Reviewerly/exHarmony huggingface dataset:
LACT+: Practical Post-Quantum Scalable Confidential Transactions

A “confidential monetary value” carries information about the real monetary value but does not disclose it. Post-quantum private blockchains with confidential monetary values—large-sized blockchains with large verification times—have the least scalability because they need to save and verify more information than those with “plain-text monetary values”. High scalability is an essential security requirement for decentralized blockchain payment systems because the more honest peers who can afford to verify the blockchain copies are, the higher the security. We propose a quantum-safe transaction protocol for confidential monetary blockchains, LACT+ (Lattice-based Aggregable Confidential Transactions), which is more scalable than previous post-quantum confidential blockchains, i.e., many input/output transactions with logarithmic sized complexity.
Robust and fast representation learning for heterogeneous information networks

Network representation learning is an important tool that can be used to optimize the speed and performance of downstream analysis tasks by extracting latent features of heterogeneous networks. However, in the face of new challenges of increasing network size, diverse latent features, and unseen network noise, existing representation models need to be further optimized. In this paper, a robust and fast representation learning model is proposed for heterogeneous networks, called RFRL. First, the global features of a heterogeneous network are divided into multiple intra-type local features and inter-type local features, and a type-aware biased sampling is designed to generate training samples for each local feature. Second, a node-type-aware and a link-type-aware shallow representation strategy are used to learn intra-type features and inter-type features respectively. This enables the model to achieve good performance while having high speed through the divide-and-conquer learning process and shallow learning model, thus coping with increasing network size and latent feature diversity. Finally, adversarial learning is used to integrate the above two representation strategies to address unseen network noise and enhance the robustness of representation learning. Extensive experiments on three network analysis tasks and three public datasets demonstrate the good performance of our RFRL model.
Towards a cybercontextual transmission model for online scamming

ABSTRACTABSTRACTThis study focuses on advance fee fraud (AFF) scamming, a specific form of online deception in which scammers rely on social engineering techniques to deceive individuals into making advance payments to them. Several industry and law enforcement reports have emphasised that AFF scamming is among the most pervasive forms of online social engineering attacks against consumers, organisations, and online users. Although AFF scamming has received significant attention worldwide, it remains an under-researched and poorly understood crime, and little work has focused on offenders. Although studies on online scammers have inferred that digital environment attributes influence online deception, few studies have empirically clarified how such contexts explain online scammers’ motivations. The present study was designed to explore the motivations and deceptive practices of modern-day AFF scammers by using data from scammers. The empirical results urge the adoption of a model for AFF scamming that conceptually builds on social learning theory (SLT)’s core concepts but functions differently from it, warranting a new IT-based conceptual model. Accordingly, our contributions identify and explain cybercontextual social learning attributes that influence AFF scamming and underscore how traditional criminological theories, such as SLT, cannot sufficiently account for online offences, such as AFF scamming. Consequently, we propose cybercontextual transmission model (CTM) as a reformulation of SLT. Additional theory and practice implications are discussed.KEYWORDS: AFF scammingsocial learning theoryqualitative researchinductive researchsocial engineering Disclosure statementNo potential conflict of interest was reported by the authors.
Semantic similarity measure of natural language text through machine learning and a <scp>keyword‐aware cross‐encoder‐ranking</scp> summarizer—A case study using <scp>UCGIS GIS</scp>&amp;T body of knowledge

Initiated by the University Consortium of Geographic Information Science (UCGIS), GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re-organization and content updating, resulting in a new digital version of the project. While the BoK topics provide useful materials for researchers and students to learn about GIS, the semantic relationships among the topics, such as semantic similarity, should also be identified so that a better and automated topic navigation can be achieved. Currently, the related topics are either defined manually by editors or authors, which may result in an incomplete assessment of topic relationship. To address this challenge, our research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text, including both deep neural networks and traditional machine learning approaches. Besides, a novel text summarization - KACERS (Keyword-Aware Cross-Encoder-Ranking Summarizer) - is proposed to generate a semantic summary of scientific publications. By identifying the semantic linkages among key topics, this work provides guidance for future development and content organization of the GIS&T BoK project. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications, and demonstrate the potential of KACERS summarizer in semantic understanding of long text documents.
Reliable and efficient emergency rescue networks: A blockchain and fireworks algorithm-based approach

In recent years, coronavirus disease 2019 (COVID-19) has been a severe issue the world faces. Emergency rescue networks concerning the distribution of relief materials have gained extensive attention to tackle COVID-19 and related emergency issues. However, it is challenging to establish reliable and efficient emergency rescue networks due to information asymmetry and lack of trust among different rescue stations. In this work, we propose blockchain-based emergency rescue networks to track every transaction of the relief materials reliably and make decisions to deliver relief materials efficiently. More specifically, we propose a hybrid blockchain architecture that employs on-chain data verification to authenticate data records and off-chain data storage to reduce storage overhead. Furthermore, we propose a fireworks algorithm to efficiently calculate the optimal allocation strategies for relief materials. The algorithm provides chaotic random screening and node request guarantee techniques with good convergence. The simulation results show that integrating blockchain technology and the fireworks algorithm can significantly improve relief materials’ operation efficiency and distribution quality.
Adversarial domain adaptation using contrastive learning

In recent research, problems with biased datasets or domain shift have presented challenges to the practical applications of deep learning methods. In this paper, we propose a simple method using adversarial learning combined with contrastive learning and domain adaptation to solve the domain-shift problem. Because domain shift is caused by differences in the distribution of data across domains, different approaches have been proposed to resolve this by rendering the data distributions closer through adversarial training. Contrastive learning is a method of acquiring valid feature representations for downstream tasks. By combining these approaches, we aim to achieve better domain adaptation. The proposed method is simple and intuitive. By introducing a domain discriminator into SimCLR, which is a typical contrastive learning model, and training it in an adversarial manner, the feature vectors of the source and target domains are rendered closer to acquire domain-invariant features. The proposed approach facilitates high-performance pretraining without labels and demonstrates a significant improvement in accuracy in comparison to standard benchmark methods, including conventional supervised models and SimCLR.
Dual-Spatial Normalized Transformer for image captioning

Self-attention modules have shown dominance in image captioning. However, current self-attention modules insufficiently consider spatial correlations between objects in an image and easily suffer from distribution shifts. In this work, we aim to improve self-attention modules in two ways to solve the above problems. We first propose a Spatial-Enhanced Attention (SEA) module, which incorporates dual spatial encodings to enhance geometric correlations between visual instances. We further introduce a Gated-Normalized Attention (GNA) module to fix the distributions inside self-attention module. GNA module also smooths gradients and reduces misleading information. To prove our designs, we apply SEA module and GNA module to the standard Transformer to construct a novel Dual-Spatial Normalized Transformer (DSNT), which captures a more accurate spatial representations and improves performance of image captioning. Extensive experiments are conducted to verify the effectiveness of our proposal. Compared to non-pretrained state-of-the-art methods, quantitative results on the MSCOCO dataset show that our DSNT achieves competitive performance on both the offline and online test sets, i.e., 134.8% CIDEr score on the Karpathy split and 134.0% CIDEr score on the official split. Our source code is available at https://github.com/TBI805/DSNT.
Efficient diagnosis of hematologic malignancies using bone marrow microscopic images: A method based on MultiPathGAN and MobileViTv2

Hematologic malignancies, including the associated multiple subtypes, are critically threatening to human health. The timely detection of malignancies is crucial for their effective treatment. In this regard, the examination of bone marrow smears constitutes a crucial step. Nonetheless, the conventional approach to cell identification and enumeration is laborious and time-intensive. Therefore, the present study aimed to develop a method for the efficient diagnosis of these malignancies directly from bone marrow microscopic images.A deep learning-based framework was developed to facilitate the diagnosis of common hematologic malignancies. First, a total of 2033 microscopic images of bone marrow analysis, including the images for 6 disease types and 1 healthy control, were collected from two Chinese medical websites. Next, the collected images were classified into the training, validation, and test datasets in the ratio of 7:1:2. Subsequently, a method of stain normalization to multi-domains (stain domain augmentation) based on the MultiPathGAN model was developed to equalize the stain styles and expand the image datasets. Afterward, a lightweight hybrid model named MobileViTv2, which integrates the strengths of both CNNs and ViTs, was developed for disease classification. The resulting model was trained and utilized to diagnose patients based on multiple microscopic images of their bone marrow smears, obtained from a cohort of 61 individuals.MobileViTv2 exhibited an average accuracy of 94.28% when applied to the test set, with multiple myeloma, acute lymphocytic leukemia, and lymphoma revealed as the three diseases diagnosed with the highest accuracy values of 98%, 96%, and 96%, respectively. Regarding patient-level prediction, the average accuracy of MobileViTv2 was 96.72%. This model outperformed both CNN and ViT models in terms of accuracy, despite utilizing only 9.8 million parameters. When applied to two public datasets, MobileViTv2 exhibited accuracy values of 99.75% and 99.72%, respectively, and outperformed previous methods.The proposed framework could be applied directly to bone marrow microscopic images with different stain styles to efficiently establish the diagnosis of common hematologic malignancies.
Training a quantum measurement device to discriminate unknown non-orthogonal quantum states

Here, we study the problem of decoding information transmitted through unknown quantum states. We assume that Alice encodes an alphabet into a set of orthogonal quantum states, which are then transmitted to Bob. However, the quantum channel that mediates the transmission maps the orthogonal states into non-orthogonal states, possibly mixed. If an accurate model of the channel is unavailable, then the states received by Bob are unknown. In order to decode the transmitted information we propose to train a measurement device to achieve the smallest possible error in the discrimination process. This is achieved by supplementing the quantum channel with a classical one, which allows the transmission of information required for the training, and resorting to a noise-tolerant optimization algorithm. We demonstrate the training method in the case of minimum-error discrimination and show that it achieves error probabilities very close to the optimal one. In particular, in the case of two unknown pure states our proposal approaches the Helstrom bound. A similar result holds for a larger number of states in higher dimensions. We also show that a reduction of the search space, which is used in the training process, leads to a considerable reduction in the required resources. Finally, we apply our proposal to the case of the dephasing channel reaching an accurate value of the optimal error probability.
FACTORS THAT INCREASE THE EFFECTIVENESS OF HYBRID TEACHING IN A DIGITAL EDUCATIONAL ENVIRONMENT

In this article, we present the functional characteristics of the qualities of students' professional competence in the hybrid educational environment. We have shown the criteria and indicators of students' analysis-synthesis algorithm. We have considered ways of developing students' creative activity and professional competencies. We have provided information on virtual learning tools based on a mutually beneficial combination of the communicative level and self-motivational preparation factors in the cognitive domain.
Information sharing and security investment for substitutable firms: A game-theoretic analysis

There are several types of relations between information assets of two firms, among which substitutable relation is such that even when one of the firms is successfully breached by one hacker, both firms would incur loss and the hacker gets benefit. This paper examines security investment and information sharing of two substitutable firms through constructing a game-theoretic model between these firms and one hacker. We derive the following interesting results: (a) the firm with more efficient security investment suffers weaker cyber-attack from the hacker because the rational hacker would switch its emphasis to the less efficient firm; (b) one firm’s aggregate defense increases with its unit investment cost for the positive interdependence when this cost remains high due to more shared information and enhanced security investment from the other firm; (c) although a widely-used compensation mechanism can urge the firms to invest more when security loss remains low, it increases their expected costs because of the excess investment; (d) unlike simultaneous game, each firm’s aggregate defense always increases with the unit cost of cyber-attacks under sequential game since the hacker has an advantage of learning the firms’ security decisions. These results give fresh managerial guidelines to security experts of firms.
Self-supervised Dynamic Graph Embedding with evolutionary neighborhood and community

Real-world data and systems are generally evolutionary over time, which can be formalized as dynamic graphs, and Dynamic Graph Embedding (DGE) as a fundamental technique for analyzing dynamic graphs, has shown considerable potential in recent years. However, the majority of existing DGEs focus only on the local neighborhood structure, ignoring the global community structure, which is also an important property of dynamic graphs. Therefore, how to fuse both local and global information to learn more comprehensive graph representations and their variation patterns is a meaningful and promising topic. In this paper, we propose a novel DGE method to explore the graph properties and evolution patterns from Neighborhood and Community views, named DGNC. The neighborhood view learns the node embeddings in line with timestamped interaction events, and models the local variation of graphs via the time-varying attention vector. While the community view encodes the alternative transformation of node features and community features according to community correlation, which aims to extract the global evolution of graphs. To better integrate the two views, cross-supervised contrastive learning is introduced to enable them to collaboratively provide guidance and complementary information to each other, which can significantly improve the quality of graph embedding. We evaluate DGNC through diverse downstream tasks on four real-world dynamic graph datasets, and the experimental results verify the effectiveness and superiority of our proposed method.
A machine learning-based optimization approach for pre-copy live virtual machine migration

Abstract Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.
Fully convolutional network for automated detection and diagnosis of mammographic masses

Breast cancer, though rare in male, is very frequent in female and has high mortality rate which can be reduced if detected and diagnosed at the early stage. Thus, in this paper, deep learning architecture based on U-Net is proposed for the detection of breast masses and its characterization as benign or malignant. The evaluation of the proposed architecture in detection is carried out on two benchmark datasets- INbreast and DDSM and achieved a true positive rate of 99.64% at 0.25 false positives per image for INbreast dataset while the same for DDSM are 97.36% and 0.38 FPs/I, respectively. For mass characterization, an accuracy of 97.39% with an AUC of 0.97 is obtained for INbreast while the same for DDSM are 96.81%, and 0.96, respectively. The measured results are further compared with the state-of-the-art techniques where the introduced scheme takes an edge over others.
Application of elemental chemostratigraphy to refine the stratigraphy of the Adavale Basin, Queensland

A prerequisite to understanding the evolution and resource potential of a basin is to establish a reliable stratigraphic framework that enables the correlation of rock units across multiple depocentres. Establishing a stratigraphic model for the Adavale Basin is challenging due to its structurally complexity, lack of well penetration and its lateral changes in facies. Biostratigraphy appears broad-scale, and despite providing chronostratigraphic control for the Lower Devonian Gumbardo Formation when combined with U/Pb zircon geochronology, the rest of the Devonian succession is hampered by a lack of microfossil assemblages and their poor preservation. The aim of this study is to establish an independent chemostratigraphic correlation across the Adavale Basin using whole rock inorganic geochemistry. Within this study, a total of 1489 cuttings samples from 10 study wells were analysed by Inductively Coupled Plasma – Optical Emission Spectrometry and Inductively Coupled Plasma – Mass Spectrometry for whole rock geochemistry, in order to establish an independent chemostratigraphic zonation scheme. Based on key elemental ratios selected to reflect changes in feldspars, clay minerals and provenance, the Devonian-aged stratigraphy is characterised into four chemostratigraphic mega-sequences that encompass the Gumbardo Formation (Mega-sequence 1); the Eastwood Formation, the Log Creek Formation and the Lissoy Sandstone (Mega-sequence 2); the Bury Limestone and the Boree Salt formations (Mega-sequence 3); and the Etonvale and the Buckabie formations (Mega-sequence 4). These mega-sequences have been further subdivided into a series of chemostratigraphic sequences that can be correlated across the study wells, establishing a regional correlation framework.
NRPose: Towards noise resistance for multi-person pose estimation

The high signal-to-noise ratio is one of the main challenges of multi-person pose estimation (MPE) and receives little attention. In this work, we find that MPE suffers from two types of noise: aleatoric noise and epistemic noise. The former represents the noise inherent in the observations, such as the background. The latter indicates the noise brought by the priori hypotheses, such as the inappropriate keypoint relations. Both of them reduce the saliency of information available for keypoint localization. We propose the noise-resistance pose estimation (NRPose) that integrates keypoint-oriented region proposal module (KRPM) and pose-aware sparse relation module (PSRM). To mitigate aleatoric noise, KRPM generates keypoint-level RoIs by circumscribing semantically significant regions. To reduce epistemic noise, PSRM filters out the noisy relations dynamically by modeling the noise propagation and keypoint interaction. NRPose outperforms the state-of-the-art methods by at least 1.0 AP on COCO and OCHuman dataset.