Miracle-dz/UniFAR-FTUs huggingface dataset:
The Small-Signal Model Comparison and Analysis between AlGaN/GaN FinFETs and HEMTs on the Same Wafer

In this letter, we proposed the small-signal models of AlGaN/GaN fin-shaped field-effect transistors (FinFETs) and AlGaN/GaN high electron mobility transistors (HEMTs). The parasitic parameters of device been extracted from small signal model, respectively. The comparisons of parasitic parameters and S-parameters between planer and 3D GaN device also are shown in this work.
Image Annotation through Adaptive Dependency Fusion

In order to improve the performance of image annotation, recently proposed methods build their model combining multiple dependencies from relations between image and label (image/label), between images (image/image) and between labels (label/label). However, most of these methods cannot make multiple dependencies work jointly, and their performances is largely depending on the results predicted by image/label dependency. To address this problem, we propose an end-to-end image annotation model to associate these dependencies with the prediction path, which is composed of a series of labels in the order they are detected. Specially, our model can adaptively adjust the prediction path: from those easy-to-detect relevant labels to these hard-to-detect relevant ones. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.
Processing Big Data in Streaming for Fault Prediction: An Industrial Application

In the current data-driven industrial scenario, Big Data processing plays a leading role in enhancing business performance. An ever increasing number of working machines are equipped with smart devices (such as sensors and actuators) which are in charge of monitoring machine status in real time and implement corrective actions before the workpiece quality is compromised or machine is damaged. However, many manufacturing companies do not take advantage of the use of Big Data coming from their production systems. In some cases, Big Data analytics is an un-explored issue since it is considered time and resources consuming. Moreover, the real benefits of processing, in real time, industrial data are usually underestimated. The European project TOREADOR wants to extend and facilitate the diffusion of Big Data analytics within industrial contexts, in order to generate greater value for companies. Focusing on the aerospace components manufacturing process (as one of the case studies of the Project), the paper aims to describe the main developments and lessons learned by the customization of TOREADOR Big Data analytics platform to process and analyze data from CNC machines sensors.
Automatic Personality Perception from Speech in Mandarin

Researches on speech-based personality perception have been actively conducted in recent years. However, most of them are focus on French, while research on Chinese corpus has not been reported. This paper investigates automatic perception of speakers’ personality from speech in Mandarin. The studied personality traits are extended from standard Big-Five to 6 sub-traits of each, leading to a total of 30 traits. As for modeling approach, traditional SVM system with standard prosodic feature extraction is studied as baseline. A novel skip-frame LSTM system is proposed, which resorts to skip-frame sampling to augment training data while retaining prosodic variation across a long time. The LSTM system learns personality information directly from frame level descriptor (MFCCs) instead of manually designed prosodic features as SVM system does. The results of both the two systems show that, the extraversion trait is the most easily perceived while openness is the most difficult one, which is in agreement with the results in French. In contrast, the high accuracy on agreeableness is peculiar to the previous result in French, which may relate to different culture backgrounds. In addition, relation between Big-Five and their sub-traits are discussed. Finally, the proposed skip-frame LSTM system outperforms obviously than SVM overall, demonstrating that LSTM can serve as an effective method for automatic personality perception even in low-resource condition.
Deep Reinforcement Learning Based Service Migration Strategy for Edge Computing

Edge Computing (EC) is an emerging technology to cope with the unprecedented growth of user demands for access to low-latency computation and content data. However, user mobility and limited coverage of Edge Computing Server (ECS) result in service discontinuity and reduce Quality of Service (QoS). Service migration has a great potential to address this issue. In the scenario of service migration, how to choose the optimal migration strategy and communication strategy is a key challenge. In this paper, we innovatively propose solving the service migration using reinforcement learning based model which can take a long-term goal into consideration and make service migration and communication decisions more efficient. we consider a single-user EC system with exploiting predefined movement of user, where user passes through many ECSs and its corresponding Virtual Machine (VM) in ECS decides the migration strategy and communication strategy. We design a Reinforcement Learning (RL)-based framework for a single-user EC service migration system. Q-learning based and Deep Q Network (DQN) based themes are analyzed in detail respectively. Simulation results shows that our RL-based system can achieve the optimal result compared with other two methods under different system parameters.
Policy-Based Access Control for Robotic Applications

With the wide application of modern robots, more concerns have been raised on security and privacy of robotic systems and applications. Although the Robot Operating System (ROS) is commonly used on different robots, there have been few work considering the security aspects of ROS. As ROS does not employ even the basic permission control mechanism, applications can access any resources without limitation, which could result in equipment damage, harm to human, as well as privacy leakage. In this paper we propose an access control mechanism for ROS based on an extended policy-based access control (PBAC) model. Specifically, we extend ROS to add an additional node dedicated for access control so that it can provide user identity and permission management services. The proposed mechanism also allows the administrator to revoke a permission dynamically. We implemented the proposed method in ROS and demonstrated its applicability and performance through several case studies.
Performance Comparison of Client-Centered Counseling Agents

Nowadays, a lot of people such as IT workers, jobseekers, and elderly persons are suffering from distress in their life or work. Meanwhile, there is a shortage of counselors for offloading their spiritual burden. Therefore, we developed a counseling agent (CA) replacing human counselors. VCA (Virtual Counseling Agent) is a further evolution of CRECA (Context Respectful Counseling Agent). CRECA has context narrowing mechanisms besides mirroring of ELIZA. However, it has only a text interface. To much more imitate a human counselor, VCA has an image avatar and a voice conversation using the Google Cloud audio API. Further, VCA is made independent of counseling content fields. Thus, it is more generalized than CRECA in order for every people to easily use everywhere in every situation. In this paper, VCA, CRECA, and ELIZA are comparatively evaluated by questionnaire after the use of 10 college students having career problems. As a result, VCA significantly exceeded the average value of ELIZA along with the significant difference at the level of 5%. Still more, average of the evaluation value is not worse than CRECA. Compared with CRECA, VCA does not limit the content and field of consultation. It can be used generally at any time in everyday natural conversation. It is easily used by elderly people and the digital divide due to voice conversion. Thus, it is shown VCA is also more useful than CRECA.
An Empirical Study of Scalability Frameworks in Open Source Microservices-based Systems

Microservice-based systems (MBS) are distributed systems consistent of smaller components (possibly developed by separate teams using different technologies) that use lightweight mechanisms to communicate with each other. One of the main quality attributes that motivate MBS is scalability, and there are several available frameworks to face scalability challenges in MBS. However, there is still little empirical data about the architectural concerns of using these frameworks to support the scalability dimensions. This article builds upon a previous study that defined a pattern language for MBS scalability, and aims to identify and analyze which MBS frameworks are actually used in open source MBS projects to address the scalability dimensions. Results show that (1) nine common frameworks satisfy the scalability dimensions defined in the pattern language, (2) frameworks produce trade-offs among scalability dimensions, and (3) few frameworks address several scalability dimensions at once. Finally, we identify five reusable design decisions to address scalability requirements and propose them as microservices architectural tactics.
Dual-Camera Facial Landmark Tracking for Clinical Infant Monitoring

This paper presents a new algorithm for dualcamerafacial landmark tracking, intended for clinical infant monitoring. For landmark tracking of in-the-wild faces, state-of-the-art solutions use deep networks for face detection and landmark localization. For clinical infant monitoring, however, there is currently not enough data to reliably train such networks. The proposed algorithm overcomes this by incorporating 3D head tracking, to supplement face detection and to enable a refinement version of landmark localization that is more easily learned from less data. The paper reports experimental results on real-life video of infants in bed in hospital (in visual light or near-infrared light), showing good landmark tracking behavior. For challenging sequences without severe occlusions, the mean tracking error, in terms of the RMS error measured for a subset of landmarks and divided by the maximum eye-philtrum distance, remains below 20%. The proposed algorithm is used successfully in an infant monitoring system under development.
Cost-effective Congestion-aware Load Balancing for Datacenters

Most existing datacenters still use Equal-Cost Multi-Path (ECMP) to achieve network load balancing. But such a stateless load balancing mechanism is not able to react adaptively when network congestion occurs. It will cause datacenters unable to make good use of network resources under the unbalanced network state. The main reason most datacenters still using ECMP is that the cost of realizing the state-of-art load balancing technologies is too high since all switches in datacenters need to be replaced with programmable switches, such as P4 switches. In this paper, we propose a cost-effective congestion-aware load balancing (CCLB) scheme that only needs to replace a portion of switches with programmable switches to achieve congestion-aware load balancing. CCLB makes good use of Explicit Congestion Notification (ECN) in the IP layer to be aware of network congestion and uses flowlet switching that slices large flows into small sub-flows to achieve load balancing. Experiment results show that the average flow completion time of the proposed CCLB is 27% shorter than that of ECMP in large flows. Compared to a classical congestion-aware mechanism, HULA, the average flow completion time of the proposed CCLB is slightly longer, but our switch replacement cost is much lower than HULA's.
Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes

Data mining can be used for data analysis of social media users who visit E-Commerce. This study uses data mining techniques aimed at comparing the classification in sentiment analysis from the views of E-Commerce customers who have been written on Twitter. The data set is derived from tweets about E-Commerce in Tokopedia and Bukalapak. Text mining techniques, transform, tokenize, stem, classification, etc. are used to build classification and analysis of sentiment analysis. Rapidminer is also used to assist in making analysis sentiments for comparison by using three different classifications in the dataset with the Decision Tree, K-NN, and Naïve Bayes Classifier approaches to find the best accuracy. The highest result of this study is the Naïve Bayes approach with an accuracy of 77%, precision 88.50% and recall of 64%.
Estimation of Scenes Contributing to Score in Tennis Video Using Attention

The use of image processing technology for sports is increasing. By analyzing athletes and teams based on video analysis, scientific and objective analysis can be conducted separately from subjective analysis of experts, and individuals or teams can be evaluated with the same index. As can be seen from such a trend, recently, technological progress has made it possible to detect and analyze what can be visually confirmed in the sports video. It is certain that these technologies are helping coaches in the analysis of movement. However, these cannot play the role of coaches. The role is to judge what is important from what can be visually confirmed. There are also empirical and qualitative parts in coach’s judgment, and no reproducibility of understanding the important parts unless it is a specialist. Based on this background, we thought that it would be useful to extract more important scenes during the game using quantitative information. Such technology can be applied to various sports, but this paper, we focused on tennis. In this thesis, the aim is to estimate which play was largely contributed to the tennis game result (score, failure). In addition, we don’t use supervised information that can be obtained from an empirical point of view. This is to eliminate dataset dependency caused by using data created from qualitative information. Specifically, based on quantitative information such as athletes’ movement and score result, we attempted to estimate the attention from unsupervised method.
Activation of the Remaining Hemisphere Following Stimulation of the Blind Hemifield in Hemispherectomized Subjects

We used functional magnetic resonance imaging (fMRI) to investigate the neural substrates mediating residual vision in the "blind" hemifield of hemispherectomized patients. The visual stimuli were semicircular gratings moving in opposite directions on a dynamic random-dot background. They were specifically constructed to eliminate intra- and extraocular light scatter and optimize the activation of extrastriate cortical areas and their subcortical relays. Multislice T2*-weighted gradient echo (GE) echoplanar imaging (EPI) images (TR/TE = 4 s/45 ms, flip angle 90 degrees ) were acquired during activation and baseline visual stimulation. An activation minus baseline subtraction was performed, and the acquired t statistic map transformed into the stereotaxic coordinate space of Talairach and Tournoux. In seven normal control subjects, right hemifield stimulation produced significant activation foci in contralateral V1/V2, V3/V3A, VP, and V5 (MT). Significant activation was also produced in homologous regions of the right occipital lobe with left hemifield stimulation. Stimulation of the intact hemifield in hemispherectomized patients resulted in activation of similar areas exclusively within the contralateral hemisphere. Stimulation of the anopic hemifield produced statistically significant activation in the ipsilateral occipital lobe (putative area V5 or MT) and areas V3/V3A in the only subject with blindsight. We conclude that the remaining hemisphere may contribute to residual visual functions in the blind hemifield of hemispherectomized patients, possibly through the collicular-pulvinar route since the activated areas are known to receive their afferents from these subcortical nuclei.
The Development of Hand Controller for Persons with Disabilities by Myo Sensor

Development of hand controller for persons with disabilities used the Myo Sensor aims to study, design and develop a device to facilitate the handicapped in living normally as possible. This proposed device can detect the gestures from muscle with EMG waveform by Myo sensor and use to control the mechanic's hand via servo motors and process tasks with microcontroller. The model of hand and fingers was designed by 3D printer. The functionality of Myo Sensor represents in a form of the electromyography (EMG) to command a microcontroller to control the processing of arm gestures such as the fist and movement of fingers. The results show that it can work under various conditions and the development of this model or device provides the benefits to persons with disabilities.
A Linear Programming Approach to Max-Sum Problem: A Review

The max-sum labeling problem, defined as maximizing a sum of binary (i.e., pairwise) functions of discrete variables, is a general NP-hard optimization problem with many applications, such as computing the MAP configuration of a Markov random field. We review a not widely known approach to the problem, developed by Ukrainian researchers Schlesinger et al. in 1976, and show how it contributes to recent results, most importantly, those on the convex combination of trees and tree-reweighted max-product. In particular, we review Schlesinger et al.'s upper bound on the max-sum criterion, its minimization by equivalent transformations, its relation to the constraint satisfaction problem, the fact that this minimization is dual to a linear programming relaxation of the original problem, and the three kinds of consistency necessary for optimality of the upper bound. We revisit problems with Boolean variables and supermodular problems. We describe two algorithms for decreasing the upper bound. We present an example application for structural image analysis.
Detection of Harbour Porpoise with Low-Level Feature Extraction and Deep Learning Based Classification

This paper proposes a method for detection of harbour porpoise from very high resolution images. The approach involves the detection of salient objects of very small sizes with the feature accelerated segment test method, creation of an annotated database and classification of those objects with different convolutional neural networks. Multiple deep models based on VGGNet, ResNet, and GoogLeNet are modified and used in this work while a shallow custom network is developed as well. This method is computationally less expensive because of the network application on smaller candidate patches instead of the entire image. Provided the versatile and challenging environmental conditions during the period of the video recording, the networks show a good and robust performance.
Combining Phase-based Features for Replay Spoof Detection System

Automatic Speaker Verification (ASV) systems are developed to verify the claimed identity of a speaker based on speech samples. The technological advances have given pathways to practical ASV systems that showcase the threat towards spoofing attacks. Replay is one of the spoofing attacks where the ASV systems are fooled with pre-recorded speech samples of a target speaker. In this context, both magnitude-based and phase-based spectral features get affected by the quality of intermediate devices and their environments. There have been only a few studies reported to detect the replay attacks based on the phase features. In this paper, we explore the relative significance of various phase-based features for detecting replay attacks. The magnitude-based features are chosen to perform score-level fusion with phase-based features to capture the possible complementary information. Among various possible combinations of magnitude and phase-based features, we obtain 12.25 % as the best Equal Error Rate (EER) which is less than that obtained with individual feature set, while the score-level fusion of phase-based features gave an EER of 13.14 % on the evaluation set of ASVspoof 2017 version 1 database.
Improving the Performance of Collaborative Filtering Using Outlier Labeling, Clustering, and Association Rule Mining

Collaborative Filtering (CF) is a popular recommendation method because it can provide recommendations personally. Under conditions of data sparsity, CF recommendation systems are known to have low accuracy because available historical information is not enough to properly identify preferences. Based on experiments conducted by researchers, the factor that limits the accuracy of the CF recommendation system is the number of recommendation items that exceed the system requirements. The existence of outliers with too much items also affect the recommendation results. This study attempted to apply model-based CFs to improve the accuracy of CF recommendations under data sparsity conditions. Conduct outlier labeling, clustering, and association rule mining implemented from preprocessing as a combination of data processing methods to generate recommendation items. Experiments conducted on Groceries dataset, a real-world point-of-sale transactions data from grocery outlet. The results of the evaluation indicate that the proposed method can improve the accuracy of basic CF by 26% with the quality improvement of the recommendation result by 24%.
A Study on Acoustic Modeling for Child Speech Based on Multi-Task Learning

This paper describes a study on acoustic modeling of child speech for large-vocabulary speech recognition of Cantonese. This study is driven and enabled by a new speech corpus recently collected for developing acoustic assessment systems for speech sound disorders in Cantonese-speaking children. The speech corpus, named CUChild127, contains 127 Chinese words spoken by 1, 500 pre-school children in Hong Kong. A small amount of manually transcribed child speech is used to initialize a GMM-HMM based speech recognition system, which is subsequently used to generate speech transcriptions for a large amount of training data. Multi-task learning approach is adopted to train a conventional DNN model and a timedelay neural network (TDNN) model. The primary and secondary tasks are context-dependent phone modeling for child speech and adult speech respectively. The training data of adult speech are obtained from an existing phonetically-rich speech corpus. Experimental results show that TDNN based acoustic model significantly outperforms DNN and GMM-HMM systems. Multi-task learning leads to further performance improvement of the TDNN model. The best syllable error rate attained in our experiments is 8.96%, with the weights of the primary and secondary tasks being 0.8 and 0.2.
Speech Enhancement Based on A New Architecture of Wasserstein Generative Adversarial Networks

Most conventional speech enhancement methods work poorly at low SNRs. And the speech enhancement method based on generative adversarial network (SEGAN) gets lower speech quality though it has lots of parameters in its generator. To solve these problems, we propose a speech enhancement method based on a new architecture of Wasserstein generative adversarial network (SEWGAN), whose generator network and discriminator network are structured on the basis of fully convolutional neural networks (FCNNs) and deep neural networks (DNNs) respectively. In the paper, multiple noise and different signal-noise ratios (SNRs) are used to train the proposed method for improving its generalization capability. Experimental results show that the proposed method outperforms SEGAN and minimum mean square error estimators based on magnitude-squared spectrum (MMSE-MSS) in terms of both short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ). As expected, the work also demonstrates the proposed method has strong generalization capability in a real-world scenario.