Reviewerly/exHarmony huggingface dataset:
Detail enhancement-based vehicle re-identification with orientation-guided re-ranking

Vehicle re-identification (re-ID) has attracted extensive attention in the field of computer vision owing to the development of smart cities. Two issues remain in the task of vehicle re-ID: minor inter-class differences and extreme orientation variations. To address them, we propose a detailed enhancement-based vehicle re-ID method with orientation-guided re-ranking. In the proposed method, a novel enhancement module using stripe-based partition, is presented to avoid the negative influence of minor inter-class difference. The stripe-based partition subdivides the feature map of middle layers in the network into two stripes, and then the module explores more detailed information for vehicle re-ID. Furthermore, a multilayer feature fusion module is proposed to enhance the feature representation of a vehicle. To address the problem of extreme orientation variation in vehicle re-ID, we propose an orientation-guided re-ranking strategy to re-rank the retrieval result based on the orientation information and query expansion algorithm. This strategy can optimize the ranking of vehicles whose orientation is not similar to the query images in the post-processing stage. Extensive experiments on three public datasets confirm the effectiveness of our method.
Specifying Internet of Things Behaviors in Behavior-Driven Development: Concurrency Enhancement and Tool Support

The Internet of Things (IoT) systems are inherently distributed with many concurrent behaviors. In order to apply behavior-driven development (BDD), a proven agile practice of software development that brings many benefits, we must ensure that the specification of sequential and concurrent behaviors is supported at the specification level and that tool support is in place to execute the specification. This study proposes a minimal semantic enhancement to the Gherkin language, the most popular specification language in BDD, to distinguish sequential and concurrent behaviors. At the same time, a tool called concurrentSpec is developed to support the correct execution of specifications written in the enhanced Gherkin language. With two IoT examples involving both sequential and concurrent behaviors, it is shown that the enhanced Gherkin with concurrentSpec can correctly specify and execute the specifications, while the original Gherkin with existing tools is unable to do so. Hence, the contribution of this study is to eliminate a technical impediment for the IoT development community to adopt BDD and receive its benefits.
A novel color image privacy-preserving method: Combining breadth and depth visual encryption with chaotic system

As the saying goes, one image is worth ten thousand words. Because of the rich visual information carried by the image itself, previewers can easily find what they need. On the other hand, the large amount of detail information carried by the image may invade the privacy of the parties, which needs to be protected. Traditional image encryption schemes can protect privacy, but they indiscriminately erase all visual information, which also gives up the visual usability of the image. Motivated by this, we propose a novel color image privacy-preserving method, which can be regarded as a variant of thumbnail-preserving encryption (TPE). The scheme preserves the rough visual information of the image, and considers the information redundancy in the depth direction for the first time compared with other TPE schemes. Specifically, we propose a TPE scheme combining breadth and depth, and a matching encryption framework is proposed, which is divided into three steps: cube permutation, block substitution, and block permutation. Meanwhile, a prototype is constructed by using a chaotic system and sufficient experiments are carried out with it. The experimental results show that this scheme has excellent properties, can well balance privacy and availability, and has high time efficiency.
Connecting Textiles: Exploring Textile Interior Surfaces for Power Supply, Communication and User Interaction in the IoT

We investigate textile-integrated components for power supply, communication and user interaction in and on home interior surfaces. A wallpaper with an integrated functional layer serves as basic infrastructure for power supply. We developed patches as core interaction elements that integrate IoT functionality and can be freely positioned on the wallpaper. In addition to interacting with these patches, users can perform touch gestures directly on the wallpaper. We describe the iterative prototyping approach of these key components of ConText (Connecting Textiles), and present design guidelines. We report on the results of a user study that highlights the potential of ConText, and points on future development needs.
Free-view Face Relighting Using a Hybrid Parametric Neural Model on a SMALL-OLAT Dataset

Abstract The development of neural relighting techniques has by far outpaced the rate of their corresponding training data ( e.g., OLAT) generation. For example, high-quality relighting from a single portrait image still requires supervision from comprehensive datasets covering broad diversities in gender, race, complexion, and facial geometry. We present a hybrid parametric neural relighting (PN-Relighting) framework for single portrait relighting, using a much smaller OLAT dataset or SMOLAT. At the core of PN-Relighting, we employ parametric 3D faces coupled with appearance inference and implicit material modelling to enrich SMOLAT for handling in-the-wild images. Specifically, we tailor an appearance inference module to generate detailed geometry and albedo on top of the parametric face and develop a neural rendering module to first construct an implicit material representation from SMOLAT and then conduct self-supervised training on in-the-wild image datasets. Comprehensive experiments show that PN-Relighting produces comparable high-quality relighting to TotalRelighting (Pandey et al., 2021), but with a smaller dataset. It further improves shape estimation and naturally supports free-viewpoint rendering and partial skin material editing. PN-Relighting also serves as a data augmenter to produce rich OLAT datasets beyond the original capture.
Students' Learning Perception in Engineering, Health and Education During Emergency Remote Education in Chile

&lt;p style="text-align:justify"&gt;Higher education institutions around the world had to implement an emergency remote education (ERE) modality due to the COVID-19 pandemic. This study aimed to determine the individual and relational factors that affect the perception of learning in engineering, education, and health students during emergency distance education. Through a quantitative, non-experimental, and correlational study, an online survey was administered to a sample of 929 higher education students regarding their ERE experience. They were surveyed regarding their perceived learning, empathy, classroom interaction, self-regulation of learning, positive agency, student engagement, learning approaches, and use of digital resources. Data was analyzed using quantitative methods, namely correlations, comparisons of means and a linear regression analysis. Education students showed a more positive perception of learning, used a deep learning approach, and rated more positively the empathy shown by their professors and the possibilities for class participation than other students. Engineering students had the lowest scores on perceived learning and on almost all other variables. In general, students with a better perception of learning presented a higher engagement in their studies and a lower use of surface learning approach.&lt;/p&gt;
TrEnD: A transformer‐based encoder‐decoder model with adaptive patch embedding for mass segmentation in mammograms

Breast cancer is one of the most prevalent malignancies diagnosed in women. Mammogram inspection in the search and delineation of breast tumors is an essential prerequisite for a reliable diagnosis. However, analyzing mammograms by radiologists is time-consuming and prone to errors. Therefore, the development of computer-aided diagnostic (CAD) systems to automate the mass segmentation procedure is greatly expected.Accurate breast mass segmentation in mammograms remains challenging in CAD systems due to the low contrast, various shapes, and fuzzy boundaries of masses. In this paper, we propose a fully automatic and effective mass segmentation model based on deep learning for improving segmentation performance.We propose an effective transformer-based encoder-decoder model (TrEnD). Firstly, we introduce a lightweight method for adaptive patch embedding (APE) of the transformer, which utilizes superpixels to adaptively adjust the size and position of each patch. Secondly, we introduce a hierarchical transformer-encoder and attention-gated-decoder structure, which is beneficial for progressively suppressing interference feature activations in irrelevant background areas. Thirdly, a dual-branch design is employed to extract and fuse globally coarse and locally fine features in parallel, which could capture the global contextual information and ensure the relevance and integrity of local information. The model is evaluated on two public datasets CBIS-DDSM and INbreast. To further demonstrate the robustness of TrEnD, different cropping strategies are applied to these datasets, termed tight, loose, maximal, and mix-frame. Finally, ablation analysis is performed to assess the individual contribution of each module to the model performance.The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 92.20% and 85.81% on the mix-frame CBIS-DDSM, while 91.83% and 85.29% for the mix-frame INbreast, respectively. The segmentation performance outperforms the current state-of-the-art approaches. By adding the APE and attention-gated module, the Dice and IoU have improved by 6.54% and 10.07%.According to extensive qualitative and quantitative assessments, the proposed network is effective for automatic breast mass segmentation, and has adequate potential to offer technical assistance for subsequent clinical diagnoses.
Enhancing the Anonymity and Auditability of Whistleblowers Protection

In our democracy a trade-off between checks and balances is mandatory. To play the role of balances, it is necessary to have information that is often only obtainable through channels that ensure the anonymity of the source. Here we present a work in progress of a system that provides anonymity to sources in a open and auditable system, oriented to audit systems of critical infrastructure and built on our previous work autoauditor [5].
Histogram Filter with Smoothing Parameter Setting

A histogram filter with smoothing parameter settings is discussed in the article. The histogram filter can be effectively applied in the problems of identification (recognition) of distribution laws for small amounts of data. The smoothing parameter is determined taking into account the available a priori information regarding the proposed distribution law. The relationship between the mathematical expectations of the chi-square fit criterion of the standard estimation histogram and the use of the histogram filter has been determined. This ratio is determined by the smoothing factor. The numerical value of the smoothing coefficient depends on the following parameters: the amount of data, the number of grouping intervals, and the shape parameters of the distribution law. The paper analyzes the feasibility of using a histogram filter, depending on the ratio of the above parameters. The dependence of the smoothing coefficient on the specified parameters allows one to determine the relationship between the number of data grouping intervals and their volume. The histogram filter is an easy-to-implement tool that can be easily integrated into any open distribution law identification (recognition) algorithm
Emerging blockchain solutions in the mobility ecosystem: Associated risks and areas for applications

This study aims to identify the most influential blockchain types for potential implementation areas in the transforming mobility ecosystem, considering application area-specific needs such as transparency, transaction speed, scalability, energy usage, security, and operating cost. The study demonstrated the hybrid blockchain suitability for most of the 13 distinguished mobility applications, while private and consortium blockchains are found applicable based on the needs of specific use cases. A public blockchain is only found suitable for two of the use cases. Proof of Authority and Proof of Stake matches well with most use cases, while Practical Byzantine Fault Tolerance and Proof of Work could be suitable in particular.
Evaluation and Comparison of a Private and a Public Blockchain Solution for Use in Supply Chains of SMEs Based on a QOC Analysis

SMEs (Small and Medium Enterprises) are more and more interested in using blockchain technologies nowadays and one of the first questions that arises when dealing with the subject of blockchains is whether to choose a private or a public blockchain. This research utilizes requirements of a blockchain solution of three different SMEs for a QOC analysis (Questions, Options and Criteria) and consults the opinions of three blockchain experts to make a decision between a private and a public blockchain based on these evaluations and the corresponding weightings from the interviewed SMEs. In addition, the mentioned criteria also highlight IT requirements that are important for SMEs with regard to blockchain applications. Considering the specified requirements, their weightings, and the evaluation of the two given alternatives, it turns out that a public blockchain is preferable for this particular use case.
“Are You What You Claim to Be?” Attribute Validation with IOTA for Multi Authority CP-ABE

Ciphertext-Policy Attribute-Based-Encryption (CP-ABE) is a one-to-many encryption scheme that generates secret keys according to attributes held by users. It is assumed that attribute authorities (AAs) know this matching between users’ identities and their attributes and always generate valid secret keys. However, this approach is not scalable in systems with numerous users or several AAs. This lack of scalability makes attribute management difficult, leading to attackers abusing this situation to escalate privileges by spoofing attributes. We consider attribute spoofing a security risk and propose an attribute spoofing prevention scheme using a DAG-type DLT. We analyze the proposal by studying its effectiveness against the identified attack vectors. We also apply the proposal to a value-chain use case and provide a qualitative evaluation of the system by analyzing its ability to prevent attribute spoofing and other attack vectors identified in the literature review.
SmartTwin: A Blockchain-Based Software Framework for Digital Twins Using IoT

A Digital Twin provides a virtual representation of a physical object, complementing theoretical models with real-world data provided by the Internet of Things. Currently, most cloud-based IoT platforms provide device virtualization with different levels of detail and complexity. Nonetheless, this approach has inherited the disadvantages and risks of cloud computing, such as centralizing and isolating the information. Thus, a blockchain-based Digital Twin could provide a unique combination of properties to address these challenges while ensuring availability, integrity, and confidentiality. Current research on blockchain-based Digital Twin has focused on the data-sharing functionalities. However, the role of the IoT sensors as secure, trustworthy data sources is still an issue that needs to be addressed in the Digital Twin domain. In this work, we propose SmartTwin as a software framework to develop blockchain-based Digital Twins. The architecture's key is considering IoT as direct actors on the blockchain system, taking advantage of the cryptographic capabilities of Blockchain to create a root-of-trust for the data feeding the Digital Twin. In addition, our proposal uses smart contracts as a software platform to define complex business logic. Finally, to illustrate the benefits of SmartTwin, we describe two use cases where the framework was used.
Digital Identity Using Hyperledger Fabric as a Private Blockchain-Based System

Recently, with high technology development the need for digital identity as personal identification has become more essential and not limited for the Cybernet world and can also be used every day in the real world. Many popular identity management systems such as uPort, Sovrin and shoCard use the digital identity that will be compared with our method. This extremely personal data must be kept hidden and secure. Moreover, these day-to-day documents are used in their physical form, adding to the risk of being stolen, lost, or forged. In this paper an approach model is proposed for a private blockchain-based software application that converts supported documents into a digital format using Hyperledger fabric, which is one of the popular blockchain technologies. In addition, a verification method using face and text recognition technologies is proposed to ensure the credibility of information access. The main purpose is to keep all the personal documents, such as ID, license, and birth certificate, in one place and accessible at all times, subsequently, reducing the risk of losing or misplacing them during regular activities.
Blockchain Adoption in the Energy Sector: A Comprehensive Regulatory Readiness Assessment Framework to Assess the Regulatory Readiness Levels of Countries

As we leverage blockchain technology, there is potential for it to become ubiquitous by advancing the operability and functionality of various sectors. Blockchain technology has gained significant scholarly attention in recent years, particularly due to its ability to manoeuvre and disrupt existing value chains and business models. Regulations play a primary role as the building blocks in successfully adopting blockchain technology. This research considers the legal and regulatory challenges circumjacent to blockchain-enabled energy solutions. It develops a set of regulatory criteria to proffer a comprehensive regulatory readiness assessment framework (RRAF) to assess the regulatory readiness levels (RRLs) of countries. It offers a valuable tool for evaluating the maturity, comprehensiveness, and efficacy of the existing regulatory mechanisms. The readiness and maturity levels of blockchain adoption in the energy sector can be evaluated in various jurisdictions using the RRAF model proposed in this paper. The RRAF model comprises multiple regulatory readiness criteria that can contribute to the practical and theoretical advancement of blockchain in the energy sector.
Cryptocurrencies, Systematic Literature Review on Their Current Context and Challenges

The financial crisis of 2008 breached society’s trust in banks and centralized financial institutions. Bitcoin appeared as a response to those transgressions, proving that the blockchain technology it implements is reliable. Since then, this technology has become widespread in the form of new cryptocurrencies and an increasing number of people have begun to invest in them. In the absence of a body to regulate the cryptocurrency market, users play with and encourage the fluctuations in their price, causing them to be treated as investment assets rather than transaction assets. This work carries out and then discusses a systematic study of the state of the art and of the general challenges faced by cryptocurrencies.
ChronoEOS: Configuration Control System Based on EOSIO Blockchain for On-Running Forensic Analysis

In almost any industrial field, there are critical devices and elements that must be trusted. Therefore, it is meaningful to make a secure and reliable record of the events occurring in that environment, which may be used for a possible future forensic investigation in an industrial environment. Blockchain technology covers this need. This project presents ChronoEOS, an application that allows different versions of critical files to be stored securely and immutably, using EOSIO’s blockchain technology. In this way, changes to the configuration files of a robotic arm are stored in the blockchain. In addition, a REST API has been deployed that allows the authorized user to make changes to the corresponding file by interacting with a Smart Contract deployed on the blockchain. Finally, there is a Lookout Agent pointing to the files that are being monitored. This code detects when the version stored in the blockchain is different from the original file, updating one or the other depending on where the last change has been applied.
Improving long-tailed classification by disentangled variance transfer

Image classification is very important in the system of internet of things (IoT), and long-tailed distribution data are common in our daily life. Extremely imbalanced classes in long-tailed classification lead to a huge performance gap between training and testing. A number of methods have been proposed to transfer knowledge from head classes to tail classes, which expects to augment semantic information in tail. However, by projecting feature vectors onto classifier vectors, we find that the projection part and the orthogonal part behave differently in testing phase as the number of instances decreases. In order to properly transfer covariance information in long-tailed classification task, we propose a novel class-based covariance transfer method from the perspective of disentangling. Extensive experimental results on CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT and iNaturalist 2018 illustrate the effectiveness of our method, which will further improve the validity of IoT system.
Determination of equilibrium transaction fees in the Bitcoin network: A rank-order contest

Bitcoin users compete with one another over the timing of the settlement of their on-[block]chain transactions. Earlier settlement is valuable. Bitcoin users can accelerate the confirmation of the settlement of their transactions in the cryptocurrency by voluntarily bidding transaction fees with a user's optimal fee level dependent upon his or her impatience. We characterize this transaction competition as a contest with a rank-order allocation of prizes. Under this characterization, we derive the fee-bidding strategy at the symmetric equilibrium. Based on this equilibrium, we discuss the Bitcoin network and highlight the impacts of changes in different factors related to the mining industry. We find that fee–security feedback can amplify the impacts of these events.