nils-herrmann/clean_scirepeval huggingface dataset:
Spinal curvature determination from scoliosis X-Ray image using sum of squared difference template matching

Scoliosis is a disorder in which there is a sideways curve of the spine. Curve are often S-shaped or C-shaped. One of the methods to ascertain a patient with scoliosis is through using Cobb angle measurement. The importance of the automatic spinal curve detection system is to detect any spinal disorder quicker and faster. This study is intended as a first step based on computer-aided diagnosis. The spinal detection method that we propose is using template matching based on Sum of Squared Difference (SSD). This method is used to estimate the location of the vertebra. By using polynomial curve fitting, a spinal curvature estimation can be done. This paper discusses the performance of SSD method used to detect a variety of data sources of X-Ray from numerous patients. The results from the implementation indicate that the proposed algorithm can be used to detect all the X-ray images. The best result in this experiment has 96.30% accuracy using 9-subdivisions poly 5 algorithm, and the average accuracy is 86.01%.
Experimental Evaluation of Sketching on Surfaces in VR

Sketching in immersive 3D virtual reality (VR) environments has great potential for a variety of interactive 3D design applications. Precisely sketching the intended strokes in mid-air, however, can be a challenge. In this paper, we present a set of controlled studies to analyze the factors affecting human ability to sketch freely in a 3D VR environment. In our first study, we directly compare traditional sketching on a physical surface to sketching in VR, with and without a physical surface to rest the stylus on. Our results indicate that the lack of a physical drawing surface is a major cause of inaccuracies in VR drawing, and that the effect is dependent on the orientation of the drawing surface. In a second experiment, we evaluate the extent to which visual guidance can compensate for the loss of sketching precision in VR. We found that while additional visual guidance improves positional accuracy, it can be detrimental to the aesthetic quality of strokes. We conclude by distilling our experimental findings into design guidelines for sketching tools in immersive 3D environments.
A modular architecture for building automation systems

The deployment of building automation systems (BAS) allows to increase comfort, safety and security and to reduce operational cost. Today such systems typically follow a two-layered hierarchical approach. While control networks interconnect distributed sensors, actuators and controllers, a backbone provides the necessary infrastructure for management tasks hosted by configuration and management devices. In addition, devices interconnecting the control network with the backbone and the backbone with further networks (e.g., the Internet) play a strategic role. All BAS devices contributing to a particular functionality differ in their requirements for hardware. This paper discusses requirements for devices used in the building automation domain and presents our work in progress to assemble platforms with different purposes relying on a modular architecture.
Hardware-Oblivious Parallelism for In-Memory Column-Stores

The multi-core architectures of today’s computer systems make parallelism a necessity for performance critical applications. Writing such applications in a generic, hardware-oblivious manner is a challenging problem: Current database systems thus rely on laborintensive and error-prone manual tuning to exploit the full potential of modern parallel hardware architectures like multi-core CPUs and graphics cards. We propose an alternative design for a parallel database engine, based on a single set of hardware-oblivious operators, which are compiled down to the actual hardware at runtime. This design reduces the development overhead for parallel database engines, while achieving competitive performance to hand-tuned systems. We provide a proof-of-concept for this design by integrating operators written using the parallel programming framework OpenCL into the open-source database MonetDB. Following this approach, we achieve efficient, yet highly portable parallel code without the need for optimization by hand. We evaluated our implementation against MonetDB using TPC-H derived queries and observed a performance that rivals that of MonetDB’s query execution on the CPU and surpasses it on the GPU. In addition, we show that the same set of operators runs nearly unchanged on a GPU, demonstrating the feasibility of our approach.
Tracking and Segmentation of the Airways in Chest CT Using a Fully Convolutional Network

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes such as lung cancer detection, chronic obstructive pulmonary disease (COPD), and surgical navigation. However, due to the complex tree-like structure of the airways, obtaining segmentation results with high accuracy for a complete 3D airway extraction remains a challenging task. In recent years, deep learning based methods, especially fully convolutional networks (FCN), have improved the state-of-the-art in many segmentation tasks. 3D U-Net is an example that optimized for 3D biomedical imaging. It consists of a contracting encoder part to analyze the input volume and a successive decoder part to generate integrated 3D segmentation results. While 3D U-Net can be trained for any 3D segmentation task, its direct application to airway segmentation is challenging due to differently sized airway branches. In this work, we combine 3D deep learning with image-based tracking in order to automatically extract the airways. Our method is driven by adaptive cuboidal volume of interest (VOI) analysis using a 3D U-Net model. We track the airways along their centerlines and set VOIs according to the diameter and running direction of each airway. After setting a VOI, the 3D U-Net is utilized to extract the airway region inside the VOI. All extracted candidate airway regions are unified to form an integrated airway tree. We trained on 30 cases and tested our method on an additional 20 cases. Compared with other state-of-the-art airway tracking and segmentation methods, our method can increase the detection rate by 5.6 while decreasing the false positives (FP) by 0.7 percentage points.
A curriculum for teaching computer science through computational textiles

The field of computational textiles has shown promise as a domain for diversifying computer science culture by drawing a population with broad and non-traditional interests and backgrounds into creating technology. In this paper, we present a curriculum that teaches computer science and computer programming through a series of activities that involve building and programming computational textiles. We also describe two new technological tools, Modkit and the LilyPad ProtoSnap board, that support implementation of the curriculum. In 2011-12, we conducted three workshops to evaluate the impact of our curriculum and tools on students' technological self-efficacy. We conclude that our curriculum both draws a diverse population, and increases students' comfort with, enjoyment of, and interest in working with electronics and programming.
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks

Designing optimal treatment plans for patients with comorbidities requires accurate cause-specific mortality prognosis. Motivated by the recent availability of linked electronic health records, we develop a nonparametric Bayesian model for survival analysis with competing risks, which can be used for jointly assessing a patient’s risk of multiple (competing) adverse outcomes. The model views a patient’s survival times with respect to the competing risks as the outputs of a deep multi-task Gaussian process (DMGP), the inputs to which are the patients’ covariates. Unlike parametric survival analysis methods based on Cox and Weibull models, our model uses DMGPs to capture complex non-linear interactions between the patients’ covariates and cause-specific survival times, thereby learning flexible patient-specific and cause-specific survival curves, all in a data-driven fashion without explicit parametric assumptions on the hazard rates. We propose a variational inference algorithm that is capable of learning the model parameters from time-to-event data while handling right censoring. Experiments on synthetic and real data show that our model outperforms the state-of-the-art survival models.
News Sentiment Analysis Based on Cross-Domain Sentiment Word Lists and Content Classifiers

The main task of sentiment classification is to automatically judge sentiment polarity (positive or negative) of published sentiment data (e.g. news or reviews). Some researches have shown that supervised methods can achieve good performance for blogs or reviews. However, the polarity of a news report is hard to judge. Web news reports are different from other web documents. The sentiment features in news are less than the features in other Web documents. Besides, the same words in different domains have different polarity. So we propose a selfgrowth algorithm to generate a cross-domain sentiment word list, which is used in sentiment classification of Web news. This paper considers some previously undescribed features for automatically classifying Web news, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms, and also validates the selfgrowth algorithm for the cross-domain word list.
Mars Exploration Rover mobility assembly design, test and performance

In January 2004, NASA landed two mobile robotic spacecraft, or rovers, on opposite sides of the planet Mars. These rovers, named Spirit and Opportunity, were each sent on their own scientific mission of exploration. Their objective was to work as robotic geologists. After more than a year the vehicles are still in excellent health, and returning vast amounts of scientific information on the ancient water processes that helped form Mars. Key to the success of the rovers was the development of their advanced mobility system. In this paper the mobility assembly, the mechanical hardware that determines the vehicles mobility capability, is described. The details of the design, test, and performance of the mobility assembly are shown to exceed the mission requirements. The rovers' ability to traverse the Mars terrain with its combination of rocks, craters, soft soils, and hills was verified, and the system design validated.
Three-Dimensional Dual-Polarized Frequency Selective Structure With Wide Out-of-Band Rejection

A new three-dimensional bandpass frequency-selective structure (FSS) is proposed for dual-polarized applications. Each square unit cell of the proposed FSS consists of vertical and horizontal double-sided parallel-strip lines (DSPSLs) and a thin metallic plate. Furthermore, the parallel-strip lines of every DSPSL are connected together through via holes. Using this arrangement, multimode resonators are established in the proposed structure. At lower frequencies, the substrate mode propagates and two resonators along the DSPSL are constructed, leading to a bandpass response with two transmission poles. At higher frequencies, the air mode is reflected by two resonates along the inserted plate, resulting in a stopband with two transmission zeros. The operating principle for this FSS is explained with the aid of an equivalent circuit model. Moreover, a parametric study of the proposed FSS is also carried out and design guidelines are formulated. As an example, a prototype of the proposed FSS is designed, fabricated, and tested. Measured results demonstrate that the FSS exhibits dual polarizations, high out-of-band rejection, and stable frequency response under both transverse electric (TE) and transverse magnetic (TM) polarizations of an obliquely incident wave.
Information Security Awareness: Literature Review and Integrative Framework

Individuals’ information security awareness (ISA) plays a critical role in determining their securityrelated behavior in both organizational and private contexts. Understanding this relationship has important implications for individuals and organizations alike who continuously struggle to protect their information security. Despite much research on ISA, there is a lack of an overarching picture of the concept of ISA and its relationship with other constructs. By reviewing 40 studies, this study synthesizes the relationship between ISA and its antecedents and consequences. In particular, we (1) examine definitions of ISA; (2) categorize antecedents of ISA according to their level of origin; and (3) identify consequences of ISA in terms of changes in beliefs, attitudes, intentions, and actual security-related behaviors. A framework illustrating the relationships between the constructs is provided and areas for future research are identified.
GaN-based high frequency totem-pole bridgeless PFC design with digital implementation

Totem-pole bridgeless PFC is a very promising topology for GaN devices because of very low reverse recovery. However, inherent challenges exist for this topology at zerocrossing point of AC voltage where inductor, current spike is observed which may cause higher harmonics and EMI issue. This paper looks deeply into this phenomena and found two main causes: the transition delay of low frequency leg and the minimum pulse width limit of high frequency leg. A DSP-based digital modulator is designed to address those two issues by applying lead time to the low frequency leg and digital dithering to increase equivalent resolution at the high frequency leg. Some other rules are also summarized to enable robust modulator design, which is the main challenge in this topology. A 500 kHz GaN totem-pole bridgeless PFC converter operating in continuous conduction mode verified the effectiveness of those methods to eliminate the current spike at zero-crossing. All those findings will help the implementation of wide band gap devices in very high frequency PFC circuit.
Evaluating User Privacy in Bitcoin

Bitcoin is quickly emerging as a popular digital payment system. However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly announced in the system. In this paper, we investigate the privacy provisions in Bitcoin when it is used as a primary currency to support the daily transactions of individuals in a university setting. More specifically, we evaluate the privacy that is provided by Bitcoin (i) by analyzing the genuine Bitcoin system and (ii) through a simulator that faithfully mimics the use of Bitcoin within a university. In this setting, our results show that the profiles of almost 40% of the users can be, to a large extent, recovered even when users adopt privacy measures recommended by Bitcoin. To the best of our knowledge, this is the first work that comprehensively analyzes, and evaluates the privacy implications of Bitcoin.
End-to-end text recognition with convolutional neural networks

Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003.
Cross Domain Regularization for Neural Ranking Models using Adversarial Learning

Unlike traditional learning to rank models that depend on hand-crafted features, neural representation learning models learn higher level features for the ranking task by training on large datasets. Their ability to learn new features directly from the data, however, may come at a price. Without any special supervision, these models learn relationships that may hold only in the domain from which the training data is sampled, and generalize poorly to domains not observed during training. We study the effectiveness of adversarial learning as a cross domain regularizer in the context of the ranking task. We use an adversarial discriminator and train our neural ranking model on a small set of domains. The discriminator provides a negative feedback signal to discourage the model from learning domain specific representations. Our experiments show consistently better performance on held out domains in the presence of the adversarial discriminator---sometimes up to 30% on precision$@1$.
Fast, Large-Scale String Match for a 10Gbps FPGA-Based Network Intrusion Detection System

Intrusion Detection Systems such as Snort scan incoming packets for evidence of security threats. The most computation-intensive part of these systems is a text search against hundreds of patterns, and must be performed at wire-speed. FPGAs are particularly well suited for this task and several such systems have been proposed. In this paper we expand on previous work, in order to achieve and exceed a processing bandwidth of 11Gbps. We employ a scalable, low-latency architecture, and use extensive fine-grain pipelining to tackle the fan-out, match, and encode bottlenecks and achieve operating frequencies in excess of 340MHz for fast Virtex devices. To increase throughput, we use multiple comparators and allow for parallel matching of multiple search strings. We evaluate the area and latency cost of our approach and find that the match cost per search pattern character is between 4 and 5 logic cells.
Sentence Boundary Detection: A Long Solved Problem?

We review the state of the art in automated sentence boundary detection (SBD) for English and call for a renewed research interest in this foundational first step in natural language processing. We observe severe limitations in comparability and reproducibility of earlier work and a general lack of knowledge about genreand domain-specific variations. To overcome these barriers, we conduct a systematic empirical survey of a large number of extant approaches, across a broad range of diverse corpora. We further observe that much previous work interpreted the SBD task too narrowly, leading to overly optimistic estimates of SBD performance on running text. To better relate SBD to practical NLP use cases, we thus propose a generalized definition of the task, eliminating textor language-specific assumptions about candidate boundary points. More specifically, we quantify degrees of variation across ‘standard’ corpora of edited, relatively formal language, as well as performance degradation when moving to less formal language, viz. various samples of user-generated Web content. For these latter types of text, we demonstrate how moderate interpretation of document structure (as is now often available more or less explicitly through mark-up) can substantially contribute to overall SBD performance.
DTCTH: a discriminative local pattern descriptor for image classification

Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST.
Increasing The Performance Of Mobile Smartphones Using Partition And Migration Of Mobile Applications To Cloud Computing

With the increasing use of smartphones devices, mobile applications with richer functionalities are becoming ubiquitous but mobile devices are limited by their resources for computing and power consumption. Cloud the place for abundant resources. Clouds provide opportunity to do huge computations quickly and accurately so we can use cloud for mobile computations. Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of a major discussion thread in the IT world in the recent few years. We developed an architecture that uses cloud to do computations that consume resources badly on mobiles. It aims at finding the right spots in an application automatically where the execution can be partitioned and migrated to the cloud. Thus, an elastic application can augment the capabilities of a mobile device including computation power, storage, and network bandwidth, with the light of dynamic execution configuration according to device‟s status including CPU load, memory, and battery level. We demonstrate results of the proposed application model using data collected from one of our elastic application.
Exploiting Passive Dynamics with Variable Stiffness Actuation in Robot Brachiation

This paper explores a passive control strategy with variable stiffness actuation for swing movements. We consider brachiation as an example of a highly dynamic task which requires exploitation of gravity in an efficient manner for successful task execution. First, we present our passive control strateg y considering a pendulum with variable stiffness actuation. Then, we formulate the problem based an optimal control framework with temporal optimization in order to simultaneously find an appropriate stiffness profile and movement duration such that the resultant movement will be able to exploit the passive dynamics of the robot. Finally, numerical evaluations on a twolink brachiating robot with a variable stiffness actuator (VSA) model are provided to demonstrate the effectiveness of our approach under different task requirements, modelling errors and switching in the robot dynamics. In addition, we discuss the issue of task description in terms of the choice of cost function for successful task execution in optimal control.