Reviewerly/exHarmony huggingface dataset:
Interferometric stitching method for testing cylindrical surfaces with large apertures

Cylindrical surfaces widely used in high-energy laser systems can have nearly semi-meter-scale dimensions, and aperture angles can exceed R/3. State-of-the-art interferometric stitching test methods involve stitching only along the arc direction, and the reported dimensions of ∼50 × 50 mm 2 are far smaller than those required in high-energy laser systems. To rectify this limitation, an interferometric stitching method for cylindrical surfaces with large apertures is proposed. Moreover, a subaperture stitching algorithm that can stitch along both the linear and arc directions is developed. An interferometric stitching workstation equipped with a six-axis motion stage and a series of computer-generated holograms is established, where cylindrical surfaces with R/# values as large as R/0.5 and apertures up to 700 mm can be tested based on the theoretical analysis. A convex cylindrical surface with a 350 × 380 mm 2 aperture is tested to validate the proposed method’s feasibility in enlarging the testable aperture of cylindrical surfaces significantly from Ф50 mm to Ф700 mm, thereby promoting the application of large cylindrical surfaces in high-energy laser systems.
An End-to-End Multi-Task Learning Model for Drivable Road Detection via Edge Refinement and Geometric Deformation

This paper presents a road detection method for autonomous driving based on an end-to-end neural network model. Our method takes advantage of both the characteristics of road boundary and multi-task learning of a deep convolutional network. By reassigning the label and rebalancing the loss of road pixels, we focus on the learning of hard examples on the boundary to refine its performance. Then, a road geometric transformation-based data augmentation method is proposed to enable the network model to be robust under traffic scenes. Based on these two novel methods, a unified architecture consisting of a shared deep residual encoder network and multi-branch decoder sub-networks is integrated. It adopts road scene classification as a supervised learning task to realize road segmentation and scene classification simultaneously. Experiments demonstrate that the proposed method has achieved the highest MaxF value in most road scenes. Both qualitative and quantitative evaluations on the KITTI-Road benchmark demonstrate our superior performance.
A Taxonomy of Explainable Bayesian Networks

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made.
Enhancement of E-Commerce Database System During the COVID-19 Pandemic

The Database Management System (DBMS) is a database development and management system programme. As for end-users, DBMS makes it easy to build, read, edit and uninstall data in a database. These days, the databases only treated individual bits of specially formatted files. However, today's databases are more developed, and can accommodate various forms of less formatted data and bind them together in more elaborate ways. Online meat shopping rose during the COVID-19 pandemic. However, more than half of the participants did not choose to order meat goods online because of worries about the spoilage of the meat before the products were shipped. If there was a smartphone monitoring device that helps consumers to monitor food conditions during shipment, 56 percent of participants who did not originally choose to shop online for meat indicated that they would buy meat online. This research has demonstrated that it is very important to incorporate mobile control systems for the stability of the food chain in processes such as pandemics. This study will lead to the development, in the future, of mobile monitoring systems for the food industry. The DBMS handles three main segments, namely the information, the database engine that permits the information to be retrieved, locked and updated, and the database schema that determines the sensible structure of the database. Every one of the three elements help to ensure continuity, confidentiality, data integrity and consistency in data management procedures. Database management functions assisted by the DBMS include change management, performance monitoring and tuning, security, backup and recovery.
On the Design of Autonomous Agents From Multiple Data Sources

This paper is concerned with the problem of designing agents able to dynamically select information from multiple data sources in order to tackle tasks that involve tracking a target behavior while optimizing a reward. We formulate this problem as a data-driven optimal control problem with integer decision variables and give an explicit expression for its solution. The solution determines how (and when) the data from the sources should be used by the agent. We also formalize a notion of agent's regret and, by relaxing the problem, give a regret upper bound. Simulations complement the results.
Delving Deep into Many-to-many Attention for Few-shot Video Object Segmentation

This paper tackles the task of Few-Shot Video Object Segmentation (FSVOS), i.e., segmenting objects in the query videos with certain class specified in a few labeled support images. The key is to model the relationship between the query videos and the support images for propagating the object information. This is a many-to-many problem and often relies on full-rank attention, which is computationally intensive. In this paper, we propose a novel Domain Agent Network (DAN), breaking down the full-rank attention into two smaller ones. We consider one single frame of the query video as the domain agent, bridging between the support images and the query video. Our DAN allows a linear space and time complexity as opposed to the original quadratic form with no loss of performance. In addition, we introduce a learning strategy by combining meta-learning with online learning to further improve the segmentation accuracy. We build a FSVOS benchmark on the Youtube-VIS dataset and conduct experiments to demonstrate that our method outperforms baselines on both computational cost and accuracy, achieving the state-of-the-art performance. Code is available at https://github.com/scutpaul/DANet.
Multi-Task Wavelet Corrected Network for Image Splicing Forgery Detection and Localization

Although the existing image splicing forgery detection networks can achieve a promising performance, most of these networks utilize regular pooling operations (max-pooling and mean-pooling) and a single task strategy, which limits the comprehensiveness and representativeness of the features learned by the networks. In this paper, we propose a multi-task wavelet corrected network (MWC-Net) that can learn more comprehensive and representative features for image splicing forgery detection and localization. MWC-Net exploits wavelet-pooling and wavelet un-pooling to compress and reconstruct the features of splicing forgery images, which can reduce information loss during learning features. Mean-while, MWC-Net implements a multi-task strategy to improve its ability to learn and utilize more comprehensive and representative features. The experimental results demonstrate that MWC-Net outperforms the state-of-the-art methods in splicing forgery detection and localization on four public datasets.
FAST: Fair Auctions via Secret Transactions

Sealed-bid auctions are a common way of allocating an asset among a set of parties but require trusting an auctioneer who analyses the bids and determines the winner. Many privacy-preserving computation protocols for auctions have been proposed to eliminate the need for a trusted third party. However, they lack fairness, meaning that the adversary learns the outcome of the auction before honest parties and may choose to make the protocol fail without suffering any consequences. In this work, we propose efficient protocols for both first and second-price sealed-bid auctions with fairness against rational adversaries, leveraging secret cryptocurrency transactions and public smart contracts. In our approach, the bidders jointly compute the winner of the auction while preserving the privacy of losing bids and ensuring that cheaters are financially punished by losing a secret collateral deposit. We guarantee that it is never profitable for rational adversaries to cheat by making the deposit equal to the bid plus the cost of running the protocol, i.e., once a party commits to a bid, it is guaranteed that it has the funds and it cannot walk away from the protocol without forfeiting the bid. Moreover, our protocols ensure that the winner is determined and the auction payments are completed even if the adversary misbehaves so that it cannot force the protocol to fail and then rejoin the auction with an adjusted bid. In comparison to the state-of-the-art, our constructions are both more efficient and furthermore achieve stronger security properties, i.e., fairness. Interestingly, we show how the second-price can be computed with a minimal increase of the complexity of the simpler first-price case. Moreover, in case there is no cheating, only collateral deposit and refund transactions must be sent to the smart contract, significantly saving on-chain storage.
Influence of squeezing on the weak-to-strong measurement transition

In this work, we study the measurement transition for a coherent squeezed pointer state through a transition factor $\Gamma$ that involves a system-pointer coupling by using an arbitrary measured observable $A$. In addition, we show that the shift in the pointer's position and momentum establishes a relationship with a new value defined as the transition value, which generalizes the weak value as well as the conditional expectaction value. Furthermore, a new strategy is introduced to achieve different measurement regimes by just adjusting the $r$ and $\phi_{\xi}$ parameters of the coherent squeezed pointer state, opening an interesting way to test quantum mechanics foundations. Our scheme has been theoretically applied in a trapped ion illuminated by a bichromatic laser beam, with a high potential to be implemented in future experimental setups.
Blockchain based Cryptocurrency Scope in India

As the internet is becoming more accessible and convenient, larger numbers of people and businesses are shifting towards digital transactions. Digital payment methods are quicker, cheaper and much more efficient. Therefore, it's not surprising that newer forms of digital payment systems are rapidly being developed. When compared no other method comes even close to the giant that is cryptocurrency. Cryptocurrencies like Bitcoin and Ethereum are among the most popular forms of digital payments. Cryptocurrencies could be popularized in India as a viable option for digital currency, but it has both pros and cons which need to be acknowledged. Through the means of literature review, this research paper will analyze the cryptocurrency, its working and perform a comparative study between China and India. Alos, it considers the current status as well as the scope of cryptocurrency in India.
Event-triggered optimal Kalman consensus filter with upper bound of error covariance

In this paper, we present an event-triggered optimal Kalman consensus filter (EOKCF) for state estimation in distributed wireless sensor networks. The local message is exchanged only when the local estimation violates a predetermined Send-on-Delta (SoD) data transmission condition in order to reduce the amount of data transfer. The neighboring measurement and estimation information are integrated into the consensus filter, and a recursive upper bound of the error covariance is derived for uncertainties of interval communication from the event-based systems. The optimal Kalman gain and consensus gain matrices are obtained by minimizing the upper bound of state error covariance. The Crameˇr−−Rao lower bound (CRLB) of the state estimates is also introduced to measure the mean-square estimation error performance. The effectiveness of event-triggered communication protocol and the advantages of filtering performance of EOKCF are demonstrated using Monte Carlo simulations on a target tracking application in a distributed sensor network.
Detection and tracking of the trajectories of dynamic UAVs in restricted and cluttered environment

The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV's initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm.
Moments of sharing, language style and resources for solidarity on social media: A comparative analysis

This research draws on Androutsopoulos (2014) to analyse the language, language mixing phenomena and participation in online exchanges among members of a transnational community: colleagues in a project team who have been working together for over four years. Informed by sociolinguistics and Computer-mediated Discourse Analysis theory (Herring, 2004), this contribution compares the colleagues’ usual moments of sharing on WhatsApp with their interactions using the same medium during three extraordinary online moments of sharing, which are also significant off-line moments for the group. These three special moments of sharing took place after the terrorist attacks in Manchester in May 2017 and in Turku in August 2017, and the outbreak of the coronavirus (COVID-19) pandemic in Europe in March 2020. An examination of the messages exchanged during these extraordinary moments provides considerable evidence that moment, style and audience engagement are closely intertwined. While graphical elements, such as emoji, as well as textese language, are pragmatically unmarked in the typical practices of the community, their absence is the marked choice during the three special moments of sharing analysed. Importantly, the study also shows that code-switching is a key strategy to demonstrate group solidarity during moments of sharing that are special and highly significant for the participants.
Efficient Local Navigation Approach for Autonomous Driving Vehicles

This paper presents an efficient and practical approach for a car navigation system (CVM-Car) based on the velocity space optimization paradigm. The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane' s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.
Modeling Instant User Intent and Content-Level Transition for Sequential Fashion Recommendation

Fashion recommendation, aiming to explore specific user preference in fashion, has become an important research topic for its practical significance to the fashion business sector. However, little work has been done on an important sub-task called sequential fashion recommendation, which aims to capture additional short-term fashion interest of users by modeling the item-to-item transitions. In this paper, we propose a novel Attentional Content-level Translation-based Recommender (ACTR) framework, which simultaneously models the instant user intent of each transition and the intent-specific transition probability. Specifically, we define instant intent with the relationships between adjacent items that the users interacted, which are the three fundamental domain-specific relationships of: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">match</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">substitute</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">others</i> . To further exploit the characteristics of fashion domain and alleviate the item transition sparsity problem, we augment the item-level transition modeling with multiple sub-transitions using various content-level attributes. An attention mechanism is further devised to effectively aggregate multiple content-level transitions. To the best of our knowledge, this is the first work that specifies the implicit user actions in online fashion shopping with explicit instant intent, which enhances the connectivity of fashion items and boosts the recommendation performance. Extensive experiments on two real-world fashion E-commerce datasets demonstrate the effectiveness of the proposed method in sequential fashion recommendation.
A sustainable-development approach for self-adaptive cyber–physical system’s life cycle: A systematic mapping study

Cyber–Physical Systems (CPS) refer to a new generation of systems where the cyber and physical layers are –strongly– interconnected. The development of these systems requires two fundamental parts. First, the design of sustainable architectures –centered on adaptation, throughout a System-Development Life-Cycle (SDLC)– to develop robust and economically profitable products. Second, the use of self-adaptive techniques to adjust CPSs to the evolving circumstances of their operation context. This work presents a systematic mapping study (SMS) that discusses different approaches used to develop self-adaptive CPSs (SA-CPSs) at each stage of the SDLC, focused on sustainability. The results show trends such as (i) Designs are not limited to particular application domains, (ii) Performance was the most commonly used attribute, and (iii) Monitor–Analyze–Plan–Execute over a shared Knowledge (MAPE-K) is the predominant feedback loop applied in the cyber layer. The results also raise challenges such as (i) How to design and evaluate sustainable SA-CPSs, (ii) How to apply unit and integration testing in the development of SA-CPSs, and (iii) How to develop feedback loops on SA-CPSs with the integration of machine-learning techniques.
A Novel Classifier Ensemble Method Based on Subspace Enhancement for High-Dimensional Data Classification

High-dimensional small-size data seriously affects the performance of classifiers. By combining classifiers, ensemble learning obtains higher accuracy and more robust predictions. However, these classifier ensemble methods suffer from several limitations: 1) ensemble with sample space suffers from noise and redundant features; 2) constructing sample subspace on small-size data leads to an insufficient description of sample space; 3) ensemble with feature space leads to information loss, which will degrade performance of classifiers. To overcome the above limitations, a new classifier ensemble method based on subspace enhancement (CESE) is proposed for high-dimensional data classification. First, a superior subspace enhancement scheme (SSE) is designed to effectively implement feature selection and transformation for high-dimensional data, followed by generating multiple superior feature subspaces with diversity and discrimination, which enhances the representative ability of features. Second, we develop a mixed space enhancement process (MSE) based on multiscale rotation reconstruction and various subspace enhanced features of SSE. Furthermore, to improve the capacity of our method, we design various feature combination strategies for enhanced features from both SSE and MSE. Comparative results on 33 high-dimensional data sets indicate that our approach CESE outperforms different mainstream integrated system
Comparative evaluation of WIMP and immersive natural finger interaction: a user study on CAD assembly modeling

Abstract Virtual and augmented reality allows the utilization of natural user interfaces, such as realistic finger interaction, even for purposes that were previously dominated by the WIMP paradigm. This new form of interaction is particularly suitable for applications involving manipulation tasks in 3D space, such as CAD assembly modeling. The objective of this paper is to evaluate the suitability of natural interaction for CAD assembly modeling in virtual reality. An advantage of the natural interaction compared to the conventional operation by computer mouse would indicate development potential for user interfaces of current CAD applications. Our approach bases on two main elements. Firstly, a novel natural user interface for realistic finger interaction enables the user to interact with virtual objects similar to physical ones. Secondly, an algorithm automatically detects constraints between CAD components based solely on their geometry and spatial location. In order to prove the usability of the natural CAD assembly modeling approach in comparison with the assembly procedure in current WIMP operated CAD software, we present a comparative user study. Results show that the VR method including natural finger interaction significantly outperforms the desktop-based CAD application in terms of efficiency and ease of use.