nils-herrmann/clean_scirepeval huggingface dataset:
Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories

This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems are addressed jointly and a solution that takes both into consideration is proposed. To do so, a manipulation capability index is defined, which is a function of both the task execution waypoints and the object grasping contact points. We build on recent state-of-the-art grasp-learning methods, to show how this index can be combined with a likelihood function computed by a probabilistic model of grasp selection, enabling the planning of grasps which have a high likelihood of being stable, but which also maximise the robot's capability to deliver a desired post-grasp task trajectory. We also show how this paradigm can be extended, from a single arm and hand, to enable efficient grasping and manipulation with a bi-manual robot. We demonstrate the effectiveness of the approach using experiments on a simulated as well as a real robot.
Treatment of posterolateral tibial plateau fractures with modified Frosch approach : a cadaveric study and case series

This study aimed to investigate the surgical techniques and the clinical efficacy of a modified Frosch approach in the treatment of posterolateral tibial plateau fractures. The standard Frosch approach was performed on 5 fresh-frozen cadavers. The mean bony surface area was measured upon adequate exposure of the proximal tibial cortex. Lateral proximal tibial plate and posterolateral T-plates were placed and the ease of the procedure was noted. In the study, 12 clinical cases of posterolateral tibial plateau fractures were treated via modified or standard Frosch approaches. The outcome was assessed over short to medium follow-up period. Cadaver studies allowed the inspection of the posterolateral joint surface of all specimens from the lateral side. The mean bony surface areas of the exposed lateral and posterolateral tibial plateau were (6.78 ± 1.13) cm2 and (3.59 ± 0.65) cm2, respectively. Lateral and posterolateral plates were implanted successfully. Lateral proximal tibial plates were fixed in 10 patients via a modified Frosch approach while posterolateral plates were fixed in 2 patients via a standard Frosch approach. Patients were followed up for 10 to 24 months (average: 15.7 months) and no complications were observed during this period. Based on the Rasmussen knee function score system, the results were recorded as excellent, good, and fair in 6, 4, and 2 patients, respectively. In conclusion, the modified Frosch approach has offers advantages of clear clarity in exposure, convenient for reduction and internal fixation of a fracture, and good positive clinical results over the normal approach.
An overview of body sensor networks in enabling pervasive healthcare and assistive environments

The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the growing needs in ubiquitous communications and recent advances in very-low-power wireless technologies, there has been considerable interest in the development and application of wireless networks around humans. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultra low-power processing / communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This review paper presents an up-to-date report of the current research and enabling applications and addresses some of the challenges and implementation issues.
Trajectory Tracking Control of a Four-Wheel Differentially Driven Mobile Robot

We consider the trajecto ry tracking control problem for a 4-wheel differentially driven mobile robot moving on an outdoor terrain. A dynamic model is presented accounting for the effects of wheel skidding. A model-based nonlinear controller is designed, following the dynamic feedback linearization paradigm. A n operational nonholonomic constraint is added at this stage, so as to obtain a predictable behavior for the instantaneous center of rotation thus preventing excessive skidding. The controller is then robustijied, using conventional linear techniques, against uncertainty in the soil parameters at the ground-wheel contact. Simulation results show the good performance in tracking spline-type trajectories on a virtual terrain with varying characteristics.
Part-of-speech Taggers for Low-resource Languages using CCA Features

In this paper, we address the challenge of creating accurate and robust partof-speech taggers for low-resource languages. We propose a method that leverages existing parallel data between the target language and a large set of resourcerich languages without ancillary resources such as tag dictionaries. Crucially, we use CCA to induce latent word representations that incorporate cross-genre distributional cues, as well as projected tags from a full array of resource-rich languages. We develop a probability-based confidence model to identify words with highly likely tag projections and use these words to train a multi-class SVM using the CCA features. Our method yields average performance of 85% accuracy for languages with almost no resources, outperforming a state-of-the-art partiallyobserved CRF model.
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics

Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration. Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.
Evaluation of Quranic text retrieval system based on manually indexed topics

This paper investigates the effectiveness of a state of the art information retrieval (IR) system in the verse retrieval problem for Quranic text. The evaluation is based on manually indexed topics of the Quran that provides both the queries and the relevance judgments. Furthermore, the system is evaluated in both Malay and English environment. The performance of the system is measured based on the MAP, the precision at 1, 5 and 10, and the MRR scores. The results of the evaluation are promising, showing the IR system has many potential for the Quranic text retrieval.
Diverse Concept-Level Features for Multi-Object Classification

We consider the problem of image classification with semantic features that are built from a set of base classifier outputs, each reflecting visual concepts. However, existing approaches consider visual concepts independently from each other whereas they are often linked together. When those relations are considered, existing models strongly rely on image low-level features, yielding in irrelevant relations when the low-level representation fails. On the contrary, the approach we propose, uses existing human knowledge, the application context itself and the human categorization mechanism to reflect complex relations between concepts. By nesting this human knowledge and the application context in the concept detection and selection processes, our final semantic feature captures the most useful information for an effective categorization. Thus, it enables to give good representation, even if some important concepts failed to be recognized. Experimental validation is conducted on three publicly available benchmarks of multi-class object classification and leads to results that outperforms comparable approaches.
RNA-Seq: a revolutionary tool for transcriptomics

RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
A Low-Complexity GPS Anti-Spoofing Method Using a Multi-Antenna Array

Spoofing attacks can completely mislead the position solution provided by global navigation satellite systems (GNSS). Recently, several anti-spoofing techniques have been introduced to combat spoofing attacks. However, in most cases the available anti-spoofing techniques are computationally complex or limited to a specific spoofing scenario. This paper provides a low computational complexity approach to mitigate the spoofing signals based on an antenna array processing technique. The proposed method is established based on the fact that all spoofing pseudo random noise (PRN) codes are transmitted from the same source in space. This technique steers a null toward a spatial sector where the maximum spatial energy is coming from. To avoid unintentional attenuation of authentic signals due to the beam pattern profile, the received power of each authentic signal is maximized individually after spoofing mitigation. The proposed anti-spoofing technique provides a low computational complexity process that performs prior to the acquisition stage of GPS receivers. Moreover, this method does not require any antenna array calibration process. Therefore, it can be easily employed as a standalone anti-spoofing module at the receiver input. Simulation results show that the proposed anti-spoofing method performs well for a wide range of received spoofing power. To verify the applicability of this method in real-world scenarios, a real data set has been collected and processed without any outdoor propagation. The data processing results demonstrate the desired performance of the proposed technique for real spoofing scenarios.  GNSS signals are vulnerable to in–band interference such as jamming and spoofing signals. Spoofing is an intentional interfering signal that aims to coerce GNSS receivers into generating false position/navigation solutions (Humphreys et al 2008). A spoofing attack is more menacing than jamming since the target receiver is not aware of the threat. The rapid advance in software defined radio (SDR) technology has made GNSS spoofing more flexible and less costly; therefore, GNSS spoofers can be made available for civilian misapplications at a low cost. Several anti-spoofing techniques have been proposed in the open literature recently. These methods can be generally divided into two main categories, namely spoofing detection and spoofing mitigation. Spoofing detection algorithms concentrate on discriminating the spoofing attack while spoofing mitigation techniques aim to neutralize the spoofing threat. Most of the previously proposed techniques focus on spoofing detection rather than spoofing mitigation. Amplitude discrimination, time of arrival (TOA) discrimination, consistency cross-check
Synthesis of inline filters with arbitrarily placed attenuation poles by using nonresonating nodes

A general and direct synthesis technique of pseudoelliptic inline filters with arbitrarily placed attenuation poles (APs) (transmission zeros) at real frequencies is presented. The APs are brought about and independently controlled by dedicated resonators, which are coupled to nonresonating nodes. Simple rules to properly determine the phases of the reflection coefficients at the input and output are given. To reduce the effect of roundoff errors, especially for higher order filters, the extraction of the elements of the network is performed from the input and output simultaneously. Multiplicity and scaling properties of the solutions are discussed. Synthesis examples are presented to demonstrate the soundness of the procedure. Theoretical results are compared with measurement to demonstrate the validity of the presented theory.
Active suction cup actuated by ElectroHydroDynamics phenomenon

Designing and manufacturing actuators using soft materials are among the most important subjects for future robotics. In nature, animals made by soft tissues such as the octopus have attracted the attention of the robotics community in the last years. Suckers (or suction cups) are one of the most important and peculiar organs of the octopus body, giving it the ability to apply high forces on the external environment. The integration of suction cups in soft robots can enhance their ability to manipulate objects and interact with the environment similarly to what the octopus does. However, artificial suction cups are currently actuated using fluid pressure so most of them require external compressors, which will greatly increase the size of the soft robot. In this work, we proposed the use of the ElectroHydroDynamics (EHD) principle to actuate a suction cup. EHD is a fluidic phenomenon coupled with electrochemical reaction that can induce pressure through the application of a high-intensity electric field. We succeeded in developing a suction cup driven by EHD keeping the whole structure extremely simple, fabricated by using a 3D printer and a cutting plotter. We can control the adhesion of the suction cup by controlling the direction of the fluidic flow in our EHD pump. Thanks to a symmetrical arrangement of the electrodes, composed by plates parallel to the direction of the channel, we can change the direction of the flow by changing the sign of the applied voltage. We obtained the pressure of 643 Pa in one unit of EHD pump and pressure of 1428 Pa in five units of EHD pump applying 6 kV. The suction cup actuator was able to hold and release a 2.86 g piece of paper. We propose the soft actuator driven by the EHD pump, and expand the possibility to miniaturize the size of soft robots.
Sparse Non-rigid Registration of 3D Shapes

Non-rigid registration of 3D shapes is an essential task of increasing importance as commodity depth sensors become more widely available for scanning dynamic scenes. Non-rigid registration is much more challenging than rigid registration as it estimates a set of local transformations instead of a single global transformation, and hence is prone to the overfitting issue due to underdetermination. The common wisdom in previous methods is to impose an ℓ2-norm regularization on the local transformation differences. However, the ℓ2-norm regularization tends to bias the solution towards outliers and noise with heavy-tailed distribution, which is verified by the poor goodness-of-fit of the Gaussian distribution over transformation differences. On the contrary, Laplacian distribution fits well with the transformation differences, suggesting the use of a sparsity prior. We propose a sparse non-rigid registration (SNR) method with an ℓ1-norm regularized model for transformation estimation, which is effectively solved by an alternate direction method (ADM) under the augmented Lagrangian framework. We also devise a multi-resolution scheme for robust and progressive registration. Results on both public datasets and our scanned datasets show the superiority of our method, particularly in handling large-scale deformations as well as outliers and noise.
A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval

With deep convolutional features, cross-region matching (CRM) has recently shown superior performance on image retrieval. It evaluates image similarity by comparing image regions at different locations and scales, and is, therefore, more robust to geometric variance of objects. This paper first scrutinizes CRM-based image retrieval to provide a rigorous probabilistic interpretation by following the probability ranking principle. In addition to manifesting the assumptions implicitly taken by CRM, our interpretation highlights a fundamental issue hindering the performance of CRM—when comparing two image regions, CRM ignores modeling the distribution of the visual concept class associated with an image region, making the similarity comparison less precise. Taking advantage of the unprecedented representation capability of deep convolutional features, this paper proposes one approach to tackle that issue. It treats locally clustered image regions as a pseudo-labeled class sharing the same visual concept and utilizes them to model the distribution of the visual concept class associated with an image region. Both non-parametric and parametric methods are developed for this purpose, with careful probabilistic justification. Extensive experimental study on multiple benchmark data sets demonstrates the superior performance of the proposed pseudo-label approach to CRM and other comparable methods, with the maximum improvement of more than 10 percentage points over CRM.
From content delivery today to information centric networking

Today, content delivery is a heterogeneous ecosystem composed by various independent infrastructures. The ever increasing growth of Internet traffic has encouraged the proliferation of different architectures to serve content provider needs and user demand. Despite the differences among the technology, their low level implementation can be characterized in a few fundamental building blocks: network storage, request routing, and data transfer. Existing solutions are inefficient because they try to build an information centric service model over a network infrastructure which was designed to support host-to-host communications. Information-Centric Networking (ICN) paradigm has been proposed as a possible solution to this mismatch. ICN integrates content delivery as a native network feature. The rationale is to architect a network that automatically interprets, processes, and delivers content (information) independently of its location. This paper makes the following contributions: 1) it identifies a set of building blocks for content delivery, 2) it surveys the most popular approaches to realize the above building blocks, 3) it compares content delivery solutions relying on the current Internet infrastructure with novel ICN approaches.
User Authentication from Mouse Movement Data Using Multiple Classifiers

The biometric authentication is becoming very familiar due to its unique characteristics. This paper presents a user authentication system using mouse movement data. The mouse movement data are captured using an existing tool named Jitbit macro recorder. The acquired data are preprocessed and sampled into N blocks based on specific number of actions and stored in database. From each block twelve features are generated: Number of Points in the trajectory, Delay Time, Number of Delay, Number of Action, STDEV of Trajectory Length, Total Length of Trajectory, STDEV of Slope, STDEV of Slope to Slope Difference, Number of Curvatures, Curvature of Trajectory, Number of Changes in Horizontal Position and Number of Changes in Vertical Position. This system uses three separate classifiers: SVM, K-Nearest Neighbor and Naive Bayes to recognize the user. The system is trained and tested using a benchmark data. The experimental result shows that K-nearest Neighbor has the lowest error rate.
Formalization, Annotation and Analysis of Diverse Drug and Probe Screening Assay Datasets Using the BioAssay Ontology (BAO)

Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens.