Reviewerly/exHarmony huggingface dataset:
Deep Neural Network for User Attribute Recognition in Metaverse Hand Images

This work develops a Deep Neural Network (DNN) that can recognize user attributes such as gender, age group, skin color, accessories, nail polish, palm and back of the hand, and left and right hands from hand images to understand user attributes of virtual-real interactions in metaverse. The proposed DNN is based on DenseNet-169 in which outputs from four dense blocks are convoluted and concatenated to form multi-scale features. These features then go through the spatial coordinate attention module, which emphasizes meaningful parts. To deal with unbalanced data, loss scaling parameters and dynamically learnable weighted loss function are adopted along with data augmentation to improve the performance. In particular, dorsal hand images are better suited for classifying gender, age group, and skin color than palmar images due to the presence of abundant skin textures, wrinkles, age spots, veins and so on. Accordingly, the proposed DNN includes a post- processing module to classify these three attributes in terms of dorsal hands. Our DNN achieves superior performance in gender identification with 89.74% accuracy using NTU-PI v1, 94.03% and 92.69% in dorsal and palmar hand recognition using 11K hands, respectively, and an average multi-attribute accuracy of 93.17% on 11K hands. Therefore, the DNN proposed herein can be widely applied to various metaverse applications to provide users with customized service experiences.
Adversarial Attacks on Deep Learning Visual Object Detectors: Optimization and Evaluation of Physical Attack Vectors using YOLOv4

Adversarial attacks refer to an intentional manipulation of the visual input of a deep neural network (DNN). Such a modification can be aligned as an adversarial perturbation able to provoke critical failures of a detection model. Recent research has shown that DNNs are vulnerable against such adversarial attacks, especially in the field of digital attacks. In this work, to demonstrate the threat of adversarial attacks in a non-digital, real-world autonomous driving setting, an optimization pipeline is implemented to generate physical attacks. As the target model, the YOLOv4 algorithm model is chosen to validate the results based on a state-of-the-art object detector. The proposed method improves the success rate of attacks by using an adapted adversarial loss to increase the amount of information during training. In the customized optimization function of this work, several cost terms, including vector distance norms, total-variation loss, and grey-scale loss, are defined to shape the design of the attack. The combination and outbalancing of these terms result in inconspicuous adversarial attacks. Experiments are conducted to validate the approach. The adversarial attacks generated by this approach successfully reduce the confidence by more than 50% in a real indoor and outdoor testing scenes.
Viewpoint Invariant 3D Driver Body Pose-Based Activity Recognition

Driver monitoring will be required in many countries for all new vehicles with automation functions. While the common approach for this task is face and eye gaze monitoring, cameras with a wider field of view have already been introduced in some cars. However, their mounting position can change between vehicle models. To minimize data collection efforts and to facilitate data reuse it is important for algorithms to be able to deal with a changing environment. We conduct an experiment comparing the performance of video-based models with 3D body pose-based activity recognition methods with regards to sensor and position changes. We introduce a modular activity recognition pipeline which uses a sensor independent representation including the 3D body pose of the driver, 3D dynamic object positions and 3D interior positions. We show that while video-based models offer the best quality overall, when trained and tested on the same camera view, body pose-based methods can be far more robust to positional changes. Moreover, augmentation reduces the performance drop across views to 35% compared to 83% without augmentation and 90% for video-based models.
Multi-frame Correlated Representation Network for Video Super-Resolution

Video super-resolution aims to reconstruct the high-resolution video from its degraded observation. Existing methods primarily utilize explicit alignment strategies, including optical flow and motion compensation, and implicit alignment strategies, including deformable convolution and non-local attention, to model the temporal information. However, these alignment strategies with motion estimation and motion compensation (MEMC) introduce inaccurate inter-frame information to some extent, which largely affects the reconstruction performance. To avoid the error compensation information, we propose to model the temporal information from the perspective of the self-similarity between frames and design a multi-frame correlated representation network (MCRNet) for video super-resolution. To address the issue of temporal information distortion caused by large-scale pixel displacement and object occlusion, MCRNet extracts temporal information through similar regions on multiple frames, which are aggregated with allocated weights for information compensation. Moreover, we design a multi-scale non-local information fusion module for non-local correlation matching of spatio-temporal features in the multi-scale space, which maintains the scale consistency of spatio-temporal features. Experimental results indicate that MCRNet achieves promising gains over other competing methods which employ explicit and implicit alignment strategies on different datasets.
Hierarchical Federated Learning with Mean Field Game Device Selection for Connected Vehicle Applications

In this paper, a client-edge-cloud hierarchical federated learning (FL) model has been developed for connected vehicle applications. Generalized models are aggregated on the cloud server, while customized models trained on local data with similar data distribution are aggregated on the edge server, which mitigates the impact of data heterogeneity. To reduce the communication overhead of FL, clients will periodically update to the edge server and edge servers will periodically update to the cloud server. Moreover, we propose a mean field game-based probabilistic device selection scheme. Jointly considering their contributions and the population diversity, a fraction of devices will be selected to join the FL iteration. Taking driving range estimation as an example of connected vehicle applications in the experiment, we have shown that the proposed FL frameworks can increase the prediction accuracy by 28.9% with 5 times fewer clients’ participation, compared with the vanilla FL.
Augmentation-based Domain Generalization for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are two research areas that aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unseen domains. While UDA methods have access to unlabeled target images, domain generalization does not involve any target data and only learns generalized features from a source domain. Image-style randomization or augmentation is a popular approach to improve network generalization without access to the target domain. Complex methods are often proposed that disregard the potential of simple image augmentations for out-of-domain generalization. For this reason, we systematically study the in- and out-of-domain generalization capabilities of simple, rule-based image augmentations like blur, noise, color jitter and many more. Based on a full factorial design of experiment design we provide a systematic statistical evaluation of augmentations and their interactions. Our analysis provides both, expected and unexpected, outcomes. Expected, because our experiments confirm the common scientific standard that combination of multiple different augmentations outperforms single augmentations. Unexpected, because combined augmentations perform competitive to state-of-the-art domain generalization approaches, while being significantly simpler and without training overhead. On the challenging synthetic-to-real domain shift between Synthia and Cityscapes we reach 39.5% mIoU compared to 40.9% mIoU of the best previous work. When additionally employing the recent vision transformer architecture DAFormer we outperform these benchmarks with a performance of 44.2% mIoU.
An Efficient Tracking Method Based on Multipath Utilization in Traffic Scenarios

Radar has been widely used in the field of traffic with the development of radar detection and tracking technology. However, the traffic scene is complex, where some targets generate multipath due to the reflection of electromagnetic waves. The multipath phenomenon affects the tracking results and cause many errors. Most existing techniques identify multipath targets and eliminate them, which do not utilize the target information in multipath measurements. This paper proposes an algorithm to reduce the tracking error by using the multipath target information. By modeling the propagation path of multipath and the tracking process, the target information is obtained. Our algorithm can not only remove the false multipath target but also feed back the real world information contained in the multipath target in the tracking process. Moreover, the experimental results show that our algorithm can effectively improve the tracking accuracy and robustness of multipath targets.
Lightweight Hash Function based on Fermat Number Transform (FNT)

This paper presents a lightweight hash function using the Fermat number transform (FNT). The FNT is one of the most important number-theoretic transforms, with many applications in digital signal processing. It can be implemented using only addition, subtraction and shifting without any multiplication, making it suitable for lightweight hash functions. In this work, the FNT is used as the core of a 128-bit hash function with a simple structure, and some of its key properties are evaluated. FNT-based lightweight hash functions can offer good security with simple structures.
Open-world driving scene segmentation via multi-stage and multi-modality fusion of vision-language embedding

In this study, a pixel-text level multi-stage multi-modality fusion segmentation method is proposed to make the open-world driving scene segmentation more efficient. It can be used for different semantic perceptual needs of autonomous driving scenarios for real-world driving situations. The method can finely segment unseen labels without additional corresponding semantic segmentation labels, only using the existing semantic segmentation data. The proposed method consists of 4 modules. A visual representation embedding module and a segmentation command embedding module are used to extract the driving scene and the segmentation category command. A multi-stage multi-modality fusion module is used to fuse the driving scene visual information and segmentation command text information for different sizes at the pixel-text level. Finally, a cascade segmentation head is used to ground the segmentation command text to the driving scene for encouraging the model to generate corresponding high-quality semantic segmentation results. In the experiment, we first verify the effectiveness of the method for zero-shot segmentation using a popular driving scene segmentation dataset. We also confirm the effectiveness of synonyms unseen label and hierarchy unseen label for the open-world semantic segmentation.
Magpie: Efficient Big Data Query System Parameter Optimization based on Pre-selection and Search Pruning Approach

With the rapid development of big data technology, big data systems are more and more used to process SQL query jobs. However, intricate big data system parameters have big effects on the quality of service for big data query performance. Moreover, the types of query jobs and data sizes of data sets are diversified, which have different optimal parameters for best running performance. There exist high complexity and difficulties in tuning the big data system parameters for efficient query job execution. To address this issue, we propose Magpie, an efficient big data query tuning system based on the genetic algorithm with customized pruning strategies. The experimental results show that the proposed approaches can reduce big data query job execution time by 35.5% on average compared with the cuttingedge big data system parameter tuning methods.
An Extensive Study on Model Architecture and Program Representation in the Domain of Learning-based Automated Program Repair

Bug fixing is one of the most time-consuming and resource-intensive tasks in the software development life cycle.Automated Program Repair (APR) might be able to help in this process, but it still has to overcome many obstacles.Deep learning models have shown promise for automated program repair in recent years, but their effectiveness can depend on the representation of the source code used as input.In this paper, we conduct an experimental study to compare the performance of deep learning models on two popular programming languages, Java and JavaScript, using three different code representations: raw text, command sequences, and abstract syntax trees (ASTs).We also experiment with varying models, including T5, CodeT5, (for solving sequence-to-sequence tasks) RoBERTa, and GPTNeo (to encode/decode AST graph information).We evaluate the models on a set of real-world defects from open-source projects and compare the performance, and the repair patches generated by the models.Our results show that training on command sequence representation outperforms most other configurations.We achieve a best of 19.88% accuracy on the java-small dataset, and 11.87% on java-medium, using text representation.Using command sequence representation, we achieve 30.64% on javasmall and 18.53% on the medium dataset.However, when representing the source with ast+text information, our models significantly underperform compared to other representations, achieving results below one percent.Our findings contribute to a better understanding of the strengths and limitations of deep learning models for automated program repair and provide practical guidance for their use in practice.
Integrating All-optical Switching and Entangled Photon Source Placement for Entanglement Routing

Entanglement routing plays a vital role in quantum networks. Previous works on entanglement routing did not note that all-optical switching capacity can be used to improve the network throughput or ignored that the placement of Entangled Photon Sources (EPS), another type of precious (and costly) quantum source, which also limits the network throughput. In this paper, we propose OptEPS to jointly optimize all-optical switching and EPS placement in entanglement routing to maximize the quantum network throughput. The main challenge of OptEPS lies in two folds: i). an entanglement may fail to be created; ii). the joint optimization problem suffers from a large time complexity. To overcome these challenges, we first formulate the joint optimization problem as a link-path based model and prune out some candidate paths in order to reduce the time complexity. Then, efficient algorithms are proposed to derive a near-optimal solution in a timely manner. Extensive simulations show that compared with the solutions ignoring EPS placement or without introducing all-optical switching, OptEPS will increase the network throughput by 1557% and 109%, respectively.
A unified model of data uncertainty and data relation uncertainty

Data uncertainty (DU), one of the most interesting topics in information science, inevitably leads to data relation uncertainty (DRU). Moreover, DU greatly affects the results of information processing problems such as sorting and top-k queries with uncertain data. However, studies have not clarified the mechanism, mode, and degree of impact of DU on DRU. Hence, we established a unified mathematical model to reveal the precise relation between DU and DRU. The experimental results showed this model’s high accuracy under various conditions and its relative error and absolute error of less than 0.2%. This model can be used to quantitatively estimate DRU according to a given DU and vice versa. Unexpectedly, the experiments showed that under given conditions, high DU will not necessarily lead to high DRU. This characteristic is highly significant to the design of data sorting and query algorithms with uncertain data.
Design and optimization of an SOI-based electro-absorption-type VOA

A silicon-on-insulator (SOI) variable optical attenuator (VOA) based on the plasma dispersion effect is optimized and realized, and the effects of doping concentration and distance about the VOA's modulation depth and attenuation efficiency are investigated. Two structures of the VOA component are designed to achieve low power consumption, high stability, and high modulation efficiency. The modulation depth of the series VOA scheme reached 60.11 dB, and the insertion loss is only 4.87 dB. Compared with conventional components, our optimized VOA can not only improve the modulation accuracy and efficiency but also reduce the wavelength dependence.
FlowerHub: An Online Flower Shop

The study aimed to design and develop an online flower shop using object-oriented analysis and the Laravel PHP Framework. It aims to enhance local flower shops in Surigao City by improving manual ordering processing with online shopping convenience. The app offers a user-friendly interface, empowering flower shop owners with efficient inventory management, marketing strategies, and expanded reach. The system usability score of 85 out of 100 reflects higher user satisfaction and usability.
Quantum boosting using domain-partitioning hypotheses

Boosting is an ensemble learning method that converts a weak learner into a strong learner in the PAC learning framework. Freund and Schapire designed the Godel prize-winning algorithm named AdaBoost that can boost learners, which output binary hypotheses. Recently, Arunachalam and Maity presented the first quantum boosting algorithm with similar theoretical guarantees. Their algorithm, which we refer to as QAdaBoost henceforth, is a quantum adaptation of AdaBoost and only works for the binary hypothesis case. QAdaBoost is quadratically faster than AdaBoost in terms of the VC-dimension of the hypothesis class of the weak learner but polynomially worse in the bias of the weak learner. Izdebski et al. posed an open question on whether we can boost quantum weak learners that output non-binary hypothesis. In this work, we address this open question by developing the QRealBoost algorithm which was motivated by the classical RealBoost algorithm. The main technical challenge was to provide provable guarantees for convergence, generalization bounds, and quantum speedup, given that quantum subroutines are noisy and probabilistic. We prove that QRealBoost retains the quadratic speedup of QAdaBoost over AdaBoost and further achieves a polynomial speedup over QAdaBoost in terms of both the bias of the learner and the time taken by the learner to learn the target concept class. Finally, we perform empirical evaluations on QRealBoost and report encouraging observations on quantum simulators by benchmarking the convergence performance of QRealBoost against QAdaBoost, AdaBoost, and RealBoost on a subset of the MNIST dataset and Breast Cancer Wisconsin dataset.
Gaussian quantum steering in a nondegenerate three-level laser

Steering is a type of nonseparable quantum correlation, where its inherent asymmetric feature makes it distinct from Bell nonlocality and entanglement. In this paper, we investigate quantum steering in a two-mode Gaussian state [Formula: see text] coupled to a two-mode vacuum reservoir. The mode [Formula: see text] ([Formula: see text]) is emitted during the first (second) transition of a nondegenerate three-level cascade laser. By means of the master equation of the state [Formula: see text], we derive the analytical expression of the steady-state covariance matrix of the modes [Formula: see text] and [Formula: see text]. Using realistic experimental parameters, we show that the state [Formula: see text] can exhibit asymmetric steering. Furthermore, by an appropriate choice of the physical parameters of the state [Formula: see text], we show that one-way steering can be achieved. Essentially, we demonstrate that one-way steering can, in general, occur only from [Formula: see text]. Besides, we perform a comparative study between the steering of the two laser modes and their Gaussian Rényi-2 entanglement. As a result, we found that the entanglement and steering behave similarly in the same circumstances, i.e. both of them decay under dissipation effect; moreover, they can be well enhanced by inducing more and more quantum coherence in the state [Formula: see text]. In particular, we found that the steering remains always less than the Gaussian Rényi-2 entanglement.
Incentive-driven long-term optimization for hierarchical federated learning

Hierarchical federated learning (HFL), an emerging paradigm of the client-edge-cloud architecture, can effectively leverage nearby edge servers to conduct model aggregation, significantly reducing transmission overhead. HFL faces both technical and economic challenges: first, in the online setting, computation resources and network bandwidth can only reveal themselves when clients participate in HFL model training. Second, the model training process consumes substantial resources at the clients, such as energy, computation, and bandwidth. It is unrealistic to assume that all clients contribute their resources voluntarily. Thorough investigation is lacking for these challenges in existing HFL research. This work develops a novel online algorithm AUCS, based on the auction and combinatorial multi-armed bandit, to minimize the overall latency of HFL training. AUCS utilizes the ratio of upper confidence bound-based reward to the bid as a criterion for winner determination. Then, AUCS computes the key payment for each winner to guarantee truthfulness of the incentive mechanism. Theoretically, AUCS can achieve sub-linear regret, truthfulness, individual rationality and computational efficiency, and guarantees model convergence. Simulations on real-world datasets and training tasks demonstrate the advantages of AUCS in terms of training latency, model accuracy, and system efficiency.
Drone-Based Quantum Key Distribution

Quantum networks between mobile platforms enable secure communication, distributed quantum sensors, and quantum computing networks. Here we present data from a quantum key distribution system between two aerial drones separated by 10 m.
Chapter 5. Recent claims of human-machine parity in translation highlight core issues surrounding the human evaluation of machine translation

In 2018, the first claims of empirical backing for human-machine parity in translation (HMPT) emerged at the WMT18 Conference on Machine Translation and in a study using WMT resources. Other researchers quickly refuted these claims, pointing to a flawed human evaluation campaign. Subsequent HMPT claims at WMT19 were also empirically refuted. This chapter discusses the evolution of recommendations for human evaluation of MT stemming from these claims to HMPT and evaluates possibilities of HMPT at WMT20 in the context of these recommendations. Finally, we summarize all criteria for human evaluation of MT based on recent literature.