Reviewerly/exHarmony huggingface dataset:
Cohesion Intensive Hash Code Book Coconstruction for Efficiently Localizing Sketch Depicted Scenes

We investigate the problem of efficiently localizing sketch depicted scenes in a remote sensing image dataset. We pose the problem as that of remote sensing image retrieval with sketch queries and explore the use of hashing techniques to achieve efficient retrieval. Given two training datasets of sketches and remote sensing images that have a common set of class labels, we develop a hashing strategy that coconstructs two hash code books for the sketches and the remote sensing images separately. The hash code book coconstruction strategy encourages hash codes for the sketches and remote sensing images from different classes to be far away from one another and those from the same class to be close. This property is maintained by two cohesion intensive cues: 1) an interclass pairwise disperse cue (InterPDC) and 2) an intraclass pairwise balance cue (IntraPBC). We use the two coconstructed hash code books for training two linear mapping models that generate hash codes for sketches and remote sensing images separately. Sorting the Hamming distance between the sketch hash codes and the remote sensing image hash codes renders efficient remote sensing image retrieval with sketch queries. This enables localizing the sketch depicted scenes in the remote sensing image dataset. In addition, our method can also be used for fast localizing sketch depicted scenes in a remote sensing image of large size. Extensive experiments on public datasets validate the effectiveness and efficiency of our method.
Multi‐scale attention encoder for street‐to‐aerial image geo‐localization

Abstract The goal of street‐to‐aerial cross‐view image geo‐localization is to determine the location of the query street‐view image by retrieving the aerial‐view image from the same place. The drastic viewpoint and appearance gap between the aerial‐view and the street‐view images brings a huge challenge against this task. In this paper, we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street‐view images. To bridge the domain gap between these two view images, we first use an inverse polar transform to make the street‐view images approximately aligned with the aerial‐view images. Then, the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information. Finally, we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars. Experiments on standard benchmark datasets show that our approach obtains 81.39% top‐1 recall rate on the CVUSA dataset and 71.52% on the CVACT dataset, achieving the state‐of‐the‐art performance and outperforming most of the existing methods significantly.
Formal Verification of Blockchain Smart Contracts via ATL Model Checking

A blockchain is a list of data blocks as a publicly distributed ledger, which are linked together using cryptography.By allowing Turing-complete programming languages to implement smart contracts, recent blockchains such as Ethereum can reduce needs in trusted intermediators, arbitrations and enforcement costs.However, subtle errors in smart contracts have induced an enormous financial loss-for examples, the DAO attack, Parity multisignature wallet attacks, and integer underflow/overflow attacks.To identify such errors in smart contracts, various researches are performed, which are based on static analysis and theorem proving.However, they only support inspection for pre-defined error patterns, or they cannot explore the whole searching space exhaustively or be fully automatic.Hence, in this paper, we propose a novel formal verification technique to analyze blockchain smart contracts by using ATL model checking.In our methodology, we represent the interaction between users and smart contracts into a two-player game and verify properties we want to check using MCMAS that is an efficient ATL model checker for multi-agent systems.Moreover, we present three case studies to show that our proposal can successfully identify subtle flaws in real world smart contracts.Blockchain, ethereum, INDEX TERMS
School principals’ educational goals and leadership styles for digital transformation: results from case studies in upper secondary schools

Digital technologies are transforming the job market and pose new challenges to education, yet there is little research that specifically examines how school principals’ goals and leadership styles influence digital development in upper secondary schools. The present study explores how their ambitions and educational goals affect their approaches to leadership for digital transformation in their schools. The study employs an exploratory and qualitative research approach based on interviews with a full sample of nine upper secondary school principals from Central Switzerland. Transcripts have been analyzed using qualitative content analysis. The findings suggest that all principals express a clear orientation toward student-oriented teaching when integrating technology in their schools. However, there are substantial differences in terms of leadership processes to address this change: One principal oriented toward transformational leadership reported more rapid and more profound changes than all the principals oriented toward a more transactional type of leadership. In this case, transformational leadership is associated with faster implementation of digital technologies and also with a clearer focus on educational goals rather than on tools. This study expands the knowledge on principals’ technology leadership practices as it provides insights into school leaders’ attitudes and concepts and highlights the benefits of transformational leadership.
Blockchain in Environmental Compliance and Enforcement

The blockchain applications are a relatively new innovation, especially in the field of envirinmental law enforcement. The review of the existing literature on blockchain applications seems to point out the availability of great potential for these application in the field of environmental compliance and enforcement. The study aims to review the existing blockchain technologies in environmental compliance and enforcement and other relevant fields. The study recommend that Future research is required to further investigate the potential and impact of blockchain application on environmental enforcement.
A Color Image Encryption Scheme Combining Hyperchaos and Genetic Codes

In consideration of the reduced chaotic range and susceptibility of a single chaotic map, we exploit the 4D-hyperchaotic system for creating three S-boxes i.e., red, green and blue S-boxes and a logistic map to transform a plain image into DNA strands. Afterwards, a logistic map based fake image is also generated which is also mapped to deoxyribonucleic acid (DNA) strands. Then DNA operations based on logistic map sequence are performed among the DNA strands and the resultant strands are decoded. The decoded strands are substituted by three substitution-boxes (s-boxes) to create an encrypted image. In this research, a cryptanalysis driven design approach is used to prove the security of a proposed encryption scheme. The proposed scheme operates on numerous image dimensions N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> M and different image file sizes and formats. Experimental results and analysis are completed for visual analysis, key space, key sensitivity, energy analysis, homogeneity analysis, contrast analysis, entropy analysis, histogram analysis, correlation analysis, chosen-plaintext attacks, number of pixels change rate (NPCR), universal average changing intensity (UACI), mean absolute error, robustness against noises and occlusion attacks and encryption efficiency analysis. The visual as well as numerical simulations demonstrate that the proposed algorithm is safe and reliable.
Learners' flow experience during peer revision in a virtual writing course during the global pandemic

The global Covid-19 pandemic that hit educational contexts worldwide transformed our regular educational practices and some tasks such as peer revision, a staple in many additional language (AL) writing courses (Hyland & Hyland, 2019), were put aside. As teachers have become more familiar with many new technologies since the start of the pandemic, there is a need to implement peer revision tasks in virtual spaces and to understand learners' experiences in this process. The aims of the present study were to examine AL learners’ subjective experience through flow theory as they engage in peer revision tasks with two components: an individual peer feedback component with a follow-up shared feedback component. Flow, a positive experiential state characterized by focus and involvement in challenging yet doable tasks, has been associated with enhanced self-confidence and task performance (Csikszentmihalyi, 2008). Participants of French as an AL (n = 18) engaged in two peer revision tasks (PR) which included both an individual (I-PR) and a follow-up sharing component (S-PR). Flow perception questionnaires were completed immediately following each task. Findings provide empirical evidence that despite being physically isolated and having limited experience with online technologies, learners experienced flow, especially during the S-PR component.
Modeling the blockchain readiness challenges for product recovery system

Product Recovery System (PRS) transfers products from their typical final place to their source to arrest some value on the product. There are obstructions, such as costs, associated with the modification of accounts and assessment of products and refunds associated with the implementation of PRS. Blockchain Technology (BCT) emerged as an innovative approach to constructing trust in a trust less environment and assures the availability, traceability, and security in data management. It also presents a valuable solution to PRS. This study aims to analyze the Blockchain Readiness Challenges (BRCs) to PRS in the context of manufacturing industries. The study observes 20 readiness challenges linked with the implementation of BCT in PRS. The BRCs are identified from the literature survey and confirmed after consequent examinations with industry experts and researchers. The study employed a Multi-Criteria Decision-Making (MCDM) i.e., the Decision-Making Trial And Evaluation Laboratory (Fuzzy DEMATEL) approach to find the cause-and-effect interactions to prioritize BRCs. The Maximum Mean De-Entropy (MMDE) algorithm was adopted to establish the threshold value based on the information entropy of the interactions among the BRCs for PRS. The fuzzy set theory was adopted to tackle the uncertainty and vagueness of personnel biases and data deficiency problems. The findings from this study reveal that inadequate financing for PRS exercises, lack of governance and standards, and security challenges to BCT implementation are the most influential readiness challenges for the adoption of blockchain in PRS. The study is useful to manufacturing organizations for identifying the potential BRCs to implement PRS among all existing readiness challenges so that they can take suitable measures before proceeding to adopt blockchain in PRS. The managers are suggested to eliminate the readiness challenges and widen the blockchain technology adoption in PRS.
How to Build Your Own ASP-based System?!

Abstract Answer Set Programming, or ASP for short, has become a popular and sophisticated approach to declarative problem solving. Its popularity is due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. However, in contrast to ASP’s ease of use, the high degree of sophistication of the underlying technology makes it even hard for ASP experts to put ideas into practice whenever this involves modifying ASP’s machinery. For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system clingo can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how metaprogramming can be used for extending ASP. This is a rather light approach that relies on clingo ’s reification feature to use ASP itself for expressing new functionalities. The second part of this tutorial uses traditional programming (in Python) for manipulating clingo via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is clingo ’s new Application class that allows us to draw on clingo ’s infrastructure by customizing processes similar to the one in clingo . For instance, we may apply manipulations to programs’ abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. A cross-sectional structure, spanning meta as well as application programming, is clingo ’s intermediate format, aspif , that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several nontrivial case studies. In particular, we show how clingo can be extended by difference constraints and how guess-and-check programming can be implemented with both meta and application programming.
Cloud Computing and Comparison based on Service and Performance between Amazon AWS, Microsoft Azure, and Google Cloud

Cloud computing is the on-request supply of computing resources through the Web with pay-as-you-use billing. Instead of purchasing, operating, and maintaining physical computers, hardware, and servers, cloud solutions providers such as Microsoft Azure of Microsoft, Amazon Web Services (AWS) of Amazon, and Google Cloud Platform (GCP) by Google offer cloud solutions such as processing power, memory, and databases on an as-needed basis. This research paper discusses the architecture and types of cloud computing services, as well as comparison of the performance and service among three main Cloud Computing platforms: Microsoft Azure, Amazon AWS, and Google Cloud Platform. All three systems have been evaluated in identical virtual environments, specifically micro instance of Ubuntu 16.04. The benchmark application Phoronix Test Suite 10.4 is used to assess performance, and the results for the Apache, Dbench, and RAM speed benchmarks are evaluated in this paper.
SignBERT: A BERT-Based Deep Learning Framework for Continuous Sign Language Recognition

Continuous sign language recognition (CSLR) is a very challenging task in intelligent systems, since it requires to produce real-time responses while performing computationally intensive video analytics and language modeling.Previous studies mainly focus on adopting hidden Markov models or recurrent neural networks with a limited capability to model specific sign languages, and the accuracy can drop significantly when recognizing the signs performed by different signers with non-standard gestures or non-uniform speeds.In this work, we develop a deep learning framework named SignBERT, integrating the bidirectional encoder representations from transformers (BERT) with the residual neural network (ResNet), to model the underlying sign languages and extract spatial features for CSLR.We further propose a multimodal version of SignBERT, which combines the input of hand images with an intelligent feature alignment, to minimize the distance between the probability distributions of the recognition results generated by the BERT model and the hand images.Experimental results indicate that when compared to the performance of alternative approaches for CSLR, our method has better accuracy with significantly lower word error rate on three challenging continuous sign language datasets.
Adaptive Deadline Determination for Mobile Device Selection in Federated Learning

Owing to dynamically changing resources and channel conditions of mobile devices (MDs), when a static deadline-based MD selection scheme is used for federated learning, resource utilization of MDs can be degraded. To mitigate this problem, we propose an adaptive deadline determination (ADD) algorithm for MD selection, where a deadline for each round is adaptively determined with the consideration of the performance disparity of MDs. Evaluation results demonstrate that ADD can achieve the fastest average convergence time among the comparison schemes.
Deep Representation Learning using Multilayer Perceptron and Stacked Autoencoder for Recommendation Systems

Deep learning-based collaborative filtering methods are studied in recommendation systems as efficient feature mapping techniques. The aim of these methods is to project the users and items to a common representation space and obtain their latent features. Although these methods have been widely used in the literature, they suffer from the limited expressiveness of Dot product function. In other words, Dot product cannot describe different impacts of various latent factors. To solve this issue, we propose a novel recommender system named Deep-MSR which exploits the multilayer perceptron (MLP) neural network and stacked auto-encoder network (SAN) to extract item latent factors and user latent factors from user-item interaction matrix. The obtained latent factors are used in the proposed rating prediction module which integrates user preferences and item features in the recommendation process. Our experiments on two well-known datasets show that our method can outperform the competitive baseline recommendation methods.
Adaptive Hierarchical Federated Learning Over Wireless Networks

Federated learning (FL) is promising in enabling large-scale model training by massive devices without exposing their local datasets. However, due to limited wireless resources, traditional cloud-based FL system suffers from the bottleneck of communication overhead in core network. Fueled by this issue, we consider a hierarchical FL system and formulate a joint problem of edge aggregation interval control and resource allocation to minimize the weighted sum of training loss and training latency. To quantify the learning performance, an upper bound of the average global gradient deviation, in terms of the edge aggregation interval, the training latency, and the number of successfully participating devices, is derived. Then an alternative problem is formulated, which can be decoupled into an edge aggregation interval control problem and a resource allocation problem, and solved by an iterative optimization algorithm. Specifically, given the resource allocation strategy, a relaxation and rounding method is proposed to optimize the edge aggregation interval. The problem of resource allocation including training time allocation and bandwidth allocation is solved separately based on the convex optimization theory. Simulation results show that the proposed algorithm, compared to the benchmarks, can achieve higher learning performance with lower training latency, and is capable of adaptively adjusting the edge aggregation interval and the resource allocation strategy according to the training process.
Affective Computing in Marketing: Practical Implications and Research Opportunities Afforded by Emotionally Intelligent Machines

Abstract After years of using AI to perform cognitive tasks, marketing practitioners can now use it to perform tasks that require emotional intelligence. This advancement is made possible by the rise of affective computing, which develops AI and machines capable of detecting and responding to human emotions. From market research, to customer service, to product innovation, the practice of marketing will likely be transformed by the rise of affective computing, as preliminary evidence from the field suggests. In this Idea Corner, we discuss this transformation and identify the research opportunities that it offers.
Chain Reductions for Multi-signatures and the HBMS Scheme

Existing proofs for existing Discrete Log (DL) based multi-signature schemes give only weak guarantees if the schemes are implemented, as they are in practice, in 256-bit groups. This is because the underlying reductions, which are mostly in the standard model and from DL, are loose. We show that relaxing either the model or the assumption suffices to obtain tight reductions. Namely we give (1) tight proofs from DL in the Algebraic Group Model, and (2) tight, standard-model proofs from well-founded assumptions other than DL. We first do this for the classical 3-round schemes, namely \(\mathsf {BN} \) and \(\mathsf {MuSig} \). Then we give a new 2-round multi-signature scheme, \(\mathsf {HBMS} \), as efficient as prior ones, for which we do the same. These multiple paths to security for a single scheme are made possible by a framework of chain reductions, in which a reduction is broken into a chain of sub-reductions involving intermediate problems. Overall our results improve the security guarantees for DL-based multi-signature schemes in the groups in which they are implemented in practice.
Carbon Emission Monitoring and Credit Trading: The Blockchain and IOT Approach

Over the years, carbon dioxide emissions are the primary cause of climate change due to its quantum release by anthropogenic activities. However, inconsistencies in current carbon dioxide emission data make it difficult to ascertain the actual companies' emissions as well as difficulties in assessing trading routes between emitters and cleaners. These inconsistencies impede the sustainable development of low-carbon cities in current times. Therefore, a decentralized, transparent, traceable, and trustless system for carbon emission monitoring and credit trading is imperative in meeting low carbon cities' development. This paper proposes a Blockchain and IoT-based framework on the FISCO bcos platform to monitor enterprise carbon emissions coupled with carbon credit trading using a smart contract. An evaluation of our system demonstrates that our system outperforms other solutions with comparisons on system median response time. The smart contract code for this work is available at: https://github.com/De-miles1/Carbon/tree/master.
Fast and Accurate U-Net Model for Fetal Ultrasound Image Segmentation

U-Net based algorithms, due to their complex computations, include limitations when they are used in clinical devices. In this paper, we addressed this problem through a novel U-Net based architecture that called fast and accurate U-Net for medical image segmentation task. The proposed fast and accurate U-Net model contains four tuned 2D-convolutional, 2D-transposed convolutional, and batch normalization layers as its main layers. There are four blocks in the encoder-decoder path. The results of our proposed architecture were evaluated using a prepared dataset for head circumference and abdominal circumference segmentation tasks, and a public dataset (HC18-Grand challenge dataset) for fetal head circumference measurement. The proposed fast network significantly improved the processing time in comparison with U-Net, dilated U-Net, R2U-Net, attention U-Net, and MFP U-Net. It took 0.47 seconds for segmenting a fetal abdominal image. In addition, over the prepared dataset using the proposed accurate model, Dice and Jaccard coefficients were 97.62% and 95.43% for fetal head segmentation, 95.07%, and 91.99% for fetal abdominal segmentation. Moreover, we have obtained the Dice and Jaccard coefficients of 97.45% and 95.00% using the public HC18-Grand challenge dataset. Based on the obtained results, we have concluded that a fine-tuned and a simple well-structured model used in clinical devices can outperform complex models.