Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-Driven Sensor Scheduling for Mission-Critical Control Applications

In this paper, we consider a mission-critical control system, where an unstable dynamic plant is monitored by a number of distributed sensors connected to the controller over the wireless fading channels. We focus on the dynamic sensor scheduling to stabilize the unstable dynamic plant. The dynamic sensor scheduling is modeled as a non-convex drift-plus-penalty minimization problem. To improve the scheduling efficiency, the proposed scheme adapts to both the fading channel state as well as the dynamic plant state. To overcome the non-convexity in the minimization problem, we propose a novel transformation technique for the scheduling variables and the objective function (using the Lyapunov drift). Based on that, we can derive a low complexity dynamic sensor scheduling scheme and also obtain a closed-form stability analysis (despite the non-convexity) of the mission-critical control system via a randomized state-independent policy. Compared with various baselines, the proposed scheme has higher power efficiency and superior scalability performance.
Why Are We Not Teaching Machine Learning at High School? A Proposal

Although there are many online resources for youngsters to start learning about machine learning on their own, a majority of them require solid mathematical or programming background. In addition, there is a lack of material on how to effectively teach machine learning. This paper is motivated by a survey conducted among young students in a technical high-school to inquire on their interest in learning how computers learn. Given the fact that almost every high-school student has heard about artificial intelligence and is curious about it, this article proposes a way to give a friendly introduction to machine learning in the context of a short workshop. Through a series of problem-based activities, students are expected to understand the foundations of what does ‘learning’ mean for a computer. In addition, through analogies as well as toy and real problems, this short workshop will tackle students’ preconceptions, give them an insight of what tools are important in order to deal with this popular topic and address the ethical issues that arise. Moreover, it is desirable that this stimulates students’ interest for STEM degrees. Finally, the activities proposed can be easily adapted for an introductory engineering course.
Word Level Language Identification in Assamese-Bengali-Hindi-English Code-Mixed Social Media Text

The content posted over social media platforms today are characterized by code-mixing, phonetic typing, lexical borrowing and neologism making word level language identification an important pre-requisite for various natural language processing applications. Existing studies in word level language identification are not applicable for low resource languages due to the lack of essential tools and resources like dictionaries, annotated resources, transliteration tools etc. In this paper, we address the problem of word level language identification in a highly multilingual environment for low resource languages. We investigate the performance of different word level language identification frameworks over a corpus of transliterated Assamese-Bengali-Hindi-English messages collected from Facebook social media platform. From various experimental observations, it is evident that global semantic similarities help in identifying borrowed words, and local contextual similarity helps in resolving words that are valid in multiple languages.
Improving Phrase-Based Statistical Machine Translation with Preprocessing Techniques

This work presents an improvement to phrase-based statistical machine translation models which incorporates linguistic knowledge, namely parts-of-speech information and preprocessing techniques. Any Statistical Machine Translation (SMT) System needs large parallel corpora for exact performance. So, non-availability of corpora limits the success achievable in machine translation to and from those languages. In this study, we choose Sinhala to Tamil translation which gains importance since both of them are acknowledged as official languages of Sri Lanka and also resource-poor languages. Even though findings presented here is for Sinhala to Tamil translation, the concept of pre-processing is language neutral and can be transcended to any other language pair with different parameters. To overcome the translation challenges in traditional SMT, preprocessing techniques are used. Preprocessing described in the research is related to generating phrasal units, Parts of Speech (POS) integration and segmentation. At the end, automatic evaluation of the system is performed by using BLEU as evaluation metrics. We observed all preprocessing techniques outperform the baseline system. The best performance is reported with PMI based chunking for Sinh ala to Tamil translation. We could improve performance by 12% BLEU (3.56) using a small Sinhala to Tamil corpus with the help of proposed PMI based preprocessing. Notably, this increase is significantly higher compared to the increase shown by prior approaches for the same language pair.
A Hybrid Deep Learning Architecture for Sentence Unit Detection

Automatic speech recognition systems currently deliver an unpunctuated sequence of words which is hard to peruse for human and degrades the performance of the downstream natural language processing tasks. In this paper, we propose a hybrid approach for Sentence Unit Detection, in which the focus is on adding the full stop [. ]to the unstructured text. Our model profits from the advantage of two dominant deep learning architectures: (i)the ability to learn the long dependencies in both directions of a bidirectional Long Short-Term Memory; (ii)the ability to capture the local context with Convolutional Neural Networks. We also empirically study the training objective of our networks using extra-loss and further investigate the impacts of each model component on the overall result. Experiments conducted on two large-scale datasets demonstrated that the proposed architecture outperforms previous separated methods by a substantial margin of 1.82–1.91% of F1. Availability: the source code and model are available at https://github.com/catcd/LSTM-CNN-SUD
An Improved Method for Human Eye State Detection Based on Least Square Ellipse Fitting Algorithm

In order to accurately judge eyes open or closed state of the online learners, we propose an improved method of judging eye state based on the least square ellipse fitting algorithm under natural light. Firstly, this method models human eyes, then obtains contour maps of the eye region from the image captured by the camera. Secondly, the improved least square ellipse fitting algorithm is used to search for the optimal solution to obtain the fitting ellipses of human eyes contours. Thirdly, after accurately fitting to the human eyes, the fitting ellipses are adjusted according to the threshold to obtain the actual width and height of human eyes. Finally, the human eyes state is judged by the actual aspect ratio. The experimental result shows that the accuracy of human eyes state detection based on this improved method is 91.9% (without glasses) and 85.0% (with glasses). Meanwhile, this improved method can eliminate the interference of glasses well, and compared with other traditional methods, it has higher accuracy and robustness, and has certain practical value.
Oscillations Induced by Brain Connectivity Changes in Basal ganglia-cortex Network

Synchronous oscillatory activities of the biological neural network are the basis for the brain to implement different functions, while abnormal oscillations of basal ganglia-cortex circuits in the range of12-35 Hz are correlated with Parkinson‘s disease. A kind of lumped parameter model to characterize the properties of the neural mass is proposed to reproduce the observed neural activities both under normal and Parkinsonian state. By analyzing the firing pattern and power spectrum characteristic of each brain nucleus under different brain connectivities, it is shown that increased intra-coupling strengths perform an important role in inducing abnormal oscillations in the high-beta frequency band (20-35Hz) in cortex and the low-beta frequency band (12-20Hz) in basal ganglia. To elucidate the mechanism underlying the transition between different brain states, the dynamics of each nucleus and the connectivity properties are explored. Simulation results suggest that the normal neural system could be driven into Parkinsonian state due to the increase of inhibitory projections from basal ganglia to cortex or excitatory projections from cortex to basal ganglia. This work may pave the way to the exploration of the generation mechanism of the abnormal oscillations related to Parkinsonian movement disorders.
A Grid Map Fusion Algorithm Based on Maximum Common Subgraph

Grid map fusion is a key problem for multi-robot simultaneous localization and mapping (SLAM), and single robot graph-SLAM. To solve this problem, this paper proposes a novel approach to fuse maps based on maximum common subgraph without the requirements of any assumption on the robot's pose. In the approach, the problem of grid map fusion is transformed into an image registration problem, and then the objective function is established to calculate a transformation matrix. Firstly, a corner extraction algorithm is applied to extract corner points from grid maps. Secondly, according to the extracted corner points the isomorphism scheme of triangle is constructed, as a result the initial optimal transformation matrix to being calculated. Finally, the transformation matrix is optimized by finding a maximum common subgraph based on iterating the polygon isomorphism scheme with more corner points. Experimental results show that the proposed approach can solve the grid map merging problem high accurately and robustly.
An Investigation of Word Embeddings with Deep Bidirectional LSTM for Sentence Unit Detection in Automatic Speech Transcription

This work investigates the effectiveness of using the word based and sub-word based embedding representations as input for a deep bidirectional Long Short-Term Memory Network for Sentence Unit Detection in Automatic Speech Recognition transcription. Our experimental results show that using sub-word based embedding can significantly improve the SUD performance when a limited text is used to train both the word embedding and the SUD model. The SUD model using the sub-word based embedding gains up to 2.07% absolute improvement in F1-score as compared to the best model trained with the word-based embedding. When tested on a domain-mismatch condition, the SUD model with sub-word based embedding trained from the in-domain data gives an approximate 2 % and 1 % improvement over the best model using out-of-domain embedding with reference and ASR transcription with 29.5% Word Error Rate respectively.
An 82–107.6-GHz Integer- $N$  ADPLL Employing a DCO With Split Transformer and Dual-Path Switched-Capacitor Ladder and a Clock-Skew-Sampling Delta–Sigma TDC

A W-band integer-N all-digital phase-locked loop (ADPLL) aiming for wide frequency tuning range (TR) and low phase noise is proposed. The W-band ADPLL employs a digitally controlled oscillator (DCO) with split transformer and dual-path exponentially scaled switched-capacitor ladder and a clock-skew-sampling delta–sigma time-to-digital converter (TDC). The 65-nm CMOS W-band ADPLL measures a frequency TR of 27% from 82 to 107.6 GHz and phase noise from −106 to −110 dBc/Hz at 10-MHz offset and −84 to −87 dBc/Hz at 100-kHz offset while consuming 35.5 mW and occupying a 0.36 mm<sup>2</sup> core area, corresponding to a figure of merit (FOM) of −171 ~ −173 dB and FOM<sub>T</sub> of −178 ~ −181 dB.
Community-based anomaly detection

Network behaviour anomaly detection systems can detect zero-day attacks and work even with encrypted traffic. They maintain a model of normal behaviour and report any deviation as anomaly. Typically, a separated model for each host is generated or there is one model for the whole network. The model of normal can be built for the whole network or for each network host separately. The per host models suffer from a small amount of noisy data as the behaviour of a single user is typically not very stable. The single model for the whole network is more robust to fluctuations, but it is trying to find a normal behaviour of a group of hosts with possibly diverse behaviourWe propose a method for clustering network hosts based on their network behaviour to create groups of hosts that behave similarly. The anomaly detection models trained on such groups of network hosts are more robust to fluctuations of the behaviour of individual hosts when compared to the per host models. It is able to detect finer anomalies (e.g. stealthy data ex-filtration) that would be otherwise hidden by modelling diversely behaving network hosts together.
Evaluating the Morphological and Capitalization Features for Word Embedding-Based POS Tagger in Bahasa Indonesia

In this paper, morphological and capitalization features are employed to improve the current word embedding-based POS tagger for Bahasa Indonesia. The experiments are conducted with an architecture based on neural network model, that is a simple feedforward neural network with two input layers, one merge layer, and two hidden layers. The first input layer uses word embeddings (CBOW and Skip-gram) feature as the input while the second input layer uses morphological and capitalization features. The results show that the selected additional features improve the performance and accuracy of current word embedding-based POS tagger, although it is not really significant. The F1 score averages of all word embedding types are increasing from 93% to 94% and the accuracies are increasing from 92-93% to 94-95% on manually tagged corpus of about 250,000 tokens (12,775 unique tokens).
Relation Detection for Indonesian Language Using Deep Neural Network - Support Vector Machine

Relation Detection is a task to determine whether two entities is related or not. In this paper, we employ neural network to do relation detection between two named entities for Indonesian Language. We used feature such as word embedding, position embedding, POS-Tag embedding, and character embedding. For the model, we divide the model into two parts: Front-part classifier (Convolutional layer or LSTM layer)and Back-part classifier (Dense layer or SVM). We did grid search method of neural network hyper parameter and SVM. We used 6000 Indonesian sentences for training process and 1,125 for testing. The best result is 0.8083 on F1-Score using Convolutional Layer as front-part and SVM as back-part.
Adaptive Iterative Learning Control of Robotic Manipulator with Second-Order Terminal Sliding Mode Method

To solve the trajectory tracking problem of robotic manipulators with uncertain model information and unknown external disturbances, an adaptive iterative learning control method with second-order terminal sliding mode method is proposed in this paper. This method adopts nonsingular fast terminal sliding mode surface and second-order sliding mode control to improve the convergence speed of system states and robustness. Adaptive iterative learning control is used to approximate system model and bounded external disturbance for getting rid of the dependence on specific mathematical model and improving control precision. The convergence of this controller along iterative times is proved by composite energy function. With Denso VP6242G manipulator as the controlled object, this proposed controller has better performance comparing to traditional iterative learning controller designs.
Neuroanatomical Correlates of Creativity: Evidence From Voxel-Based Morphometry

Creativity was a special cognitive capacity which was crucial to human survival and prosperity. Remote associates test (RAT), identifying the relationships among remote ideas, was one of the most frequently used methods of measuring creativity. However, the structural characteristics associated with RAT remains unclear. In the present study, the relationship between gray matter density (GMD)/white matter density (WMD) and RAT was explored using voxel-based morphometry (VBM) in a larger healthy college student sample (144 women and 117 men). Results showed that the score of RAT was significantly positively related with the GMD in the right anterior superior temporal gyrus (aSTG) and negatively correlated with the GMD in the right dorsal anterior cingulate cortex (dACC). Meanwhile, results also showed that the score of RAT was significantly positively related with the WMD in the right dACC and negatively correlated with the WMD in the left inferior frontal gyrus (IFG). These findings indicate that individual creativity, as measured by the RAT, was mainly related to the regional gray /white matter density of brain regions in the aSTG, dACC and IFG, which might have been involved in the forming of novel combinations, breaking of mental set, monitoring of conflict and semantic integration.
An Optimal Sparse Signals Recovery Using Constraint Programming

The emerging field of Compressive Sensing (CS) has shown that sparse signals can be acquired using a rate far less than the one required by the classical Shannon-Nyquist theorem. In that, CS acquires a sparse signal by correlating it with the rows of a sensing matrix to form a small set of measurements. And then these measurements are used to reconstruct the original sparse signal using an optimization algorithm. In this paper, we extend our recently published work, to more investigate the power of Constraint Programming (CP) solvers with the CS problem. We show that a statistical property of the sensing matrix can highly affects the performance of CP solvers in the case of CS problem. Which enable us to improve the CP solvers performance and even reach the optimal case. The effectiveness of our method is demonstrated via simulation results.
Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures

The brain-inspired spiking neural network neuromorphic architecture offers a promising solution for a wide set of cognitive computation tasks at a very low power consumption. Due to the practical feasibility of hardware implementation, we present a memristor-based model of hardware spiking neural networks which we simulate with Neural Network Scalable Spiking Simulator (N2S3), our open source neuromorphic architecture simulator. Although Spiking neural networks are widely used in the community of computational neuroscience and neuromorphic computation, there is still a need for research on the methods to choose the optimum parameters for better recognition efficiency. With the help of our simulator, we analyze and evaluate the impact of different parameters such as number of neurons, STDP window, neuron threshold, distribution of input spikes, and memristor model parameters on the MNIST hand-written digit recognition problem. We show that a careful choice of a few parameters (number of neurons, kind of synapse, STDP window, and neuron threshold) can significantly improve the recognition rate on this benchmark (around 15 points of improvement for the number of neurons, a few points for the others) with a variability of four to five points of recognition rate due to the random initialization of the synaptic weights.
Chinese Dialogue Analysis Using Multi-Task Learning Framework

Dialogue analysis contains Dialogue Act (DA) analysis and Adjacent Pair Dependency (APD) analysis. DA represents a function of a speaker's utterance, while APD represents the relation of the adjacent utterances. DA analysis is a difficult point for further exploration of dialogue understanding, and the context information provides the extra information for DA which is very important. APD shows the relation between adjacent pairs, as a result it can provide the corresponding precondition and background contextual information for the dialogue analysis. However, previous studies ignored the relation between DA and APD analysis, and there is no well-designed dialogue model for this issue. Based on a Chinese daily conversation annotation corpus, we propose a multi-task learning framework. In this method, DA task is responsible for extracting content information from one utterance and classify its function, while APD task is responsible for classifying relation between adjacent utterances. By this way, APD can provide DA with additional context information, and DA has the ability to assist in training APD parameters during this process. Our experimental results demonstrate that multi-task learning model can improve the accuracy of the DA and APD tasks while compared with the state-of-art of DA and APD analysis models.
12-Gb/s Over Four Balanced Lines Utilizing NRZ Braid Clock Signaling With No Data Overhead and Spread Transition Scheme for 8K UHD Intra-Panel Interfaces

This paper presents a new pin/energy-efficient data and clock signaling scheme, named braid clock signaling (BCS). This signaling scheme efficiently embeds clock information into the data stream without data overhead, unnecessary pins, and channels for clock. To remove the data overhead, the clock information is embedded in every other data period. This high transition density (TD) leads to the enhanced jitter tracking performance of a receiver and increased stability. Furthermore, a spread transition scheme (STS) removes the additional power consumption for the embedded clock with little electromagnetic interference (EMI). As non-return-to-zero (NRZ) signaling uses only two voltage levels, the NRZ BCS secures a large input voltage margin at the receiver side, unlike other pin-efficient multi-level signaling schemes. An analysis of the secured voltage margin shows improved energy efficiency over conventional pin-efficient multi-level signaling schemes, even without consideration of their clocking power dissipation. The prototype transceiver is fabricated in a 28-nm CMOS process with a 12-Gb/s delay-locked loop (DLL)-based receiver over four lines.
Review Rating with Both Positive and Negative Sentiment Intensity Measurements

As a fundamental task in sentiment analysis, review rating has normally been modeled as a multi-class classification problem or a real-valued regression problem. In this study, we focus on regression modeling for review rating. Compared with all existing studies which perform regression learning with the intensity scores towards only the positive sentiment (abbreviated as positive scores). We introduce the concept of negative scores to leverage both positive and negative sentiment intensity measurements to improve the regression performance. To achieve this, we propose a joint learning framework to integrate the two regression tasks, i.e., the sentiment regression tasks with either positive or negative scores. Empirical studies demonstrate the effectiveness of the proposed joint learning framework to review rating.
A Digitally Assisted Dual-Input Dual-Band Doherty Power Amplifier With Enhanced Efficiency and Linearity

This brief presents a dual-input dual-band Doherty power amplifier (DPA) that operates at 850 and 2100 MHz. The presented DPA utilizes a compact input matching network and composite right-left handed phase offset line. This DPA delivers a peak output power of around 44.5 dBm with an efficiency enhancement region upto 9–10 dB output power back off at both the operating frequencies. Further, the phase offset angles between main and auxiliary power amplifiers are controlled digitally at both the operating frequencies in order to improve the efficiency and linearity throughout the back-off region. As compared to the conventional phase offset of 90°, the optimum offset angle of 120° at 850 MHz provides an improvement of 13% in drain efficiency and 8.5 dB in C/IMD3. Similarly, an improvement of 4 dB in C/IMD3 is achieved by digitally setting a phase offset of 80° at 2100 MHz.