nils-herrmann/clean_scirepeval huggingface dataset:
Reciprocal myocardial-endocardial interactions pattern the delay in atrioventricular junction conduction.

Efficient blood flow depends on two developmental processes that occur within the atrioventricular junction (AVJ) of the heart: conduction delay, which entrains sequential chamber contraction; and valve formation, which prevents retrograde fluid movement. Defects in either result in severe congenital heart disease; however, little is known about the interplay between these two crucial developmental processes. Here, we show that AVJ conduction delay is locally assigned by the morphogenetic events that initiate valve formation. Our data demonstrate that physical separation from endocardial-derived factors prevents AVJ myocardium from becoming fast conducting. Mechanistically, this physical separation is induced by myocardial-derived factors that support cardiac jelly deposition at the onset of valve formation. These data offer a novel paradigm for conduction patterning, whereby reciprocal myocardial-endocardial interactions coordinate the processes of valve formation with establishment of conduction delay. This, in turn, synchronizes the electrophysiological and structural events necessary for the optimization of blood flow through the developing heart.
Socket overloading for fun and cache-poisoning

We present a new technique, which we call socket overloading, that we apply for off-path attacks on DNS. Socket overloading consists of short, low-rate, bursts of inbound packets, sent by off-path attacker to a victim host. Socket overloading exploits the priority assigned by the kernel to hardware interrupts, and enables an off-path attacker to illicit a side-channel on client hosts, which can be applied to circumvent source port and name server randomisation. Both port and name server randomisation are popular and standardised defenses, recommended in [RFC5452], against attacks by off-path adversaries. We show how to apply socket overloading for DNS cache poisoning and name server pinning against popular systems that support algorithms recommended in [RFC6056] and [RFC4097] respectively.
 Our socket overloading technique may be of independent interest, and can be applied against other protocols for different attacks.
Lip augmentation.

Lip augmentation has become increasingly popular in recent years as a reflection of cultural trends emphasizing youth and beauty. Techniques to enhance the appearance of the lips have evolved with advances in biotechnology. An understanding of lip anatomy and aesthetics forms the basis for successful results. We outline the pertinent anatomy and aesthetics of the preoperative evaluation. A summary of various filler materials available is provided. Augmentation options include both injectable and open surgical techniques. The procedures and materials currently favored by the authors are described in greater detail.
A Yokeless Linear Permanent Magnet Machine

This paper presents a novel yokeless linear permanent magnet (PM) machine topology. The armature windings employ fractional slot concentric configuration and can be prewound on separate teeth consecutively or alternatively with a high slot filling factor. A number of advantages are obtained from the proposed yokeless topology. First, the volume of the iron core is reduced and more space is available for the windings, which leads to higher force density, lower copper loss, and higher efficiency. The absence of the yoke iron also reduces the iron loss and like-hood of saturation, and hence improves efficiency and linearity of thrust force characteristic. Without the yoke, the slot leakage flux and the magnetomotive force drop in the iron core are also reduced and consequently the utilization of PMs and the machine force capability are improved. These benefits are demonstrated through a design case study.
Bounding Box Improvement With Reinforcement Learning

In this thesis, I explore a reinforcement learning technique for improving bounding box localizations of objects in images. The model takes as input a bounding box already known to overlap an object and aims to improve the fit of the box through a series of transformations that shift the location of the box by translation, or change its size or aspect ratio. Over the course of these actions, the model adapts to new information extracted from the image. This active localization approach contrasts with existing bounding-box regression methods, which extract information from the image only once. I implement, train, and test this reinforcement learning model using data taken from the Portland State Dog-Walking image set . The model balances exploration with exploitation in training using an -greedy policy. I find that the performance of the model is sensitive to the -greedy configuration used during training, performing best when the epsilon parameter is set to very low values over the course of training. With = 0.01, I find the algorithm can improve bounding boxes in about 78% of test cases for the ‘dog’ object category, and 76% for the ‘human’ category.
Manifold bootstrapping for SVBRDF capture

Manifold bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance, based on the idea that reflectance over a given material sample forms a low-dimensional manifold. It provides a high-resolution result in both the spatial and angular domains by decomposing reflectance measurement into two lower-dimensional phases. The first acquires representatives of high angular dimension but sampled sparsely over the surface, while the second acquires keys of low angular dimension but sampled densely over the surface.
 We develop a hand-held, high-speed BRDF capturing device for phase one measurements. A condenser-based optical setup collects a dense hemisphere of rays emanating from a single point on the target sample as it is manually scanned over it, yielding 10 BRDF point measurements per second. Lighting directions from 6 LEDs are applied at each measurement; these are amplified to a full 4D BRDF using the general (NDF-tabulated) microfacet model. The second phase captures N=20-200 images of the entire sample from a fixed view and lit by a varying area source. We show that the resulting N-dimensional keys capture much of the distance information in the original BRDF space, so that they effectively discriminate among representatives, though they lack sufficient angular detail to reconstruct the SVBRDF by themselves. At each surface position, a local linear combination of a small number of neighboring representatives is computed to match each key, yielding a high-resolution SVBRDF. A quick capture session (10-20 minutes) on simple devices yields results showing sharp and anisotropic specularity and rich spatial detail.
An angle-based interest model for text recommendation

Building an interest model is the key to realize personalized text recommendation. Previous interest models neglect the fact that a user may have multiple angles of interest. Different angles of interest provide different requests and criteria for text recommendation. This paper proposes an interest model that consists of two kinds of angles: persistence and pattern, which can be combined to form complex angles. The model uses a new method to represent the long-term interest and the short-term interest, and distinguishes the interest in object and the interest in the link structure of objects. Experiments with news-scale text data show that the interest in object and the interest in link structure have real requirements, and it is effective to recommend texts according to the angles.
Quantum Computability

In this paper some theoretical and (potentially) practical aspects of quantum computing are considered. Using the tools of transcendental number theory it is demonstrated that quantum Turing machines (QTM) with rational amplitudes are sufficient to define the class of bounded error quantum polynomial time (BQP) introduced by Bernstein and Vazirani [ 1993, pp. 11–20,  (1997), . On the other hand, if quantum Turing machines are allowed unrestricted amplitudes (i.e., arbitrary complex amplitudes), then the corresponding BQP class has uncountable cardinality and contains sets of all Turing degrees. In contrast, allowing unrestricted amplitudes does not increase the power of computation for error-free quantum polynomial time (EQP). Moreover, with unrestricted amplitudes, BQP is not equal to EQP. The relationship between quantum complexity classes and classical complexity classes is also investigated. It is shown that when quantum Turing machines are restricted to have transition amplitudes which are algebraic numbers, BQP, EQP, and nondeterministic quantum polynomial time (NQP) are all contained in PP, hence in P#P and PSPACE. A potentially practical issue of designing “machine independent” quantum programs is also addressed. A single (“almost universal”) quantum algorithm based on Shor’s method for factoring integers is developed which would run correctly on almost all quantum computers, even if the underlying unitary transformations are unknown to the programmer and the device builder.
Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer

In this paper, novel 2 one-dimensional (1D) Haar-like filtering techniques are proposed as a new and low calculation cost feature extraction method suitable for 3D acceleration signals based human activity recognition. Proposed filtering method is a simple difference filter with variable filter parameters. Our method holds a strong adaptability to various classification problems which no previously studied features (mean, standard deviation, etc.) possessed. In our experiment on human activity recognition, the proposed method achieved both the highest recognition accuracy of 93.91% while reducing calculation cost to 21.22% compared to previous method.
Engaging with practices: design case studies as a research framework in CSCW

Information and communications technology (ICT) pervades most aspects of our lives and changes everyday's practices in work and leisure time. When designing innovative ICTs, we need to engage with given practices, institutional arrangements, and technological infrastructures. We describe the research framework used at the University of Siegen. It is based on a collection of design case studies in particular fields of practice and identifies cross-cutting issues to compare and aggregate insights between these cases. To illustrate this framework, we describe our research activities and discuss three themes which became important in different design case studies.
Object recognition using saliency maps and HTM learning

In this paper a pattern classification and object recognition approach based on bio-inspired techniques is presented. It exploits the Hierarchical Temporal Memory (HTM) topology, which imitates human neocortex for recognition and categorization tasks. The HTM comprises a hierarchical tree structure that exploits enhanced spatiotemporal modules to memorize objects appearing in various orientations. In accordance with HTM's biological inspiration, human vision mechanisms can be used to preprocess the input images. Therefore, the input images undergo a saliency computation step, revealing the plausible information of the scene, where a human might fixate. The adoption of the saliency detection module releases the HTM network from memorizing redundant information and augments the classification accuracy. The efficiency of the proposed framework has been experimentally evaluated in the ETH-80 dataset, and the classification accuracy has been found to be greater than other HTM systems.
Arabic speech recognition using MFCC feature extraction and ANN classification

This research addresses a challenging issue that is to recognize spoken Arabic letters, that are three letters of hijaiyah that have indentical pronounciation when pronounced by Indonesian speakers but actually has different makhraj in Arabic, the letters are sa, sya and tsa. The research uses Mel-Frequency Cepstral Coefficients (MFCC) based feature extraction and Artificial Neural Network (ANN) classification method. The result shows the proposed method obtain a good accuracy with an average acuracy is 92.42%, with recognition accuracy each letters (sa, sya, and tsa) prespectivly 92.38%, 93.26% and 91.63%.
Text versus non-text distinction in online handwritten documents

The aim of this paper is to explore how well the task of text vs. nontext distinction can be solved in online handwritten documents using only offline information. Two systems are introduced. The first system generates a document segmentation first. For this purpose, four methods originally developed for machine printed documents are compared: x-y cut, morphological closing, Voronoi segmentation, and whitespace analysis. A state-of-the art classifier then distinguishes between text and non-text zones. The second system follows a bottom-up approach that classifies connected components. Experiments are performed on a new dataset of online handwritten documents containing different content types in arbitrary arrangements. The best system assigns 94.3% of the pixels to the correct class.
ORTHODONTIC TREATMENT ALTERNATIVE TO A CLASS III SUBDIVISION MALOCCLUSION

Class III malocclusions are considered one of the most complex and difficult orthodontic problems to diagnose and treat. Skeletal and/or dental asymmetries in patients presenting with Class III malocclusions can worsen the prognosis. Recognizing the dentoalveolar and skeletal characteristics of subdivision malocclusions and their treatment possibilities is essential for a favorable nonsurgical correction. Therefore, this article presents a nonsurgical asymmetric extraction approach to Class III subdivision malocclusion treatment which can significantly improve the occlusal and facial discrepancies.
A Countermeasure against Spoofing and DoS Attacks based on Message Sequence and Temporary ID in CAN

The development of Information and Communications Technologies (ICT) has affected various fields including the automotive industry. Therefore, vehicle network protocols such as Controller Area Network (CAN), Local Interconnect Network (LIN), and FlexRay have been introduced. Although CAN is the most widely used for vehicle network protocol, its security issue is not properly addressed. In this paper, we propose a security gateway, an improved version of existing CAN gateways, to protect CAN from spoofing and DoS attacks. We analyze sequence of messages based on the driver’s behavior to resist against spoofing attack and utilize a temporary ID and SipHash algorithm to resist against DoS attack. For the verification of our proposed method, OMNeT++ is used. The suggested method shows high detection rate and low increase of traffic. Also, analysis of frame drop rate during DoS attack shows that our suggested method can defend DoS attack.
Innovative and Reliable Power Modules: A Future Trend and Evolution of Technologies

Since the introduction of the first power module by Semikron in 1975, many innovations have been made to improve the thermal, electrical, and mechanical performance of power modules. These innovations in packaging technology focus on the enhancement of the heat dissipation and thermal cycling capability of the modules. Thermal cycles, caused by varying load and environmental operating conditions, induce high mechanical stress in the interconnection layers of the power module due to the different coefficients of thermal expansion (CTE), leading to fatigue and growth of microcracks in the bonding materials. As a result, the lifetime of power modules can be severely limited in practical applications. Furthermore, to reduce the size and weight of converters, the semiconductors are being operated at higher junction temperatures. Higher temperatures are especially of great interest for use of wide-?bandgap materials, such as SiC and GaN, because these materials leverage their material characteristics, particularly at higher temperatures. To satisfy these tightened requirements, on the one hand, conventional power modules, i.e., direct bonded Cu (DBC)-based systems with bond wire contacts, have been further improved. On the other hand, alternative packaging techniques, e.g., chip embedding into printed circuit boards (PCBs) and power module packaging based on the selective laser melting (SLM) technique, have been developed, which might constitute an alternative to conventional power modules in certain applications.
Autonomous Vehicle With Monocular Vision for Object Detection Using Haar-cascade Classifiers

The biggest challenge in case of an autonomous driving system is proper integration of all the functions to be carried out for a safe driving experience. Detection of obstacles and prevention of collision plays an important role in road safety in case of such vehicles. The use of Haar-feature based cascade classifiers provides a light weight solution to object detection with extensive use of computer vision. With the help of monocular vision, image processing algorithms (Gaussian blur), identifying brightest spots (e.g. break lights of a car) and distance measuring algorithms, quick collision preventing actions can be taken. Multilayer Perceptron (MLP-ANN) can be used to classify objects and paths to create a simple decision making data structure for proper navigation. This paper illustrates on an intelligent self-driving vehicle that uses all the above methods tied together to drive on a predefined path. The monocular vision camera takes in image inputs that are processed and the above algorithms are applied on them. This acts as input to the ANN that recognizes the road direction. The road direction is classified into 4 classesleft, right, forward and reverse. The neural network then takes the correct decisions based on the road direction and obstacles that lie on it.
3D Reconstruction of Indoor Scenes Using RGB-D Monocular Vision

Aiming at the problem of low speed of 3D reconstruction of indoor scenes with monocular vision, the color images and depth images of indoor scenes based on ASUS Xtion monocular vision sensor were used for 3D reconstruction. The image feature extraction using the ORB feature detection algorithm, and compared the efficiency of several kinds of classic feature detection algorithm in image matching, Ransac algorithm and ICP algorithm are used to point cloud fusion. Through experiments, a fast 3D reconstruction method for indoor, simple and small-scale static environment is realized. Have good accuracy, robustness, real-time and flexibility.
Lehmer ’ s ENIAC computation

A complete reconstruction of D.H. Lehmer’s ENIAC set-up for computing the exponents of p modulo 2 is given. This program served as an early test program for the ENIAC (1946). The reconstruction illustrates the difficulties of early programmers to find a way between a man operated and a machine operated computation. These difficulties concern both the content level (the algorithm) and the formal level (the logic of sequencing operations).
Improving native language (L1) identifation with better VAD and TDNN trained separately on native and non-native English corpora

Identifying a speaker's native language (L1), i.e., mother tongue, based upon non-native English (L2) speech input, is both challenging and useful for many human-machine voice interface applications, e.g., computer assisted language learning (CALL). In this paper, we improve our sub-phone TDNN based i-vector approach to L1 recognition with a more accurate TDNN-derived VAD and a highly discriminative classifier. Two TDNNs are separately trained on native and non-native English, LVCSR corpora, for contrasting their corresponding sub-phone posteriors and resultant supervectors. The derived i-vectors are then exploited for improving the performance further. Experimental results on a database of 25 L1s show a 3.1% identification rate improvement, from 78.7% to 81.8%, compared with a high performance baseline system which has already achieved the best published results on the 2016 ComParE corpus of only 11 L1s. The statistical analysis of the features used in our system provides useful findings, e.g. pronunciation similarity among the non-native English speakers with different L1s, for research on second-language (L2) learning and assessment.
5G in space: PHY-layer design for satellite communications using non-orthogonal multi-carrier transmission

For the 5th generation of terrestrial mobile communications, Multi-Carrier (MC) transmission based on non-orthogonal waveforms is a promising technology component compared to orthogonal frequency division multiplex (OFDM) in order to achieve higher throughput and enable flexible spectrum management. Coverage extension and service continuity can be provided considering satellites as additional components in future networks by allowing vertical handover to terrestrial radio interfaces. In this paper, the properties of Filter Bank Multicarrier (FBMC) as potential MC transmission scheme is discussed taking into account the requirements for the satellite-specific PHY-Layer like non-linear distortions due to High Power Amplifiers (HPAs). The performance for specific FBMC configurations is analyzed in terms of peak-to-average power ratio (PAPR), computational complexity, non-linear distortions as well as carrier frequency offsets sensitivity (CFOs). Even though FBMC and OFDM have similar PAPR and suffer comparable spectral regrowth at the output of the non linear amplifier, simulations on link level show that FBMC still outperforms OFDM in terms of CFO sensitivity and symbol error rate in the presence of non-linear distortions.
On Time-Series Topological Data Analysis: New Data and Opportunities

This work introduces a new dataset and framework for the exploration of topological data analysis (TDA) techniques applied to time-series data. We examine the end-toend TDA processing pipeline for persistent homology applied to time-delay embeddings of time series – embeddings that capture the underlying system dynamics from which time series data is acquired. In particular, we consider stability with respect to time series length, the approximation accuracy of sparse filtration methods, and the discriminating ability of persistence diagrams as a feature for learning. We explore these properties across a wide range of time-series datasets spanning multiple domains for single source multi-segment signals as well as multi-source single segment signals. Our analysis and dataset captures the entire TDA processing pipeline and includes time-delay embeddings, persistence diagrams, topological distance measures, as well as kernels for similarity learning and classification tasks for a broad set of time-series data sources. We outline the TDA framework and rationale behind the dataset and provide insights into the role of TDA for time-series analysis as well as opportunities for new work.
Reconhecimento de padrões faciais : Um estudo

O reconhecimento facial tem sido utilizado em diversas áreas para identificação e autenticação de usuários. Um dos principais mercados está relacionado a segurança, porém há uma grande variedade de aplicações relacionadas ao uso pessoal, conveniência, aumento de produtividade, etc. O rosto humano possui um conjunto de padrões complexos e mutáveis. Para reconhecer esses padrões, são necessárias técnicas avançadas de reconhecimento de padrões capazes, não apenas de reconhecer, mas de se adaptar às mudanças constantes das faces das pessoas. Este documento apresenta um método de reconhecimento facial proposto a partir da análise comparativa de trabalhos encontrados na literatura.