Reviewerly/exHarmony huggingface dataset:
Few-Shot Domain Adaptation with Polymorphic Transformers

Deep neural networks (DNNs) trained on one set of medical images often experience severe performance drop on unseen test images, due to various domain discrepancy between the training images (source domain) and the test images (target domain), which raises a domain adaptation issue. In clinical settings, it is difficult to collect enough annotated target domain data in a short period. Few-shot domain adaptation, i.e., adapting a trained model with a handful of annotations, is highly practical and useful in this case. In this paper, we propose a Polymorphic Transformer (Polyformer), which can be incorporated into any DNN backbones for few-shot domain adaptation. Specifically, after the polyformer layer is inserted into a model trained on the source domain, it extracts a set of prototype embeddings, which can be viewed as a “basis” of the source-domain features. On the target domain, the polyformer layer adapts by only updating a projection layer which controls the interactions between image features and the prototype embeddings. All other model weights (except BatchNorm parameters) are frozen during adaptation. Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images. We demonstrate the effectiveness of Polyformer on two medical segmentation tasks (i.e., optic disc/cup segmentation, and polyp segmentation). The source code of Polyformer is released at https://github.com/askerlee/segtran.
SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization

Inspired by the classic Sauvola local image thresholding approach, we systematically study it from the deep neural network (DNN) perspective and propose a new solution called SauvolaNet for degraded document binarization (DDB). It is composed of three explainable modules, namely, Multi-Window Sauvola (MWS), Pixelwise Window Attention (PWA), and Adaptive Sauolva Threshold (AST). The MWS module honestly reflects the classic Sauvola but with trainable parameters and multi-window settings. The PWA module estimates the preferred window sizes for each pixel location. The AST module further consolidates the outputs from MWS and PWA and predicts the final adaptive threshold for each pixel location. As a result, SauvolaNet becomes end-to-end trainable and significantly reduces the number of required network parameters to 40K – it is only 1% of MobileNetV2. In the meantime, it achieves the State-of-The-Art (SoTA) performance for the DDB task – SauvolaNet is at least comparable to, if not better than, SoTA binarization solutions in our extensive studies on the 13 public document binarization datasets. Our source code is available at https://github.com/Leedeng/SauvolaNet.
A Metaheuristic Approach to Secure Multimedia Big Data for IoT-Based Smart City Applications

Media streaming falls into the category of Big Data. Regardless of the video duration, an enormous amount of information is encoded in accordance with standardized algorithms of videos. In the transmission of videos, the intended recipient is allowed to receive a copy of the broadcasted video; however, the adversary also has access to it which poses a serious concern to the data confidentiality and availability. In this paper, a cryptographic algorithm, Advanced Encryption Standard, is used to conceal the information from malicious intruders. However, in order to utilize fewer system resources, video information is compressed before its encryption. Various compression algorithms such as Discrete Cosine Transform, Integer Wavelet transforms, and Huffman coding are employed to reduce the enormous size of videos. moving picture expert group is a standard employed in video broadcasting, and it constitutes of different frame types, viz., I, B, and P frames. Later, two frame types carry similar information as of foremost type. Even I frame is to be processed and compressed with the abovementioned schemes to discard any redundant information from it. However, I frame embraces an abundance of new information; thus, encryption of this frame is sufficient enough to safeguard the whole video. The introduction of various compression algorithms can further increase the encryption time of one frame. The performance parameters such as PSNR and compression ratio are examined to further analyze the proposed model’s effectiveness. Therefore, the presented approach has superiority over the other schemes when the speed of encryption and processing of data are taken into consideration. After the reversal of the complete system, we have observed no major impact on the quality of the deciphered video. Simulation results ensure that the presented architecture is an efficient method for enciphering the video information.
A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction

Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such approaches often derive the training signal from the mean-squared error (MSE) between the generated frame and the ground truth, which can lead to sub-optimal training, especially when the predictive uncertainty is high. Towards this end, we introduce Neural Uncertainty Quantifier (NUQ) - a stochastic quantification of the model’s predictive uncertainty, and use it to weigh the MSE loss. We propose a hierarchical, variational framework to derive NUQ in a principled manner using a deep, Bayesian graphical model. Our experiments on three benchmark stochastic video prediction datasets show that our proposed framework trains more effectively compared to the state-of-the-art models (especially when the training sets are small), while demonstrating better video generation quality and diversity against several evaluation metrics.
Product verification using OCR classification and Mondrian conformal prediction

The retail sector is undergoing an apparent digital transformation that completely revolutionises shopping operations. To stay competitive, retailer stakeholders are forced to rethink and improve their business models to provide an attractive personalised experience to consumers. The self-service checkout process is at the heart of this transformation and should be designed to identify the products accurately and detect any possible anomalous behaviour. In this paper, we introduce a product verification system based on OCR classification and Mondrian conformal prediction. The proposed system includes three components: OCR reading, text classification and product verification. By using image data from existing grocery stores, the system can detect anomalies with high performance, even when there is partial text information on the products. This makes the system applicable for reducing shrinkage loss (caused, for example, by employee theft or shoplifting) in grocery stores by identifying fraudulent behaviours such as barcode switching and miss-scan. Additionally, OCR reading with NLP classification shows that it is in itself a powerful classifier of products.
Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline

In mammography and tomosynthesis, radiologists use the geometric relationship of the four standard screening views to detect breast abnormalities. To date, computer aided detection methods focus on formulations based only on a single view. Recent multi-view methods are either black box approaches using methods such as relation blocks, or perform extensive, case-level feature aggregation requiring large data redundancy. In this study, we propose Retina-Match, an end-to-end trainable pipeline for detection, matching, and refinement that can effectively perform ipsilateral lesion matching in paired screening mammography images. We demonstrate effectiveness on a private, digital mammography data set with 1,016 biopsied lesions and 2,000 negative cases.
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets

Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition. Mostly, these tasks are solved with deep learning methods. Due to the fuzzy nature of textual data, we consider using classification methods based on fuzzy rough sets.
Anomaly Detection in Sequence Data Based on MSM-H with EXPoSE

One efficient way of anomaly detection is to accurately predict future observations by using existing data sets then evaluate the anomaly degree or score of the data by comparing the residuals between the observed values and the predicted values. Therefore, reasonable prediction of future observation values and appropriate anomaly scores are the keys to the effectiveness of anomaly detection. In this work, we proposed a new method Multi-Sub-Model with Heteroscedasticity (MSM-H) modelling for pre-processing sequence data to get residuals together with EXPected Similarity Estimation (EXPoSE) for detection using the residuals. We compare the proposed pre-processed residuals with two more datasets and compare the detection methods with five more latest and proven effective detection algorithms as the anomaly detector. Experiments are carried out using real-word traffic flow data. It firstly generates multiple small models from the traffic flow sequence data, then adapted the model to the specific data of each day, and finally processed and solves the heteroscedasticity in the data to obtain relatively accurate and reasonable residuals. Anomaly detection on the residuals shows promising results and gives 41% improvement considering Area Under Curve (AUC).
Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-the-art violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation

Weakly supervised instance segmentation (WSIS) with only image-level labels has recently drawn much attention. To date, bottom-up WSIS methods refine discriminative cues from classifiers with sophisticated multi-stage training procedures, which also suffer from inconsistent object boundaries. And top-down WSIS methods are formulated as cascade detection-to-segmentation pipeline, in which the quality of segmentation learning heavily depends on pseudo masks generated from detectors. In this paper, we propose a unified parallel detection-and-segmentation learning (PDSL) framework to learn instance segmentation with only image-level labels, which draws inspiration from both top-down and bottom-up instance segmentation approaches. The detection module is the same as the typical design of any weakly supervised object detection, while the segmentation module leverages self-supervised learning to model class-agnostic foreground extraction, following by self-training to refine class-specific segmentation. We further design instance-activation correlation module to improve the coherence between detection and segmentation branches. Extensive experiments verify that the proposed method outperforms baselines and achieves the state-of-the-art results on PASCAL VOC and MS COCO.
Eye Movement-Based Methods for Human-System Interaction. A Comparison of Different Approaches

The paper focuses on utilizing eye movements for controlling computer programs. Four different approaches were considered, including gazing at an element, blinking, and two solutions for eye gestures. The methods were implemented in a simple photo viewer. However, the developed functionalities can be utilized in other applications (switching e-book pages or on-screen keyboard elements). Various sizes for gazed components were verified, as well as the time required for performing particular actions, the number of unwanted element choices, and needed gesture repetitions. The methods were also assessed by the participants of the experiments. Based on the obtained results, it may be reasoned that components of size 200px are convenient for the application control. The users' opinions and the experiments' outcomes revealed that the gaze-based method and gestures based on joining points are satisfactory solutions.
Distributed Multi-Agent Navigation Based on Reciprocal Collision Avoidance and Locally Confined Multi-Agent Path Finding

Avoiding collisions is the core problem in multiagent navigation. In decentralized settings, when agents have limited communication and sensory capabilities, collisions are typically avoided in a reactive fashion, relying on local ob-servations/communications. Prominent collision avoidance techniques, e.g. ORCA, are computationally efficient and scale well to a large number of agents. However, in numerous scenarios, involving navigation through the tight passages or confined spaces, deadlocks are likely to occur due to the egoistic behaviour of the agents and as a result, the latter can not achieve their goals. To this end, we suggest an application of the locally confined multi-agent path finding (MAPF) solvers that coordinate sub-groups of the agents that appear to be in a deadlock (to detect the latter we suggest a simple, yet efficient ad-hoc routine). We present a way to build a grid-based MAPF instance, typically required by modern MAPF solvers. We evaluate two of them in our experiments, i.e. Push And Rotate and a bounded-suboptimal version of Conflict Based Search (ecbs), and show that their inclusion into the navigation pipeline significantly increases the success rate, from 15% to 99% in certain cases.
The Impact of Organizational Practices on the Information Security Management Performance

Information explosion and pressures are leading organizations to invest heavily in information security to ensure that information technology decisions align with business goals and manage risks. Limited studies have been done using small- and-medium-sized enterprises (SMEs) in the manufacturing sector. Furthermore, a small number of parameters have been used in the previous studies. This research aims to examine and analyze the effect of security organizational practices on information security management performance with many parameters. A model has been developed together with hypotheses to evaluate the impact of organizational practices on information security management performance. The data is collected from 171 UK employees at manufacturing SMEs that had already implemented security policies. The structure equation model is employed via the SPSS Amos 22 tool for the evaluation of results. Our results state that security training, knowledge sharing, security education, and security visibility significantly impact information security performance. In addition, this study highlights a significant impact of both security training and knowledge sharing on trust in the organization. Business leaders and decision-makers can reference the proposed model and the corresponding study results to develop favourable tactics to achieve their goals regarding information security management.
Attack-Guided Perceptual Data Generation for Real-world Re-Identification

In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e.g., cross-resolution and insufficient lighting. Due to the limited variation range of training data, existing models are difficult to generalize to scenes with unknown perceptual interference types. To address the above problem, in this paper, we propose two disjoint data-generation ways to complement existing training samples to improve the robustness of Re-ID mod-els. Firstly, considering the sparsity and imbalance of samples in the perceptual space, a dense resampling method from the estimated perceptual distribution is performed. Secondly, to dig more representative generated samples for identity representation learning, we introduce a graph-based white-box attacker to guide the data generation process with intra-batch ranking and discriminate attention. In addition, two synthetic-to-real feature constraints are introduced into the Re-ID training to prevent the generated data from bringing domain bias. Our method is effective, easy-to-implement, and independent of the specific network architecture. Applying our approach to a ResNet-50 base-line can already achieve competitive results, surpassing state-of-the-art methods by +1.2% at Rank-1 on the MLR-CUHK03 dataset.
Joint Segmentation and Quantification of Main Coronary Vessels Using Dual-Branch Multi-scale Attention Network

Joint segmentation and quantification of main coronary vessels are important to the diagnosis and intraoperative treatment of coronary artery disease. They can help clinicians decide whether to carry out coronary revascularization and choose the interventional stent. However, joint segmentation and quantification in a framework is still challenging because of intrinsic distinction of optimization objects for these two tasks. In this paper, we propose a dual-branch multi-scale attention network (DMAN) to achieve synergistic optimization process in a framework. Our DMAN consists of a nested residual module and a attentive regression module. The nested residual module is used to extract and aggregate multi-level and multi-scale features. The attentive regression module introduces a two-phase attention block to express interactive correlation of separated regions and capture the informativeness of the important region in the image. Our DMAN is evaluated over 1893 X-ray coronary angiography images collected from 529 subjects. DMAN achieves the dice coefficient of 0.916 for segmentation and the MAE of 1.30 ± 0.62 mm for quantification.
GenClass: A parallel tool for data classification based on Grammatical Evolution

<h2>Abstract</h2> A genetic programming tool is proposed here for data classification. The tool is based on Grammatical Evolution technique and it is designed to exploit multicore computing systems using the OpenMP library. The tool constructs classification programs in a C-like programming language in order to classify the input data, producing simple if-else rules and upon termination the tool produces the classification rules in C and Python files. Also, the user can use his own Backus Normal Form (BNF) grammar through a command line option. The tool is tested on a wide range of classification problems and the produced results are compared against traditional techniques for data classification, yielding very promising results.
A Federated Deep Learning Framework for 3D Brain MRI Images

Deep learning has shown unique advantages in multiple applications. However, medical image data such as 3D brain MRI scans contain private information of subjects and cannot be shared and used without special permits. This makes it difficult for aggregated analysis of medical images from multiple sites. Federated learning provides an approach to aggregate learned features from different sites without transferring raw data, ensuring the security of subject information. Traditional federated learning algorithms such as FEDAVG has been proved to be effective, but the performance is always poor due to high data heterogeneity across sites. Moreover, federated learning for 3D medical images has rarely been explored. Here we proposed a Guide-Weighted Federated Deep Learning framework for multisite 3D brain MRI images. We used weighted aggregation on gradients and then used the guide gradients to update federated models to deal with cross-site heterogeneity. The framework was validated on the publicly accessible multi-site brain MRI database of Autism Spectrum Disorder (ASD) - ABIDE I&II. Results showed that federated models outperform local models with improvements in accuracy of 0.92-4.02%. As the first federated learning framework for 3D MRI data, the proposed framework is expected to facilitate imaging data sharing across sites and accelerate the Medical Imaging Alliance's formation. In this way, a robust federated model with better performance can then be generated for future diagnostic classification tasks and assisted in the diagnosing and screening of neuropsychiatric disorders.
One-Time Traceable Ring Signatures

A ring signature allows a party to sign messages anonymously on behalf of a group, which is called ring. Traceable ring signatures are a variant of ring signatures that limits the anonymity guarantees, enforcing that a member can sign anonymously at most one message per tag. Namely, if a party signs two different messages for the same tag, it will be de-anomymized. This property is very useful in decentralized platforms to allow members to anonymously endorse statements in a controlled manner.
Learning to Remove: Towards Isotropic Pre-trained BERT Embedding

Research in word representation shows that isotropic embeddings can significantly improve performance on downstream tasks. However, we measure and analyze the geometry of pre-trained BERT embedding and find that it is far from isotropic. We find that the word vectors are not centered around the origin, and the average cosine similarity between two random words is much higher than zero, which indicates that the word vectors are distributed in a narrow cone and deteriorate the representation capacity of word embedding. We propose a simple, and yet effective method to fix this problem: remove several dominant directions of BERT embedding with a set of learnable weights. We train the weights on word similarity tasks and show that processed embedding is more isotropic. Our method is evaluated on three standardized tasks: word similarity, word analogy, and semantic textual similarity. In all tasks, the word embedding processed by our method consistently outperforms the original embedding (with average improvement of 13% on word analogy and 16% on semantic textual similarity) and two baseline methods. Our method is also proven to be more robust to changes of hyperparameter.