Reviewerly/exHarmony huggingface dataset:
Optimized convolutional pose machine for 2D hand pose estimation

Hand pose estimation is a challenging task owing to the high flexibility and serious self-occlusion of the hand. Therefore, an optimized convolutional pose machine (OCPM) was proposed in this study to estimate the hand pose accurately. Traditional CPMs have two components, a feature extraction module and an information processing module. First, the backbone network of the feature extraction module was replaced by Resnet-18 to reduce the number of network parameters. Furthermore, an attention module called the convolutional block attention module (CBAM) is embedded into the feature extraction module to enhance the information extraction. Then, the structure of the information processing module was adjusted through a residual connection in each stage that consist of a series of continuous convolutional operations, and requires a dense fusion between the output from all previous stages and the feature extraction module. The experimental results on two public datasets showed that the OCPM network achieved excellent performance.
A brief introduction to quantum algorithms

Quantum algorithms are demonstrated to outperform classical algorithms for certain problems and thus are promising candidates for efficient information processing. Herein we aim to provide a brief and popular introduction to quantum algorithms for both the academic community and the general public with interest. We start from elucidating quantum parallelism, the basic framework of quantum algorithms and the difficulty of quantum algorithm design. Then we mainly focus on a historical overview of progress in quantum algorithm research over the past three to four decades. Finally, we clarify two common questions about the study of quantum algorithms, hoping to stimulate readers for further exploration.
A random grouping-based self-regulating artificial bee colony algorithm for interactive feature detection

As the dimensionality and complexity of datasets increase, such as biological data, the demand for fast and efficient technologies to detect relevant feature information from these data is also increasing. Extensive progress has been made in the development of evolutionary algorithms (EAs), which can detect the association between a single feature and class label in high-dimensional data. However, when facing the high-dimensional problem caused by interactions between features, the search space of EAs increases exponentially with an increase in the number of features, resulting in unsatisfactory solution quality. Therefore, this study proposed a random grouping-based self-regulating artificial bee colony algorithm (RCABC). RCABC first decomposes all dataset features into a series of dynamic subsets using a dynamic random grouping (DRG) strategy. Thereafter, a self-regulating bee colony optimizer (S-Optimizer) is used to detect relevant interactive features in each subset. This study demonstrates the superiority of the proposed algorithm through experimental results on synthetic and real-world biological datasets.
Development of a Learning-aid tool using Hand Gesture Based Human Computer Interaction System

A hand gesture recognition system is a natural and simple way of communicating in today&#x2019;s world. The development of teaching methods by using technology-dependent useful items to increase communication and interaction between the teacher and the student is a major part of today&#x2019;s e-learning. In this paper, we have proposed an interactive learning-aid tool based on a vision-based hand gesture recognition system. The system uses MediaPipe for hand gesture recognition. The recognized hand gestures use a virtual-mouse-based object controlling system to control various virtual objects created using Unity. The system has been tested using six hand gestures and it is found that the system can be used effectively for controlling various virtual objects.
Few-weight codes over a non-chain ring associated with simplicial complexes and their distance optimal Gray image

In this paper, we construct new infinite families of codes over the ring R=F2+uF2+vF2+uvF2,u2=v2=0,uv=vu by employing simplicial complexes. When the simplicial complexes are all generated by a single maximal element, we compute the Lee weight distributions of these classes of codes. By the Gray map, we show that some classes of codes are minimal and some classes of these codes are distance optimal.
Quantune: Post-training quantization of convolutional neural networks using extreme gradient boosting for fast deployment

To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision representation is converted to a lower bit representation. To overcome problems such as sensitivity of the training dataset, high computational requirements, and large time consumption, post-training quantization methods that do not require retraining have been proposed. In addition, to compensate for the accuracy drop without retraining, previous studies on post-training quantization have proposed several complementary methods: calibration, schemes, clipping, granularity, and mixed-precision. To generate a quantized model with minimal error, it is necessary to study all possible combinations of the methods because each of them is complementary and the CNN models have different characteristics. However, an exhaustive or a heuristic search is either too time-consuming or suboptimal. To overcome this challenge, we propose an auto-tuner known as Quantune, which builds a gradient tree boosting model to accelerate the search for the configurations of quantization and reduce the quantization error. We evaluate and compare Quantune with the random, grid, and genetic algorithms. The experimental results show that Quantune reduces the search time for quantization by approximately 36.5× with an accuracy loss of 0.07–0.65% across six CNN models, including the fragile ones (MobileNet, SqueezeNet, and ShuffleNet). To support multiple targets and adopt continuously evolving quantization works, Quantune is implemented on a full-fledged compiler for deep learning as an open-sourced project.
On Black-Box Explanation for Face Verification

Given a facial matcher, in explainable face verification, the task is to answer: how relevant are the parts of a probe image to establish the matching with an enrolled image. In many cases, however, the trained models cannot be manipulated and must be treated as "black-boxes". In this paper, we present six different saliency maps that can be used to explain any face verification algorithm with no manipulation inside of the face recognition model. The key idea of the methods is based on how the matching score of the two face images changes when the probe is perturbed. The proposed methods remove and aggregate different parts of the face, and measure contributions of these parts individually and in-collaboration as well. We test and compare our proposed methods in three different scenarios: synthetic images with different qualities and occlusions, real face images with different facial expressions, poses, and occlusions and faces from different demographic groups. In our experiments, five different face verification algorithms are used: ArcFace, Dlib, FaceNet (trained on VGGface2 and CasiaWebFace), and LBP. We conclude that one of the proposed methods achieves saliency maps that are stable and interpretable to humans. In addition, our method, in combination with a new visualization of saliency maps based on contours, shows promising results in comparison with other state-of-the-art art methods. This paper presents good insights into any face verification algorithm, in which it can be clearly appreciated which are the most relevant face areas that an algorithm takes into account to carry out the recognition process.
A Light-Weight CNN for Object Detection with Sparse Model and Knowledge Distillation

This study details the development of a lightweight and high performance model, targeting real-time object detection. Several designed features were integrated into the proposed framework to accomplish a light weight, rapid execution, and optimal performance in object detection. Foremost, a sparse and lightweight structure was chosen as the network’s backbone, and feature fusion was performed using modified feature pyramid networks. Recent learning strategies in data augmentation, mixed precision training, and network sparsity were incorporated to substantially enhance the generalization for the lightweight model and boost the detection accuracy. Moreover, knowledge distillation was applied to tackle dropping issues, and a student–teacher learning mechanism was also integrated to ensure the best performance. The model was comprehensively tested using the MS-COCO 2017 dataset, and the experimental results clearly demonstrated that the proposed model could obtain a high detection performance in comparison to state-of-the-art methods, and required minimal computational resources, making it feasible for many real-time deployments.
bi-directional Bayesian probabilistic model based hybrid grained semantic matchmaking for Web service discovery

Abstract Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and preferences. In this paper, we present a novel service discovery approach that can support user queries with various-size-grained text elements. Compared with existing approaches that only support semantics matchmaking in single texture granularity (either word level or paragraph level), our approach enables the requester to search for services with any type of query content with high performance, including word, phrase, sentence, or paragraph. Specifically, we present an unsupervised Bayesian probabilistic model, bi-Directional Sentence-Word Topic Model (bi-SWTM), to achieve semantic matchmaking between possible textual types of queries (word, phrase, sentence, paragraph) and the texts in web service descriptions, by mapping words and sentences in the same semantic space. The bi-SWTM captures textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible method to build the semantic links from user queries to service descriptions. The novel approach is validated using a collection of comprehensive experiments on ProgrammableWeb data. The results demonstrate that the bi-SWTM outperforms state-of-the-art methods on service discovery and classification. The visualization of the nearest-neighbored queries and descriptions shows the capability of our model on capturing the latent semantics of web services.
A first look at HTTP/3 adoption and performance

The third version of the Hypertext Transfer Protocol (HTTP) is in the final standardization phase by the IETF. In addition to better security and greater flexibility, it promises performance benefits. HTTP/3 uses a more efficient header compression scheme and replaces TCP with QUIC, a transport protocol over UDP that was originally proposed by Google and is also currently being standardized. Although initial implementations of HTTP/3 already exist and some websites have announced their support, few studies have been conducted to assess its benefits. We measure the adoption and performance of HTTP/3 and show how it has been adopted by some of the leading Internet companies such as Google, Facebook, and Cloudflare in 2020. We conduct a large-scale measurement campaign on thousands of websites using HTTP/3 to understand the extent to which it outperforms HTTP/2 in web browsing applications. We find that websites using HTTP/3 often host most web page objects on third-party servers that only support HTTP/2 or even HTTP/1.1. Websites that load objects from a limited number of third-party domains are the ones that see larger performance gains. However, our experiments show that HTTP/3 offers significant benefits only in high-latency or mobile networks. Finally, we run an experimental campaign to study the impact of HTTP/3 on video streaming applications. In this direction, our results show that HTTP/3 currently does not provide benefits.
Can Agile Enterprise Architecture be Implemented Successfully in Distributed Agile Development? Empirical Findings

A potential solution to the high failure rate in distributed agile development and enhance the success of projects is through implementing agile enterprise architecture, though the success is still to be established. The present paper empirically investigates the gap, by defining the role and commitment of implementing agile enterprise architecture on distributed agile development. The data were collected by interviewing 12 key team members and observing four team meetings over 2 months and analyzing using thematic analysis. The present study suggests that implementing agile enterprise architecture is possible in distributed agile development and may have a positive impact on project success. However, many questions demand further investigation.
CSG: Classifier-Aware Defense Strategy Based on Compressive Sensing and Generative Networks for Visual Recognition in Autonomous Vehicle Systems

Visual classification algorithms based-on Deep Neural Networks (DNN) have been widely adopted in autonomous vehicle design. However, DNN suffers from adversarial attacks including pixel attacks and patch attacks, and its adoption may introduce new vulnerability into such security-critical scenarios. Existing defense techniques only focus on defending against one category, either pixel attacks or patch attacks, but does not translate to the other. Hence, the design of a practical comprehensive real-time defense algorithm for DNN-based classifiers presents a challenging task in this adversarial context. This paper attempts to address the abovementioned problem by combining Compressive Sensing with Generative neural networks (CSG) to construct an efficient defense framework, in conjunction with the proposal of a classifier-aware adversarial training way. Extensive experiments have been conducted using the LISA road sign dataset to evaluate the performance of CSG. The results show its superiority in comprehensively defending adversarial examples generated using attacks including CW-L2, FGSM and Sticker, compared with other state-of-the-art defense techniques.
The Role of Blockchain Technology in Harnessing the Sustainability of Chinese Digital Finance

In recent years, blockchain, with its features of decentralization, security, reliability, transparency of information, and low maintenance cost, has been applied in the fintech industry in more and more cases in China. The maturity of blockchain-enabled technology has improved. This is expected to rebuild the underlying internet technology or will trigger a new round of development and innovation in the Chinese fintech industry. This chapter aims to investigate the current application of blockchain technology in China's fintech industry by analyzing case studies. Through the comparison and analysis of the cases in the fintech industry, this chapter attempts to examine how and whether digital transformation and other innovative technologies can drive new business models and create valuable experiences for consumers and/or firms in China's fintech industry. A variety of challenges and opportunities is analyzed to draw a blueprint for Chinese blockchain development. Finally, this chapter also provides some recommendations to the government.
ConNOC: A Practical Timing Channel Attack on Network-on-chip Hardware in a Multicore Processor

Shared hardware resources in today's microprocessors have emerged as a target for adversaries to leak secret information via timing-based software side channels. This paper characterizes such attacks on the non-persistent network-on-chip (NoC) hardware, and demonstrates its practicality on a real multicore machine. State-of-the-art 4-core baseline setup shows an average of less than 2-cycle latency variation due to contention at the NoC hardware resources. This noisy and unpredictable timing channel achieves ~18% accuracy when the attacker is assumed to have no replay capability. However, in the presence of a high number of replays, accuracy improves significantly but slows down the speed of the attack. ConNOC proposes a novel attack setup that better exploits interference at NoC hardware to make it less noisy and more predictable. It demonstrates ~7-cycle latency variation under no replay capability. The evaluation of covert communication and information leakage attacks shows 100% accuracy using five replays to leak information. This translates to 2 mbps (mega bits per second) attack throughput on the Tilera TileGx72 multicore processor executing at 1GHz.
Relating individual motion sickness levels to subjective discomfort ratings

High levels of vehicle automation are expected to increase the risk of motion sickness, which is a major detriment to driving comfort. The exact relation between motion sickness and discomfort is a matter of debate, with recent studies suggesting a relief of discomfort at the onset of nausea. In this study, we investigate whether discomfort increases monotonously with motion sickness and how the relation can best be characterized in a semantic experiment (Experiment 1) and a motion sickness experiment (Experiment 2). In Experiment 1, 15 participants performed pairwise comparisons on the subjective discomfort associated with each item on the popular MIsery SCale (MISC) of motion sickness. In Experiment 2, 17 participants rated motion sickness using the MISC during exposures to four sustained motion stimuli, and provided (1) numerical magnitude estimates of the discomfort experienced for each level of the MISC, and (2) verbal magnitude estimates with seven qualifiers, ranging between feeling 'excellent' and 'terrible'. The data of Experiment 1 show that the items of the MISC are ranked in order of appearance, with the exception of 5 ('severe dizziness, warmth, headache, stomach awareness, and sweating') and 6 ('slight nausea'), which are ranked in opposite order. However, in Experiment 2, we find that discomfort associated with each level of the MISC, as it was used to express motion sickness during exposure to a sickening stimulus, increases monotonously; following a power law with an exponent of 1.206. While the results of Experiment 1 replicate the non-linearity found in recent studies, the results of Experiment 2 suggest that the non-linearity is due to the semantic nature of Experiment 1, and that there is a positive monotonous relation between MISC and discomfort in practice. These results support the suitability of MISC to assess motion sickness.
A Two-Party Quantum Parliament

This paper introduces the first functional model of a quantum parliament that is dominated by two parties or coalitions, and may or may not contain independent legislators. We identify a single crucial parameter, aptly named free will radius, which can be used as a practical measure of the quantumness of the parties and the parliament as a whole. The free will radius used by the two parties determines the degree of independence that is afforded to the representatives of the parties. Setting the free will radius to zero degrades the quantum parliament to a classical one. On the other hand, setting the free will radius to its maximum value 1 makes the representatives totally independent. Moreover, we present a quantum circuit in Qiskit with which we simulate the operation of the quantum parliament under various scenarios. The experimental results allow us to arrive at some novel and fundamental conclusions that, we believe, provide new insights into the operation and the traits of a possible future quantum parliament. Finally, we propose the game “Passing the Bill,” which captures the operation of the quantum parliament and basic options available to the leadership of the two parties.
From Demonstration to Flight: Realization of Autonomous Aerobatic Maneuvers for Fast, Miniature Fixed-Wing UAVs

Performing agile maneuvers autonomously remains a challenge for miniature fixed-wing unmanned aerial vehicles (UAVs) because they are required to fly along complex trajectories while approaching their physical limits. This article proposes an integrated maneuver planning and tracking method to address this issue. The developed approach is built upon the learning from demonstration (LFD) framework. First, an experienced pilot performs several sophisticated maneuvers manually. Then, instead of directly controlling the UAV to track the demonstrated trajectory, we split the teaching maneuvers into multiple segments. A novel dynamic motion primitive in terms of a unit dual quaternion (DQ-DMP) is then developed to encode and store those segments’ rotational and translational features. Besides, a robust connecting method for the learned DQ-DMPs is introduced, resulting in a smooth maneuvering trajectory that even the pilot has not taught yet. Additionally, a cascaded dynamic inversion control scheme with stable tracking performance is also developed. Our system is integrated into an autonomous fixed-wing drone and implements several complex maneuvers in the outdoor environment. The results of comparative experiments and outdoor flight reveal that our approach can learn, adapt and generalize maneuvers in real-world scenarios.
Pathways to Cybersecurity Awareness and Protection Behaviors in South Korea

Cybersecurity awareness plays an important role in the human arena of cybersecurity. Using a nationally representative South Korean sample, this current study examines two pathways leading to enhanced cybersecurity protection behaviors in relation to cybersecurity awareness and cybersecurity risks. In addition, this study aims to identify generational differences between pathways. Data were derived from Information Security Survey, which is the latest public access survey on this topic. The study found that cybersecurity awareness and cybersecurity risk have a positive association with cybersecurity behaviors. In a multigroup analysis, the influence of cybersecurity risk on cybersecurity behaviors was statistically significant for all generational groups. Across the younger generation, acceptance of cybersecurity risk was more influenced by cybersecurity protection behaviors, while cybersecurity awareness became more important for the older generation. Practical, policy, and research implications for increasing cybersecurity awareness and risk to promote safe cybersecurity protection behaviors are also discussed.
ACSiam: Asymmetric convolution structures for visual tracking with Siamese network

Object trackers based on Siamese network usually transform the tracking task into a matching problem between the candidate samples and the target template. However, with the increasing depth and width of backbone networks, researches on Siamese trackers using backbone networks are not very advanced. Therefore, it is necessary for us to further investigate the characteristics of backbone network. As a fact, the ability of backbone network to extract features can directly determine the performance of object tracker. Given this, in this paper, we first propose an asymmetric convolutional network to improve the representational capability of backbone network. And then, the strip convolution is employed to enhance the operational capability of square kernel convolution in the backbone network. Besides, we also construct a novel module named Feature Dropblock (i.e., FD) to simulate the occlusion of hidden space, which goal is to improve the performance of backbone network in the target tracking under occlusion. To demonstrate the effectiveness of the proposed tracker, extensive ablation studies are conducted. Better results are obtained on the tracking benchmarks OTB100 and VOT2018, compared to other state-of-the-art trackers.