Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Map Learning with Self-Organizing Adaptive Recurrent Incremental Network

Biological information inspires the advancement of a navigational mechanism for autonomous robots to help people explore and map real-world environments. However, the robot’s ability to constantly acquire environmental information in real-world, dynamic environments has remained a challenge for many years. In this paper, we propose a self-organizing adaptive recurrent incremental network that models human episodic memory to learn spatiotemporal representations from novel sensory data. The proposed method termed as SOARIN consists of two main learning process that is active learning and episodic memory playback. For active learning (robot exploration), SOARIN quickly learns and adapts incoming novel sensory data as episodic neurons via competitive Hebbian Learning. Episodic neurons are connecting with each other and gradually forms a spatial map that can be used for robot localization. Episodic memory playback is triggered whenever the robot is in an inactive mode (charging or hibernating). During playback, SOARIN gradually integrates knowledge and experience into more consolidate spatial map structures that can overcome the catastrophic forgetting. The proposed method is analyzed and evaluated in term of map learning and localization through a series of real robot experiments in real-world indoor environments.
Performance Evaluation of Route Selection Schemes over a Clustered Cognitive Radio Network

Cognitive radio (CR) is a promising next-generation wireless communication system that provides efficient utilization of radio spectrum by enabling unlicensed users (or secondary users, SUs) to sense for and use underutilized radio spectrum (or white spaces) owned by licensed users (or primary users, PUs). In this paper, we investigate the effects of a larger network size (or higher number of routes) on non-clustered, clustered and clustered-reinforcement learning (RL)-based route selection schemes in a USRP/ GNU radio platform focusing on the network layer. Experimental results show that the enhanced variant of reinforcement learning (RL)-based route selection scheme (C-ERL) selects stable route(s) over a clustered CRN in a USRP/ GNU radio platform. C-ERL improves cluster stability by reducing the number of route breakages caused by route switches, and network scalability by reducing the number of clusters in the network without significant deterioration of QoS, including throughput, packet delivery rate, and end-to-end delay.
AnxietyDecoder: An EEG-based Anxiety Predictor using a 3-D Convolutional Neural Network

In this paper, we propose and implement an EEG-based three-dimensional Convolutional Neural Network architecture, ‘AnxietyDecoder’, to predict anxious personality and decode its potential biomarkers from the participants. Since Goal-Conflict-Specific-Rhythmicity (GCSR) in the EEG is a sign of an anxiety-related system working, we first propose a two-dimensional Conflict-focused CNN (2-D CNN). It simulates the GCSR extraction process but with the advantages of automatic frequency band selection and functional contrast calculation optimization, thus providing more comprehensive trait anxiety predictions. Then, to generate more targeted hierarchical features from local spatio-temporal scale to global, we propose a three-dimensional Conflict-focused CNN (3-D CNN), which simultaneously integrates information in the temporal and brain-topology-related spatial dimensions. In addition, we embed Layer-wise Relevance Propagation (LRP) into our model to reveal the essential brain areas that are correlated to anxious personality. The experimental results show that the percentage variance accounted for by our three-dimensional Conflict-focused CNN is 33%, which is almost four times higher than the previous theoretically derived GCSR contrast (7%). Meanwhile, it also outperforms the 2-D model (26%) and the t-test difference between the 3-D and 2-D models is significant (t(4) = 5.4962, p = 0.0053). What’s more, the reverse engineering results provide an interpretable way to understand the prediction decision-making and participants’ anxiety personality. Our proposed AnxietyDecoder not only sets a new benchmark for EEG-based anxiety prediction but also reveals essential EEG components that contribute to the decision-making, and thus sheds some light on the anxiety biomarker research.
Study of the impact of grid connected PV system on PQ through a comparative analysis by scenarios

Over the last years, the implementation of PV systems has been growing rapidly. The PV penetration in power systems means new concerns about their impacts on the power quality (PQ) of the distribution networks. Both, intermittent nature of solar PV and electronic power devices used in PV systems could affect the voltage profile, harmonic distortions, and voltage unbalance, among others. The aim of this paper is to evaluate and quantify the impact of PV systems on some PQ parameters of low voltage (LV) distribution networks. This, by performing a comparative statistical analysis through the application of hypothesis testing of the difference between two population means, where those populations are the data with and without PV generation classified into PV power and load power scenarios. The methodology covers the selection of PQ parameters and their monitoring, the application of a scenario matrix for grouping data with similar load and PV injection conditions, and, lastly, the Wilcoxon rank sum test (non-parametric test) is applied. This methodology is applied to evaluate the effect of the integration of a PV system (9.8 kWp) on the LV network of a university building.
Relearning procedure to adapt pollutant prediction neural model: Choice of relearning algorithm

Predict the indoor air quality becomes a global public health issue. That’s why Airbox lab® company develops a smart connected object able to measure different physical parameters including concentration of pollutants (volatile organic compounds, carbon dioxide and fine particles). This smart object must embed prediction capacities in order to avoid the exceedance of an air quality threshold. This task is performed by neural network models. However, when some events occur (change of people’s behaviors, change of place of the smart connected object as example), the embedded neural models become less accurate. So a relearning step is needed in order to refit the models. This relearning must be performed by the smart connected object, and therefore, it must use the less computing time as possible. To do that, this paper propose to combine a control chart in order to limit the frequency of relearning, and to compare three learning algorithms (backpropagation, Levenberg-Marquardt, neural network with random weights) in order to choose the more adapted to this situation.
Dynamic Bayesian Network Approach to Evaluate Vehicle Driving Risk Based on On-Road Experiment Driving Data

In this work, we utilize a dynamic Bayesian network for an inferential analysis of driving-related risks based on our assessment of real-world driving data. On-road driving tests are carried out to gather and analyze data to identify different evaluation indicators of driving-related risks. These indicators include the distance between vehicles, acceleration, steering entropy, visual distraction duration, visual glance speed, and blink frequency. Moreover, these indicators are processed to build a driving-related risk evaluation model based on the dynamic Bayesian network. The validity of this model is further tested by experimental data. The results show that this model can achieve a reasonable quantitative evaluation of driving-related risks. Vehicle operation-related risks can be further divided into four levels of safety, namely, levels I to IV. The lowest risk is observed at level I, whereas level IV has the highest risk. Among the indicators for risk evaluation, the distance between vehicles is the most sensitive control indicator of vehicle operation-related risks. The research findings provide various methodologies that could be utilized for evaluation and early warning of driving-related risks. Thus, a theoretical foundation to seek solutions for the safety of advanced driving assistance system is formed.
A Hybrid Character Representation for Chinese Event Detection

For the Chinese language, event triggers in a sentence may appear inside or across words after word segmentation. Thus recent works on Chinese event detection often formulate the task as a character-wise sequence labeling problem instead of a word-wise one. Due to a limited amount of corpus, however, it is more difficult in practice to train character-wise models to capture the inner structure of event triggers and the semantics of sentence-level context compared with word-wise ones. In this paper, we propose to improve character-wise models by incorporating word information and language model representation into Chinese character representation. More specifically, the former consists of the position of the character inside a word and the word’s embedding, which can aid structural pattern learning; the latter is obtained by BERT, which contains long-distance semantic information. We construct a sequence tagging model equipped with the hybrid representation and evaluate our model on ACE 2005 Chinese corpus. Experiment results show that both word information and language model representation are effective enhancements, and our model gains an increase of 4.5 (6.5%) and 6.1 (9.4%) in F1-score in event trigger identification task and classification task respectively over the state-of-the-art method.
Using Local Convolutional Units to Defend Against Adversarial Examples

Deep neural networks are known to be sensitive to adversarial examples – inputs that are created in such a way that they are similar (if viewed by people) to clean inputs, but the neural network has high confidence that they belong to another class.In this paper, we study a new type of neural network unit similar to the convolutional units, but with a more local behavior. The unit is based on the Gaussian radial basis function. We show that if we replace the first convolutional layer in a convolutional network by the new layer (called RBFolutional), we obtain better robustness towards adversarial samples on the MNIST and CIFAR10 datasets, without sacrificing the performance on the clean examples.
A Spiking Neural Network with a Global Self-Controller for Unsupervised Learning Based on Spike-Timing-Dependent Plasticity Using Flash Memory Synaptic Devices

Neuromorphic engineering aims to implement a brain-inspired computing architecture as an alternative paradigm to the von Neumann processor. In this work, we propose a hardware-based spiking neural network (SNN) architecture for unsupervised learning with spike-timing-dependent plasticity (STDP) synapse array using flash memory synaptic array. This novel architecture includes a global self-controller to make each neuron in a single neuron layer operate systematically, which can be also an excellent benefit in terms of area required for system configuration. Therefore, the proposal of this architecture configuration is significant in terms of suggesting a methodology for extending a single neuron into a network. We perform circuit simulation using HSPICE to verify systematic operations of multiple neuron system such as feed-forward and -back pulses generation, a refractory period, a lateral inhibition, and a homeostasis. Various operation in the proposed architecture are designed based on MATLAB simulation result of 28 × 28 MNIST handwritten digit learning and recognition in the SNN having an array of thin-film transistor (TFT)-type NOR flash memory synaptic devices. The results of circuit simulation reflect the specifications required for the STDP operation using the long-term potentiation (LTP) and long-term depression (LTD) characteristics of the proposed synaptic device. The pulse scheme required for STDP in this paper is shown to be suitable for unsupervised learning with flash memory synaptic device.
Multiple Hash Matching Units (MHMU): An Algorithmic Ternary Content Addressable Memory Design for Field Programmable Gate Arrays

As applications and user requirements are constantly evolving, there is a need to provide flexible networks that are able to process packets at high speed. One of the basic functions used for packet processing is matching a key formed by some fields of the incoming packet header against a set of stored rules. This is done for example to determine the next hop of a packet or to apply security checks on a firewall. In many cases, the stored rules have do not care bits as that enables a more flexible and compact representation of the rules. Therefore, the matching can be done in hardware using Ternary Content Addressable Memories (TCAMs). However, TCAMs pose several problems in many implementations. For example, for ASICs they require much more circuit area and power than standard SRAMs. On the other hand, designs based on programmable logic such as Field Programmable Gate Arrays (FPGAs) can only use the blocks provided by the FPGA that do not typically include TCAMs. In this last case, a TCAM can be emulated using the FPGA logic resources but with a large cost. To reduce the cost of implementing TCAMs, a number of algorithmic solutions have been proposed and are known as Algorithmic TCAMs or A-TCAMs. Most of those schemes target either software or ASIC implementations. In this paper we present Multiple Hash Matching Units (MHMU) an A-TCAM solution targeted towards FPGA implementations. The proposed scheme exploits the massive parallelism of FPGAs to implement many hash based matching units that use the embedded block RAM memories of the FPGA. The proposed MHMU scheme has been mapped to a Xilinx series 7 FPGA to check its efficiency in terms of resource usage and its scalability. To validate the effectiveness of MHMU, a simple configuration has been tested with Classbench generated sets of rules. The results show that the MHMU is able to consistently accommodate sets with several tens of thousands of rules with large keys.
A survey of applications research on content-centric networking

As a named data-based clean-slate future Internet architecture, Content-Centric Networking (CCN) uses entirely different protocols and communication patterns from the host-to-host IP network. In CCN, communication is wholly driven by the data consumer. Consumers must send Interest packets with the content name and not by the host's network address. Its nature of in-network caching, Interest packets aggregation and hop-by-hop communication poses unique challenges to provision of Internet applications, where traditional IP network no long works well. This paper presents a comprehensive survey of state-of-the-art application research activities related to CCN architecture. Our main aims in this survey are (a) to identify the advantages and drawbacks of CCN architectures for application provisioning; (b) to discuss the challenges and opportunities regarding service provisioning in CCN architectures; and (c) to further encourage deeper thinking about design principles for future Internet architectures from the perspective of upper-layer applications.
Confidence Identification Based on the Combination of Verbal and Non-Verbal factors in Human Robot Interaction

Not only verbal information but also Non-verbal information is an essential factor in Human-Robot Interaction (HRI). In order to understand Human partner’s perception, confidence status detection plays an important role for the robot to be capable to provide further information in the conversation. Moreover, the confident/uncertain feature can be utilized on robot expression to smooth the interaction and gain social skill. On another aspect, certain/uncertain expression style makes it possible to form extraversion/introversion personality which might spice up the interaction. This paper presented the concept of confident expression in HRI and an experiment to analyze both verbal and non-verbal features for confident expression. The confidence conditions were defined as 5-level status according to the questionnaire answer: most confident, comparative certainty, neutral, relative uncertain, most unconfident. The results showed that verbal and nonverbal features are possible to be classified into most confident/uncertain condition with the accuracy of 73.2143 % analyzed by multinomial logistic regression model via Weka. However, the ambiguous differences between the expression style are difficult to be classified. The accuracy of predicting comparative certainty and relative uncertain status can merely reach 66.67%. The classification of comparative certainty and relative uncertainty still need more features to increase prediction accuracy.
Cascaded Neural Network for Memristor based Neuromorphic Computing

Recent years, several memristor-based neuromor-phic processing chips have been proposed. However, there is few architectures to consider the cascading problems, and the scalability is not strong while dealing some tasks. To address this issue, we present a memristor-based cascaded method with some basic computation unit, several neural network processing chips can be cascaded by this means to improve the processing capability of the dataset. Compared with VGGNet and GoogLeNet, the proposed cascaded framework can achieve 93.54% Fashion-MNIST accuracy under the 4.15M parameters. Extensive experiments are conducted show that the circuit simulation results can still provide a high recognition accuracy, the recognition accuracy loss after circuit simulation can be controlled at around 0.26%.
Learning to Parse Grounded Language using Reservoir Computing

Recently new models for language processing and learning using Reservoir Computing have been popular. However, these models are typically not grounded in sensorimotor systems and robots. In this paper, we develop a model of Reservoir Computing called Reservoir Parser (ResPars) for learning to parse Natural Language from grounded data coming from humanoid robots. Previous work showed that ResPars is able to do syntactic generalization over different sentences (surface structure) with the same meaning (deep structure). We argue that such ability is key to guide linguistic generalization in a grounded architecture. We show that ResPars is able to generalize on grounded compositional semantics by combining it with Incremental Recruitment Language (IRL). Additionally, we show that ResPars is able to learn to generalize on the same sentences, but not processed word by word, but as an unsegmented sequence of phonemes. This ability enables the architecture to not rely only on the words recognized by a speech recognizer, but to process the sub-word level directly. We additionally test the model's robustness to word error recognition.
Leveraging Recursive Processing for Neural-Symbolic Affect-Target Associations

Explaining the outcome of deep learning decisions based on affect is challenging but necessary if we expect social companion robots to interact with users on an emotional level. In this paper, we present a commonsense approach that utilizes an interpretable hybrid neural-symbolic system to associate extracted targets, noun chunks determined to be associated with the expressed emotion, with affective labels from a natural language expression. We leverage a pre-trained neural network that is well adapted to tree and sub-tree processing, the Dependency Tree-LSTM, to learn the affect labels of dynamic targets, determined through symbolic rules, in natural language. We find that making use of the unique properties of the recursive network provides higher accuracy and interpretability when compared to other unstructured and sequential methods for determining target-affect associations in an aspect-based sentiment analysis task.
Extreme Dimensionality Reduction for Network Attack Visualization with Autoencoders

The visualization of network traffic flows is an open problem that affects the control and administration of communication networks. Feature vectors used for representing traffic commonly have from tens to hundreds of dimensions and hardly tolerate visual conceptualizations. In this work we use neural networks to obtain extremely low-dimensional data representations that are meaningful from an attack-detection perspective. We focus on a simple Autoencoder architecture, as well as an extension that benefits from pre-knowledge, and evaluate their performances by comparing them with reductions based on Principal Component Analysis and Linear Discriminant Analysis. Experiments are conducted with a modern Intrusion Detection dataset that collects legitimate traffic mixed with a wide variety of attack classes. Results show that feature spaces can be strongly reduced up to two dimensions with tolerable classification degradation while providing a clear visualization of the data. Visualizing traffic flows in two-dimensional spaces is extremely useful to understand what is happening in networks, also to enhance and refocus classification, trigger refined analysis, and aid the security experts’ decision-making. We additionally developed a tool prototype that covers such functions, therefore supporting the optimization of network traffic attack detectors in both design and application phases.
Representation Learning vs. Handcrafted Features for Music Genre Classification

In this work we present a comprehensive set of experiments aiming to perform music genre classification using learned and handcrafted features plus the fusion of them. Handcrafted features were obtained from the audio signal itself, lyrics, chords and spectrogram images extracted from the audio. The rationale behind this investigation is based on the assumption that one can find some complementarity between classifiers created from these different resources. The experimental protocol was conducted on the Brazilian Music Dataset using the artist filter restriction and they confirm the power of non-handcrafted features to perform audio classification tasks. The experimental results have shown a significant complementarity among the handcrafted features for which the evaluated fusion strategies allowed an improvement in the classification accuracy up to 4 percent points. On the other hand, the fusion of learned and handcrafted features provided similar accuracy than the best individual CNN (0.7815).
Beyond the Power of Mere Repetition: Forms of Social Communication on Twitter through the Lens of Information Flows and Its Effect on Topic Evolution

Understanding how people interact and exchange messages on social networks is significant for managing online contents and making predictions of future behaviors. Most existing research on the communication characteristics simply focuses on the user involvement. The current work largely neglects the content changes that imply how wide and deep the discussion in a topic goes, and to what degree people set forth their own views with the additional information supplemented. We are highly motivated to propose a theoretical framework to target those issues. In this paper, we define the communication modality constructs, and classify topics based on three dimensions: user involvement, information flow depth, and topic inter-relations, which substantially extend the traditional focus in user interaction analysis. The communication modality constructs comprise of (i) topic dialogicity, (ii) discussion intensiveness, and (iii) discussion extensibility. We introduce a quantitative model based on the topology of information flow graph, and use the information addition as well as the emotion attachment along the path to measure the pattern divergence between topic groups. Our model is empirically validated by using 78 million tweets, and experiments on Twitter demonstrate our contributions.