nils-herrmann/clean_scirepeval huggingface dataset:
An enhanced smart card based remote user password authentication scheme

Smart card based password authentication is one of the simplest and efficient authentication mechanisms to ensure secure communication in insecure network environments. Recently, Chen et al. have pointed out the weaknesses of some password authentication schemes and proposed a robust smart card based remote user password authentication scheme to improve the security. As per their claims, their scheme is efficient and can ensure forward secrecy of the session key. However, we find that Chen et al.’s scheme cannot really ensure forward secrecy, and it cannot detect the wrong password in login phase. Besides, the password change phase of Chen et al.’s scheme is unfriendly and inefficient since the user has to communicate with the server to update his/her password. In this paper, we propose a modified smart card based remote user password authentication scheme to overcome the aforementioned weaknesses. The analysis shows that our proposed scheme is user friendly and more secure than other related schemes. & 2013 Elsevier Ltd. All rights reserved.
Usability Dimensions for Mobile Applications-A Review

Usability has been increasingly recognized as a significant quality dimension to determine the success of mobile applications. Due to its importance, a number of usability guidelines have been proposed to direct the design of usable applications. The guidelines are intended particularly for desktop and web-based applications. Mobile applications on the other hand are different in many ways from those applications due to the mobility nature of mobile devices. To date, the usability guidelines for mobile applications are very limited. They in fact are isolated, which makes usability evaluation for mobile devices more difficult. This study aims to address this issue by proposing a set of usability dimensions that should be considered for designing and evaluating mobile applications. The dimensions are illustrated as a model that considers four contextual factors: user, environment, technology and task/activity. The model was proposed based on the reviews of previous related studies, which were analyzed by using content analysis approach. Twenty-five dimensions were found from the analysis. The dimensions however were synthesized and prioritized based on their importance towards designing usable mobile applications. As a result, ten most important dimensions were outlined in the model. The model can be used by practitioners and researchers as a guideline to design usable mobile applications and further research can be conducted in the near future.
A 6LoWPAN Accelerator for Internet of Things Endpoint Devices

The Internet of Things (IoT) is revolutionizing the Internet of the future and the way smart devices, people, and processes interact with each other. Challenges in endpoint devices (EDs) communication are due not only to the security and privacy-related requirements but also due to the ever-growing amount of data transferred over the network that needs to be handled, which can considerably reduce the system availability to perform other tasks. This paper presents an IPv6 over low power wireless personal area networks (6LoWPAN) accelerator for the IP-based IoT networks, which is able to process and filter IPv6 packets received by an IEEE 802.15.4 radio transceiver without interrupting the microcontroller. It offers nearly 13.24% of performance overhead reduction for the packet processing and the address filtering tasks, while guaranteeing full system availability when unwanted packets are received and discarded. The contribution is meant to be deployed on heterogeneous EDs at the network edge, which include on the same architecture, field-programmable gate array (FPGA) technology beside a microcontroller and an IEEE 802.15.4-compliant radio transceiver. Performed evaluations include the low FPGA hardware resources utilization and the energy consumption analysis when the processing and accepting/rejection of a received packet is performed by the 6LoWPAN accelerator.
Use of uterine electromyography to diagnose term and preterm labor.

Current methodologies to assess the process of labor, such as tocodynamometry or intrauterine pressure catheters, fetal fibronectin, cervical length measurement and digital cervical examination, have several major drawbacks. They only measure the onset of labor indirectly and do not detect cellular changes characteristic of true labor. Consequently, their predictive values for term or preterm delivery are poor. Uterine contractions are a result of the electrical activity within the myometrium. Measurement of uterine electromyography (EMG) has been shown to detect contractions as accurately as the currently used methods. In addition, changes in cell excitability and coupling required for effective contractions that lead to delivery are reflected in changes of several EMG parameters. Use of uterine EMG can help to identify patients in true labor better than any other method presently employed in the clinic.
Asirra: a CAPTCHA that exploits interest-aligned manual image categorization

The typical CAPTCHA GUI consists of two parts: a character image with noise, and an and J. Saul, "Asirra: a CAPTCHA that exploits interest-aligned manual image categorization," in 14th ACM conference on Computer and Communications. CAPTCHAs (short for "Completely Automated Public Turing test to tell Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization.  2007,
Automatic construction of multifaceted browsing interfaces

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Interfaces that use multifaceted hierarchies represent a new powerful browsing paradigm which has been proven to be a successful complement to keyword searching. Thus far, multifaceted hierarchies have been created manually or semi-automatically, making it difficult to deploy multifaceted interfaces over a large number of databases. We present automatic and scalable methods for creation of multifaceted interfaces. Our methods are integrated with traditional relational databases and can scale well for large databases. Furthermore, we present methods for selecting the best portions of the generated hierarchies when the screen space is not sufficient for displaying all the hierarchy at once. We apply our technique to a range of large data sets, including annotated images, television programming schedules, and web pages. The results are promising and suggest directions for future research.
Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering

This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be beneficial for the case where hierarchical clustering is already performed. Numerical experiments revealed that the outputs of the algorithm moderately preserve the original hierarchical structures.
A virtual shared metadata storage for HDFS

Hadoop is a popular open-source framework that allows distributed analysis of large datasets using the MapReduce programming model. A distributed file system HDFS is implemented to provide high-throughput access to datasets. HDFS can achieve high performance metadata service but has two disadvantages. First, when the metadata server stores metadata on persistent devices, it is restricted to read and write operations of local disks. Second, it also lacks effective methods for metadata synchronization and replication, which is critical for metadata availability and reliability. In this research, we introduce a novel Virtual Shared Storage Pool (VSSP) concept and design for storing and sharing metadata in HDFS. The VSSP is a virtual storage device which is built on existing servers and transparent to upper layers. Two strategies, a journal synchronization based on the 2PC protocol and a fine-grained image replication, are introduced in the VSSP according to different metadata access features. The VSSP not only reduces the overhead on metadata modification operations, but also improves the I/O performance for namespace storage. Experimental results show that the VSSP improved the average performance by 40.51% and 23.46% when writing logs compared with the BookKeeper and Hadoop QJM. The average image read and write throughput was nearly 5 times and 2.4 times better than NFS and the original approach. These results confirm that the proposed VSSP solution significantly improves the metadata access performance, scalability, and reliability for HDFS.
Cumulus: A distributed and flexible computing testbed for edge cloud computational offloading

While smart devices are increasing in numbers, compute resources, and communication capabilities, current application requirements are often outpacing such hardware improvement. Recently, offloading computation to remote cloud resources or closely located computing resources, such as cloudlets or other mobile and IoT devices have been proposed. In this paper, we propose “Cumulus”, an open source platform for edge cloud computational offloading. Cumulus aims at helping engineers and researchers build, test, and evaluate their edge-computing applications, scheduling methods, etc. We present a generic and flexible architecture of Cumulus as well as a proof-of-concept testbed prototype leveraging a large spectrum of heterogeneous devices, communication methods, and OSs. The testbed consists of standalone worker IoT devices and clustered edge mobile clouds of IoT devices. We enhance Cumulus with a visualization tool that allows real time monitoring of resources used by the devices running on the testbed. We evaluate Cumulus in action by demonstrating its performance under several offloading algorithms in different settings.
Racing Bib Numbers Recognition

Running races, such as marathons, are broadly covered by professional as well as amateur photographers. This leads to a constantly growing number of photos covering a race, making the process of identifying a particular runner in such datasets difficult. Today, such identification is often done manually. In running races, each competitor has an identification number, called the Racing Bib Number (RBN), used to identify that competitor during the race. RBNs are usually printed on a paper or cardboard tag and pined onto the competitor’s T-shirt during the race. We introduce an automatic system that receives a set of natural images taken in running sports events and outputs the participants’ RBN.
More than Just Two Sexes: The Neural Correlates of Voice Gender Perception in Gender Dysphoria

Gender dysphoria (also known as "transsexualism") is characterized as a discrepancy between anatomical sex and gender identity. Research points towards neurobiological influences. Due to the sexually dimorphic characteristics of the human voice, voice gender perception provides a biologically relevant function, e.g. in the context of mating selection. There is evidence for a better recognition of voices of the opposite sex and a differentiation of the sexes in its underlying functional cerebral correlates, namely the prefrontal and middle temporal areas. This fMRI study investigated the neural correlates of voice gender perception in 32 male-to-female gender dysphoric individuals (MtFs) compared to 20 non-gender dysphoric men and 19 non-gender dysphoric women. Participants indicated the sex of 240 voice stimuli modified in semitone steps in the direction to the other gender. Compared to men and women, MtFs showed differences in a neural network including the medial prefrontal gyrus, the insula, and the precuneus when responding to male vs. female voices. With increased voice morphing men recruited more prefrontal areas compared to women and MtFs, while MtFs revealed a pattern more similar to women. On a behavioral and neuronal level, our results support the feeling of MtFs reporting they cannot identify with their assigned sex.
Neighborhood-Level LGBT Hate Crimes and Bullying Among Sexual Minority Youths: A Geospatial Analysis.

The goal of this study was to evaluate a novel measure of environmental risk factors for bullying among sexual minority youths. Data on lesbian, gay, bisexual, and transgender (LGBT) assault hate crimes were obtained from police records, geocoded, and then linked to individual-level data on bullying and sexual orientation from the 2008 Boston Youth Survey Geospatial Dataset (N = 1,292; 108 sexual minorities). Results indicated that sexual minority youths who reported relational and electronic bullying were more likely to reside in neighborhoods with higher LGBT assault hate crime rates. There was no asso- ciation between LGBT assault hate crimes and bullying among heterosexual youths, pro- viding evidence for specificity to sexual minority youth. Moreover, no relationships were observed between sexual minority bullying and neighborhood-level violent and property crimes, indicating that the results were specific to LGBT assault hate crimes.
Deep Aligned Representations

We capitalize on large amounts of readily-available, synchronous data to learn a deep discriminative representations shared across three major natural modalities: vision, sound and language. By leveraging over a year of sound from video and millions of sentences paired with images, we jointly train a deep convolutional network for aligned representation learning. Our experiments suggest that this representation is useful for several tasks, such as cross-modal retrieval or transferring classifiers between modalities. Moreover, although our network is only trained with image+text and image+sound pairs, it can transfer between text and sound as well, a transfer the network never observed during training. Visualizations of our representation reveal many hidden units which automatically emerge to detect concepts, independent of the modality.
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

Current end-to-end machine reading and question answering (Q&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score1 on the test set, which is significantly better than the best published F1 score of 81.8.
Strategic play for a pool-playing robot

Playing pool is not a trivial task for an autonomous robotic system: In order to excel, the tight coupling of accurate perception, planning and highly dynamic while precise manipulation is required. To investigate scientific challenges arising from this tight coupling requirement an anthropomorphic robotic system has been developed, which is capable of playing pool. Focusing in this paper on the planning part, a well-calibrated pool simulator is developed in order to predict the outcome of possible strokes. An optimization based framework applying a discounted return term is presented for suggesting an optimized next stroke by planning several strokes ahead. Different to existing planners so far, our planner considers the player's and opponent's skill. Therefore, strategic considerations (e.g. safety shots) can now be evaluated depending on the outcome of the next strokes for both player and opponent.
Performance analysis of the IEEE 802.1 ethernet audio/video bridging standard

Switched Ethernet is widely used for all kinds of applications. However, for demanding multimedia streaming applications in a fully-loaded network, legacy Ethernet is not the best choice due to its limited Quality-of-Service (QoS) support. The new specification of the IEEE 802.1 Audio/Video Bridging (AVB) standard, an extension to legacy Ethernet, provides the QoS features needed for multimedia streaming: Time synchronized low latency streaming services and bandwidth reservation on Layer-2. In this work, we study and analyze the AVB standard based on a simulation approach to verify the specified constraints and to determine the feasibility of the mechanisms in a switched Ethernet network. We use the simulation tool OMNeT++ and extend the existing INET-framework to support all the required protocols specified in the IEEE 802.1 AVB standard.
Accelerating cine phase-contrast flow measurements using k-t BLAST and k-t SENSE.

Conventional phase-contrast velocity mapping in the ascending aorta was combined with k-t BLAST and k-t SENSE. Up to 5.3-fold net acceleration was achieved, enabling single breath-hold acquisitions. A standard phase-contrast (PC) sequence with interleaved acquisition of the velocity-encoded segments was modified to collect data in 2 stages, a high-resolution under sampled and a low-resolution fully sampled training stage. In addition, a modification of the k-t reconstruction strategy was tested. This strategy, denoted as "plug-in," incorporates data acquired in the training stage into the final reconstruction for improved data consistency, similar to conventional keyhole. "k-t SENSE plug-in" was found to provide best image quality and most accurate flow quantification. For this strategy, at least 10 training profiles are required to yield accurate stroke volumes (relative deviation <5%) and good image quality. In vivo 2D cine velocity mapping was performed in 6 healthy volunteers with 30-32 cardiac phases (spatial resolution 1.3 x 1.3 x 8-10 mm(3), temporal resolution of 18-38 ms), yielding relative stroke volumes of 106 +/- 18% (mean +/- 2*SD) and 112 +/- 15% for 3.8 x and 5.3 x net accelerations, respectively. In summary, k-t BLAST and k-t SENSE are promising approaches that permit significant scan-time reduction in PC velocity mapping, thus making high-resolution breath-held flow quantification possible.
Image-based indoor positioning system: fast image matching using omnidirectional panoramic images

In this paper, we developed an image-based indoor localization system using omnidirectional panoramic images to which location information is added. By the combination of the robust image matching by PCA-SIFT and fast nearest neighbor search algorithm based on Locality Sensitive Hashing (LSH), our system can estimate users' positions with high accuracy and in a short time. To improve the precision, we introduced the "confidence" of the image matching results. We conducted experiments at the Railway Museum and we obtained 426 omnidirectional panoramic reference images and 1067 supplemental images for image matching. Experimental results using 126 test images demonstrated that the location detection accuracy is above 90% with about 2.2s of processing time.
A multi-view deep learning architecture for classification of breast microcalcifications

In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. Classification of clustered breast microcalcifications into benign and malignant categories is an extremely challenging task for computerized algorithms and expert radiologists alike. We describe a deep-learning classification method that is based on two view-level decisions, implemented by two neural networks, followed by a single-neuron layer that combines the viewlevel decisions into a global decision that mimics the biopsy results. Our method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). Experimental results show that our network structure significantly improves on previously suggested methods.
Clustering of periodic multichannel timeseries data with application to plasma fluctuations

A periodic datamining algorithm has been developed and used to extract distinct plasma fluctuations in multichannel oscillatory timeseries data. The technique uses the Expectation Maximisation algorithm to solve for the maximum likelihood estimates and cluster assignments of a mixture of multivariate independent von Mises distributions (EM-VMM). The performance of the algorithm shows significant benefits when compared to a periodic k-means algorithm and clustering using non-periodic techniques on several artificial datasets and real experimental data. Additionally, a new technique for identifying interesting features in multichannel oscillatory timeseries data is described (STFT-clustering). STFTclustering identifies the coincidence of spectral features overmost channels of amulti-channel array using the averaged short time Fourier transform of the signals. These features are filtered using clustering to remove noise. This method is particularly good at identifying weaker features and complements existing methods of feature extraction. Results from applying the STFT-clustering and EM-VMM algorithm to the extraction and clustering of plasma wave modes in the time series data from a helical magnetic probe array on the H-1NF heliac are presented.
jFAST: a java finite automata simulator

Visualization and interactivity are valuable active learning techniques that can improve mastery of difficult concepts. In this paper we describe jFAST, an easy-to-use graphical software tool for teachers and students, with an emphasis on introductory level finite state machine topics. The jFAST software enables visual design, exploration and simulation of a variety of finite state machines, with a primary goal of enhancing teaching effectiveness in this subject, particularly for less advanced computer science students. The architecture and functionality of jFAST are explained, and results of preliminary evaluation are provided.