Reviewerly/exHarmony huggingface dataset:
Comparative accuracy of sonography, mammography and the BI-RADS characterization of breast masses among adult women at Mulago Hospital, Uganda

Background Breast cancer is common among Ugandan women and mortality rate is significantly high. Uganda, like many low-income countries, has no national regular mammography screening programme, a gold standard in breast cancer imaging. Breast ultrasound and Breast Imaging Reporting and Data System (BI-RADS) classification is thus being promoted as a supplemental evaluation tool for breast masses. However, studies on the comparative accuracy of breast sonography, mammography and BI-RADS from low-income settings are limited. This study aimed to determine the accuracy of breast ultrasound, mammography and BI-RADS classification against histology in the evaluation of breast masses among adult women. Methods This was a cross-sectional study conducted at Mulago National Referral Hospital involving women with breast masses. The women underwent breast ultrasound, mammography and needle biopsy for histological analysis. The breast lesions were also evaluated against the BI-RADS characterization criteria. Results Overall, 212 breast masses were evaluated and included in the analysis. Of these, 50% (106) were benign and 50% (106) were malignant. The sensitivity for ultrasound and mammography separately was 68.5% and 72.5% respectively. The sensitivity for ultrasound and mammography combined was 85%. BI-RADS 5 category had the highest sensitivity at 59.9% while the highest specificity was noted among BI-RADS 3 and 5 at 100%. The highest accuracy was noted in the BI-RADS 5 category at 79.9%. Conclusions Findings from this study demonstrate that combining ultrasound and mammography could have a higher sensitivity and diagnostic accuracy than when these imaging modalities are used independently. In addition, the BI-RADS reporting classification has an optimum positive predictive value and should be promoted to standardize breast imaging reporting.
Multimodal Approach for Code-Mixed Speech Sentiment Classification

Sentiment analysis is a natural language processing (NLP) technique used to classify a statement into three polarities, namely, positive negative and neutral. Thus, speech sentiment analysis invariably becomes a NLP task rather than a direct speech processing task, making the available speech recognition model just a tool to carry out NLP. Nevertheless, we do not have a way to directly use the pre-trained speech models to carry out sentiment analysis on speech utterances. The present study proposes to achieve this by directly using the speech signal to carry out sentiment analysis. We evaluate two such approaches on our custom made dataset consisting of movie and political reviews. The first approach used a fully connected neural network (FCNN) model and the second one used a 3-shot few shot learning (FSL) framework. For the FCNN model, the proposed framework provides a classification accuracy of 61.53%, whereas we get an accuracy of 99.83% for the 3-shot FSL framework. The performance is comparable to the current state-of-the-art (SOTA) system in place.
Cultural Implications Regarding Privacy in Digital Contact Tracing Algorithms: Method Development and Empirical Ethics Analysis of a German and a Japanese Approach to Contact Tracing

Digital contact tracing algorithms (DCTAs) have emerged as a means of supporting pandemic containment strategies and protecting populations from the adverse effects of COVID-19. However, the impact of DCTAs on users' privacy and autonomy has been heavily debated. Although privacy is often viewed as the ability to control access to information, recent approaches consider it as a norm that structures social life. In this regard, cultural factors are crucial in evaluating the appropriateness of information flows in DCTAs. Hence, an important part of ethical evaluations of DCTAs is to develop an understanding of their information flow and their contextual situatedness to be able to adequately evaluate questions about privacy. However, only limited studies and conceptual approaches are currently available in this regard.This study aimed to develop a case study methodology to include contextual cultural factors in ethical analysis and present exemplary results of a subsequent analysis of 2 different DCTAs following this approach.We conducted a comparative qualitative case study of the algorithm of the Google Apple Exposure Notification Framework as exemplified in the German Corona Warn App and the Japanese approach of Computation of Infection Risk via Confidential Locational Entries (CIRCLE) method. The methodology was based on a postphenomenological perspective, combined with empirical investigations of the technological artifacts within their context of use. An ethics of disclosure approach was used to focus on the social ontologies created by the algorithms and highlight their connection to the question about privacy.Both algorithms use the idea of representing a social encounter of 2 subjects. These subjects gain significance in terms of risk against the background of a representation of their temporal and spatial properties. However, the comparative analysis reveals 2 major differences. Google Apple Exposure Notification Framework prioritizes temporality over spatiality. In contrast, the representation of spatiality is reduced to distance without any direction or orientation. However, the CIRCLE framework prioritizes spatiality over temporality. These different concepts and prioritizations can be seen to align with important cultural differences in considering basic concepts such as subject, time, and space in Eastern and Western thought.The differences noted in this study essentially lead to 2 different ethical questions about privacy that are raised against the respective backgrounds. These findings have important implications for the ethical evaluation of DCTAs, suggesting that a culture-sensitive assessment is required to ensure that technologies fit into their context and create less concern regarding their ethical acceptability. Methodologically, our study provides a basis for an intercultural approach to the ethics of disclosure, allowing for cross-cultural dialogue that can overcome mutual implicit biases and blind spots based on cultural differences.
Adaptive Enhanced Detection Network for Low Illumination Object Detection

Any small environmental changes in the driving environment of a traffic vehicle can become a risk factor directly leading to major safety incidents. Therefore, it is necessary to assist drivers in automatically detecting risk factors during the driving process using algorithms. However, besides making it more difficult for drivers to judge environmental changes, the performance of automatic detection networks in low illumination scenarios can also be greatly affected and cannot be used directly. In this paper, we propose a risk factor detection model based on deep learning in low illumination scenarios and test the optimization of low illumination image enhancement problems. The overall structure of this model includes dual discriminators, encoder–decoders, etc. The model consists of two main stages. In the first stage, the input low illumination scene image is adaptively converted into a standard illumination image through a lighting conversion module. In the second stage, the converted standard illumination image is automatically assessed for risk factors. The experiments show that the detection network can overcome the impact of low lighting and has high detection accuracy.
Detecting Fake Reviews in Google Maps—A Case Study

Many customers rely on online reviews to make an informed decision about purchasing products and services. Unfortunately, fake reviews, which can mislead customers, are increasingly common. Therefore, there is a growing need for effective methods of detection. In this article, we present a case study showing research aimed at recognizing fake reviews in Google Maps places in Poland. First, we describe a method of construction and validation of a dataset, named GMR–PL (Google Maps Reviews—Polish), containing a selection of 18 thousand fake and genuine reviews in Polish. Next, we show how we used this dataset to train machine learning models to detect fake reviews and the accounts that published them. We also propose a novel metric for measuring the typicality of an account name and a metric for measuring the geographical dispersion of reviewed places. Initial recognition results were promising: we achieved an F1 score of 0.92 and 0.74 when detecting fake accounts and reviews, respectively. We believe that our experience will help in creating real-life review datasets for other languages and, in turn, will help in research aimed at the detection of fake reviews on the Internet.
Against data individualism: Why a pandemic accord needs to commit to data solidarity

Abstract An accord to increase global pandemic preparedness is currently negotiated at the World Health Organisation (WHO). One aspect is conspicuously missing from the draft version: data governance. As the COVID‐19 pandemic has shown, the availability of good quality data, and prudent ways of using this data while preventing other forms of data use that is harmful to people and communities, is literally a matter of life and death. It is of utmost importance for the pandemic accord to include a data governance strategy. We argue that data solidarity provides a suitable foundation for such a data governance strategy in the context of pandemics. It includes instruments to facilitate data use in cases where data use is likely to create benefits for many people without entailing high risks for individuals or communities. Data solidarity also calls to refrain from data use in cases where it would pose unacceptable risks. Finally, data solidarity helps to mitigate harm where it occurs. In sum, data solidarity helps to enhance the benefits from digital practices in health crises without playing out collective interests against the rights and needs of individuals.
RAIF: A deep learning‐based architecture for multi‐modal aesthetic biometric system

Abstract Human aesthetics play a significant role in video game development, emotional‐aware robot design, online recommender systems, digital human, and other domains of research focusing on human‐computer interactions. Social network user recognition based on aesthetic preferences is an emerging research domain. In this paper, a novel deep learning architecture is proposed for multi‐modal audio‐visual person identification that combines audio and visual aesthetic features. A pre‐trained ResNet architecture is utilized to extract high‐level features from a set of user‐preferred audio and image samples. A novel deep learning‐based fusion technique called residual‐aided intermediate fusion (RAIF) is introduced in order to effectively merge the audio and visual features. The proposed RAIF method achieved an accuracy of 98% and a loss of 0.01 on a proprietary multi‐modal dataset, indicating its effectiveness in fusing audio and visual information.
Innovative Promotion Technologies: Brand Management in the Digital Environment

As innovative technologies spread, the role of means of interaction with customers in the digital environment is growing. Virtual reality technologies are becoming one of the most promising tools for significantly expanding customer interaction in the context of digital transformation. The purpose of this article is to analyze the brand strategies of the leading companies and show the possibilities of using virtual reality technology and the concept of the metaverse for brand formation and promotion. We also aim to assess the prospects and formulate recommendations on the use of the digital environment in brand management strategies. To achieve the set goal, several tasks were completed. The authors analyzed the experience of using the digital environment for interacting with customers and studied the influence of VR, AR, MR, and XR technologies on brand management. Additionally, the possibilities of using the metaverse concept for brand management was defined. In this work, general scientific theoretical and empirical research methods were applied. The study furthers the theoretical foundations of brand management in the digital environment via innovative branding technologies that support an increase in the competitive performance of companies.
Joint Modal Alignment and Feature Enhancement for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification aims to solve the matching problem between cross-camera and cross-modal person images. Existing methods strive to perform better cross-modal alignment, but often neglect the critical importance of feature enhancement for achieving better performance. Therefore, we proposed an effective method that combines both modal alignment and feature enhancement. Specifically, we introduced Visible-Infrared Modal Data Augmentation (VIMDA) for visible images to improve modal alignment. Margin MMD-ID Loss was also used to further enhance modal alignment and optimize model convergence. Then, we proposed Multi-Grain Feature Extraction (MGFE) Structure for feature enhancement to further improve recognition performance. Extensive experiments have been carried out on SYSY-MM01 and RegDB. The result indicates that our method outperforms the current state-of-the-art method for visible-infrared person re-identification. Ablation experiments verified the effectiveness of the proposed method.
Adoption of Academic Staff to use the Learning Management System (LMS): Applying Extended Technology Acceptance Model (TAM2) for Jordanian Universities

Starting from March of 2020, the outbreak of the COVID-19 caused Jordanian universities to close their campuses and forced them to initiate online teaching. Learning management system (LMS) is an important tool and suitable as tool for learning and activities in a higher education. However, each university has a different LMS tool that allows users to use it in their activities. This paper is interested in investigating the acceptance of Jordanian academic staff in using an LMS after getting back to work post the COVID-19 period. The study sample consisted of 500 faculty members of different ranks, selected randomly from all Jordanian universities, for the second semester of the academic year 2020-2021. The Technology Acceptance Model (TAM2) has been adopted to identify the Acceptance and use of LMS. The questionnaire developed by the researcher was based on past research studies. Simple linear regression was used to calculate the relationships between factors. The research results presented that there is a statistically positive effect of Perceived Usefulness(PU), Perceived Ease of Use (PEOU) and Subjective Norm(SN) on adoption to use LMS, and Perceived Ease of Use(PEOU), lecturers' Image(LI), Subjective Norm(SN), Job Relevance(JR), and, Result Demonstrability(RD) Output Quality(OQ) on Perceived Usefulness(PU). This study indicates that we must always conduct workshops and training programs related to learning management systems and computers to enhance the usability of learning management systems among faculty members
3D Smooth Trajectory Planning for UAVs under Navigation Relayed by Multiple Stations Using Bézier Curves

Navigation relayed by multiple stations (NRMS) is a promising technique that can significantly extend the operational range of unmanned aerial vehicles (UAVs) and hence facilitate the execution of long-range tasks. However, NRMS employs multiple external stations in sequence to guide a UAV to its destination, introducing additional variables and constraints for UAV trajectory planning. This paper investigates the trajectory planning problem for a UAV under NRMS from its initial location to a pre-determined destination while maintaining a connection with one of the stations for safety reasons. Instead of line segments used in prior studies, a piecewise Bézier curve is applied to represent a smooth trajectory in three-dimensional (3D) continuous space, which brings both benefits and complexity. This problem is a bi-level optimisation problem consisting of upper-level station routing and lower-level UAV trajectory planning. A station sequence must be obtained first to construct a flight corridor for UAV trajectory planning while the planned trajectory evaluates it. To tackle this challenging bi-level optimisation problem, a novel efficient decoupling framework is proposed. First, the upper-level sub-problem is solved by leveraging techniques from graph theory to obtain an approximate station sequence. Then, an alternative minimisation-based algorithm is presented to address the non-linear and non-convex UAV trajectory planning sub-problem by optimising the spatial and temporal parameters of the piecewise Bézier curve iteratively. Computational experiments demonstrate the efficiency of the proposed decoupling framework and the quality of the obtained approximate station sequence. Additionally, the alternative minimisation-based algorithm is shown to outperform other non-linear optimisation methods in finding a better trajectory for the UAV within the given computational time.
Generation of Musculoskeletal Ultrasound Images with Diffusion Models

The recent advances in deep learning have revolutionised computer-aided diagnosis in medical imaging. However, deep learning approaches to unveil their full potential require significant amounts of data, which can be a challenging task in some scientific fields, such as musculoskeletal ultrasound imaging, in which data privacy and security reasons can lead to important limitations in the acquisition and the distribution process of patients’ data. For this reason, different generative methods have been introduced to significantly reduce the required amount of real data by generating synthetic images, almost indistinguishable from the real ones. In this study, the power of the diffusion models is incorporated for the generation of realistic data from a small set of musculoskeletal ultrasound images in four different muscles. Afterwards, the similarity of the generated and real images is assessed with different types of qualitative and quantitative metrics that correspond well with human judgement. In particular, the histograms of pixel intensities of the two sets of images have demonstrated that the two distributions are statistically similar. Additionally, the well-established LPIPS, SSIM, FID, and PSNR metrics have been used to quantify the similarity of these sets of images. The two sets of images have achieved extremely high similarity scores in all these metrics. Subsequently, high-level features are extracted from the two types of images and visualized in a two-dimensional space for inspection of their structure and to identify patterns. From this representation, the two sets of images are hard to distinguish. Finally, we perform a series of experiments to assess the impact of the generated data for training a highly efficient Attention-UNet for the important clinical application of muscle thickness measurement. Our results depict that the synthetic data play a significant role in the model’s final performance and can lead to the improvement of the deep learning systems in musculoskeletal ultrasound.
Multiview Fusion Using Transformer Model for Recommender Systems: Integrating the Utility Matrix and Textual Sources

Recommender systems are challenged with providing accurate recommendations that meet the diverse preferences of users. The main information sources for these systems are the utility matrix and textual sources, such as item descriptions, users’ reviews, and users’ profiles. Incorporating diverse sources of information is a reasonable approach to improving recommendation accuracy. However, most studies primarily use the utility matrix, and when they use textual sources they do not integrate them with the utility matrix. This is due to the risk of combined information causing noise and reducing the effectiveness of good sources. To overcome this challenge, in this study we propose a novel method that utilizes the Transformer Model, a deep learning model that efficiently integrates textual and utility matrix information. The study suggests feature extraction techniques suitable for each information source and an effective integration method in the Transformer model. The experimental results indicate that the proposed model significantly improves recommendation accuracy compared to the baseline model (MLP) for the Mean Absolute Error (MAE) metric, with a reduction range of 10.79% to 31.03% for the Amazon sub-datasets. Furthermore, when compared to SVD, which is known as one of the most efficient models for recommender systems, the proposed model shows a decrease in the MAE metric by a range of 34.82% to 56.17% for the Amazon sub-datasets. Our proposed model also outperforms the graph-based model with an increase of up to 108% in Precision, a decrease of up to 65.37% in MAE, and a decrease of up to 59.24% in RMSE. Additionally, experimental results on the Movielens and Amazon datasets also demonstrate that our proposed model, which combines information from the utility matrix and textual sources, yields better results compared to using only information from the utility matrix.
Hardware-Software Co-design for Side-Channel Protected Neural Network Inference

Physical side-channel attacks are a major threat to stealing confidential data from devices. There has been a recent surge in such attacks on edge machine learning (ML) hardware to extract the model parameters. Consequently, there has also been work, although limited, on building corresponding defenses against such attacks. Current solutions take either fully software-or fully hardware-centric approaches, which are limited in performance and flexibility, respectively. In this paper, we propose the first hardware-software co-design solution for building side-channel-protected ML hardware. Our solution targets edge devices and addresses both performance and flexibility needs. To that end, we develop a secure RISCV-based coprocessor design that can execute a neural network implemented in C/C++. Our coprocessor uses masking to execute various neural network operations like weighted summations, activation functions, and output layer computation in a sidechannel secure fashion. We extend the original RV32I instruction set with custom instructions to control the masking gadgets inside the secure coprocessor. We further use the custom instructions to implement easy-to-use APIs that are exposed to the end-user as a shared library. Finally, we demonstrate the empirical sidechannel security of the design up to 1M traces.