Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Virtual Reality Sickness and Visual Attention

It is a well-known fact that virtual reality (VR) sickness is an obstacle to an immersive VR experience, however, an objective analysis of the physiological responses for VR sickness has been insufficient. In this study, our analysis uncovers how the users' visual attention varies with the level of VR sickness and how the level of VR sickness influences the center-bias tendency. Toward this, we first conduct a large-scale eye-tracking experiment of 21 inexperienced users while they experience VR sickness-oriented database VR-SP [15]. Then, we quantify the tendency of visual behavior according to VR sickness. To do this, we newly define a visual entropy measurement of VR visual attention. The experimental results clearly suggest that the center-bias effect becomes stronger as the degree of VR sickness increases. In other words, this implies that the users' explorativeness in VR content may be restricted by the VR sickness and this leads to the restraint of the immersive experience. For a more clear demonstration, we also show the visual entropy can be used to predict VR sickness with an accuracy of 80% on the VR-SP database.
Mandarin Electro-Laryngeal Speech Enhancement based on Statistical Voice Conversion and Manual Tone Control

Electro-Larynx can help the laryngectomees re-pronounce the voice, while the Electro-Laryngeal (EL) speech has a poor intelligibility and naturalness. Recently, voice conversion (VC) has been applied to enhance the EL speech, which achieves a good result. However, the complicated tone variation rule of continuous Mandarin EL speech takes a new challenge into enhancement of EL speech by VC. In this paper, a novel framework combining manual tone control (MTC) and statistical VC is proposed to enhance the continuous Mandarin EL speech. As statistical VC methods, GMM-based VC and CLDNN-based VC are implemented for the proposed framework. The objective and subj ective evaluations are designed to validate the proposed framework. The experimental results have demonstrated that 1) the combination of MTC and statistical VC yields significant improvements in both naturalness and intelligibility of the enhanced Mandarin EL speech, 2) the word perception error rates of the enhanced Mandarin EL speech is decreased from 11.35% of Mandarin EL speech with MTC to 5.61 % by using statistical VC, and 3) the proposed framework achieves the average tone accuracy of 26.59% higher than that of original continuous Mandarin EL speech.
Model-Based Soft Actor-Critic

Deep reinforcement learning has been successfully developed for many challenging applications. However, collecting new data in actual environment requires a lot of costs which make the agent to learn slowly for high-dimensional states and actions. It is crucial to enhance the sample efficiency and learn with long-term planning. To tackle these issues, this study presents a stochastic agent driven by a new model-based soft actor-critic (MSAC). The dynamics of the environment as well as the reward function are represented by a learnable world model which allows the agent to explore latent representation of environment which conducts stochastic prediction and foresight planning. An off-policy method is proposed by combining with an online learning for world model. The actor, critic and world model are jointly trained to fulfill multi-step foresight imagination. To further enhance the performance, an overshooting scheme is incorporated for long-term planning, and the multi-step rollout is applied for stochastic prediction. The experiments on various tasks with continuous actions show the merit of the proposed MSAC for data efficiency in reinforcement learning.
A Semi-Empirical Data-Rate Estimation Method of 5G RAN Slicing

Network Slicing is a novel 5G technique enabling a single net to meet differential needs from multiple tenants. In this paper, a semi-empirical method called linear reduction factor (LRF) for the estimation and prediction of data goodput rates is proposed for radio access network (RAN) slicing. The method is derived from our fundamental understanding of peak data throughput calculation, and a bold but ambitious assumption that the linearity of the RAN-sliced data goodput should hold with the amount of allocated resource, in units of resource block group (RBG). The paper presents theoretical derivation steps in a semi-empirical way toward how to estimate the slice goodput directly from the measured reduction factor of system throughput with the full frequency bandwidth. When combined the linearity with the number of allocated REGs, the proposed method can precisely predict the data goodput for a RAN slice with any given number of RBGs. This research also successfully ran a software-defined platform to conduct the verification experiments using the Mosaic5G FlexRAN real-time controller for configuring and monitoring the emulated RAN. The desired linearity and precision of the proposed LRF method are fully supported by two case studies, namely equal-slicing and prioritized-slicing.
Noise Removal for Dynamic Mode Decomposition Based on Plug-and-Play ADMM

Dynamic mode decomposition (DMD) is useful for video background/foreground separation. This decomposition algorithm can decompose a video into a set of dynamic modes, called the DMD mode, and then separate the near-zero mode as the stationary background and the other modes as the moving foreground components. However, When fore-ground/background separation is performed on noisy video, noisy background/foreground components are obtained because the DMD mode is degraded by noise. This paper proposes a novel noise removal method for the DMD mode of a noisy video. Specifically, we formulate a minimization problem that simultaneously reduces the noise of the DMD mode and the re-constructed video. The proposed minimization problem is solved by an algorithm based on Plug-and-Play alternating direction method of multipliers (PnP-ADMM). The experimental results demonstrate the advantages of the proposed method compared with a conventional noise removal method.
Comparison between Ground-based Synoptic Data and ERA5 Reanalysis Data in Iran

This work investigates the performance of ERA5 reanalysis data released by ECMWF to estimate the air surface temperature over Fars province in southern Iran. The monthly average temperature extracted from ERA5 gridded data for 5 place in region is compared with the observational data obtained from synoptic station. The correlation coefficient and mean bias error are used to evaluate data. The monthly temperature estimated by ERA5 shows positive biases (ranges from 1.0 to 4.66°C) and against the ground-based synoptic data except in Kazerun station with -6.08 MBE. The value of biases can be used to correct-bias the estimations. Generally, the results show ERA5 underestimates the monthly average temperature in most stations.
In-Silico Screening of Potential Anti-Glycoprotein of Nipah Virus

Nipah virus (NiV) poses a health threat. It causes severe respiratory and neurological diseases with no treatment available. We aim to screen 7,676 compounds from ZINC15 database, 52 compounds from anti-Nipah webserver, as well as 3 antivirals (Ribavirin, Acyclovir and Favipiravir) and their conformers as probable inhibitors for Nipah virus G Attachment Glycoprotein. For molecular docking we used Glide docking of Schrödinger software. The compounds were subjected to drug likeness and ADMET. Molecular dynamic (MD) simulation was done using GROMACS software. Benzamide, acetic acid, acetamide, ethanone, pyrimidin and oxamide derivatives (from ZINC15 database) had the best minimum binding energies of -7.554 Kcal/mol to -6.773 Kcal/mol, whereas Ribavirin, Acyclovir and Favipiravir had binding energies of -6.727 Kcal/mol, -5.695 Kcal/mol and -5.245 Kcal/mol respectively. From anti-Nipah website, Bradykinin and EICAR had binding energies of -7.906 Kcal/mol and -6.892 Kcal/mol respectively. MD simulation showed that all ligands were stable except EICAR and Bradykinin. All protein ligand complexes showed stable complexes except Favipiravir. ZINC19693736, ZINC19738967, ZINC408709522 and Ribavirin were the most stable ligand-protein complexes and ZINC222903805, ZINC257273320, ZINC19738967 and Ribavirin complexes were the most flexible among others. Therefore, 5 out of 7676 small molecules retrieved from ZINC15 database (ZINC222903805, ZINC257273320, ZINC19693736, ZINC19738967 and ZINC408709522) could be possible candidates for anti-Nipah virus. We recommend that these compounds should be studied further in vitro, in vivo, and in clinical trials as potential anti-Nipah.
Extreme Values and Risk Analysis: EMC Design Approach Through Metamodeling

EMC design using only numerical simulations remains out of reach. There are at least three main hurdles to overcome, i) the impossible description of all physical parameters involved; ii) their intrinsic uncertainty and / or the endless exploration of their combined variations if controlled by design; iii) the unavailability of some models such as susceptibility models. Therefore, performing parametric studies based on available models does not provide neither an efficient analysis nor a complete picture of the governing features of an EMC scenario. How to prevent and quantify situations at risk remains an open question. In this paper, we examine an EMC scenario as a standard source / victim interaction under a probabilistic approach based on available models. As far as the ‘victim’ is concerned, without access to any susceptibility model, statistical inference based on a limited set of experiments provides a relevant probabilistic information. Then, we suppose that the source and path of interactions are described through numerical simulations involving a complex combination of multiple parameters (intrinsically uncertain or adjustable by design). In this frame, we highlight the interest of statistical inference, learning techniques and specific tools dedicated to extreme events exploration. More precisely, we present the combination of kriging, which provides metamodels (i.e. surrogate models) with negligible cost of calculation, and sensitivity analysis for EMC design parameters. Overall, these statistical tools (and others) offer many new perspectives for conventional EMC software packages.
Speech Reconstruction from The Larynx Vibration Feature Captured by Laser-Doppler Vibrometer Sensor

There are many deep learning (DL)-based models with the contact sensors (e.g., throat microphone, TM) to reconstruct the speech from the vibration signals of the larynx. The TM can obtain robust speech information than an air-conducted microphone (ACM) sensor in noisy environments. However, it needs tight contact with the user's skin, which causes discomfort for users. Therefore, we assume that a non-contact sensor allows users to have a better experience. Following this concept, the DL-based models with a non-contact sensor, a laser-Doppler vibrometer (LDV), are proposed to reconstruct the speech from the vibration signals of the larynx. Notably, the recognition and speech synthesis modules were adopted in the proposed system. The experimental results showed that, on average, the word error rate (WER) of the recognition module in the proposed system achieves similar performance as TM did in both quiet and noisy testing conditions. Furthermore, the listening test showed that the synthesis module's reconstructed speech provided a higher preference rate and naturalness than an original recorded speech of the LDV sensor. These results suggested that the proposed system is a potential approach to reconstruct speech from the vibration signals of the larynx with DL technology, captured by a non-contact LDV sensor.
Task Scheduling Strategies for Batched Basic Linear Algebra Subprograms on Many-core CPUs

Batched Basic Linear Algebra Subprograms (BLAS) provides an interface that allows multiple problems for a given BLAS routine (operation) - with different parameters and sizes independent of each other - to be computed in a single routine. The efficient use of cores on many-core processors has been introduced for computing multiple minor problems for which sufficient parallelism cannot be extracted from a single problem. The major goal of this study is to automatically generate high-performance batched routines for all BLAS routines using nonbatched BLAS implementation and OpenMP on CPUs. Furthermore, the primary challenge is the task scheduling method for allocating batches to cores. In this study, we propose a scheduling method based on a greedy algorithm, which allocates batches based on their costs in advance to eliminate load imbalance when the costs of batches vary. Then, we investigate the performance of five scheduling methods, including ones implemented in OpenMP and our proposed method, on matrix multiplication (GEMM) and matrix-vector multiplication (GEMV) under several conditions and environments. As a result, we found that the optimal scheduling strategy differs depending on the problem setting and environment. Based on this result, we propose an automatic generation scheme of batched BLAS from nonbatched BLAS that can introduce arbitrary task scheduling. This scheme facilitates the development of batched routines for a full set of BLAS routines and special BLAS implementations such as high-precision versions.
Towards Reference-Independent Rhythm Assessment of Solo Singing

Rhythm is an important aspect of singing in music information retrieval. From the principles of music theory, note duration is related to the time signature of a song, therefore it provides a representation for rhythm analysis. However, in a reference-independent setup, where the reference musical score of a song is not available, measurement of note duration and hence assessment of rhythm quality of solo singing is difficult. In this work, we analyse the correlation between the duration distributions of musical note lengths and singing syllable lengths, and propose a reference-independent framework that uses singing syllable duration distribution to assess the rhythm quality of solo singing. We show improved prediction of rhythm assessment score using syllable duration histogram compared to existing reference-independent frameworks.
Image Fusion Based Selective Optimal Restoration of Colors in Single Underwater Images

In this work, we put forward the approach of fusion of two-color corrected images of the same single underwater image obtained by two different color correction methods to optimize the restoration of colors of the image. Also, selective color correction is followed instead of general color correction. By this approach, we can reduce the color casts, a common drawback of the existing enhancement methods. The quality of the result of our fusion method is compared with the existing two enhancement methods of single underwater images. The experimental result shows that our method outperforms the existing methods in compensating for the color distortions.
Enriching Under-Represented Named Entities for Improved Speech Recognition

Automatic speech recognition (ASR) for under-represented named-entity (UR-NE) is challenging due to such named-entities (NE) have insufficient instances and poor contextual coverage in the training data to learn reliable estimates and representations11In this paper, UR-NE refers to the named-entity (NE) words that have low-frequency count, say, the count is in [1], [9] in this work, or do not appear in the training data at all, i.e. the count is 0. In this paper, we propose approaches to enriching UR-NEs to improve speech recognition performance. Specifically, our first priority is to ensure those UR-NEs to appear in the word lattice if there is any. To this end, we employ class-based language model (LM) philosophy, making exemplar utterances for those UR-NEs according to their classes (e.g, location, person, organization, etc.), ending up with an improved LM that boosts the UR-NE occurrence in the word lattice. Then we boost the recognition performance through lattice rescoring methods. We first enrich the representations of UR-NEs in a pretrained recurrent neural network LM (RNNLM) by borrowing the embedding representations of the rich-represented NEs (RR-NEs), yielding the lattices that statistically favor the UR-NEs. Finally, we directly boost the likelihood scores of the utterances containing UR-NEs and gain further performance improvement.
Zero-shot Domain Adaptation with Inference Relation Paths for Spoken Language Understanding

Zero-shot slot filling methods are proposed to tackle the problems of adapting to new domains with unseen slots. Due to lacking share information across domains based on semantic slot descriptions, the challenge in zero-shot slot filling is handling the unseen slots that are less similar to the training slots semanti-cally. Since people utilizes not only explicit semantic information as estimation cues but also implicit semantic relations, this study attempts to find implicit sematic relation cues between slots and values to tackle unseen slots in zero-shot slot filling. We speculate that the inference paths between slots and values may be one of the implicit semantic relation cues and then investigated an amount of inference paths in the SnipsNLU dataset via the knowledge graph. The inference relation paths (IRPs) are found to implicitly build up semantic relations between slots and their values. Accordingly, we proposed a method to utilize the IRPs for zero-shot slot filling. Experimental results showed that the proposed method outperformed a strong baseline by 3.61 in terms of F1 score and achieved an improvement rate of 40 % for semantically dissimilar unseen slots. These results demonstrate that the proposed method can provide effective share information for handling unseen slots.
Received Signal Power based Sensor Zone Estimation with Maximum Likelihood Approach

In this paper, we divide a region of interest into several zones, where a sensor node could exist in one of them, and consider the problem of estimating the zone to which it belongs. We propose a zone estimation method from the information of received signal power observed at the sensor node using maximum likelihood (ML) approach without using any distance measuring device. The estimation success probability of the proposed method is demonstrated via computer simulations. We also demonstrate the success probability of the proposed method using real data of the received power obtained through measurements.
Fault Prediction for Rotating Bearing United Genetic Algorithm Optimize Variational Mode Decomposition and Support Vector Machine

The vibration signal of rotating bearing is continuous and non-stationary. The fault information contained in the signal can not be reflected directly due to the excessive noise background of the operating environment. Based on the above characteristic and shortcomings, a new fault prediction method is proposed in this paper. Firstly, aiming at the disadvantage of modal aliasing caused by improper parameter selection of variational mode decomposition(VMD) algorithm, a parameter optimization model based on genetic algorithm(GA) is established to search the best combination of parameters globally. Due to the small dataset of early rotating bearing failures, the support vector machine (SVM) method based on parameter optimization is used for fault prediction, and its prediction accuracy is as high as 90%, which provides a new idea for rotating bearing fault prediction.
Aizuchi as a sign of internal information processing and its interpretations by listeners

A back-channel is a short utterance made by non-primary speakers. In the Japanese language back-channel is called “Aizuchi,” Aizuchi has long been considered as a positive reaction to the primary speaker showing agreement or appraisal and or giving the right to continue speaking. Togashi argues that, in contrast to the conventional view, the essence of aizuchi is a sign of internal information processing in the mind of the one who uses aizuchi. The present study examined the usage of aizuchi as a non-primary speaker's internal information processing using Japanese map task dialogue. The result of our analysis showed that aizuchi often occurred when the primary speakers introduced an object known by the non-primary speakers into the conversation as predicted by Togashi's view of aizuchi. It was also suggested that the presence/absence of aizuchi was not necessarily a clear sign indicating the non-primary speakers have knowledge about the target object but rough but useful information to predict the knowledge state of the non-primary speakers.
Inter-Locking Dependency Evaluation Schema based on Block-chain Enabled Federated Transfer Learning for Autonomous Vehicular Systems

Federated Transfer Learning is a new approach to optimizing information and data training frameworks to achieve a higher order of distributed learning. Currently, the Internet of Vehicles (IoV) model is trained through online learning by exchanging the local and global updates. Context-based distributed learning with minimum communication becomes challenging due to the dynamic nature of autonomous systems. This paper aims to understand the past and future challenges, trends, and applications of new learning adopted in autonomous systems with a proposed schema of FTL-enabled autonomous models training. The goal behind minimizing the latency through the proposed FTL Interlocking Dependency Evaluation schema in exchanging the local and global updates is to match with the real-time application scenario by ensuring security and privacy features. The evaluation parameters used in estimating the performance of new learning are window size, back-off time, recovery policy (retransmission), scalability, peak data rate (Gbps), user-experienced data rate (Mbps), mobility (Kmph), latency (ms), end-to-end delay analysis, connection density (devices/km2), and area traffic capacity (Mb/s/m2). The evaluation results have validated the reliability of the proposed model for computing autonomous systems using a novel FTL Interlocking Dependency Evaluation schema for higher order of inter-dependency on vehicular environment processing.
Deep Siamese network for low-resolution face recognition

Real-world surveillance face images are usually of low-resolution (LR) because the faces are captured at a distance. Matching the LR query faces with high-resolution (HR) gallery faces is still challenging and remains an open problem. The existing face recognition networks fail to extract discriminative features from the LR face images as they never encounter any LR face images during training. One intuitive way to solve the problem is to randomly downsample the training face images to different resolutions for training. This implicitly makes the face recognition network invariant to the resolution change. To better address this problem, we propose to train a face recognition network using a deep Siamese network, which is simple yet effective. Firstly, a shared classifier is used to classify the deep features extracted from HR and LR facial image pairs, explicitly narrowing the domain gap between the HR and LR deep features. Secondly, on top of the deep Siamese network, a new loss function, namely the cross-resolution triplet loss, is used to pull the matching pairs further while pushing the non-matching pairs in the learned feature space. Therefore, the trained network can extract discriminative features across different resolutions. Experiments demonstrate the superiority of our proposed method on a synthetic LR face dataset, LFW, and two real-world LR face datasets, SCface and QMUL-SurvFace.