Reviewerly/exHarmony huggingface dataset:
DeNoL: A Few-Shot-Sample-Based Decoupling Noise Layer for Cross-channel Watermarking Robustness

Cross-channel (e.g. Screen-to-Camera) robustness is an urgent requirement for modern watermarking systems. To realize such robustness, training a network that can precisely simulate the cross-channel distortion as the noise layer for deep watermarking training is an effective way. However, network training requires massive data, and generating the data is laborious. Meanwhile, directly using limited data to train may lead to an over-fitting issue. To address such limitation, we proposed DeNoL, a decoupling noise layer for cross-channel simulation which only needs few-shot samples. We believe the overfitting issue comes from the overlearning of the training image content rather than only simulating the distortion style. Consequently, we design a network that can decouple the image content and the distortion style into different components. Thus, by fixing the content representation component and fine-tuning a new style component accordingly, the network can efficiently learn and only learn the distortion style. Such learning can be done with only few-shot samples. Besides, in order to enhance adaptability, we also proposed a diversification operation to cooperate with DeNoL. Experimental results show that DeNoL can effectively simulate cross-channel distortion with only 20 image pairs and assist in training a general and robust watermarking network.
Post-Quantum Security: Opportunities and Challenges

Cryptography is very essential in our daily life, not only for confidentiality of information, but also for information integrity verification, non-repudiation, authentication, and other aspects. In modern society, cryptography is widely used; everything from personal life to national security is inseparable from it. With the emergence of quantum computing, traditional encryption methods are at risk of being cracked. People are beginning to explore methods for defending against quantum computer attacks. Among the methods currently developed, quantum key distribution is a technology that uses the principles of quantum mechanics to distribute keys. Post-quantum encryption algorithms are encryption methods that rely on mathematical challenges that quantum computers cannot solve quickly to ensure security. In this study, an integrated review of post-quantum encryption algorithms is conducted from the perspective of traditional cryptography. First, the concept and development background of post-quantum encryption are introduced. Then, the post-quantum encryption algorithm Kyber is studied. Finally, the achievements, difficulties and outstanding problems in this emerging field are summarized, and some predictions for the future are made.
Enhancing Approximate Nearest Neighbor Search: Binary-Indexed LSH-Tries, Trie Rebuilding, and Batch Extraction

Locality-Sensitive-Hashing (LSH) plays a crucial role in approximate nearest neighbour search and similarity-based queries. In this paper, we present a study on the performance of LSH for indexing and searching high-dimensional binary vectors under the hamming distance metric. The paper presents a simple implementation of LSH-tries, utilizing binary indexing to seamlessly traverse to neighbouring buckets. To speed up queries, two optimization techniques, Batch Extraction & Trie Rebuilding, are introduced and their impact on performance analyzed. For evaluation, we conducted benchmarks using 1024-bit binary sketches from the LAION dataset, showcasing the accuracy and efficiency of our solution w.r.t. the brute-force approach, optimization strategies, and dataset size. The results show that SWANN outperforms the brute-force approach, and exhibits sub-linear growth as the size of the dataset increases. Our results w.r.t. the aforementioned optimizations showcase the importance of the bucket distribution, and the impact of hash function configuration.
General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo

Despite the efficacy of graph-based algorithms for Approximate Nearest Neighbor (ANN) searches, the optimal tuning of such systems remains unclear. This study introduces a method to tune the performance of off-the-shelf graph-based indexes, focusing on the dimension of vectors, database size, and entry points of graph traversal. We utilize a black-box optimization algorithm to perform integrated tuning to meet the required levels of recall and Queries Per Second (QPS). We applied our approach to Task A of the SISAP 2023 Indexing Challenge and got second place in the 10 M and 30 M tracks. It improves performance substantially compared to brute force methods. This research offers a universally applicable tuning method for graph-based indexes, extending beyond the specific conditions of the competition to broader uses.
Investigating “organizational maturity” in order to provide “blockchain banking service” based on “FinTech” (through “the CMMI” in “Parsian Bank”)

Purpose The description of “blockchain banking”, the determination of “the sub-processes” of “blockchain banking” as a “business process”, and the assessment of “maturity level” in Parsian Bank. Design/methodology/approach Theoretical sources on “blockchain banking” were initially investigated. Then the “sub-processes” of “blockchain banking” as a “business process” were extracted by Parsian Bank's experts through the “Delphi method”. Next, the “sequence” of the “sub-processes” was determined by means of the “AHP”. Eventually, Parsian Bank's maturity levels for all the sub-processes as well as the overall maturity level were specified on the basis of the “CMMI” V1.3 in order for Business Process Management (BPM). Findings Blockchain banking’ combines traditional banking with cryptocurrencies, which can be provided by merging “hybrid e-wallet” with “bank account” and “bank card” – all together as “crypto bank account”. Plus, “hybrid e-wallet” is a form of mobile e-wallet on blockchain that supports both cryptocurrencies and traditional currencies in the same platform by which the purchase and sale of cryptocurrencies are possible. Besides, “Blockchain banking service” can also be offered within the framework of “open banking” aligned with “open innovation” through a FinTech (or a beta bank) in collaboration with a licensed bank via “open API”, which is called “blockchain banking based on FinTech”. At last, the eight sub-processes of “blockchain banking” were determined and Parsian Bank's “maturity level” was specified. Originality/value This is the very first practical guide to “blockchain banking service”.
Inferring nonlinear fractional diffusion processes from single trajectories

Abstract We present a method to infer the arbitrary space-dependent drift and diffusion of a nonlinear stochastic model driven by multiplicative fractional Gaussian noise from a single trajectory. Our method, fractional Onsager-Machlup optimisation (fOMo), introduces a maximum likelihood estimator by minimising a field-theoretic action which we construct from the observed time series. We successfully test fOMo for a wide range of Hurst exponents using artificial data with strong nonlinearities, and apply it to a data set of daily mean temperatures. We further highlight the significant systematic estimation errors when ignoring non-Markovianity, underlining the need for nonlinear fractional inference methods when studying real-world long-range (anti-)correlated systems.
Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform

The presence of speckle noise severely hampers the interpretability of synthetic aperture radar (SAR) images. While research on despeckling single-temporal SAR images is well-established, there remains a significant gap in the study of despeckling multi-temporal SAR images. Addressing the limitations in the acquisition of the “superimage” and the generation of ratio images within the RABASAR despeckling framework, this paper proposes an enhanced framework. This enhanced framework proposes a direction-based segmentation approach for multi-temporal SAR non-local means filtering (DSMT-NLM) to obtain the “superimage”. The DSMT-NLM incorporates the concept of directional segmentation and extends the application of the non-local means (NLM) algorithm to multi-temporal images. Simultaneously, the enhanced framework employs a weighted averaging method based on wavelet transform (WAMWT) to generate superimposed images, thereby enhancing the generation process of ratio images. Experimental results demonstrate that compared to RABASAR, Frost, and NLM, the proposed method exhibits outstanding performance. It not only effectively removes speckle noise from multi-temporal SAR images and reduces the generation of false details, but also successfully achieves the fusion of multi-temporal information, aligning with experimental expectations.
Fractal-Based Hybrid Cryptosystem: Enhancing Image Encryption with RSA, Homomorphic Encryption, and Chaotic Maps

Protecting digital data, especially digital images, from unauthorized access and malicious activities is crucial in today’s digital era. This paper introduces a novel approach to enhance image encryption by combining the strengths of the RSA algorithm, homomorphic encryption, and chaotic maps, specifically the sine and logistic map, alongside the self-similar properties of the fractal Sierpinski triangle. The proposed fractal-based hybrid cryptosystem leverages Paillier encryption for maintaining security and privacy, while the chaotic maps introduce randomness, periodicity, and robustness. Simultaneously, the fractal Sierpinski triangle generates intricate shapes at different scales, resulting in a substantially expanded key space and heightened sensitivity through randomly selected initial points. The secret keys derived from the chaotic maps and Sierpinski triangle are employed for image encryption. The proposed scheme offers simplicity, efficiency, and robust security, effectively safeguarding against statistical, differential, and brute-force attacks. Through comprehensive experimental evaluations, we demonstrate the superior performance of the proposed scheme compared to existing methods in terms of both security and efficiency. This paper makes a significant contribution to the field of digital image encryption, paving the way for further exploration and optimization in the future.
Improving the Cybersecurity Awareness of Finnish Podiatry SMEs

In the health and welfare sector, many entrepreneurs and employees are not skilled in information and cybersecurity, even when they are constantly dealing with sensitive data. This case study research examines a team of private Finnish podiatrists and their cybersecurity capabilities. The goal of the study is to gather the most important information and cybersecurity topics and create an easy-to-read guide that helps businesses find the framework for their information and cybersecurity and address it in more detail. The results of the case study show that the target organization's most important information and cybersecurity areas are phishing, secure environment, secure communication, passwords, software updates, backups, and physical security. Understanding these topics and following the planned guidelines will strengthen the security posture of all small and medium-sized enterprises (SMEs) in the health and welfare sector.
Accessibility of a COVID-19 Interactive Display: An Evaluation of the Johns Hopkins COVID-19 Dashboard

This paper highlights the importance of heuristic accessibility analysis by investigating the Johns Hopkins COVID-19 U.S. dashboard. It suggests possible future research directions for technical communicators to continue to explore the issue of accessibility in interactive data visualizations.
Layer-Wise Filter Thresholding Based CNN Pruning for Efficient IoT Edge Implementations

This paper presents a novel approach for efficiently running convolutional neural networks (CNNs) on Internet of Things (IoT) edge devices. The proposed method utilizes threshold-based pruning to optimize pre-trained CNN models, enabling inference on resource-constrained IoT and edge devices. The pruning thresholds for each layer are iteratively adjusted using a range-based threshold pruning technique. The pre-trained network evaluates the accuracy of the pruned model and dynamically adjusts the pruning thresholds to maximize accuracy. The effectiveness of the proposed approach is validated on the widely-used LeNet benchmark network, with MNIST, Fashion-MNIST, and SVHN datasets. Our experimental results show that for the MNIST dataset, we can prune 62–64% of weights for an accuracy loss of 1–4%. Similarly, for Fashion-MNIST, we can prune around 64% for an accuracy loss of around 2.92%, and for the SVHN dataset, we can prune around 55% of weights for an accuracy loss of 1.7% on average, saving resources.
Vistrust: a Multidimensional Framework and Empirical Study of Trust in Data Visualizations

Trust is an essential aspect of data visualization, as it plays a crucial role in the interpretation and decision-making processes of users. While research in social sciences outlines the multi-dimensional factors that can play a role in trust formation, most data visualization trust researchers employ a single-item scale to measure trust. We address this gap by proposing a comprehensive, multidimensional conceptualization and operationalization of trust in visualization. We do this by applying general theories of trust from social sciences, as well as synthesizing and extending earlier work and factors identified by studies in the visualization field. We apply a two-dimensional approach to trust in visualization, to distinguish between cognitive and affective elements, as well as between visualization and data-specific trust antecedents. We use our framework to design and run a large crowd-sourced study to quantify the role of visual complexity in establishing trust in science visualizations. Our study provides empirical evidence for several aspects of our proposed theoretical framework, most notably the impact of cognition, affective responses, and individual differences when establishing trust in visualizations.
A novel data balancing technique via resampling majority and minority classes toward effective classification

Classification is a predictive modelling task in machine learning (ML), where the class label is determined for a specific example of predefined features. In determining handwriting characters, identifying spam, detecting disease, identifying signals, and so on, classification requires training data with many features and label instances. In medical informatics, high precision and recall are mandatory issues besides the high accuracy of the ML classifiers. Most of the real-life datasets have imbalanced characteristics that hamper the overall performance of the classifiers. Existing data balancing techniques perform the whole dataset at a time that sometimes causes overfitting and underfitting. We propose a data balancing technique that follows the divide and conquer procedure to cluster the dataset into several segments, and both oversampling and undersampling operation is performed on each cluster. Finally, the cluster joined together and built a balanced dataset. We chose the sample data of two heart disease datasets: Hungarian and Long Beach. Logistic regression and random forest classifier are the representatives of ML algorithms. We compare our proposed techniques with existing SMOTE, NearMiss, and SMOTETomek data balancing techniques. Both algorithms perform better on the proposed technique-balanced dataset. This technique can be the optimal solution for the imbalanced data handling strategy.
An Improved Deeplabv3+ Model for Semantic Segmentation of Urban Environments Targeting Autonomous Driving

This paper proposes an improved Deeplabv3+ model for semantic segmentation of urban scenes targeting autonomous driving applications. A high-quality semantic segmentation dataset is constructed from 2,967 manually labeled aerial images captured at 200m height with a 5-eye camera. The images contain 5 classes - buildings, vegetation, ground, lake and playgrounds. The improved Deeplabv3+ network enriches high-level semantics by replacing max pooling with depthwise separable convolutions. Dilated convolutions extract multi-scale features to avoid overfitting. Experiments demonstrate that the model achieves an overall mean IoU of 0.87 on the test set, with IoU scores of 0.90, 0.92 and 0.94 on buildings, vegetation and water respectively. The model shows promising results for extracting semantic information from complex urban environments to support navigation for autonomous vehicles.
Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement

Multimodal Named Entity Recognition (MNER) aims to use images to locate and classify named entities in a given free text. The mainstream MNER method based on a pre-trained model ignores the syntactic relations in the text and associations between different data; however, these relations can provide crucial missing auxiliary information for the MNER task. Therefore, we propose an auxiliary and syntactic relation enhancement graph fusion (ASGF) method for MNER based on the cross-modal information between similar texts and long-distance inter-word syntactic dependencies in the text. First, for each text image pair (training sample), we search for a sample that is most similar to its text because similar samples may contain similar entity information. We then exploit a multimodal relation graph to model the association between different modal data of the two similar samples; that is, we use the similar sample to supplement the entity information of the text to be recognized. Second, we parse the syntax of the text to capture the syntactic dependencies between different words and integrate them into the relation graph to further enhance its semantic information. Finally, the relation graph is input into the graph neural network, multimodal information is interactively fused through the attention and gating mechanisms, and final MNER label sequence is predicted through CRF decoding. Extensive experimental results show that compared to mainstream methods, the proposed model achieved competitive recognition accuracy on public datasets.
Blockchain and cloud platforms in banking services: A paradox perspective

PURPOSE: The banking sector is under intense pressure from digitalization. One of the accompanying processes is the development of digital platforms and platform ecosystems in banking services. The paper aims to present the dynamic pattern of behavior among partners stemming from the tensions between governance costs and co-created value within platforms in banking services. METHODOLOGY: The study employs an approach based on a systematic literature review of 54 publications selected from Scopus and WoS databases. We applied an approach that consists of two steps. The first step of the research was a literature review and critical analysis of the sources related to our research questions. In the second step, we propose a causal loop diagram research procedure, which is a research system dynamic tool used in modeling system dynamics. FINDINGS: There are different types of platforms, and among the most important are blockchain-based and cloud-based platforms. In both types, the relations between owners, complementors, and customers are important. The tension between governance costs and co-created value informs behavior patterns among platform partners. The degree of interconnectedness between platform participants and the level of centralization of banking services depends on the platform type. The study highlights that blockchain-based and cloud-based platforms play a significant role in the transformation of the current banking services. The choice of platform type has important implications for the platformization of banking services. IMPLICATIONS: The pattern of behavior among platform partners identifies the self-reinforcing dynamics that suggest how managers can navigate the tension over time amidst the asymmetry of benefits and risks. The research findings can be informative for financial regulators and they help work out a policy that reduces the asymmetry of benefits and contributes to the more sustainable development of digital platforms. ORIGINALITY AND VALUE: This paper addresses the paradox perspective on the banking sector changes during the intensive processes of digitalization and the creation of new ‘platform ecosystems.’ This topic has not been studied in this context so far.
A stereo vision depth estimation method of binocular wide-field infrared camera

With the development of binocular stereo vision ranging technology, traditional visible light cameras which have a narrow field of vision gradually cannot meet the need of some special environments. For this reason, this paper proposes to use large field of view infrared cameras to replace traditional visible light cameras. Aiming at the distortion defects caused by the large field of view lens and the low calibration accuracy of the infrared camera, an active infrared radiation calibration board is used. The internal and external parameters of the camera are accurately calibrated under Taylor model which is different from the traditional pinhole model. In addition, we use the SIFT+RANSAC algorithm for feature point extraction and matching, and then get the depth information. The experimental results of target ranging at different distances show that when the distance reaches 9 m, the error rates of the Taylor model (7.11% and 10.1%) are better than the pinhole model (16.4% and 29.8%). The image correction effect of the Taylor model calibration is much better than that of the pinhole model, and the accuracy of depth estimation is also better than that of the pinhole model.
SMS Spam Detection Using Multiple Linear Regression and Extreme Learning Machines

With the growth of the use mobile phones, people have become increasingly interested in using Short Message Services (SMS) as the most suitable communications service. The popularity of SMS has also given rise to SMS spam, which refers to any unwanted message sent to a mobile phone as a text. Spam may cause many problems, such as traffic bottlenecks or stealing important users' information. This paper, presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham. To evaluate the proposed model, the UCI benchmark dataset was used. The proposed model produced recall, precision, F-measure, and accuracy values of 98.7%, 93.3%, 95.9%, and 98.2%, respectively.
The Need for Artificial Intellingence (AI) in Tourism Management

The paper presents a model for evaluating the influence of Artificial Intelligence (AI) on the tourist experience. The proposed AIDE model performs AI evaluation in all travel stages, from pre-travel, during travel and post-travel. The results were obtained through the practical application of the model by using the Chat GPT application in the tourist-tourist destination interaction. The four evaluation components are: attract attention (A) and generate interest (I), stimulates the desire (D) to travel and creates emotion (E) through the experience of visitors.