nils-herrmann/clean_scirepeval huggingface dataset:
A Protocol of Comparing Information without Leaking

At present, research on secure multi-party computation is of great interest in modern cryptography. It should be acknowledged that if any function can be computed securely, then it results in a very powerful tool. In fact, all natural protocols are, or can be rephrased to be, special cases of the multi-party computation problems. Design and analysis of the special multi-party computation protocols is meaningful and has attracted much interest in this field. Based on the combination of a public-key cryptosystem of the homomorphic encryption and on the theoretic construction relying on the Φ-hiding assumption, a protocol for comparing information of equality is proposed. The protocol needs only a single round of interaction and ensures fairness, efficiency and security. The protocol is fair, which means that one party knows the sound result of the comparison if and only if the other one knows the result. The protocol is efficient with the help of an oblivious third party for calculating. However, the third party cannot learn any information about the participant’s private inputs and even about the comparison result, and cannot collude with any participant. The protocol is secure for the two participants, that is, any information about their secret input will not leak except the final computation result. A precise proof of security of the protocol is presented. Applications of this protocol may include private bidding and auctions, secret ballot elections, ∗ Supported by the National Natural Science Foundation of China under Grant No.60373039 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2001AA144040, 2003AA144151 (国家高技术研究发展计划 (863)); the National Grand Fundamental Research 973 Program of China under Grant No.G1999035802 (国家重点基础研究发展规划 (973)); the National Outstanding Young Scientists Foundation of China under Grant No.60025205 (国家杰出青年科学基金); the Natural Science Foundation of Shandong Province of China under Grant No.Y2003A03 (山东省自然科学基金) 作者简介: 秦静(1960-),女,山东济南人,教授,主要研究领域为密码学,安全协议;张振峰(1972-),男,副研究员,主要研究领域 为密码学/网络与信息安全;冯登国(1965-),男,研究员,博士生导师,主要研究领域为信息与网络安全;李宝(1962-),男,研究员,博士生 导师,主要研究领域为信息安全,安全协议. 422 Journal of Software 软件学报 2004,15(3) commercial business, identification in a number of scenarios and so on. It is believed that the protocol may be of practical significance for electronic transaction.
Statistical Learning Algorithms Applied to Automobile Insurance

We recently conducted a research project for a large North American automobile insurer. This study was the most exhaustive ever undertaken by this particular insurer and lasted over an entire year. We analyzed the discriminating power of each variable used for ratemaking. We analyzed the performance of several models within five broad categories: linear regressions, generalized linear models, decision trees, neural networks and support vector machines. In this paper, we present the main results of this study. We qualitatively compare models and show how neural networks can represent high-order nonlinear dependencies with a small number of parameters, each of which is estimated on a large proportion of the data, thus yielding low variance. We thoroughly explain the purpose of the nonlinear sigmoidal transforms which are at the very heart of neural networks’ performances. The main numerical result is a statistically significant reduction in the out-of-sample meansquared error using the neural network model and our ability to substantially reduce the median premium by charging more to the highest risks. This in turn can translate into substantial savings and financial benefits for an insurer. We hope this paper goes a long way in convincing actuaries to include neural networks within their set of modeling tools for ratemaking.
Cache Template Attacks: Automating Attacks on Inclusive Last-Level Caches

Recent work on cache attacks has shown that CPU caches represent a powerful source of information leakage. However, existing attacks require manual identification of vulnerabilities, i.e., data accesses or instruction execution depending on secret information. In this paper, we present Cache Template Attacks. This generic attack technique allows us to profile and exploit cachebased information leakage of any program automatically, without prior knowledge of specific software versions or even specific system information. Cache Template Attacks can be executed online on a remote system without any prior offline computations or measurements. Cache Template Attacks consist of two phases. In the profiling phase, we determine dependencies between the processing of secret information, e.g., specific key inputs or private keys of cryptographic primitives, and specific cache accesses. In the exploitation phase, we derive the secret values based on observed cache accesses. We illustrate the power of the presented approach in several attacks, but also in a useful application for developers. Among the presented attacks is the application of Cache Template Attacks to infer keystrokes and—even more severe—the identification of specific keys on Linux and Windows user interfaces. More specifically, for lowercase only passwords, we can reduce the entropy per character from log2(26) = 4.7 to 1.4 bits on Linux systems. Furthermore, we perform an automated attack on the Ttable-based AES implementation of OpenSSL that is as efficient as state-of-the-art manual cache attacks.
Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser.

We demonstrate ultrahigh speed swept source retinal OCT imaging using a Fourier domain mode locked (FDML) laser. The laser uses a combination of a semiconductor optical amplifier and an ytterbium doped fiber amplifier to provide more than 50 mW output power. The 1050 nm FDML laser uses standard telecom fiber for the km long delay line instead of two orders of magnitude more expensive real single mode fiber. We investigate the influence of this "oligo-mode" fiber on the FDML laser performance. Two design configurations with 684,400 and 1,368,700 axial scans per second are investigated, 25x and 50x faster than current commercial instruments and more than 4x faster than previous single spot ophthalmic results. These high speeds enable the acquisition of densely sampled ultrawide-field data sets of the retina within a few seconds. Ultrawide-field data consisting of 1900 x 1900 A-scans with ~70° angle of view are acquired within only 3 and 6 seconds using the different setups. Such OCT data sets, more than double as large as previously reported, are collapsed to a 4 megapixel high definition fundus image. We achieve good penetration into the choroid by hardware spectral shaping of the laser output. The axial resolution in tissue is 12 µm (684 kHz) and 19 µm (1.37 MHz). A series of new data processing and imaging extraction protocols, enabled by the ultrawide-field isotropic data sets, are presented. Dense isotropic sampling enables both, cross-sectional images along arbitrary coordinates and depth-resolved en-face fundus images. Additionally, we investigate how isotropic averaging compares to the averaging of cross-sections along the slow axis.
Information & Environment: IoT-Powered Recommender Systems

Internet of Things (IoT) infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. The evaluation and analysis herein clarified the nature of users’ requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The results indicated that users of IoTbased recommendation are interested in a broad distribution of subjects, with a short-head distribution from this collection in American and English Literature. A long-tail finding showed a diversity of topics that are recommended to users in the library book stacks with IoT-powered recommendations.
Is ultrasound screening for DDH in babies born breech sufficient?

To review our incidence of developmental dysplasia of the hip (DDH) in breech infants referred for ultrasound screening and to determine if subsequent follow-up radiographs are necessary in these patients with normal clinical and ultrasound examinations.



A review of the clinical data and imaging studies of all children with the risk factor of breech presentation that were referred for orthopedic evaluation over a 5-year period was conducted. All patients were examined by a fellowship-trained pediatric orthopedic surgeon and all ultrasounds were done at approximately 6 weeks of age by an experienced ultrasonographer. Ultrasounds were evaluated using the dynamic method as described by Harcke. As per our protocol, all patients with normal screening ultrasounds were brought back for a final clinical examination and radiographic check at 4-6 months. Acetabular dysplasia was indicated by radiographic parameters-if there was severe blunting of the sourcil, abnormal acetabular index for age, or if there was significant asymmetry of acetabular indices side-to-side-in the setting of clinical parameters-if there was greater than 10° difference in side-to-side abduction or symmetric abduction of less than 60°.



Three hundred patients with the risk factor of breech presentation were included. Thirty-four patients had clinically unstable hips; 266 had clinically stable hips and were screened by ultrasound. Sixty-four percent were female and 36% were male. Twenty-seven percent of these breech patients had abnormal screening ultrasounds and were subsequently treated. Of the remaining 73% with normal ultrasounds, who were returned per protocol at a mean of 5 months, 29% had evidence of dysplasia and underwent treatment. The diagnosis of dysplasia following a normal ultrasound was based on both radiographic and clinical parameters. Of the hips treated with a Pavlik harness, 62% had acetabular indices at least two standard deviations from the age-corrected average versus 26% of patients not treated. The average length of follow-up was 10 months.



Retrospectively, we found that, at approximately 6 weeks of age, ultrasound screening of breech patients with clinically stable hips produces an incidence of DDH of 27%. In those patients with a normal ultrasound, 29%, at 4-6 months radiographic follow-up, were found to have dysplasia requiring treatment. This data supports breech as the most important risk factor for hip dysplasia and we, therefore, recommend careful and longitudinal evaluation of these patients with: a careful newborn physical examination, an ultrasound at age 6 weeks, and an anteroposterior (AP) pelvis and frog lateral radiograph at 6 months, as the risk of subsequent dysplasia is too high to discharge patients after a normal ultrasound.
Generalization of TORCS car racing controllers with artificial neural networks and linear regression analysis

Designing controllers for simulated cars is a challenging task because there are numerous sensor inputs and a lot of actuators to be controlled. Although it is possible to use domain-expert knowledge on the car racing, it is not trivial to represent the knowledge into controllers and tune the parameters for them. Therefore, it is natural to adopt machine learning approach into the knowledge-oriented controllers to enhance their performance and minimize tedious parameter tunings. In this paper, we try to enhance our own heuristic controllers using machine learning models which decide the appropriate parameters of the heuristic controllers given the current sensory inputs. At first, we predict the desired speed only using the equations derived by the linear regression analysis for the both curved and straight track segments. Because the decision on reducing speed before the corner is more complex than the one in the straight line and the corners, it is necessary to use a non-linear model such as artificial neural networks. Secondly, the linear regression and artificial neural networks are specialized to predict desired speed in different situations. Experimental results on TORCS-based car racing simulations show that the combination of the two machine learning algorithms with the heuristic outperforms other alternatives (heuristic only, heuristicþ linear regression, and heuristicþartificial neural networks). & 2012 Elsevier B.V. All rights reserved.
The Colletotrichum boninense species complex

Although only recently described, Colletotrichum boninense is well established in literature as an anthracnose pathogen or endophyte of a diverse range of host plants worldwide. It is especially prominent on members of Amaryllidaceae, Orchidaceae, Proteaceae and Solanaceae. Reports from literature and preliminary studies using ITS sequence data indicated that C. boninense represents a species complex. A multilocus molecular phylogenetic analysis (ITS, ACT, TUB2, CHS-1, GAPDH, HIS3, CAL) of 86 strains previously identified as C. boninense and other related strains revealed 18 clades. These clades are recognised here as separate species, including C. boninense s. str., C. hippeastri, C. karstii and 12 previously undescribed species, C. annellatum, C. beeveri, C. brassicicola, C. brasiliense, C. colombiense, C. constrictum, C. cymbidiicola, C. dacrycarpi, C. novae-zelandiae, C. oncidii, C. parsonsiae and C. torulosum. Seven of the new species are only known from New Zealand, perhaps reflecting a sampling bias. The new combination C. phyllanthi was made, and C. dracaenae Petch was epitypified and the name replaced with C. petchii. Typical for species of the C. boninense species complex are the conidiogenous cells with rather prominent periclinal thickening that also sometimes extend to form a new conidiogenous locus or annellations as well as conidia that have a prominent basal scar. Many species in the C. boninense complex form teleomorphs in culture.


TAXONOMIC NOVELTIES
New combination - Colletotrichum phyllanthi (H. Surendranath Pai) Damm, P.F. Cannon & Crous. Name replacement - C. petchii Damm, P.F. Cannon & Crous. New species - C. annellatum Damm, P.F. Cannon & Crous, C. beeveri Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. brassicicola Damm, P.F. Cannon & Crous, C. brasiliense Damm, P.F. Cannon, Crous & Massola, C. colombiense Damm, P.F. Cannon, Crous, C. constrictum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. cymbidiicola Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. dacrycarpi Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. novae-zelandiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. oncidii Damm, P.F. Cannon & Crous, C. parsonsiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. torulosum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir.  Epitypifications - C. dracaenae Petch.
Making Decisions about Self-Disclosure in Online Social Networks

This paper explores privacy calculus decision making processes for online social networks (OSN). Content analysis method is applied to analyze data obtained from face-to-face interviews and online survey with open-ended questions of 96 OSN users from different countries. The factors users considered before self-disclosing are explored. The perceived benefits and risks of using OSN and their impact on self-disclosure are also identified. We determine that the perceived risks of OSN usage hinder selfdisclosure. It is not clear, however, whether the perceived benefits offset the impact of the risks on selfdisclosure behavior. The findings as a whole do not support privacy calculus in OSN settings.
Search-Oriented Conversational AI (SCAI)

The aim of SCAI@ICTIR2017 is to bring together IR and AI communities to instigate future direction of search-oriented conversational systems. We identified the number of research areas related to conversational AI which is of actual interest to both communities and which have not been fully explored yet. We think it's beneficial to exchange our visions. We solicit the paper submissions and more importantly proposals for panel discussions where researchers can exchange opinions and experiences. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to search-oriented conversational systems which are a new and promising area.
Pileus: protecting user resources from vulnerable cloud services

Cloud computing platforms are now constructed as distributed, modular systems of cloud services, which enable cloud users to manage their cloud resources. However, in current cloud platforms, cloud services fully trust each other, so a malicious user may exploit a vulnerability in a cloud service to obtain unauthorized access to another user's data. To date, over 150 vulnerabilities have been reported in cloud services in the OpenStack cloud. Research efforts in cloud security have focused primarily on attacks originating from user VMs or compromised operating systems rather than threats caused by the compromise of distributed cloud services, leaving cloud users open to attacks from these vulnerable cloud services. In this paper, we propose the Pileus cloud service architecture, which isolates each user's cloud operations to prevent vulnerabilities in cloud services from enabling malicious users to gain unauthorized access. Pileus deploys stateless cloud services "on demand" to service each user's cloud operations, limiting cloud services to the permissions of individual users. Pileus leverages the decentralized information flow control (DIFC) model for permission management, but the Pileus design addresses special challenges in the cloud environment to: (1) restrict how cloud services may be allowed to make security decisions; (2) select trustworthy nodes for access enforcement in a dynamic, distributed environment; and (3) limit the set of nodes a user must trust to service each operation. We have ported the OpenStack cloud platform to Pileus, finding that we can systematically prevent compromised cloud services from attacking other users' cloud operations with less than 3% additional latency for the operation. Application of the Pileus architecture to Open-Stack shows that confined cloud services can service users' cloud operations effectively for a modest overhead.
Measuring the perceived quality of an AR-based learning application: a multidimensional model

Augmented reality (AR) technologies could enhance learning in several ways. The quality of an AR-based educational platform is a combination of key features that manifests in usability, usefulness, and enjoyment for the learner. In this paper, we present a multidimensional model to measure the quality of an AR-based application as perceived by students. The model is specified by a second-order factor (perceived quality) and three dimensions: ergonomic quality, learning quality, and hedonic quality. The purpose of this model is to embody previous research into a coherent framework for the evaluation of AR-based educational platforms and to provide guidance for researchers and practitioners. The model was empirically validated on a Chemistry learning scenario and the results confirm the importance of both the learning and hedonic quality. ARTICLE HISTORY Received 23 July 2014 Revised 2 December 2015 Accepted 9 December 2015
10 kV/120 A SiC DMOSFET half H-bridge power modules for 1 MVA solid state power substation

In this paper, the extension of SiC power technology to higher voltage 10 kV/10 A SiC DMOSFETs and SiC JBS diodes is discussed. A new 10 kV/120 A SiC power module using these 10 kV SiC devices is also described which enables a compact 13.8 kV to 465/√3 solid state power substation (SSPS) rated at 1 MVA.
Driver Fatigue Detection Based on Eye State Recognition

Driving fatigue is a main factor caused the traffic accidents. Our faces contain a lot of useful information, we can use the state of eyes to detect the fatigue, but the eye state would be affected by wearing sunglasses. In this paper, to solve above problems and make the algorithm keep the accuracy and real-time at the same time, we use the infrared videos for detecting and propose an eye state recognition method based on convolution neural network (CNN), eventually calculating percentage of eyelid closure over the pupil over time (PERCLOS), blink frequency to detect the fatigue. The experimental results show that the proposed method has high recognition accuracy of state of eyes when wearing glasses and can detect the fatigue effectively.
Web-Based Ontology Languages and its Based Description Logics

The main object of semantic web languages is to add semantics to the existing information on the Web. These web languages have been developed to represent or express ontologies. Therefore, these description languages provide richer constructors for forming complex class expressions and axioms. An ontology is expressed in a knowledge representation language, which provides a formal frame of semantics. Since description logic is the basis of most ontology language, it is appropriate to explain briefly the base of the description to understand the ontology language clearly. The aim of this paper is to provide a brief survey of state-of-the-art ontology languages which are used to express ontology over the Web is provided. Also the gaol of this paper is to provide a basic understanding of ontologies and description logics, which are the basis of ontology languages.
Supporting attribute-based access control with ontologies

In highly open systems like the Internet, attribute-based access control (ABAC) has proven its appropriateness. The specification and maintenance of ABAC policies however has turned out to be complex and error-prone, especially if heterogeneous attribute schemes are involved. Here, the arising semantic Web can contribute to a solution. This paper presents an approach based on an extension of the established XACML standard. It simplifies the policies by providing an ontology-based attribute management facility.
An Image Encryption Algorithm Based on Knight's Tour and Slip Encryption-Filter

A new image encryption algorithm is proposed in the paper. Its essential idea is that, firstly, we produce a knight's tour matrix emended as slip encryption-filter template matrix; And then, we produce a knight's tour matrix again with the same size of the image waiting for encryption as a knight's tour slip matrix; Finally, we let the center of the slip encryption-filter template move along with the knight's tour slip matrix to do convolvsion operation, and achieve the cipher image. Experimental results show that the algorithm has perfert encryption effect, high encryption efficiency, simplity to achieve, huge secret key space and high security.
Detecting Satire in the News with Machine Learning

We built models with Logistic Regression and linear Support Vector Machines on a large dataset consisting of regular news articles and news from satirical websites, and showed that such linear classifiers on a corpus with about 60,000 articles can perform with a precision of 98.7% and a recall of 95.2% on a random test set of the news. On the other hand, when testing the classifier on “publication sources” which are completely unknown during training, only an accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we showed that the same algorithm can distinguish between news written by the news agency itself and paid articles from customers. Here the results had an accuracy of 99%.