Reviewerly/exHarmony huggingface dataset:
Time-Optimal Design of Finite Field Arithmetic for SIKE on Cortex-M4

The advances in quantum technologies and the fast move toward quantum computing are threatening classical cryptography and urge the deployment of post-quantum (PQ) schemes. The only isogeny-based candidate forming part of the third round of the standardization, the Supersingular Isogeny Key Encapsulation (SIKE) mechanism, is a subject of constant latency optimizations given its attractive compact key size lengths and, thus, its limited bandwidth and memory requirements. In this work, we present a new speed record of the SIKE protocol by implementing novel low-level finite field arithmetics targeting ARMv7-M architecture. We develop a handcrafted assembly code for the modular multiplication and squaring functions where we obtain 8.71% and 5.38% of speedup, respectively, compared to the last best-reported assembly implementations for p434. After deploying the finite field optimized architecture to the SIKE protocol, we observe 5.63%, 3.93%, 3.48%, and 1.61% of latency reduction for SIKE p434, p503, p610, and p751, respectively, targeting the NIST recommended STM32F407VG discovery board for our experiments.
5G-AKA, Revisited

The 5G primary Authentication and Key Agreement (5G-AKA) protocol has received much attention in the literature. However, most of the 5G-AKA and relevant AKA protocols do not guarantee forward secrecy. In this paper, we propose a secure AKA (for short, $$\textsf {AKA}^{\star }$$ ) protocol that provides UE (User Equipment) anonymity and forward secrecy for 5G and beyond networks. Also, we formally prove the security of the $$\textsf {AKA}^{\star }$$ protocol in the random oracle model under the CDH (Computational Diffie-Hellman) problem. Moreover, we discuss several aspects of the $$\textsf {AKA}^{\star }$$ protocol, and compare the $$\textsf {AKA}^{\star }$$ and relevant protocols (EAP-AKA, EAP-AKA’, EAP-AKA’ FS, 5G-AKA and 5G-AKA’) in terms of efficiency, forward secrecy, UE anonymity and UE unlinkability.
Analysis of Radioactive Decay Based Entropy Generator in the IoT Environments

In cryptography, random numbers are the most important element for security. Random numbers are generated through RNG (Random Number Generator), and collecting sufficient entropy sources is essential to generate random numbers. However, it is difficult to collect sufficient entropy sources due to limited resources in the IoT environment. In this paper, we have $$\alpha $$ - and $$\beta $$ -based radioactive decay noise sources to solve this problem, and generated entropy using proper entropy generation methods respectively. The result is as follows: $$\beta $$ -based noise sources can generate entropy about 32 times faster than $$\alpha $$ -based noise sources.
TLS Goes Low Cost: When TLS Meets Edge

Recently, we have witnessed an upward trend in adopting the Transport Layer Security version 1.3 (TLS 1.3) to numerous applications (Google Cloud [25], Microsoft software products [20], CloudFlare [27]). Although TLS 1.3 provides higher efficiency than the previous versions of TLS, its handshake protocol still requires the server to send its certificate to the client which consumes a significant amount of network bandwidth. Moreover, the client becomes idle while it is waiting for the certificate to arrive. This latency is one of the causes of the TLS handshake delay. Adequate adoption of edge computing can increase the efficiency of traditional server client architectures. In this paper, we envision a new paradigm to adopt edge computing into TLS to improve the efficiency of session establishment. Our new architecture will motivate researchers to consider the edge in improving the TLS protocol in the future. TLS-EC (TLS with Edge Computing) protocol improves the TLS 1.3 handshake efficiency by reducing server-side certificate transmission overhead and network latency between server and client through edge computing. We also present the implementation of TLS-EC, which shows a reduction in both the handshake time and the bandwidth consumption between the server and the client during the TLS handshake. In particular, our experiments indicate that bandwidth consumption can be reduced by 33% and 49%, respectively, for ECDSA and RSA-based certificates with 128-bit security level compared to TLS 1.3 full handshake.
Does Virtual Reality Enhance Taxation (Capital Allowances) Learning? An International Study

Taxation undergraduate students in Wales, Singapore and China experienced virtual reality (VR) in their learning of taxation, where a real-world capital allowances scenario supported student learning of the topic. Experiential learning can help students improve their understanding of the subject and gain valuable skills for employment or further studies e.g. professional qualifications. Virtual reality is an immersive learning experience that can help students improve their understanding. As a visual learning approach, students are more likely to be able to recall their visual experiences. VR has the benefit of participators experiencing “presence” in their learning, putting themselves in the virtual world making it a memorable and often an enjoyable experience. This study compares student perceptions from students at each of the three universities using a voluntary questionnaire, accessed via a QR reader, before the VR session to capture their knowledge and use of technology plus their thoughts on using VR for learning. A further questionnaire after the VR session captured student’s thoughts on their VR learning experience, did they enjoy the experience, did it help their understanding, and would they like to use it more in their studies? A summary of the results demonstrates the majority of students indicated: they were positive about using VR for learning; VR enhanced their learning experience; VR improved tax understanding; They enjoyed using VR; and they would like to see more VR in their studies.
Convolution, Correlation and Generalized Shift Operations Based on the Fresnel Transform

The Fresnel transform (FrT) is commonly used to describe the free-space propagation of optical waves. In this work, we present new definitions for the convolution, correlation and generalized shift operations based on the FrT. The generalized shift operation is defined by using simultaneous space and phase shifts. The generalized shift operation is useful for centred optical systems in the Fresnel domain (FrD) when the data distributions at the input plane of the optical system are shifted. The new convolution and correlation operations defined in terms of the FrT, the wavelength and the propagation distance, can be considered as a generalization of the usual convolution and correlation operations. The sampling theorem for distributions, whose resulting FrT has finite support, is formulated by using the new convolution operation introduced in this work and a new definition of the Dirac comb function. These new definitions and results could be applied to describe, design and implement optical processing systems related to the FrT. Finally, we present a centred optical systems used in holography and optical security systems that can be described or modelled by the new definitions of the operations proposed in this paper.
Conceptualisation of the Relational Governance of Artificial Intelligence

Having established AI as an element of Relational Economics, this chapter proceeds to conceptualise the Relational Governance of AI. To this end, it is divided into three sections: First, it delimits Wieland’s (2018, 2020) concept of Relational Governance from existing research about ‘Relational Governance’. Thereafter, critical elements of Wieland’s (2018, 2020) theory of Relational Governance are summarised and interpreted for the AI context. Second, it addresses the identified theoretical need for additions and adaptations of Wieland’s (2018, 2020) governance approach for the AI context. Hence, it begins with a depiction of the relational transaction underlying AI governance by presenting each element of the transaction and the interlinkages among them. Third, it proceeds to present its conceptualisation of Relational AI Governance by introducing the governance formula depicting this underlying transaction for AI governance.1 The formula depicts the overall governance structure and the governance parameters existing in the AI context. The governance parameters, in particular, subsume important content-related themes, e.g., AI ethics, providing the base for the operationalisation of Relational AI Governance in organisations.
Nyquist Sampling Conditions of Some Diffraction Algorithms with Adjustable Magnification

Diffraction algorithms with adjustable magnification are dominant in holographic projection and imaging. However, the algorithms are limited by the Nyquist sampling conditions, and simulation results with inappropriate parameters sometimes appear with aliasing. At present, many diffraction algorithms have been proposed and improved, but there is a need for an overall analysis of their sampling conditions. In this paper, some classical diffraction algorithms with adjustable magnification are summarized, and their sampling conditions in the case of plane wave or spherical wave illumination are analyzed and compared, which helps to select the appropriate diffraction algorithm according to the specific parameter conditions of the simulation to avoid aliasing.
UX Framework including Imbalanced UX Dataset Reduction Method for Analyzing Interaction Trends of Agent Systems

The performance of game AI can significantly impact the purchase decisions of users. User experience (UX) technology can evaluate user satisfaction with game AI by analyzing user interaction input through a user interface (UI). Although traditional UX-based game agent systems use a UX evaluation to identify the common interaction trends of multiple users, there is a limit to evaluating UX data, i.e., creating a UX evaluation and identifying the interaction trend for each individual user. The loss of UX data features for each user should be minimized and reflected to provide a personalized game agent system for each user. This paper proposes a UX framework for game agent systems in which a UX data reduction method is applied to improve the interaction for each user. The proposed UX framework maintains non-trend data features in the UX dataset where overfitting occurs to provide a personalized game agent system for each user, achieved by minimizing the loss of UX data features for each user. The proposed UX framework is applied to a game called "Freestyle" to verify its performance. By using the proposed UX framework, the imbalanced UX dataset of the Freestyle game minimizes overfitting and becomes a UX dataset that reflects the interaction trend of each user. The UX dataset generated from the proposed UX framework is used to provide customized game agents of each user to enhanced interaction. Furthermore, the proposed UX framework is expected to contribute to the research on UX-based personalized services.
Does the Centrual Economic Work Conference (CEWC) affect the stock market?

Purpose This paper focuses on the orientation of the economy expressed in the communication of the Central Economic Work Conference (CEWC) of China and its relation with the stock market. This study seeks to explore which orientation of the economy may have a stronger impact on the rise of the stock market. It proposes words connoting orientation of the economy (WOE) that is closely related to the stock market, and different WOE has different impacts on the stock market in terms of intensity. The study aims to provide investors with better investment strategies by identifying the stronger developmental WOE. Design/methodology/approach The paper opted for an exploratory study using the textual analysis approach, based on a corpus of 28 CEWC communications spanning from 1994 to 2021. The raw corpus amounted to 50,754 words in total that are treated with noise reduction method and record an effective corpus of 39,591. Findings The paper provides empirical insights into the close relationship of the WOE of the CEWC to the stock market, and different WOE has different impacts on the stock market in terms of intensity. It suggests that WOE connoting development may forecast a rising stock market if it is nearly 40% higher than the other two WOEs by impact index. Research limitations/implications As WOE is only proven in the CEWC, this paper has its limitations in the scope of samples. It is necessary to apply WOE to more Central Bank communication (CBC) and countries. It is desirable to apply the Gunning–Fog index. Practical implications The paper includes implications for investors to read out the orientation of the economy and the degree of different WOEs. Investors are keener to know “what” degree of the CEWC leads to the rise/fall of the stock market. The impact index can be an indicator of a tendency of the stock market, which upgrades the rationality of investment decisions. Social implications This paper fulfills words connoting the orientation of economy as an identified linguistic feature, which the impact of CEWC on stockmarket can be measured. Originality/value Previous academic research studies mostly focus on the impact on stock market from the language features of CBC, rather than that from the more influential body, CEWC communication. This study seeks to provide the relationship of CEWC communication and the time length of the impact on the stock prices.
ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation

Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user's identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings.
Fast object detector with center localization confidence based on FCOS for environment perception in urban traffic scene

Environment perception on urban roads is one of the most important tasks in Automatic Driving System, because object detection is necessary in identifying and localizing cars and pedestrians. However, the accuracy and inference speed are still far away from satisfying, and the problem of missed detection occurs frequently. To address the problems, this paper first uniformly scales all dimensions of depth, width, and resolution in feature extraction module, and then fuses the semantic information in bidirectional cross-scale connections, which extract more significant features and detect objects of various scales. To decrease the undetected rate of small objects, this paper further introduces an IoU-based center localization confidence, which predicts centerness and the distance between the center of predicted box and the center of the ground-truth box, so that the predicted point approaches the center of an object. The experimental results on Cityscapes show that the proposed detector brings an improvement of 6.5% Bbox mAP and reaches an inference speed of 36.6 FPS. Compared with the other widely-used detectors such as Cascade R-CNN, FCOS, and YOLOX, it has obvious advantages in both precision and real-time performance. With additional ticks, the detection precision can be further improved by 4.4%. The proposed detector can accurately and quickly identify and localize objects, which is beneficial for the safety of self-driving cars.
Privacy preserving multi-party multiplication of polynomials based on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e556" altimg="si3.svg"><mml:mrow><mml:mo>(</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> threshold secret sharing

In applications such as cloud analytics, multi-party computation enables a set of participants to compute an arbitrary function of their inputs jointly, without revealing these inputs to one another. In this study, we introduce secure multi-party multiplication using (k,n) threshold secret sharing without increasing the required number of computing servers. This is achieved using encrypted shares and a recombination vector, whereby two encrypted shares of the same secret are sent to each server. Moreover, our method supports multi-input (as opposed to only two-input) multiplication. This is important for operations such as exponential functions that are commonly used in domains including deep learning.
Research on linkage of science and technology in the library and information science field

Scientific research serves as both the root and the driving force behind technological innovation. While the transformation of science to technology is more commonly observed in hard science disciplines, the application of knowledge from soft science disciplines to technological innovation has received less attention, so is the Library and Information Science (abbreviated as LIS). This study seeks to examine the role of LIS knowledge in bridging the link between science and technology, how the LIS knowledge contributes to patented inventions, and and discover the future trend of scientific and technological innovation in LIS. The study collected LIS papers cited by patents and the citing patents’ data to explore the relationship between LIS papers and their citing patents based on time, impact, and topic. Six hypotheses were developed to analyze the flow of knowledge between science and technology in LIS, providing insight into the characteristics of knowledge flow and transformation of this field into technology. The study found that the amount of LIS scientific knowledge transformed into technology applications has been increasing, and the average “paper-patent” transformation time lag per year has been getting longer, etc.. Additionally, the topics “Knowledge organization and strategic planning”, “Network service and reference consultation,” and “Library and public services” in the LIS have the potential to further contribute to the technology field.
Design and Simulation of Pitch Optimizer and Stall Protection for a Typical UAV

Flight dynamics of an Unmanned Aerial Vehicle (UAV) is the science of its orientation and control in three dimensions. Primary orientations include Pitch, Roll, and Yaw. The rear part of the tailplane is known as elevator control of UAV pitch. An airplane stall is an aerodynamic condition in which the airplane exceeds its given critical angle of attack and is no longer able to produce the required lift of normal flight. The main goal of this paper is to design a controller that optimizes the response speed of pitch control and design stall protection for a typical UAV. This protection system operates according to certain conditions, which are known stall conditions. When UAV reaches these conditions, the system generates a warning that UAV is at stall point and the pilot must adjust the situation to stabilize UAV. A PID controller has been implemented on MATLAB, also mathematically calculations were done to obtain suitable PID gains for a faster and accurate response of UAV elevator, after that, these mathematically PID gains have been applied to MATLAB/Simulink. Results showed the control parameters improved, such that settling time is 4.6220 seconds, the rise time is 0.2788 seconds, the percentage overshoot is 36.8192%, and steady-state error less than 2%.
Leukocyte deep learning classification assessment using Shapley additive explanations algorithm

A peripheral blood smear is a basic test for hematological disease diagnosis. This test is performed manually in many places worldwide, which requires both time and qualified staff. Large laboratories are equipped with digital morphology analyzers, some of which are based on deep learning methods. However, it is difficult to explain to scientists how they work. In this paper, we proposed to add an explanatory factor to enhance the interpretability of deep learning models in leukocyte classification.10 297 single images of leukocytes obtained from peripheral blood smears were included in this study. Pre-trained and fully trained VGG16 and VGG19 models were used to classify the leukocytes, and Shapley Additive Explanations (SHAP) DeepExplainer was applied to visualize the area of cells that were significant for classification. The output images from the DeepExplainer were compared with cellular elements that are essential to laboratory practice.The accuracy of our fully trained models was 99.81% for VGG16 and 99.79% for VGG19. It achieved slightly better results than the partially trained model, which scored 98.67% for VGG16 and 98.33% for VGG19. Their SHAP explanations indicated the significance of cellular structures in microscopic examination. Explanations in the pre-trained models have proved the cell and nucleus contours to be relevant to classification, while explanations in the fully trained models pointed to the cytoplasm area.Despite different SHAP DeepExplainer explanations for fully and partially trained models, this method appears to be helpful for the verification of leukocyte classification in automated peripheral blood smear examination.