Reviewerly/exHarmony huggingface dataset:
Wavefront measurements using the Ronchi test for high-NA lithography projection lenses

The Ronchi test is widely used for wavefront measurements in advanced lithography tools, and a physical optics explanation of the Ronchi test based on scalar diffraction theory can be found in numerous publications. However, for high-numerical aperture (high-NA) lithography projection lenses, the vector nature of light should be considered when performing wavefront measurements, especially the effect of polarization aberrations on the wavefront test results. In this paper, a vector model for describing shearing interferometry for high-NA lithography projection lenses is established. In addition to considering the vector nature of light, the vector model also calculates the Ronchigram on the screen of a detector at any distance from a diffraction grating, as opposed to the distance restriction for the Fraunhofer diffraction approximation used by the existing methods. Using the developed mathematical model of the Ronchi test, the Ronchigrams of high-NA lithography projection lenses under non-polarized illumination are simulated, and the effect of the distance between the diffraction grating and the detection screen on the wavefront measurement accuracy are discussed.
Exploring the Impact of Augmented Reality on Customer Experiences and Attitudes: A Comparative Analysis with Websites

Augmented Reality is used by various customers’ concerned companies to augment their stimuli, shopping experiences, and customers’ purchasing behavior. Hence, AR driven enabling stimulates customer engagement and their shopping attitude by providing a vivid technology experience. Prior studies found that AR novelty, trust, and vividness influenced the customer usage attitude. There have extant literature presented in the context of AR applications, it is widely used through numerous gadgets but the author intends to explore holistic features that tend to grasp customers’ experiences and attitudes to find the variance between AR vs. Websites. AR potentially disrupted the real-world environment by providing hedonic visual benefits and eliminating the physical efforts that tend to stimulate customers who mesmerize with AR compared to a website. The author chose the Houzz AR app and its website to gather data and provide a better understanding of both scenarios. The author conducted two-field experiments to illustrate positive customer attitudes that satisfied their experience towards the AR app rather than the traditional website. AR app is vivid and novel to engage a customer and moderate their attitude towards the technology immersion. Finally, this study provides multiple implications such as unique visual illustrations, sensational features, and innovation resulting in satisfied customer experience, considering both features in real and virtual world environments.
Neuromorphic sensing for yawn detection in driver drowsiness

Driver monitoring systems (DMS) are a key component of vehicular safety and essential for the transition from semiautonomous to fully autonomous driving. A key task for DMS is to ascertain the cognitive state of a driver and to determine their level of tiredness. Neuromorphic vision systems, based on event camera technology, provide advanced sensing of facial characteristics, in particular the behavior of a driver’s eyes. This research explores the potential to extend neuromorphic sensing techniques to analyze the entire facial region, detecting yawning behaviors that give a complimentary indicator of tiredness. A neuromorphic dataset is constructed from 952 video clips (481 yawns, 471 not-yawns) captured with an RGB colour camera, with 37 subjects. A total of 95,200 neuromorphic image frames are generated from this video data using a video-to-event converter. From these data 21 subjects were selected to provide a training dataset, 8 subjects were used for validation data, and the remaining 8 subjects were reserved for an ‘unseen’ test dataset. An additional 12,300 frames were generated from event simulations of a public dataset to test against other methods. A convolutional neural network (CNN) with self-attention and a recurrent head was trained and tested with these data. Respective precision and recall scores of 95.9% and 94.7% were achieved on our test set, and 89.9% and 91% on the simulated public test set, demonstrating the feasibility to add yawn detection as a sensing component of a neuromorphic DMS.
Generate adversarial network based on binary priors and conditions research on image deblurring algorithm of QR code

QR codes are black and white two-dimensional bar code graphics that record data information, with fast readability and powerful storage capabilities. With the rapid development of mobile communication and the popularity of intelligent devices, the speed of information exchange has been greatly accelerated, QR codes are widely used in various areas of life. This paper proposes a defuzzification algorithm based on binary priori properties and conditions of black and white pixels to generate adversarial networks. The potential feature representation of the QR code image is extracted by the selfsupervised pretraining of the encoder, which is used for the training of the conditional generation adverssion network. The trained generator network can realize the pixel-level binary segmentation restoration of the degraded QR code image with different fuzzy degrees. The experimental results show that the proposed network framework can recover the QR code images with different degrees of degradation end to end, pixel level, and improve the recognition rate.
Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms

Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor’s algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The NIST has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two PQC algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing.
cq100: a high-quality image dataset for color quantization research

Color quantization (cq) is a classical image processing operation that reduces the number of distinct colors in a given image. Although the idea of cq dates back to the early 1970s, the first true cq algorithm, median-cut, was proposed later in 1980. Since then, hundreds of publications have investigated the topic of cq, proposing dozens of algorithms. A vast majority of these publications demonstrate their results on small datasets, containing a handful of images of mixed quality. Furthermore, the reproducibility of cq research is often limited due to the use of private test images or public test images with multiple non-identical copies on the World Wide Web or restrictive licenses. To address these problems, we curated a large, diverse, and high-quality dataset of 24-bit color images called cq100 and released it under a permissive license. We present an overview of cq100 and demonstrate its use in comparing cq algorithms.
Virtual Machine Selection and Migration: Challenges and Future Directions

Over the past ten years, cloud computing has significantly altered many aspects of human life by providing access to hardware and software resources through the internet. Businesses or individuals can use resources without setting up and maintaining their IT infrastructure. A data center is the core computation unit of any cloud environment, and it consists of hardware-oriented machines termed physical machines (PM) and software-oriented machines that are termed virtual machines (VM). The fundamental method of generating different resources from the given physical infrastructure is virtualization. The use of virtual machines is expanding as people use more smart gadgets that are highly computational and require virtual machines to run efficiently. An enormous amount of energy is required to run a cloud’s services when they are established on a large scale. Resource usage and energy management must be carefully managed in a cloud environment to accomplish virtualization. To do this, it should be necessary to manage the workload by dividing it equally among the physical machines. But due to rapid development and growing user requests, the workload cannot be equally divided between the physical machines. There is a need to apply a virtual machine selection process, which will identify under-utilized and over-utilized PMs based on resource utilization. To minimize energy consumption, there is a need to migrate VMs from over-utilized PMs to other PMs and bring all PMs to a neutral state without compromising the quality of service. This paper presents the challenges and future directions in the VM selection and migration process of cloud computing.
A Genetic Programming Approach with Building Block Evolving and Reusing to Image Classification

Genetic programming (GP) has shown promising results in image classification in the last decade. However, most existing GP-based image classification methods often have a complex tree/program structure and a large search space, which may lead to poor performance. To address this, this paper develops a two-stage-based GP approach to automatically evolving solutions/ensembles for image classification. In the new approach, the process of constructing an image classification solution is divided into two stages, i.e., evolving small building blocks for feature extraction and evolving ensembles of classifiers by reusing these blocks. Accordingly, at each stage, a simple tree structure can be designed to facilitate the search. In the first stage, a simple block representation and a new search mechanism including a population updating strategy are developed to evolve diverse and effective blocks. In the second stage, a small set of diverse blocks are selected and transformed into primitives, which produces a new tree representation to evolve ensembles of classifiers for image classification. The new designs allow the proposed approach to construct sufficiently but not over complex solutions for difficult tasks by using/searching small trees. The new approach outperforms most GP-based and non-GP-based comparison methods on five image datasets including CIFAR10, Fashion_MNIST and SVHN. Deep analysis is conducted to provide more insights into the proposed approach.
Separating Oil and Vinegar with a Single Trace

Due to recent cryptanalytical breakthroughs, the multivariate signature schemes that seemed to be most promising in the past years are no longer in the focus of the research community. Hence, the cryptographically mature UOV scheme is of great interest again. Since it has not been part of the NIST process for standardizing post-quantum cryptography so far, it has not been studied intensively for its physical security.In this work, we present a side-channel attack on the latest implementation of UOV. In the first part of the attack, a single side-channel trace of the signing process is used to learn all vinegar variables used in the computation. Then, we employ a combination of the Kipnis-Shamir attack and the reconciliation attack to reveal the complete secret key. Our attack, unlike previous work, targets the inversion of the central map and not the subsequent linear transformation. It further does not require the attacker to control the message to be signed.We have verified the practicality of our attack on a ChipWhisperer-Lite board with a 32-bit STM32F3 ARM Cortex-M4 target mounted on a CW308 UFO board. We publicly provide the code and both reference and target traces. Additionally, we discuss several countermeasures that can at least make our attack less efficient.
Polarization response of spin-lasers under amplitude modulation

Lasers with injected spin-polarized carriers show an outstanding performance in both static and dynamic operation. In addition to the intensity response of conventional lasers, without spin-polarized carriers, both intensity and polarization of light can be exploited for optical communication in spin-lasers. However, the polarization dynamics of spin-lasers under amplitude modulation has been largely overlooked. Here, we reveal, analytically and numerically, a nontrivial polarization response that accompanies the well-known intensity dynamics of a spin-laser under amplitude modulation. We evaluate the polarization and intensity response under the same amplitude modulation and further assess the capability of such a polarization response in digital data transfer with eye diagram simulations. Our results provide a more complete understanding of the modulation response in spin-lasers and open up unexplored opportunities in optical communication and spintronics.
Cost assignment based adaptive active forensics scheme for image tampers

With the rapid development of multimedia editing technology and DeepFake technology, image integrity and authenticity meet more challenges. Most existing methods only focus on improving the accuracy of tamper detection and localization, but ignore the potential tampering risk, which is related to the saliency. There are uneven potential tamper threats to any graphic images, and it is interesting to exploit saliency to adaptively assign embedding cost. We propose an active forensics scheme for tamper localization by adaptively adjusting cost assignment. The experimental results demonstrate a significant improvement in transparency, localization accuracy, and robustness against unintentional attacks.
Machine learning approach to uncover customer plastic bag usage patterns in a grocery store

Plastic bags are used by many people because they are inexpensive, lightweight, durable, and waterproof. Plastic bags, on the other hand, do not break down and can pollute the environment if not handled properly. Indonesia produces a lot of plastic waste and is one of the top ten countries that has a problem with plastic waste. In this study, we used three months of data of real transactions from a grocery store. This study shows how the decision tree can identify patterns on plastic bag usage at a small grocery store by using demography and products purchase. The attribute weights showed that in the hometown, the total of several products bought were the factors that affected the use of plastic bags.
Research on English Vocabulary Teaching in Senior High School under the New Curriculum Standards—Taking a Senior High School in Yingcheng Hubei as an Example

Vocabulary is the core of language and the basis of communication.According to the 2017 new curriculum standards for the amount of senior high school English vocabulary and English discipline core accomplishment, i.e. language ability, cultural awareness, thinking ability and learning ability, teachers should adopt a variety of teaching methods to help students to achieve these requirements.Based on questionnaire surveys of 12 English teachers and 111 students in a senior high school in Yingcheng, Hubei province, this paper investigates the current situation of English vocabulary teaching and proposes some corresponding teaching methods to improve the current situation of English vocabulary teaching in senior high schools.
Historical charcoal-burning mounds in the Engure area, Latvia, and their significance for the history of forest exploitation

The application of airborne laser scanning (LiDAR) has led to the discovery of numerous man-made mounds encircled by pits in the forests near Engure, north-western Latvia. Field survey revealed these to be historical features remaining from charcoal-burning in mound kilns. Archaeological excavation was undertaken at one of the features, along with analysis of recovered wood charcoal, dendrochronological and radiocarbon (14C) dating and archival research into documents relating to historical charcoal production in the area. All of the analysed charcoal samples are from Scots pine (Pinus sylvestris L.), the dominant tree species in this area. It is concluded that the large-scale charcoal production served the needs of the ironworks operating at Engure in the 17th–18th century. The study of the charcoal-burning mounds has permitted estimation of the source area and volume of the wood supplied for charcoal production, and the period during which it was undertaken. Wood harvesting on such a scale had a significant impact on the surrounding forests and also had some effect on subsequent forest growth in the area. These findings demonstrate that features of this kind deserve to be regarded as archaeological remains of considerable research potential. Keywords: Duchy of Courland and Semigallia, forest history, Scots pine, charcoal-burning mounds, charcoal kilns, wood charcoal
Simulated gold-standard for quantitative evaluation of monocular vision algorithms

In the physical universe, truth for computer vision (CV) is impractical if not impossible to obtain. As a result, the CV community has resorted to qualitative practices and sub-optimal quantitative measures. This is problematic because it limits our ability to train, evaluate, and ultimately understand algorithms such as single image depth estimation (SIDE) and structure from motion (SfM). How good are these algorithms, individually and relatively, and where do they break? Herein, we discuss that while truth evades both the real and simulated (SIM) universes, a SIM CV gold-standard can be achieved. We outline an extensible SIM framework and data collection workflow using unreal engine with the Robot Operating System (ROS) for three-dimensional mapping on low altitude aerial vehicles. Furthermore, voxel-based mapping measures from algorithm output to a SIM gold-standard are discussed. The proposed metrics are demonstrated by analyzing performance across changes in platform context. Ultimately, the current article is a step towards an improved process for comparing algorithms, evaluating their strengths and weaknesses, and automating algorithm design.
Black-box Fuzzing for Security in Managed Networks: An Outline

Service providers are adopting open-source technology and open standards in their next-generation networks. This gives them great flexibility and spurs innovation. But it also means that they must ensure proper interoperability between components; otherwise, vulnerabilities might get introduced in their networks. Unfortunately, state-of-the-art vulnerability scanning tools are unable to handle the complexity of service provider networks. In this paper we show how interoperability issues between seemingly reliable components introduce an injection vulnerability that allows us to control a firewall-protected network management system. We also extend the state-of-the-art in black-box fuzzing to give service providers a tool for combating similar issues.
Neural Inference Search for Multiloss Segmentation Models

Semantic segmentation is vital for many emerging surveillance applications, but current models cannot be relied upon to meet the required tolerance, particularly in complex tasks that involve multiple classes and varied environments. To improve performance, we propose a novel algorithm, neural inference search (NIS), for hyperparameter optimization pertaining to established deep learning segmentation models in conjunction with a new multiloss function. It incorporates three novel search behaviors, i.e., Maximized Standard Deviation Velocity Prediction, Local Best Velocity Prediction, and n -dimensional Whirlpool Search. The first two behaviors are exploratory, leveraging long short-term memory (LSTM)-convolutional neural network (CNN)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. A scheduling mechanism is also introduced in NIS to manage the contributions of these three novel search behaviors in stages. NIS optimizes learning and multiloss parameters simultaneously. Compared with state-of-the-art segmentation methods and those optimized with other well-known search algorithms, NIS-optimized models show significant improvements across multiple performance metrics on five segmentation datasets. NIS also reliably yields better solutions as compared with a variety of search methods for solving numerical benchmark functions.
BLACK MIRROR: A NOVEL APPLICATION OF SPECULATIVE DESIGN TO FACILITATE CONTEXT-AWARE DESIGN THINKING

Abstract As design thinking became popularized, practitioners in the field pointed out that design thinking rarely takes stakeholders and contexts into account as a result of focusing solely on end-users. We believe such a limitation can be complemented with speculative design, which critiques the future that an idea depicts and interrogates relationships with social, cultural, economical, and political systems. This paper, through pilot interviews with 10 participants and a thematic analysis on interviews with 12 participants, explores possible interaction between design thinking and speculative and critical depiction of the future. Speculative and critical inquiries allowed the participants to consider various social contexts and stakeholders, and further helped them shape and brainstorm the ideas’ potential values and challenges faced during adoption. The overall critical speculation influenced various steps of the design thinking process. The paper contributes to the literature by reporting a novel application of speculative design to the conventional design thinking process. In the future, we hope to further explore other elements of speculative design and their impact on design outcomes.
ENHANCED FINDABILITY AND REUSABILITY OF ENGINEERING DATA BY CONTEXTUAL METADATA

Abstract Complex research problems are increasingly addressed by interdisciplinary, collaborate research projects generating large amounts of heterogeneous amounts of data. The overarching processing, analysis and availability of data are critical success factors for these research efforts. Data repositories enable long term availability of such data for the scientific community. The findability and therefore reusability strongly builds on comprehensive annotations of datasets stored in repositories. Often generic metadata schema are used to annotate data. In this publication we describe the implementation of discipline specific metadata into a data repository to provide more contextual information about data. To avoid extra workload for researchers to provide such metadata a workflow with standardised data templates for automated metadata extraction during the ingest process has been developed. The enriched metadata are in the following used in the development of two repository plugins for data comparison and data visualisation. The added values of discipline-specific annotations and derived search features to support matching and reusable data is then demonstrated by use cases of two Collaborative Research Centres (CRC 1368 and CRC 1153).