Reviewerly/exHarmony huggingface dataset:
Real-Time Optimal Trajectory Planning for Autonomous Driving with Collision Avoidance Using Convex Optimization

Abstract An online trajectory planning method for collision avoidance is proposed to improve vehicle driving safety and comfort simultaneously. The collision-free trajectory for autonomous driving is formulated as a nonlinear optimization problem. A novel approximate convex optimization approach is developed for the online optimal trajectory in both longitudinal and lateral directions. First, a dual variable is used to model the non-convex collision-free constraint for driving safety and is calculated by solving a dual problem of the relative distance between vehicles. Second, the trajectory is further optimized in a model predictive control framework considering the safety. It realizes continuous-time and dynamic feasible motion with collision avoidance. The geometry of object vehicles is described by polygons instead of circles or ellipses in traditional methods. In order to avoid aggressive maneuver in the longitudinal and lateral directions for driving comfort, rates of the acceleration and the steering angle are restricted. The final formulated optimization problem is convex, which can be solved by using quadratic programming solvers and is computationally efficient for online application. Simulation results show that this approach can obtain similar driving performance compared to a state-of-the-art nonlinear optimization method. Furthermore, various driving scenarios are tested to evaluate the robustness and the ability for handling complex driving tasks.
PhishCatcher: Client-Side Defense Against Web Spoofing Attacks Using Machine Learning

Cyber security confronts a tremendous challenge of maintaining the confidentiality and integrity of user’s private information such as password and PIN code. Billions of users are exposed daily to fake login pages requesting secret information. There are many ways to trick a user to visit a web page such as, phishing mails, tempting advertisements, click-jacking, malware, SQL injection, session hijacking, man-in-the-middle, denial of service and cross-site scripting attacks. Web spoofing or phishing is an electronic trick in which the attacker constructs a malicious copy of a legitimate web page and request users’ private information such as password. To counter such exploits, researchers have proposed several security strategies but they face latency and accuracy issues. To overcome such issues, we propose and develop client-side defence mechanism based on machine learning techniques to detect spoofed web pages and protect users from phishing attacks. As a proof of concept, a Google Chrome extension dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PhishCatcher</i> , is developed that implements our machine learning algorithm that classifies a URL as suspicious or trustful. The algorithm takes four different types of web features as input and then random forest classifier decides whether a login web page is spoofed or not. To assess the accuracy and precision of the extension, multiple experiments were carried on real web applications. The experimental results show remarkable accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs. Furthermore, to measure the latency of our tool, we performed experiments over forty phished URLs. The average recorded response time of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PhishCatcher</i> was just 62.5 milliseconds.
Logical perspectives on the foundations of probability

Abstract We illustrate how a variety of logical methods and techniques provide useful, though currently underappreciated, tools in the foundations and applications of reasoning under uncertainty. The field is vast spanning logic, artificial intelligence, statistics, and decision theory. Rather than (hopelessly) attempting a comprehensive survey, we focus on a handful of telling examples. While most of our attention will be devoted to frameworks in which uncertainty is quantified probabilistically, we will also touch upon generalisations of probability measures of uncertainty, which have attracted a significant interest in the past few decades.
Forensic analysis of ephemeral messaging applications: Disappearing messages or evidential data?

Ephemeral messaging or ‘disappearing messages’ is the mobile-to-mobile transmission of multimedia messages that automatically disappear from the recipient's screen after the message has been viewed. This new feature can be enabled by users for more privacy when using instant messaging apps. A user can set messages to disappear within a certain timeframe: 24 hours, 7 days, or 90 days, after the time they are sent. While disappearing messages provide additional privacy to users, its anti-forensics capability creates challenges for investigators in the recovery of evidential artefacts that could be crucial to an investigation. In this paper, we conduct a comprehensive forensic analysis of ‘disappearing messages’ across different digital platforms (mobile, desktop, and cloud) and instant messaging apps (WhatsApp, Snapchat, and Telegram) to determine whether they can be recovered within a limited timeframe. The results from this study provide valuable information to investigators dealing with instant messaging apps that have this feature enabled and provides detailed understanding of how disappearing messages are stored, managed, and deleted compared to messages sent without this feature enabled.
A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems.In such applications, a large amount of data is generated about diverse aspects of our life.Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy.Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle.Each of these stages can introduce unique threats to individual's privacy and have an impact on ethical processing and protection of data.In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions.We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices.We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks.We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems.The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle.In each phase of the AI life cycle, open challenges have been identified.
A quantitative framework for security assurance evaluation and selection of cloud services: a case study

Abstract Due to the high adoption of cloud services, the protection of data and information is critical. Cloud service customers (CSCs) need help to obtain the authoritative assurances required for the cloud services and negotiate the cloud service contract based on the terms and conditions set by cloud service providers (CSPs). Several standards and guidelines are available for assessing cloud security. However, most of these standards and guidelines are complex and time-consuming to select a service or make an informed decision for CSCs. Moreover, the existing methods are insufficient to solve this problem because they are process-oriented, neglect the importance of stakeholder requirements, and lack a comprehensive and rigid analytic method that can aid decision-makers in making the right decisions. In this paper, we developed two evaluation techniques: (i) a quantitative cloud security assurance method to assess the security level of cloud services by measuring the critical security properties and (ii) a novel and rigid categorical analytical method that enables CSPs to identify the major problems in the system and assess how much gain can be achieved by solving each of them. The cloud security assurance method is based on two important metrics: security requirement and vulnerability . It assists CSCs in avoiding severe mistakes and making informed decisions while selecting a cloud service. Moreover, these methods support CSPs in improving the security level of cloud services and meet customer requirements. The proposed methods are validated using different case scenarios on a private cloud platform.
Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32

The baseband signal of the 406 MHz COSPAS-SARSAT emergency beacon is a 144-bit or 112-bit data with 0 and 1 code, including the information bits and error correcting check bits. In order to improve the coding efficiency of the baseband signal for the 406 MHz transmitter in the STM32 micro-controller, this paper proposes a calculation method for the first BCH error correcting code (BCH-1) and the second BCH error correcting code (BCH-2) of the COSPAS-SARAST transmitter. First, conduct the data coding process by 64-bit integral data; then, compute the BCH (127, 106) code and the BCH (63, 51) code to obtain the respective error correcting codes for improving coding rate; next, generate the baseband signal according to the GB 14391-2021 coding format; finally, modulate the baseband signal into a 406 MHz-RF signal, send it out and complete the task of distress alerting. The experimental results show that the proposed STM32 microcontroller-based baseband signal generation method is effective, and the EPIRB (Emergency Position Indicating Radio Beacon) designed based on this method has reached the industry standard.
An Invitation Model Protocol (IMP) for the Bitcoin Asymmetric Lightning Network

The Lightning Network (LN), a second-layer protocol built atop Bitcoin, promises swift, low-cost transactions, thereby addressing blockchain scalability and enhancing user privacy. As the global financial technology landscape evolves, the LN’s importance in the future of fintech and the Fourth Industrial Revolution (4IR) becomes increasingly pivotal. The anticipated rise of blockchain-based payments and smart contracts in businesses demands a more agile and secure payment system. However, the LN’s early stage raises valid concerns about security and reliability, especially when implemented on a huge asymmetric network such as the Internet, potentially hindering its broader adoption. Malicious nodes could intentionally cause payment failures or initiate attacks, such as DDoS attacks, by overwhelming other nodes in the network with channel-opening requests. As a result, users will be discouraged from using the LN; hence, the technology will become obsolete as individuals will not waste the time and power investment required for using this technology. Addressing these issues, this paper proposes an innovative invitation model protocol (IMP) to reinforce the LN’s security and reliability. The IMP creates an exclusive ‘Club’ within the LN, admitting only nodes verified as honest, thereby bolstering network security and reliability. The protocol empowers Club Founders to expel members exhibiting malicious activities, thereby preserving the invested time, energy, and funds of the network’s users. The IMP was rigorously tested using Amazon Web Services Virtual Machines within the Bitcoin and Lightning Network’s Testnet environment, which is a highly asymmetric network. The results demonstrated the protocol’s efficacy in fulfilling its objectives, marking a significant step towards a safer and more efficient blockchain transaction network. As the blockchain continues to revolutionize the financial sector, implementing robust security measures such as the IMP becomes essential. This research paper introduces a novel approach to enhancing the reliability and security of a Lightning Network (LN), and thus distinguishes itself from the existing literature, by introducing an additional step before establishing or joining such a network. The research underscores the critical role of such protocols in realizing the potential of the LN in powering the next wave of fintech and industrial innovation.
Compiling arbitrary single-qubit gates via the phase shifts of microwave pulses

We give an arbitrary single-qubit gate compilation scheme on superconducting processors that takes advantage of tuning the phase shift of microwave pulses to obtain a continuous gate set. This scheme is compatible with any two-qubit gate, and we only need to calibrate the $X_\pi$ and $X_{\pi/2}$ pulses. We implement this on fluxonium and obtain state-of-the-art fidelities. We give two other schemes: the first requires one $X_\pi$ pulse and one pulse with a variable rotation angle, and the second requires four $X_{\pi/2}$ pulses. We also find that if we can do virtual $Z$ gates, then we can also do virtual gates around any axis. Our results apply to any physical platform that natively supports virtual $Z$.
Automated assessment of pen and paper tests using computer vision

Abstract Computer vision is one of the artificial intelligence’s most challenging fields, enabling computers to interpret, analyse and derive meaningful information from the visual world. There are various utilizations of computer vision algorithms, and most of them, from simpler to more complicated, have an object and shape recognition in common. Traditional pen and paper tests are designed in a pre-established format and consist of numerous basic shapes, which designate the important parts of the test itself. With that in mind, many computer vision applications regarding pen and paper tests arise as an opportunity. Massive courses and large schooling organizations mostly conduct their exams in paper format and assess them manually, which imposes a significant burden on the teaching staff. Any kind of automatization that will facilitate the grading process is highly desirable. Hence, an automated answer recognition system in assessment was developed to mitigate the problems above. The system uses images of scanned test pages obtained from the test scanning process and performs the necessary image manipulation steps to increase target recognition accuracy. Further, it manages to identify regions of interest containing multiple-choice questions and contours. Finally, the system verifies obtained results using the knowledge of the whereabouts of the test template regions of interest.
The impacts of the cyber-trust program on the cybersecurity maturity of government entities in the Kingdom of Bahrain

Purpose This study aims to determine the impacts of the Bahrain Government framework [cyber-trust program (CTP)] on the cybersecurity maturity of government entities and how the CTP can impact the cybersecurity of government entities in the Kingdom of Bahrain. Design/methodology/approach The authors used a quantitative and qualitative approach. The data were collected by conducting semi-structured interviews with the information technology experts in the Bahrain Government entities participating in the CTP. Also, quantitative data was obtained through a questionnaire distributed to relevant people in the information technology field. Findings The findings of this study suggest that the CTP had a significant impact on the cybersecurity assurance of the government entities that participated in the CTP; it increased the employees’ awareness, reduced the number of cyberattacks and optimized the available resources. The findings also highlighted the role of top management in the success of the implementation of the CTP. The results also ensure that the CTP’s maturity model affected the cybersecurity compliance of an organization and the implementation of cybersecurity policies and controls. Practical implications This study enhances cybersecurity researchers’ and practitioners’ understanding of the impact of the CTP and its components and evaluates its influence on Bahrain’s cybersecurity assurance. Originality/value This study implies that to achieve better cybersecurity, managers should focus on implementing the policies and controls provided by cybersecurity frameworks to enhance cybersecurity assurance.
Assessment of Shareholder Strategy – An Internal Corporate Social Responsibility Perspective on Organizational Commitment in Five-Star Hotels in Kenya

In Kenya, Five-Star hotels are leading in employees’ turnover within the hotel industry at 68%. This surpasses the healthy turnover range (0-15%) and affect organizational performance through the high cost incurred to replace experienced workers. Workers’ commitment in an organization plays a vital role in addressing turnover intentions. The objective of this study was to assess the influence of shareholder strategy on organizational commitment in five-star hotels in Kenya. The study applied descriptive research design, cross-sectional approach and quantitative method to examine the study variables. A total of 216 hotel managers in five-star hotels in Kenya was the target population of the study, out of this, 144 hotel managers were selected as sample size in 2021. A self-administered questionnaire was used to collect data and a response rate of 86.8% was obtained. The study applied both descriptive and inferential statistical approaches to analyze data with tabulation, figures and narrative output presentation. The study found that Shareholder Strategy has statistically significant and positive effect and explains 53.1% variation of the Organizational Commitment in Five-Star hotels in Kenya. Empowering the workers with ability for decision-making, problem solving, and planning activities fosters loyalty and commitment which drastically reduces turnover intentions. The study recommends to the hotel management and Kenya Association of Hotelkeepers &amp; Caterers to strive to formulate and implement CSR embedded Shareholder Strategy for raising workers’ Commitment with the aim to attract, motivate, and retain workers. The study suggests replication of the study in the same or other sectors to develop further the Internal CSR field.
Internet search engines as auxiliary tools for translators

The paper focuses on using internet search engines as an auxiliary tool in the work of a translator. Emphasis is placed on the translator's skills in searching for information to support the translation process. Several search engines are discussed in terms of their use by translators to find additional and reference information on Internet resources. Aspects of the privacy policy of the systems in question are highlighted. The advantages of metasearch systems are outlined. A set of search options for translators has been defined - keyword searches, subject searches, and metadata searches. It is suggested to take into account the search engines of the countries for which the translation is being done.
Feature diversity learning with sample dropout for unsupervised domain adaptive person re-identification

Clustering-based approach has proved effective in dealing with unsupervised domain adaptive person re-identification (ReID) tasks. However, existing works along this approach still suffer from noisy pseudo labels and the unreliable representation ability during the whole training process. In order to solve these problems, in this paper, we propose a new approach to learn the feature representation with better generalization ability through limiting noisy pseudo labels. At first, we propose a Sample Dropout (SD) method to prevent the training of the model from falling into the vicious circle caused by samples that are frequently assigned with noisy pseudo labels, our method can correct the noisy labels and boost the representation ability. In addition, we put forward a new method referred as to Feature Diversity Learning (FDL) under the classic mutual-teaching architecture, which can significantly improve the generalization ability of the feature representation on the target domain in an unsupervised fashion. Experimental results show that our proposed FDL-SD achieves the state-of-the-art performance on multiple well-known benchmark datasets.
A deep learning-based approach to solve the height-slope ambiguity in phase measuring deflectometry

Abstract Phase measuring deflectometry (PMD) shows excellent performance in measuring the shape of specular surfaces. However, the existing height-slope ambiguity affects the measurement of PMD. In this paper, we propose a deep learning-based method to solve the ambiguity and reconstruct the specular surface. A neural network was built to reconstruct the surface. A set of simulation surfaces were generated using two-dimensional Legendre polynomials to train and test the network. A spherical-shaped surface was reconstructed in a simulation to evaluate the validity of the proposed method and compare our method to the state-of-the-art method. Experimental results demonstrate a 35.88% improvement in this method’s accuracy. Our method provides a great improvement in measurement accuracy.
Concurrent quantum eigensolver for multiple low-energy eigenstates

We propose a quantum algorithm that diagonalizes a Hamiltonian by implementing an ansatz that satisfies the generalized Rayleigh-Ritz variational principle. This algorithm uses a purification technique to target many quantum states in one quantum circuit and allows multiple eigenstates to be optimized and determined simultaneously. Moreover, it requires a reasonable circuit depth compared to existing algorithms and enables flexible postprocessing on the accurately determined eigensubspace. Using the transverse-field Ising model, we confirm that the eigenvalues obtained with the algorithm converge efficiently and uniformly with the iteration steps, tested by both simulations and IBM platform measurements. As limited quantum resources are needed, this algorithm is promising for noise resilience, better performances, and versatile applications.
Spectrally Resolved Hong-Ou-Mandel Interferometry with Discrete Color Entanglement

Hong-Ou-Mandel (HOM) interferometry is a powerful quantum metrology technique to reconstruct the surface-depth profile of transparent samples with complementary benefits of measurement precision and experimental robustness. It is generally assumed that a trade-off exists between the standard HOM interferometry sensitivity and its ambiguity-free dynamic range. Here we challenge this ``well-known'' assumption through the implementation of spectrally resolved HOM interferometry with discrete color entanglement, and show that the well-separated frequencies embedded in a quantum state suffices to achieve great advantages in enhancing the temporal resolution but without limiting its ambiguity-free dynamic range. Backed by the Fisher-information analysis, spectrally resolved coincidence detection is an optimal measurement strategy as it enables us to recover the quantum Cram\'er-Rao bound. These results may significantly facilitate the use of spectrally resolved quantum interference for real-time quantum information processing, such as ultrafast phase transition and spectral-temporal photography.