Miracle-dz/UniFAR-FTUs huggingface dataset:
Unpacking the CNN’s Algorithmic “Glass Ceiling” of Inferring Psychological Traits from Facial Images

Deep learning techniques have brought novel analytic tools into multiple scientific fields. As one of the traditional domains in social psychology, face perception, which investigates the association between facial physical features and inferred psychological traits, has begun to benefit from deep learning techniques either, specifically for Convolutional Neural networks (CNNs). However, CNN’s predicting performances are not identical among various traits, even when utilizing the same CNN algorithm. Several researchers pointed out that it is due to the variations of inter-rater agreements on different traits. Besides this, we suggest that the variability of the trait ratings in the same image databases (i.e., standard deviation of trait evaluation) and the belonging clarity degree of a trait positioning in the underlying structure of many traits (i.e., absolute maximum load) could also explain the performance variation of CNN algorithm in trait judgment predictions. Drawing on the 10k US Adult Faces Database and the results of 40 social traits obtained by Song, A. et al. (2017) using the CNN algorithm, we employed inter-rater agreement, standard deviation of evaluation, and absolute maximum load in a regression equation to predict the variation of the fitting degree between CNN’s predicting evaluations and human’s ratings (goodness of fit of CNN). The result showed that inter-rater agreement explains a large part of the variance, as well as the standard deviation of evaluation and absolute maximum load, could independently account for a substantial part of the variance. Then, we utilized the structural equation model (SEM) to further lay out the above predictors’ pathway effects on the CNN’s goodness of fit. The finding has important implications for us to understand how deep neural networks learn human face perception and the prediction accuracy of CNN on human face perception judgments could be increased.
DSPU: An Efficient Deep Learning-Based Dense RGB-D Data Acquisition With Sensor Fusion and 3-D Perception SoC

3-D red, green, blue, and depth (RGB-D) and 3-D perception are essential information for 3-D applications such as autonomous driving and augmented reality (AR)/virtual reality (VR) systems. However, battery- and resource-limited mobile devices face difficulties in obtaining dense RGB-D data and 3-D perception information in low-power (LP) and real-time. Specifically, an RGB-D sensor is used to acquire 3-D RGB-D data, but it consumes high power and produces sparse depth data. Moreover, preprocessing for RGB-D data requires a long execution time. Previous 3-D perception accelerators also have limited reconfigurability, making them incapable of executing diverse 3-D perception tasks. In this article, an LP and real-time depth signal processing system-on-chip (SoC), depth signal processing unit (DSPU), is presented. The DSPU produces accurate dense RGB-D data using a convolutional neural network (CNN)-based monocular depth estimation (MDE) and a sensor fusion with an LP ToF sensor. Then, the DSPU performs 3-D perception inferring a point cloud-based neural network (PNN). The DSPU executes the depth signal processing system with the following features: 1) a unified point processing unit (UPPU) with a flexible window based-search algorithm for simplifying the complexity of point processing algorithms and saving the arithmetic units and buffers; 2) a unified matrix processing unit (UMPU) with bit-slice-level sparsity exploitation to accelerate various matrix processing algorithms; 3) a band matrix encoder and decoder to decrease the data transactions in the conjugate-gradient (C-Grad) method; and 4) a point feature (PF) reuse method with a pipelined architecture for low-latency and LP PNN inference. Finally, the DSPU achieves real-time implementation with 281.6 mW of the end-to-end 3-D B-box extraction system.
Study of the Angular Dependence of a Prompt Gamma Detector Response during Proton Radiation Therapy

Abstract Purpose: Several studies have recently shown that the characteristics of prompt gamma (PG) rays emitted during proton radiation therapy are beneficial for verifying proton beam range during treatment delivery. Since PG rays are produced instantaneously upon the proton beam delivery, the viability of in vivo beam range verification using PG rays depends greatly on the design optimization of not only intrinsically highly efficient detectors, but also detector location around the beam to maximize detection efficiency. The purpose of this study is to characterize angular dependence of the PG detection rates as a function of proton beam energy to help develop the design of clinically feasible detectors. Materials and Methods: In this study as a part of the long-term goal of developing a clinically feasible multistage Compton camera, we performed a Monte Carlo–based study of the detector response in a water phantom over the clinical range of beam energies, 50 to 200 MeV, and characterized PG emission s.
FPGA Based Galvanically Isolated Cascaded Multilevel Inverter with Matlab GUI for Realtime Monitoring and Control

In growing electricity markets, there is a demand for more effective, secure, and pure sine wave inverters due to the fast expansion in the use of solar photovoltaics in renewable power generation. Despite the fact that certain inverters are affordable price and have better efficiency, they fail to reduce the Total Harmonic Distortion (THD) of their output voltage and load current, and also lack galvanic isolation between source and load. This paper aims to design and simulate the 9-Level Cascaded H-Bridge Multilevel inverter with Low THD and Galvanic Isolation (CHB MLI) and to compare the results with the conventional two-level inverter output. Also, to develop the proposed MLI topology in hardware using Field Programmable Gate Array (FPGA) technology and to develop a Matlab application for real-time controlling and monitoring.
ATOFIS, an AR Training System for Manual Assembly: A Full Comparative Evaluation against Guides

This paper reports on a user study to comparatively evaluate two AR training systems designed for step-by-step manual operations: ATOFIS - recently proposed in the literature, and Microsoft Dynamics 365 Guides (hereinafter Guides) - one of the most relevant state-of-the-art commercial solutions. The user study (N=16) was conducted in two stages- i.e., training and authoring, on a partial replica of a real-world assembly workstation. During training, the participant learns a sequence of manual operations by performing two assembly cycles, guided by each of the two AR training systems. During authoring, the participant creates the two sets of AR work instructions used in the next training session, one set with each of the two authoring systems. We bound the authoring and training procedures during the experiment to comparatively assess the AR systems overall, and address at the same time an evaluation gap observed in the literature. The experimental results demonstrated advantages of the authoring approach proposed by ATOFIS (i.e., low-cost, formalized, in-situ, immersive and on-the-fly), proved the usability and effectiveness of the AR instructions authored with ATOFIS and validated a set of hypotheses formulated by the authors of the system. ATOFIS authoring was $1.72\times$ faster and unanimously preferred by the participants; ATOFIS training reported zero assembly errors and was 13% faster than Guides. ATOFIS reported excellent system usability (i.e., SUS) and mental workload (i.e., NASA-TLX) scores for both authoring and training, outperforming Guides on all dimensions.
Low Texture Artifacts Recognition and Visual Servo Position Based on YOLOv4-Tiny

Traditional artifact detection needs to manually select and extract suitable image features, but some artifacts have weak surface textures, which may cause problems such as difficulty in feature extraction or poor robustness. In order to apply visual servoing technology to the detection and positioning of low-texture artifacts, this paper proposes a target detection platform based on YOLOv4-Tiny. Using four types of low-texture water pipe connectors with different shapes as image samples, a data set was made, which not only realized the accurate identification of low-texture artifacts, but also reduced the actual hardware requirements. For the positioning of the target artifact, the relative pose estimation method based on the target detection frame is proposed for further improving the visual servo system. The experimental results show that the system solves the problem of difficulty in extracting visual features of low-texture artifacts, and the positioning error can be controlled within ±1 mm or ±1 degree in practical application.
GVA: Gated Variational Autoencoder for de novo molecule generation

Molecule generation refers to the process of designing new chemicals with certain chemical properties and then optimising these properties. Following prior research, we encode chemicals as continuous vectors and decode the embedding vectors back into molecules using the variational autoencoder architecture. The encoder and decoder of the proposed variational atoencoder are based on gated recurrent unit cells. The gated recurrent unit cells limit the amount of learnable parameters in the variational autoencoder. The variational autoencoder based on gated recurrent units provides validity of 92.32 % and reconstruction accuracy of 89.63% percent, which is superior to other state-of-the-art techniques. The proposed model is effective in generating compounds with diverse properties.
Towards Spatial Airflow Interaction: Schlieren Imaging for Augmented Reality

This work integrates Schlieren Imaging, a unique sensing modality, into Augmented Reality (AR) to explore ways to utilize invisible airflows for AR. Schlieren imaging is an imaging technique that visualizes the flow of fluids, which is normally invisible to the eyes. Theoretically, the technique can calculate the motion, pressure, temperature, and density of the airflow in our physical world. This unique, but less applied modality may expand interaction paradigms in AR and VR. We build a proof-of-concept AR system combined with Schlieren imaging that allows real airflow to affect virtual objects. The results of quantitative analyses show that our system can integrate different types of airflow with pressure values ranging from weak breathing actions to a heat gun up to 10m/s or 0.25m3/min airflow. We also showcase AR use cases including blowing out a virtual candle and a heat gun.
A Passive Technique for Detecting Islanding Using Voltage Sequence Component

Islanding is a well-known phenomenon in Distributed Generation (DG) in which the microgrid continues to provide power to an isolated load area even after it is disconnected from the utility grid. Fast detection of islanding is very important to maintain stability in voltage and frequency, restrict the disturbances within permissible limits, and prevent faults and cascade tripping in the grid. In case of delayed islanding detection, the system may have increased harmonics, distorted voltage profile during the transition, faults, and service interruption during critical loads. The main disadvantage of passive detection approaches is their large non-detection zone. Due to external signal injection at a common coupling point, active islanding reduces power quality and system performance. The suggested passive islanding detection approach is based on measuring the negative sequence voltage rate of change (ROCONSV) at the point of common connection (PCC). The negative sequence voltage is calculated using symmetrical component transformation and the derivative is then compared to predefined threshold values. Based on the system decision, the islanding trip signal is released for the circuit breaker at the microgrid. The proposed method is simple, reliable, and fast without the use of any external signal injection and power quality degradation.
Detection and Analysis of ERPs for Social Cognition Evaluation

This paper describes an approach for elicitation, acquisition, and analysis of event-related potentials (ERPs) for social cognition evaluation. We used images of emotional content that were classified into three groups according to their valence: pleasant, unpleasant, and neutral. An application for stimuli generation based on the emotional oddball paradigm (EOP) was developed, and a commercial wireless EEG headset was used for signal acquisition. The ERPs of 13 volunteers for the three types of stimuli were obtained and analyzed to extract the N100 and P300 components. The results show increased amplitudes in ERP components due to unpleasant stimuli and longer latencies observed in neutral stimuli.
High-Gain Low-Side-Lobe Circularly Polarized Slot Antenna Array for Satellite Communications

In this article, a high-gain low-side-lobe circularly polarized (CP) substrate integrated waveguide (SIW) slot antenna array is proposed for Ku-band satellite communication. Based on a matched two-compound slot pair structure, a 16 × 24 element CP substrate integrated waveguide slot array is designed, and the proposed array exhibits advantages of high gain and low sidelobe. Utilizing based on Taylor distribution based array synthesis method, a AR <1.5 dB and a max SLL suppression over 20 dB at 11.95 GHz can be achieved. Detailed design and simulated results of the proposed slot array are provided in this paper. The simulated results show that it can be a good candidate for satellite communications.
Applications of Human Activity Recognition in Different Fields: A Review

Human activity recognition is a field where not enough work has been done yet, but its applications can be helpful in numerous domains. In this paper, we have discussed different methods by which implementation of human activity recognition can be carried away and compared those methods in terms of advantages, performance, accuracy, techniques, datasets, and limitations. We have also discussed numerous domains where this can be helpful such as healthcare, security, and augmented reality, along with the challenges and the type of method used.
Taiwanese Across Taiwan Corpus And Its Applications

Taiwanese across Taiwan (TAT) corpus is the first large-scale and publicly released Taiwanese speech corpus which represents the modern Taiwanese around Taiwan. This paper briefly reviews the TAT corpus and a corresponding parallel Chinese, Hàn-Lô-Tâi-bûn, Tai-Luo and Péh-ōe-jī lexicon and demonstrate some of their potential applications including ASR, TTS and voice conversion. The corresponding pretrained ASR and TTS models, sample model usage codes and training scripts will also be released. More information could be found on the Formosa Speech in the Wild website: https: //sites. google. com/nycu.edu. tw/fsw.
Optimization of attribute-based access control policy with priority filtering

The ABAC model currently has a wide range of application scenarios, such as cloud environments, the Internet of Things, and fine-grained access control for NoSQL databases. Although the use of attribute-based control techniques can solve problems such as insufficient access control granularity, it immediately brings the problem of inefficient access control. In this paper, we design a new ABAC policy matching algorithm, when the guest sends an access request, we first filter out the access requests that are unlikely to be decided using a cuckoo filter and reject the access request directly. If the access request passes the filter, then the policy that can match the access request is found through the primary index table and secondary index table of the policy and the decision result is given. Experiments prove that the algorithm performs best in the worst case and performs well in the general case, and can greatly improve the efficiency of attribute-based access control.
Co-simulation of a Two Area Power System Using an Adaptive Macro Step Size Method Based on Richardson Extrapolation

Co-simulation plays an important role in the analysis and testing of novel protection and control systems in low-inertia power systems and Smart Energy scenarios. These scenarios are increasing in complexity and heterogeneity, where systems that used to be analyzed in isolation are being integrated into a system of systems. With incremental methodologies for scenario development, the incorporation of new subsystems into existing co-simulation scenarios can degrade performance indicators such as numerical accuracy and real-time execution. Starting from a two-area power system co-simulation with primary control only, we show the impact of incorporating an Automatic Generation Control (AGC) as an additional subsystem using an adaptive step size method based on Richardson Extrapolation. Simulation results show how the closed-loop dynamics change with the AGC, in terms of the number of step sizes required for completing the simulation while maintaining the global error below a predefined tolerance.
Nature Inspired Node Localization Algorithm for Anisotropic WSNs

This study resolves the node localization problem in O-shaped anisotropic wireless sensor networks (AWSNs) using a nature-inspired algorithm. The localization problem is resolved by using location aware anchor or beacon node. The distance between the target and anchor node is estimated with the help of a reliable anchor pair selection approach. The target nodes' coordinates are obtained using the nature-inspired salp swarm algorithm (SSA). The proposed node localization algorithm's localization error is calculated for different node densities and anchor node numbers. Comparisons are made using a geometric method and a node localization algorithm based on the particle swarm optimization (PSO) algorithm. The simulations are performed on MATLAB software.
Statistical mechanics of source coding with a fidelity criterion

We provide a method to evaluate the typical performance of lossy data compression schemes for general (discrete or continuous) memoryless sources using the replica method (RM). The proposed method reproduces a known formula to compute the rate-distortion function representing the optimal tradeoff in the limit of infinite data lengths between the compression rate and permissible distortion level, which is consistent with existing methods in information theory literature. The advantage of the RM-based method is the ability to accurately assess the performance of sub-optimal code ensembles, demonstrated here for Gaussian memoryless sources. The obtained result is used to construct a family of error correcting codes that are composed of practical-size codebooks and asymptotically achieve the capacity of the Gaussian channel.
Cognitive load Classification with a Stroop task in Virtual Reality based on Physiological data

Cognitive load (CL) when using Virtual Reality (VR) requires more experimental inputs, especially to determine how VR affects human psychophysiology depending on the task. Classifying humans’ physiological variations in a controlled setup is essential. We randomly assigned 92 participants to three experimental conditions: control, stereoscopy, and dual-task. Participants fulfilled a rest-baseline period, a Stroop task (25 congruent, 25 incongruent words), and a NASA-TLX questionnaire. We recorded behavioral and physiological data from eye-tracking(ET), electrocardiogram(ECG), and electrodermal activity (EDA). NASA-TLX scores of control and stereoscopy were statistically different with dual-task conditions. We used NASA-TLX scores to create three classes and train a CL classifier based on physiological variations. We deployed linear models penalized with the L1 norm to select the most relevant features correlated with subjective CL levels. The ECG sensor provided the most selected features compared to EDA and ET. We compared SVM, Logistic Regression, and Gradient boosting classifier models. The Gradient boosting method with 87.23% accuracy and an 87.13% F1 score is the most performant. Future works will try to compare such an approach with stressful stimuli.
DSKE: Digital Signature with Key Extraction

In general, digital signatures can be used to prove authenticity for as long as the signature scheme is not broken and the signing key is kept secret. While this “long-lived” authenticity might be useful in some scenarios, it is inherently undesirable for certain types of sensi-tive communication, for instance, whistleblowing. A particular concern in this case is that the communication could be leaked in the future, which might lead to potential retaliation and extortion. Therefore, a natural question to ask is whether it is possible to design a signature scheme that allows the signers to prove authenticity for a limited period of time, and then afterwards be able to deny having signed any messages in the ﬁrst place. We argue that this could oﬀer a desirable degree of protection to the signers through deniability against future leaks. This also reduces the incentives for criminals to obtain leaked communications for the sole purpose of blackmailing. This paper introduces the concept of digital signature with key extraction (DSKE). In such schemes, signers can have plausible deniability by demonstrating that a group of recipients can collectively extract the signing key, while, within a certain threshold, the signature deter-ministically proves message authenticity. We give a formal deﬁnition of DSKE, as well as two provably secure constructions, one based on hash-based digital signatures and the other based on polynomial commitments. Later, we propose a forward-forgeable signature construction, GroupForge, by combining DSKE constructions with Merkle trees and timestamps to have a “short-lived” signature with extractable
Atomic-Order Planarization of Ultrathin SiO2/Si(001) Interfaces

Conventional wet cleaning and ultrahigh vacuum (UHV) heated cleaning prior to thermal oxidation were compared with respect to their effects on extremely thin SiO2/Si(001) interface roughness. Atomically flat SiO2/Si(001) interfaces were realized by preparing the Si(001) reconstructed surface in UHV followed by thermal oxidation. In contrast to conventional wet cleaning, this planarization is significant in the range of oxide thickness (T ox)<9 nm which is an important thickness for future gate oxides. As for the conventional wet-cleaned surfaces, maximum roughness of the interface was observed at T ox≃4 nm which corresponds to the “initial oxide thickness” which appeared in the oxide growth kinetics.
RMC-PVC: A Multi-Client Reusable Verifiable Computation Protocol (Long version)

The verification of computations performed by an untrusted server is a cornerstone for delegated computations, especially in multi-clients setting where inputs are provided by different parties. Assuming a common secret between clients, a garbled circuit offers the attractive property to ensure the correctness of a result computed by the untrusted server while keeping the input and the function private. Yet, this verification can be guaranteed only once . Basedonthenotion of multi-keyhomomorphicencryption ( MKHE ), we propose RMC - PVC a multi-client verifiable computation protocol, able to verify the correctness of computations performed by an untrusted server for inputs (encoded for a garbled circuit) provided by multiple clients. Thanks to MKHE , the garbled circuit is reusable an arbitrary number of times. In addition, each client can verify the computation by its own. Compared to a single-key FHE scheme, the MKHE usage in RMC - PVC allows to reduce the workload of the server and thus the response delay for the client. It also enforce the privacy of inputs, which are provided by different clients.