Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Mapping with IoE-based Heterogeneous Vehicles for Enhanced Situational Awareness

The development of autonomous vehicles or advanced driving assistance platforms has had a great leap forward to get closer to human daily life over the last decade. Nevertheless, it is still challenging to achieve an efficient and fully autonomous vehicle or driving assistance platform due to many strict requirements and complex situations or unknown environments. One of the main remaining challenges is a robust situation awareness in autonomous vehicles when the environment is unknoen. An autonomous system with a poor situation awareness due to low quantity or quality of data may directly or indirectly cause serious consequences. For instance, a person’s life might be at risk due to a delay caused by a long or incorrect path planning of an autonomous ambulance. Internet of Everything (IoE) is currently becoming a prominent technology for many applications such as automation. In this paper, we propose an IoE-based architecture consisting of a heterogeneous team of cars and drones for enhancing situational awareness in autonomous cars, especially when dealing with critical cases of natural disasters. In particular, we show how an autonomous car can plan in advance the possible paths to a given destination, and send orders to other vehicles. These, in turn, perform terrain reconnaissance for avoiding obstacles and dealing with difficult situations. Together with a map merging algorithm deployed into the team autonomous vehicles, the proposed architecture can help to save traveling distance and time significantly in case of complex scenarios.
RSS-based Carrier Sensing and Interference Estimation in 802.11 Wireless Networks

We analyze the carrier sensing and interference relations between the two wireless links and measure the impact of these relations on link capacity in two indoor 802.11a mesh network testbeds. We show that asymmetric carrier sensing and/or interference relations happen frequently in wireless networks; these asymmetric relations affect not only the level of performance degradation, but also the fairness of channel access. We then propose a new methodology that predicts the relation of carrier sensing and interference based on radio signal strength measurements. The measurement complexity increases only linearly with the number of wireless nodes. To our knowledge, the proposed methodology is the first trial that considers physical layer capture, and detects the source of interference that is out of the communication range. We validate the prediction methodology on an 11-node wireless mesh network testbed.
An Analysis of Decoding for Attention-Based End-to-End Mandarin Speech Recognition

Many of the current state-of-the-art Mandarin Large Vocabulary Continuous Speech Recognition (LVCSR) Systems are built either with a hybrid of Deep Neural Network (DNN) and Hidden Markov Models (HMM) or with Neural Network model trained with Connectionist Temporal Classification (CTC) criterion. In both of these models, decoding is conducted by Weighted Finite State Transducer (WFST) that searches the word sequence which matches best with the speech given the acoustic and language models. Recently, attention-based end-to-end method is becoming more and more popular for Mandarin speech recognition. This new method advocates replacing complex data processing pipelines with a single neural network trained in an end-to-end fashion.In this paper, we investigate the decoding process for attention-based Mandarin models using syllable and character as acoustic modeling units and discuss how to combine word information into the decoding process. We also conduct a detailed analysis on various factors that affect the performance of decoding including beam size, label smoothing, softmax temperature, attention smoothing and coverage.
Keyword Spotting Based On CTC and RNN For Mandarin Chinese Speech

The state-of-the-art acoustic modeling for Keyword Spotting (KWS) systems is mainly based on the hybrid model of Hidden Markov Model (HMM) and Neural Network (NN). However, it is challenging to efficiently train such a hybrid system, since the dependence of the intermediate phonetic representation. Motivated by the end-to-end speech recognition systems, we propose a Mandarin KWS system using the end-to-end method, which directly predict the posterior of phonetic units. The system is based on Connectionist Temporal Classifier (CTC) and Recurrent Neural Network (RNN). The main difference between our system and other CTC-based KWS system is the output alphabet and its corresponded keyword searching mechanism. We adopt Mandarin syllables as the output labels, rather than the phonemes or characters. Extensive experiments are conducted on the Mandarin Chinese speech dataset. Experimental results indicate that: 1) Compared with HMM-based KWS system, the end-to-end KWS system achieves a significant improvement, without any increase of computational cost. 2) Our syllable-based end-to-end KWS system obtains better performance than the state-of-the-art ones based on Chinese context independent (CI) phonemes or Chinese characters.
DNN i-vector based Fishervoice and PLDA SVM scoring for NIST SRE 2016

Our ongoing work that applies Fishervoice to map joint factor analysis (JFA)-mean supervectors 1 into a compressed discriminant subspace has shown that performing cosine distance scoring on the Fishervoice projected vectors outperforms classical JFA. In this paper, we refine Fishervoice for low-dimensional i-vectors by only using the nonparametric between-class scatter matrix to substitute the parametric one in linear discriminative analysis (LDA). The task of 2016 speaker recognition evaluation (SRE16) only has unlabeled in-domain training data and labeled out-of-domain training data for model training. Support vector machine (SVM) scoring can capture the discriminative information embedded in the unlabeled in-domain training data. We perform probabilistic linear discriminant analysis (PLDA) before SVM scoring for inter-session compensation with speaker label information from out-of-domain training data. This approach constitutes CUHK’s submission for SRE16. In this paper, we present a detailed analysis of the approaches and the performance gains with refined Fishervoice and PLDA SVM scoring.1The JFA-mean supervector of an utterance is a GMM supervector obtained from the JFA model.
A Refined Query-by-Example Approach to Spoken-Term-Detection on ESL learners’ Speech

A refined Query-by-Example (QbE) approach is proposed to improve Spoken-Term-Detection (STD) performance on L2 English learners’ speech data. A Hidden Markov Model (HMM) is built for each keyword and a computationally efficient, iterative Viterbi decoding is adopted to detect spoken keywords in test. The approach is evaluated on an English as Second Language (ESL) speech database collected over L2 learners with different English proficiency levels. The experimental results show that the new approach achieves a performance better than the traditional DTW-based QbE. Also, it is comparable to that of an LVCSR-based STD but with significant lower complexities and computations. The refined QbE and LVCSR approach to STD are complementary to each other. By fusing the two systems together, we can further improve the MAP and MP@N performance by 6.1%-13.4% and 7.5%-14.4%, respectively, in testing sets of 3 different English proficiency levels over the best performance of either system.
IoT based Schistosomiasis Monitoring for More Efficient Disease Prediction and Control Model

The urinary and intestinal schistosomiasis are a significant public health problem in Senegal with a prevalence rate varying between 0.3% and 1%. After malaria, bilharzia (or Schistosomiasis) is the second disease that calls for admission to hospital. In Senegal, treatment is based on "Praziquantel" that is not effective and may aggravate symptoms. In fact, schistosoma that transmits the illness lives in water points. Firstly, our proposed Sensors-Based Bilharzia Detection (SB2D) architecture uses data collected by wireless sensors network that are deployed in natural environment. SB2D is able to collect in real time different physical and chemical parameters such as solar irradiation, water temperature, water point pH and then predicts whether the environmental factors are favourable to bilharzia life cycle transmission. Secondly, event detection algorithms were developed in order to assess the transmission contamination risk when anomalies are detected. The obtained results show that Support Vector Machines (SVM) gives good anomalies detection rate compared to other anomalies detection test.
The La-ICPMS U-Pb Ages of Detrital Zircons from the BAYANKALA Group: Implication for the Provenance

Songpan-Ganzi Orogenic belt is covered by thick Triassic turbidite complex-Bayankala Group. The provenance of Triassic turbidites is still controversial. In order to constrain the detrital provenance of the siliciclastic rocks, We carried out La-ICP-MS zircon U–Pb dating and Hf isotope analyses on 44 detrital zircons obtained from Bayankala group. There are three peak values for, i.e. 247-500Ma, 1711-2000Ma, and 2200-2580Ma. On the basis of regional geologic background. We suggest that the the formation age of Bayankala Group was derived mainly from Qinling, Qilian, and Kunlun orogens along the southern margin of the North China Craton with minor the South China.
What is AI Software Testing? and Why

— With the fast advance of artificial intelligence technology and data-driven machine learning techniques, building high-quality AI-based software in different application domains is becoming a very hot research topic in both academic and industry communities. Today, many machine learning models and artificial technologies have been developed to build smart application systems based on multimedia inputs to achieve intelligent functional features, such as recommendation, object detection, classification, and prediction, natural language processing and translation, and so on. This brings strong demand in quality validation and assurance for AI software systems. Current research work seldom discusses AI software testing questions, challenges, and validation approaches with clear quality requirements and criteria. This paper focuses on AI software quality validation, including validation focuses, features, and process, and potential testing approaches. Moreover, it presents a test process and a classification-based test modeling for AI classification function testing. Finally, it discusses the challenges, issues, and needs in AI software testing.
FlexNode: a reconfigurable Internet of Things node for design evaluation

Accurate evaluation of Ultra Low Power Systems on Chip (ULP SoC) is a huge challenge for designers and developers. In embedded applications, especially for Internet of Things end-node devices, ULP SoCs have to interact with their environment, but modelling a complete SoC and the peripheral components, their interaction and low-power policies, can be very complex in terms of developments and benchmarking. In order to cope with this challenge, an approach is to implement the desired system on FPGA with a monitoring infrastructure dedicated to fast and accurate evaluation. This paper presents a reconfigurable prototyping platform used for SoC architecture exploration and real-time application evaluation.
Decision Support System for the Analysis of Traffic Accident Big Data

Today, as traffic accidents continue to occur, road safety has become a major concern of contemporary social problems. Many factors explain traffic accidents, such as the nature of the accident site, its environment, time, driver behavior, weather, and other uncertain complex factors. As a result, the occurrence of road accidents is non-linear, so it is necessary to explore the correlation between data from many different aspects to minimize the risk. After pre-processing the data and a classification using the data mining tools, relevant information can be deduced on the causes of the high-frequency accidents. Depending on the actual results obtained, we can verify the accuracy of the extracted information, and this can help predict new situations with similar data in the future. The ultimate aim of data analysis is to choose the most accurate extraction process after validation, by analyzing the characteristics of the data and their relationship to the analysis and extraction process. In this paper, we propose a decision-making system for the analysis of traffic accident data in order to extract information relevant to the prevention of road risk. In addition, we introduce the concept of crowdsourcing in the collection phase to gather more information from the users involved in an accident. The proposed system is based on appropriate data mining and Big Data techniques for the extraction, pre-processing and exploration of accident data, entirely managed by the Hadoop framework, in order to categorize road accidents and detect problematic sites.
Emotion Recognition using Sequence Mining

With the development of human-computer interaction technology, user emotion recognition, as an important factor in the process of natural language communication, has become a hot research topic. Current studies mainly analyze the emotional within a single long sentence, but in real communication, the transmission of information and emotion is more often achieved by multi-round dialogue. In this paper, we construct the emotional sequence between people and people in different scenarios to identify their multi-round conversational emotions, and analyze their emotional changes through sequence mining. This article takes several novels as the analysis corpus, and proceeds the scene segmentation according to the chapters of the novel. Then we analyze the emotional bias of each conversation between different people in each scene, and then construct the emotional matrix between people in each scene. Finally, the LSTM algorithm is used to mine different emotional patterns and changing trends between people compared with machine learning algorithm. Experimental results show that the proposed sequential-based emotion recognition method can recognize the emotions between people very well and predict the future emotional patterns.
Non-intrusive Speech Quality Assessment Using Deep Belief Network and Backpropagation Neural Network

In this paper, we present a new speech quality assessment method to estimate the quality of degraded speech without the reference speech. The traditional non-intrusive assessment methods cannot meet the requirement of high consistency with subjective results owing to the lack of original reference signals. To solve these issues, deep belief network is trained to produce pseudo-reference speech signal of degraded speech. Then mel-frequency cepstrum coefficients of pseudo-reference speech and degraded speech are extracted to calculate feature differences. The feature differences are mapped to speech quality score using backpropagation neural network. Experiments are conducted in a dataset containing various degraded speech signals and subjective listening scores. When compared with the standardization ITU-T P.563, Gaussian Mixture Model method and the autoencoder-based method, the proposed method brings about a higher correlation coefficient between predicted scores and subjective scores.
Novel Amplitude Weighted Frequency Modulation Features for Replay Spoof Detection

In this paper, we propose the use of Amplitude Modulation and Frequency Modulation (AM-FM) features for replay detection task. In AM-FM signal, AM component is known to be severely affected by noise (in this case, due to replay mechanism) which is exploited in proposed feature extraction. In particular, we explore this damage in AM component to corresponding instantaneous frequency. Thus, the novelty of proposed Amplitude Weighted Frequency Cosine Coefficients (AWFCC) feature set is the use of frequency components along with weighted amplitude components that are degraded due to replay noise. The AWFCC features have the information of AM-FM and hence, gave discriminatory information in the spectral characteristics. The experiments were performed on ASVspoof 2017 Challenge database using AWFCC feature set that gave an Equal Error Rate (EER) of 6.37 % (dev) and 11.72 % (eval) set. We have compared our results with CQCC, LFCC and MFCC using Gaussian Mixture Model (GMM) classifier and found that proposed AWFCC feature set performed better than the other feature sets on both dev and eval datasets. In addition, we have used score-level fusion of CQCC and AWFCC to obtain a lower EER of 3.60 % and 11.22 % on dev and eval set.
Convolutional Neural Turing Machine for Speech Separation

Long short-term memory (LSTM) has been successfully developed for monaural speech separation. Temporal information is learned by using dynamic states which are evolved through time and stored as an internal memory. The spectro-temporal data matrix of mixed signal is flattened as input vectors. There are twofold limitations. First, the internal memory in LSTM could not sufficiently characterize long-term information from different sources. Second, the temporal correlation and frequency neighboring in the flattened vectors were smeared. To deal with these limitations, this paper presents a convolutional neural Turing machine (ConvNTM) where the feature maps of spectro-temporal data are extracted and embedded in an external memory at each time step. ConvNTM aims to preserve the spectro-temporal structure in long sequential signals which is exploited to estimate the separated spectral signals. An addressing mechanism is introduced to continuously calculate the read and write heads to retrieve and update memory slots, respectively. The memory augmented source separation is implemented for single-channel speech enhancement. Experimental results illustrate the superiority of ConvNTM to LSTM, NTM and convolutional LSTM for speech enhancement in terms of short-term objective intelligibility measure.
Utterance-level Permutation Invariant Training with Discriminative Learning for Single Channel Speech Separation

The challenge in deep learning for speaker independent speech separation comes from the label ambiguity or permutation problem. Utterance-level permutation invariant training (uPIT) technique solves this problem by minimizing the mean square error (MSE) over all permutations between outputs and targets. It is a state-of-the-art deep learning architecture. However, uPIT only minimizes the chosen permutation with the lowest MSE, not discriminates it with other permutations. This may lead to increase the possibility of remixing the separated sources. In this paper, we propose a uPIT with discriminative learning (uPITDL) method to solve this problem by adding one regularization at the cost function. In other words, we minimize the difference between the outputs of model and their corresponding reference signals. Moreover, the dissimilarity between the prediction and the targets of other sources is maximized. We evaluate the proposed model on WSJ0-2mix dataset. Experimental results show 22.0% and 24.8% relative improvements under both closed and open conditions compared with the uPIT baseline.
End-to-End Mongolian Text-to-Speech System

Speech synthesis, or text-to-speech (TTS), generates a speech waveform of the given text. To build a satisfactory TTS system, a large natural speech corpus is requested. In the traditional approach, the corpus should be accompanied with precise annotations. However, the annotation is difficult and costly. Recently, end-to-end speech synthesis methods are proposed, which eliminated the requirement of annotation. The end-to-end methods make the development of TTS system less costly and easier. We used the state-of-the-art end-to-end Tacotron model in the Mongolian TTS task. With much more unannotated speech data (about 17 hours), the new system beats the old best Mongolian TTS system, which is trained on a small amount of annotated data (about 5 hours), with a big margin. The new mean opinion score (MOS) is 3.65 vs 2.08 which is the old one. The proposed system becomes the first Mongolian TTS system can be utilized in real applications.
Measurement of Gait Speed using a Doppler Radar: Influence of Acceleration and Deceleration Zones

This paper examines the reliability of the measurement of the gait speed using a Doppler radar, and studies the influence of the acceleration and deceleration zones on this measurement. To do that, a study sample consisted of ten healthy young adults aged between 23 and 38, were demanded to walk back and forth over a distance of 4.57m in slow, usual, and fast pace. The test was repeated on a total of three sessions. The gait speed was determined using a Doppler radar. The repeatability of the gait speed was evaluated through the intraclass correlation coefficient. The gait speed did not vary much in the tests at each pace. A variation was however noted between the start, middle, and end of each walking pattern. These results revealed that the gait speed measured using a Doppler radar is a reliable index for evaluating pathological walking patterns. A care should however be taken of the variations between the zones of each walking pattern.
Invited Paper: Distributed Computing in Cyber-Physical Intelligence: Robotic Perception as an Example

Intelligent cyber-physical systems, such as robots, are emerging computing devices that autonomously and directly interact with the physical world. The new characteristics of these devices motivate further research questions different from those addressed in traditional computing technology. Based on an in-depth investigation of the relationship between a typical example, robotic perception, and distributed computing, this paper tries to explore the challenges and opportunities brought by intelligent cyber-physical systems to distributed computing. The preliminary answers to three questions are given: "Why should we introduce distributed computing into cyber-physical intelligence", "What kind of distributed architecture can contribute to cyber-physical intelligence" and "What challenges brought by intelligent cyber-physical systems to distributed computing infrastructure? " A multi-scale hybrid distributed architecture for cyber-physical intelligence named as Music as well as our initial practices towards enabling this architecture is also presented.
Lensfree Microfluidic On-chip Flowing Cell Imaging with Integrated Surface Acoustic Wave Pumping

One lensfree microfluidic on-chip flowing cell imaging system with integrated SAW pumping is proposed in this article. The SAW device temperature responses are characterized to be below 40 °C so as not to harm cells. Different resolution enhancing techniques are implemented and compared. It is demonstrated that machine learning based methods exceed those interpolation based ones, and a training library of individual cell type helps improve the SSIM compared with a training library of combined cell types.
A Fault Diagnosis Scheme for High-Speed Train Bogie based on Depth-wise Convolution

The fault detection and isolation system is the key element for the safe long-term operation of high-speed train. The multi-channel signals provided by parallel monitoring system are usually closely coupled and highly uncertain, which are difficult to analyze. This paper proposed a depth-wise convolution modular structure for fault diagnosis with the multi-channel signal to address the complex and dynamic operating conditions of high-speed trains. A scalable modular structure is designed to provide low coupling and high transparency, which could easily configurable function-level according to the requirements. Depth-wise convolution is employed to avoid premature channel fusion. The experimental demonstrate that the proposed scheme improves the accuracy of high-speed train bogie fault diagnosis, including cases with noise and with speed-varied condition, which has practical value to industrial applications.