Miracle-dz/UniFAR-FTUs huggingface dataset:
An Auxiliary Screening System for Autism Spectrum Disorder Based on Emotion and Attention Analysis

The screening and diagnosis of Autism Spectrum Disorder(ASD) suffer from great challenges due to insufficient professional clinicians and complex procedures. It is urgent to introduce an effective auxiliary system in the diagnosis and treatment process to assist in the completion of pathological information collection tasks, consequently simplifying the screening method and improving the accuracy of screening. We propose a computer vision-based early screening system for ASD to characterize the facial expressions and eye gaze attention which are considered to remarkable indicators for early screening of autism. The system provides the subjects with three different virtual interaction modes: video, picture, and virtual interactive game. During the interaction between the subject and the computer, the system extracts and analyzes the quantitative information of the subject’s performance. Then, through computer vision-based emotion analysis and attention analysis methods, the subject’s emotions and attention features in the three interaction modes are automatically calculated to assist in the early screening of autism. Finally, the accuracy and feasibility of the system are verified through experiments on both the publicly available dataset and the data collected from 10 ASD children.
Hashed B-tree: Adaptive Performance Enhancement of B-tree on Byte-addressable Nonvolatile Memories

As a popular data structure for block devices like hard disks, B-tree maintains a perfectly balanced tree height to minimize the number of block accesses and optimize the performance of common tree operations. However, when modern byteaddressable nonvolatile memories (NVMs) replace mechanical hard disk as the storage media, B-tree might suffer from severe performance degradation due to the lacking of concerns over the byte accessing capability, which is not available on hard disk. In particular, B-tree might experience severe performance degradation on node splits and merges, during which a large number of keys, data, and pointers must be moved across multiple B-tree nodes. Meanwhile, classical B-tree designs aim to reduce the number of nodes accessed on common B-tree operations, but underestimate the performance impacts to sort and rewrite the keys in a B-tree node on byte-addressable NVMs. While the concept to buffer newly inserted keys in a B-tree to reduce the update costs has been proposed in prior arts, the realization schemes on NVMs are still an incomplete issue. Such an observation motivates this work in proposing a hashed B-tree design which provides better practical performance on byte-addressable NVMs. According to experimental results, with different workloads, the hashed B-tree reduces the write traffic, read traffic, and normalized I/O time by 75.55%-93.54%, 36.66%-46.75%, and 66.42%-82.16%, respectively. In addition, the hashed B-tree is more adaptive than the default B-tree scheme on diversified, and even dynamically-shifting workloads.
A Phase Conjugating Metasurface

This paper presents the design of a phase conjugating metasurface operating at 10 GHz. The designed metasurface achieves phase conjugation, by heterodyning (mixing) an incident RF signal with a pump (modulation) signal at twice the frequency of the incident RF signal. Frequency mixing is achieved through the use of fast switching Schottky diodes. The unit cell of the metasurface is composed of a subwavelength metallic patch that is aperture coupled to the heterodyning circuit. Metasurfaces offer a low-profile alternative to retrodirective antenna arrays. Such metasurfaces could play a key role in far-field wireless power transfer systems.
The physics of nucleated droplets in large-scale MD Lennard-Jones simulations

We have performed large-scale Lennard-Jones molecular dynamics simulations of homogeneous vapor-to-liquid nucleation, typically with N ∼ (1 - 8) × 109. Our work continues on from [4, 5], except at lower vapor densities. The large size of these simulations is primarily necessitated by the rarity of nucleation events at these low supersaturations. A further benefit gained from large simulations is the substantial number of nucleated droplets which are able to continue growing without significantly dropping the vapor density. This allows us to study the properties of clusters as they grow, embedded within an unchanging external environment. This is particularly important in understanding the role that the droplet's surface plays in the development of the droplet-as a bustling interface between the denser (and ever-growing) core, and the stable vapor outside. The nucleation properties of the vapor as a whole are presented in a separate contribution (Diemand et al.), while here we explore the properties of the.
Leveraging Smart Contracts for Asynchronous Group Key Agreement in Internet of Things

Group Key Agreement (GKA) mechanisms play a crucial role in realizing various applications in different networks, such as sensor networks and the Internet of Things (IoT). To be suitable for IoT, a GKA must satisfy several critical requirements. First, a GKA must be robust against a compromised device attack and satisfy essential secrecy definitions without the existence of a Trusted Third Party (TTP). TTP is often used by IoT devices to establish ad hoc networks securely, and usually, these devices are resource-constrained. Second, the GKA must be able to distribute session keys successfully, even with offline devices. Third, a GKA must reduce the burden of heavy cryptographic computations for IoT devices. Based on these observations, we propose a new GKA scheme that satisfies all the requirements above. The proposed scheme leverages smart contracts to alleviate the computational and storage overheads on IoT devices induced by cryptographic functions. It also brings the advantage of asynchronism such that offline devices will be able to compute the group key once they are online.
A Review About Transcription Factor Binding Sites Prediction Based on Deep Learning

Transcription factors (TFs) recognize and bind to specific DNA sequences, thereby altering the chromatin structure and regulating transcription. TFs aid in the formation of a guide genome that facilitates the expression of genes under complex regulation. Understanding the underlying mechanism that mediates the TF-led regulation of gene expression is a popular topic in current genomic research. However, identifying the precise TF binding site (TFBS) and the specific role of the TFs in transcriptional regulation is challenging. This article summarizes the status of research concerning the prediction of TFBS. First, the experimental methods for identifying TFBS have been summarized by accessing related databases. Second, the machine learning methods for predicting TFBS, especially deep learning, have been summarized. Finally, the study elaborates on the main challenges faced in TFBS prediction. The purpose of this article is to provide researchers with a comprehensively understand the prediction of TFBS and to promote further development in this field.
Real-time and Embedded Compact Deep Neural Networks for Seagrass Monitoring

We propose an efficient and robust segmentation network for automated seagrass region detection. The proposed network has a simple architecture to save computational demands as well as inference energy cost. More importantly, the scale of network can be feasibly adjusted, to balance the network computational demands and segmentation accuracy. Experimental results show that our proposed network is robust to segment the various seagrass patterns with 90.66% mIoU (mean Intersection over Union) accuracy. It had achieved 200 frames per second (FPS, 1.42 times faster than the second-best network GCN) on desktop GPU, and 18 FPS on NVIDIA Jetson TX2. It also has 3.45M parameters and 0.587 GMACs FLOPs (FLoating Point OPerations), only 14.6% and 10.8% of those in GCN respectively. To segment a single image on the Jetson TX2, our architecture requires an average energy of 0.26 Joule. This energy cost is only 46% of DeepLab, which shows the proposed network to be an energy efficient one. The proposed network demonstrates accurate and real-time segmentation capability, and it can be deployed to low-energy embedded AUVs for sea habitat protection.
Compressing Head-Related Transfer Function databases by Eigen decomposition

A method to reduce the memory footprint of Head- Related Transfer Functions (HRTFs) is introduced. Based on an Eigen decomposition of HRTFs, the proposed method is capable of reducing a database comprising 6,344 measurements from 36.30 MB to 2.41MB (about a 15:1 compression ratio). Synthetic HRTFs in the compressed database were set to have less than 1dB spectral distortion between 0.1 and 16 kHz. The differences between the compressed measurements with those in the original database do not seem to translate into degradation of perceptual location accuracy. The high degree of compression obtained with this method allows the inclusion of interpolated HRTFs in databases for easing the real-time audio spatialization in Virtual Reality (VR).
The Adaptive Distributed Event-Triggered Observer Approach to Cooperative Output Regulation of Discrete-Time Linear Multi-Agent Systems Under Directed Switching Networks

In the paper, we solve the cooperative output regulation problem of discrete-time linear multi-agent systems under directed switching networks. Firstly, a new adaptive distributed event-triggered observer is proposed, which does not require each follower to know the leader matrix information. In addition, we propose relative threshold and novel switching threshold event-triggered strategy, which can be used in different situations according to their respective characteristics. Then, we solve the cooperative output regulation problem for discrete-time linear multi-agent systems under directed switching networks by both distributed state feedback and distributed dynamic output feedback control laws utilizing the adaptive distributed event-triggered observer. Finally, a corresponding numerical simulation on the discrete-time linear multi-agent systems is conducted, and the cooperative output regulation performance is verified.
Body Weight Perception of Females using Photorealistic Avatars in Virtual and Augmented Reality

The appearance of avatars can potentially alter changes in their users’ perception and behavior. Based on this finding, approaches to support the therapy of body perception disturbances in eating or body weight disorders by mixed reality (MR) systems gain in importance. However, the methodological heterogeneity of previous research has made it difficult to assess the suitability of different MR systems for therapeutic use in these areas. The effects of MR system properties and related psychometric factors on body-related perceptions have so far remained unclear. We developed an interactive virtual mirror embodiment application to investigate the differences between an augmented reality see-through head-mounted-display (HMD) and a virtual reality HMD on the before-mentioned factors. Additionally, we considered the influence of the participant’s body-mass-index (BMI) and the BMI difference between participants and their avatars on the estimations. The 54 normal-weight female participants significantly underestimated the weight of their photorealistic, generic avatar in both conditions. Body weight estimations were significantly predicted by the participants’ BMI and the BMI difference. We also observed partially significant differences in presence and tendencies for differences in virtual body ownership between the systems. Our results offer new insights into the relationships of body weight perception in different MR environments and provide new perspectives for the development of therapeutic applications. Index Terms: Human-centered computing—Human computer interaction (HCI)—Empirical studies in HCI; Human-centered computing—Human computer interaction (HCI)—Mixed / augmented reality; Human-centered computing—Human computer interaction (HCI)—Virtual reality
Local Luminance Patterns for Point Cloud Quality Assessment

In recent years, there has been an increase in the popularity of Point Clouds (PC) as the preferred data structure for representing 3D visual contents. Examples of PC applications range from 3D representations of small objects up to large maps. The advent of PC adoption triggered the development of new coding, transmission, and presentation methodologies. And, along with these, novel methods for evaluating the visual quality of PC contents. This paper presents a new objective full-reference visual quality metric for PC contents, which uses a proposed descriptor entitled Local Luminance Patterns (LLP). It extracts the statistics of the luminance information of reference and test PCs and compares their statistics to assess the perceived quality of the test PC. The proposed PC quality assessment method can be applied to both large and small scale PCs. Using publicly available PC quality datasets, we compared the proposed method with current state-of-the-art PC quality metrics, obtaining competing results.
Efficient Bearing Fault Diagnosis by Fast-Residual Network With 2-D Representation of Vibration Signals

Effective intelligent bearing fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditional diagnosis methods require complex computation of feature vectors and rely on domain knowledge and diagnostic expertise. In recent years, many deep learning-based methods have been applied in this field, such as deep belief network and convolutional neural network. However, these diagnosis methods are hard to train and lack of efficiency. In this paper, we use a new residual network structure to solve the bearing fault diagnosis problem. Firstly, we give a brief description to the original residual network. Then, we redesign the structure and propose a new paradigm: Fast-Residual Network. To fit in the new model, we develop an effective pre-processing method using frequency slice wavelet transform and shape the one-dimensional representation signals to two-dimensional representation image. The final experimental results show that proposed model achieves higher classification and is faster and more accurate than other deep learning-based models.
Generalizing the Split Factor of the Minimizing Delta Debugging Algorithm

One of the first attempts at the automation of test case reduction was the minimizing delta debugging algorithm, widely known as ddmin. Despite its age, it is still an unavoidable cornerstone of this field of research. One criticism against ddmin is that it can take too long to reach the granularity where it can perform actual reduction. Therefore, in this paper, ddmin is generalized with respect to the granularity by introducing a new split factor parameter, leading to the formalization of a parametric algorithm variant. The complexity analysis of this parametric variant reveals that the theoretical worst and best-case behavior of ddmin can be improved. Moreover, the results of experiments with the generalized algorithm show that the reduction can be sped up significantly by choosing the right split factor: up to 84% of the test steps can be eliminated in practice.
Selecting Feature Sets and Comparing Classification Methods for Cognitive State Estimation

Acute stress and high workload are part of everyday work at safety critical fields (e.g. health care). Adaptive human computer interaction systems could support and guide a nurse or a doctor in these hectic situations. Seamless interaction between human and computer requires accurate cognitive state estimation of the person. Currently studies are mainly focused on detecting between two cognitive states with full set of physiologically inspired features. This study demonstrates a classification of different types of stress during Maastricht Acute Stress Test by using feature combinations from electro-oculogram (EOG) and electrocardiogram (ECG) signals in general and personalized approaches, comparing three different classifiers. The classification is evaluated for features extracted from both signals separately and together, and the most important features are selected and reported. Results indicate that the best performance is achieved when features from both EOG and ECG signals are used, and approximately twenty features from EOG and ECG signals are enough to distinguish the two/three states. A personalized approach together with feature selection and support vector machine classifier achieves accuracies of 96.9% and 86.3% in classifying between two states (relaxation and stress) and three states (relaxation, psycho-social stress, and physiological stress), respectively, which exceed state-of-the-art performance. Thus cognitive state estimation benefits from combining selected eye and heart parameters which suggests a promising basis for real-time estimation in the future.
Job Satisfaction Recognition of Power Distribution Staff Based on Voice Analysis

Safety production is important for power grid enterprises. It is not only related to enterprise factors, but also closely related to the individual behavior of employees. Among them, employee job satisfaction is significantly related to work safety. Therefore, to realize the timely perception of the psychological state and emotion of staff in the operation is of great importance to the information-based safe management. In this study, the research object is the staff involved in power distribution and operation in the power grid. Through the analysis of audio characteristics and job satisfaction, this research attempts to explore and establish its internal relationship, which provides a reference for predicting job satisfaction from the perspective of voice.
Design Optimization of a Direct-Drive PM Wind Generator With Flux-Modulation Structure

The flux modulation technique based on the basic principles of magnetic gears has become a focus in high torque density and low speed applications. This paper presents the design optimization of a 1.5-MW direct-drive flux-modulated wind power permanent magnet (PM) generator. Through the employment of the modulation iron segments, the stator pole pair number and the slot number of the generator are effectively reduced, and the cogging torque is efficaciously limited by the flux-modulation effect. In order to maximum the power while minimizing the cogging torque, the structural parameters of the generator are optimized by the coupling of genetic algorithm (GA) and finite element analyses. The output performances of the optimized generator with different working conditions are numerically investigated and validated.
In vitro evaluation of red blood cell flow in bifurcating microchannel

To investigate the partitioning properties of the red blood cells (RBCs) in bifurcating capillary vessels, bifurcating microchannels with different daughter channel widths of several $\mu {\mathrm {m}}$ were prepared, and in vitro experiments were performed using a suspension of human RBC. In particular, it was observed that the RBC distribution changed and was biased toward the inner wall as the RBCs passed through the bifurcation. The effect of different daughter channel widths on the RBC partitioning was also investigated. As the channel width narrowed, the flow rate changed and a phase separation occurred. In addition, the experimental results deviated from the empirical model, indicating that the interaction between different RBCs in the bifurcation may also affect the RBC partitioning. The obtained findings could therefore contribute to a better understanding of the RBC partitioning properties in the microcirculatory system.
A CNN Model for Herb Identification Based on Part Priority Attention Mechanism

Automated herb identification plays an important role in protecting and investigating the herbs for botanists, which has been widely applied in the field of cosmetic, medical and food industry areas. Traditionally, due to the complicated background and various herb patterns, herb discriminative feature extractions is a hard work. And some existing methods may not be applicable when the herbs exist in actual wild environment. Therefore, how to locate the valid herb regions and extract the effective features is an open issue. In this paper, a novel CNN model is proposed for herb identification by using the part-information perception module (PPM) and species classification module (SCM). A new attention mechanism, namely part priority attention mechanism (PPAM), is proposed by training PPM independently with herb part labels. It should be pointed out that the proposed PPAM can guide the model to focus on the position of herb parts and suppress the irrelevant noisy regions. Moreover, depthwise separable convolution and label smoothing technique are introduced to decrease the model complexity and regularize the impact of mistake labels. In the experiment part, a large-scale herb dataset is constructed, which consists many kinds of challenging herb species in the wild environment. Additionally, these images contains both species labels and part labels. Experimental results demonstrate that our model achieves obviously improvement in term of accuracy and model size compared with other classical deep learning models.
Efficient Implementation of Active Exterior Cloaking in Three Dimensions

We describe a cloaking strategy for the 3D Helmholtz equation that uses a number of multipolar sources located at the vertices of an imaginary Platonic solid to construct a silent region within their interior which shields any object inside from the incoming wave and minimizes the scattering. The symmetry of the configuration means that we only need to know the amplitude of one of the sources to determine those of the remaining ones as they are shown to satisfy a system of linear equations. The cloaking approach is applicable to various areas involving scalar fields like electromagnetics and acoustics.
Fuzzy Multi-Criteria Selection of Non-Ferrous Scrap Metal Suppliers

Selecting non-ferrous scrap metal suppliers involves evaluating each supplier and its available scrap metals in terms of quantitative and qualitative criteria in each selection round. This paper presents a new approach for developing and selecting fuzzy multi-criteria decision making (MCDM) methods to address the non-ferrous scrap metal supplier selection problem with fuzzy data. The approach develops different fuzzy MCDM methods by combining three normalization processes and three aggregation processes commonly used in MCDM research. In particular, a specific defuzzification process is used to reflect the decision maker’s risk attitude about the current global commodity market. A validation procedure using fuzzy c-means clustering is applied to select among inconsistent ranking results produced by different fuzzy MCDM methods. An empirical study on a non-ferrous scrap metal company in China is conducted to illustrate how fuzzy MCDM methods are developed and selected.