Reviewerly/exHarmony huggingface dataset:
An open-source digital contact tracing system tailored to haulage

Digital contact tracing presents numerous advantages compared to manual contact tracing methods, especially in terms of enhanced speed and automation. Nevertheless, a lack of comprehensive evaluation regarding functionality, efficiency, benefits, and acceptance within communities remains. Here we primarily focus on the functionality of THEA-GS, an open-source digital contact tracing tool developed through consultation with stakeholders. Additionally, we provide insights from its implementation on a limited sample of haulage drivers in Uganda, serving as a representative case for a low- and middle-income country. THEA-GS comprises two primary components: (a) a smartphone application, and (b) a suite of server-programs responsible for data processing and analysis, including databases and a web-based interface featuring dashboards. In essence, the mobile application records the timestamped location of haulage drivers within the road network and identifies possible transmission hotspots by analyzing factors such as the duration of stops and the communities associated with them. The tool can be integrated with national infrastructure to compare drivers’ diagnostic results and contact structure, thereby generating individual and community risk assessments relative to the road network. During the Omicron-variant wave of the COVID-19 pandemic, a total of 3,270 haulage drivers were enrolled between October 2021 and October 2022. Around 75% of these drivers utilized THEA-GS for approximately two months. Based on an analysis of 3,800 test results, which included 48 positive cases, 125 contacts, and 40 million time-stamped GPS points, THEA-GS shows a significant speed improvement, being approximately 90 times faster than MCT. For instance, the average time from sample collection to notifying a case and their contacts was approximately 70 and 80 min, respectively. The adoption of this tool encountered challenges, mainly due to drivers’ awareness of its purpose and benefits for public health. THEA-GS is a place-based digital contact tracing tool specifically designed to assist National Public Health Institutions in managing infectious disease outbreaks involving the haulage industry as a high-risk group. While its utility, acceptance, and accuracy have not been fully evaluated, our preliminary tests conducted in Uganda indicate the tool’s functionality is robust, but social acceptance and adoption are heavily reliant on establishing trust among users.
MobileRec: A Large Scale Dataset for Mobile Apps Recommendation

Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing recommendation datasets, such as Amazon Product Reviews and MovieLens, greatly facilitated the research and development of recommender systems in their respective domains. While the number of mobile users and applications (aka apps) has increased exponentially over the past decade, research in mobile app recommender systems has been significantly constrained, primarily due to the lack of high-quality benchmark datasets, as opposed to recommendations for products, movies, and news. To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec. We constructed MobileRec from users' activity on the Google play store. MobileRec contains 19.3 million user interactions (i.e., user reviews on apps) with over 10K unique apps across 48 categories. MobileRec records the sequential activity of a total of 0.7 million distinct users. Each of these users has interacted with no fewer than five distinct apps, which stands in contrast to previous datasets on mobile apps that recorded only a single interaction per user. Furthermore, MobileRec presents users' ratings as well as sentiments on installed apps, and each app contains rich metadata such as app name, category, description, and overall rating, among others. We demonstrate that MobileRec can serve as an excellent testbed for app recommendation through a comparative study of several state-of-the-art recommendation approaches. The MobileRec dataset is available at https://huggingface.co/datasets/recmeapp/mobilerec.
A new approach for modelling uncertainty in expert systems knowledge bases

The current paradigm of modelling uncertainty in expert systems knowledge bases using Certainty Factors (CF) has been critically evaluated.A way to circumvent the awkwardness, non-intuitiveness and constraints encountered while using CF has been proposed.It is based on introducing Data Marks for askable conditions and Data Marks for conclusions of relational models, followed by choosing the best suited way to propagate those Data Marks into Data Marks of rule conclusions.This is done in a way orthogonal to the inference using Aristotelian Logic.Using Data Marks instead of Certainty Factors removes thus the intellectual discomfort caused by rejecting the notion of truth, falsehood and the Aristotelian law of excluded middle, as is done when using the CF methodology.There is also no need for changing the inference system software (expert system shell): the Data Marks approach can be implemented by simply modifying the knowledge base that should accommodate them.The methodology of using Data Marks to model uncertainty in knowledge bases has been illustrated by an example of SWOT analysis of a small electronic company.A short summary of SWOT analysis has been presented.The basic data used for SWOT analysis of the company are discussed.The rmes_EE SWOT knowledge base consisting of a rule base and model base have been presented and explained.The results of forward chaining for this knowledge base have been presented and critically evaluated.
Watermarking of Frequency and Steganography for Protection of Medical Images Based on Bacterial Foraging Optimization and Genetic Algorithm

The art of watermarking and steganography are techniques to hide a secret message in an image or other media. They can be used to protect the privacy and security of medical images and patient data in the healthcare industry. We propose two algorithms for watermarking and steganography based on discrete wavelet transform (DWT), discrete cosine transform (DCT), parallel bacterial foraging optimization (PBFO), and genetic algorithm (GA). These algorithms can embed a secret message in a grayscale medical image of size 512x512 pixels, with high imperceptibility and capacity. Imperceptibility means that the stego image does not have noticeable distortions from the cover image. Capacity means that the stego image can carry as much information as possible without compromising imperceptibility. Algorithm I uses DWT to decompose the cover image into four sub-bands, and DCT to transform each sub-band into a matrix of coefficients. It uses PBFO to select the best coefficients for embedding the message, and GA to embed the message using a binary encoding scheme. Algorithm II is similar to Algorithm I, but it does not use PBFO. It uses GA to select and embed the coefficients directly. We compare the performance of Algorithm I and Algorithm II using peak signal-to-noise ratio (PSNR) and payload. PSNR measures the similarity between two images, where higher values indicate better quality. Payload measures how many bits of information can be embedded per pixel in an image, where higher values indicate more capacity. We use grayscale medical images from a public database as cover images, and random binary strings as secret messages. We vary the length of the secret message from 0.1 to 0.5 bits per pixel. The results show that Algorithm I has better performance than Algorithm II in terms of PSNR and payload. Algorithm I produces less distortion and can embed more information than Algorithm II. Therefore, Algorithm I is more suitable for watermarking and steganography in medical images than Algorithm II. We also discuss some possible applications and challenges of watermarking and steganography in the healthcare domain.
What We See Part 1: Morphological Properties of Particles in a Fixed Mount

We often identify what we expect to see, what is familiar, or what we want to see based on limited information (1). For example, while reading we may skim a word based on the first and last letters; however, it may not be the actual word. The brain is very busy, and it often skips ahead when it comes to a conclusion based on a limited set of data. We may have to go back and view all the letters to identify the actual word. Looking at particles under the light microscope is similar. Working memory is limited at any given time and must be regularly updated while the many visible properties of a particle are examined. There are more than 40 optical properties that may be used to characterize a particle in a fixed mounting medium. Information on how the particle was generated, where it came from, how it was transported, its composition, and other aspects of its history are often written in its optical properties. A list of the morphological properties is presented here with information on how to look for them. Without such a list it is easy to overlook important characteristics that may be critical in understanding how a particle relates to the analytical questions being asked.
Implementation of Discovery Model Assisted by Flipbook Media on Learning to Write Poetry Text

In the preliminary survey that has been conducted in the classroom, students have the value of poetry writing skills under the minimum completeness criteria that have been set by the school. The background of this study is the low ability to write poetry texts of Junior High School students. Students ' ability to write poetry texts needs to be improved because students must be trained in pouring words with imagination in the surrounding environment. This study aims to determine the effectiveness of the discovery learning learning model with the help of flipbook media, the results of writing poetry texts after the implementation of the learning model and the activities of teachers and students during the learning process. This study uses a qualitative approach with descriptive analysis techniques. The Data were obtained based on the results of the application of the discovery learning learning model assisted by flipbook media which can improve the writing skills of Al Inayah Kutamukti Junior High School students. The presentation includes: 1) learning syntax, 2) the results of students ' poetry writing skills, and 3) the results of observations of teacher and student activities. The data obtained in this study are presented in Tabular Form and analyzed descriptively. The data processing is done by researchers, namely display, reduction and conclusions. The results of descriptive analysis after the first application, the steps of learning to write poetry texts using the discovery learning method assisted by flipbook media, namely the provision of stimuli or stimuli with flipbook media. 2) identify problems on how to write poetry 3) collect data from several sources facilitated by teachers 4) process information 5) proof and 5) draw conclusions. Second, the value of students in writing poetry obtained an average of 82 with the lowest value of 79 and the highest 86. The Data showed that the results of writing poetry students have reached the minimum criteria of completeness is 75. Then the last activity of teachers and students in the learning process based on the observation of 88% which means it has a good category. These results can be concluded that the learning model of discovery learning assisted by flipbook media is effective for learning to write poetry.
Case-Based Cleaning of Text Images

Old documents suffer from the passage of time: the paper is becoming more yellow, the ink is fading and the handling of these documents can still cause them to be damaged (e.g. stains may appear). The online edition of such documents can have profit of a cleaning step, the aim of which is to improve its readability. Some image filtering systems exist that require proper parameters to perform a cleaning. This article presents $${\textsc {Georges}}$$ , a CBR system designed to predict these parameters, for images of texts written in French, with several variants based on approximation, interpolation and extrapolation, based respectively of similarity, betweenness, and analogical proportion relations. The images are characterized by a dirtiness index, with the assumption that two images with similar dirtiness indexes would require similar sets of parameters for being cleaned. This index is based on the detection of the occurrences of a frequent French word on the pages and of averaging these occurrences. Human and automatic evaluations show that the proposed approach (with its variants) provides high-quality results.
Comparative relation mining of online reviews: a hierarchical multi-attention network model

Comparative relations behind online reviews contain rich information concerning customers' assessments of different products or services, thereby supporting upcoming consumers' purchase decisions, as well as helping to identify enterprises' market competitiveness. Instead of using the pattern recognition method, this paper proposes a hierarchical multi-attention network (HMAN) model to extract the comparative relations, in order to greatly reduce the requirements of artificial features and the manual annotation in the relation mining process. Such model outperforms both traditional classification models and text classification models in terms of accuracy, with its F1-score up to 81%. Besides, the proposed model has a good performance on extracting comparative relations from long texts where comparison information is relatively scattered. In this study, we visualise results of different experiments in order to demonstrate the interpretability of this model, and furthermore explore the mechanism of multi-attention method in comparative relations mining. This study applies the deep learning method instead of pattern recognition to automatically capture deep features of comparative relations, and therefore it redefines the identification process of comparative relations.
Reverse Engineering of Generative Models: Inferring Model Hyperparameters From Generated Images

State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. Identifying and understanding manipulated media are crucial to mitigate the social concerns on the potential misuse of GMs. We propose to perform reverse engineering of GMs to infer model hyperparameters from the images generated by these models. We define a novel problem, “model parsing”, as estimating GM network architectures and training loss functions by examining their generated images – a task seemingly impossible for human beings. To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints. To evaluate our approach, we collect a fake image dataset with 100 K images generated by 116 different GMs. Extensive experiments show encouraging results in parsing the hyperparameters of the unseen models. Finally, our fingerprint estimation can be leveraged for deepfake detection and image attribution, as we show by reporting SOTA results on both the deepfake detection (Celeb-DF) and image attribution benchmarks.
Code Execution with Pre-trained Language Models

Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code.However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures.In this paper, we investigate how well pre-trained models can understand and perform code execution.We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution, which challenges existing models such as Codex.We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension.We evaluate CodeExecutor on code execution and show its promising performance and limitations.We also demonstrate its potential benefits for code intelligence tasks such as zero-shot code-tocode search and text-to-code generation.Our analysis provides insights into the learning and generalization abilities of pre-trained models for code execution.
Adaptive Contrastive Knowledge Distillation for BERT Compression

In this paper, we propose a new knowledge distillation approach called adaptive contrastive knowledge distillation (ACKD) for BERT compression.Different from existing knowledge distillation methods for BERT that implicitly learn discriminative student features by mimicking the teacher features, we first introduce a novel contrastive distillation loss (CDL) based on hidden state features in BERT as the explicit supervision to learn discriminative student features.We further observe sentences with similar features may have completely different meanings, which makes them hard to distinguish.Existing methods do not pay sufficient attention to these hard samples with less discriminative features.Therefore, we propose a new strategy called sample adaptive reweighting (SAR) to adaptively pay more attention to these hard samples and strengthen their discrimination abilities.We incorporate our SAR strategy into our CDL and form the adaptive contrastive distillation loss, based on which we construct our ACKD framework.Comprehensive experiments on multiple natural language processing tasks demonstrate the effectiveness of our ACKD framework.
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking

Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks.Unfortunately, some dimensions of this progress are illusory: the majority of the popular IR benchmarks today focus exclusively on downstream task accuracy and thus conceal the costs incurred by systems that trade away efficiency for quality.Latency, hardware cost, and other efficiency considerations are paramount to the deployment of IR systems in user-facing settings.We propose that IR benchmarks structure their evaluation methodology to include not only metrics of accuracy, but also efficiency considerations such as a query latency and the corresponding cost budget for a reproducible hardware setting.For the popular IR benchmarks MS MARCO and XOR-TyDi, we show how the best choice of IR system varies according to how these efficiency considerations are chosen and weighed.We hope that future benchmarks will adopt these guidelines toward more holistic IR evaluation.
AnEMIC: A Framework for Benchmarking ICD Coding Models

Diagnostic coding, or ICD coding, is the task of assigning diagnosis codes defined by the ICD (International Classification of Diseases) standard to patient visits based on clinical notes.The current process of manual ICD coding is time-consuming and often error-prone, which suggests the need for automatic ICD coding.However, despite the long history of automatic ICD coding, there have been no standardized frameworks for benchmarking ICD coding models.We open-source an easy-to-use tool named AnEMIC, which provides a streamlined pipeline for preprocessing, training, and evaluating for automatic ICD coding.We correct errors in preprocessing by existing works, and provide key models and weights trained on the correctly preprocessed datasets.We also provide an interactive demo performing real-time inference from custom inputs, and visualizations drawn from explainable AI to analyze the models.We hope the framework helps move the research of ICD coding forward and helps professionals explore the potential of ICD coding.The framework and the associated code are available here.
BANDA: A Novel Blockchain-Assisted Network for Drone Authentication

The development of a comprehensive and decisive drone defense integrated control system (DDS) that can provide maximum security is crucial for smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study developed a robust drone defense control system that uses blockchain technology to verify and authenticate the legitimacy of a drone operation and verify its package delivery to forestall possible disruption of aerial vehicular mobility, malicious attacks, and invasions. The system accepts inputs about the drone information and attached package in real-time over the cloud and verifies them using a proof of authority algorithm and smart contract and authenticates them using a decentralized application that is deployed on the drone, the ground control station, and the DDS before executing the appropriate neutralization response. To maintain a proper balance between scalable authentication, security, and speed of responsiveness in taking action, the legacy and the proposed blockchain-assisted approach run in parallel, and the disparity in the result is adjudged a perceived illegitimate aerial mobility intention in real-time. With the deployment of the BANDA on three (3) public networks, the Avalanche Fuji achieved a 97.7 ms authentication latency adjudging it an efficient and effective drone defense security approach for ensuring the sustainability of DTS as an autonomous vehicle for mobility.
Computing Mellin Representations and Asymptotics of Nested Binomial Sums in a Symbolic Way: The RICA Package

Nested binomial sums form a particular class of sums that arise in the context of particle physics computations at higher orders in perturbation theory within QCD and QED, but that are also mathematically relevant, e.g., in combinatorics. We present the package RICA (Rule Induced Convolutions for Asymptotics), which aims at calculating Mellin representations and asymptotic expansions at infinity of those objects. These representations are of particular interest to perform analytic continuations of such sums.
Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation

Most existing ultra-high resolution (UHR) segmentation methods always struggle in the dilemma of balancing memory cost and local characterization accuracy, which are both taken into account in our proposed Guided Patch-Grouping Wavelet Transformer (GPWFormer) that achieves impressive performances. In this work, GPWFormer is a Transformer (T)-CNN (C) mutual leaning framework, where T takes the whole UHR image as input and harvests both local details and fine-grained long-range contextual dependencies, while C takes downsampled image as input for learning the category-wise deep context. For the sake of high inference speed and low computation complexity, T partitions the original UHR image into patches and groups them dynamically, then learns the low-level local details with the lightweight multi-head Wavelet Transformer (WFormer) network. Meanwhile, the fine-grained long-range contextual dependencies are also captured during this process, since patches that are far away in the spatial domain can also be assigned to the same group. In addition, masks produced by C are utilized to guide the patch grouping process, providing a heuristics decision. Moreover, the congruence constraints between the two branches are also exploited to maintain the spatial consistency among the patches. Overall, we stack the multi-stage process in a pyramid way. Experiments show that GPWFormer outperforms the existing methods with significant improvements on five benchmark datasets.