nils-herrmann/clean_scirepeval huggingface dataset:
Gray-scale skeletonization of small vessels in magnetic resonance angiography

Interpretation of magnetic resonance angiography (MRA) is problematic due to complexities of vascular shape and to artifacts such as the partial volume effect. The authors present new methods to assist in the interpretation of MRA. These include methods for detection of vessel paths and for determination of branching patterns of vascular trees. They are based on the ordered region growing (ORG) algorithm that represents the image as an acyclic graph, which can be reduced to a skeleton by specifying vessel endpoints or by a pruning process. Ambiguities in the vessel branching due to vessel overlap are effectively resolved by heuristic methods that incorporate a priori knowledge of bifurcation spacing. Vessel paths are detected at interactive speeds on a 500-MHz processor using vessel endpoints. These methods apply best to smaller vessels where the image intensity peaks at the center of the lumen which, for the abdominal MRA, includes vessels whose diameter is less than 1 cm.
Evaluation of Stiffness and Strength of Scots Pine Wood Using Resonance Frequency and Ultrasonic Techniques

The aim of this paper was to predict the static bending modulus of elasticity (MOES) and modulus of rupture (MOR) of Scots pine (Pinus sylvestris L.) wood using three nondestructive techniques. The mean values of the dynamic modulus of elasticity based on flexural vibration (MOEF), longitudinal vibration (MOELV), and indirect ultrasonic (MOEUS) were 13.8, 22.3, and 30.9 % higher than the static modulus of elasticity (MOES), respectively. The reduction of this difference, taking into account the shear deflection effect in the output values for static bending modulus of elasticity, was also discussed in this study. The three dynamic moduli of elasticity correlated well with the static MOES and MOR; correlation coefficients ranged between 0.68 and 0.96. The correlation coefficients between the dynamic moduli and MOES were higher than those between the dynamic moduli and MOR. The highest correlation between the dynamic moduli and static bending properties was obtained by the flexural vibration technique in comparison with longitudinal vibration and indirect ultrasonic techniques. Results showed that there was no obvious relationship between the density and the acoustic wave velocity that was obtained from the longitudinal vibration and ultrasonic techniques.
Multifunctional and compact 3D FMCW MIMO radar system with rectangular array for medium-range applications

Ever since the era of autonomous systems started, multisensor platforms have become a key topic in aerospace, automotive, and robotics industries. Multifunctional systems integrate more than one sensor and/or actuator in one device and are capable of creating synergies between them. As a consequence, their performance is superior compared to single-sensor systems, for different reasons [1]-[3].
Incremental Solid Modeling from Sparse and Omnidirectional Structure-from-Motion Data

We introduce a new incremental 2-manifold surface reconstruction method. Compared to the previous works, its input is a sparse 3D point cloud estimated by a Structure-from-Motion (SfM) algorithm instead of a more common dense input. The main argument against such a method is that the lack of points implies an inaccurate scene surface. However, the advantages like point quality (thanks to the SfM machinery including bundle adjustment and interest point detection) and simplified resulting surface makes it worth of exploration. Our algorithm is incremental since the surface is locally updated for every new camera pose (and its 3D points) estimated by SfM. This is an advantage compared to global methods like  or  for applications which require a surface while reading the video sequence. Compared to , our method avoids prohibitive time complexity in presence of loops in the camera trajectory. Last but not least, unlike other incremental methods like  the output surface is a 2-manifold, i.e. it is a list of triangles in 3D such that the neighborhood of every surface point is topologically a disk. This property is needed to define surface normal and curvature  and thus is used by many mesh processing and computational geometry algorithms. Now we introduce notations. Let P be a set of 3D points on the unknown scene surface. The 3D Delaunay triangulation of P is a list T of tetrahedra which partition the convex hull of P. A list O of tetrahedra (O⊆ T ) represents the reconstructed object whose volume is |O|, the union of the O tetrahedra. Border δO is the list of triangles (tetrahedra faces) which are included in exactly one tetrahedra of O. The union of triangles |δO| is our target surface and should be a 2-manifold. SfM also provides visibility knowledge Ri: every point pi ∈ P is computed from camera locations c j where j ∈ Ri. This implies that |δO| should not intersect the rays (line segments) c jpi, j ∈ Ri except at pi. The tetrahedra intersected by a ray are labeled freespace, the others are matter. Let F be the set of free-space tetrahedra. In practice, |δF | is not a 2-manifold. One iteration of our algorithm is shown in Fig. 1. At image (or time) t +1, we have the following input
RaptorQ code based concurrent transmissions in dual connectivity LTE network

Dual Connectivity(DC) is one of the key technologies standardized in Release 12 of the 3GPP specifications for the Long Term Evolution (LTE) network. It attempts to increase the per-user throughput by allowing the user equipment (UE) to maintain connections with the MeNB (master eNB) and SeNB (secondary eNB) simultaneously, which are inter-connected via non-ideal backhaul. In this paper, we focus on one of the use cases of DC whereby the downlink U-plane data is split at the MeNB and transmitted to the UE via the associated MeNB and SeNB concurrently. In this case, out-of-order packet delivery problem may occur at the UE due to the delay over the non-ideal backhaul link, as well as the dynamics of channel conditions over the MeNB-UE and SeNB-UE links, which will introduce extra delay for re-ordering the packets. As a solution, we propose to adopt the RaptorQ FEC code to encode the source data at the MeNB, and then the encoded symbols are separately transmitted through the MeNB and SeNB. The out-of-order problem can be effectively eliminated since the UE can decode the original data as long as it receives enough encoded symbols from either the MeNB or SeNB. We present detailed protocol design for the RaptorQ code based concurrent transmission scheme, and simulation results are provided to illustrate the performance of the proposed scheme.
Ultralow anterior resection and coloanal anastomosis for distal rectal cancer: functional and oncological results

We evaluated the functional and oncological outcome of ultralow anterior resection and coloanal anastomosis (CAA), which is a popular technique for preserving anal sphincter in patients with distal rectal cancer. Forty-eight patients were followed up for 6–100 months regarding fecal or gas incontinence, frequency of bowel movement, and local or systemic recurrence. The main operative techniques were total mesorectal excision with autonomic nerve preservation; the type of anastomosis was straight CAA, performed by the perianal hand sewn method in 38 cases and by the double-stapled method in 10. Postoperative complications included transient urinary retention (n=7), anastomotic stenosis (n=3), anastomotic leakage (n=3), rectovaginal fistula (n=2), and cancer positive margin (n=1; patient refused reoperation). Overall there were recurrences in seven patients (14.5%): one local and one systemic recurrence in stage B2; and one local, two systemic, and two combined local and systemic in C2. The mean frequency of bowel movements was 6.1 per day after 3 months, 4.4 after 1 year, and 3.1 after 2 years. The Kirwan grade for fecal incontinence was 2.7 after 3 months, 1.8 after 1 year, and 1.5 after 2 years. With careful selection of patients and good operative technique, CAA can be performed safely in distal rectal cancer. Normal continence and acceptable frequency of bowel movements can be obtained within 1 year after operation without compromising the rate of local recurrence.
Spam filtering using hybrid local-global Naive Bayes classifier

This paper propose a novel learning framework for classification of messages into spam and legit. We introduce a classification method based on feature space segmentation. Naive Bayes (NB) model is a statistical filtering process which uses previously gathered knowledge. Instead of using a single classifier, we propose the use of local and global classifier, based on Bayesian hierarchal framework. This helps in achieving multi-task learning, as simultaneous extraction of knowledge can be achieved while achieving classification accuracy. Knowledge among different task can be shared while learning for task specific.
Efficient depth peeling via bucket sort

In this paper we present an efficient algorithm for multi-layer depth peeling via bucket sort of fragments on GPU, which makes it possible to capture up to 32 layers simultaneously with correct depth ordering in a single geometry pass. We exploit multiple render targets (MRT) as storage and construct a bucket array of size 32 per pixel. Each bucket is capable of holding only one fragment, and can be concurrently updated using the MAX/MIN blending operation. During the rasterization, the depth range of each pixel location is divided into consecutive subintervals uniformly, and a linear bucket sort is performed so that fragments within each subintervals will be routed into the corresponding buckets. In a following fullscreen shader pass, the bucket array can be sequentially accessed to get the sorted fragments for further applications. Collisions will happen when more than one fragment is routed to the same bucket, which can be alleviated by multi-pass approach. We also develop a two-pass approach to further reduce the collisions, namely adaptive bucket depth peeling. In the first geometry pass, the depth range is redivided into non-uniform subintervals according to the depth distribution to make sure that there is only one fragment within each subinterval. In the following bucket sorting pass, there will be only one fragment routed into each bucket and collisions will be substantially reduced. Our algorithm shows up to 32 times speedup to the classical depth peeling especially for large scenes with high depth complexity, and the experimental results are visually faithful to the ground truth. Also it has no requirement of pre-sorting geometries or post-sorting fragments, and is free of read-modify-write (RMW) hazards.
FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scans

The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket. This paper tackles this problem by proposing FloorNet, a novel deep neural architecture. The challenge lies in the processing of RGBD streams spanning a large 3D space. FloorNet effectively processes the data through three neural network branches: 1) PointNet with 3D points, exploiting the 3D information; 2) CNN with a 2D point density image in a top-down view, enhancing the local spatial reasoning; and 3) CNN with RGB images, utilizing the full image information. FloorNet exchanges intermediate features across the branches to exploit the best of all the architectures. We have created a benchmark for floorplan reconstruction by acquiring RGBD video streams for 155 residential houses or apartments with Google Tango phones and annotating complete floorplan information. Our qualitative and quantitative evaluations demonstrate that the fusion of three branches effectively improves the reconstruction quality. We hope that the paper together with the benchmark will be an important step towards solving a challenging vector-graphics reconstruction problem. Code and data are available at https://github.com/art-programmer/FloorNet.
Forensic data acquisition from cloud-of-things devices: windows Smartphones as a case study

The continued amalgamation of cloud technologies into all aspects of our daily lives and the technologies we use (i.e. cloud-of-things) creates business opportunities, security and privacy risks, and investigative challenges (in the event of a cybersecurity incident). This study examines the extent to which data acquisition fromWindows phone, a common cloud-of-thing device, is supported by three popular mobile forensics tools. The effect of device settings modification (i.e. enabling screen lock and device reset operations) and alternative acquisition processes (i.e. individual and combined acquisition) on the extraction results are also examined. Our results show that current mobile forensic tool support for Windows Phone 8 remains limited. The results also showed that logical acquisition support was more complete in comparison to physical acquisition support. In one example, the tool was able to complete a physical acquisition of a Nokia Lumia 625, but its deleted contacts and SMSs could not be recovered/extracted. In addition we found that separate acquisition is needed for device removable media to maximize acquisition results, particularly when trying to recover deleted data. Furthermore, enabling flight-mode and disabling location services are highly recommended to eliminate the potential for data alteration during the acquisition process. These results should provide practitioners with an overview of the current capability of mobile forensic tools and the challenges in successfully extracting evidence from the Windows phone platform.
SOCRADES: A Web Service Based Shop Floor Integration Infrastructure

On the one hand, enterprises manufacturing any kinds of goods require agile production technology to be able to fully accommodate their customers’ demand for flexibility. On the other hand, Smart Objects, such as networked intelligent machines or tagged raw materials, exhibit ever increasing capabilities, up to the point where they offer their smart behaviour as web services. The two trends towards higher flexibility and more capable objects will lead to a service-oriented infrastructure where complex processes will span over all types of systems — from the backend enterprise system down to the Smart Objects. To fully support this, we present SOCRADES, an integration architecture that can serve the requirements of future manufacturing. SOCRADES provides generic components upon which sophisticated production processes can be modelled. In this paper we in particular give a list of requirements, the design, and the reference implementation of that integration architecture.
Multi-policy optimization in decentralized autonomic systems

Autonomic computing systems are those that are capable of managing themselves based only on highlevel objectives given by humans. In such systems the details of how to meet their objectives, even in the face of changing operating conditions, are left to the systems themselves. Therefore, autonomic systems are required to be able to self-optimize, self-heal, self-protect, and self-configure. Enabling autonomic behaviour is particularly challenging in decentralized autonomic systems, where central control is not tractable or even possible, due to the large number and geographical dispersion of the entities involved. In such systems, entities only have local views of their immediate environments and no global view of the system exists. Decentralized autonomic systems can be implemented as multiagent systems, in which each entity is modelled as an intelligent agent. These agents can self-organize based only on local actions and interactions, so that the global behaviour of the system, required to meet its objectives, emerges from the agents’ local behaviours. This thesis addresses self-optimization in decentralized autonomic systems. Examples of techniques used to self-optimize autonomic systems include ant-colony optimization, evolutionary algorithms, neural networks, and reinforcement learning (RL). RL is considered particularly suitable for use in large-scale autonomic systems, as it does not require a predefined model of the environment. However, most applications of RL in decentralized autonomic computing address systems that optimize their behaviours towards only a single policy, while in reality management of most autonomic systems requires optimization towards multiple, often conflicting policies. These policies can be heterogeneous (i.e., implemented on different sets of agents, be active at different times and have different levels of priority), leading to the heterogeneity of the agents of which the system is composed. The cooperation required for self-optimization is particularly challenging in such heterogeneous multi-agent environments, as agents might not be aware of other agents’ policies and their relative priority for the system. Additionally, since agents operate in the same shared environment, dependencies can arise between their performance and therefore between policy implementations as well. To address self-optimization in such decentralized autonomic systems in the presence of agent
Symptomatic Tarlov cyst: report and review.

Tarlov or perineural cysts are nerve root cysts found most commonly at the sacral spine level arising between covering layers of the perineurium and the endoneurium near the dorsal root ganglion. The cysts are relatively rare and most of them are asymptomatic. Some Tarlov cysts can exert pressure on nerve elements resulting in pain, radiculopathy and even multiple radiculopathy of cauda equina. There is no consensus on the appropriate therapeutic options of Tarlov cysts. The authors present a case of two sacral cysts diagnosed with magnetic resonance imaging. The initial symptoms were low back pain and sciatica and progressed to cauda equina syndrome. Surgical treatment was performed by sacral laminectomy and wide cyst fenestration. The neurological deficits were recovered and had not recurred after a follow-up period of nine months. The literature was reviewed and discussed. This is the first reported case in Thailand.
Fair Enough: Guaranteeing Approximate Maximin Shares

We consider the problem of fairly allocating indivisible goods, focusing on a recently introduced notion of fairness called maximin share guarantee: each player’s value for his allocation should be at least as high as what he can guarantee by dividing the items into as many bundles as there are players and receiving his least desirable bundle. Assuming additive valuation functions, we show that such allocations may not exist, but allocations guaranteeing each player 2/3 of the above value always exist. These theoretical results have direct practical implications.
Modified Viola–Jones algorithm with GPU accelerated training and parallelized skin color filtering-based face detection

Face detection is a prominent research domain in the field of digital image processing. Out of various algorithms developed so far, Viola–Jones face detection has been highly successful. However, because of its complex nature, there is need to do more exploration in its various phases including training as well as actual face detection to find the scope of further improvement in terms of efficiency as well as accuracy under various constraints so as to detect and process the faces in real time. Its training phase for the screening of large amount of Haar features and generation of cascade classifiers is quite tedious and computationally intensive task. Any modification for improvement in its features or cascade classifiers requires re-training of all the features through example images, which are very large in number. Therefore, there is need to enhance the computational efficiency of training process of Viola–Jones face detection algorithm so that further enhancement in this framework is made easy. There are three main contributions in this research work. Firstly, we have achieved a considerable speedup by parallelizing the training as well as detection of rectangular Haar features based upon Viola–Jones framework on GPU. Secondly, the analysis of features selected through AdaBoost has been done, which can give intuitiveness in developing more innovative and efficient techniques for selecting competitive classifiers for the task of face detection, which can further be generalized for any type of object detection. Thirdly, implementation of parallelization techniques of modified version of Viola–Jones face detection algorithm in combination with skin color filtering to reduce the search space has been done. We have been able to achieve considerable reduction in the search space and time cost by using the skin color filtering in conjunction with the Viola–Jones algorithm. Time cost reduction of the order of 54.31% at the image resolution of 640*480 of GPU time versus CPU time has been achieved by the proposed parallelized algorithm.
Surface urban heat island across 419 global big cities.

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.
Analysis and defense of vulnerabilities in binary code

David Brumley is an Assistant Professor in Electrical and Computer Engineering at Carnegie Mellon University. He earned his Ph.D. in Computer Science from Carnegie Mellon University, a Masters in Computer Science from Stanford University, and a Bachelors in Mathematics from the University of Northern Colorado. His current work focuses on software security. His research and interests include all areas of security, as well as programming languages, compilers, formal methods, and systems. Analysis and Defense of Vulnerabilities in Binary Code
Internet Banking Adoption : Integrating Technology Acceptance Model and Trust

Rejection of Internet banking is one of the most important problems that faces banks in developing countries. So far, very few academic studies have been conducted on Internet banking adoption in Arab countries. Hence, this research aims to investigate factors that influence the intention to use Internet banking in Yemen. Cross-sectional data were collected from 1286 respondents through a survey. Structural equation modeling was employed to analyze data. The findings supported the research hypotheses and confirmed that perceived relative advantages, perceived ease of use, trust of the Internet banking all impact attitude toward the intention of adopting Internet banking. This paper makes a contribution to Internet banking literature. It sheds light on the factors that affect Internet banking adoption. The findings made a contribution in terms of understanding the factors that can contribute to the adoption of Internet banking by Yemeni consumers..