nils-herrmann/clean_scirepeval huggingface dataset:
An adaptive vision-based attentive tracker for human intended actions

Advances in vision technology are accelerating developments of non-verbal communication channels from human behaviors toward understanding their intention. An adaptive vision-based attentive tracker is proposed for isolating such human intended actions from ordinary walking behavior. The algorithm which drives the attentive tracker is divided into two sub-processes: one is for modeling the movement of human body parts as the environment using hidden Markov models, and the other is for learning the model of the tracker's action using a temporal difference algorithm. We describe the integration of the two sub-processes in detail and finally show an experimental result of isolating the human sign action during his natural walking motion for demonstrating the feasibility of our proposal. Identification of the signed shape determines the attentive action of the tracker with the simulated rewards supplied.
Design a microstrip antenna with defected ground structure

In this paper, a compact design for bandwidth enhancement of microstrip antenna is present. A basic rectangle microstrip is selected to research. The ground element of the proposed antenna is taken in the form of Defected Ground Structure (DGS). The antenna is based on substance Duroid RO4700JXR and is fed by a feeding microstrip line. The results of a fabricated antenna have an agreement with simulation results with wide bandwidth and good return loss.
A Robust Fuzzy Algorithm Based on Student's t-Distribution and Mean Template for Image Segmentation Application

Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. Student's t-distribution has come to be regarded as an alternative to Gaussian distribution, as it is heavily tailed and more robust for outliers. In this letter, we propose a new algorithm to incorporate the merits of these two approaches. The advantages of our method are as follows: First, we incorporate the local spatial information and pixel intensity value by considering the labeling of an image pixel influenced by the labels in its immediate neighborhood. Second, we introduce additional parameter a to control the extent of this influence. The larger a indicates heavier extent of influence in the neighborhoods. Finally, we utilize a mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. Compared with HMRF, our method is simple, easy and fast to implement. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of our approach.
Achieving High Code Coverage in Android UI Testing via Automated Widget Exercising

Automated functional user interface (UI) testing of mobile apps is a challenging task due to the highly interactive nature of the app UIs, and thus it commonly fails to provide high code coverage. In this paper, we present DroidDEV, an automated UI exerciser which aims to achieve high code coverage for real-world Android apps. DroidDEV dynamically builds a finite UI flow graph, generates and injects UI-context-aware inputs to exercise all the widgets on all the UI screens through a best-first search path finding algorithm. We have evaluated DroidDEV on 20 real-world open-source Android apps and compared it with manual UI testing and two other prevalent automated approaches including MobiGUITAR and Monkey. On these subjects, DroidDEV reports code coverage similar to manual UI testing, and it outperforms MobiGUITAR and Monkey in terms of code coverage and exercising time.
Airspace Collision Risk Hot-Spot Identification using Clustering Models

A key safety indicator for airspace is its collision risk estimate, which is compared against a target level of safety to provide a quantitative basis for judging the safety of operations in airspace. However, this quantitative basis fails to provide any insight regarding the magnitude, location, and timing of the risk of collision, distributed within a given airspace. In this paper, we propose a methodology for the identification of collision risk hot spots in a given airspace. The proposed methodology consists of processing air traffic data and developing traffic routes based on entry and exit points within the airspace. These routes and other flight information are then used to project air-traffic crossings and cluster potential collisions. The proposed method then estimates the collision risk for each identified cluster, culminating in risk assessment for the entire airspace. The model extends and adopts the state-of-the art clustering models, systemically identifies airspace collision risk hot spots, and further analyses hot spots by analyzing cluster features (number of points and contribution to overall risk) with flight levels and time of day. Experiments were conducted using one-month traffic data (25 440 flights) from Bahrain en-route airspace. By visualizing crossing points and clustering them in a 2-D geographic information system model we are able to identify collision risk hot spots, which contribute significantly to overall collision risk.
Cross-based local multipoint filtering

This paper presents a cross-based framework of performing local multipoint filtering efficiently. We formulate the filtering process as a local multipoint regression problem, consisting of two main steps: 1) multipoint estimation, calculating the estimates for a set of points within a shape-adaptive local support, and 2) aggregation, fusing a number of multipoint estimates available for each point. Compared with the guided filter that applies the linear regression to all pixels covered by a fixed-sized square window non-adaptively, the proposed filtering framework is a more generalized form. Two specific filtering methods are instantiated from this framework, based on piecewise constant and piecewise linear modeling, respectively. Leveraging a cross-based local support representation and integration technique, the proposed filtering methods achieve theoretically strong results in an efficient manner, with the two main steps' complexity independent of the filtering kernel size. We demonstrate the strength of the proposed filters in various applications including stereo matching, depth map enhancement, edge-preserving smoothing, color image denoising, detail enhancement, and flash/no-flash denoising.
Design of Low Hardware Complexity Filter Banks for Communications Systems Employing Folding Number System

In this paper, a new architecture to compute 1-D Discrete Wavelet Transform (DWT) based on Folding Number System (FNS) is introduced. Numbers for FNS are extracted directly from analog input without any forward converter like in the Residue Number System (RNS) based DWT. The large Dynamic Range makes the FNS DWT more efficient than those implemented using RNS systems. The proposed 1- D DWT architecture makes use of the relation between the coefficients of the low-pass and high-pass decomposition filters for orthogonal wavelets families to compute the transform with minimum numbers of modular multipliers. These multipliers are based on pipelined Look-Up Tables (LUT). A 6-tap Daubechies analysis filter bank is synthesized at a structural level using VHDL. FLEX10K devices of Altera are used in the simulation of DWT using binary arithmetic, RNS and FNS to evaluate its performance. Simulations show a significant throughput improvement is achieved in the proposed FNS DWT architecture when {2k - 1, 2k, 2k + 1} moduli set is used.
A fusing switch for fault suppression in the SNS High Voltage Converter Modulators

The High Voltage Converter Modulators (HVCMs) at the Spallation Neutron Source (SNS) have operated in excess of a combined 250,000 hours. Performance and reliability improvements to the HVCM are ongoing to increase modulator availability as accelerator system demands increase. There is a relatively large amount of energy storage in the HVCMs, ∼180 kJ. This energy can be released into unsuppressed faults, cause damage, and increase the time to repair. The “fusing switch” would provide a means to isolate this stored energy from the location of the most common faults. This paper introduces this concept and its application to the HVCMs.
A MODFET-based optoelectronic integrated circuit receiver for optical interconnects

The design, fabrication, and characterization of a 0.85- mu m sensitive photoreceiver is described. The monolithically integrated optoelectronic receiver is based on pseudomorphic InGaAs on GaAs modulation-doped field-effect transistors (MODFETs) and metal-semiconductor-metal (MSM) photodetectors. High-performance quarter-micrometer MODFETs with f/sub t/'s of approximately 70 GHz are utilized in a two-stage transimpedance amplifier. An asymmetric and a symmetric amplifier design are compared. The symmetric design is found to provide the desired zero DC offset voltage for a variety of supply voltages. Excess MSM-detector dark current and low-frequency internal gain are greatly reduced through the use of a silicon passivation layer and/or AlGaAs cap layer. Receiver transimpedances between 100 and 5000 Omega are obtained by varying the bias on an active feedback resistor. The parasitic capacitances of this common-gate feedback FET are studied. A transimpedance amplifier bandwidth as high as 14 GHz and an overall photoreceiver bandwidth of 11 GHz are measured.
A new algorithm of group scheduling in OBS core nodes

Scheduling is one of the activities which have a great impact on the communication performance of optical burst switching networks. There have been many scheduling algorithms proposed but most of them are online. Recently, several algorithms of group scheduling have been published, but they have the complexity of NP-complete. Moreover, the timeslot of group scheduling is predetermined and fixed without considering the dependence on the rate of arriving data. This article proposes a new algorithm of group scheduling which has the linear complexity and has the timeslot of group scheduling that could be changeable and adapt to the rate of arriving data. The analyses and evaluations of the effectiveness based on the simulation results will confirm the advantages of our algorithm.
Cooperative-cum-Constrained Maximum Likelihood algorithm for UWB-based localization in wireless BANs

Wireless Body Area Network (BAN) is a mainstream technology for numerous application fields (medicine, security, sport science...) and precise determination of wireless sensors' positions responses to the great needs in many applications. In addition, Ultra Wide Band (UWB) radio is an attractive technology to achieve the centimeter-level distance measurements. However, the aggregation of the distance information remains a challenge and this paper presents a cutting-edge method for performing the accurate localization in wireless BAN. To this aim, by fully exploiting its unique features, a novel Cooperative-cum- Constrained Maximum Likelihood (CCML) localization algorithm is developed. Simulation results and UWB-based platform validation show absolute agreement with theoretical prediction and improvement over previous studies by Hamie et al and Mekonnen et al.
A Novel Online Control Method for Hybrid Active Power Filter with Injection Circuit

This paper proposes a novel online control method for Hybrid Active Power Filter with Injection Circuit (IHAPF). It includes two closed-control loops. The lower closed-control loop contains a conventional PI controller and IHAPF model, while the upper closed-control loop is composed of predictive neural network model, optimization adjustor using generalized predictive control criterion (GPC) and IHAPF model. The purpose of the upper closed-control loop is to find a set of optimal PI parameters for the lower closed-control loop. Compared to the conventional PI control method, the simulation and experimental results show that the proposed control method has the advantages of shorter response time, able to online control very well and very effective in reducing harmonics.
Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification

This paper presents a novel framework for visual object recognition using infinite-dimensional covariance operators of input features, in the paradigm of kernel methods on infinite-dimensional Riemannian manifolds. Our formulation provides a rich representation of image features by exploiting their non-linear correlations, using the power of kernel methods and Riemannian geometry. Theoretically, we provide an approximate formulation for the Log-Hilbert-Schmidt distance between covariance operators that is efficient to compute and scalable to large datasets. Empirically, we apply our framework to the task of image classification on eight different, challenging datasets. In almost all cases, the results obtained outperform other state of the art methods, demonstrating the competitiveness and potential of our framework.
A Group Dynamic Source Routing protocol (GDSR) using the passive clustering for wireless mobile ad hoc networks

A Group Dynamic Source Routing (GDSR) protocol that employs the passive clustering technique is proposed in order to improve overhead related to the formation and maintenance of clusters. This approach does not use any additional message to build and maintain a cluster topology. A route request message establishes a disjoint set of clusters and at the same time, explores group path which is expressed as a sequence of cluster labels or clusterheads' IDs. All data packets and control messages contribute to the formation and/or maintenance of clusters, thus significantly reducing control overhead. The nodes with the same label collaborate to deliver packets to a node with next label on the group path. Since there does exist multiple links that connect two adjacent clusters, the group path is highly robust. Simulation results show that the proposed GDSR with the passive clustering far outperforms its predecessors DSR and CBRP in various simulation scenarios.
A Fast Decoder Using Less Memory

Statistical Machine Translation (SMT) uses large amount of text corpus and complex calculation operation for translation process, which makes this method require more system resources for fast translation. In this paper, we introduce an approach of decoding in SMT using less memory but translating faster, which is more suitable for mobile applications and embedded systems. In our approach, the SMT models are stored in tree structures in order to speed up the loading process and the decoding algorithm is optimized to reduce operations. We apply our approach to English-Vietnamese and Vietnamese-English SMT systems. When translating 20,000 English sentences, which are 7.45 word lengths in average, we achieve 37.8 BLEU score, the average speed is 0.052 s. In case of Vietnamese-English system, we translate 20,000 Vietnamese sentences, which are 8.42 word lengths in average, the BLEU score is 34.63 with an average speed of 0.091 s.
Accurate friction compensation for precision stage using synchronous piezoelectric device driver

It is well-known that one of the major limitations to achieve a good performance in mechanical systems is the presence of friction. High resolution positioning systems operating with accuracies in nanometer region usually exhibit relatively large steady-state tracking errors or even oscillations if controllers are designed without considering friction. Consequently, this paper aims at improving the position control of a high precision stage using a synchronous piezoelectric device driver (SPIDER) by comparing the performances of three friction compensators. These friction compensators detect friction in the system and use this information to modify the control input. The first using bang-bang control is based on the well-known static friction model. The second is a friction state observer based on the dynamic friction LuGre model, and the third is a feed-forward compensator based on the LuGre model. In order to effect a fair comparison, three friction compensators use the same identified friction parameters in controller synthesis. The performance comparisons are presented by means of experimental results on the proposed high precision stage using SPIDER
An Effective Architecture of Memory Built-In Self-Test for Wide Range of SRAM

Together with highly increasing integration on one chip currently, usage of memories, mainly Static Random Access Memory (SRAM), applied to wide range of functions is inevitable. However, memory faults are greatly concerned due to purpose of achieving high yield. As a result, a memory built-in self-test (MBIST) has become essential in any system obviously. As regards MBIST, while increasing criteria associating with area, frequency as well as various test algorithms has posed, current approaches have not adapted both such silicon requirements and ability of covering errors with complex algorithms yet. In this work, an effective architecture of MBIST for SRAM type with different configurations is proposed for not only ensuring high ability of detecting memory faults supported by the most popular algorithms namely MARCH C-and TLAPNPSF but also satisfy strict silicon criteria. Indeed, achieving great performance based on necessary experiments on 130nm technology with Application Specific Integrated Circuit (ASIC) design flow has confirmed strong competition to current designs.
Designing a multi-user 3D environment system

This study developed a multi-user 3D virtual environment system. The system must achieve two goals: portability, it can be embedded to any kind of platform; and coherence with other systems, it can reuse shared objects such as worlds and avatars from other systems. The following work has been done as part of this research: the design issues have been explored in detail; an architecture is proposed that overcomes several existing limitations; a functional prototype system that implements the architecture has been implemented; and the system has been tested and results are given.
Classification of meta-processes and their models

Identifies a taxonomy to characterize meta-process categories, and their characteristics. The taxonomy is applied to several existing software process-centered support environments (PSEs) and their support technologies. Up to now, meta-processes have been poorly understood, and thus hardly exploited properly. In some PSEs, meta-processes are supported, but their properties are not reflected clearly in any systematic framework. In this paper, some typical characteristics of meta-processes are recognized by clarifying: (i) the reasons why meta-processes are necessary for software development and maintenance; (ii) which effects they have on other components or fragments of a software process; (iii) how and when they should be accomplished; and (iv) who is responsible for performing them. These characteristics also contribute to recognizing the phases of meta-processes in software engineering. Identified characteristics and phases, serving as a framework for assessment, are then applied to evaluate five European PSEs, namely Adele2, EPOS, SPADE, Process Weaver and Process Wise Integrator. The intention of this evaluation is to illustrate the strengths and weaknesses of the identified PSE systems, providing a motivation for their further research and enhancement. In addition, it is also an initial step to validate and demonstrate the usefulness of this proposed assessment framework w.r.t. studying software meta-processes.<<ETX>>
Coordinative optical manipulation of multiple microscopic objects using micro-hands with multiple fingertips

Current control techniques for optical manipulation of multiple micro-objects employ laser tweezers to directly trap and manipulate the target microscopic objects, and thus it is not capable of coordinating and manipulating micro-objects with arbitrary types in the micro-world, including large micro-objects, laser sensitive biological cells, or optically untrappable ones. In this paper, we propose a robotic control technique for optical manipulation of multiple microscopic objects by using micro-hands with multiple fingertips. In this control technique, several micro-hands are first formed by coordinating multiple optically trapped micro-particles that serve as the laser-driven fingertips, and then utilized for grasping and coordinative manipulation of multiple micro-objects. The proposed technique offers a robotic control framework for coordination and optical manipulation of multiple microscopic objects with arbitrary types in the micro-world, including large micro-objects, lasersensitive cells, or even untrappable microscopic objects.
A Unified Framework for Symbiosis of Evolutionary Mechanisms with Application to Water Clusters Potential Model Design

This article presents a theoretic model for facilitating the emergence of productive search profiles transpiring from the symbiosis of gene (stochastic variation) and meme (lifetime learning) working in synergy. The evolvability measure of the symbiotic search profiles for each individual is quantified by means of statistical learning on distinct sample vectors encountered along the search. The most productive search profile inferred for an individual, as defined by evolvability measure, is subsequently used to work on it, leading to the self-configuration of solvers that acclimatizes to suit the given problem of interest. Empirical studies on representative problems are presented to reflect the characteristics of symbiotic evolution. Assessment made against several recent state-of-the-art evolutionary and adaptive search algorithms highlighted the efficacy of the theoretic formalism of evolutionary mechanisms in symbiosis for autonomic search. As the design of computationally cheap advanced empirical water models for the understanding of enigmatic properties of water remains an important and unsolved problem, the article presents an illustration of symbiotic evolution for the design of (H2O)n or water clusters potential model.