Reviewerly/exHarmony huggingface dataset:
Physics-Inspired Optimization in the QUBO Framework: Key Concepts and Approaches

Quantum computing promises to have a tremendous advantage over its classical counterpart for solving computationally hard problems, yet remains in a relatively early stage for practical applications owing to the limited capabilities of today’s quantum computers. The approach to the special purposes of quantum computers by exploiting the special-purpose physics-inspired or quantum-inspired computers is emerging as a novel alternative to its quantum counterpart in tackling hard problems in high-performance computing. Inspired by physics, the Ising machine — a type of special-purpose computer that implements or emulates physics or quantum effects of the Ising model to speed up finding solutions to optimization problems — has recently become an active research area in the field of combinatorial optimization. This paper is to address the key enabling software and hardware technology underlying physics-inspired optimization using Ising machines in the unified quadratic unconstrained binary optimization (QUBO) framework for modeling and solving computationally hard combinatorial optimization problems, and with an aim to shed some light on the challenges and opportunities associated with the ever-growing landscape of this novel high-performance computing.
Determining perceptions of banking customers regarding fingerprint ATMs

Biometrics, such as fingerprint technology, is a novel technology that verifies an individual's identity based on their distinct physical or behavioural features. Despite the technology's increasing prevalence in the public and private sectors due to its potential benefits, its adoption by end users is proceeding slowly. This study proposes a framework that explores the influence of various dimensions of perceived benefits and risks on banking customers’ intentions to use fingerprint ATMs. Empirical data was collected from banking customers of Jordanian banks that have implemented fingerprint technology at their ATMs. The proposed framework was evaluated with potential adopters of the target technology. The findings suggest that personal innovativeness positively influences banking customers’ perceived value of fingerprint ATMs. Perceived benefits, including time convenience, cognitive effort saving, increased security and perceived enjoyment, also positively influence the perceived value of fingerprint ATMs. Perceived risks, except for physical risk, have negative effects on perceived value. In addition, perceived value is recognised as a key enabler of fingerprint ATM adoption intention. The findings of this study provide valuable insights that can help Jordanian banks promote and encourage their customers to use fingerprint ATMs.
A lightweight adaptive random testing method for deep learning systems

Abstract In recent years, deep learning (DL) systems are increasingly used in the safety‐critical fields such as autonomous driving, medical diagnosis, and financial service. Although these systems have demonstrated an outstanding performance in enhancing the accuracy of decision‐making, they pose significant challenges to the trustworthiness due to their limited interpretability and inherent uncertainty. Adaptive random testing (ART) has been proved as an effective approach for ensuring the reliability of DL systems. However, existing ART methods for DL systems incur a heavy overhead in test case selection due to the computation of distances. To address this issue, we propose a lightweight adaptive random testing (Lw‐ARTDL) method for DL systems. In our improved algorithm, we employ the K‐Means technique to divide the entire test suite into several subsets. Then, for a candidate test case, we only calculate distances between it and the test cases within the category to which it belongs. This partition strategy ensures that the selected test cases are more representative while significantly reducing the computational cost. To validate the proposed algorithm, the comparison experiments between Lw‐ARTDL and the original ARTDL algorithm are conducted on two typical DL systems. The experimental results show that Lw‐ARTDL significantly reduces the overhead of failure detection, and exhibits stronger failure detection capability compared to ARTDL in most similarity metrics.
The Prediction of Lahar Flood Event Impact on the Inundation Areas in Gendol River, Indonesia

One of the natural phenomena that inflict harm and fatalities is lahar flow. The Gendol River frequently experiences lahar flows from Mount Merapi, necessitating study. Predictions can be used to anticipate lahar flows and minimize casualties and material losses. The Nakayasu synthetic unit hydrograph is used in this study utilizing a simulation method based on the theory of Ashida, Takahashi, and Mizuyama, which modifies the hydrograph pattern and rainfall intensity values using the SIMLAR V2.1 application. Based on the simulation results, it was discovered that each simulated design had a different flow area, velocity, lahar volume, and flood height. Based on the result, it can be said that this study's hyetograph pattern significantly impacts how debris flows affect people. The more the hyetograph patterns, the higher the peak hydrograph value. Additionally, the increase in the hydrograph value is directly related to the increase in flow velocity. The rain intensity and pattern value also influence the number of areas affected by the lahar flow.
AVX-TSCHA: Leaking information through AVX extensions in commercial processors

Modern x86 processors support an AVX instruction set to boost performance. However, this extension set may also cause security issues. We discovered that there are vulnerable properties in the implementation of the masked load/store instructions. First, these instructions can suppress exceptions caused by invalid or inaccessible memory access. Second, the execution time of these instructions leaks the current state of the page mappings, permissions, and TLB states. Based on this, we present a novel AVX timing side-channel attack that can defeat address space layout randomization. We demonstrate the significance of our side-channel attack by showing User and Kernel ASLR breaks on the recent Intel and AMD processors in various environments, including cloud computing systems (Amazon AWS, Google GCP, and Microsoft Azure), an SGX enclave (a fine-grained ASLR break), and major OSes (Linux, Windows, and macOS). Our attack can identify the Linux kernel's base address in 0.29 ms as well as those of loaded kernel modules in 2.24 ms, with a near-zero error rate. We further demonstrate that our attack can be used to infer user behavior, such as mouse movements and data transmissions over the network. Our evaluation results on multiple mobile, desktop, and server processors (a total of 26 Intel and AMD CPUs) show that 1) the AVX timing side-channel works on the vast majority of Intel processors (from the Sandy Bridge microarchitecture) as well as AMD processors (from the Zen microarchitecture onward) and 2) our KASLR breaks are very fast and reliable. To the best of our knowledge, our attack is the first to demonstrate a KASLR break on both the recent Intel Alder Lake and AMD Zen 3 CPUs. We highlight that more robust isolation or fine-grained randomization should be adopted to mitigate our presented attacks successfully.
RDPCF: Range-based differentially private user data perturbation for collaborative filtering

Collaborative filtering recommends potentially interesting content to users based on historical data that it collects from users, which can lead to privacy breaches by untrusted servers. Differential privacy is a strict definition of privacy, and user privacy protection in collaborative filtering through differential privacy methods has drawn widespread attention from researchers. However, perturbing user data in collaborative filtering through existing differential privacy techniques will lead to the problem of poor recommendation accuracy. This is because differential privacy techniques generally perturb user data independently and will destroy the data similarity required by collaborative filtering. This paper proposes a differentially private user data perturbation method RDPCF, which perturbs the user data within a given content similarity range, and constrains the perturbation probability by differential privacy definition, thus ensuring both the accuracy of using the perturbation results for collaborative filtering and user privacy protection. Experimental results show that the RDPCF considerably outperforms the existing methods regarding privacy protection level and recommendation accuracy.
Lifted inference with tree axioms

We consider the problem of weighted first-order model counting (WFOMC): given a first-order sentence ϕ and domain size n∈N, determine the weighted sum of models of ϕ over the domain {1,…,n}. Past work has shown that any sentence using at most two logical variables admits an algorithm for WFOMC that runs in time polynomial in the given domain size [1], [2]. The same property was later also shown to hold for C2, the two-variable fragment with counting quantifiers [3]. In this paper, we further expand this result to any C2 sentence ϕ with the addition of a tree axiom, stating that some distinguished binary relation in ϕ forms a tree in the graph-theoretic sense.
Noise induced dynamics of two-qubit entangled Bell’s states

The stability of different types two-qubit entangled states was analyzed by a direct calculations of the time dependent probability to find the system in its initial state in the presence of Gaussian noise. The entangled states time evolution was studied in the presence of both classical and quantum noise. It was demonstrated that one pair of Bell’s states is robust to the Gaussian fluctuations, while the another one decays. The decay law of unstable entangled states is determined by the noise correlation function. The developed general formalism was applied for analysis the dynamics of entangled states in the presence of fluctuations produced by the electron–phonon interaction. It was revealed that unstable entangled states interacting with phonon bath can demonstrate the crossover between short time exponential decay law and long time power law decay. Obtained results can be effectively exploited for quantum computation, in which it is essential to be able to retrieve information about the initial quantum state even in the presence of interaction with the environment.
Robust peer-to-peer learning via secure multi-party computation

To solve the data island problem, Federated Learning (FL) provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation. Peer-to-peer (P2P) federated learning further improves the robustness of the system, in which there is no server and each client communicates directly with the other. For secure aggregation, secure multi-party computing (SMPC) protocols have been utilized in peer-to-peer manner. However, the ideal SMPC protocols could fail when some clients drop out. In this paper, we propose a robust peer-to-peer learning (RP2PL) algorithm via SMPC to resist clients dropping out. We improve the segment-based SMPC protocol by adding a check and designing the generation method of random segments. In RP2PL, each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training. Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.
DSG-GAN: Multi-turn text-to-image synthesis via dual semantic-stream guidance with global and local linguistics

Multi-turn text-to-image synthesis task aims to manipulate desired visual content according to the user's intention step by step, which has recently attracted a lot of research interest in the community of language and vision. Different from traditional text-to-image synthesis, multi-turn text-to-image synthesis is more challenging as 1) it needs to continuously recognize the user's intention from spoken instruction and perceive the visual information from the source image; 2) it requires reasoning about the position, appearance, and characteristics of fresh modifications in target images as well as connecting objects in instructions with visual components in source images. To deal with this issue, in this paper, we propose a Dual Semantic-stream Guidance with global and local linguistics Generative Adversarial Network (DSG-GAN), which reasons and learns the user's intention from text description and iteratively manipulates visual information. Specifically, we design a novel dual semantic-stream discriminator, which combines with a hierarchical instruction encoder to evaluate the logic and quality between human intention in linguistic instruction and generates visual content from the perspective of global and fine-grained consistency matching. Meanwhile, the discriminator's backpropagation gradient is used to optimize the instruction encoder, which incentivizes it to purify the user's intention into global and local information that is consistent with the manipulation's visual representation. Extensive experiments show that even when producing high-resolution images and making deep iterative turns, our method performs significantly better due to local fine-grained linguistic information being combined with cross-modal correlation.
P‐59: A Pupil Distance based Monocular Camera Eye Tracking Algorithm for Naked Eye 3D Display

Naked eye 3D display makes it possible for viewers to watch stereoscopic scenes without wearing any auxiliary equipment. Eye tracking is an essential technology for viewers to see high quality stereo scenes at different positions. This paper proposed a pupil distance‐based monocular camera eye tracking algorithm, which detected pupils from face and estimated head pose to acquire the distances between eyes and the display. The method further adopted the camera projection to predict the 3D pupil positions. Experimental results demonstrated that the proposed algorithm had high accuracy and was effective on naked eye 3D display devices.
P‐45: An Evaluation Method of Moving Picture Response Time for Organic Light‐Emitting Diode Motion Blur

The motion image quality of organic light‐emitting diode (OLED) degrades by motion blur due to the hold‐type display. Moving picture response time (MPRT) is a representative index to evaluate the performance of the moving image. A method is proposed to evaluate the motion blur of OLED display.
P‐175: AI‐based Multimodal Architecture for Video Anomaly Detection in Large OLED Panel Manufacturing

This paper proposes an AI‐based multimodal architecture for anomaly detection in a video footage of a smart carrier which is used for monitoring alignment status, grinding of rollers and vibration of a carrier for manufacturing line of OLED (Organic Light Emitting Diodes) panels. The proposed multimodal architecture has a two‐stream structure. The first stream extracts a candidate anomaly region in image frames sampled from the video by DL‐based segmentation. Thereafter, the candidate region is determined whether it is a true failure or not by comparing the region with a reference image which was previously obtained in a normal setup status. The second stream calculates a dense optical flow with sparse areas using consecutive video frames. By observing change between (n)th, reference, (n‐3)th and (n‐7)th images, it is determined with sound analysis whether the smart carrier experiences vibration during movement. The overall detection rate of three anomalies is 97%, and processing takes only 40% of total video time.
89‐1: Usability of Glove‐based Handling Devices in Virtual Training: Use Case in Automotive Sector

This work analyzes the impact of subjective factors on users’ immersive experience, while using glove‐based handling devices as input system. A user‐study has been conducted on a Virtual Training framework, evaluating usability and mental effort perceived with such devices.
P‐53: A Real‐time Method for Preventing AM Mini‐LED COG Image Sticking based on Display Content History and Heat Diffusion

The MLED (Mini‐LED) has excellent color saturation and contrast. MLED based on COG (Chip On Class) has ultra low current. But it suffers a difficulty that its red light luminescent efficiency is drastically hindered by high temperature of screen. Image sticking occurs to the full‐screen gray picture because of temperature differ‐ ence among areas. To prevent image sticking problem and improve the visual quality of splicing screen, we propose a novel method to estimate the compensation value for red channel and apply this compensation to acquire chroma uniformity. Our method is imple‐ mented in FPGA to achieve real‐time performance. Experimental results validate the effectiveness of the proposed method.
88‐1: E‐book with Animation Playing Capability Based on Liquid Crystal Display Technology

Electronic ink (E‐ink) displays have become increasingly popular in e‐books due to their paper‐like appearance, readability in direct sunlight, low power consumption, and flexible sizing. However, the slow switching speed of bistable LCDs and E‐ink limits their ability to play animations. This paper proposes a novel method for fabricating fast‐ switching bistable liquid crystal displays capable of playing animations. The research is based on a new type of bistable LCD technology that achieves lower driving voltages than existing E‐ink displays. The research team has significant experience developing bistable displays and has invented many new technologies in their laboratory. The proposed technology combines the benefits of bistable displays with the ability to play animations, a significant advancement in display technology.
P‐150: A New Multitasking Demura Algorithm for Display Defect Compensation

This paper proposes a multitasking Demura algorithm based on backlight modulation and liquid crystal voltage adjustment that significantly increase the luminance uniformity and improves the details of high gray image without saturation issues. The multitasking Demura benefit was experimentally validated in a 55‐inch splicing module display, and the experimental results show that the multitasking Demura can eliminate severe mura, increasing the module's luminance uniformity from 34%, 60% and 88% to 83%, 95% and 98% at L25, L128 and L240 gray levels.
P‐167: Autoencoder with Variables Added Model for Manufacturing Process Anomaly Detection Using Multivariate Time‐series Data that Minimizes Data Information Loss

In the display industry, the technology of the FAB process is continuously being advanced. As process quality management technology improves, data can be stored, analyzed, and monitored in real time using various types of sensors. The manufacturing process is so complex that it is difficult to detect anomalies simply by analyzing data from on e or two sensors. For t his reason, multivariate data analysis is essential, and time‐series data analysis is particularly effective for detecting process state changes. In this study, we propose a multimodal AVA that can detect process anomalies using multivariate time‐series sensor data. In order to minimize t he information loss of the original data, multivariate time‐series data are processed into tabular and image forms and applied to the proposed model. We demonstrate that the proposed model performs better in anomaly detection compared to a general anomaly detection model in highly imbalanced datasets. The proposed method is expected to reduce cost and time due to defects by detecting abnormal situations in the process in real time and responding quickly when abnormal situations occur.
P‐83: Optimizing Water‐proofing Algorithm with Machine Learning for Wearable OLED In‐Cell Touch Panel

In this paper, a wearable OLED in‐cell self‐capacitive sensor using machine learning algorithm for water‐proofing is proposed. This paper also shows the experiment and the test result. The result shows that the success rate of water‐proofing on bezel can be largely improved (from 30.7% to 77.1%) by machine learning method on the wearable device with Novatek TDDI NT38350.
20‐5: Novel Interactive Mixed Reality System Based on Dual Depth Camera Gesture Recognition for Human‐Computer Interaction

This work presents a novel human‐computer interactive mixed reality (MR) system using two depth cameras to recognize users’ gestures, which are used to make control commands. Moreover, the display screen of the computer is streaming to MR via transmission control protocol/internet protocol (TCP/IP). By using the proposed system, users can easily interact with computers anywhere without the limitations of the display screen, mouse, and keyboard.
22‐2: Low power consumption optimization using conformalized quantile regression

AMOLED IT develops low‐frequency technology to meet the market's low power consumption needs. If the frequency is lowered to 48hz, the logic power consumption compared to 120hz can be reduced by about 30%. However, the flicker phenomenon during low‐frequency operation is a significant defect that customers can immediately recognize. The cause of the flicker phenomenon is known to have various effects, such as process, design, and device characteristics. However, there needed to be a way to calculate it, considering all parameters accurately. To solve this issue, we developed an AI that can predict by collecting information on the process, panel design, device characteristics, and flicker data mentioned above and using XAI to analyze the AI. As a result, we could determine the cause of the flicker under what circumstances. We applied synthetic generation AI to improve insufficient experimental data and improved predictive consistency through optimization. We additionally discovered a cause that was not well known before, and the importance of each parameter was identified. As a result, using this technology to predict the flicker phenomenon in advance when changing the process, prevent failures, and predict the risk of new IT models. Moreover, the prediction reliability of the model was verified by conformalized quantile regression to minimize the risk.
22‐3: An Adaptive Frequency Domain Demoiré Method for Display Test Images

A deep learning based demoiré algorithm is applied to display test images to remove moiré patterns and improve measurement accuracy. The preliminary result showed better results than traditional frequency domain filtering methods, without any additional cost on hardware.
20‐4: An AI‐Driven Aquarium Guide System for Intelligent Museum

Visitors to aquariums often struggle to access information and differentiate between similar marine species. To address this issue, ITRI has developed an AI‐driven Aquarium Guide System that uses real‐time face tracking and recognition technology, along with fish identification and gesture recognition. When visitors stand in front of a fish tank, the system detects their gaze and facial features, and identifies the species they are looking at. The backend database then displays the fish's name and provides relevant information to visitors. This system has a 98% accuracy rate in recognizing marine life and can adjust the information based on visitor gestures with an accuracy error of less than 1.0 mm. It also uses virtual and augmented reality technology, paving the way for real‐time, immersive mapping technology. The AI‐driven Aquarium Guide System improves the visitor experience at tourist attractions by offering accurate and interactive information about marine life. This increases visitor understanding and appreciation of marine creatures while providing an engaging and enjoyable experience. We are thrilled to announce that this system has won the 2023 CES Innovation Award, recognizing its excellence and innovation. It has already been installed at the National Museum of Marine Science &amp; Technology (NMMST), transforming the way visitors experience and learn about marine life.