Reviewerly/exHarmony huggingface dataset:
Communication Pattern Models: An Extension of Action Models for Dynamic-Network Distributed Systems

Halpern and Moses were the first to recognize, in 1984, the importance of a formal treatment of knowledge in distributed computing. Many works in distributed computing, however, still employ informal notions of knowledge. Hence, it is critical to further study such formalizations. Action models, a significant approach to modeling dynamic epistemic logic, have only recently been applied to distributed computing, for instance, by Goubault, Ledent, and Rajsbaum. Using action models for analyzing distributed-computing environments, as proposed by these authors, has drawbacks, however. In particular, a direct use of action models may cause such models to grow exponentially as the computation of the distributed system evolves. Hence, our motivation is finding compact action models for distributed systems. We introduce communication pattern models as an extension of both ordinary action models and their update operator. We give a systematic construction of communication pattern models for a large variety of distributed-computing models called dynamic-network models. For a proper subclass of dynamic-network models called oblivious, the communication pattern model remains the same throughout the computation.
Efficient Hardware Implementation of Finite Field Arithmetic AB+C for Binary Ring-LWE Based Post-Quantum Cryptography

Post-quantum cryptography (PQC) has gained significant attention from the community recently as it is proven that the existing public-key cryptosystems are vulnerable to the attacks launched from the well-developed quantum computers. The finite field arithmetic <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$AB+C$</tex> </formula> , where A and C are integer polynomials and <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$B$</tex> </formula> is a binary polynomial, is the key component for the binary Ring-learning-with-errors (BRLWE)-based encryption scheme (a low-complexity PQC suitable for emerging lightweight applications). In this paper, we propose a novel hardware implementation of the finite field arithmetic <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$AB+C$</tex> </formula> through three stages of interdependent efforts: (i) a rigorous mathematical formulation process is presented first; (ii) an efficient hardware architecture is then presented with detailed description; (iii) a thorough implementation has also been given along with the comparison. Overall, (i) the proposed basic structure ( <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$u=1$</tex> </formula> ) outperforms the existing designs, e.g., it involves 46.3\% less area-delay product (ADP) than \cite{b14b} for <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$n=512$</tex> </formula> ; (ii) the proposed design also offers very efficient performance in time-complexity and can be used in many future applications.
Evaluation Design Methodology for an AR App for English Literacy Skills

Augmented Reality (AR) is a powerful tool for supporting students' learning processes, but sound research findings regarding the systematic evaluation of AR-enhanced teaching and learning processes are scarce especially with regards to literacy attainment. Hence, against the background of a systematic literature review the evaluation approach in the European H2020 ARETE project is introduced. The effects of Augmented Reality (AR) on fourth to sixth grade primary school students' literacy skills acquisition are assessed. The evaluation approach has been designed systematically to respond to important research desiderata such as the development of multimethod and multi-perspective evaluation approaches combining different target groups and measurements. The aim of this paper is the design clarification and the provision of the research desideratum of evaluation design and metrics that are suitable for systematically assessing students' literacy attainment when utilising AR.
A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

As one of the most valuable technologies, blockchains have received extensive attention from researchers and industry circles and are widely applied in various scenarios. However, data on a blockchain cannot be deleted. As a result, it is impossible to clean invalid and sensitive data and correct erroneous data. This, to a certain extent, hinders the application of blockchains in supply chains and Internet of Things. To address this problem, this study presents a deletable and modifiable blockchain scheme (DMBlockChain) based on record verification trees (RVTrees) and the multisignature scheme. (1) In this scheme, an RVTree structure is designed and added to the block structure. The RVTree can not only ensure that a record is true and valid but, owing to its unique binary structure, also verify whether modification and deletion requests are valid. (2) In DMBlockChain, the multisignature mechanism is also introduced. This mechanism requires the stakeholders’ signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily. A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature mechanism. The analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.
An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs

Mobile ad hoc networks (MANETs) play a highly significant role in the Internet of Things (IoT) for managing node mobility. MANET opens the pathway for different IoT-based communication systems with effective abilities for a variety of applications in several domains. In IoT-based systems, it provides the self-formation and self-connection of networks. A key advantage of MANETs is that any device or node can freely join or leave the network; however, this makes the networks and applications vulnerable to security attacks. Thus, authentication plays an essential role in protecting the network or system from several security attacks. Consequently, secure communication is an important prerequisite for nodes in MANETs. The main problem is that the node moving from one group to another may be attacked on the way by misleading the device to join the neighboring group. To address this, in this paper, we present an authentication mechanism based on image hashing where the network administrator allows the crosschecking of the identity image of a soldier (i.e., a node) in the joining group. We propose the node joining and node migration algorithms where authentication is involved to ensure secure identification. The simulation tool NS-2 is employed to conduct extensive simulations for extracting the results from the trace files. The results demonstrate the effectiveness of the proposed scheme based on the memory storage communication overhead and computational cost. In our scheme, the attack can be detected effectively and also provides a highly robust assurance.
Multi-modal Bifurcated Network for Depth Guided Image Relighting

Image relighting aims to recalibrate the illumination setting in an image. In this paper, we propose a deep learning-based method called multi-modal bifurcated network (MB-Net) for depth guided image relighting. That is, given an image and the corresponding depth maps, a new image with the given illuminant angle and color temperature is generated by our network. This model extracts the image and the depth features by the bifurcated network in the encoder. To use the two features effectively, we adopt the dynamic dilated pyramid modules in the decoder. Moreover, to increase the variety of training data, we propose a novel data process pipeline to increase the number of the training data. Experiments conducted on the VIDIT dataset show that the proposed solution obtains the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place in terms of SSIM and PMS in the NTIRE 2021 Depth Guide One-to-one Relighting Challenge.
A Privacy Preserving System for AI-assisted Video Analytics

The emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. Face recognition, object detection, or movement tracing are prominent examples enabled by this technology. However, the widespread deployment of such mechanism in public areas are a growing cause of privacy and security concerns. Data protection strategies need to be appropriately designed and correctly implemented in order to mitigate the associated risks. Most existing approaches focus on privacy and security related operations of the video stream itself or protecting its transmission. In this paper, we propose a privacy preserving system for AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information. The system ensures that applications leveraging extracted data have no access to the video stream. An attribute-based authorization scheme allows applications to only query a predefined subset of extracted data. We demonstrate the feasibility of our approach by evaluating an application motivated by the recent COVID-19 pandemic, deployed on typical edge computing infrastructure.
Underwater Image Mosaic Algorithm Based on Improved Image Registration

In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance.
Single-Image Dehazing Using Extreme Reflectance Channel Prior

Image dehazing algorithms based on dark channel prior principle have achieved good results for most scenes. However, the popular dark channel prior tends to underestimate transmissions of bright areas or objects, such as the skies, white areas and self-luminous bodies, which may cause color distortions during dehazing. A complementary prior called the extreme reflectance channel prior (ERC), which combines the dark channel prior with the bright channel prior, is proposed to estimate the transmission map. The extreme reflectance channel is the union of dark and bright channel's pixels which satisfy the corresponding channel. Based on the scattering analysis results that the intensities of pixels in ERC are often close to 0 or 1 for the natural haze-free images or close to global atmospheric light if hazes occur in the air, the pixels in a hazy image can be recovered according to ERC to calculate the transmission map and then solve the haze imaging mode. Experiments show that ERC method outperforms state-of-the-art methods in PSNR, SSIM and visual perception effects.
Limitations of Post-Hoc Feature Alignment for Robustness

Feature alignment is an approach to improving robustness to distribution shift that matches the distribution of feature activations between the training distribution and test distribution. A particularly simple but effective approach to feature alignment involves aligning the batch normalization statistics between the two distributions in a trained neural network. This technique has received renewed interest lately because of its impressive performance on robustness benchmarks. However, when and why this method works is not well understood. We investigate the approach in more detail and identify several limitations. We show that it only significantly helps with a narrow set of distribution shifts and we identify several settings in which it even degrades performance. We also explain why these limitations arise by pinpointing why this approach can be so effective in the first place. Our findings call into question the utility of this approach and Unsupervised Domain Adaptation more broadly for improving robustness in practice.
TaskAllocator: A Recommendation Approach for Role-based Tasks Allocation in Agile Software Development

In this paper, we propose a recommendation approach - TaskAllocator - in order to predict the assignment of incoming tasks to potential befitting roles. The proposed approach, identifying team roles rather than individual persons, allows project managers to perform better tasks allocation in case the individual developers are over-utilized or moved on to different roles/projects. We evaluated our approach on ten agile case study projects obtained from the Taiga. io repository. In order to determine the TaskAllocator's performance, we have conducted a benchmark study by comparing it with contemporary machine learning models. The applicability of the TaskAllocator was assessed through a plugin that can be integrated with JIRA and provides recommendations about suitable roles whenever a new task is added to the project. Lastly, the source code of the plugin and the dataset employed have been made public.
Spot-welding path planning method for the curved surface workpiece of body-in-white based on a memetic algorithm

Aiming at the problem of complex path planning in the processing of curved surface workpieces of body-in-white, a hybrid path planning method based on a memetic algorithm is proposed. The method is divided into two parts: welding sequence planning and welding path planning between weld points. By establishing the kinematic model of a spot welding robot based on the pipper criterion and z-y-z Euler angle solution method, the motion constraints of path optimization are analyzed. Under the framework of the memetic algorithm, the improved A-star algorithm with redundant node deletion and a post-smoothing process is used to obtain the smooth collision-free optimal path set between weld points and to construct the objective function of travelling all weld points with the shortest path length and highest smoothness. The multiobjective elitist-simulated annealing genetic algorithm (MESAGA) is used to achieve the welding sequence planning of all weld points. The variable neighborhood search method improves the mutation operator; the elitist strategy is introduced to improve the probability of elitist individual crossover and mutation operation, and a simulated annealing algorithm is used to jump out of local search to obtain the global optimal solution. According to the motion constraints, the joint space path is obtained by the optimal path in Cartesian space. Simulation analysis results demonstrate that the hybrid path planning method based on the memetic algorithm can effectively optimize the path of spot welding robots and lay the foundation for control and trajectory planning during welding processes.
A No-Reference Visual Quality Metric For 3D Color Meshes

To improve the viewers’ quality of experience (QoE) in computer graphics applications, the visual quality assessment (VQA) of 3D meshes is becoming a popular task in the multimedia area. Since 3D meshes are quite sensitive to the processing operations like simplification and compression, many studies concerning the VQA of 3D meshes have been carried out to measure the caused degradations. However, the previous studies mostly utilize full-reference metrics and focus mainly on the geometry attributes. While in some application scenarios such as 3D reconstruction, digital entertainment, and medical modeling, the reference 3D mesh is not always available and the color information can not be ignored. Therefore, in this paper, we propose a no-reference visual quality metric for 3D color meshes, which is based on the statistical parameters and entropy estimated from the probability distributions of curvature, dihedral angles, face area, face angle, and diffuse color information. The performance of the proposed method is validated on a database specially built for VQA of 3D color meshes. Experimental results show that our metric operates stably and achieves the highest correlation with subjective judgement.
Approximate decoherence free subspaces for distributed sensing

We consider the sensing of scalar valued fields with specific spatial dependence using a network of sensors, e.g. multiple atoms located at different positions within a trap. We show how to harness the spatial correlations to sense only a specific signal, and be insensitive to others at different positions or with unequal spatial dependence by constructing a decoherence-free subspace for noise sources at fixed, known positions. This can be extended to noise sources lying on certain surfaces, where we encounter a connection to mirror charges and equipotential surfaces in classical electrostatics. For general situations, we introduce the notion of an approximate decoherence-free subspace, where noise for all sources within some volume is significantly suppressed, at the cost of reducing the signal strength in a controlled way. We show that one can use this approach to maintain Heisenberg-scaling over long times and for a large number of sensors, despite the presence of multiple noise sources in large volumes. We introduce an efficient formalism to construct internal states and sensor configurations, and apply it to several examples to demonstrate the usefulness and wide applicability of our approach.
Implementation of web resources using cloud technologies to demonstrate and organize students’ research work

Abstract The possibility of increasing the efficiency of research work of students by introducing specialized web applications for group work and presentation of research results is substantiated. The main directions of using web resources in research work are determined. Possibilities of cloud services for the organization of cooperation of teachers and students on scientific projects are opened. The expediency of creating specialized websites and mobile applications to present research results is emphasized. Requirements for the content of web resources used for the presentation and organization of research activities of students, which should provide the ability to manage research projects, communication between all participants in the learning process, as well as providing the necessary scientific and methodological materials are determined. An interactive prototype of a specialized site and a corresponding mobile application has been created, which contains files and services necessary for coverage of research areas and activities of collective scientific work at the department. An experimental study of the effectiveness of using a specialized web service has been carried out. The final survey of the students involved in the experiment indicates a significant increase in the number of students interested in scientific work. This confirms the expediency of using the proposed resources for the development of research work.
Expressivity of Quantitative Modal Logics : Categorical Foundations via Codensity and Approximation

A modal logic that is strong enough to fully characterize the behavior of a system is called expressive. Recently, with the growing diversity of systems to be reasoned about (probabilistic, cyber-physical, etc.), the focus shifted to quantitative settings which resulted in a number of expressivity results for quantitative logics and behavioral metrics. Each of these quantitative expressivity results uses a tailor-made argument; distilling the essence of these arguments is non-trivial, yet important to support the design of expressive modal logics for new quantitative settings. In this paper, we present the first categorical framework for deriving quantitative expressivity results, based on the new notion of approximating family. A key ingredient is the codensity lifting-a uniform observation-centric construction of various bisimilarity-like notions such as bisimulation metrics. We show that several recent quantitative expressivity results (e.g. by König et al. and by Fijalkow et al.) are accommodated in our framework; a new expressivity result is derived, too, for what we call bisimulation uniformity.
A Ranking Strategy to Promote Resources Supporting the Classroom Environment

Popular search engines (SE) favored by children are optimized neither to respond to their search behavior and abilities, nor retrieve resources that align with classroom standards. Further, attempts to adapt SE to support children's inquiries have been one-dimensional, e.g., satisfy users' reading skills or search expertise. To ease the process of locating resources relevant to children in the classroom, we introduce KORSCE, a ranking strategy designed to complement the functionality of existing SE. KORSCE employs a multi-objective approach to re-rank resources retrieved by popular SE to fit a specific target audience and setting based on varied criteria: appropriateness, readability, objectivity, and curriculum-alignment. Experimental results and insights from an expert appraiser showcase KORSCE'S ability to prioritize resources that assist children's information-seeking activities at school.
A Study-Based Review on Blockchain Technology for IoT

In this modern internet-enabled age of technology, the two major disruptive emerging components are both Blockchain and Internet of Things (IoT). In research and industry, the IoT has experienced exponential growth, although it suffers from a number of limitations like poor interoperability, decentralization, privacy, and security vulnerabilities. In the vision of IoT, conventional devices become autonomous and smart. In a heterogeneous environment, a technology is required to allow secure communication among IoT nodes. In the coming years considering the predicted progression of the IoT, it is essential to provide assurance in this enormous information source. Blockchain technology brought opportunities to address the shortcomings of IoT and it also offered a favorable solution to resolve the limitation of current peer-to-peer networks. To use openly and publicly Blockchain is a safe technology. It has developed as a vital technology that will transform the way of information sharing. In this paper, the architecture and model of IoT and the basic functioning of blockchain have been presented followed by the existing blockchain technology for IoT environment. Further, this paper studies the current challenges in blockchain IoT applications and incorporation of blockchain with IoT. The last survey shows the relevant work to present the potential of blockchain technology to improve the IoT.