Reviewerly/exHarmony huggingface dataset:
Towards sustainable health - detection of tumor changes in breast histopathological images using deep learning

The paper presents issues related to methods used in breast histopathological images tumor changes detection. The problem is connected with sustainable health issues which focus on the improvement of health and better delivery of healthcare, rather than late intervention in disease, with resulting benefits to patients and to the environment on which human health depends, thus serving to provide high quality healthcare. The main purpose of the paper is to develop a model based on deep artificial neural networks for the cancer detection in histopathological breast images. The implementation of the proposed model fits in with the concept of sustainable health through the support of the work of doctors in their decisions, diagnosis and in the reduction of the human workload and time, which can be referred to improve the health services. Data set contains 277524 samples from 163 breast histopathological images taken with the WSI scanner. The model is based on a convolutional neural network in the ResNet-18 architecture, which consists of residual blocks. During the final validation on the test set, the network achieved an accuracy of 93.6% and a 87.3% sensitivity in the detection of cancer tissues. The overall performance of the model is characterized by an F1-score of 0.887. The obtained results indicate the possibility of using the system in clinical conditions.
A Machine Learning Approach for Phishing Websites Prediction with Novel Feature Selection Framework

Considering the recent events, Bangladesh stands highly vulnerable to cyberattacks. During this COVID-19 pandemic, since people have become more active on the Internet, the frequency of phishing attacks is escalating concurrently. However, this study used the two latest phishing datasets and proposed an efficient machine learning approach to predict the potential phishing and legitimate websites by analyzing the features of URLs. The proposed study introduced a two-phase empirical hybrid framework for feature selection, which includes the filter, embedded, and wrapper methods. In the primary phase—four filter methods are applied, and to obtain the cumulative feature subset the function perturbation ensemble is performed using set union operation. In the final stage, the filtered feature subset is fed in the wrapper method—where a statistical model is employed to select features with a p-value test, and in the embedded method—which used an algorithmic technique (random forest). The best features are derived from the final feature subsets of the wrapper and embedded methods applying set intersection operation. Using this hybrid ensemble approach, this case study obtained only 37.84% of the features from the datasets for experimentation. Afterward, the study performed cross-validation using three optimized machine learning models (Random Forest Classifier, XGBoost Classifier, Artificial Neural Network) on Data-I, and the XGBoost model outperformed with a 96.10% accuracy score. Besides, the model secured a significant accuracy of 97.40% on Data-II using <38% of the original features.
Invariant Inference with Provable Complexity from the Monotone Theory

Invariant inference algorithms such as interpolation-based inference and IC3/PDR show that it is feasible, in practice, to find inductive invariants for many interesting systems, but non-trivial upper bounds on the computational complexity of such algorithms are scarce, and limited to simple syntactic forms of invariants. In this paper we achieve invariant inference algorithms, in the domain of propositional transition systems, with provable upper bounds on the number of SAT calls. We do this by building on the monotone theory, developed by Bshouty for exact learning Boolean formulas. We prove results for two invariant inference frameworks: (i) model-based interpolation, where we show an algorithm that, under certain conditions about reachability, efficiently infers invariants when they have both short CNF and DNF representations (transcending previous results about monotone invariants); and (ii) abstract interpretation in a domain based on the monotone theory that was previously studied in relation to property-directed reachability, where we propose an efficient implementation of the best abstract transformer, leading to overall complexity bounds on the number of SAT calls. These results build on a novel procedure for computing least monotone overapproximations.
A high-capacity text watermarking method based on geometric micro-distortion

Motivated by the increasing necessity of copyright protection for textual documents, we propose an automatic generation method for high-capacity text watermarking that leverages Chinese character structure and variable font features. The framework was developed by hiding a watermark in Chinese textual files. By replacing the original characters in the documents with ideologically equivalent but morphologically different glyphs generated using our proposed method, we embedded the watermark into documents. Previous watermarking methods mainly face the challenge of low embedding capacity and the replacement glyph was not automatically generated. To address this problem, we focused on a new glyph variation-generating approach. we first devised four types of geometric micro-distortion methods for Chinese characters, including changing the font-weight, panning the stroke components, adjusting the thickness of strokes and altering the spatial layout of components. Glyph variations were then generated by applying one or more than one of the four types of micro-distortion methods. Using variable font features, numerous glyph variations can be automatically generated in a feature-continuous manner, which provides our algorithm with a relatively high capacity. The experimental results indicate that our approach has better capacity and imperceptibility performance than those of state-of-the-art Chinese text watermarking approaches.
Undeclared Work Prediction Using Machine Learning: Dealing with the Class Imbalance and Class Overlap Problems

Undeclared work is a complex and ever-changing problem severely impacting society and the economy. It is one of the structural parts of the informal sector and undermines the well-being of workers and businesses and the foundations of the welfare state. Labour inspectorates are among the leading public institutions dealing with undeclared work, but they face difficulties lacking human and financial resources and the appropriate tools. Yet, they own large volumes of data produced by the increasing use of e-Government services and ICT tools, which, if properly processed and analysed employing advanced machine learning techniques, are able to provide significant assistance in undeclared work prediction and understanding its features. Notably, classification algorithms may learn from datasets containing past labour inspection findings and produce classifiers that effectively predict labour law violations and provide understandable explanations for these predictions. Still, undeclared work is usually underrepresented in such datasets since it is not often detected in onsite inspections due to its hidden and multifaceted nature. In addition, several onsite inspection cases with similar characteristics may usually reveal different findings. These facts introduce the issues of class imbalance and class overlap in datasets of this application domain, which impede the machine learning process. The current research work focuses on data engineering techniques to address them. It uses data from real-life inspections and presents the effects of these techniques by creating several different classifiers and assessing their performance in predicting undeclared work, concluding with identifying the best approach.
Fuzzy Twin Bounded Large Margin Distribution Machines

Twin bounded large distribution machine (TBLDM) considers structural risk and marginal distribution issues, obtaining good efficiency, robustness, and generalization performance. However, it ignores the influence of noise and uncertainty of the input data, which is inevitable in reality. Aiming at the problem, this paper introduces the idea of fuzzy set theory and proposes a novel fuzzy TBLDM (FTBLDM). The fuzzy membership function is set to be related to the distance of the sample to the class center, which describes its importance and credibility level. By incorporating the membership degree into the object function, FTBLDM reduces the influence of outliers and noise on the optimal hyperplane. The effectiveness of our method is validated by experiments on a synthetic dataset and UCI benchmark datasets. Besides, to verify the anti-noise performance of the model, we conduct experiments on UCI datasets with noise. The results show that FTBLDM is able to produce promising results compared to several benchmark and state-of-the-art algorithms.
Inferring Metamorphic Relations from JavaDocs: A Deep Dive into the MeMo Approach

Identifying and selecting suitable Metamorphic Relations is a complex process since it necessitates a thorough grasp of the system under test and its problem domain. Recently, an approach supporting unit testing at the method level called MeMo was proposed. Through a module called MR-Finder, MeMo infers Equivalent Metamorphic Relations (EMRs) by identifying sentences in Javadoc’s comments that describe equivalent behaviours between different methods of the same class. MR-Finder has three main components: (i) a predefined set of 10 words that express an equivalence (S10W). (ii) A mechanism that measures the semantic similarity between two sentences by using Word Move Distance (WDM). (iii) A binary classifier that decides whether a given sentence points to an EMR. The goal of our research is to determine if MeMo’s MR-Finder module can be improved. For that purpose, we first re-build the MR-Finder module and use the same dataset provided by MeMo’s authors to verify the reported results in the original study and establish the basis for further experiments. Second, we explore two strategies, STRTG No.1 and STRTG No.2, to improve the MR-Finder. In STRTG No.1, we increase the set S10W. In STRTG No.2, we keep S10W unchanged but add a second template sentence to the MR-Finder module. We successfully re-implemented the MR-Finder module and achieved comparable results using the same S10W. Our results indicate that the overall performance of MR-Finder is very likely to improve when the initial set of equivalent words increases, i.e., with STRTG No.1.
Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed algorithm, in comparison with several benchmark FL schemes and algorithms.
VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data

While monocular 3D pose estimation seems to have achieved very accurate results on the public datasets, their generalization ability is largely overlooked. In this work, we perform a systematic evaluation of the existing methods and find that they get notably larger errors when tested on different cameras, human poses and appearance. To address the problem, we introduce VirtualPose, a two-stage learning framework to exploit the hidden "free lunch" specific to this task, i.e.generating infinite number of poses and cameras for training models at no cost. To that end, the first stage transforms images to abstract geometry representations (AGR), and then the second maps them to 3D poses. It addresses the generalization issue from two aspects: (1) the first stage can be trained on diverse 2D datasets to reduce the risk of over-fitting to limited appearance; (2) the second stage can be trained on diverse AGR synthesized from a large number of virtual cameras and poses. It outperforms the SOTA methods without using any paired images and 3D poses from the benchmarks, which paves the way for practical applications. Code is available at https://github.com/wkom/VirtualPose .
Complex Video Action Reasoning via Learnable Markov Logic Network

Profiting from the advance of deep convolutional networks, current state-of-the-art video action recognition models have achieved remarkable progress. Nevertheless, most of existing models suffer from low interpretability of the predicted actions. Inspired by the observation that temporally-configured human-object interactions often serve as a key indicator of many actions, this work crafts an action reasoning framework that performs Markov Logic Network (MLN) based probabilistic logical inference. Crucially, we propose to encode an action by first-order logical rules that correspond to the temporal changes of visual relationships in videos. The main contributions of this work are two-fold: 1) Different from existing black-box models, the proposed model simultaneously implements the localization of temporal boundaries and the recognition of action categories by grounding the logical rules of MLN in videos. The weight associated with each such rule further provides an estimate of confidence. These collectively make our model more explainable and robust. 2) Instead of using hand-crafted logical rules in conventional MLN, we develop a data-driven instantiation of the MLN. In specific, a hybrid learning scheme is proposed. It combines MLN's weight learning and reinforcement learning, using the former's results as a self-critic for guiding the latter's training. Additionally, by treating actions as logical predicates, the proposed framework can also be integrated with deep models for further performance boost. Comprehensive experiments on two complex video action datasets (Charades & CAD-120) clearly demonstrate the effectiveness and explainability of our proposed method.
Testing machine learning systems in industry

Machine learning becomes increasingly prevalent and integrated into a wide range of software systems. These systems, named ML systems, must be adequately tested to gain confidence that they behave correctly. Although many research efforts have been devoted to testing technologies for ML systems, the industrial teams are faced with new challenges on testing the ML systems in real-world settings. To absorb inspirations from the industry on the problems in ML testing, we conducted an empirical study including a survey with 87 responses and interviews with 7 senior ML practitioners from well-known IT companies. Our study uncovers significant industrial concerns on major testing activities, i.e., test data collection, test execution, and test result analysis, and also the good practices and open challenges from the perspective of the industry. (1) Test data collection is conducted in different ways on ML model, data, and code and faced with different challenges. (2) Test execution in ML systems suffers from two major problems: entanglement among the components and the regression on model performance. (3) Test result analysis centers on quantitative methods, e.g., metric-based evaluation, and is combined with some qualitative methods based on practitioners' experience. Based on our findings, we highlight the research opportunities and also provide some implications for practitioners.
Smart Contract Document Authentication for Digital Clothing Design Specification based on Blockchain and QR Code

In the current situation, graphical illustrations are used in Digital clothing fashion. These clothing design specifications are built using computer technologies. In the internet world, the major challenges in fashion technology are forged products and untimely product delivery. Counterfeiting has always been a severe problem in the luxury goods business. To solve this issue, the proposed system authenticates the smart contract document for digital clothing design specifications based on the blockchain and QR code. Smart contracts are a set of simple self-executing codes or programs. It can run automatically when the predetermined conditions are met. It enables secure transactions and agreements to be completed without the need for centralised authority. Blockchain is a distributed, immutable ledger that makes it easier to record transactions and manage assets in a business network. QR codes are square-shaped matrices of dark or light pixels that are used to encode and retrieve data from computing devices more quickly. QR codes are used in a wide range of applications, from supply chain management to crypto wallet addresses. The proposed system has the following steps. Select the digital garment design specification file from the smart contract approved list. Verification authority checks whether the file has been approved file or not with the recorded blockchain data. After the verification process, a secure QR code is generated with the secret message. The generated QR code is included in the design specification file. This encoded file is uploaded to the IPFS (Interplanetary File System). This QR code symbol and a design specification file’s IPFS hash code are sent to the stitching section. This QR code is used to determine if the file has been tampered with or forged. The QR code is decoded by using the secret message and it displays the decoded product information. Finally, the downloaded data has been validated using the blockchain. This QR code encoding and decoding process experimented with the collected dataset which has 60 images. The action of the QR code analysed based on the with and without attacks.
SIMBAR: Single Image-Based Scene Relighting For Effective Data Augmentation For Automated Driving Vision Tasks

Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a controllable manner, presents an opportunity to augment datasets with a richer variety of lighting conditions, similar to what would be encountered in the real-world. This paper presents a novel image-based relighting pipeline, SIMBAR, that can work with a single image as input. To the best of our knowledge, there is no prior work on scene relighting leveraging explicit geometric representations from a single image. We present qualitative comparisons with prior multi-view scene relighting baselines. To further validate and effectively quantify the benefit of leveraging SIMBAR for data augmentation for automated driving vision tasks, object detection and tracking experiments are conducted with a state-of-the-art method, a Multiple Object Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained from scratch and tested on Virtual KITTI. For more details and sample relit datasets, please visit our project website (https://simbarv1.github.io).
A Homomorphic Encryption Service to Secure Data Processing in a Cloud/Edge Continuum Context

For years, one of the major issues against the adoption of Cloud computing has been security. Typically, with traditional symmetric and asymmetric encryption schemes, data can be encrypted and sent into Cloud data centers, but this approach presents a great weakness: data must be transferred to the client-side to be deciphered and then processed. Nevertheless, such an approach is not feasible for a large amount of data. In this paper, we address such an issue through the adoption of homomorphic encryption in a Cloud/Edge continuum environment. Specifically, pieces of data are encrypted in the Edge and transferred into the Cloud for storage and processing without the need to decode them. Experiments performed on different homomorphic encryption schemes in a Cloud/Edge testbed highlight the goodness of our approach.
Large Pose Detection and Facial Landmark Description for Pose-invariant Face Recognition

Face recognition is an important computer vision task affected by several factors. These factors include the face pose of the input image, features used to describe the image, illumination conditions, and facial expression. In this study, a pose-invariant face recognition framework based on large pose detection and facial landmark description is proposed. During the training phase, a large pose detector model is proposed to process the 2D spatial distributions of the detected facial landmarks on a set of face images. This model can detect whether the yaw angle of the face is large or small (semi-frontal face image). This results in two face pose scenarios. Then, a feature descriptor is applied to a set of predefined facial landmarks on a face image for obtaining the feature vectors. These feature vectors are used to train two face recognition models for each person in the database. One for the large pose scenario and the other for the semi-frontal pose scenario. During the testing phase, the large pose detector is used to select a type of face recognition model (large pose or semi-frontal one). The selected model is utilized to determine the identity of the person. In this study, the CMU-PIE database is employed. Three feature descriptors, SIFT, HOG, and LBP, are adopted for comparison. The models used for face recognition are SVM, GMM, and Naive Bayes. The novelty of the proposed method is using a large pose detector to improve the face recognition rate. After performing experimental trials on face images with pose angles ±90°, a performance comparable with state-of-the-art methods is obtained.