Reviewerly/exHarmony huggingface dataset:
Deep ensemble of texture maps for false positive reduction in mammograms

Abstract Worldwide, breast cancer is a life-threatening disease attributing to increased mortality rates among women. Mammograms are commonly used for screening breast cancer in asymptomatic stages. However, the subtle nature of abnormalities in early stages makes mammogram analysis a cumbersome task. A computer aided diagnosis (CAD) system can complement subjective diagnosis of physicians with its objective assessment. Mass detection is the most important task in breast cancer diagnosis, as masses are the prominent indicators of the disease. Nevertheless, it is the most challenging task due to the ambiguity between masses and the surrounding normal tissues, especially in dense breasts. Though CAD systems are effective in detecting masses with high sensitivity, the price paid is usually high false positive rates (FPR). Texture analysis is normally employed to reduce the FPR in mass detection, where texture features extracted from suspicious regions are used to build a classifier model to discriminate between actual masses and false positives. Deep learning (DL) is a data-driven model that is gaining increased importance in diverse fields, including medical diagnosis, that involve voluminous amounts of data. In particular, convolutional neural network (CNN) plays an important role in image analysis in various applications, including mammogram analysis. Converting raw images to texture maps can enhance the performance of CNN for false positive reduction. In this work, textural image maps based on Hilbert curve, forest fire model, Radon transform, discrete wavelet transform (DWT) and curvelet transform are analysed using CNN. More specifically, an ensemble of CNNs based on these individual textural image representations is constructed. The proposed work is validated on CBIS-DDSM, a publicly available benchmark dataset, demonstrating 100% accuracy for mass detection with 0% FPR.
Analysis of MRI as a screening tool for the diagnosis of schizophrenia

Abstract Schizophrenia (SCZ) is a clinical disorder that affects 0.01% of the world population. It affects people in late teen hood or early maturity resulting in lifelong social and mental disturbance. At present, there is no cure, but this can be diagnosed and treated. Classification of SCZ imposes great challenges even for the most experienced neurologists. A non-intrusive technique like MRI is taken for diagnosing various diseases which are used as a base for our tool. Many researchers used large datasets of SCZ and normal for analyzing SCZ using various parameters like Grey matter, white matter, voxel-based morphometry, etc., This work proposes a simpler but effective approach to classify the same. This paper determined statistical and complexity features from 32 SCZ and 18 normal MRI images. Totally 9 features are determined out of these, novel features Hausdorff dimension and Euclidean distance played an important role in classification. Hausdorff dimension is selected as the most significant feature by student’s t-test with p&lt;0.001 . The back propagation neural network receives substantial information from the t-test as input. Our promising approach, with a minimal dataset, classified the subjects with 100% sensitivity, 88.9% specificity, and 94.4% accuracy.
Design and Development of a Caretaker Alert System Using Eye Blink Detection

Abstract The patient who are bedridden due of weakening of muscles of hands, feet or had lost voice, finds difficult to communicate their needs. These conditions occur when there is an issue in the passage of messages between the brain and the muscles. Some of them are affected with quadriplegia cannot move their limbs and some could move only their eyes. Therefore, the aim of this study is to provide an interactive system which would assist them to communicate with caretakers. Communication was done using pre- defined messages and an alert buzzer to grab attention, for corresponding delay in eye blinks. Once the eye blinks, the eye sensor senses it and compares the duration with the defined delays. If the condition is true then corresponding defined message appears on the display, if the condition is false, then it checks for next condition and so on. The user needs to be trained to communicate through this device. The time delays set for each message can be adjusted as per user’s need. More messages can be displayed by changing the time of closure of the eye. The system is employed to control, manage and communicate with the other people through eye blink action.
Face Mask Detection Using AI through Python

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdown to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. As a surveillance task performer, it can also detect a face along with a mask in motion. The method attains accuracy up to 95.77% and 94.58% respectively on two different datasets. We explore optimized values of parameters using the Sequential Convolutional Neural Network of Deep Learning model to detect the presence of masks correctly without causing over-fitting.
Contact Tracing Apps for the COVID-19 Pandemic: A Responsible Innovation Perspective

Abstract The COVID-19 pandemic has brought about the first real opportunity to test the efficacy of the Responsible Research and Innovation framework or RRI in a global health crisis. This is in view of the bold new approaches to health research and innovation that the pandemic has paved the way for. One such approach is the digital contact tracing application (CTA). Although contact tracing has been a fundamental part of infectious disease control for decades, this is the first time this technique has been used in mobile applications. Based on a Multivocal Literature Review, the development of CTAs in four countries – France, Germany, Spain, and the UK – was assessed to understand what dimensions of RRI can be identified in the governments’ response to COVID-19. This chapter shows that although from 2011, RRI has been promoted as a governance approach for increasing societal desirability of the processes and products of science and technology, very little is known about how the framework may be applied in a health crisis. Notwithstanding that RRI was not explicitly referenced during the development of CTAs in France, the UK, Spain, and Germany, the analysis has identified some interesting linkage to this framework. It shows that while no RRI approach was explicitly embraced by these governments, some key components were present – even though inadequately. It also indicates that, while it is challenging to apply RRI in crises, there is value in using it as an analytical tool for techno-social responses in situations, like those created by the COVID-19 health crisis.
Anonymization and Analysis of User Trajectories and Revisit Patterns

Spatiotemporal data of individuals open up new business opportunities, lead to the adoption of better marketing strategies, and enable a better understanding of customers. Raw spatiotemporal data can be used for malicious activities, therefore stressing the need for complete anonymization of the data before using it for business purposes. This gives rise to the need for anonymization techniques that hide users' sensitive information. A differential privacy-based approach has been proposed in this paper to anonymize the revisit patterns of individuals. A novel clustering algorithm based on CASTLE has been proposed to anonymize the sensitive information in user trajectory. Both the methods were tested on real-world datasets and stream data. The algorithm's time complexity was studied, and trade-offs are proposed to cater to the needs of an organization.
High Dimensional Optimization for Electronic Design

Bayesian optimization (BO) samples points of interest to update a surrogate model for a blackbox function. This makes it a powerful technique to optimize electronic designs which have unknown objective functions and demand high computational cost of simulation. Unfortunately, Bayesian optimization suffers from scalability issues, e.g., it can perform well in problems up to 20 dimensions. This paper addresses the curse of dimensionality and proposes an algorithm entitled Inspection-based Combo Random Embedding Bayesian Optimization (IC-REMBO). IC-REMBO improves the effectiveness and efficiency of the Random EMbedding Bayesian Optimization (REMBO) approach, which is a state-of-the-art high dimensional optimization method. Generally, it inspects the space near local optima to explore more points near local optima, so that it mitigates the over-exploration on boundaries and embedding distortion in REMBO. Consequently, it helps escape from local optima and provides a family of feasible solutions when inspecting near global optimum within a limited number of iterations.
Automatic Analog Schematic Diagram Generation based on Building Block Classification and Reinforcement Learning

Schematic visualization is important for analog circuit designers to quickly recognize the structures and functions of transistor-level circuit netlists. However, most of the original analog design or other automatically extracted analog circuits are stored in the form of transistor-level netlists in the SPICE format. It can be error-prone and time-consuming to manually create an elegant and readable schematic from a netlist. Different from the conventional graph-based methods, this paper introduces a novel analog schematic diagram generation flow based on comprehensive building block classification and reinforcement learning. The experimental results show that the proposed method can effectively generate aesthetic analog circuit schematics with a higher building block compliance rate, and fewer numbers of wire bends and net crossings, resulting in better readability, compared with existing methods and modern tools.
Towards Neural Hardware Search: Power Estimation of CNNs for GPGPUs with Dynamic Frequency Scaling

Machine Learning (ML) algorithms are essential for emerging technologies such as autonomous driving and application-specific Internet of Things(IoT) devices. Convolutional Neural Network(CNN) is one of the major techniques used in such systems. This leads to leveraging ML accelerators like GPGPUs to meet the design constraints. However, GPGPUs have high power consumption, and selecting the most appropriate accelerator requires Design Space Exploration(DSE), which is usually time-consuming and needs high manual effort. Neural Hardware Search(NHS) is an upcoming approach to automate the DSE for Neural Networks. Therefore, automatic approaches for power, performance, and memory estimations are needed.
Efficient Design Rule Checking Script Generation via Key Information Extraction

Design rule checking (DRC) is a critical step in integrated circuit design. DRC requires formatted scripts as the input to the design rule checker. However, these scripts are always generated manually in the foundry, and such a generation process is extremely inefficient, especially when encountering a large number of design rules. To mitigate this issue, we first propose a deep learning-based key information extractor to automatically identify the essential arguments of the scripts from rules. Then, a script translator is designed to organize the extracted arguments into executable DRC scripts. In addition, we incorporate three specific design rule generation techniques to improve the performance of our extractor. Experimental results demonstrate that our proposed method can significantly reduce the cost of script generation and show remarkable superiority over other baselines.
Genetic algorithm-based adaptive weighted fuzzy logic control (awFLC) for traction power control

One of the most important issues for FLC systems is the problem of finding the right balance between interpretability and accuracy. For this delicate balance, several methods which can be integrated into fuzzy logic, and tune the fuzzy logic parameters adaptively, have been proposed. One of these popular approaches is the heuristic optimization method. However, in terms of optimization, designing fuzzy logic control is a complex optimization problem that is discrete in terms of rule optimization and numerical in terms of optimization of membership degrees parameters. In this context, heuristic-based adaptive fuzzy control systems focus on either fuzzy rule optimization, weighting fuzzy rules, or parameter optimization. In this paper, unlike the others, an adaptive weighted fuzzy logic control (awFLC) method, which weights the inputs instead of the rules, is proposed. First, the membership degree of each input is calculated. Then, the resultant weight is determined by combining the weighted input membership degrees. For a crisp result, the average of the membership degrees of the resultant weight to the output membership functions is calculated. In awFLC, the interaction between membership functions is achieved by average membership degree, communication between inputs is achieved by the weighting of inputs, and mapping between inputs-outputs is achieved by the resultant weight value. Thus, the approach, which turns into a purely numerical optimization problem, provides convenience for heuristic search. In awFLC, optimal values for input weights and variable parameters are searched by the genetic algorithm. The performance of the method was tested on traction power control, and the results were compared with the ANFIS results. With awFLC, an 8.13% average error was obtained, while ANFIS produced solutions with an average error rate of 8.97%.
Computations in the Electroweak Theory

Written for a two-semester Master's or graduate course, this comprehensive treatise intertwines theory and experiment in an original approach that covers all aspects of modern particle physics. The author uses rigorous step-by-step derivations and provides more than 100 end-of-chapter problems for additional practice to ensure that students will not only understand the material but also be able to apply their knowledge. Featuring up-to-date experimental material, including the discovery of the Higgs boson at CERN and of neutrino oscillations, this monumental volume also serves as a one-stop reference for particle physics researchers of all levels and specialties. Richly illustrated with more than 450 figures, the text guides students through all the intricacies of quantum mechanics and quantum field theory in an intuitive manner that few books achieve.
Is it really flexible? Examining definitions of flexibility against contemporary practice in online education

COVID-19 has recently driven a shift in formal education environments through the transition from on-campus to online learning. The urgency of this transition has challenged the traditional perception of what a higher education experience should look like for educators and higher education institutions. However, flexibility in the delivery of formal education environments is not new. This paper explores the development of flexibility as a concept within higher education against an established flexibility framework. This paper adapts the 'Roberts Model' to reflect contemporary definitions of flexibility and uses this adapted framework to evaluate 12 online courses to validate/moderate. It then considers the implications for institutions who are increasingly exploring online delivery models appropriate for the contemporary student.
A rendezvous block‐based authentication framework for service‐level security in decentralized cloud resources

Abstract Cloud services are distributed from manifold sources with autonomous security procedures. The conventional authentication methods result in asynchronous security towards multi‐resource access and sharing. This provides volatile authentication for sequential service sessions. For alleviating this issue, a Rendezvous Block‐based Authentication Framework (RBAF) is proposed. This framework is backboned with a blockchain paradigm that differentiates authentication based on rendezvous and asynchronous attributes. The modifications in initial and final attributes are observed, intended for training, and supplanted with service‐dependent authentications. In the different sessions, attribute‐based authentication and agreed end‐to‐end security are administered using agreed keys that are valid within the sessions. This key generation is modified using the new session and user attributes based on learning recommendations. The ledger paradigm records the session and its associated attributes for different training instances, based on which service flexibility is ensured. The proposed framework's performance is verified using the metrics service distribution ratio, false rate, session uptime, and service delay.
Optimizing Tree-Based Contrast Subspace Mining Using Genetic Algorithm

Abstract Mining contrast subspace is a task of finding contrast subspace where a given query object is most similar to a target class but dissimilar to non-target class in a multidimensional data set. Recently, tree-based contrast subspace mining method has been introduced to find contrast subspace in numerical data set effectively. However, the contrast subspace search of the tree-based method may be trapped in local optima within the search space. This paper proposes a tree-based method which incorporates genetic algorithm to optimize the contrast subspace search by identifying global optima contrast subspace. The experiment results showed that the proposed method performed well on several cases compared to the variation of the tree-based method.
An AR User Interface for the Evaluation of Visuospatial Abilities

Early pathogen processes of Alzheimer´s dementia (AD) develop years before the time of diagnosis. The process starts in hippocampal regions, which play an important role in spatial cognitive abilities including spatial memory. The decrease of these abilities leads to deficits in orientation and navigation and has a high impact on everyday live. A simple, fast and exact evaluation of the spatial memory impairment could help to detect the disease at an early stage (e.g. during a regular screening) and could thus enable stopping or slowing down the process. This work aims at the development of such a screening tool by combining a version of the established Smith-Milner paradigm (SMP) [26, 27] for analysis of spatial-cognitive deficits with the technical possibilities of the augmented-reality (AR) technology. A new AR-application was developed and the user acceptance was evaluated with a first group of healthy subjects (N = 40). The user interface of the AR application was specifically tailored for easy use by older participants and non-technical physicians.
Creation of In-Situ Instructions Made Easy: Development and Evaluation of a Prototypical AR Tool

In-situ overlays are considered forward-looking for technical service instructions. Creating these instructions usually requires expertise in 3D modeling, AR environment creation, and the domain of the instruction being created. The goal of this work was to develop a prototype that simplifies the creation of in-situ overlays. For this purpose, concepts for functions and operation of the tool were elaborated, a functional prototype was developed and subsequently evaluated in a user study. Modeling 3D representations was simplified with the approach of creating objects by assembling basic geometric shapes. By using augmented reality, the basic shapes can be placed directly around the real object. The virtual objects can then be positioned in relation to real objects through the flexible use of an AR marker. The augmented instructions created in this way can be retrieved at any time. In our user study, all participants were able to successfully create in-situ instructions quickly and without prior knowledge. This work contributes to a better usage of augmented in-situ instructions without 3D modeling knowledge.
Elevating Stress Levels - Exploring Multimodality for Stress Induction in VR

Simulation training in Virtual Reality (VR) has gained attraction in recent years. With its broad application possibilities and implicit safety for users, simulation-based training may be imagined for safety-critical situations and exposure therapy. Beyond visual and auditory representation of the environment and stressors, upcoming hardware supports olfactory and haptic feedback. To examine the benefits of these technological advances in stress training, we present a Wizard of Oz pilot study (N=12). Therein, a bimodal presentation of the scenario ‘being stuck in an elevator’ was compared to a multimodal one. For the comparison, we measured qualitative feedback, the iGroup presence questionnaire scores, and physiological stress reactions by recording changes in cardiac and pulmonary activity. Results show trends for moderately more pronounced stress levels and perceived presence for the multimodal presentation. Thus, we argue that multimodal stress induction may better simulate hazardous situations in stress training.
The Impact of Varying Resolution and Motion Realism of Avatars in Augmented Reality-Supported, Virtually Co-located Sales Consultations

The use of avatars in AR-based remote cooperation has gained a lot of interest in research and practice, as it allows remote cooperators to be virtually co-located. The realism of avatars has been found to be key to their successful use and is mainly affected by avatar resolution and movement realism. As hardware limits and difficulties to create human-like animations create challenges for designers to develop realistic avatars, design knowledge is needed to create suitable and feasible representations of avatars. However, there is hardly any work addressing this challenge. This paper presents an initial study that investigates this area and compares the impact of avatar resolution and movement realism on remote cooperation.
The Negative Effect on Postural Ergonomics of Non-Sedentary Workplace Desks in Virtual Reality

Virtual reality (VR) is gaining increasing importance in an increasing number of places in daily life, particularly when gaming or working. Moreover, immersive activities are often performed while standing at physical desks and current devices can even register the physical properties of a virtual workplace to match the virtual content with haptics in the real world in front of the user. However, little is known about the effects of VR on how users perceive and ergonomically adapt to workplace desks when wearing a head-mounted display (HMD). In this user study, we conducted an experiment with 19 participants to investigate the effects of non-sedentary VR on the postural risk level, workload, and preferred desk height. The results indicate that being in VR negatively influences objective and subjective measures of ergonomics and increases postural risk while the preferred desk height remained unaffected. We found evidence that wearing the HMD negatively affects the neck posture at non-sedentary workplace desks. We contribute with our findings and highlight the need for improving the field-of-view and weight of HMDs for lower postural risk levels at workplace desks in non-seated VR.