Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of framework based on roles for application development of service robots

The modern approach for developing service robot applications is by the use of robotics middleware. Various issues are still present and several projects attempt to address them. However, an important issue that impacts the developers and integrators has not yet been thoroughly analyzed: usability. The usability of the development tools needs to be improved; it is required to access and work with service robots (and their components) with the least effort possible. This issue affects the introduction of service robots into the real world. In this article, we propose the Framework for Integration of Elements and Resources by Roles (FIERRo) to address the usability problem by making the items needed by developers and integrators as easily available and organized as possible. Description of example applications include: modeling of two independent robot systems, system development using and not using the FIERRo (hypotheses were set), and a simulated GUI. Social impact and future works are discussed thereafter.
First results of automatic fault-injection in an AUTOSAR tool-chain

Fault-injection is a commonly used method for testing safety-relevant embedded systems. Especially after the introduction of the ISO 26262 the importance of this technique has gained attention in the automotive domain to test the proper implementation of safety concepts. To enable meaningful tests, the possible random hardware faults need to be aligned in early stages of the development process between the hardware and software developers. Since it is difficult to reason that a system nominal behavior is not affected by the injected fault-code, we aim at an automatic injection of faults into the software units using an AUTOSAR tool-chain. An extension in the AUTOSAR design tool is proposed in order to capture fault definitions that shall be used to automatically create trigger-able defects in the behavioral models designed with SIMULINK/Targetlink. As first results, it is demonstrated how to integrate faults in Targetlink to be able to use AUTOSAR simulation environments without any further changes to perform fault-injection tests. Furthermore, automatically generated test-vectors from requirements are used to trigger the fault injection at runnable level.
A 3-Dimensional Trimeric β-Barrel Model for Chlamydia MOMP Contains Conserved and Novel Elements of Gram-Negative Bacterial Porins

Chlamydia trachomatis is the most prevalent cause of bacterial sexually transmitted diseases and the leading cause of preventable blindness worldwide. Global control of Chlamydia will best be achieved with a vaccine, a primary target for which is the major outer membrane protein, MOMP, which comprises ∼60% of the outer membrane protein mass of this bacterium. In the absence of experimental structural information on MOMP, three previously published topology models presumed a16-stranded barrel architecture. Here, we use the latest β-barrel prediction algorithms, previous 2D topology modeling results, and comparative modeling methodology to build a 3D model based on the 16-stranded, trimeric assumption. We find that while a 3D MOMP model captures many structural hallmarks of a trimeric 16-stranded β-barrel porin, and is consistent with most of the experimental evidence for MOMP, MOMP residues 320–334 cannot be modeled as β-strands that span the entire membrane, as is consistently observed in published 16-stranded β-barrel crystal structures. Given the ambiguous results for β-strand delineation found in this study, recent publications of membrane β-barrel structures breaking with the canonical rule for an even number of β-strands, findings of β-barrels with strand-exchanged oligomeric conformations, and alternate folds dependent upon the lifecycle of the bacterium, we suggest that although the MOMP porin structure incorporates canonical 16-stranded conformations, it may have novel oligomeric or dynamic structural changes accounting for the discrepancies observed.
The influence of partial discharge on crack propagation in piezoelectric materials

This paper studies the influence of partial discharge (PD) on crack propagation in piezoelectric materials under combined mechanical and electric loads. According to Stroh formalism, the electric field is obtained based on the semi-permeable electrical boundary condition inside the crack. Then, the crack propagation effects of the electric field without PD are discussed through considering the contribution of the induced electric field inside the deformed crack space to the total energy release rate, and the effect of partial discharge on the crack propagation is also derived under the assumption of the streamer-type mechanism. Finally, the electric field curves and the energy release rate before PD are numerically calculated in the condition of different environments under remote mechanic-electric loads to investigate its effects on crack growth. It is found that the process of partial discharge is very complex and accompanied with randomness in a semi-permeable crack, and the applied mechanical stress, the electric field and dielectric ratio have influence on the crack propagation of piezoelectric materials.
On the Use of Ontologies for an Optimal Representation and Exploration of the Web

The use and definition of ontology for the representation and the exploration of knowledge are critical issues for approaches dealing with information retrieval. In this paper, we propose a new ontology-based approach for improving the quality, in terms of relevance, of the results obtained when searching documents on the Internet. This is done by a coherent integration of ontologies, Web data and query languages. We propose new data structures built upon ontologies: the WPGraph and the W3Graph which allow Web data to be modelled. We also discuss the use of ontologies for an efficient exploration of the knowledge contained in our conceptual structures using ASK, a specific query language introduced in this paper. An experimental validation of our approach is proposed through a prototype supporting our innovative framework. Categories and Subject Descriptors D.3. 3 (Language Constructs and Features); H.2. 3 (Data Description Languages) General Terms Ontology, Web graph, Web data
Proper use of Agent Technologies in Design and Implementation of Software Intensive Systems

Envisions of future network enabled socio-technical systems are in focus of several international ongoing efforts by industry and academia. Different interest groups, e.g., the agent and the grid computing communities, have to that end put forward several roadmaps. However, those roadmaps by and large presuppose a key role to be played by their favorite technologies. We propose another complementary approach with a focus on the requirements on the goal system (network enabled capabilities) and a generic configurable framework to support design, implementation, monitoring, and maintenance of future dependable and secure socio-technical systems. Our methodological approach is grounded on IEEE standards on software intensive systems and on own experiences in development of such systems. We propose the use of agent technologies foremost in requirement engineering and high-level design whence the implementation platform is in our cases preferably based on service oriented architectures as in grid computing
Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints

Discovering association rules from time-series data is an important data mining problem. The number of potential rules grows quickly as the number of items in the antecedent grows. It is therefore difficult for an expert to analyze the rules and identify the useful. An approach for generating representative association rules for transactions that uses only a subset of the set of frequent itemsets called frequent closed itemsets was presented by Saquer and Deogun (2000). We employ formal concept analysis to develop the notion of frequent closed episodes. The concept of representative association rules is formalized in the context of event sequences. Applying constraints to target highly, significant rules further reduces the number of rules. Our approach results in a significant reduction of the number of rules generated, while maintaining the minimum set of relevant association rules and retaining the ability to generate the entire set of association rules with respect to the given constraints. We show how our method can be used to discover associations in a drought risk management decision support system and use multiple climatology datasets related to automated weather stations.
Intrinsic parameter fluctuations in decananometer MOSFETs introduced by gate line edge roughness

In this paper, we use statistical three-dimensional (3-D) simulations to study the impact of the gate line edge roughness (LER) on the intrinsic parameters fluctuations in deep decananometer (sub 50 nm) gate MOSFETs. The line edge roughness is introduced using a Fourier synthesis technique based on the power spectrum of a Gaussian autocorrelation function. In carefully designed simulation experiments, we investigate the impact of the rms amplitude /spl Delta/ and the correlation length /spl Lambda/ on the intrinsic parameter fluctuations in well scaled, but simple devices with fixed geometry as well as the channel length and width dependence of the fluctuations at fixed LER parameters. For the first time, we superimpose in the simulations LER and random discrete dopants and investigate their relative contribution to the intrinsic parameter fluctuations in the investigated devices. For particular MOSFET geometries, we were able to identify the regions where each of these two sources of intrinsic parameter fluctuations dominates.
Teacher Perceptions of Inquiry-Based Instruction vs . Teacher-Based Instruction

This applied research was designed to analyze the e ffectiveness of inquiry-based instruction as compared to teacher-directed instruction in 5th-gra de mathematics and science courses at the subject school. The subject school modified mathematics and science curriculum for 3 years and became an Alabama Math, Science, and Technology (AMSTI) [2009 ] school. AMSTI provided extensive professional development and ongoing support for th e use of inquiry-based mathematics and science instruction. AMSTI also regularly sent kits of nece ssary materials to the subject school to support th e implementation of inquiry-based mathematics and sci ence instruction. The research interviewed current 5th-grade teachers and drew conclusions abo ut the teachers’ perceptions of inquiry-based instruction in the broader curriculum as well as fe elings of self-efficacy. Of note, many teachers discussed the importance of adequate time allowance s to feelings of self-efficacy and the place of inquiry-based instruction in the broader curriculum .
Applications of low temperature sintering technology as die attach for high temperature power modules

Silver-based low temperature sintering technology (LTST) is becoming a promising alternative to bond or joint the large area joints as die attach for high temperature power modules. A review of the silver-sintered die attach technologies will be presented in this paper, as well as a roadmap of challenge of the state-of-the-art silver sintering. Then, highlighted are the methodology of sintering process, the different types and application of sintered Ag paste, the effects of the process parameters on the sintering layer, and the quality evaluations of sintered joints by means of destructive or non-destructive tests. Finally, the applications as well as challenges of silver sintering for high temperature power modules are proposed.
Predicting phrase breaks with memory-based learning

We investigate whether Memory-Based Learning (MBL) can be used to predict Phrase Breaks (PBs) in speech production reliably. The MBL approach is compared to the HMM approach described in [Taylor and Black, 1998] using the same corpus and information sources. We show that a simple memory-based learning algorithm that uses only minimal context and information outperforms the HMM approach, in terms of precision and recall. An exhaustive search of variants of algorithms, metrics and information sources does not bring any significant further improvement.
Detection of SCMA signal with channel estimation error

Fifth generation wireless communication systems should support, among other things, very large number of simultaneous connections. To address this requirement, various schemes of non-orthogonal multiple access (NOMA) were proposed that allow to increase the number of simultaneously active users. One of NOMA schemes is sparse code multiple access (SCMA), where sparse multidimensional codewords allow to use iterative detecting algorithms with reasonable complexity. In the paper, SCMA detection is investigated in the presence of channel estimation error. Uncoded and turbo coded SCMA is analyzed. Uplink channel with Rayleigh flat block fading is assumed. Simulation results show that required accuracy of the channel estimation depends on the turbo code block length. For full utilization of turbo code error-correction capability with short blocks (40 bits) normalized variance of channel estimation error should be less than 10-3, the same value applies to the case of uncoded SCMA. For turbo code with long blocks (1024 bits), estimation can be less accurate, with normalized variance up to 10-2. With such channel estimation accuracy, power loss is about 0.6-0.7 dB compared with the case of perfect estimation. Two different types of codebooks have shown the same performance for coded SCMA, that leads to conclusion that codebook with more simple structure that provides less complexity of detection algorithm is a good candidate for use in SCMA schemes. The comparison with traditional orthogonal multiple access scheme with the same overall spectral efficiency is provided for both uncoded and coded SCMA systems. In case of coded system, SCMA scheme is shown to have smaller BER in the range of bit error probabilities below 10-4-10-5 for long blocks. The power gain is 0.5-1 dB for long blocks and a few tenths of dB for short blocks.
Effects of posture on dynamic back loading during a cable lifting task

This study evaluated spinal loads associated with lifting and hanging heavy mining cable in a variety of postures. This electrical cable can weigh up to 10 kg per metre and is often lifted in restricted spaces in underground coal mines. Seven male subjects performed eight cable lifting and hanging tasks, while trunk kinematic data and trunk muscle electromyograms (EMGs) were obtained. The eight tasks were combinations of four postures (standing, stooping, kneeling on one knee, or kneeling on both knees) and two levels of cable load (0 N or 100 N load added to the existing cable weight). An EMG-assisted model was used to calculate forces and moments acting on the lumbar spine. A two-way split-plot ANOVA showed that increased load (p<0.05) and changes in lifting posture (p<0.05) independently affected trunk muscle recruitment and spinal loading. The increase in cable load resulted in higher EMG activity of all trunk muscles and increased axial and lateral bending moments on the spine (p<0.05). Changes in posture caused more selective adjustments in muscle recruitment and affected the sagittal plane moment (p<0.05). Despite the more selective nature of trunk EMG changes due to posture, the magnitude of changes in spinal loading was often quite dramatic. However, average compression values exceeded 3400 N for all cable lifting tasks.
DOROTHY: Enhancing Bidirectional Communication between a 3D Programming Interface and Mobile Robots

Dorothy is an integrated 3D/robotics educational tool created by augmenting the Alice programming environment for teaching core computing skills to students without prior programming experience. The tool provides a drag and drop interface to create graphical routines in virtual worlds; these routines are automatically translated into code to provide a real-time or offline enactment on mobile robots in the real world. This paper summarizes the key capabilities of Dorothy, and describes the contributions made to: (a) enhance the bidirectional communication between the virtual interface and robots; and (b) support multirobot collaboration. Specifically, we describe the ability to automatically revise the virtual world based on sensor data obtained from robots, creating or deleting objects in the virtual world based on their observed presence or absence in the real world. Furthermore, we describe the use of visually observed behavior of teammates for collaboration between robots when they cannot communicate with each other. Dorothy thus helps illustrate sophisticated algorithms for fundamental challenges in robotics and AI to teach advanced computing concepts, and to emphasize the importance of computing in real world applications, to beginning programmers.
Submodular Point Processes

We introduce a class of discrete point processes that we call the Submodular Point Processes (SPPs). These processes are characterized via a submodular (or supermodular) function, and naturally model notions of information, coverage and diversity, as well as cooperation. Unlike Log-submodular and Log-supermodular distributions (Log-SPPs) such as determinantal point processes (DPPs), SPPs are themselves submodular (or supermodular). In this paper, we analyze the computational complexity of probabilistic inference in SPPs. We show that computing the partition function for SPPs (and Log-SPPs), requires exponential complexity in the worst case, and also provide algorithms which approximate SPPs up to polynomial factors. Moreover, for several subclasses of interesting submodular functions that occur in applications, we show how we can provide efficient closed form expressions for the partition functions, and thereby marginals and conditional distributions. Finally, we argue how SPPs complement existing Log-SPP distributions, and are a natural model for several applications.
Evaluation for Tensile Performance of Recently Developed FRP Hybrid Bars

This paper deals with an experimental study on the tensile performance of recently developed "FRP Hybrid Bars" by authors. The objective of this study is to investigate tensile strength of FRP Hybrid Bars and to overcome the shortcomings of the FRP material with its low elastic modulus. This uses the concept of material hybridization to increase elastic modulus to be used in concrete structures, especially for marine and port concrete structures. The effect of hybridization on tensile properties of FRP Hybrid Bars was evaluated by comparing the results of tensile test with those of non-hybrid FRP bars. The results of this study indicated that the elastic modulus of the hybrid GFRP bar was increased by up to approximately 5 to 204 percent by the material hybridization. To ensure long-term durability of the hybrid GFRP bars in waterfront structure applications, the individual and combined effects of environmental conditions on hybrid GFRP rebar itself as well as on the interface between rebar and concrete should be accessed in the future study.
Real-Time Semantic Search Using Approximate Methodology for Large-Scale Storage Systems

The challenges of handling the explosive growth in data volume and complexity cause the increasing needs for semantic queries. The semantic queries can be interpreted as the correlation-aware retrieval, while containing approximate results. Existing cloud storage systems mainly fail to offer an adequate capability for the semantic queries. Since the true value or worth of data heavily depends on how efficiently semantic search can be carried out on the data in (near-) real-time, large fractions of data end up with their values being lost or significantly reduced due to the data staleness. To address this problem, we propose a near-real-time and cost-effective semantic queries based methodology, called FAST. The idea behind FAST is to explore and exploit the semantic correlation within and among datasets via correlation-aware hashing and manageable flat-structured addressing to significantly reduce the processing latency, while incurring acceptably small loss of data-search accuracy. The near-real-time property of FAST enables rapid identification of correlated files and the significant narrowing of the scope of data to be processed. FAST supports several types of data analytics, which can be implemented in existing searchable storage systems. We conduct a real-world use case in which children reported missing in an extremely crowded environment (e.g., a highly popular scenic spot on a peak tourist day) are identified in a timely fashion by analyzing 60 million images using FAST. FAST is further improved by using semantic-aware namespace to provide dynamic and adaptive namespace management for ultra-large storage systems. Extensive experimental results demonstrate the efficiency and efficacy of FAST in the performance improvements.
High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings

A complete neurobiological understanding of speech motor control requires determination of the relationship between simultaneously recorded neural activity and the kinematics of the lips, jaw, tongue, and larynx. Many speech articulators are internal to the vocal tract, and therefore simultaneously tracking the kinematics of all articulators is nontrivial—especially in the context of human electrophysiology recordings. Here, we describe a noninvasive, multi-modal imaging system to monitor vocal tract kinematics, demonstrate this system in six speakers during production of nine American English vowels, and provide new analysis of such data. Classification and regression analysis revealed considerable variability in the articulator-to-acoustic relationship across speakers. Non-negative matrix factorization extracted basis sets capturing vocal tract shapes allowing for higher vowel classification accuracy than traditional methods. Statistical speech synthesis generated speech from vocal tract measurements, and we demonstrate perceptual identification. We demonstrate the capacity to predict lip kinematics from ventral sensorimotor cortical activity. These results demonstrate a multi-modal system to non-invasively monitor articulator kinematics during speech production, describe novel analytic methods for relating kinematic data to speech acoustics, and provide the first decoding of speech kinematics from electrocorticography. These advances will be critical for understanding the cortical basis of speech production and the creation of vocal prosthetics.
Source camera linking using enhanced sensor pattern noise extracted from images

Source camera linking, i.e., establishing whether or not the images of interest are taken by the same camera without the camera and its digital fingerprint in the investigator's possession, is an important aspect of image forensics. Sensor pattern noises (SPNs), extracted from digital images as device fingerprints, have been proved as an effective way for digital device identification and have been used for device linking as well. However, as we demonstrate in this work, the limitation of the current method of extracting the sensor pattern noise is that the SPNs extracted from images can be severely contaminated by the details from scenes. This makes device linking a more challenging problem than source device identification because the absence of the camera prohibits the acquisition of a clean fingerprint of the camera. In this work we propose a novel approach for attenuating the influence of the details from the scenes on sensor pattern noises so as to improve the correct rate of device linking. The hypothesis underlying our SPN enhancement method is that the stronger a signal component in a SPN is, the less trustworthy the component should be and thus should be attenuated. This hypothesis suggests that an enhanced SPN can be obtained by assigning weighting factors inversely proportional to the magnitude of the SPN components. (6 pages)