Reviewerly/exHarmony huggingface dataset:
Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer’s Disease from MRI Images

Alzheimer’s Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on classifying brain images into one of three major categories: healthy, MCI, and AD; or categorizing MCI patients into (1) progressive: those who progress from MCI to AD at a future time, and (2) stable: those who stay as MCI and never progress to AD. This misses the opportunity to accurately identify the trajectory of progressive MCI patients. In this paper, we revisit the AD identification task and re-frame it as an ordinal classification task to predict how close a patient is to the severe AD stage. To this end, we construct an ordinal dataset of progressive MCI patients with a prediction target that indicates the time to progression to AD. We train a Siamese network (SN) model to predict the time to onset of AD based on MRI brain images. We also propose a Weighted variety of SN and compare its performance to a baseline model. Our evaluations show that incorporating a weighting factor to SN brings considerable performance gain. Moreover, we complement our results with an interpretation of the learned embedding space of the SNs using a model explainability technique.
A Contrastive Learning Scheme with Transformer Innate Patches

This paper presents Contrastive Transformer (CT), a contrastive learning scheme using the innate transformer patches. CT enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation. The scheme performs supervised patch-level contrastive learning, selecting the patches based on the ground truth mask, subsequently used for hard-negative and hard-positive sampling. The scheme applies to all patch-based vision-transformer architectures, is easy to implement, and introduces minimal additional memory footprint. Additionally, the scheme removes the need for huge batch sizes, as each patch is treated as an image. We apply and test CT for the case of aerial image segmentation, known for low-resolution data, large class imbalance, and similar semantic classes. We perform extensive experiments to show the efficacy of the CT scheme on the ISPRS Potsdam aerial image segmentation dataset. Additionally, we show the generalizability of our scheme by applying it to multiple inherently different transformer architectures. Ultimately, the results show a consistent increase in mean Intersection-over-Union (IoU) across all classes.
On the Application of Feed-Forward Artificial Neural Networks to the Maritime Target Motion Analysis Problem

This work presents a novel approach for the fast prediction of future positions of marine vessels utilizing a simple feed-forward artificial neural network. It is shown that this simple network architecture with a single hidden layer, containing three hidden neurons is capable of predicting the future position of a maritime vessel with an accuracy of 99.26%. For this research a simulation was developed, in order to generate enough track data needed to train the network. The input data had to be converted from common polar coordinate system used by navigators into Cartesian coordinates in order to increase the accuracy of the predictions. The predictions are based on three previous observed positions and their corresponding observation times. It was shown that the accuracy decreased linearly with an increasing noise level of the observations. If the noise level exceeded a maximum noise level c of 20 m, the performance of the network degraded beyond its practical use.
Confidence Preservation Property in Knowledge Distillation Abstractions

Social media platforms prevent malicious activities by detecting harmful content of posts and comments. To that end, they employ large-scale deep neural network language models for sentiment analysis and content understanding. Some models, like BERT, are complex, and have numerous parameters, which makes them expensive to operate and maintain. To overcome these deficiencies, industry experts employ a knowledge distillation compression technique, where a distilled model is trained to reproduce the classification behavior of the original model. The distillation processes terminates when the distillation loss function reaches the stopping criteria. This function is mainly designed to ensure that the original and the distilled models exhibit alike classification behaviors. However, besides classification accuracy, there are additional properties of the original model that the distilled model should preserve to be considered as an appropriate abstraction. In this work, we explore whether distilled TinyBERT models preserve confidence values of the original BERT models, and investigate how this confidence preservation property could guide tuning hyperparameters of the distillation process.
Creating an App with Prisma

In this chapter, we will use Prisma to build the back end of a blog app. This is an ORM tool that autogenerates a schema and other things for GraphQL.
Optimal Manufacturing Controller Synthesis Using Situation Calculus

In this paper, we discuss a framework for synthesising manufacturing process controllers using situation calculus, a well-known second-order logic for reasoning about actions in AI. Using a library of high-level ConGolog programs and logical action theories for production resources, we demonstrate how to efficiently synthesise an ‘optimal’ plan, i.e. the plan that minimises the number of actions for a target high-level program of a process recipe.
Profiling Power Consumption for Deep Learning on Resource Limited Devices

The introduction of convolutional neural networks (CNN) has had a significant impact on various computer vision tasks. The process of inference, where a CNN takes images as input and produces corresponding predictions, is a complex and resource hungry task that consumes significant power. Originally much of the processing was done on well resourced machines locally or hosted on various cloud platforms but there has been a recent trend towards moving the processing of data closer to where it is produced within resource limited ‘edge’ devices. It is important to understand the implications and limitations for deployment of a CNN to a device such as an earth observation satellite, which does not have a constant power source. Quantisation is a model optimising technique, where the precision of weights, biases, and activations are reduced, such that they consume less memory and power, and is a common approach to facilitate such deployments. This paper investigates the power consumption behaviour of CNN models from the DenseNet, EfficientNet, MobileNet, ResNet, ConvNeXt & RegNet architecture families, processing imagery on board a Nvidia Jetson Orin Nano platform. It was found that energy consumption varied from 6 mJ to 26 mJ per image for different base (non-quantised) models. Accuracy varied from 69% to 82% and latency varied from 1.3 ms to 7.5 ms per image. The effectiveness of quantisation in reducing the power requirements of CNNs during inference was also investigated, focusing on the use case of deployment to an earth observation satellite. A large difference was found between architectures with some reducing the energy consumption by up to 87% while others achieve less than 10%. The metrics “accuracy-per-joule” and “latency-by-joule” were introduced and used to benchmark and more effectively compare models energy-effectiveness and the impact of quantisation. After quantisation, an improvement in accuracy-per-joule of up to 700% and a latency-by-joule reduction of 99% was achieved.
The error-correcting pair for direct sum codes

The error-correcting pair is a general algebraic decoding method for linear codes, which exists for many classical linear codes. In this paper, we focus our study on the error-correcting pair for the direct sum code of two linear codes with an error-correcting pair. Firstly, for the direct sum code $ \mathcal{C} $ of two linear codes with an error-correcting pair, several sufficient conditions for $ \mathcal{C} $ with an error-correcting pair are given. Secondly, for the direct sum code $ \mathcal{C} $ of two Maximal Distance Separable linear codes, two Near-Maximal Distance Separable linear codes, or a Maximal Distance Separable linear code and a Near-Maximal Distance Separable linear code, several sufficient conditions for $ \mathcal{C} $ with an error-correcting pair are given, respectively. And then, we introduce the corresponding decoding procedure of the direct sum code with an error-correcting pair, and give several examples.
Getting Started

In this book, you will learn about GraphQL. GraphQL is a new way to create APIs. Traditional APIs were created with REST. The problem with REST is that even when you need a subset of data, the endpoint gives all the data from the databases.
Queries

In this chapter, we will start creating an e-commerce app and learn how to use GraphQL with it.
Writing Bug-Free Scripts and Debugging the Rest

A programmer that has never written a buggy program has either not written any code or is more imaginary than Santa Claus or the Easter Bunny. Bugs are the bane of a programmer’s existence. These range from simple typing errors (causing syntax errors) to more complex bad code to faulty logic. These errors can be fixed, some easily while others can take hours or days to identify, trace, and fix.
Semi-supervised Similarity Learning in Process-Oriented Case-Based Reasoning

Supervised learning is typically challenging with insufficient amounts of labeled training data and high costs for label acquisition, creating a demand for unsupervised learning methods. In the research area of Process-Oriented Case-Based Reasoning (POCBR), this demand is created by training data that is manually-modeled and computationally-expensive labeling methods. In this paper, we propose a semi-supervised transfer learning method for learning similarities between pairs of semantic graphs in POCBR with Graph Neural Networks (GNNs). The method aims to replace the fully supervised learning procedure from previous work with an unsupervised and a supervised training phase. In the first phase, the GNNs are pretrained with a triplet learning procedure that utilizes graph augmentation and random selection to enable unsupervised training. This phase is followed by a supervised one where the pretrained model is trained on the original labeled training data. The experimental evaluation examines the quality of the semi-supervised models compared to the supervised models from previous work for three semantic graph domains with different properties. The results indicate the potential of the proposed approach for improving retrieval quality.
Distributed attribute-based encryption with small ciphertext

Currently, cloud services are widely used to manage data. Hence protecting their security is extremely im-portant. This requires meticulous control of access rights for viewers, especially for confidential corporate data in the cloud. Attribute-based encryption (ABE) is a next-generation cryptographic technique that can efficiently control access rights by associating decryption conditions based on the attributes of a given viewer with a ciphertext to allow only viewers who satisfy the decryption conditions. However, ABE requires a key generator, which is an organization that generates users public and private keys with sufficient authority to decrypt the ciphertext. To solve this security problem, distributed ABE (DABE) methods without decrypt-able institutions have been presented.DABE also involves a problem that the more ORs in the decryption condition, the larger the ciphertext size becomes, which is a major challenge for practical use. In this study, we propose a DABE method with reduced ciphertext size and processing load without the need for an au-thority that can decrypt ciphertext. To this end, based on DABE, we adopt a multi-value attribute algorithm with attributes as a set with restrictions on decryption conditions. The results of a security evaluation are presented to show that the security strength of the proposed method is equivalent to that of the existing DABE&nbsp; scheme. Furthermore, the increase in ciphertext size was greatly reduced, which was a major issue for the existing DABE.We also confirmed that the proposed method performed better in terms of decryption time under certain conditions. This research contributes to the development of ABE by realizing a highly practical implementation with both efficient processing and strong capabilities.
Analyzing Hate Speech Against Women on Instagram

Abstract The popularization of digital technologies, such as social media, has driven remarkable changes in the way citizens participate in public life. On the one hand, they gave power to social actors, who began to act in a new media environment, with a considerable impact on the political and economic spheres. On the other hand, they laid the material foundations for the dissemination of hate speech against vulnerable groups and minorities. The present investigation has as its main objective to analyze the misogynistic hate narratives that are uttered in social media. For this purpose, a netnographic study was carried out, of the qualitative type, organized in three sequential moments: extraction, exploration/treatment, and content analysis of the data from the platform of the social network Instagram. The data highlighted from 74 profiles aligned with hate speech show, essentially, the presence of 40 publications, largely linked to extreme right-wing cultures with discursive and imagery manifestations of a misogynistic nature, highlighting the use of irony and the ridicule of women. The most offensive dimension of hate speech was measured in the comments of the followers of these publications, present in the form of insult and direct offense, confirming that the digital environment has aggravated hostility and online harassment against women.
A Survey of Medical Visualization through the Lens of Metaphors

We provide an overview of metaphors that were used in medical visualization and related user interfaces. Metaphors are employed to translate concepts from a source domain to a target domain. The survey is grounded in a discussion of metaphor-based design involving the identification and reflection of candidate metaphors. We consider metaphors that have a source domain in one branch of medicine, e.g., the virtual mirror that solves problems in orthopedics and laparoscopy with a mirror that resembles the dentist's mirror. Other metaphors employ the physical world as the source domain, such as crepuscular rays that inspire a solution for access planning in tumor therapy. Aviation is another source of inspiration, leading to metaphors, such as surgical cockpits, surgical control towers, and surgery navigation according to an instrument flight. This paper should raise awareness for metaphors and their potential to focus the design of computer-assisted systems on useful features and a positive user experience. Limitations and potential drawbacks of a metaphor-based user interface design for medical applications are also considered.
DBMS-assisted Live Migration of Virtual Machines

Live migration of virtual machines (VMs) is a technique that moves active VMs between different physical hosts without losing any running states. Although it is desirable for administrators that the live migration is completed as quickly as possible, the pre-copy-based live migration, widely used in modern hypervisors, does not satisfy this demand on the current trend that VMs on which running applications are performance-critical such as database management systems (DBMSes) have quite large memory. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DMigrate</i> , presented in this paper, shortens the time for live-migrating VMs with even large memory DBMSes. To quickly produce the running state of the migrating VMs on the destination, DMigrate performs regular memory transfers while simultaneously constructing the DBMS’s buffer-pool by fetching the data items from the shared storage. We prototyped DMigrate on MySQL 5.7.30, QEMU 5.1.0, and Linux 4.18.20. The experimental results show that the migration time of the prototype is up to 1.71× and 1.71× shorter under workloads, including sysbench and TPC-C, than the default pre-copy and post-copy schemes, respectively.
An Efficient Adaptive Multi-Kernel Learning With Safe Screening Rule for Outlier Detection

Recent advances in multi-kernel-based methods for outlier detection have positioned them as an attractive way to detect instances that are markedly different from the remaining data in a dataset. Currently, most outlier detection approaches based on multi-kernel learning are simply a convex combination of various kernels with handcrafted weights, meaning that these weights may not be suitable. Meanwhile, this combination of weights does not sufficiently consider the intrinsic correlations of instances when fusing different kernels. Thus, a key challenge is how to adaptively learn an appropriate combination of weights for capturing a new feature space in which outliers can be better detected than the original space. Simultaneously, it is still a burning issue to get the optimal combination of weights due to considerable computational cost and memory usage when the feature or instance size is large. In this paper, we propose a novel method for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> fficient <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> daptive <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u> ulti-kernel for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> utlier <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> etection (EAMOD), which automatically learns the optimal weight for each training instance under different kernels using a non-negative function. In addition, we design a safe screening rule (SSR) for EAMOD to improve its training efficiency without any loss of accuracy. To the best of our knowledge, it is the first attempt to develop SSR for multi-kernel-based outlier detection methods. Extensive experiments show that EAMOD is effective and efficient.
To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream Processing

One of the most important issues in distributed data stream processing systems is using operator migration to handle highly variable workloads cost-efficiently and adapt to the needs at any given time on demand. Operator migration is a complex process involving changes in the state and stream management of a running query, typically without any data loss, and with as little disruption to the execution as possible. This tutorial aims to introduce operator migration, explain the core elements of operator migration, and provide the reader with a good understanding of the design alternatives used in existing solutions. We developed a conceptual model to explain the fundamentals of operator migration and introduce a unified terminology, leading to a taxonomy of existing solutions. The conceptual model separates mechanisms, i.e., how to migrate, and policy, i.e., when to migrate. This separation is further applied to structure the description of existing solutions, offering the reader an algorithmic perspective on various design alternatives. To enhance our understanding of the impact of various design alternatives on migration mechanisms, we also conducted an empirical study that provides quantitative insights. The operator downtime for the naïve migration approach is almost 20 times longer than when applying an incremental checkpoint-based approach.
Cooperative Saliency-Based Pothole Detection and AR Rendering for Increased Situational Awareness

Autonomous vehicles are expected to operate safely in real-life road conditions in the next years. Nevertheless, unanticipated events such as the existence of unexpected objects in the range of the road, can put safety at risk. The advancement of sensing and communication technologies and Internet of Things may facilitate the recognition of hazardous situations and information exchange in a cooperative driving scheme, providing new opportunities for the increase of collaborative situational awareness. Safe and unobtrusive visualization of the obtained information may nowadays be enabled through the adoption of novel Augmented Reality (AR) interfaces in the form of windshields. Motivated by these technological opportunities, we propose a saliency-based distributed, cooperative and rendering scheme for increasing the driver’s situational awareness through (i) automated negative obstacle (potholes) detection, (ii) AR visualization and (iii) information sharing (upcoming potential dangers) with other connected vehicles or road infrastructure. An extensive evaluation study using a variety of real datasets for pothole detection showed that the proposed method provides favorable results and features compared to other recent and relevant approaches.
Structured Light Vision 3-D Reconstruction System for Different Media Considering Refraction: Design, Modeling, and Calibration

Due to the advantages of high measurement accuracy and good robustness, structured light vision (SLV) methods have been extensively applied for 3-D reconstruction in air and water scene. However, most current SLV systems cannot adapt to different media without recalibration, and have the disadvantages of complicated calibration process including SLV model parameter calibration in air and water. In order to overcome these problems, a SLV 3-D reconstruction system for different media considering refraction is proposed. First, an accurate SLV measurement model is established for 3-D reconstruction in different media, which considers structured light plane refraction model and camera refraction model simultaneously. Specifically, the relationship of the SLV model parameters in air and water are proposed. Second, a simple and efficient calibration method of SLV model parameters is presented. After SLV model calibration in air and refraction interface parameters calibration, the SLV model parameters in different media can be obtained accurately without recalibration. Finally, a compact SLV system is developed, and calibration experiments and 3-D reconstruction experiments in air and different turbid water are conducted. Experiment results show that the proposed system could obtain satisfactory 3-D reconstruction performance in air and different turbidity water.