Reviewerly/exHarmony huggingface dataset:
PG-TFNet: Transformer-based Fusion Network Integrating Pathological Images and Genomic Data for Cancer Survival Analysis

Survival analysis is crucial to the evaluation of cancer treatment options and deep learning-based methods integrating pathological images and genomic data have been used for prognosis prediction. However, the most methods are based on the analysis of pathological image patches, thus ignoring the morphological structure information at larger field-of-view and intrinsic relationships between patches. Meanwhile, the existing models fail to exploit the powerful representation learning capabilities of the neural networks for effective multimodal feature fusion of pathological images and genomic data. In this paper, we propose a novel transformer-based fusion network integrating pathological images and genomic data (PGTFNet) for cancer survival analysis. Specifically, we present a transformer-based feature fusion module for multi-scale pathological slides to fully exploit the intra-modality relationships between image patches at various fields of view. Moreover, in order to make effective inter-modality feature fusion of pathological images and genomic data, we introduce a cross-attention transformer module that can exchange feature representations of different modalities between two transformers branches. The PG-TFNet is performed on the colorectal cancer dataset from the Cancer Genome Atlas (TCGA), which contains paired whole-slide images and genomic data with ground truth survival data. The experimental results from a 10-fold cross validation demonstrate that the proposed PG-TFNet facilitates the prognosis prediction of colorectal cancer and shows superiority over the existing methods.
Towards Adoption of Smart Contract in Construction Industry in Malaysia

The revolution of Malaysian Construction 4.0 through emerging technologies has brought a paradigm shift that has digitalized the construction sector. There is a need to adopt a computerized protocol to assist in automating the performance of a contract to meet future digital challenges. Therefore, this paper aims to serve as a pioneer study to investigate the implementation of the Malaysian construction industry to adopt smart contracts. This study adopted a qualitative scientific methodology, whereby a systematic review was conducted to gather the benefits and challenges of implementing smart contracts in the construction industry. Further, interview sessions were arranged to collect data from the construction contract management experts. The research findings unveil that due to the self-executing attribute of smart contracts, the implementation of smart contracts could provide a better apportionment of risks in a contract. The study also finds that the challenges in implementing smart contracts are severe. For instance, the smart contract is irreversible and immutable and prone to human error. The study concludes that it is more suitable to apply and implement a smart contract to a short-term contract that is not subjected to variation. Furthermore, a smart contract can enhance the efficiency in managing the contracts, such as reducing time and managing the conflicts and disputes that arise during the contract duration. The developed implementation framework is significant for the construction personnel, especially those dealing with the contract administration. The implementation of smart contracts in construction could boost contract administration and management discipline via investment in this new technology.
The effect of perceived risks and perceived cost on using online learning by high school students

As Covid19 Pandemic hit all over the world, Indonesian high schools are struggled to cope with the sudden and forced switch to fully online learning. This study employed an online survey of Indonesian high school students to understand their behaviour in using online learning. The survey gathers data from 462 respondents who resided in 24 provinces. Theory of Planned Behaviour extended with Perceived Risks and Perceived Costs is used as the theoretical framework. Perceived Risks are used to accommodate concerning security-related news that might affect online activities. Perceived Costs is used to address complaints regarding additional financial burden due to fully online learning, namely cost to access and cost to acquire equipment. SmartPLS version3 is used as the main data analysis tools. The result showed that the Theory of Planned Behaviour is indeed able to explain the use of online learning by Indonesian high school students. Perceived Risks are considered as an influence but only have minimal impact. Perceived Costs does not have any influence on online learning. This might be because Indonesian is quick to act and counter the negative impact of the Covid19 Pandemic. One of the Indonesian Government's efforts is to subsidise Internet costs for students and teachers.
Actively Secure Setup for SPDZ

We present the first actively secure, practical protocol to generate the distributed secret keys needed in the SPDZ offline protocol. As an added bonus our protocol results in the resulting distribution of the public and secret keys are such that the associated SHE ‘noise’ analysis is the same as if the distributed keys were generated by a trusted setup. We implemented the presented protocol for distributed BGV key generation within the SCALE-MAMBA framework. Our method makes use of a new method for creating doubly (or even more) authenticated bits in different MPC engines, which has applications in other areas of MPC-based secure computation. We were able to generate keys for two parties and a plaintext size of 64 bits in around 5 min, and a little more than 18 min for a 128-bit prime.
mSHAP: SHAP Values for Two-Part Models

Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models that price insurance policies are fair and non-discriminatory. Black box models can make it very difficult to know which covariates are influencing the results, resulting in model risk and bias. SHAP (SHapley Additive exPlanations) values enable interpretation of various black box models, but little progress has been made in two-part models. In this paper, we propose mSHAP (or multiplicative SHAP), a method for computing SHAP values of two-part models using the SHAP values of the individual models. This method will allow for the predictions of two-part models to be explained at an individual observation level. After developing mSHAP, we perform an in-depth simulation study. Although the kernelSHAP algorithm is also capable of computing approximate SHAP values for a two-part model, a comparison with our method demonstrates that mSHAP is exponentially faster. Ultimately, we apply mSHAP to a two-part ratemaking model for personal auto property damage insurance coverage. Additionally, an R package (mshap) is available to easily implement the method in a wide variety of applications.
A Method for Closed Frequent Subgraph Mining in a Single Large Graph

Mining frequent subgraphs is an interesting and important problem in the graph mining field, in that mining frequent subgraphs from a single large graph has been strongly developed, and has recently attracted many researchers.Among them, MNI-based approaches are considered as state-of-the-art, such as the GraMi algorithm.Besides frequent subgraph mining (FSM), frequent closed frequent subgraph mining was also developed.This has many practical applications and is a fundamental premise for many studies.This paper proposes the CloGraMi (Closed Frequent Subgraph Mining) algorithm based on GraMi to find all closed frequent subgraphs in a single large graph.Two effective strategies are also developed, the first one is a new level order traversal strategy to quickly determine closed subgraphs in the searching process, and the second is setting a condition for early pruning a large portion of non-closed candidates, both of them aim to reduce the running time as well as the memory requirements, improve the performance of the proposed algorithm.Our experiments are performed on five real datasets (both directed and undirected graphs) and the results show that the running time as well as the memory requirements of our algorithm are significantly better than those of the GraMi-based algorithm.
Modality-Oriented Graph Learning Toward Outfit Compatibility Modeling

Outfit compatibility modeling, which aims to automatically evaluate the matching degree of an outfit, has drawn great research attention. Regarding the comprehensive evaluation, several previous studies have attempted to solve the task of outfit compatibility modeling by integrating the multi-modal information of fashion items. However, these methods primarily focus on fusing the visual and textual modalities, but seldom consider the category modality as an essential modality. In addition, they mainly focus on the exploration of the intra-modal compatibility relation among fashion items in an outfit but ignore the importance of the inter-modal compatibility relation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> the compatibility across different modalities between fashion items. Since each modality of the item could deliver the same characteristics of the item as other modalities, as well as certain exclusive features of the item, overlooking the inter-modal compatibility could yield sub-optimal performance. To address these issues, a multi-modal outfit compatibility modeling scheme with modality-oriented graph learning is proposed, dubbed as MOCM-MGL, which takes both the visual, textual, and category modalities as input and jointly propagates the intra-modal and inter-modal compatibilities among fashion items. Experimental results on the real-world Polyvore Outfits-ND and Polyvore Outfits-D datasets have demonstrated the superiority of our proposed model over existing methods.
Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions.In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells.This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning.Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions.Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions.BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type.The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied.VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images.The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.
LiDAR Depth Completion Using Color-Embedded Information via Knowledge Distillation

Depth completion is the task of reconstructing dense depth images from sparse LiDAR data. LiDAR depth completion, for which LiDAR data is the only input, is an ill-posed and challenging problem owing to the underlying properties of LiDAR data: extremely few points, presence of discontinuities, and absence of texture information. Accordingly, most approaches are heavily dependent on guided color images, which leads to unsatisfactory results when the color images are degraded. To alleviate the dependency on color images but leverage this information during training, we present a deep convolutional neural network (CNN) consisting of depth and edge CNNs via transferring of knowledge. In order to compensate for the limitations of LiDAR data, we design the edge CNN to learn a gradient depth image from a powerful teacher network through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knowledge-Distillation</i> method. Since the teacher network is trained with color images, color-embedded information can be obtained in the test phase even if color images are not used as an input. We further propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Distillation</i> method for transferring the color-embedded features from the edge CNN to the depth CNN. Enforcing the depth features to contain edge information hardly observed in LiDAR data enables the depth CNN to generate more edge-attentive and structure-preserving results. Our novel methods show remarkable results in outdoor and indoor environments for KITTI and NYU-Depth-V2 datasets. Experiments performed with low-channel LiDAR data in KITTI and few depth points in the NYU-Depth-V2 dataset show that our method is robust to data sparsity and applicable in various scenarios.
Improving Generalization of Deepfake Detection With Data Farming and Few-Shot Learning

Recent advances in automated video and audio editing tools, generative adversarial networks (GANs), and social media allow creation and fast dissemination of high quality tampered videos, which are generally called deepfakes. Typically, in these videos, a face is swapped with someone else’s using GANs. Accessible open source software and apps for the face swapping led to a wide and rapid dissemination of the generated deepfakes, posing a significant technical challenge for their detection and filtering. In response to the threat, which deepfake videos can pose to our trust in video evidence, several large datasets of deepfake videos and several methods to detect them were proposed recently. However, the proposed methods suffer from a problem of overfitting on the training data and the lack of the generalization across different databases and the generative models. Therefore, in this paper, we investigate the techniques for improving the generalization of deepfake detection methods that can be employed in practical settings. We have selected two popular state of the art deepfake detectors: based on Xception and EfficientNet models, and we use five databases: from Google and Jigsaw, FaceForensics++, DeeperForensics, Celeb-DF, and our own publicly available large dataset DF-Mobio. To improve generalization, we apply different augmentation strategies used during training, including a proposed aggressive ‘data farming’ technique based on random patches. We also tested two few-shot tuning methods, when either a first convolutional layer or a last layer of a pre-trained model is tuned on 100 seconds from a training set of the test database. The experimental results clearly expose the generalization problem of deepfake detection methods, since the accuracy drops significantly when a model is trained on one dataset and evaluated on another. However, the silver lining is that an aggressive augmentation during training and a few-shot tuning on the test database can improve the accuracy of the detection methods in a cross-database scenario. As a side observation, we show the importance of database selection for training and evaluation, as FaceForensics++ is found to be better to use for training, while DeeperForensics is found to be significantly more challenging as a test database.
Cycle-Free Weakly Referring Expression Grounding With Self-Paced Learning

In this paper, we are tackling the weakly referring expression grounding task to localize the target object in an image according to a given query sentence, where the mapping between the query sentence and image regions is blind during the training period. Previous methods all follow a cyclic forward-backward pipeline to handle this task, where the query sentence is firstly converted to the result region through the forward module, and then the result region is converted back to a sentence through the backward module, with the difference between the reconstructed sentence and original query used as the loss to optimize the entire network. These existing methods, however, suffer from the deviation issue when the result region, generated through the forward module, totally deviates from the target area, but the backward module still reconstructs a similar sentence. The aforementioned loss function cannot penalize this kind of deviation because of the consistent prediction of the sentence. To overcome this limitation, we propose a cycle-free pipeline, where a region describer network is designed to predict the textual description for each candidate region, and a result region is selected according to the similarity between the predicted description and the query sentence. Furthermore, a self-paced learning mechanism is designed to avoid the drift issue during the warm-up period of the optimization process. The proposed method achieves a higher average accuracy on RefCOCO and RefCOCO+ datasets, compared with all previous state-of-the-art methods.
Intelligent Autonomous Navigation of Car-Like Unmanned Ground Vehicle via Deep Reinforcement Learning

In this paper, a car-like Unmanned Ground Vehicle (UGV) is simulated and trained as an intelligent agent to navigate and exit unknown obstacle filled environments given no prior knowledge of environment characteristics, using a Reinforcement Learning (RL) algorithm tailored for continuous action spaces. This is achieved using Deep Deterministic Policy Gradient (DDPG), an Actor-Critic network that combines multiple cutting-edge Artificial Intelligence methods including continuous Deep-Q learning, policy gradient methods and actor-critic networks. A combination of two feedforward neural networks with Rectified Linear Units (ReLU) is used for the critic and actor representations which combine both policy and value based methods to learn continuous action space policies via approximation functions. The role of the actor network in this architecture is to decide linear and angular velocity outputs from a continuous action space given current state inputs, to then be evaluated by the critic network to learn and estimate Q-values by minimizing a loss function. The proposed DDPG RL network is trained and evaluated in two obstacle filled environments for a car-like UGV with wheelbase, l of 0.3 m. During the 10,000 episode training period, the agent converges to a maximum reward value of 180 after 1100 training episodes in the first environment, and a maximum reward value of 80 after 7500 training episodes in the second, more complex environment. The agent is shown to exhibit intelligent human-like learning behavior to learn optimal policies and adapt to new environments at the end of each training period with no changes to network architecture.
Efficient Profiled Side-Channel Analysis of Masked Implementations, Extended

We extend the study of efficient profiled attacks on masking schemes initiated by Lerman and Markowitch (TIFS, 2019) in different directions. First, we study both the profiling complexity and the online attack complexity of different profiled distinguishers. Second, we extend the range of the noise levels of their experiments, in order to cover (higher-noise) contexts where masking is effective. Third, we further contextualize the investigated distinguishers (e.g., in terms of adversarial capabilities and a priori assumptions on the leakage probability density function). Finally, we complete the list of distinguishers considered in this previous work and add expectation-maximization, soft analytical side-channel attacks and multi-layer perceptrons in our comparisons. Our results allow shedding an interesting new light on the respective strengths and weaknesses of these different statistical tools, both in the context of a side-channel security evaluation and for concrete attacks. In particular, they confirm the experimental relevance of evaluation shortcuts leveraging the masking randomness during profiling, in order to speed up the evaluation process.
Discrimination between Earthquake <i>P</i> Waves and Microtremors via a Generative Adversarial Network

ABSTRACT The accurate and reliable discrimination of earthquakes from background noise is a primary task of earthquake early warning (EEW); however, ubiquitous and complex microtremor signals substantially complicate this task. To mitigate this problem, a generative adversarial network (GAN) is adopted to distinguish between earthquakes and microtremors in this study. We train a GAN based on 52,537 K-NET and KiK-net strong ground motion records from Japan, and use the well-trained discriminator to identify 5373 P waves and 5373 microtremors in the testing set. The results indicate that this algorithm can correctly identify 99.89% of P waves and 99.24% of microtremors with high confidence. In addition, a verification of the proposed algorithm on data from the Great East Japan earthquake confirms that this model can achieve robust results for local records of large events and ultimately discriminate earthquakes from microtremors. This algorithm is an exploratory test of a GAN for identifying earthquake P waves. Though the GAN uses only P waves for training (There are no microtremors in the input data.), it has extensive potential in seismological and EEW applications.
Quaternary Linear Codes and Related Binary Subfield Codes

In this paper, we mainly study quaternary linear codes and their binary subfield codes. First we obtain a general explicit relationship between quaternary linear codes and their binary subfield codes in terms of generator matrices and defining sets. Second, we construct quaternary linear codes via simplicial complexes and determine the weight distributions of these codes. Third, the weight distributions of the binary subfield codes of these quaternary codes are also computed by employing the general characterization. Furthermore, we present two infinite families of optimal linear codes with respect to the Griesmer Bound, and a class of binary almost optimal codes with respect to the Sphere Packing Bound. We also need to emphasize that we obtain at least 9 new quaternary linear codes.