nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms.



In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands.



This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed.



The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.
Object Detection using Image Processing

—An Unmanned Ariel vehicle (UAV) has greater importance in the army for border security. The main objective of this article is to develop an OpenCV-Python code using Haar Cascade algorithm for object and face detection. Currently, UAVs are used for detecting and attacking the infiltrated ground targets. The main drawback for this type of UAVs is that sometimes the object are not properly detected, which thereby causes the object to hit the UAV. This project aims to avoid such unwanted collisions and damages of UAV. UAV is also used for surveillance that uses Voila-jones algorithm to detect and track humans. This algorithm uses cascade object detector function and vision. train function to train the algorithm. The main advantage of this code is the reduced processing time. The Python code was tested with the help of available database of video and image, the output was verified.
Evaluating mobile apps for breathing training: The effectiveness of visualization

Deep and slow breathing exercises can be an effective adjunct in the treatment of stress, anxiety, post-traumatic stress disorder, chronic pain and depression. Breathing techniques are traditionally learned in courses with trainers and/or with materials such as audio CDs for home practice. Recently, mobile apps have been proposed as novel breathing training tools, but to the best of our knowledge no research has focused on their evaluation so far. In this paper, we study three different designs for breathing training apps. The first employs audio instructions as in traditional training based on audio CDs, while the other two include visualizations of the breathing process, representative of those employed in current breathing training apps. We carry out a thorough analysis, focusing on users’ physiological parameters as well as subjective perception. One visualization produces better results both objectively (measured deepness of breath) and subjectively (users’ preferences and perceived effectiveness) than the more traditional audio-only design. This indicates that a visualization can contribute to the effectiveness of breathing training apps. We discuss which features could have allowed one visualization (but not the other) to obtain better results than traditional audio-only instructions.
LaserStroke: Mid-air Tactile Experiences on Contours Using Indirect Laser Radiation

This demonstration presents a novel form of mid-air tactile display, LaserStroke, that makes use of a laser irradiated on the elastic medium attached to the skin. LaserStroke extends a laser device with an orientation control platform and a magnetic tracker so that it can elicit tapping and stroking sensations to a user's palm from a distance. LaserStroke offers unique tactile experiences while a user freely moves his/her hand in midair.
Microstrip-fed circular substrate integrated waveguide (SIW) cavity resonator and antenna

Substrate integrated waveguide (SIW) cavity resonators are among the emerging group of SIW-based circuit components that are gaining popularity and increasingly employed in integrated microwave and mm-wave circuits. The SIW cavities offer a significantly enhanced performance in comparison to the previously available planar microwave resonators. The high quality factor of the waveguide-based cavity resonators enables designing microwave oscillators with very low phase noise as well as compact high-gain antennas [1–4]. The SIW-cavity-based antennas also show much promise for implementation of low-cost lightweight fully-integrated high-gain antennas that find application in ultra-light communication satellites, low payload spacecrafts, high-frequency radar, and sensors. In this paper, a circular SIW cavity resonator, which is fed by a microstrip and via probe, is presented. The microstrip feed is optimized to achieve a return loss of better than 20dB from both simulation and measurements. A resonance frequency of 16.79GHz and quality factor of 76.3 are determined from vector network analyzer (VNA) measurements. Due to the folded slot on the upper conductor layer, the resonator is an efficient cavity backed antenna. A maximum gain of over 7dB is measured in an anechoic chamber at the resonance frequency of 16.79 GHz.
Agile Model Driven Development: An Intelligent Compromise

Both the model-driven and agile development approaches have significantly enhanced productivity and predictability of software development in practice. Agile Model Driven Development or AMDD is an attempt to effectively bring together the fast pace of agile development and the guaranteed quality of model-driven development. Despite the proliferation of processes claim to comply with AMDD principles, there has been little academic research examining merits and demerits of such an approach. This paper first presents the underlying theoretical foundations of AMDD and then reviews, classifies and compares existing AMDD processes. The results indicate significant disparity between compared processes that implies the AMDD concepts are still in their infancy.
Radian: Visual Exploration of Traceroutes

Several projects deploy probes in the Internet. Probes are systems that continuously perform traceroutes and other networking measurements (e.g., ping) towards selected targets. Measurements can be stored and analyzed to gain knowledge on several aspects of the Internet, but making sense of such data requires suitable methods and tools for exploration and visualization. We present Radian, a tool that allows to visualize traceroute paths at different levels of detail and to animate their evolution during a selected time interval. We also describe extensive tests of the tool using traceroutes performed by RIPE Atlas Internet probes.
Novel Dataset for Fine-Grained Image Categorization : Stanford Dogs

We introduce a 120 class Stanford Dogs dataset, a challenging and large-scale dataset aimed at fine-grained image categorization. Stanford Dogs includes over 22,000 annotated images of dogs belonging to 120 species. Each image is annotated with a bounding box and object class label. Fig. 1 shows examples of images from Stanford Dogs. This dataset is extremely challenging due to a variety of reasons. First, being a fine-grained categorization problem, there is little inter-class variation. For example the basset hound and bloodhound share very similar facial characteristics but differ significantly in their color, while the Japanese spaniel and papillion share very similar color but greatly differ in their facial characteristics. Second, there is very large intra-class variation. The images show that dogs within a class could have different ages (e.g. beagle), poses (e.g. blenheim spaniel), occlusion/self-occlusion and even color (e.g. Shih-tzu). Furthermore, compared to other animal datasets that tend to exist in natural scenes, a large proportion of the images contain humans and are taken in manmade environments leading to greater background variation. The aforementioned reasons make this an extremely challenging dataset.
Deep Multi-Class Segmentation Without Ground-Truth Labels

In this paper we demonstrate that through the use of adversarial training and additional unsupervised costs it is possible to train a multi-class anatomical segmentation algorithm without any ground-truth labels for the data set to be segmented. Specifically, using labels from a different data set of the same anatomy (although potentially in a different modality) we train a model to synthesise realistic multichannel label masks from input cardiac images in both CT and MRI, through adversarial learning. However, as is to be expected, generating realistic mask images is not, on its own, sufficient for the segmentation task: the model can use the input image as a source of noise and synthesise highly realistic segmentation masks that do no necessarily correspond spatially to the input. To overcome this, we introduce additional unsupervised costs, and demonstrate that these provide sufficient further guidance to produce good segmentation results. We test our proposed method on both CT and MR data from the multi-modal whole heart segmentation challenge (MM-WHS) , and show the effect of our unsupervised costs on improving the segmentation results, in comparison to a variant without them.
Speech Enhancement Using a-Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator

Absstroct-This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the " spectral subtraction " algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class.
Pitch Elevation in Male-to-female Transgender Persons-the Würzburg Approach.

The present study reports objective and subjective voice results of Wendler's glottoplasty modified by Hagen.



This is an outcomes research study.



A total of 21 patients underwent Wendler's glottoplasty modified by Hagen. Parameters in the follow-up session were laryngoscopy, voice range profile, Voice Handicap Index, Life Satisfaction Questionnaire, and a visual analog scale for individual satisfaction with the voice.



The fundamental frequency was elevated into the typical female fundamental frequency range. Furthermore, an elevation of the lower frequency limit was shown without a reduction of the frequency range. About one third of the population feels affected by the restricted dynamic range. This change of the vocal pitch is seen as part of the voice feminization by some of the patients. The Dysphonia Severity Index as a marker for voice quality was unchanged. Subjective satisfaction with the voice showed a strong correlation with the individual elevation of the pitch.



Wendler's glottoplasty modified by Hagen is an effective and low-risk method of raising the vocal pitch of male-to-female transgender persons. However, elevated Scores of the Voice Handicap Index indicated that in everyday life, transgender persons continue to feel handicapped because of their voice. Another indicator for the lack of social acceptance and integration is the reduced general life satisfaction in the Life Satisfaction Questionnaire especially in the domain "friends, acquaintances, relatives." Therefore, a better multidisciplinary therapy concept for voice feminization is necessary.
FPGA-SOPC based motion controller with ACC/DEC using digital convolution

Acceleration/deceleration (ACC/DEC) motion planning is used to generate smooth motion command. This study focuses on realizing trapezoidal, S-curve and fourth-order ACC/DEC planning using digital convolution. To test and verify the proposed ACC/DEC method, a motion controller is designed and implemented. The FPGA (Field Programmable Gate Array) is used to implement the measurement unit and pulse generator, while the SOPC (System on a Programmable Chip) is used to realize the trajectory generator and PID controller. The FPGA-SOPC based motion controller can improve the integration level of the motion control system and meet the real-time requests. Simulation and experiment results verify that the digital convolution method is effective and the FPGA-SOPC based motion controller is feasible.
Linear (Hull) and Algebraic Cryptanalysis of the Block Cipher PRESENT

The contributions of this paper include the first linear hull and a revisit of the algebraic cryptanalysis of reduced-round variants of the block cipher PRESENT, under known-plaintext and ciphertextonly settings. We introduce a pure algebraic cryptanalysis of 5-round PRESENT and in one of our attacks we recover half of the bits of the key in less than three minutes using an ordinary desktop PC. The PRESENT block cipher is a design by Bogdanov et al., announced in CHES 2007 and aimed at RFID tags and sensor networks. For our linear attacks, we can attack 25-round PRESENT with the whole code book, 2 25round PRESENT encryptions, 2 blocks of memory and 0.61 success rate. Further we can extend the linear attack to 26-round with small success rate. As a further contribution of this paper we computed linear hulls in practice for the original PRESENT cipher, which corroborated and even improved on the predicted bias (and the corresponding attack complexities) of conventional linear relations based on a single linear
Model of Customer Churn Prediction on Support Vector Machine

To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural network, decision tree, logistic regression, and naive bayesian classifier. It is found that the method enjoys the best accuracy rate, hit rate, covering rate, and lift coefficient, and therefore, provides an effective measurement for customer churn prediction.
Scalable Blockchain Based Smart Contract Execution

Blockchain, or distributed ledger, provides a way to build various decentralized systems without relying on any single trusted party. This is especially attractive for smart contracts, that different parties do not need to trust each other to have a contract, and the distributed ledger can guarantee correct execution of the contract. Most existing distributed ledger based smart contract systems process smart contracts in a serial manner, i.e., all users have to run a contract before its result can be accepted by the system. Although this approach is easy to implement and manage, it is not scalable and greatly limits the system's capability of handling a large number of smart contracts. In order to address this problem, we propose a scalable smart contract execution scheme that can run multiple smart contract in parallel to improve throughput of the system. Our scheme relies on two key techniques: a fair contract partition algorithm leveraging integer linear programming to partition a set of smart contracts into multiple subsets, and a random assignment protocol assigning subsets randomly to a subgroup of users. We prove that, our scheme is secure as long as more than $50\%$ of the computational power is possessed by honest nodes. We then conduct experiments with data from existing smart contract system to evaluate the efficiency of our scheme. The results demonstrate that our approach is scalable and much more efficient than the existing smart contract platform.
Networks of the Brain

Models of Network Growth All networks, whether they are social, technological, or biological, are the result of a growth process. Many of these networks continue to grow for prolonged periods of time, continually modifying their connectivity structure throughout their entire existence. For example, the World Wide Web has grown from a small number of cross-linked documents in the early 1 990s to an estimated 30 billion indexed web pages in 2009.3 The extraordinary growth of the Web continues unabated and has occurred without any top-down design, yet the topology of its hyperlink structure exhibits characteristic statistical patterns (Pastor-Satorras and Vespig­ nani, 2004). Other technological networks such as the power grid, global transportation networks, or mobile communication networks continue to grow and evolve, each displaying characteristic patterns of expansion and elaboration. Growth and change in social and organizational
Verification of Recurrent Neural Networks

Verification of neural networks is a relatively young research area which offers a vast range of topics to investigate. With most of the existing work focused on FeedForward Neural Networks, the demand for exploring new network topologies increases constantly. In this project we propose a first attempt towards reachability analysis on ReLU Recurrent Neural Networks based on techniques developed for the traditional neural networks. We start by defining a series of abstractions for the recurrent networks and prove their equivalence to the original one. Using the created approximations we perform reachability analysis via two different techniques: Linear Programs and Satisfiability Modulo Theories. Finally, we evaluate our contributions on some concrete neural networks and analyse the obtained results.
A compact low-power supply-insensitive CMOS current reference

A simple low-power current reference using dual-threshold MOS transistors is introduced. The area required for this reference is small and the sensitivity of the output current to the supply voltage is low. Simulation results show that an implementation of this current reference in a TSMC 0.18um CMOS process with a 1.8V power supply is constant to within ±1.25% over the temperature range of -10°C to 100°C. In this implementation, the active area is 86 μm2, the power dissipation is 72μW, and the worst process corner nonlinearity due to temperature variations is bounded by ±4.16%.
Author ' s personal copy Dynamic modeling , stability and energy consumption analysis of a realistic six-legged walking robot

This paper deals with a detailed analysis on kinematics, dynamics, stability and energy consumption of a realistic six-legged robot. The aim of this study is to extend a previous work of Roy et al. , in order to estimate optimal feet forces and joint torques of the six-legged robot generating wave-gaits with four different duty factors and deal with its stability issues. Two different approaches are developed to determine optimal feet forces. In the first approach, minimization of the norm of feet forces is carried out using a least square method, whereas minimization of the norm of joint torques is performed in the second approach. The second approach is found to be more energy efficient compared to the first one. The maximum values of feet forces and joint torques are seen to decrease with the increase of duty factor. The effects of walking parameters, namely velocity, stroke and duty factors have been studied on energy consumption and stability of the robot. The variations of average power consumption and specific energy consumption with the velocity and stroke are compared for four different duty factors. Wave gait with a low duty factor is found to be more energy-efficient compared to that with a high duty factor at the highest possible velocity. & 2012 Elsevier Ltd. All rights reserved.
Player AnalysisInput Frame Depth EstimationDepth Estimation Scene ReconstructionScene

We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device. At the heart of our paper is an approach to estimate the depth map of each player, using a CNN that is trained on 3D player data extracted from soccer video games. We compare with state of the art body pose and depth estimation techniques, and show results on both synthetic ground truth benchmarks, and real YouTube soccer footage.
Design of Compact Dual-Band SIW Slotted Array Antenna

A compact dual-band substrate integrated waveguide (SIW) slotted array antenna is presented in this letter. The proposed antenna has two layers. The bottom layer is for a 2 <inline-formula><tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> 6 longitudinal slotted rigged SIW array resonating at the X-band. The top layer is for a 4 <inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula> 8 longitudinal slotted SIW array working in the Ku-band. Two SIW power dividers are employed as the feeding networks for two slotted arrays. The effects between the two antennas are analyzed. The lower and higher band antennas on two different layers can be designed separately, which have good abilities to work independently in X-band and Ku-band, respectively. The −10 dB impedance band is 9.4–10 GHz in X-band and 14.5–15.2 GHz in Ku-band. Over the impendence band, the maximum gains are 14.5 dBi in the lower band and 17.5 dBi in the upper band.
Cross-Media Hashing with Neural Networks

Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional hamming space, has attracted intensive attention in recent years. This is motivated by the facts a) the multi-modal data is widespread, e.g., the web images on Flickr are associated with tags, and b) hashing is an effective technique towards large-scale high-dimensional data processing, which is exactly the situation of cross-media retrieval. Inspired by recent advances in deep learning, we propose a cross-media hashing approach based on multi-modal neural networks. By restricting in the learning objective a) the hash codes for relevant cross-media data being similar, and b) the hash codes being discriminative for predicting the class labels, the learned Hamming space is expected to well capture the cross-media semantic relationships and to be semantically discriminative. The experiments on two real-world data sets show that our approach achieves superior cross-media retrieval performance compared with the state-of-the-art methods.