service offerings. As with any new opportunity, cloud computing entails commensurate risks. It brings to organizations a different dimension of collaboration and human interaction, new organizational dependencies, faster resource fulfilment, and new business models.
Unsupervised Extraction of Attributes and Their Values from Product Description

This paper describes an unsupervised method for extracting product attributes and their values from an e-commerce product page. Previously, distant supervision has been applied for this task, but it is not applicable in domains where no reliable knowledge base (KB) is available. Instead, the proposed method automatically creates a KB from tables and itemizations embedded in the product’s pages. This KB is applied to annotate the pages automatically and the annotated corpus is used to train a model for the extraction. Because of the incompleteness of the KB, the annotated corpus is not as accurate as a manually annotated one. Our method tries to filter out sentences that are likely to include problematic annotations based on statistical measures and morpheme patterns induced from the entries in the KB. The experimental results show that the performance of our method achieves an average F score of approximately 58.2 points and that filters can improve the performance.
A location-based smart shopping system with IoT technology

Localization is one important part of Internet of Things(IoT) where the Location of Everything (LoE) system plays a important role to improve most services in IoT area. On the other hand, data mining techniques are essential analyses when we have big data from IoT platforms. Indeed, integration of location-based methods and data mining analysis process can make a smart system service for IoT scenarios and applications. For this purpose, we design a smart shopping platform including four components, location of everything component, data collection component, data filtering/analysing component and data mining component. Then a novel accurate localization scheme named “location orbital” is developed that estimates the current location of mobile objects (users or everything) based on both current and the previous locations. Finally, an implementation of the experiment in a shopping mall is conducted to practically examine performance evaluation of the location-based scheme. The experimental results show that the proposed scheme could achieve significant higher precision than other localization techniques.
Automatic Shoeprint Retrieval Algorithm for Real Crime Scenes

This study is to propose a fully automatic crime scene shoeprint retrieval algorithm that can be used to link scenes of crime or determine the brand of a shoe. A shoeprint contour model is proposed to roughly correct the geometry distortions. To simulate the character of the forensic experts, a region priority match and similarity estimation strategy is also proposed. The shoeprint is divided into two semantic regions, and their confidence values are computed based on the priority in the forensic practice and the quantity of reliable information. Similarities of each region are computed respectively, and the matching score between the reference image and an image in the database is the weighted sum. For regions with higher confidence value, the similarities are computed based on the proposed coarse-to-fine global invariant descriptors, which are based on Wavelet-Fourier transform and are invariant under slight geometry distortions and interference such as breaks and small holes, etc. For regions with lower confidence value, similarities are estimated based on computed similarities of regions with higher confidence value. Parameters of the proposed algorithm have learned from huge quantity of crime scene shoeprints and standard shoeprints which can cover most practical cases, and the algorithm can have better performance with minimum user intervention. The proposed algorithm has been tested on the crime scene shoeprint database composed of 210,000 shoeprints provided by the third party, and the cumulative matching score of the top 2 percent is 90.87.
ECG Recording on a Bed During Sleep Without Direct Skin-Contact

A new indirect contact (IDC) electrocardiogram (ECG) measurement method (IDC-ECG) for monitoring ECG during sleep that is adequate for long-term use is provided. The provided method did not require any direct conductive contact between the instrument and bare skin. This method utilizes an array of high-input-impedance active electrodes fixed on the mattress and an indirect-skin-contact ground made of a large conductive textile sheet. A thin cotton bedcover covered the mattress, electrodes, and conductive textile, and the participants were positioned on the mattress over the bedcover. An ECG was successfully obtained, although the signal quality was lower and the motion artifact was larger than in conventional direct-contact measurements (DC-ECG). The results showed that further studies are required to apply the provided method to an ECG diagnosis of cardiovascular diseases. However, currently the method can be used for HRV assessment with easy discrimination of R-peaks
Virtual stage sets in live performing arts (from the spectator to the spect-actor)

This paper studies the added value of VR to the art of stage setting through examples and experiments. It will also analyze the link between the audience and digital sets (VR, AR), from an aesthetic point of view as well as a practical one. It is important for us to succeed in creating a theatrical set which favors the spectator's presence for him to become a live performing spectator, and thus have a stronger link with the setting in the short as well as long run.
Reference ranges for uterine artery mean pulsatility index at 11-41 weeks of gestation.

To construct gestational age (GA)-based reference ranges for the uterine artery (UtA) mean pulsatility index (PI) at 11-41 weeks of pregnancy.



A prospective cross-sectional observational study was carried out of 20 consecutive singleton pregnancies for each completed gestational week at 11-41 weeks. UtAs were examined by color and pulsed Doppler imaging, and the mean PI, as well as the presence or absence of a bilateral protodiastolic notch, were recorded. Polynomials were fitted by means of least-square regression to estimate the relationship between the mean UtA-PI and GA.



A total of 620 women were included. A second-degree polynomial (Log(e) mean UtA-PI = 1.39 - 0.012 x GA + GA(2) x 0.0000198, with GA measured in days), after a natural logarithmic transformation, was selected to model our data. There was a significant decrease in the mean UtA-PI between 11 weeks (mean PI, 1.79; 95(th) centile, 2.70) and 34 weeks (mean PI, 0.70; 95(th) centile, 0.99). It then became more stable up until 41 weeks (mean PI, 0.65; 95(th) centile, 0.89).



The mean UtA-PI shows a progressive decrease until the late stages of pregnancy. Reference ranges for mean UtA-PI may have clinical value in screening for placenta-associated diseases in the early stages of pregnancy, and in evaluating patients with pregnancy-induced hypertension and/or small-for-gestational age fetuses during the third trimester.
Patient-centredness: a conceptual framework and review of the empirical literature.

A 'patient-centred' approach is increasingly regarded as crucial for the delivery of high quality care by doctors. However, there is considerable ambiguity concerning the exact meaning of the term and the optimum method of measuring the process and outcomes of patient-centred care. This paper reviews the conceptual and empirical literature in order to develop a model of the various aspects of the doctor-patient relationship encompassed by the concept of 'patient-centredness' and to assess the advantages and disadvantages of alternative methods of measurement. Five conceptual dimensions are identified: biopsychosocial perspective; 'patient-as-person'; sharing power and responsibility; therapeutic alliance; and 'doctor-as-person'. Two main approaches to measurement are evaluated: self-report instruments and external observation methods. A number of recommendations concerning the measurement of patient-centredness are made.
A survey of information-centric networking

The information-centric networking (ICN) concept is a significant common approach of several future Internet research activities. The approach leverages in-network caching, multiparty communication through replication, and interaction models decoupling senders and receivers. The goal is to provide a network infrastructure service that is better suited to today¿s use (in particular. content distribution and mobility) and more resilient to disruptions and failures. The ICN approach is being explored by a number of research projects. We compare and discuss design choices and features of proposed ICN architectures, focusing on the following main components: named data objects, naming and security, API, routing and transport, and caching. We also discuss the advantages of the ICN approach in general.
Performance analysis of DCO-OFDM in VLC system

The performance of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The impact of nonlinearity of Light Emitting Diode (LED) and its beam angle on the VLC system performance is studied. We later analyze the effect of modulation order, number of subcarriers, signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major issue in OFDM based VLC system. Simulation results show that the bit error rate (BER) decreases as the degree of nonlinearity increases and a better BER can be achieved as the LED behavior approaches linear model. In addition, it is shown that reducing the QAM order or increasing the number of subcarriers may reduce the effect of the LED nonlinearity, thus improving the BER performance of the VLC system. Moreover, it is demonstrated that PAPR is higher for a large number of subcarriers and modulation order. Finally, the PAPR of the visible light OFDM system can significantly be reduced by employing the signal scaling combined with biasing operation.
FLATM: A fuzzy logic approach topic model for medical documents

One of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.
BETTER GUN ENFORCEMENT , LESS CRIME *

Project Safe Neighborhoods (PSN), which for the past several years has been the major federal initiative to combat gun violence, includes several elements (such as gun locks and other efforts to reduce gun availability) that research suggests are likely to have at best modest effects on gun crime. In general, enforcement activities targeted at the “demand side” of the underground gun market currently enjoy stronger empirical support. However much of PSN’s budget has been devoted to increasing the severity of punishment, such as by federalizing gun cases, which seems to be less effective than targeted street-level enforcement designed to increase the probability of punishment for gun carrying or use in crime.
Automatic Scaling of Fish Images

In aquaculture breeding programs where large numbers of fish need to be rapidly phenotyped, the absolute physical dimensions of fish (in millimeters or inches) are often required to be extracted from electronic images in order to measure the size of the fish. While it is possible to infer the length of the fish in pixels, the absolute scale of the image (in pixels-per-millimeter or dots-per-inch) is largely unknown without a reference grid, or requires additional hardware, data collection and/or record-keeping management overheads. One cost and time effective solution is to capture the absolute scale by including a measuring ruler in the photographed scene and from which a computer program can automatically identify the scale of the photo and calculate fish morphometric measurements. To assist such workflow, this study developed an algorithm that automatically detects a ruler in a given image, and automatically extracts its scale as distance (in fractional number of pixels) between the ruler's graduation marks. The algorithm was applied to 445 publicly available images of barramundi or Asian seabass (Lates calcarifer), where a millimeter-graded ruler was included in each image. Convolutional Neural Network (CNN) was trained to segment the images into ruler, background, fish and label sections. Then the distance-extraction algorithm was applied to the ruler section of the images. The false-negative rate was less than 2%, where the ruler graduation distances could not be extracted in only 2-6 (out of 445) images even when the test images were rotated up to 90 degrees. The mean absolute relative error (MARE) of the inferred distances was 1-2%.
Generalized Mongue-Elkan Method for Approximate Text String Comparison

The Mongue-Elkan method is a general text string comparison method based on an internal character-based similarity measure (e.g. edit distance) combined with a token level (i.e. word level) similarity measure. We propose a generalization of this method based on the notion of the generalized arithmetic mean instead of the simple average used in the expression to calculate the Monge-Elkan method. The experiments carried out with 12 well-known name-matching data sets show that the proposed approach outperforms the original Monge-Elkan method when character-based measures are used to compare tokens.
Deep 6-DOF Tracking

We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.