Reviewerly/exHarmony huggingface dataset:
Explaining clinical decision support systems in medical imaging using cycle-consistent activation maximization

Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. In recent years, this has been addressed by a variety of approaches that have successfully contributed to providing deeper insight. Most notably, additive feature attribution methods are able to propagate decisions back into the input space by creating a saliency map which allows the practitioner to “see what the network sees.” However, the quality of the generated maps can become poor and the images noisy if only limited data is available—a typical scenario in clinical contexts. We propose a novel decision explanation scheme based on CycleGAN activation maximization which generates high-quality visualizations of classifier decisions even in smaller data sets. We conducted a user study in which we evaluated our method on the LIDC dataset for lung lesion malignancy classification, the BreastMNIST dataset for ultrasound image breast cancer detection, as well as two subsets of the CIFAR-10 dataset for RBG image object recognition. Within this user study, our method clearly outperformed existing approaches on the medical imaging datasets and ranked second in the natural image setting. With our approach we make a significant contribution towards a better understanding of clinical decision support systems based on deep neural networks and thus aim to foster overall clinical acceptance.
Post Training Uncertainty Calibration Of Deep Networks For Medical Image Segmentation

Neural networks for automated image segmentation are typically trained to achieve maximum accuracy, while less attention has been given to the calibration of their confidence scores.However, well-calibrated confidence scores provide valuable information towards the user.We investigate several post hoc calibration methods that are straightforward to implement, some of which are novel.They are compared to Monte Carlo (MC) dropout.They are applied to neural networks trained with cross-entropy (CE) and soft Dice (SD) losses on BraTS 2018 and ISLES 2018.Surprisingly, models trained on SD loss are not necessarily less calibrated than those trained on CE loss.In all cases, at least one post hoc method improves the calibration.There is limited consistency across the results, so we can't conclude on one method being superior.In all cases, post hoc calibration is competitive with MC dropout.Although average calibration improves compared to the base model, subject-level variance of the calibration remains similar.
Technology options to help people with dementia or acquired cognitive impairment perform multistep daily tasks: a scoping review

Purpose The purpose of this paper is to review studies that evaluated technology-based prompting systems for supporting participants with dementia or acquired cognitive impairment in their performance of multistep daily tasks. Design/methodology/approach A scoping review was conducted to identify eligible studies through a search of four electronic databases, that is, PubMed, PsycINFO, Web of Science and Institute of Electrical and Electronics Engineers. Findings The search, which covered the 2010–2020 period, led to the identification of 1,311 articles, 30 of which were included in the review. These articles evaluated six different types of prompting systems: context-aware, automatic computer prompting, context-aware, mediated computer prompting, teleoperated robot prompting, self-operated augmented reality prompting, self-operated computer or tablet prompting and time-based (preset) computer, tablet or smartphone prompting. Originality/value Technology-aided prompting to help people with dementia or acquired cognitive impairment perform relevant multistep daily tasks is considered increasingly important. This review provides a picture of the different prompting options available and of their level of readiness for application in daily contexts.
Breaking the silence on sexual harassment and assault: An analysis of #MeToo tweets

This study, using conventional content analysis, examined a corpus of #MeToo tweets from the first day the hashtag went viral, October 16th 2017. Of the 10,546 #MeToo tweets collected, three major categories were identified: these included #MeToo Facilitated Self-Disclosure, Messages of Support, and Calling Out Poor Behaviour with 5,243, 1,556, and 1207 tweets, respectively. The majority of disclosure tweets detailed experiences of sexual assault (44%) and experiences that occurred during childhood (29.4%). The results of this study offer valuable insights regarding the information users chose to share during the first day of the #MeToo movement and the nature of sexual harassment and assault experienced by these individuals. This information may be used by policymakers to identify and implement means to reduce the prevalence of sexual harassment and assault.
Small aircraft detection using deep learning

Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label (hard-label) to a queried data input. We propose a simple and efficient Bayesian Optimization (BO) based approach for developing black-box adversarial attacks. Issues with BO's performance in high dimensions are avoided by searching for adversarial examples in a structured low-dimensional subspace. We demonstrate the efficacy of our proposed attack method by evaluating both ℓ∞ and ℓ2 norm constrained untargeted and targeted hard label black-box attacks on three standard datasets - MNIST, CIFAR-10, and ImageNet. Our proposed approach consistently achieves 2x to 10x higher attack success rate while requiring 10x to 20x fewer queries compared to the current state-of-the-art black-box adversarial attacks.
Model independent feature attributions: Shapley values that uncover non-linear dependencies

Shapley values have become increasingly popular in the machine learning literature, thanks to their attractive axiomatisation, flexibility, and uniqueness in satisfying certain notions of ‘fairness’. The flexibility arises from the myriad potential forms of the Shapley value game formulation. Amongst the consequences of this flexibility is that there are now many types of Shapley values being discussed, with such variety being a source of potential misunderstanding. To the best of our knowledge, all existing game formulations in the machine learning and statistics literature fall into a category, which we name the model-dependent category of game formulations. In this work, we consider an alternative and novel formulation which leads to the first instance of what we call model-independent Shapley values. These Shapley values use a measure of non-linear dependence as the characteristic function. The strength of these Shapley values is in their ability to uncover and attribute non-linear dependencies amongst features. We introduce and demonstrate the use of the energy distance correlations, affine-invariant distance correlation, and Hilbert–Schmidt independence criterion as Shapley value characteristic functions. In particular, we demonstrate their potential value for exploratory data analysis and model diagnostics. We conclude with an interesting expository application to a medical survey data set.
Evolution of the Agile Scaling Frameworks

Abstract Over the past decade, agile methods have become the favored choice for projects undertaken in rapidly changing environments. The success of agile methods in small, co-located projects has inspired companies to apply them in larger projects. Agile scaling frameworks, such as Large Scale Scrum and Scaled Agile Framework, have been invented by practitioners to scale agile to large projects and organizations. Given the importance of agile scaling frameworks, research on those frameworks is still limited. This paper presents our findings from an empirical survey answered by the methodologists of 15 agile scaling frameworks. We explored (i) framework evolution, (ii) main reasons behind their creation, (iii) benefits, and (iv) challenges of adopting these frameworks. The most common reasons behind creating the frameworks were improving the organization’s agility and collaboration between agile teams. The most commonly claimed benefits included enabling frequent deliveries and enhancing employee satisfaction, motivation, and engagement. The most mentioned challenges were using frameworks as cooking recipes instead of focusing on changing people’s culture and mindset.
Moving beyond the mirror: relational and performative meaning making in human–robot communication

Abstract Current research in human–robot interaction often focuses on rendering communication between humans and robots more ‘natural’ by designing machines that appear and behave humanlike. Communication, in this human-centric approach, is often understood as a process of successfully transmitting information in the form of predefined messages and gestures. This article introduces an alternative arts-led, movement-centric approach, which embraces the differences of machinelike robotic artefacts and, instead, investigates how meaning is dynamically enacted in the encounter of humans and machines. Our design approach revolves around a novel embodied mapping methodology, which serves to bridge between human–machine asymmetries and socioculturally situate abstract robotic artefacts. Building on concepts from performativity, material agency, enactive sense-making and kinaesthetic empathy, our Machine Movement Lab project opens up a performative-relational model of human–machine communication, where meaning is generated through relational dynamics in the interaction itself.
Static analysis for dummies: experiencing LiSA

Semantics-based static analysis requires a significant theoretical background before being able to design and implement a new analysis. Unfortunately, the development of even a toy static analyzer from scratch requires to implement an infrastructure (parser, control flow graphs representation, fixpoint algorithms, etc.) that is too demanding for bachelor and master students in computer science. This approach difficulty can condition the acquisition of skills on software verification which are of major importance for the design of secure systems. In this paper, we show how LiSA (Library for Static Analysis) can play a role in that respect. LiSA implements the basic infrastructure that allows a non-expert user to develop even simple analyses (e.g., dataflow and numerical non-relational domains) focusing only on the design of the appropriate representation of the property of interest and of the sound approximation of the program statements.
Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems

Optimizing the quality of result (QoR) and the quality of service (QoS) of AI-empowered autonomous systems simultaneously is very challenging. First, there are multiple input sources, e.g., multimodal data from different sensors, requiring diverse data preprocessing, sensor fusion, and feature aggregation. Second, there are multiple tasks that require various AI models to run simultaneously, e.g., perception, localization, and control. Third, the computing and control system is heterogeneous, composed of hardware components with varied features, such as embedded CPUs, GPUs, FPGAs, and dedicated accelerators. Therefore, autonomous systems essentially require multi-modal multitask (MMMT) learning which must be aware of hardware performance and implementation strategies. While MMMT learning has been attracting intensive research interests, its applications in autonomous systems are still underexplored. In this paper, we first discuss the opportunities of applying MMMT techniques in autonomous systems, and then discuss the unique challenges that must be solved. In addition, we discuss the necessity and opportunities of MMMT model and hardware co-design, which is critical for autonomous systems especially with power/resource-limited or heterogeneous platforms. We formulate the MMMT model and heterogeneous hardware implementation co-design as a differentiable optimization problem, with the objective of improving the solution quality and reducing the overall power consumption and critical path latency. We advocate for further explorations of MMMT in autonomous systems and software/hardware co-design solutions.
Deep Reinforcement Learning for Tropical Air Free-cooled Data Center Control

Air free-cooled data centers (DCs) have not existed in the tropical zone due to the unique challenges of year-round high ambient temperature and relative humidity (RH). The increasing availability of servers that can tolerate higher temperatures and RH due to the regulatory bodies’ prompts to raise DC temperature setpoints sheds light upon the feasibility of air free-cooled DCs in the tropics. However, due to the complex psychrometric dynamics, operating the air free-cooled DC in the tropics generally requires adaptive control of supply air condition to maintain the computing performance and reliability of the servers. This article studies the problem of controlling the supply air temperature and RH in a free-cooled tropical DC below certain thresholds. To achieve the goal, we formulate the control problem as Markov decision processes and apply deep reinforcement learning (DRL) to learn the control policy that minimizes the cooling energy while satisfying the requirements on the supply air temperature and RH. We also develop a constrained DRL solution for performance improvements. Extensive evaluation based on real data traces collected from an air free-cooled testbed and comparisons among the unconstrained and constrained DRL approaches as well as two other baseline approaches show the superior performance of our proposed solutions.
SALSA: Self-Adjusting Lean Streaming Analytics

Counters are the fundamental building block of many data sketching schemes, which hash items to a small number of counters and account for collisions to provide good approximations for frequencies and other measures. Most existing methods rely on fixed-size counters, which may be wasteful in terms of space, as counters must be large enough to eliminate any risk of overflow. Instead, some solutions use small, fixed-size counters that may overflow into secondary structures.This paper takes a different approach. We propose a simple and general method called SALSA for dynamic re-sizing of counters, and show its effectiveness. SALSA starts with small counters, and overflowing counters simply merge with their neighbors. SALSA can thereby allow more counters for a given space, expanding them as necessary to represent large numbers. Our evaluation demonstrates that, at the cost of a small overhead for its merging logic, SALSA significantly improves the accuracy of popular schemes (such as Count-Min Sketch and Count Sketch) over a variety of tasks. Our code is released as open source [1].
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

Neural Architecture Search (NAS) aims to automatically discover optimal architectures. In this paper, we propose an hourglass-inspired approach (HourNAS) for extremely fast NAS. It is motivated by the fact that the effects of the architecture often proceed from the vital few blocks. Acting like the narrow neck of an hourglass, vital blocks in the guaranteed path from the input to the output of a deep neural network restrict the information flow and influence the network accuracy. The other blocks occupy the major volume of the network and determine the overall network complexity, corresponding to the bulbs of an hourglass. To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search. The search space of those non-vital blocks is further shrunk to only cover the candidates that are affordable under the computational resource constraints. Experimental results on ImageNet show that only using 3 hours (0.1 days) with one GPU, our HourNAS can search an architecture that achieves a 77.0% Top-1 accuracy, which outperforms the state-of-the-art methods.
Multi-Layout Unstructured Invoice Documents Dataset: A Dataset for Template-Free Invoice Processing and Its Evaluation Using AI Approaches

The daily transaction of an organization generates a vast amount of unstructured data such as invoices and purchase orders.Managing and analyzing unstructured data is a costly affair for the organization.Unstructured data has a wealth of hidden valuable information.Extracting such insights automatically from unstructured documents can significantly increase the productivity of an organization.Thus, there is a huge demand to develop a tool that can automate the extraction of key fields from unstructured documents.Researchers have used different approaches for extracting key fields, but the lack of annotated and high-quality datasets is the biggest challenge.Existing work in this area has used standard and custom datasets for extracting key fields from unstructured documents.Still, the existing datasets face some serious challenges, such as poor-quality images, domain-related datasets, and a lack of data validation approaches to evaluate data quality.This work highlights the detailed process flow for end-to-end key fields extraction from unstructured documents.This work presents a high-quality, multi-layout unstructured invoice documents dataset assessed with a statistical data validation technique.The proposed multi-layout unstructured invoice documents dataset is highly diverse in invoice layouts to generalize key field extraction tasks for unstructured documents.The proposed multi-layout unstructured invoice documents dataset is evaluated with various feature extraction techniques such as Glove, Word2Vec, FastText, and AI approaches such as BiLSTM and BiLSTM-CRF.We also present the comparative analysis of feature extraction techniques and AI approaches on the proposed multi-layout unstructured invoice document dataset.We attained the best results with BiLSTM-CRF model.
Tightrope walking in low-latency live streaming

It is highly challenging to simultaneously achieve high-rate and low-latency in live video streaming. Chunk-based streaming and playback speed adaptation are two promising new trends to achieve high user Quality-of-Experience (QoE). To thoroughly understand their potentials, we develop a detailed chunk-level dynamic model that characterizes how video rate and playback speed jointly control the evolution of a live streaming session. Leveraging on the model, we first study the optimal joint video rate-playback speed adaptation as a non-linear optimal control problem. We further develop model-free joint adaptation strategies using deep reinforcement learning. Through extensive experiments, we demonstrate that our proposed joint adaptation algorithms significantly outperform rate-only adaptation algorithms and the recently proposed low-latency video streaming algorithms that separately adapt video rate and playback speed without joint optimization. In a wide-range of network conditions, the model-based and model-free algorithms can achieve close-to-optimal trade-offs tailored for users with different QoE preferences.
DeepFoveaNet: Deep Fovea Eagle-Eye Bioinspired Model to Detect Moving Objects

Birds of prey especially eagles and hawks have a visual acuity two to five times better than humans. Among the peculiar characteristics of their biological vision are that they have two types of foveae; one shallow fovea used in their binocular vision, and a deep fovea for monocular vision. The deep fovea allows these birds to see objects at long distances and to identify them as possible prey. Inspired by the biological functioning of the deep fovea a model called DeepFoveaNet is proposed in this paper. DeepFoveaNet is a convolutional neural network model to detect moving objects in video sequences. DeepFoveaNet emulates the monocular vision of birds of prey through two Encoder-Decoder convolutional neural network modules. This model combines the capacity of magnification of the deep fovea and the context information of the peripheral vision. Unlike algorithms to detect moving objects, ranked in the first places of the Change Detection database (CDnet14), DeepFoveaNet does not depend on previously trained neural networks, neither on a huge number of training images for its training. Besides, its architecture allows it to learn spatiotemporal information of the video. DeepFoveaNet was evaluated in the CDnet14 database achieving high performance and was ranked as one of the ten best algorithms. The characteristics and results of DeepFoveaNet demonstrated that the model is comparable to the state-of-the-art algorithms to detect moving objects, and it can detect very small moving objects through its deep fovea model that other algorithms cannot detect.
Pyramidal adaptive meshing for Digital Image Correlation dealing with cracks

A mesh refinement procedure designed for Digital Image Correlation analyses of cracked media is proposed, based on the pixel color for an artificial case, and the crack opening displacement field for two experimental cases. Although evidence that this procedure can improve every case if tiny elements are sought, it is also shown that the size of matrices and consequently, the computational cost can be reduced while maintaining accuracy, especially for low surface crack densities (e.g., single macrocrack).