Reviewerly/exHarmony huggingface dataset:
The money laundering and terrorist financing risks of new and disruptive technologies: a futures-oriented scoping review

New and disruptive technologies, including cryptocurrencies and new payment methods, are revolutionising the way people engage with finance. Although they provide significant benefits to consumers, they are also inadvertently creating new money laundering and terrorist financing risks. This paper examines the risks that are, or are predicted to be, prevalent in three technology sectors—distributed ledger technologies (including cryptocurrencies), new payment methods and financial technologies (FinTech), through a systematic scoping review process. Specifically, the paper identifies enablers of both crimes, the precise criminal methods they might facilitate, at-risk stakeholders (of exploitation and/or complicity) and risk characteristics. The study involves systematic scoping reviews of the academic and futures literatures as well as a consultation exercise with experts to assess the likely veracity of the findings. In addition to identifying an array of specific risks, we identify six underlying trends that facilitate them. We discuss these, their policy implications, future directions for research and their benefit for conducting risk assessments to assess forthcoming technological developments.
A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images

This paper proposes a novel reversible data-hiding method in encrypted images to achieve both a high hiding capacity and good compression performance. The proposed method can also decrypt marked encrypted images without data extraction, so marked images containing a payload can be derived from marked encrypted images. A perceptual encryption algorithm proposed for an encryption-then-compression framework is used to generate compressible encrypted images. In addition, a predictor with high accuracy and a prediction-error expansion and histogram shifting method are used for data hiding. Consequently, the proposed method can compress marked encrypted images without loss using image coding standards and achieve a high hiding rate. Experimental results show the effectiveness of the method in terms of hiding capacity or marked-image quality and lossless compression efficiency.
NotiBike: Assessing Target Selection Techniques for Cyclist Notifications in Augmented Reality

Cyclists' attention is often compromised when interacting with notifications in traffic, hence increasing the likelihood of road accidents. To address this issue, we evaluate three notification interaction modalities and investigate their impact on the interaction performance while cycling: gaze-based Dwell Time, Gestures, and Manual And Gaze Input Cascaded (MAGIC) Pointing. In a user study (N=18), participants confirmed notifications in Augmented Reality (AR) using the three interaction modalities in a simulated biking scenario. We assessed the efficiency regarding reaction times, error rates, and perceived task load. Our results show significantly faster response times for MAGIC Pointing compared to Dwell Time and Gestures, while Dwell Time led to a significantly lower error rate compared to Gestures. Participants favored the MAGIC Pointing approach, supporting cyclists in AR selection tasks. Our research sets the boundaries for more comfortable and easier interaction with notifications and discusses implications for target selections in AR while cycling.
Augmentative contrastive learning for one-shot object detection

We present an Augmentative Contrastive Learning for One-Shot Object Detection method that is inspired by the co-attention and co-excitation (CoAE) method. In the One-shot Object Detection (OSOD) task, the target image contains rich background information that influences the outcomes of different categories of objects more significantly. As a result, the network frequently suggests objects that do not fall under this category, leading to false detections. To learn the similarity between the target object and the query patch, we built the data augmentation module of object switch by similarity, encourage and restrain module. It primarily consists of the following three components: 1) To improve the contrast between the foreground and background, we propose an object switch module based on data augmentation and feature similarity; 2) To decrease the likelihood of false detections, we propose an encourage and restrain module that allows the network to encourage the recommendation of similar objects while restricting the recommendation of different categories of objects; and 3) To increase classification results, we use the nonlinear projector. We perform tests on the PASCAL VOC and MS COCO datasets to confirm the efficacy of our technique.
Countering Popularity Bias by Regularizing Score Differences

Recommendation system often suffers from popularity bias. Often the training data inherently exhibits long-tail distribution in item popularity (data bias). Moreover, the recommendation systems could give unfairly higher recommendation scores to popular items even among items a user equally liked, resulting in over-recommendation of popular items (model bias). In this study we propose a novel method to reduce the model bias while maintaining accuracy by directly regularizing the recommendation scores to be equal across items a user preferred. Akin to contrastive learning, we extend the widely used pairwise loss (BPR loss) which maximizes the score differences between preferred and unpreferred items, with a regularization term that minimizes the score differences within preferred and unpreferred items, respectively, thereby achieving both high debias and high accuracy performance with no additional training. To test the effectiveness of the proposed method, we design an experiment using a synthetic dataset which induces model bias with baseline training; we showed applying the proposed method resulted in drastic reduction of model bias while maintaining accuracy. Comprehensive comparison with earlier debias methods showed the proposed method had advantages in terms of computational validity and efficiency. Further empirical experiments utilizing four benchmark datasets and four recommendation models indicated the proposed method showed general improvements over performances of earlier debias methods. We hope that our method could help users enjoy diverse recommendations promoting serendipitous findings. Code available at https://github.com/stillpsy/popbias.
Testing the ShakeAlert Earthquake Early Warning System Using Synthesized Earthquake Sequences

Abstract We test the behavior of the United States (US) West Coast ShakeAlert earthquake early warning (EEW) system during temporally close earthquake pairs to understand current performance and limitations. We consider performance metrics based on source parameter and ground-motion forecast accuracy, as well as on alerting timeliness. We generate ground-motion times series for synthesized earthquake sequences from real data by combining the signals from pairs of well-recorded earthquakes (4.4≤M≤7.1) using time shifts ranging from −60 to +180 s. We examine fore- and aftershock sequences, near-simultaneous events in different source regions, and simulated out-of-network and offshore earthquakes. We find that the operational ShakeAlert algorithms Earthquake Point-source Integrated Code (EPIC) and Finite-Fault Rupture Detector (FinDer) and the Propagation of Local Undamped Motion (PLUM) method perform largely as expected: EPIC provides the best source location estimates and is often fastest but can underestimate magnitudes or, in extreme cases, miss large earthquakes; FinDer provides real-time line-source models and unsaturated magnitude estimates for large earthquakes but currently cannot process concurrent events and may mislocate offshore earthquakes; PLUM identifies pockets of strong ground motion, but can overestimate alert areas. Implications for system performance are: (1) spatially and temporally close events are difficult to identify separately; (2) challenging scenarios with foreshocks that are close in space and time can lead to missed alerts for large earthquakes; and (3) in these situations the algorithms can often estimate ground motion better than source parameters. To improve EEW, our work suggests revisiting the current algorithm weighting in ShakeAlert, to continue developments that focus on using ground-motion data to aggregate alerts from multiple algorithms, and to investigate methods to optimally leverage algorithm ground-motion estimates. For testing and certification of EEW performance in ShakeAlert and other EEW systems where applicable, we also suggest that 25 of our 73 scenarios become part of the baseline data set.
Fuzzy Superpixel-based Image Segmentation

This article presents a multi-phase image segmentation methodology based on fuzzy superpixel decomposition, aggregation and merging. First, a collection of layers of dense fuzzy superpixels is generated by the variational fuzzy decomposition algorithm. Then a layer of refined superpixels is extracted by aggregating various layers of dense fuzzy superpixels using the hierarchical normalized cuts. Finally, the refined superpixels are projected into the low dimensional feature spaces by the multidimensional scaling and the segmentation result is obtained via the mean-shift-based merging approach with the spatial bandwidth adjustment strategy. Our algorithm utilizes the superimposition of fuzzy superpixels to impose more accurate spatial constraints on the final segmentation through the fuzzy superpixel aggregation. The fuzziness of superpixels also provides spatial features to measure affinities between fuzzy superpixels and refined superpixels, and guide the merging process. Comparative experiments with the existing approaches reveal a superior performance of the proposed method.
Efficient algorithms for mining closed and maximal high utility itemsets

Closed high utility itemsets (CHUIs) and maximal high utility itemsets (MaxHUIs) are two important concise representations of HUIs. Discovering these itemsets is important because they are lossless and compact, i.e., they provide a concise summary of all HUIs that can be orders of magnitude smaller. In addition, it can be more efficient to extract these representations than it would be to extract all HUIs. Mining the concise representations of HUIs is also an important step toward the generation of nonredundant high utility association rules that can reveal meaningful information to decision-makers. However, although several algorithms have been designed to mine these representations, such as EFIM-Closed, HMiner-Closed, and CHUI-Miner(Max), they have long runtimes, high memory usage, and scalability issues, especially for dense and large datasets. To address this issue, this paper proposes two efficient algorithms named C-HUIM and MaxC-HUIM for mining CHUIs, and simultaneously mining both CHUIs and MaxHUIs, respectively. These algorithms use a novel weak upper bound on the utility, which is strictly tighter than traditional upper bounds, and a corresponding pruning strategy called SPWUB to quickly eliminate low utility itemsets. The algorithms also include two novel search space reduction strategies named PSNonCHUB and LPSNonCHUB. The PSNonCHUB strategy only requires checking the inclusion relationship among a small number of itemsets, while LPSNonCHUB does not perform any inclusion check. In addition, the algorithms adopt a structure named MPUN-list to efficiently store and calculate information about each itemset's utility and support. Experimental results show that the proposed algorithms can be more than 100 times faster, are more memory efficient, and have better scalability than the state-of-the-art algorithms.
Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .
Multi-modality artificial intelligence in digital pathology

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.
Automatic Meter Reading from UAV Inspection Photos in the Substation by Combining YOLOv5s and DeeplabV3+

The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object detection and Deeplabv3+ image segmentation to obtain meter readings through the post-processing of segmented images. Firstly, YOLOv5s was introduced to detect the meter dial area and the meter was classified. Following this, the detected and classified images were passed to the image segmentation algorithm. The backbone network of the Deeplabv3+ algorithm was improved by using the MobileNetv2 network, and the model size was reduced on the premise that the effective extraction of tick marks and pointers was ensured. To account for the inaccurate reading of the meter, the divided pointer and scale area were corroded first, and then the concentric circle sampling method was used to flatten the circular dial area into a rectangular area. Several analog meter readings were calculated by flattening the area scale distance. The experimental results show that the mean average precision of 50 (mAP50) of the YOLOv5s model with this method in this data set reached 99.58%, that the single detection speed reached 22.2 ms, and that the mean intersection over union (mIoU) of the image segmentation model reached 78.92%, 76.15%, 79.12%, 81.17%, and 75.73%, respectively. The single segmentation speed reached 35.1 ms. At the same time, the effects of various commonly used detection and segmentation algorithms on the recognition of meter readings were compared. The results show that the method in this paper significantly improved the accuracy and practicability of substation meter reading detection in complex situations.
A cognitive assistance system with augmented reality for manual repair tasks with high variability based on the digital twin

Cognitive assistance systems with augmented reality are a promising solution to the increasing complexity in all industries. Our proposed assistance system is geared towards manual repair tasks with high variability in the maintenance, repair and overhaul industry. We use the digital twin for object-specific information and a human–machine interface that uses a web application for digital work instructions and augmented reality for spatial information. Context awareness of the assistance system is achieved by using two stationary 3D cameras and a barcode scanner. Camera images and point cloud data are used for precise markerless pose estimation and body tracking.
Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function

Image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem.
Digital Transformation of the Greek Banking Sector in the COVID Era

Rapid developments in digital innovation and technology in recent decades have marked a transition into the fourth industrial revolution (and the fifth social revolution), causing a significant impact on all areas of human activity. Moreover, these developments have been strongly felt with the advent of the COVID pandemic. The COVID pandemic has acted as a catalyst for imminent changes within the operational models of banks and their digitization, both locally and internationally, dealing with this peculiar crisis as an opportunity rather than a threat. In this study, after conducting a bibliographic overview of the key literature, an analysis of the digital development that took place in the banking sector in Greece was conducted, comparing the views of bank customers to those of bank employees and investigating the key factors that are believed to have a positive and/or negative effect on the use of new digital banking products and services, as well as the influence of factors determining digital banking expansion (gender, age, educational level, pandemic, etc.). The results revealed that bank customers were quite satisfied and familiar with digital transactions and wished to expand their use, while they considered the possibility of 24/7 service as an important factor in choosing to use digital banking, and the factors for expanding their use were transaction speed, transaction security, and information security. However, bank employees accept digital transformation positively and believe that for it to be successful, additional and ongoing training is required to upgrade their digital skills, which will also contribute to culture change and adaptation to the digital era. Furthermore, the age and educational level of bank customers had a statistically significant effect on the growing acceptance and expansion of the banking sector’s digital transformation. A comparison of the correlations of the demographic data with answers given by bank employees did not reveal any significant variations regarding the findings given by bank customers. These findings can be useful for bank management policy-making issues, for banks and their supervising authorities to build KPIs to monitor and assess the digitalization progress, and for academics for further research, especially as the COVID pandemic ensues and the need for digital solutions is all the more urgent, marking the digital competition between banks as one of increasing intensity.
Mastering scrum with a focus on team maturity and key components of scrum

Several studies contribute with valuable insights into agile teamwork performance. Currently, Scrum is dominating the industrial agile software development practices. Yet, there is a lack of studies that directly explores the role of team maturity and key components of the Scrum framework on being successful at Scrum. We investigate the impact of team maturity and four categories of the Scrum framework (team composition, Scrum values, Scrum roles, and Scrum events) on the perception of being successful at Scrum. Hence, we uncover and provide deeper insights into the characteristics and practices of what makes a team successful at Scrum. We carry out a large-scale and cross-sectional survey. We conduct Pearson's chi-square test of independence and logistic regression analysis for team maturity and the remaining variables on being successful. After surveying 182 Scrum team members, the results show that being successful at Scrum depends on the team maturity level. Team composition variables (fully allocated, low turnover rates, required skills and expertise, and self-management) and directing work in accordance with Scrum values (openness and courage) have an impact. All three Scrum roles are important. Particularly impactful are the developers' ability to adapt their plans, the product owners' mandate to prioritize, and the Scrum masters' ability to ensure that all events take place. Following all Scrum events has an effect on the perception of being successful at Scrum. This work constitutes a valuable contribution to agile practitioners and organizations who are already involved in agile development or plan to pursue agility. Organizations can influence Scrum teams' journey towards becoming successful at Scrum by ensuring the stability required to allow team maturation and decision-making relevant to team composition variables. This work also provides reflections that are useful for Scrum teams' practices and internal dynamics related to values, roles, and events.