nils-herrmann/clean_scirepeval huggingface dataset:
A high-frequency ultrasound imaging system combining limited-angle spatial compounding and model-based synthetic aperture focusing

High-frequency ultrasound (HFUS) imaging systems are routinely used for medical diagnostics (skin, eyes) and for medical research (small animal imaging). Although systems with array transducers are already commercially available, imaging systems with single-element transducers are still of interest and available as well, because this type of transducer is less complex, less expensive, and technically mature. Nevertheless, drawbacks exist, for example, the need for mechanical scanning units and the limited depth of field. In this paper, we present a high-frequency (20 MHz) ultrasound imaging system equipped with a spherically focused transducer. Limited-angle spatial compounding is utilized to improve the image contrast, to suppress speckle and noise, and to reduce imaging artifacts. To overcome the limitation in depth of field, the system uses a novel synthetic aperture focusing technique based on the correlation of the recorded echo signals with the simulated point spread function of the imaging system. This method results in lower side lobe levels and greater noise reduction compared with delay-and-sum focusing, which is demonstrated by wire phantom measurements. When used in combination with limited-angle spatial compounding, as presented in this paper, the resulting image quality is superior to conventional single-element HFUS imaging systems and to array systems. Examples of measurements on tissue phantoms and small animals (ex vivo) are presented and discussed in detail.
Fast embedding of multilayer networks: An algorithm and application to group fMRI

Learning interpretable features from complex multilayer networks is a challenging and important problem. The need for such representations is particularly evident in multilayer networks of the brain, where nodal characteristics may help model and differentiate regions of the brain according to individual, cognitive task, or disease. Motivated by this problem, we introduce the multi-node2vec algorithm, an efficient and scalable feature engineering method that automatically learns continuous node feature representations from multilayer networks. Multi-node2vec relies upon a second-order random walk sampling procedure that efficiently explores the innerand intralayer ties of the observed multilayer network is utilized to identify multilayer neighborhoods. Maximum likelihood estimators of the nodal features are identified through the use of the Skip-gram neural network model on the collection of sampled neighborhoods. We investigate the conditions under which multi-node2vec is an approximation of a closedform matrix factorization problem. We demonstrate the efficacy of multi-node2vec on a multilayer functional brain network from resting state fMRI scans over a group of 74 healthy individuals. We find that multi-node2vec outperforms contemporary methods on complex networks, and that multi-node2vec identifies nodal characteristics that closely associate with the functional organization of the brain.
Real-Time Vehicle Detection and Tracking Using Haar-Like Features and Compressive Tracking

This paper presents a real-time vision framework that detects and tracks vehicles from stationary camera. It can be used to calculate statistical information such as average traffic speed and flow as well as in surveillance tasks. The framework consists of three main stages. Vehicles are first detected using Haar-like features. In the second phase, an adaptive appearance-based model is built to dynamically keep track of the detected vehicles. This model is also used in the third phase of data association to fuse the detection and tracking results. The use of detection results to update the tracker enhances the overall framework accuracy. The practical value of the proposed framework is demonstrated in real-life experiments where it is used to robustly compute vehicle counts within certain region of interest under variety of challenges.
Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit

Multi-objective evolutionary algorithms (MOEAs) have achieved great progress in recent decades, but most of them are designed to solve unconstrained multi-objective optimization problems. In fact, many real-world multi-objective problems usually contain a number of constraints. To promote the research of constrained multi-objective optimization, we first propose three primary types of difficulty, which reflect the challenges in the real-world optimization problems, to characterize the constraint functions in CMOPs, including feasibility-hardness, convergencehardness and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable constrained multi-objective optimization problems (CMOPs) with three types of parameterized constraint functions according to the proposed three primary types of difficulty. In fact, combination of the three primary constraint functions with different parameters can lead to construct a large variety of CMOPs, whose difficulty can be uniquely defined by a triplet with each of its parameter specifying the level of each primary difficulty type respectively. Furthermore, the number of objectives in this toolkit are able to scale to more than two. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs named DAS-CMOP1-9. To evaluate the proposed test problems, two popular CMOEAs MOEA/D-CDP and NSGA-II-CDP are adopted to test their performances on DAS-CMOP1-9 with different difficulty triplets. The experiment results demonstrate that none of them can solve these problems efficiently, which stimulate us to develop new constrained MOEAs to solve the suggested DAS-CMOPs.
Real time patient monitoring system based on Internet of Things

The popularity of Internet of Things is increasing day by day in the area of remote monitoring system. The remote monitoring systems include, vehicle or assets monitoring, kids/pets monitoring, fleet management, parking management, water and oil leakage, energy grid monitoring etc. In this paper, we have proposed an intelligent patient monitoring system for monitoring the patients' health condition automatically through sensors based connected networks. Several sensors are used for gathering the biological behaviors of a patient. The meaningful biological information are then forwarded to the IoT cloud. The system is more intelligent that can able to detect the critical condition of a patient by processing sensors data and instantly provides push notification to doctors/nurses as well as hospital in-charge personal. The doctors and nurses get benefited from this system by observing their corresponding patients remotely without visiting in person. Patients' relatives can also get benefited from this system with limited access.
COLOR VS. TEXTURE FEATURE EXTRACTION AND MATCHING IN VISUAL CONTENT RETRIEVAL BY USING GLOBAL COLOR HISTOGRAM

Content Based Image Retrieval (CBIR) is the technique which uses visual contents to search images from large database. Image retrieval is achieved according to the similarity of image features. Color and texture is the most important visual features. CBIR systems focused on using low-level features like color, texture and shape for image representation. The color and texture features can be extracted using global color histogram. This paper measures the performance of color and textual statistical features of an image in retrieving the similar images from the data set. The performance is measured by implementing a single CBIR system in two levels. The first level uses color features for image retrieval. The second level uses the texture features for similar image extraction. Euclidean distance is used as the distance measure of two images. It is found that the system which uses texture features retrieves the most similar images from the data set.
2D Shape Decomposition Based on Combined Skeleton-Boundary Features

Decomposing a shape into meaningful components plays a strong role in shape-related applications. In this paper, we combine properties of skeleton and boundary to implement a general shape decomposition approach. It is motivated by recent studies in visual human perception discussing the importance of certain shape boundary features as well as features of the shape area; it utilizes certain properties of the shape skeleton combined with boundary features to determine protrusion strength. Experiments yield results similar to those from human subjects on abstract shape data. Also, experiments of different data sets prove the robustness of the combined skeleton-boundary approach.
100Gb/s ethernet chipsets in 65nm CMOS technology

This paper presents a complete design of 100GbE chipsets including gearbox TX/RX, LDD and TIA/LA arrays. Figure 7.3.1 shows the architecture, where 10×10Gb/s input data is serialized into 4×25Gb/s bit stream by a 10:4 serializer (i.e., gearbox TX). A 4-element LDD array subsequently drives 4 laser diodes, emitting 850nm light into 4 multimode fibers (MMFs). After traveling over 100m, these optical signals are captured and transformed into electrical domain by means of photo diodes (PDs) and a TIA/LA array. A 4:10 deserializer (gear-box RX) recovers the clock and data, and restores the data sequences into 10×10Gb/s outputs. In applications, gearbox TRX and optical frontends (i.e., LDD and TIA/LA arrays) may be separated by several inches in order to fulfill system-level integration.
Querying archetype-based EHRs by search ontology-based XPath engineering

Legacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that lead to false positive results, the precision can be increased by constraining the search to certain parts of documents.



A search ontology-based specification of queries on XML documents defines search concepts and relates them to parts in the XML document structure. Such query specification method is practically introduced and evaluated by applying concrete research questions formulated in natural language on a data collection for information retrieval purposes. The search is performed by search ontology-based XPath engineering that reuses ontologies and XML-related W3C standards.



The key result is that the specification of research questions can be supported by the usage of search ontology-based XPath engineering. A deeper recognition of entities and a semantic understanding of the content is necessary for a further improvement of precision and recall. Key limitation is that the application of the introduced process requires skills in ontology and software development. In future, the time consuming ontology development could be overcome by implementing a new clinical role: the clinical ontologist.



The introduced Search Ontology XML extension connects Search Terms to certain parts in XML documents and enables an ontology-based definition of queries. Search ontology-based XPath engineering can support research question answering by the specification of complex XPath expressions without deep syntax knowledge about XPaths.
Nutrigenomics and Cancer

Cancer incidence is projected to increase in the future and an effectual preventive strategy is required to face this challenge. Alteration of dietary habits is potentially an effective approach for reducing cancer risk. Assessment of biological effects of a specific food or bioactive component that is linked to cancer and prediction of individual susceptibility as a function of nutrient-nutrient interactions and genetics is an essential element to evaluate the beneficiaries of dietary interventions. In general, the use of biomarkers to evaluate individuals susceptibilities to cancer must be easily accessible and reliable. However, the response of individuals to bioactive food components depends not only on the effective concentration of the bioactive food components, but also on the target tissues. This fact makes the response of individuals to food components vary from one individual to another. Nutrigenomics focuses on the understanding of interactions between genes and diet in an individual and how the response to bioactive food components is influenced by an individual's genes. Nutrients have shown to affect gene expression and to induce changes in DNA and protein molecules. Nutrigenomic approaches provide an opportunity to study how gene expression is regulated by nutrients and how nutrition affects gene variations and epigenetic events. Finding the components involved in interactions between genes and diet in an individual can potentially help identify target molecules important in preventing and/or reducing the symptoms of cancer.
Performance validation of NS3-LTE emulation for live video streaming under QoS parameters

Currently, 4G mobile communication systems are supported by the 3GPP standard. In view of the significant increase in mobile data traffic, it is necessary to characterize it to improve the performance of current wireless networks. Indeed, video transmission and video streaming are fundamental assets for the upcoming smart cities and urban environments. Due to the high costs of deploying a real LTE system, emulation systems that consider real operating conditions emerge as a successful alternative. On the other hand, many studies with LTE simulations and emulations do not present information of basic adjustment parameters like the propagation model, nor of validation of the results with real conditions. This paper shows the validation with an ANOVA statistical analysis of an LTE emulation system developed in NS-3 for the live video streaming service. For the validation, different QoS parameters and real conditions have been used. Also, two protocols, namely RTMP and RTSP, have been tested. It is demonstrated that the emulation scenario is appropriate to characterize the traffic that will later allow to carry out a proper performance analysis of the service and technology under study.
GGP with Advanced Reasoning and Board Knowledge Discovery

Quality of General Game Playing (GGP) matches suffers from slow state-switching and weak knowledge modules. Instantiation and Propositional Networks offer great performance gains over Prolog-based reasoning, but do not scale well. In this publication mGDL, a variant of GDL stripped of function constants, has been defined as a basis for simple reasoning machines. mGDL allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule sheets conformed to mGDL without any modifications; the rest required minor changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile even demanding rule sheets under few seconds. For strengthening game knowledge, spatial features inspired by similar successful techniques from computer Go have been proposed. For they required an Euclidean metric, a small board extension to GDL has been defined through a set of ground atomic sentences. An SGA-based genetic algorithm has been designed for tweaking game parameters and conducting self-plays, so the features could be mined from meaningful game records. The approach has been tested on a small cluster, giving performance gains up to 20% more wins against the baseline UCT player. Implementations of proposed ideas constitutes the core of GGP Spatium a small C++/Python GGP framework, created for developing compact GGP Players and problem solvers.
Seashell effect pretouch sensing for robotic grasping

This paper introduces seashell effect pretouch sensing, and demonstrates application of this new sensing modality to robot grasp control, and also to robot grasp planning. “Pretouch” refers to sensing modalities that are intermediate in range between tactile sensing and vision. The novel pretouch technique presented in this paper is effective on materials that prior pretouch techniques fail on. Seashell effect pretouch is inspired by the phenomenon of “hearing the sea” when a seashell is held to the ear, and in particular relies on the observation that the “sound of the sea” changes as the distance from the seashell to the head varies. To turn the familiar seashell effect into a sensor for robotic manipulation, a cavity and microphone was built into a robot finger. The sensor detects changes in the spectrum of ambient noise that occur when the finger approaches an object. Environmental noise is amplified most (attenuated least) at the cavity's resonant frequency, which changes as the cavity approaches an object. After introducing the sensing modality and characterizing its performance, the paper describes experiments performed with prototype sensors integrated into a Willow Garage PR2 robot's gripper. We explore two primary applications: (1) reactive grasp control and (2) pretouch-assisted grasp planning. For reactive grasp control, we demonstrate the advantage of pretouch over pressure sensing for sensing objects that are too compliant for the PR2's tactile sensors to detect during the grasp execution. We demonstrate the benefit of seashell effect pretouch over prior pretouch methods by demonstrating that the new method succeeds with materials on which prior methods fail. For pretouch-assisted grasp planning, the pointcloud from the PR2's texture-projection / narrow stereo camera is augmented with additional points collected by recording the position of the robot's end effector when the pretouch sensor detects the object. This method can compensate for points that are missing from the pointcloud either because of depth camera failure or occlusion. For this application, pretouch is advantageous over contact sensing because contact sensing tends to unintentionally displace the object.
2K: A Distributed Operating System for Dynamic Heterogeneous Environments

The first decades of the new millennium will witness an explosive growth in the number and diversity of networked devices and portals. We foresee high degrees of mobility, heterogeneity, and interactions among computing devices connected to global networks. While previous research in distributed operating systems solved many problems related to resource management, they seldom addressed the problems of heterogeneity and dynamic adaptability. On the other hand, middleware solutions, like CORBA and Java/Jini, solve part of the heterogeneity problem by permitting seamless communication among different platforms . But, they do not address dynamic resource management and adaptability for applications requiring high-performanc e distributed computing. This paper presents 2K, an integrated operating system architecture that addresses the problems of resource management in heterogeneous networks, dynamic adaptability, and configuration of component-based distributed applications.
Darmstadt Dribblers: Team Description for Humanoid KidSize League of RoboCup 2008

This paper describes the hardware and software design of the kidsize humanoid robot systems of the Darmstadt Dribblers in 2007. The robots are used as a vehicle for research in control of locomotion and behavior of autonomous humanoid robots and robot teams with many degrees of freedom and many actuated joints. The Humanoid League of RoboCup provides an ideal testbed for such aspects of dynamics in motion and autonomous behavior as the problem of generating and maintaining statically or dynamically stable bipedal locomotion is predominant for all types of vision guided motions during a soccer game. A modular software architecture as well as further technologies have been developed for efficient and effective implementation and test of modules for sensing, planning, behavior, and actions of humanoid robots.
Simulation of First Come First Served ( FCFS ) and Shortest Job First ( SJF ) Algorithms

Development of scheduling algorithms is directly related with the development of operating system which brings difficulties in implementation. Any modification on the scheduling algorithm will appear as modification on the operating system kernel code. Processor is an important source of cpu scheduling process, so it becomes very important on accomplishing of the operating system design goals. A delicate problem of the well-functioning of SO is the case when in CPU comes two or more processes which wants to be executed. Scheduling includes a range of mechanisms and policies that SO has to follows in order that all processes take the service. In this Paper we will discuss about two main batches algorithms, such as FCFS and SJF, and i will show a manner how to improve these algorithms in the future work.
Personalization features on business-to-consumer e-commerce: Review and future directions

Personalization in e-commerce has potentials to increase sales, customers' purchase intention and acquisition, as well as improvement of customer interaction. It is understood that personalization is a controllable variable for successful e-commerce. However, previous research on personalization proposed diverse concepts from numerous fields. As a result, it leads to bias construct of e-commerce personalization development and evaluation by academia and industry. To address this gap, a study was conducted to unravel personalization features from various perspectives. A Kitchenham's systematic literature review was used to discover personalization research from Q1/Q2 journals and top conference papers between 2012-2017. A theory-driven approach was administered to extract 21 selected papers. This process classifies personalization features into four dimensions based on three characters i.e objective, method, and user model. They include architectural, relational, instrumental and commercial dimensions. The results show that instrumental and commercial personalizations have been proved as the most popular dimension in the academic literature. However, relational personalization has been consistently rising as a new interesting topic to study since the massive growth of social media data.
Syntactic entropy for main content extraction from web pages

In this paper, we present a solution for main content identification in web pages. Our solution is language-independent; Web pages may be written in different languages. It is topic-independent; no domain knowledge or dictionary is applied. And it is unsupervised; no training phase is necessary. The solution exploits the tree structure of web pages and the frequencies of text tokens to attribute scores of content density to the areas of the page and by the way identify the most important one. We tested this solution over representative examples of web pages to show how efficient and accurate it is. The results were satisfying.
Monitoring the railway infrastructure: Detection of surface defects using wavelets

For monitoring the conditions of railway infrastructures, axle box acceleration (ABA) measurements on board of trains is used. In this paper, the focus is on the early detection of short surface defects called squats. Different classes of squats are classified based on the response in the frequency domain of the ABA signal, using the wavelet power spectrum. For the investigated Dutch tracks, the power spectrum in the frequencies between 1060-1160Hz and around 300Hz indicate existence of a squat and also provide information of whether a squat is light, moderate or severe. The detection procedure is then validated relying on real-life measurements of ABA signals from measuring trains, and data of severity and location of squats obtained via a visual inspection of the tracks. Based on the real-life tests in the Netherlands, the hit rate of the system for light squats is higher than 78%, with a false alarm rate of 15%. In the case of severe squats the hit rate was 100% and zero false alarms.