Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Visual-inertial Localization using Summary Maps

Localization in a global reference frame constitutes a fundamental milestone towards high-level applications in robotics such as autonomous navigation and obstacle avoidance. Visual-inertial SLAM became a compelling approach for this task, despite its inherent drift and local pose estimation. A solution to these shortcomings, however, can be achieved by matching against maps built in previous sessions. Commonly, a careful data selection is performed to keep the map size traceable and thus enable localization in real-time. Although, such a map summarization usually guarantees global localization coverage, the accuracy suffers due to fewer matches. In this work, we aim at mitigating this effect by directly integrating the scarce 2d-3d matches with visual feature tracks and inertial measurements in the framework of a slidingwindow based optimization. We compare our approach to motion tracking data and demonstrate that such a joint estimation yields smoother and more accurate global pose estimates than related methods that loosely integrate 6-DoF localization poses with VIO. Finally, we evaluate the impact of varying map summarization parameters on the trade-off between map-size and localization accuracy and demonstrate that our approach allows for a more aggressive summarization while retaining the robustness and accuracy achieved with larger maps.
The Relationship of Structural Empowerment and Organizational Commitment Among Staff Nurses in Saudi Arabia: A Comparative Correlational Cross-sectional Study

Purpose: This study aimed to examine and compare the relationship between structural empowerment and organizational commitment in Saudi and non-Saudi registered nurses working in Saudi Arabia. The factors that predict organizational commitment among the two study population were also studied. Methods: Using a descriptive comparative correlational cross-sectional research design, the study was carried out in three governmental hospitals in the Eastern Province- Saudi Arabia. A convenience sample of (N = 398) inpatient Saudi and non-Saudi nurses were included in this study. Data analysis was performed using descriptive statistics, correlation analyses, and one-way ANOVA. Findings: Statistically significant positive partial correlations were found between structural empowerment and organizational commitment. Non-Saudi nurses perceived higher levels of structural empowerment and commitment compared to Saudis. Implications for Theory, Practice, and Policy: Through the application of Kanter’s (1977,1993) and Spreitzer’s (1995) conceptual models, this study provided imperative and currently unavailable data that should be the foundation for future studies in the Saudi context and worldwide. Findings of this study provide evidence for the nursing authority figures to understand the relationships between empowerment and organizational commitment among nurses working in Saudi Arabia and facilitate the necessary changes to improve the current working conditions and nurses’ retention.
Merging in Congested Freeway Traffic Using Multipolicy Decision Making and Passive Actor-Critic Learning

Merging in congested freeway traffic is a significant challenge toward realizing fully automated (level 4) driving. Merging vehicles need to decide not only how to merge safely into a spot, but also where to merge. We present a method for freeway merge based on multipolicy decision making coupled with a reinforcement learning technique called passive actor-critic (pAC), which learns with less knowledge of the system and without active exploration. The multipolicy decision making selects a candidate spot for merging by using the state value learned by pAC. Together, these techniques yield a method that first decides where to merge and then realizes safe merging. We evaluate our method using real traffic data. Our experiments show that pAC achieves an overall success rate of 92% for merging into a predetermined spot on a freeway, which is comparable to human decision making.
Towards Intelligent Social Robots: Social Cognitive Systems in Smart Environments

—We propose an institutional robotics approach to the design of socially-aware multi-robot systems, where cooperation among the robots and their social interactions with humans are guided using institutions. Inspired by the concepts stemming from economical sciences, robot institutions serve as coordination artifacts, which specify behavioral rules that are acceptable or desirable given the situation and which can be replaced by other rules to enforce new acceptable or desirable behaviors without changing the robot’s core code. In this paper we propose a formal methodology for consistent design of coordinated multi-robot behaviors intended for use in human-populated environments. We illustrate theoretical concepts with practical examples. Graph-based formations serve as a basis for coordinated multi-robot behaviors and concepts from the literature on human-aware navigation provide social rules that are enforced by the institutions. Experiments are carried out in a high-ﬁ delity robotic simulator to illustrate the application of the theoretical concepts.
Enhancing ERP to Fit the Business Needs: A Customization Method Selection

Enterprise resource planning and management (ERP) systems is now dominated by systems that are perceived expensive and inflexible. In order to take full advantage of the ERP systems, implementations require drastic structural and cultural changes within the organization including business process reengineering. These changes certainly allowed to do but somewhat difficult to accomplish and organizations continue to struggle with change management of ERP systems. Finding the right fit between ERP systems and the business processes is critical for successful ERP implementation [12]. In this case, the organization can respond by two approaches: ERP system customization or organizational adaptation [6][15]. However, there is also best of breed systems, which are best applied to one or a few functions, facilitating system maintenance. This research proposes a comprehensive ERP system customization selection framework in which the objective hierarchy is constructed and the appropriate criterias are specified to provide detailed guidance for the customization. The analytic hierarchy process (AHP) method [27] is applied for dealing with the ambiguities involved in the assessment of customization alternatives and relative importance weightings of criterias.
Human Face Age Recognition Based on Convolutional Neural Networks

In the field of image recognition, the issue of face age recognition has attracted the attention of many scholars, and a lot of outstanding algorithms have been proposed, but the correctness rate of age recognition is not high. To improve the AGE identification accuracy, this paper proposes a face AGE recognition based on convolutional neural network (CNN) -the AGE model, the IMDB WIKI database and Caffe framework for training and testing, and the AGE recognition has the highest accuracy of 52%. Through experiments, this model is proved to be scientific and provides new ideas and methods for the study of face age recognition. Keywords—human face age recognition; convolutional neural networks; CNN-AGE; caffe
Application of Soft Computing Techniques for Non-linear Process Modelling

— This paper deals with various soft computing techniques for modelling non-linear system. The traditional well established methods of modelling fail to describe non-linear system accurately. In this paper soft computing techniques like fuzzy, neural network and neuro fuzzy are used to model Conical Tank System (CTS). CTS has nonlinearity due to its shape. Models obtained using soft computing techniques proved to be well accurate with minimal model mismatch. The validity of the models is compared by taking mean-square error as performance criteria. ANFIS model for the systems produced superior results with minimum Root Mean Square Error (RMSE).
Neural Responses to Novel and Existing Words in Children with Autism Spectrum and Developmental Language Disorder

The formation of new phonological representations is key in establishing items in the mental lexicon. Phonological forms become stable with repetition, time and sleep. Atypicality in the establishment of new word forms is characteristic of children with developmental language disorder (DLD) and autism spectrum disorder (ASD), yet neural changes in response to novel word forms over time have not yet been directly compared in these groups. This study measured habituation of event-related-potentials (ERPs) to novel and known words within and between two sessions spaced 24 hours apart in typically developing (TD) children, and their peers with DLD or ASD. We hypothesised that modulation of the auditory N400 amplitude would mark real-time changes in lexical processing with habituation evident within and across sessions in the TD group, while the DLD group would show attenuated habituation within sessions, and the ASD group attenuated habituation between sessions. Twenty-one typically developing children, 19 children with ASD, and 16 children with DLD listened passively to known and novel words on two consecutive days, while ERPs were recorded using dry electrodes. Counter to our hypotheses, no habituation effect emerged within sessions. However, responses did habituate between sessions, with this effect being reduced in the DLD group, indicating less pre-activation of lexical representations in response to words encountered the previous day. No differences in change over time were observed between the TD and ASD groups. These data are in keeping with theories stressing the importance of sleep-related consolidation in word learning.
T-PCCE: Twitter Personality based Communicative Communities Extraction System for Big Data

The identification of social media communities has recently been of major concern, since users participating in such communities can contribute to viral marketing campaigns. In this work, we focus on users’ communication considering personality as a key characteristic for identifying communicative networks i.e., networks with high information flows. We describe the Twitter Personality based Communicative Communities Extraction (T-PCCE) system that identifies the most communicative communities in a Twitter network graph considering users’ personality. We then expand existing approaches in users’ personality extraction by aggregating data that represent several aspects of user behavior using machine learning techniques. We use an existing modularity based community detection algorithm and we extend it by inserting a post-processing step that eliminates graph edges based on users’ personality. The effectiveness of our approach is demonstrated by sampling the Twitter graph and comparing the communication strength of the extracted communities with and without considering the personality factor. We define several metrics to count the strength of communication within each community. Our algorithmic framework and the subsequent implementation employ the cloud infrastructure and use the MapReduce Programming Environment. Our results show that the T-PCCE system creates the most communicative communities.
Precipitation enhancement in stratocumulus clouds through airbourne seeding: sensitivity analysis by UCLALES–SALSA

Abstract. Artificial enhancement of precipitation via hygroscopic cloud seeding is investigated with a numerical large-eddy simulation model coupled with a spectral aerosol-cloud microphysics module. We focus our investigation on marine stratocumulus clouds and evaluate our model results by comparing them with recently published results from field observations. Creating multiple realizations of a single cloud event with the model provides a robust method to detect and attribute the seeding effects, which reinforces the analysis based on experimental data. Owing to the detailed representation of aerosol-cloud interactions, our model successfully reproduces the microphysical signatures attributed to the seeding, that were also seen in the observations. Moreover, the model simulations show up to a 2–3 fold increase in the precipitation flux due to the seeding, depending on the seeding rate and injection strategy. However, our simulations suggest that a relatively high seeding particle emission rate is needed for a substantial increase in the precipitation yield, as compared with the estimated seeding concentrations from the field campaign. In practical applications, the seeding aerosol is often produced by flare burning. It is speculated, that the required amount of large seeding particles suggested by our results could pose a technical challenge to the flare-based approach.
Comprehensive analysis of alternative splicing and transcriptome related to the prognosis of cervical squamous cell cancer

Background: Cervical squamous cell cancer (CESC) is a common malignancy tumor with high incidence and mortality in women globally. Increasing studies have indicated that there was an indivisible association between alternative mRNA splicing (AS) and multiple types of cancer. However, comprehensive analysis of alternative mRNA splicing events is scarce in CESC. Methods: In this study, alternative mRNA splicing events data and clinical information of 216 CESC patients were downloaded from TCGA SpliceSeq database and TCGA website. We used identified survival-associated splicing events (SASEs) to construct prognostic signatures. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve were performed to evaluate the clinical value of prognostic signatures. A nomogram was carried out to quantitatively predict individuals’ survival probability. Regulatory network between splicing factors (SFs) and SASE was analyzed to explore the upstream regulators of a certain SASE. Additionally, we explored potential downstream pathways of a certain SASE. Results: A total of 41776 alternative splicing events in 9961 genes were collected. After sorting out SASEs, multivariable regression analysis was used to acquire 70 SASEs that could be independent prognostic factors for overall survival in CESC. Kaplan-Meier survival analyses showed that the difference was statistically significant (P＜0.01). The area under ROC curve (AUC) for all AS pattern was 0.932. A nomogram was constructed with a concordance index of 0.82 to predict individuals’ survival probability. EIF3A positively regulated SEC23A-27346-AP with highest correlation coefficient (P＜0.001, R=0.69). Besides, the most significant SASEs and KEGG pathways were SEC23A-27346-AP and adherens junction pathway (P=0.02, R=0.65). Conclusions: Prognostic signatures of survival-associated splicing events were independent prognostic factors for overall survival among CESC patients. A nomogram could quantitatively predict individuals’ survival probability. Moreover, we proposed that splicing factor EIF3A positively regulated SEC23A-27346-AP, and adherens junction pathway may be the downstream pathway of SEC23A-27346-AP. The aforementioned signaling pathway could play a crucial role in the development of CESC.
Improving Data Integrity Auditing with Group user using Shared Dynamic Scheme in Cloud

The storage in cloud has become a rising trend these days that boosts the secure remote information auditing. Latterly, some analysis thought-about in shared dynamic information that there's a haul of secure and adequate public information integrity auditing. However there are schemes in sensible storage systems that don't seem to be secure against the connivance of cloud storage server and annul cluster user throughout user revocation. In this paper, based on the verifier-local revocation cluster signature, the connivance attack in existing scheme is work out associated equipped an adequate public integrity auditing scheme with secure cluster user revocation. The general public checking and adequate and secure cluster user revocation with the properties like traceability, with confidence, countability and adequacy are supported by this scheme.
High-Radix Scalable Modular Crossbar Switches

As process technologies have scaled, the increasing number of processor cores and memorieson a single die has also driven the need for more complex on-chip interconnection networks.Crossbar switches are primary building blocks in such networks-on-chip, as they can be usedas fast single-stage networks or as the core of the router switch in multi-stage networks.While crossbars offer non-blocking, single-hop, all-to-all communication, they tend to scalepoorly with the number of nodes due to the latency and energy of the long wires and highradixmultiplexor structures needed. In this work, we investigate how to improve crossbarperformance, energy-efficiency, and scalability.To better understand the design space and scaling limitations, we have developed an on chipswitch modeling tool calibrated using circuit-level simulations. The tool enables a designspace exploration showing how area, power, and performance vary across radix, data width,wire parameters, and circuit implementation. In addition to conventional design options,we examined capacitively coupled low-swing signaling to improve to energy consumption ofthe I/O wires. This exploration shows that the main bottlenecks are the long I/O wires andthe key to improving the performance and efficiency is to minimize the area. Using theseinsights, we present modular crossbar switches that can perform better at high radices thanthe monolithic designs. The modular sub-blocks are arranged in a controlled flow-through,pipelined scheme to eliminate global connections and maintain linear performance scalingand high throughput. Modularity also enables energy savings via deactivation of unusedI/O wires.To evaluate our design, we implemented a prototype radix-64 modular crossbar switchtestcip in 40nm CMOS bulk process. The testchip operates at 2.38GHz at 1V nominalsupply voltage and consumes 1.2W power. It offers 2.2X better throughput and 2.4X betterenergy-efficiency than published state of the art designs. We further evaluated modularcrossbar networks with the proposed crevaluation tool. The proposed design achieves more than 90% saturation throughput withan internal speed up of 1.5, supports high data line rates, and offers lower average networklatency compared to conventional crossbars. Evaluation results show that modular crossbarsare scalable to high-radices while still offering high-performance, energy-efficiency and onehopsimplicity.ossbar switches using BookSim2, a network on chip
A Compact High-Efficiency RF Rectifier With Widen Bandwidth

In this letter, a compact broadband RF rectifier is proposed and tested for ambient RF energy harvesting (EH). An uncomplicated impedance matching network with multistage transmission lines (quasi T-shaped) is presented. Quasi T-shaped network performs three different functions to complete the broadband matching process. The novel matching network widens working bandwidth using fewer transmission lines without via hole so that it has low loss and high conversion efficiency. After theoretical analysis and simulation, the rectifier is manufactured, and the measured results are consistent with the simulation results. The rectifier shows good broadband performance. The frequency range of rectifier efficiency over 50% is from 0.85 to 2.05 GHz (a bandwidth of 82.7%) at 5 dBm. The rectifier shows a bandwidth of 101.5% (0.8–2.45 GHz) with a power conversion efficiency (PEC) greater than 50% for an 8 dBm input power. In addition, the maximum conversion efficiency of the circuit is as high as 73.1% at the input power of 8 dBm.
Automatic segmentation of meniscus using locally weighted voting based on multi-atlas and edge classification in knee MR images

We propose an automatic segmentation of meniscus from knee MR images using multi-atlas segmentation and patchbased edge classification. To prevent registration to large tissues, meniscus is targeted using segmented bone and articular cartilage information. To segment the meniscus with large shape variations and remove leakage to the collateral ligaments robustly, meniscus is segmented using shape- and intensity-based locally-weighted voting (LWV) and patchbased edge classification. Experimental result shows that the Dice similarity coefficient of proposed method as comparison with two manually outlining results provides over 80% in average and is improved compared to LWV based on multi-atlas.
Effective Dynamic Control Strategy of a Key Supplier with Multiple Downstream Manufacturers Using Industrial Internet of Things and Cloud System

Intelligent data analytics-based cloud computing is a leading trend for managing a large-scale network in contemporary manufacturing environments. The data and information are shared using the cloud environments and valuable knowledge is driven using the embedded intelligence analytics. This research applied this trend to the control of a key supplier’s real-time production planning for solving joint production goals with downstream producers. As a key supplier has several downstream producers in general, several uncertainties are embedded on the supply chain network such as the quality issue in the supplier and the occurrence of unexpected orders from the downstream industries. While the control of a supply plan is difficult considering these dynamics in traditional frameworks, the proposed framework detects the dynamic changes accurately using the constructed cloud system. Moreover, the real-time control considering uncertain scenarios as well as the extracted knowledge is achieved using the provided Industrial Internet of Things (IIoT) and simulation-based control model using stochastic network. To show the effective of the suggested framework, real manufacturing cases and their numerical analyses are provided.
Unknown

The research activities on natural language >rocessing of the FGCS Project are presented, linguistic phenomena are formalized in terms of :omplex structures and constraints on them. The logic programming paradigm is adopted for implementing natural language processing systems because the basic operation for the complex structures is isomorphic with respect to unification. DUALS (Discourse Understanding Aimed at Logic-based System), CIL (Complex Indeterminate Language), and JPSG (Japanese Phrase Structure Grammar) are being developed using the unification-based approach. The large-scale machine readable and understandable dictionaries are also being developed.
Assessment and Improvement of the Pattern Recognition Performance of Memdiode-Based Cross-Point Arrays with Randomly Distributed Stuck-at-Faults

In this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive cross-point array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is considered here for the modelling of the synaptic weights implemented with memristors. Following the standard memristive approach, the QMM comprises two coupled equations, one for the electron transport based on the double-diode equation with a single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron. By modifying the state parameter in the current-voltage characteristic, SAFs of different severeness are simulated and the final outcome is analysed. Supervised ex-situ training and two well-known image datasets involving hand-written digits and human faces are employed to assess the inference accuracy of the SLP as a function of the faulty device ratio. The roles played by the memristor’s electrical parameters, line resistance, mapping strategy, image pixelation, and fault type (stuck-at-ON or stuck-at-OFF) on the CPA performance are statistically analysed following a Monte-Carlo approach. Three different re-mapping schemes to help mitigate the effect of the SAFs in the SLP inference phase are thoroughly investigated.