nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Objective Path Planning of an Autonomous Mobile Robot in Static and Dynamic Environments using a Hybrid PSO-MFB Optimisation Algorithm

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed path planning algorithm consists of three modules: in the first module, the path planning algorithm forms an optimised path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimises distance and follows path smoothness criteria; in the second module, any infeasible points generated by the proposed PSO-MFB Algorithm are detected by a novel Local Search (LS) algorithm and integrated with the PSO-MFB algorithm to be converted into feasible solutions; the third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. Simulations have been carried out that indicated that this method generates a feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Comparisons with previous examples in the literature are also included in the results.
Do cutaneous nerves cross the midline?

Standard cutaneous innervation maps show strict midline demarcation. Although authors of these maps accept variability of peripheral nerve distribution or occasionally even the midline overlap of cutaneous nerves, this concept seems to be neglected by many other anatomists. To support the statement that such transmedian overlap exists, we performed an extensive literature search and found ample evidence for all regions (head/neck, thorax/abdomen, back, perineum, and genitalia) that peripheral nerves cross the midline or communicate across the midline. This concept has substantial clinical implications, most notably in anesthesia and perineural tumor spread. This article serves as a springboard for future anatomical, clinical, and experimental research.
Bio-inspired predictive orientation decomposition of skeleton trajectories for real-time human activity prediction

Activity prediction is an essential task in practical human-centered robotics applications, such as security, assisted living, etc., which targets at inferring ongoing human activities based on incomplete observations. To address this challenging problem, we introduce a novel bio-inspired predictive orientation decomposition (BIPOD) approach to construct representations of people from 3D skeleton trajectories. Our approach is inspired by biological research in human anatomy. In order to capture spatio-temporal information of human motions, we spatially decompose 3D human skeleton trajectories and project them onto three anatomical planes (i.e., coronal, transverse and sagittal planes); then, we describe short-term time information of joint motions and encode high-order temporal dependencies. By estimating future skeleton trajectories that are not currently observed, we endow our BIPOD representation with the critical predictive capability. Empirical studies validate that our BIPOD approach obtains promising performance, in terms of accuracy and efficiency, using a physical TurtleBot2 robotic platform to recognize ongoing human activities. Experiments on benchmark datasets further demonstrate that our new BIPOD representation significantly outperforms previous approaches for real-time activity classification and prediction from 3D human skeleton trajectories.
Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case

Convolutional Neural Network(CNN) based semantic segmentation require extensive pixel level manual annotation which is daunting for large microscopic images. The paper is aimed towards mitigating this labeling effort by leveraging the recent concept of generative adversarial network(GAN) wherein a generator maps latent noise space to realistic images while a discriminator differentiates between samples drawn from database and generator. We extend this concept to a multi task learning wherein a discriminator-classifier network differentiates between fake/real examples and also assigns correct class labels. Though our concept is generic, we applied it for the challenging task of vessel segmentation in fundus images. We show that proposed method is more data efficient than a CNN. Specifically, with 150K, 30K and 15K training examples, proposed method achieves mean AUC of 0.962, 0.945 and 0.931 respectively, whereas the simple CNN achieves AUC of 0.960, 0.921 and 0.916 respectively.
Malware Fingerprinting under Uncertainty

Malware detection and classification is critical for the security of IT infrastructure. Legacy detection of malware has been highly reliant on static signatures, so malware authors have evolved code polymorphic techniques to counteract these tools, thus rendering static malware detectors ineffective. While malware writers may easily use code rewriting techniques to scramble binary images; malware processes at runtime still must conduct a sequence of operational steps to achieve its design goal, indicating an approach based on behavioral analysis where the captured invariants form a new type of forensic fingerprint. Moreover these operational steps are constrained to occur within the computers' or mobile devices' abstract system interface - a finite basis of activities that submit to effective monitoring with a variety of tools. In this work, we propose a formalism for expressing these behaviors, learning them and analyzing them to form automated malware analysis tools. Thus motivated by a need to detect and classify malware, we root its foundation in formal verification, as well as methodology from statistical and machine learning. Specifically using trace data from malware we leverage formal verification methods (such as probabilistic model checking) to construct classifiers and evaluate their efficacy in supervised learning and cross-fold validation experiments. The results inform how a fully automated reasoning mechanism may be applied to unknown software by posing its system trace as a query to various classifiers as hypothesis testing, the outputs informing belief of membership. Finally, we demonstrate the method and results on real malware data.
Sensing behavior of atomically thin-layered MoS2 transistors.

Most of recent research on layered chalcogenides is understandably focused on single atomic layers. However, it is unclear if single-layer units are the most ideal structures for enhanced gas-solid interactions. To probe this issue further, we have prepared large-area MoS2 sheets ranging from single to multiple layers on 300 nm SiO2/Si substrates using the micromechanical exfoliation method. The thickness and layering of the sheets were identified by optical microscope, invoking recently reported specific optical color contrast, and further confirmed by AFM and Raman spectroscopy. The MoS2 transistors with different thicknesses were assessed for gas-sensing performances with exposure to NO2, NH3, and humidity in different conditions such as gate bias and light irradiation. The results show that, compared to the single-layer counterpart, transistors of few MoS2 layers exhibit excellent sensitivity, recovery, and ability to be manipulated by gate bias and green light. Further, our ab initio DFT calculations on single-layer and bilayer MoS2 show that the charge transfer is the reason for the decrease in resistance in the presence of applied field.
Photostimulable Near-Infrared Persistent Luminescent Nanoprobes for Ultrasensitive and Longitudinal Deep-Tissue Bio-Imaging

In vivo fluorescence imaging suffers from suboptimal signal-to-noise ratio and shallow detection depth, which is caused by the strong tissue autofluorescence under constant external excitation and the scattering and absorption of short-wavelength light in tissues. Here we address these limitations by using a novel type of optical nanoprobes, photostimulable LiGa5O8:Cr(3+) near-infrared (NIR) persistent luminescence nanoparticles, which, with very-long-lasting NIR persistent luminescence and unique photo-stimulated persistent luminescence (PSPL) capability, allow optical imaging to be performed in an excitation-free and hence, autofluorescence-free manner. Cr(3+) nanoparticles pre-charged by ultraviolet light can be repeatedly (>20 times) stimulated in vivo, even in deep tissues, by short-illumination (~15 seconds) with a white light-emitting-diode flashlight, giving rise to multiple NIR PSPL that expands the tracking window from several hours to more than 10 days. Our studies reveal promising potential of these nanoprobes in cell tracking and tumor targeting, exhibiting exceptional sensitivity and penetration that far exceed those afforded by conventional fluorescence imaging.
How effective is UML modeling ?

Modeling has become a common practice in modern software engineering. Since the mid 1990s the Unified Modeling Language (UML) has become the de facto standard for modeling software systems. The UML is used in all phases of software development: ranging from the requirement phase to the maintenance phase. However, empirical evidence regarding the effectiveness of modeling in software development is few and far apart. This paper aims to synthesize empirical evidence regarding the effectiveness of modeling using UML in software development, with a special focus on the cost and benefits.
Genotype and Phenotype of Transthyretin Cardiac Amyloidosis: THAOS (Transthyretin Amyloid Outcome Survey).

Transthyretin amyloidosis (ATTR) is a heterogeneous disorder with multiorgan involvement and a genetic or nongenetic basis.



The goal of this study was to describe ATTR in the United States by using data from the THAOS (Transthyretin Amyloidosis Outcomes Survey) registry.



Demographic, clinical, and genetic features of patients enrolled in the THAOS registry in the United States (n = 390) were compared with data from patients from other regions of the world (ROW) (n = 2,140). The focus was on the phenotypic expression and survival in the majority of U.S. subjects with valine-to-isoleucine substitution at position 122 (Val122Ile) (n = 91) and wild-type ATTR (n = 189).



U.S. subjects are older (70 vs. 46 years), more often male (85.4% vs. 50.6%), and more often of African descent (25.4% vs. 0.5%) than the ROW. A significantly higher percentage of U.S. patients with ATTR amyloid seen at cardiology sites had wild-type disease than the ROW (50.5% vs. 26.2%). In the United States, 34 different mutations (n = 201) have been reported, with the most common being Val122Ile (n = 91; 45.3%) and Thr60Ala (n = 41; 20.4%). Overall, 91 (85%) of 107 patients with Val122Ile were from the United States, where Val122Ile subjects were younger and more often female and black than patients with wild-type disease, and had similar cardiac phenotype but a greater burden of neurologic symptoms (pain, numbness, tingling, and walking disability) and worse quality of life. Advancing age and lower mean arterial pressure, but not the presence of a transthyretin mutation, were independently associated with higher mortality from a multivariate analysis of survival.



In the THAOS registry, ATTR in the United States is overwhelmingly a disorder of older adult male subjects with a cardiac-predominant phenotype. Val122Ile is the most common transthyretin mutation, and neurologic phenotypic expression differs between wild-type disease and Val122Ile, but survival from enrollment in THAOS does not.
Evaluating Link Prediction on Large Graphs

Exploiting network data (i.e., graphs) is a rather particular case of data mining. The size and relevance of network domains justifies research on graph mining, but also brings forth severe complications. Computational aspects like scalability and parallelism have to be reevaluated, and well as certain aspects of the data mining process. One of those are the methodologies used to evaluate graph mining methods, particularly when processing large graphs. In this paper we focus on the evaluation of a graph mining task known as Link Prediction. First we explore the available solutions in traditional data mining for that purpose, discussing which methods are most appropriate. Once those are identified, we argue about their capabilities and limitations for producing a faithful and useful evaluation. Finally, we introduce a novel modification to a traditional evaluation methodology with the goal of adapting it to the problem of Link Prediction on large graphs.
The reduced genomes of Parcubacteria (OD1) contain signatures of a symbiotic lifestyle

Candidate phylum OD1 bacteria (also referred to as Parcubacteria) have been identified in a broad range of anoxic environments through community survey analysis. Although none of these species have been isolated in the laboratory, several genome sequences have been reconstructed from metagenomic sequence data and single-cell sequencing. The organisms have small (generally <1 Mb) genomes with severely reduced metabolic capabilities. We have reconstructed 8 partial to near-complete OD1 genomes from oxic groundwater samples, and compared them against existing genomic data. The conserved core gene set comprises 202 genes, or ~28% of the genomic complement. "Housekeeping" genes and genes for biosynthesis of peptidoglycan and Type IV pilus production are conserved. Gene sets for biosynthesis of cofactors, amino acids, nucleotides, and fatty acids are absent entirely or greatly reduced. The only aspects of energy metabolism conserved are the non-oxidative branch of the pentose-phosphate shunt and central glycolysis. These organisms also lack some activities conserved in almost all other known bacterial genomes, including signal recognition particle, pseudouridine synthase A, and FAD synthase. Pan-genome analysis indicates a broad genotypic diversity and perhaps a highly fluid gene complement, indicating historical adaptation to a wide range of growth environments and a high degree of specialization. The genomes were examined for signatures suggesting either a free-living, streamlined lifestyle, or a symbiotic lifestyle. The lack of biosynthetic capabilities and DNA repair, along with the presence of potential attachment and adhesion proteins suggest that the Parcubacteria are ectosymbionts or parasites of other organisms. The wide diversity of genes that potentially mediate cell-cell contact suggests a broad range of partner/prey organisms across the phylum.
Finite-Element-Based Multiobjective Design Optimization Procedure of Interior Permanent Magnet Synchronous Motors for Wide Constant-Power Region Operation

This paper proposes the design optimization procedure of three-phase interior permanent magnet (IPM) synchronous motors with minimum weight, maximum power output, and suitability for wide constant-power region operation. The particular rotor geometry of the IPM synchronous motor and the presence of several variables and constraints make the design problem very complicated. The authors propose to combine an accurate finite-element analysis with a multiobjective optimization procedure using a new algorithm belonging to the class of controlled random search algorithms. The optimization procedure has been employed to design two IPM motors for industrial application and a city electrical scooter. A prototype has been realized and tested. The comparison between the predicted and measured performances shows the reliability of the simulation results and the effectiveness, versatility, and robustness of the proposed procedure.
iScape: A Collaborative Memory Palace for Digital Library Search Results

Massive amounts of data are available in today’s Digital Libraries (DLs). The challenge is to find relevant information quickly and easily, and to use it effectively. A standard way to access DLs is via a text -based query issued by a single user. Typically, the query results in a potentially very long ordered list of matching documents, that makes it hard for users to find what they are looking for. This paper presents iScape, a shared virtual desktop world dedicated to the collaborative exploration and management of information. Data mining and information visualization techniques are applied to extract and visualize semantic relationships in search results. A three-dimensional (3-D) online browser system is exploited to facilitate complex and sophisticated human-computer and human-human interaction. Informal user studies have been conducted to compare the iScape world with a text -based, a 2-D visual Web interface, and a 3-D non-collaborative CAVE interface. We conclude with a discussion.
Mission-focused cyber situational understanding via graph analytics

This paper describes CyGraph, a prototype tool for improving network security posture, maintaining situational understanding in the face of cyberattacks, and focusing on protection of mission-critical assets. CyGraph captures complex relationships among entities in the cyber security domain, along with how mission elements depend on cyberspace assets. Pattern-matching queries traverse the graph of interrelations according to user-specified constraints, yielding focused clusters of high-risk activity from the swarm of complex interrelationships. Analytic queries are expressed in CyGraph Query Language (CyQL), a domain-specific language for expressing graph patterns of interest, which CyGraph translates to the backend native query language. CyGraph automatically infers the structure of its underlying graph model through analysis of the ingested data, which it presents to the user for generating queries in an intuitive way. CyGraph has been experimentally validated in both enterprise and tactical military environments.
A Comparative Study on Approaches of Vector Space Model in Information Retrieval

The vector space model is one of the classical and widely applied information retrieval models to rank the web page based on similarity values. The retrieval operations consist of cosine similarity function to compute the similarity values between a given query and the set of documents retrieved and then rank the documents according to the relevance. In this paper, we are presenting different approaches of vector space model to compute similarity values of hits from search engine for given queries based on terms weight. In order to achieve the goal of an effective evaluation algorithm, our work intends to extensive analysis of the main aspects of Vector space model, its approaches and provides a comprehensive comparison for Term-Count
A real-time remote rendering system for interactive mobile graphics

Mobile devices are gradually changing people's computing behaviors. However, due to the limitations of physical size and power consumption, they are not capable of delivering a 3D graphics rendering experience comparable to desktops. Many applications with intensive graphics rendering workloads are unable to run on mobile platforms directly. This issue can be addressed with the idea of remote rendering: the heavy 3D graphics rendering computation runs on a powerful server and the rendering results are transmitted to the mobile client for display. However, the simple remote rendering solution inevitably suffers from the large interaction latency caused by wireless networks, and is not acceptable for many applications that have very strict latency requirements.
 In this article, we present an advanced low-latency remote rendering system that assists mobile devices to render interactive 3D graphics in real-time. Our design takes advantage of an image based rendering technique: 3D image warping, to synthesize the mobile display from the depth images generated on the server. The research indicates that the system can successfully reduce the interaction latency while maintaining the high rendering quality by generating multiple depth images at the carefully selected viewpoints. We study the problem of viewpoint selection, propose a real-time reference viewpoint prediction algorithm, and evaluate the algorithm performance with real-device experiments.
Visual-Inertial Combined Odometry System for Aerial Vehicles

The requirement to operate aircraft in GPS-denied environments can be met by using visual odometry. Aiming at a full-scale aircraft equipped with a high-accuracy inertial navigation system (INS), the proposed method combines vision and the INS for odometry estimation. With such an INS, the aircraft orientation is accurate with low drift, but it contains high-frequency noise that can affect the vehicle motion estimation, causing position estimation to drift. Our method takes the INS orientation as input and estimates translation. During motion estimation, the method virtually rotates the camera by reparametrizing features with their depth direction perpendicular to the ground. This partially eliminates error accumulation in motion estimation caused by the INS high-frequency noise, resulting in a slow drift. We experiment on two hardware configurations in the acquisition of depth for the visual features: 1) the height of the aircraft above the ground is measured by an altimeter assuming that the imaged ground is a local planar patch, and 2) the depth map of the ground is registered with a two-dimensional laser in a push-broom configuration. The method is tested with data collected from a full-scale helicopter. The accumulative flying distance for the overall tests is approximately 78 km. We observe slightly better accuracy with the push-broom laser than the altimeter.
Articulated minimally invasive surgical instrument based on compliant mechanism

In minimally invasive surgery, instruments are inserted from the exterior of the patient’s body into the surgical field inside the body through the minimum incision, resulting in limited visibility, accessibility, and dexterity. To address this problem, surgical instruments with articulated joints and multiple degrees of freedom have been developed. The articulations in currently available surgical instruments use mainly wire or link mechanisms. These mechanisms are generally robust and reliable, but the miniaturization of the mechanical parts required often results in problems with size, weight, durability, mechanical play, sterilization, and assembly costs. We thus introduced a compliant mechanism to a laparoscopic surgical instrument with multiple degrees of freedom at the tip. To show the feasibility of the concept, we developed a prototype with two degrees of freedom articulated surgical instruments that can perform the grasping and bending movements. The developed prototype is roughly the same size of the conventional laparoscopic instrument, within the diameter of 4 mm. The elastic parts were fabricated by Ni-Ti alloy and SK-85M, rigid parts ware fabricated by stainless steel, covered by 3D- printed ABS resin. The prototype was designed using iterative finite element method analysis, and has a minimal number of mechanical parts. The prototype showed hysteresis in grasping movement presumably due to the friction; however, the prototype showed promising mechanical characteristics and was fully functional in two degrees of freedom. In addition, the prototype was capable to exert over 15 N grasping that is sufficient for the general laparoscopic procedure. The evaluation tests thus positively showed the concept of the proposed mechanism. The prototype showed promising characteristics in the given mechanical evaluation experiments. Use of a compliant mechanism such as in our prototype may contribute to the advancement of surgical instruments in terms of simplicity, size, weight, dexterity, and affordability.