Reviewerly/exHarmony huggingface dataset:
Merging Inductive Relations

Inductive relations offer a powerful and expressive way of writing program specifications while facilitating compositional reasoning. Their widespread use by proof assistant users has made them a particularly attractive target for proof engineering tools such as QuickChick, a property-based testing tool for Coq which can automatically derive generators for values satisfying an inductive relation. However, while such generators are generally efficient, there is an infrequent yet seemingly inevitable situation where their performance greatly degrades: when multiple inductive relations constrain the same piece of data. In this paper, we introduce an algorithm for merging two such inductively defined properties that share an index. The algorithm finds shared structure between the two relations, and creates a single merged relation that is provably equivalent to the conjunction of the two. We demonstrate, through a series of case studies, that the merged relations can improve the performance of automatic generation by orders of magnitude, as well as simplify mechanized proofs by getting rid of the need for nested induction and tedious low-level book-keeping.
Whither the Event Study in Security Litigation?

Of the eight Cammer-Krogman factors, only the empirical factor (an event study) is sufficient to determine cause and effect between new information and stock price reaction, a corollary of market efficiency, and a pre-requisite to certify a class in security litigation; but it is not necessary because the information events used in the study may not be strong enough or may not constitute a complete measure of relevant information to affect the stock price. The other 7 factors, namely trading volume, analyst coverage, market makers, S-3 registration, bid-ask spread, market capitalization, and float are neither sufficient nor necessary for cause and effect. As the only sufficient factor, the event study is the most important when it finds evidence of stock price reactivity to news, regardless of the status of the indicia factors. Despite not being sufficient or necessary, there is some evidence that such indicia factors are correlated with stock price reactivity in event studies.
Identification of micro expressions in a video sequence by Euclidean distance of the facial contours

This paper presents an automatic facial micro-expression recognition system (FMER) from video sequence. Identification and classification are performed on basic expressions: happy, surprise, fear, disgust, sadness, anger, and neutral states. The system integrates three main steps. The first step consists in face detection and tracking over three consecutive frames. In the second step, the facial contour extraction is performed on each frame to build Euclidean distance maps. The last task corresponds to the classification which is achieved with two methods; the SVM and using convolutional neural networks. Experimental evaluation of the proposed system for facial micro-expression identification is performed on the well-known databases (Chon and Kanade and CASME II), with six and seven facial expressions for each classification method.
Model-less prediction filter for adaptive adjustment process noise

In this study, a filtering scheme suitable for high-precision sensors was proposed to extract high-precision sensor information. According to the principle of Kalman gain based on data fusion, a model-less prediction filter with minimum gain measurement noise compensation and process noise posteriori constraint adjustment was developed. In comparison to various Kalman filter methods, the proposed algorithm demonstrated better accuracy in the steady state. The high precision performance and effectiveness of the model-less prediction filter were verified under a digitally controlled linear power supply.
Platforms, programmability, and precarity: The platformization of research repositories in academic libraries

We investigate in this article how repository platforms change the sharing and preservation of digital objects in academic libraries. We use evidence drawn from semi-structured interviews with 31 data repository managers working at 21 universities using the product Figshare for institutions. We first show that repository managers use this platform to bring together actors, technologies, and processes usually scattered across the library to assign to them the tasks that they value less—such as data preparation or IT maintenance—and spend more time engaging in activities they appreciate—such as raising awareness of data sharing. While this platformization of data management improves their job satisfaction, we reveal how it simultaneously accentuates the outsourcing of libraries’ core mission to private actors. We eventually discuss how this platformization can deskill librarians and perpetuate precarity politics in university libraries.
The Sonic Interactions in Virtual Environments (SIVE) Toolkit

In this paper, the Sonic Interactions in Virtual Environments (SIVE) toolkit, a virtual reality (VR) environment for building musical instruments using physical models, is presented. The audio engine of the toolkit is based on finite-difference time-domain (FDTD) methods and works in a modular fashion. The authors show how the toolkit is built and how it can be imported in Unity to create VR musical instruments, and future developments and possible applications are discussed.
Language Report Czech

Abstract This chapter provides basic data about Language Technology for the Czech language. After a brief introduction with general facts about the language (history, linguistic features, writing system, dialects), we touch upon Czech in the digital sphere. The main achievements in the field of NLP are presented: important datasets (corpora, treebanks, lexicons etc.) and tools (morphological analyzers, taggers, automatic translators, voice recognisers and generators, keyword extracters etc).
Blockchain-Enabled Internet of Things Application in Supply Chain Operations Sustainability Management

In recent decades, sustainability and green supply chain management have played essential roles in business operations. This chapter examines how the textile and apparel supply chains can comply with the United Nations Sustainable Development Goals. In particular, verifying the source of raw materials and maintaining visibility of merchandise products and related services while moving through the value-chain network is challenging. The internet of things (IoT) application can help textile and apparel supply chain operation managers observe, monitor, and track products and relevant business processes within their respective value chain networks. However, the IoT infrastructural components have a shortage of computational processing power and local saving capability, and these components are vulnerable to the privacy and security of collected data. This chapter presents blockchain technology with IoT-based infrastructural elements and service-oriented computing architecture as a solution for information processing for apparel supply chain management.
Blockchain Supply Chain Integration Relation

In this chapter, the authors aim to focus to integrate blockchain with the existing infrastructure, such that it works with the underlying core infrastructure to automate the supply chain integration as much as possible to decrease human intervention to the bare minimum level and solve the underlying problems that might occur due to the human factor in the supply chain integration. The authors will also shed light on the various regulatory issues associated with blockchain technologies. At the same time, one will also try to keep in mind that the newly proposed system can be well integrated with the existing and well-established system. The goal is to address problems that can arise from the human factor in supply chain management. The proposed system can be used in various industries requiring inventory management, the authenticity of goods, digital escrow services, etc. The authors have also discussed various regulatory issues as well as environmental concerns and procurement of raw materials associated with blockchain technologies.
The Dynamics of the Relationship Between Price and Volume of Cryptocurrencies

In this study, the authors investigate the volume as the pricing driver of the top three cryptocurrencies (Bitcoin, Ethereum, and Binance) based on a wavelet analysis from January 1, 2019 to December 31, 2021. The dynamics of the relationship between price and volume in the cryptocurrency market could have valuable market implications for stakeholders and investors and contribute to making optimal investment decisions via portfolio diversification strategies. The results reveal that the relationship between price and volume is positive in the medium and long term and that price is the leading volume for both Bitcoin and Binance markets. The findings suggest that the COVID-19 pandemic significantly affected the cryptocurrency price and volume series links. Indeed, these results contribute to the emerging and growing literature on cryptocurrencies in the time of COVID-19, which has received limited attention during the pandemic compared to the classical asset financial classes.
The Prospects of Quantum Secure Direct Communication: An Overview

Quantum-secure communication and computation have garnered increasing attention due to their powerful search capabilities for certain complex problems and their ability to provide unconditional security. This indicates that classical cryptography, such as asymmetric ciphers based on complex mathematics, faces a significant threat from quantum algorithms. However, quantum cryptography can detect any attempt to eavesdrop on a channel. As a result, the investigation and implementation of quantum computers and quantum-secure communication networks have become essential objectives for numerous research teams. This paper provides an overview of quantum cryptography, including its development, and focuses on the theoretical aspect of quantum secure direct communication (QSDC). This helps readers understand the theoretical operating principles and gain a better understanding of the entire quantum characteristic and protocol operation in the QSDC field. QSDC enables secure communication without previously shared private keys, making it a crucial topic in quantum cryptography research. This paper provides an overview of several well-known versions of QSDC in theory, discussing their applications, challenges and potential future directions.
AttCST: attention improves style transfer via contrastive learning

Arbitrary style transfer has flexible applicability and aims to learn the painting styles of different artists through one model training and re-render to everyday photos. Existing domain-to-domain style transfer methods based on generative adversarial networks achieve good results in image generation quality but need more flexibility in various style transfer tasks. In the image-to-image style transfer method, focusing on the fusion of content image and style image can learn local detail texture very well. Still, there are some defects in the preservation of the content structure. To alleviate the above two problems, we leverage both advantages, using attention improves style transfer via contrastive learning. Specifically, the attention mechanism is used to fuse the multi-scale features of the content and style images. The previous shallow features are fully utilized to supplement the texture information lost by the deep features. Then the currently learned style image is distinguished from other style images by contrastive learning modules, thereby enhancing the ability of the network to learn style images. We also derive a local loss based on the transferred blocks to improve the visual quality of the generated images. We conduct extensive qualitative and quantitative analyses, the results of which demonstrate the effectiveness of our approach.
The ‘Coal Measures’ of Grand Ledge, Michigan, USA

Abstract The best-exposed natural bedrock outcrops in Michigan's southern peninsula are Upper Carboniferous (Pennsylvanian) sandstone ledges underlain by silts, clays, and coals in classic midcontinent ‘cyclothem’ stratigraphy, exposed along the Grand River in the city of Grand Ledge. Native Americans used the underclay in pottery; later commercial mining was the basis for clay-products factories. In the 1800s, the ledges provided a picturesque setting for a resort economy. Among the beneficiaries of repurposed quarries (now parks) are generations of Michigan geoscience students. A trip to Grand Ledge is often the first geology field experience for many students and plays an important role in the professional development of practising geoscientists and geoscience educators in Michigan. After an announcement of plans to expand the city's water treatment plant, adjacent to the cyclothem exposure, geologists from academia, government, and industry united to support preservation of the outcrops. City officials, previously unaware of the geoheritage value of the rocks exposed in their parks, are willing to work with geoscientists to preserve the site. This experience underscored the importance of documenting the geoheritage of the Grand Ledge area and establishing relationships with those who have oversight of these ‘greatest outcrops’ to ensure their preservation for future generations.
10 Application of virtual reality and augmented reality technologies to boost the technical capabilities for a constructive and effective learning in education for a performing virtual eco-system: a design thinking approach

This research article introspects into the use of the technical capabilities of virtual reality and augmented reality to boost constructive learning in the domain of effective education. It examines a model approach for instructional and developmental design for creating a virtual educational ecosystem. It creates a team for participation, employing VR/AR technology to combat the hurdles in ease of learning, and assess its feasibility of performance. The adoption of the design thinking model handles and navigates, through its macro strategy, identifying subject topics for presentation. Similarly, the micro strategy ensures the effective and efficient presentation of the academic content, which is designed and intended for learning. The model is developed for the implementation of outcomes, along with developing the components of the virtual ecosystem of optimal education inputs, with a specialized evaluation of individual learners and small groups. The research article has employed exploratory study with critical literature review in a mixed method approach. It engaged the method for a deeper understanding of the constructivism learning, applied to the virtual reality technologies for better knowledge.
The evaluation of denoising techniques on microscopic blood smear images of Acute Lymphoblastic Leukemia (ALL)

Preprocessing is the first step in the image processing for any digital image before it goes further step. Denoising techniques are one of the important techniques used in preprocessing. The digitized microscopic blood smear image contains unwanted noise due to poor illumination, electronic interference, different variation in lighting condition etc. The processing of these images without the filtering techniques, can produce inaccurate results in the further step of image processing such as segmentation, feature extraction and classification. So, it is necessary to preprocess the images with proper techniques for each type of preprocessing. In this paper, we have tried to evaluate the different types of the filtering techniques on the blood smear images of Acute Lymphoblastic Leukemia (ALL) for the removal of noise. We have reviewed many research work which have used various filtering techniques used for removal of noise.
PENERAPAN ASSOCIATION RULE PADA SISTEM REKOMENDASI PRODUK PROPERTI BERDASARKAN POLA INTERAKSI PENGGUNA

The increasing need for property makes various property companies have to compete to promote the best property options. Information system facilities are one of the main reasons for prospective buyers to get property information that best suits their needs. Each customer has a unique pattern of needs that can be obtained from the search pattern of the information system. This study tries to provide the best property product recommendations using the association rule method. The association rule method itself is a data mining method that is used to provide product recommendations based on user search activity patterns while opening an information system. After the association rule method is applied, the system can provide recommendations for the three best products with an attractive and easy-to-use information system.
4 Maximizing the data-drivenness of grammaticalization research

This paper looks into the grammaticalization of to death from a resultative phrase into an intensifying expression, by describing the process in terms of changes in compositionality, productivity and schematicity and subsequently outlining a procedure in which the investigation of changes along these parameters is approached in a ‘maximally data-driven’ way. By doing so, we show that functionalsemantic explorations of grammaticalization need not be at odds with a radically data-driven, ‘hands-off’, quantitative methodology. Yet, at the same time, we acknowledge that there are still some limitations associated with ‘hands-off’ functional- semantic analysis, and argue that, to determine its reliability, it will prove valuable to extensively revisit known cases of grammaticalization.
Track initiation based on adaptive gates and fuzzy Hough transform

In order to select reliable tracks from the limited measurement cycles, the track initiation has been studied, which affects all subsequent stages of target tracking. Numerous false target tracks were produced by traditional logic algorithms, and the Hough transform method was sensitive to noise. Based on adaptive gates and fuzzy Hough transform, a track initiation algorithm is proposed in this paper, which combines the advantages of the two types of methods to reduce the amount of false alarm, and completes the high-quality track initiation. Firstly, an adaptive gate is designed to filter the detection clutter. And then the fuzzy theory is used to obtain the accumulated matrix in the Hough transform process. Finally, the tracks are determined by the threshold method, and the track initiation is completed. The proposed method can reduce the false track occupancy rate, and complete higher quality track initiation in short detection cycles in simulation experiments. It is more suitable for multi-target track initiation under the surroundings of intensive clutter.
Nonreciprocal unconventional photon blockade in atom-cavity with χ(2) nonlinear medium

In this paper, we show that the photon blockade (PB) can be observed in a nonreciprocal way with a driven spinning atom-cavity coupled filled with [Formula: see text] nonlinear medium. We analytically derive the optimal conditions for strong photon antibunching, which are in good agreement with those obtained by numerical simulations. Under the weak driving condition, we discuss the physical origins of the nonreciprocal unconventional photon blockade (UPB), which originates from the destructive quantum interference between different paths from the ground state to two-photon states by driving the device from the left side. While the quantum interference paths are broken when the device is driven from the right side, which leads to the occurrence of the photon bunching. Moreover in the opposite direction of driving the device, we also observe another nonreciprocal UPB effect, which can occur by generating bunching and antibunching effects via driving the device from the left and right sides under the derived optimal conditions, respectively. Finally, we cannot observe the nonreciprocal UPB phenomenon with the large cavity decay compared to the atomic spontaneous emission rate (or the bad cavity limit), and give the physical reason behind it. Possible experimental implementation for this scheme is presented.
Machine Learning and Automation for Enhanced Efficiency and Productivity

In recent years, the rapid development of machine learning and automation technologies has opened up unprecedented opportunities for various industries to increase efficiency, accuracy, and productivity. This research paper explores the integration of machine learning algorithms and automation technologies in various domains, highlighting their transformative potential and addressing the associated challenges. By analysing existing literature and real-world case studies, the paper aims to provide insight into the current state of machine learning and automation, as well as future directions for research and practical applications. This paper aims to contribute to the understanding of the benefits and limitations of these technologies and to encourage further innovation in this evolving field
Research and Design of Cloud Computing Security Framework

As a new technique, cloud computing has a rapid development in recent years. However, the security problems have caused great influences to the development and popularization of cloud computing, the importance and urgency has not to be ignored. This paper introduces cloud computing and security situation, studies the main security problems of cloud computing, and comes up with a cloud computing security framework which can effectively solve these security problems, and points out that only to solve the security problems, cloud computing can unceasingly expanded, and the application will be more and more widely.
Analyzing Customer Satisfaction using Support Vector Machine and Naive Bayes Utilizing Filipino Text

The study aimed to compare the classification performance of Support Vector Machine (SVM) and Naive Bayes (NB) machine learning models for estimating customer satisfaction utilizing Filipino text. Specifically, it analyzed the characteristics of the customer satisfaction data. It also examined the impact of different model configurations, including n-gram, stop words, and stemming, on the classification performance of the two models. The research employed qualitative and quantitative methods, utilizing text analytics and sentiment analysis to extract and analyze valuable information from unstructured responses from a satisfaction survey of the University President’s leadership performance conducted among PSU personnel and students. The dataset comprised 56,000 Filipino and English-word responses, manually annotated and randomly split into training and testing datasets. The study followed a general framework encompassing data pre-processing, modeling, and model comparison. To validate the classifiers’ classification performance, a 10-fold cross-validation approach was employed. The findings revealed that most personnel and students expressed positive sentiment toward the University President’s leadership performance. SVM outperformed the NB model across all different model configurations. With both stop word removal and stemming, the SVM trigram model achieved the highest classification performance for estimating customer satisfaction, using 75% of the data for training and 25% for testing. The proposed model holds the potential for estimating customer satisfaction using other unstructured customer satisfaction data utilizing Filipino text.