Reviewerly/exHarmony huggingface dataset:
Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications

The biggest concern about diabetes-related complications is that they are unrecognised in the early stages but can be immutable and devastating with time. Identifying the population at high risk of developing such complications can help intervene in preventative care at an early stage. This study aims to present a data-driven approach to predict the patients at higher risk for diabetes-related complications using real-world data. We used comorbid diagnostic features from the electronic health records called “Cerner Health Facts EMR Data” to build machine learning-based prediction models for three diabetes-related long-term complications: (a) eye diseases, (b) kidney diseases, and (c) neuropathy. Our developed pipeline was able to generate highly accurate models for predictions. We deduced from the F1-scores that applying the class balancing techniques improved the overall performance of the models, and SVM with oversampling technique was the most consistent classifier for all three cohorts.
Face Recognition for Identification and Verification in Attendance System: A Systematic Review

Attendance is an important concept to be implemented in order to enhance organizational performance. Measuring attendance may be a concern issue for many organizations, especially with the rapid changes that have occurred in this era of digitalisation. Face recognition is one of many approaches to support long distance attendance, unfortunately, there are some issues that need to be overcome regarding face recognition, for example, its verification. The purpose of this systematic literature review is to provide either academics and practitioner with insight and knowledge related to face recognition implementation, how to verify it, and what is its critical success factors towards an attendance system. Therefore, the results of this study hopes to give both practitioners and academics knowledge on how to build a relevant attendance system based on the findings of this study. The review was conducted through a systematic literature review stages that was adopted from Kitchenham. It began with declaring protocol review and ended with an analysis of the prior studies that was obtained from five relevant sources. There were 22 of 516 studies that met the criteria after several filtering stages. It is found that academic reasons are mostly used on verifying face recognition. Moreover, based on the findings, information security has been considered the most utilized security methods regarding face recognition in attendance system. Furthermore, 78% of the analysed papers stated that security issues is the most critical factors towards implementing a successful attendance system.
A Column Streaming-Based Convolution Engine and Mapping Algorithm for CNN-based Edge AI Accelerators

Edge AI accelerators have been emerging as a solution for near customers' applications in areas such as image recognition sensors, remote sensing satellites, robotics, wearable devices, and drones. These applications require meeting performance targets and strict area and power constraints due to their portable mobility feature and limited power sources. As a result, a column streaming-based convolution engine has been proposed in this paper that includes column sets of processing elements design for flexibility in terms of the applicability for different CNN algorithms in edge AI accelerators. Compared to a commercialized CNN accelerator, the key results reveal that the column streaming-based convolution engine requires similar execution cycles to process a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$227 \times 227$</tex> feature map and avoid zero-padding penalties.
Exploring Designers’ Practice of Online Example Management for Supporting Mobile UI Design

The use of digital examples plays a critical role in mobile UI design. Yet, it remains unclear how UX/UI designers manage (i.e., collect, archive, and utilize) examples to facilitate their design processes at different stages, and what possible challenges are imposed on the design of proper tools to support these practices. In this paper, we conduct a qualitative interview study with mobile UI/UX designers (12 experts and 12 novices), deriving the commonality in practices and analyzing possible differences across four design phases (Discover, Define, Develop, and Deliver) and expertise. In brief, we find that there is more diverse and frequent use of examples in the Discover and Develop phases, and that experts take more diverse advantage of the information from examples compared to novices. We further identify the challenges faced by designers when using existing example management services, and propose potential design implications for the development of more supportive design tools in the future.
This Explains That: Congruent Image–Report Generation for Explainable Medical Image Analysis with Cyclic Generative Adversarial Networks

We present a novel framework for explainable labeling and interpretation of medical images. Medical images require specialized professionals for interpretation, and are explained (typically) via elaborate textual reports. Different from prior methods that focus on medical report generation from images or vice-versa, we novelly generate congruent image–report pairs employing a cyclic-Generative Adversarial Network (cycleGAN); thereby, the generated report will adequately explain a medical image, while a report-generated image that effectively characterizes the text visually should (sufficiently) resemble the original. The aim of the work is to generate trustworthy and faithful explanations for the outputs of a model diagnosing chest X-ray images by pointing a human user to similar cases in support of a diagnostic decision. Apart from enabling transparent medical image labeling and interpretation, we achieve report and image-based labeling comparable to prior methods, including state-of-the-art performance in some cases as evidenced by experiments on the Indiana Chest X-ray dataset.
Model‐Based Systems Product Line Engineering of Physical Protection Systems

Abstract A physical protection system (PPS) aims to protect a system against adversarial attacks by deterrence, detection, delay, and response. PPSs have targeted the protection of various systems, each of which has its specific requirements. On the other hand, PPSs also share a large set of features that are implemented for each system. To reduce the development cost, reduce time‐to‐market, and increase the quality of systems, a large‐scale systematic reuse approach as defined by systems product line engineering (SPLE) can be applied. So far, PPS methods have mainly considered the systems engineering of single PPSs. In this paper, we report on our industrial experiences and lessons learned for adopting SPLE for PPSs. An explicit model‐based systems engineering approach is adopted in which the focus is on the formalized application of models in the overall systems engineering life cycle. Hereby, the presented approach adopts and integrates model‐based systems engineering, product line engineering, and the PPS method to provide a systematic large‐scale reuse approach for systems engineering of PPSs. We discuss the detailed steps of the approach and report on the lessons learned for adopting model‐based systems product line engineering for PPS.
TT2INet: Text to Photo-realistic Image Synthesis with Transformer as Text Encoder

A text-to-image (T2I) generation method is mainly evaluated from two aspects, one is the quality and diversity of the generated images, and the other is the semantic consistency between the generated images and the input sentences. The feature extraction of the text is a very important part. In this paper, we propose a Transformer based Text-to-Image Network (TT2INet). we use the pre-trained Transformer model (ALBERT) to extract the sentence feature vectors and word feature vectors of the input sentences as the basis for the Generative Adversarial Networks (GANs) to generate images. In addition, we also added self-attention mechanism and spectral normalization method to the model. Adding a self-attention mechanism can make the model pay attention to more local features when generating images. Using the spectral normalization method can make the training of GANs more stable. The Inception Scores of our method on Oxford-102, CUB and COCO datasets are 3.90, 4.89 and 26.53, and R-precision scores are 92.55, 87.72 and 92.29, respectively.
Mapping Bauxite Mining Residues Using Remote Sensing Techniques

Bauxite residuals from abandoned mining sites are both an environmental challenge and a possible source of secondary raw materials. Processing of multispectral and hyperspectral images with the best available techniques can help to produce multiscale spatial maps of elements inside and around the mining sites. The authors propose a procedure for mapping elements concentration using multiple data sets at different scales and resolutions. A comparison between multispectral Sentinel-2 images and hyperspectral PRISMA processing is performed over some case studies of bauxite residues in the Mediterranean area. Specifically, a case study from Italy is composed regarding artificial canyons created by past artisanal mining activities and by stockpiles of extracted bauxite. Hyperspectral punctual measurements (spectroradiometer surveys) were taken in various zones of the bauxite site, where infield topsoil samples were also taken for X-ray fluorescence chemical analysis. Final concentration maps were estimated by performing geostatistical techniques.
Evaluation of an Integer Optimized Shape Matching Algorithm

Computer vision and machine learning algorithms are often used for quality control for industrial products. Nowadays, neural networks can perform very well to detect the desired objects. Sometimes, the system has limited resources and is not capable of processing complex algorithms or use neural networks. Here, simpler algorithms are used for shape or object detection. The scope of the present work is to even lower the complexity of the shape matching algorithm by converting a shape detection algorithm to an integer version and evaluate the results. This allows to remove floating-point units (FPU) of processors and reduce the area of a System-on-Chip (SoC) design of a smart image sensor.
Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation

Learning a disentangled representation of the latent space has become one of the most fundamental problems studied in computer vision. Recently, many Generative Adversarial Networks (GANs) have shown promising results in generating high fidelity images. However, studies to understand the semantic layout of the latent space of pre-trained models are still limited. Several works train conditional GANs to generate faces with required semantic attributes. Unfortunately, in these attempts, the generated output is often not as photo-realistic as the unconditional state-of-the-art models. Besides, they also require large computational resources and specific datasets to generate high fidelity images. In our work, we have formulated a Markov Decision Process (MDP) over the latent space of a pre-trained GAN model to learn a conditional policy for semantic manipulation along specific attributes under defined identity bounds. Further, we have defined a semantic age manipulation scheme using a locally linear approximation over the latent space. Results show that our learned policy samples high fidelity images with required age alterations, while preserving the identity of the person.
Energy-based linear PCM audio recovery method of impaired MP4 file stored in dashboard camera memory

Sounds recorded by a dashboard camera presents essential evidence on a vehicle's driving condition for analyzing car accidents. In reality, when a direct impact of a vehicle equipped with a dashboard camera occurs, the power supply to the dashboard camera is abruptly terminated, and the digital video being recorded at that time may be incompletely stored; thus, there is no information such as the location or size to be read for the image and audio decoding. In this paper, we propose a method for recovering the linear pulse code modulation (PCM) audio from the impaired MP4 file partially stored in a video event data recorder (VEDR), known as a dashboard camera, during road vehicle accidents. This method is based on the principle of extracting an actual audio signal using the energy of each frame in the pseudo audio, which is generated from all data in the “mdat” area of the impaired MP4 file. For performance evaluation, the various video files with linear PCM audio format in the MP4 container were obtained through the different dashboard cameras. As the objective evaluation, we measured the audio recovery ratio, distance based on dynamic time warping (DTW) and comparison of the waveform and spectrogram. Moreover, the performance of the proposed method was evaluated using the multiple stimuli with a hidden reference and anchor (MUSHRA) test as the subjective assessment. As a result, it is shown from the evaluation that the proposed method can recover significantly better the linear PCM audio signal than the conventional methods from an impaired MP4 file.
Spreadsheet Data Transformation for Ontology Engineering in Petrochemical Equipment Inspection Tasks

Currently, ontologies remain one of the most effective ways to conceptualize and formalize domain knowledge. The process of their creation requires automation and improvement including the use of various information sources. One of the domains that require the use of ontology engineering is the diagnosis and assessment of the technical state of petrochemical equipment and technological complexes. In turn, spreadsheets are one of the most accessible and common ways of representing and storing information. They are characterized by a great variety and heterogeneity of layouts, styles, and content. Spreadsheets are a valuable source of structured domain knowledge. In this paper, we propose to automate the ontology engineering in petrochemical equipment inspection tasks (including diagnosis and assessment of the technical states) based on the analysis and transformation of spreadsheet data. For this purpose, we present a new technique that provides the restoration of tabular data semantics, conceptualization, and formalization of tabular content in the form of ontologies. The main activities of our technique are the following: transforming input arbitrary spreadsheets into a canonicalized form; obtaining ontology fragments based on the analysis and transformation of canonical spreadsheets; aggregating ontology fragments into a complete ontological model; generating ontological model code in the OWL format. The technique proposed is implemented in the form of a prototype of the software that was evaluated when solving tasks of ontology engineering for industrial petrochemical equipment. Spreadsheets from reports on industrial safety inspection were used as a data source.
Performance Comparison of Spatial Domain-based Watermarking Techniques

Reversible watermarking is one of the main methods to authenticate and secure digital media security and revert to its original state without any losses. There are multiple reversible watermarking schemes proposed over the years. Often the proposed reversible watermarking scheme was focused on general grey-scale images. Their performance on medical images is not often evaluated. This study compares the performance of various general reversible watermarking schemes using multi-frame medical images and general grey-scale images in terms of 3 main factors, (1) computational complexity, (2) imperceptibility, and (3) embedding capacity. The result has shown that the Least Significant bits have the highest imperceptibility and embedding capacity. However, it is the least secure where outside can modify the watermarked images as long as they have the essential information.
Text to Image Synthesis based on Multi - Perspective Fusion

In this paper, we propose a multi-perspective fusion method to improve the performance of text-to-image synthesis. From the perspective of the generator, we introduce a dynamic selection method to make the text feature match the corresponding image feature better, while the multi-class discriminant method with mask segmentation image as the extra type is introduced from the perspective of the discriminator to improve its discrimination ability. Through the effective integration of these two aspects of improvement, more excellent results by our method are obtained. Experiments on the Caltech-UCSD Birds 200 (CUB) and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our method's effectiveness and superiority. The qualitative and quantitative experiments validate that our method is superior to the existing state-of-the-art methods.
Multi-Class Mobile Money Service Financial Fraud Detection by Integrating Supervised Learning with Adversarial Autoencoders

Given the actual volume and speed of financial transactions, financial fraud detection systems are constantly evolving based on new computational intelligence algorithms. Therefore, transaction monitoring and analysis prevent monetary losses caused by fraudsters. Since the fraud detection process is a labor-intensive task for human auditors given the huge amount of daily transactions processed by financial services information systems. Credit card is the financial product most explored in the financial fraud detection literature, while mobile money service is becoming a popular option for payments, fraud detection for such financial product has not yet been deeply explored. Therefore, it is interesting to optimize the auditing process and test new quantitative techniques, such as deep learning, to support human auditors before double-checking a suspicious transaction. Thus, we propose an integration of adversarial autoencoders and machine learning methods to perform an objective classification among three transaction types: regular, local, and global anomaly. The integration consists of using the autoencoder's generated latent vectors as features for the supervised learning algorithms. The experiments considered different latent vector space forms concerning their dimensionality and the clusters generated by a prior Gaussian mixture. The results show that some classifiers may accept latent characteristics well, getting better or similar performance when using all the original characteristics.
Predicting Clinical Events Based on Raw Text: From Bag-of-Words to Attention-Based Transformers

Identifying which patients are at higher risks of dying or being re-admitted often happens to be resource- and life- saving, thus is a very important and challenging task for healthcare text analytics. While many successful approaches exist to predict such clinical events based on categorical and numerical variables, a large amount of health records exists in the format of raw text such as clinical notes or discharge summaries. However, the text-analytics models applied to free-form natural language found in those notes are lagging behind the break-throughs happening in the other domains and remain to be primarily based on older bag-of-words technologies. As a result, they rarely reach the accuracy level acceptable for the clinicians. In spite of their success in other domains, the superiority of deep neural approaches over classical bags of words for this task has not yet been convincingly demonstrated. Also, while some successful experiments have been reported, the most recent break-throughs due to the pre-trained language models have not yet made their ways into the medical domain. Using a publicly available healthcare dataset, we have explored several classification models to predict patients' re-admission or a fatality based on their discharge summaries and established that 1) The performance of the neural models used in our experiments convincingly exceeds those based on bag-of-words by several percentage points as measured by the standard metrics. 2) This allows us to achieve the accuracy typically acceptable by the clinicians as of practical use (area under the ROC curve above 0.70) for the majority of our prediction targets. 3) While the pre-trained attention-based transformer performed only on par with the model that averages word embeddings when applied to full length discharge summaries, the transformer still handles shorter text segments substantially better, at times with the margin of 0.04 in the area under the ROC curve. Thus, our findings extend the success of pre-trained language models reported in other domains to the task of clinical event prediction, and likely to other text-classification tasks in the healthcare analytics domain. 4) We suggest several models to overcome the transformers' major drawback (their input size limitation), and confirm that this is crucial to achieve their top performance. Our modifications are domain agnostic, and thus can be applied in other applications where the text inputs exceed 200 words. 5) We have successfully demonstrated how non-text attributes (such as patient age, demographics, type of admission etc.) can be combined with text to gain additional improvements for several prediction targets. We include extensive ablation studies showing the impact of the training size, and highlighting the tradeoffs between the performance and the resources needed.
Third-party induced cyber incidents—much ado about nothing?

Abstract Growing reliance on third-party services, such as cloud computing, is believed to increase client firms’ exposure to third-party induced cyber incidents. However, we lack empirical research on the prevalence and scale of third-party induced cyber incidents. Moreover, we do not know who pays more of the price for experiencing these incidents—the client firm and/or the third-party provider firm. We study these questions using a sample of 1397 cyber incidents in public firms between 2000 and 2020 of which 246 are third-party induced incidents. Our findings offer several novel insights. Third-party induced cyber incidents are not growing in prevalence any faster than other incidents, but they do compromise greater volumes of confidential data per incident. As to the price paid for third-party induced incidents, the picture is more nuanced. Client (first-party) firms suffer drops in equity returns that are comparable to those for homegrown incidents, while small third-party provider firms suffer significantly larger drops in equity returns and large third-party provider firms do not suffer a discernible drop in equity returns. We discuss implications of these findings for client firms and service providers.
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models

Recent works have shown that powerful pretrained language models (PLM) can be fooled by small perturbations or intentional attacks.To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs.However, it is still challenging to augment semantically relevant examples with sufficient diversity.In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs.Based on the original token embeddings, we construct a multinomial mixture for augmenting virtual data embeddings, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity.Furthermore, a regularized training strategy is proposed to balance the two aspects.Extensive experiments on six datasets show that our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks.Our codes and data are publicly available at https://github.com/RUCAIBox/VDA. She is a good student Virtual Data AugmentationShe is a perfect student Gradient based noise