nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge Representation in pAninI Framework Using Neural Network Model

Knowledge representation is base for expressing semantic content of input in intelligent information retrieval systems. Identification of semantic requires processing of input language at various levels. To make system understand text or speech is a challenging task as it involves extracting semantics of the language which itself is a complex problem. At the same time languages posses with multiple ambiguities and uncertainty which needs to be resolved at various phases of language processing. Level of understandability depends upon the grammar, syntactic and semantic representation of the language and methods employed for these analysis. Processing depends on the type of language, grammar of the language, ambiguities present and size of corpus available. Order free language posses different features as compared to rigid order language. Most of the Indian languages are order free; hence mechanism for such language needs to be formulated. One of the ancient Indian Sanskrit grammarians, pAninI has defined grammar of Sanskrit language in such a way that it is suitable for computational analysis. Six main semantic class identified under this theory is a baseline model for knowledge representation. This paper exploits the features of the language, applicability of rules and resolving ambiguities using neural network model. A hybrid model incorporating the features of rules based and neural network the is designed and implemented for pAninI based semantic analysis, generating case frames as output. Index Terms pAninI Grammar framework, Knowledge Representation, Case Frame, Natural Language Processing, Semantic.
Learning continuous grasp stability for a humanoid robot hand based on tactile sensing

Grasp stability estimation with complex robots in environments with uncertainty is a major research challenge. Analytical measures such as force closure based grasp quality metrics are often impractical because tactile sensors are unable to measure contacts accurately enough especially in soft contact cases. Recently, an alternative approach of learning the stability based on examples has been proposed. Current approaches of stability learning analyze the tactile sensor readings only at the end of the grasp attempt, which makes them somewhat time consuming, because the grasp can be stable already earlier. In this paper, we propose an approach for grasp stability learning, which estimates the stability continuously during the grasp attempt. The approach is based on temporal filtering of a support vector machine classifier output. Experimental evaluation is performed on an anthropomorphic ARMAR-IIIb. The results demonstrate that the continuous estimation provides equal performance to the earlier approaches while reducing the time to reach a stable grasp significantly. Moreover, the results demonstrate for the first time that the learning based stability estimation can be used with a flexible, pneumatically actuated hand, in contrast to the rigid hands used in earlier works.
Modeling, parameter identification and model-based control of a lightweight robotic manipulator

Nowadays many robotic tasks require close and compliant tracking of desired positions, paths, or forces. To achieve these goals, model-based control schemes provide a possible solution, allowing to directly consider the nonlinear dynamics. One of the key challenges, however, is the derivation of suitable models, which allow sufficiently fast evaluation, as well as the parameterization of these models based on measurements. In this work we outline and review a structured approach for model-based controller design for robots. In a first step we derive suitable models for multi-link robots. In a second step we review how such models can be parameterized and how optimal identification experiments can be designed. Based on the model we then derive a simple model based controller to experimentally validate the results considering a lightweight robot. The single steps of the derivation and controller design are supported by a newly developed freely available model toolbox for the considered lightweight robot.
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a “holistic” approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their 3D poses in the form of 2D projections of the corners of their 3D bounding boxes. This, however, is not sufficient for handling objects from the recent T-LESS dataset: These objects exhibit an axis of rotational symmetry, and the similarity of two images of such an object under two different poses makes training the CNN challenging. We solve this problem by restricting the range of poses used for training, and by introducing a classifier to identify the range of a pose at run-time before estimating it. We also use an optional additional step that refines the predicted poses. We improve the state-of-the-art on the LINEMOD dataset from 73.7% [2] to 89.3% of correctly registered RGB frames. We are also the first to report results on the Occlusion dataset  using color images only. We obtain 54% of frames passing the Pose 6D criterion on average on several sequences of the T-LESS dataset, compared to the 67% of the state-of-the-art [10] on the same sequences which uses both color and depth. The full approach is also scalable, as a single network can be trained for multiple objects simultaneously.
Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes

Additive Manufacturing is increasingly used in the development of new products: from conceptual design to functional parts and tooling. However, today, variability in part quality due to inadequate dimensional tolerances, surface roughness, and defects, limits its broader acceptance for high-value or mission-critical applications. While process control in general can limit this variability, it is impeded by a lack of adequate process measurement methods. Process control today is based on heuristics and experimental data, yielding limited improvement in part quality. The overall goal is to develop the measurement science 1 necessary to make in-process measurement and real-time control possible in additive manufacturing. Traceable dimensional and thermal metrology methods must be developed for real-time closed-loop control of additive manufacturing processes. As a precursor, this report presents a review on the additive manufacturing control schemes, process measurements, and modeling and simulation methods as it applies to the powder bed fusion process, though results from other processes are reviewed where applicable. The aim of the review is to identify and summarize the measurement science needs that are critical to real-time process control. We organize our research findings to identify the correlations between process parameters, process signatures, and product quality. The intention of this report is to serve as a background reference and a go-to place for our work to identify the most suitable measurement methods and corresponding measurands for real-time control. 1 Measurement science broadly includes: development of performance metrics, measurement and testing methods, predictive modeling and simulation tools, knowledge modeling, protocols, technical data, and reference materials and artifacts; conduct of inter-comparison studies and calibrations; evaluation of technologies, systems, and practices, including uncertainty analysis; development of the technical basis for standards, codes, and practices in many instances via test-beds, consortia, standards and codes development organizations, and/or other partnerships with industry and academia.
A Novel Interest Flooding Attacks Detection and Countermeasure Scheme in NDN

As one of the promising candidates for the next generation network, Named Data Networking (NDN) has more advantages than the TCP/IP network in areas such as mobility, content distribution and security. Although NDN is designed to defense the majority Distributed Denial of Service (DDoS) attack in the current Internet, it anticipates some new varietal DDoS attacks. A representative DDoS form is called Interest Flooding Attacks (IFA), which can be launched easily by overflowing the PIT and can do immeasurable damage to the NDN. The existing IFA detection and countermeasure methods are mainly based on the PIT abnormal state statistics. However, these methods may cause misjudgment and damage the legitimate users, especially in the case of low-rate DDoS attacks or network congestion. In this paper, we propose an IFA detection scheme based on cumulative entropy by monitoring the content request abnormal distribution and then provide the malicious prefix identification method by relative entropy theory. An Interest traceback countermeasure is also used to restrain the attacker after detection. Therefore, the proposed scheme can reduce the IFA misjudgment and protect the legitimate user, and at the same time, can avoid overreaction to normal traffic fluctuation. Simulation results reveal that our methods can effectively mitigate the IFA in NDN.
Software and Algorithms for Graph Queries on Multithreaded Architectures

Search-based graph queries, such as finding short paths and isomorphic subgraphs, are dominated by memory latency. If input graphs can be partitioned appropriately, large cluster-based computing platforms can run these queries. However, the lack of compute-bound processing at each vertex of the input graph and the constant need to retrieve neighbors implies low processor utilization. Furthermore, graph classes such as scale-free social networks lack the locality to make partitioning clearly effective. Massive multithreading is an alternative architectural paradigm, in which a large shared memory is combined with processors that have extra hardware to support many thread contexts. The processor speed is typically slower than normal, and there is no data cache. Rather than mitigating memory latency, multithreaded machines tolerate it. This paradigm is well aligned with the problem of graph search, as the high ratio of memory requests to computation can be tolerated via multithreading. In this paper, we introduce the multithreaded graph library (MTGL), generic graph query software for processing semantic graphs on multithreaded computers. This library currently runs on serial machines and the Cray MTA-2, but Sandia is developing a run-time system that will make it possible to run MTGL-based code on symmetric multiprocessors. We also introduce a multithreaded algorithm for connected components and a new heuristic for inexact subgraph isomorphism We explore the performance of these and other basic graph algorithms on large scale-free graphs. We conclude with a performance comparison between the Cray MTA-2 and Blue Gene/Light for s-t connectivity.
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods

Despite the great developments in information technology, particularly the Internet, computer networks, global information exchange, and its positive impact in all areas of daily life, it has also contributed to the development of penetration and intrusion which forms a high risk to the security of information organizations, government agencies, and causes large economic losses. There are many techniques designed for protection such as firewall and intrusion detection systems (IDS). IDS is a set of software and/or hardware techniques used to detect hacker's activities in computer systems. Two types of anomalies are used in IDS to detect intrusive activities different from normal user behavior. Misuse relies on the knowledge base that contains all known attack techniques and intrusion is discovered through research in this knowledge base. Artificial intelligence techniques have been introduced to improve the performance of these systems. The importance of IDS is to identify unauthorized access attempting to compromise confidentiality, integrity or availability of the computer network. This paper investigates the Intrusion Detection (ID) problem using three machine learning algorithms namely, BayesNet algorithm, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The algorithms are applied on a real, Management Information Based (MIB) dataset that is collected from real life environment. To enhance the detection process accuracy, a set of feature selection approaches is used; Infogain (IG), ReleifF (RF), and Genetic Search (GS). Our experiments show that the three feature selection methods have enhanced the classification performance. GS with bayesNet, MLP and SVM give high accuracy rates, more specifically the BayesNet with the GS accuracy rate is 99.9%.
PPG-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks

Non invasive and continuous measurement of blood pressure can enable an effective 24/7 monitoring of hypertensive patients to timely prevent cardiovascular problems and precisely regulate anti-hypertension cures. Unfortunately, to date, blood pressure can be only measured through either cuff-based or invasive instruments that cannot be continuously used due to their characteristics. This paper proposes an overview of techniques and approaches based on the photoplethysmographic (PPG) signal for non invasive and continuous measurement of blood pressure. In particular, the PPG signal can be easily acquired from an optical sensor applied on the epidermis and used, alone or integrated with the ECG signal, to estimate the blood pressure. On the basis of such methods new instruments and sensor-based systems can be developed and integrated with computer-based health care systems that aim at supporting continuous and remote monitoring of assisted livings.
Will the Pedestrian Cross? A Study on Pedestrian Path Prediction

Future vehicle systems for active pedestrian safety will not only require a high recognition performance but also an accurate analysis of the developing traffic situation. In this paper, we present a study on pedestrian path prediction and action classification at short subsecond time intervals. We consider four representative approaches: two novel approaches (based on Gaussian process dynamical models and probabilistic hierarchical trajectory matching) that use augmented features derived from dense optical flow and two approaches as baseline that use positional information only (a Kalman filter and its extension to interacting multiple models). In experiments using stereo vision data obtained from a vehicle, we investigate the accuracy of path prediction and action classification at various time horizons, the effect of various errors (image localization, vehicle egomotion estimation), and the benefit of the proposed approaches. The scenario of interest is that of a crossing pedestrian, who might stop or continue walking at the road curbside. Results indicate similar performance of the four approaches on walking motion, with near-linear dynamics. During stopping, however, the two newly proposed approaches, with nonlinear and/or higher order models and augmented motion features, achieve a more accurate position prediction of 10-50 cm at a time horizon of 0-0.77 s around the stopping event.
Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data

A variety of human movement datasets are represented in an Origin-Destination(OD) form, such as taxi trips, mobile phone locations, etc. As a commonly-used method to visualize OD data, flow map always fails to discover patterns of human mobility, due to massive intersections and occlusions of lines on a 2D geographical map. A large number of techniques have been proposed to reduce visual clutter of flow maps, such as filtering, clustering and edge bundling, but the correlations of OD flows are often neglected, which makes the simplified OD flow map present little semantic information. In this paper, a characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms. Then, an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space. To enhance the readability of sampled OD flows, a set of meaningful visual encodings are designed to present the interactions of OD flows. We design and implement a visual exploration system that supports visual inspection and quantitative evaluation from a variety of perspectives. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in reducing the visual clutter and enhancing correlations of OD flows.
A Weighted Probabilistic Neural Network

The Probabilistic Neural Network (PNN) algorithm represents the likelihood function of a given class as the sum of identical, isotropic Gaussians. In practice, PNN is often an excellent pattern classifier, outperforming other classifiers including backpropagation. However, it. is not. robust with respect to affine transformations of feature space, and this can lead to poor performance on certain data. We have derived an extension of PNN called Weighted PNN (WPNN) which compensates for this flaw by allowing anisotropic Gaussians, i.e. Gaussians whose covariance is not a multiple of the identity matrix. The covariance is optimized using a genetic algorithm, some interesting features of which are its redundant, logarithmic encoding and large population size. Experimental results validate our claims.
Planar reconfigurable antenna with integrated switching control circuitry

In this paper we propose a pattern reconfigurable planar antenna with integrated control circuitry, for flexible applications with Software Defined Radio (SDR) platforms. The antenna's layout is made by four folded microstrip elements placed symmetrically on a low cost FR-4 substrate. By using PIN diodes, the metallic elements can be switched to generate a single omnidirectional beam or six directional beams around the azimuth plane. The design includes MOSFET based control circuitry for switching the PIN diodes. The RF performance has been experimentally evaluated under different levels of control voltage. The radiation of omnidirectional and directional beams along with an integrated control board, allows for convenient utilization of the antenna in numerous SDR MIMO platforms.
Collection, exploration and analysis of crowdfunding social networks

Crowdfunding is a recent financing phenomenon that is gaining wide popularity as a means for startups to raise seed funding for their companies. This paper presents our initial results at understanding this phenomenon using an exploratory data driven approach. We have developed a big data platform for collecting and managing data from multiple sources, including company profiles (CrunchBase and AngelList) and social networks (Facebook and Twitter). We describe our data collection process that allows us to gather data from diverse sources at high throughput. Using Spark as our analysis tool, we study the impact of social engagement on startup fund raising success. We further define novel metrics that allow us to quantify the behavior of investors to follow and make investment decisions as communities rather than individuals. Finally, we explore visualization techniques that allow us to visualize communities of investors that make decisions in a close-knit fashion vs looser communities where investors largely make independent decisions. We conclude with a discussion on our ongoing research on causality analysis and new community detection algorithms.
Empirical evidence in global software engineering: a systematic review

Recognized as one of the trends of the 21st century, globalization of the world economies brought significant changes to nearly all industries, and in particular it includes software development. Many companies started global software engineering (GSE) to benefit from cheaper, faster and better development of software systems, products and services. However, empirical studies indicate that achieving these benefits is not an easy task. Here, we report our findings from investigating empirical evidence in GSE-related research literature. By conducting a systematic review we observe that the GSE field is still immature. The amount of empirical studies is relatively small. The majority of the studies represent problem-oriented reports focusing on different aspects of GSE management rather than in-depth analysis of solutions for example in terms of useful practices or techniques. Companies are still driven by cost reduction strategies, and at the same time, the most frequently discussed recommendations indicate a necessity of investments in travelling and socialization. Thus, at the same time as development goes global there is an ambition to minimize geographical, temporal and cultural separation. These are normally integral parts of cross-border collaboration. In summary, the systematic review results in several descriptive classifications of the papers on empirical studies in GSE and also reports on some best practices identified from literature.
Absolute Head Pose Estimation From Overhead Wide-Angle Cameras

Most surveillance cameras have a wide-angle field of view and are situated unobtrusively in overhead positions. For this type of application, head pose estimation is very challenging because of the limitations of the quality and resolution of the incoming data. In addition, as a person moves, their pose relative to the camera changes, but the desired pose is the absolute pose with respect to the room or space. In this paper, we present a solution to estimate absolute coarse head pose for wide-angle overhead cameras by integrating 3D head position and pose information. The work involves image-based learning, pose correction based on 3D position, and real-time multi-camera integration of low-resolution imagery. The system can be applied to an active face cataloguer to obtain the best view of the face for surveillance, to customer relationship management to record behavior in retail stores or to virtual reality as an input device.
An Evaluation of Fine and Gross Motor Skills in Adolescents with Down Syndromes

Down syndrome (DS) is the most common genetic cause of intellectual disability. Motor development of children with DS is delayed. The aim of the present study was to evaluate of fine and gross motor skills of adolescents with DS. The study sample of a total of 34 participants aged between 14 to 20 years comprised 16 adolescents with DS and a normally developing group of 18 adolescents without DS. Fine and gross motor skills of the participants were assessed by Bruininks-Oseretsky Test of Motor Proficiency, second edition short form (BOT-2 SF). The highest score of the test is 88 and higher score shows higher performance. The average age of adolescents with DS and without DS who participated in the study were 17.06±2.79 and 16.56±1.09, respectively. All partcipants were male. It was found a significant differences for all BOT-2 SF subtests and total scores when compared between adolescents with DS and adolescents without DS (p<0.05). Adolescents without DS has higher scores than adolescents with DS. In conclusion, both fine and gross motor skill performance of adolescents with DS is lower than normally developing adolescents. This study stresses the importance of interventions facilitating motor skills.