Reviewerly/exHarmony huggingface dataset:
Evaluation of Histopathological Images Segmentation Techniques for Breast Cancer Detection

Breast cancer classification and detection using histopathological images is considered a difficult process due to the complexity of the characteristics of histopathology images. This paper presents an automated system for the classification and detection of breast cancer from microscopic histological images where the images are classified into benign, in situ, invasive, and normal. The proposed approach involves several steps which are image preprocessing (Enhancement), image segmentation, feature extraction, feature selection, and finally image classification. The proposed approach utilizes and compares two segmentation methods which are clustering and Global thresholding using Otsu's method. Initially, images are segmented using K-means and Global thresholding methods. Then, features (morphological and texture) are extracted from the images for the two methods. Moreover, feature selection is done by using Principal Component Analysis (PCA). Finally, K-means and Global thresholding methods are compared in the classification process by using different classifiers. The results show better performance for the Global thresholding.
MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques

Coronavirus disease 2019 (COVID-19) is currently spreading in several countries around the world. The wearing of face masks during the COVID-19 pandemic is one of major protection method that has received varying recommendations from different public health agencies and governments. In this paper we have proposed a face mask detector based on deep learning techniques to classify each face as with mask or without mask in real-time. This study will focus on the Prajna Bhandary dataset. In this work a comparison between transfer learning and training from scratch has been provided, this comparison based on accuracy, model size and computer time. We have also analyzed the role of number of images dataset and learning rate in the classification accuracy and training time. In additions, the extracted model can achieve an accuracy of more than 98% and can be implemented in embedded system and used for many applications.
SPARQLIt: Interactive SPARQL Query Refinement

We propose to demonstrate an interactive system called SPARQLIt, assisting users in the formulation of SPARQL queries. In SPARQLIt, users start by specifying a well-structured SPARQL query; the challenge is that the query may not be grounded to a specific ontology schema or content. The space of possible grounded queries to which the user query may be mapped is then well-defined, and SPARQLIt allows the user to interactively explore this space in a systematic way. A key component of this exploration is a pruning mechanism that is based on identifying query parts that are correct/incorrect, and using them to efficiently filter candidate queries of a certain form. Our demonstration will use the Yago Ontology to exemplify the ease of constructing queries with SPARQLIt without prior knowledge of its contents. We will allow participants to specify queries of their choice and will walk them through the interactive process of query refinement.
Collaborative Fog Computing Architecture for Privacy-Preserving Data Aggregation

The increased digitization of several critical infrastructure services on Internet like home banking, online payments, etc. exposes them to a range of sophisticated information security attacks. Thus, there is an urgent need for strong collaboration between all governmental and non-governmental organizations in order to form the defenses by sharing. Sharing and analyzing sensitive traffic data is an important aspect to protect critical infrastructures. However, privacy concerns of the data contributors about sharing their sensitive data prevent them from gaining the benefits from collaboration, or at least weaken it to a degree of insufficiency. To cope with those privacy concerns, we extend our preceding work about constructing an efficient framework for personal collaborative event processing permitting information sharing and processing amongst administratively and geographically disjoint organizations. The structure is able to aggregating and correlating events coming from the organizations in near real-time while preserving the privacy of sensitive data even in case of coalition among the entities in the environment. The key novelty of the structure is the use of a pseudorandom oracle capability dispensed among the use of FOG structure among the organizations collaborating to the system for obfuscating the data, that permits for achieving a good level of privacy at the same time as guaranteeing scalability in both dimensions: horizontally (range of collaborators) and vertically (range of dataset per collaborator).
Using Machine Learning Models to Study Medication Adherence in Hypertensive Patients Based on National Stroke Screening Data

Stroke, with high incidence, prevalence and mortality, has brought a heavy burden to families as well as society in China nowadays. In 2009, the China national stroke screening and intervention program was launched by the Ministry of Health of China. In the national program, risk factors of stroke are screened and people aged over 40 with high-risk of stroke will be followed-up. From the experience, it is found that hypertension is an important risk factor of stroke. Improving the adherence of hypertension medication can effectively control blood pressure and further decrease stroke incidence. In this study, firstly, we employ oversampling and undersampling method to process the imbalanced dataset. Then, we build four machine learning models, namely logistic regression model, decision tree model, neural network model and random forest model, to classify medication adherence in hypertensive patients. We use the recall and precision to evaluate these models, and considering these two criteria, the model based on decision tree achieves best performance. The models constructed in this paper can be used to identify people with low adherence of antihypertensive drugs in the stroke screening program and improve the efficiency of the follow-up interventions, which can effectively control blood pressure and reduce the possibility of stroke.
Landmark based Outliers Detection in Pervasive Applications

The combination of the Internet of Things and the Edge Computing gives many opportunities to support innovative applications close to end users. Numerous devices present in both infrastructures can collect data upon which various processing activities can be performed. However, the quality of the outcomes may be jeopardized by the presence of outliers. In this paper, we argue on a novel model for outliers detection by elaborating on a `soft' approach. Our mechanism is built upon the concepts of candidate and confirmed outliers. Any data object that deviates from the population is confirmed as an outlier only after the study of its sequence of magnitude values as new data are incorporated into our decision making model. We adopt the combination of a sliding with a landmark window model when a candidate outlier is detected to expand the sequence of data objects taken into consideration. The proposed model is fast and efficient as exposed by our experimental evaluation while a comparative assessment reveals its pros and cons.
Exploring Relations in Neuroscientific Literature using Augmented Reality: A Design Study

To support scientists in maintaining an overview of disciplinary concepts and their interrelations, we investigate whether Augmented Reality can serve as a platform to make automated methods more accessible and integrated into current literature exploration practices. Building on insights from text and immersive analytics, we identify information and design requirements. We embody these in DatAR, a system design and implementation focussed on analysis of co-occurrences in neuroscientific text collections. We conducted a scenario-based video survey with a sample of neuroscientists and other domain experts, focusing on participants' willingness to adopt such an AR system in their regular literature review practices. The AR-tailored epistemic and representational designs of our system were generally perceived as suitable for performing complex analytics. We also discuss several fundamental issues with our chosen 3D visualisations, making steps towards understanding in which ways AR is a suitable medium for high-level conceptual literature exploration.
3D City Cadaster Data Visualization and Viewer Behavior Analysis in Virtual Reality Environment

The construction of smart city is inseparable from the application of data visualization technology, especially the integration of complex urban data into a reasonable expression. The development of Virtual Reality technology provides a new way for the 3D visualization of city models and the study of user behavior. Therefore, understanding user behavior in VR environment is critical to various application fields like 3D visualization and analysis of user behavior patterns. This study proposes a set of 3D visual variables model in VR environment based on Swedish three-dimensional city models. The user recognition experiment is conducted with HTC Vive HMD and Unity 3D platform to verify the validity of proposed model and user behavior data is collected. We use statistical analysis method and LSTM model to analyze user behavior data from 50 participants. The results show that the proposed visual variables model visualization is effective and the overall performance is superior to traditional visualization.
Work in Progress: Towards Usable Updates for Smart Home Devices

Background. Smart home device updates are important tools for remediating security vulnerabilities. Aim. We aim to understand smart home users' perceptions of and experiences with updates. Method. We interviewed 40 smart home users and analyzed a subset of data related to updates. We are also planning a broader, follow-on survey. Results. Users experienced inconsistency in update transparency and methods, were confused about how and if updates are applied, and seldom linked updates to security. Conclusion. Our efforts will provide a new understanding of smart home updates from a usable security perspective and how those are similar/different to views on updates of conventional IT.
The Incomplete Tyranny of Dynamic Stimuli: Gaze Similarity Predicts Response Similarity in Screen‐Captured Instructional Videos

Abstract Although eye tracking has been used extensively to assess cognitions for static stimuli, recent research suggests that the link between gaze and cognition may be more tenuous for dynamic stimuli such as videos. Part of the difficulty in convincingly linking gaze with cognition is that in dynamic stimuli, gaze position is strongly influenced by exogenous cues such as object motion. However, tests of the gaze‐cognition link in dynamic stimuli have been done on only a limited range of stimuli often characterized by highly organized motion. Also, analyses of cognitive contrasts between participants have been mostly been limited to categorical contrasts among small numbers of participants that may have limited the power to observe more subtle influences. We, therefore, tested for cognitive influences on gaze for screen‐captured instructional videos, the contents of which participants were tested on. Between‐participant scanpath similarity predicted between‐participant similarity in responses on test questions, but with imperfect consistency across videos. We also observed that basic gaze parameters and measures of attention to centers of interest only inconsistently predicted learning, and that correlations between gaze and centers of interest defined by other‐participant gaze and cursor movement did not predict learning. It, therefore, appears that the search for eye movement indices of cognition during dynamic naturalistic stimuli may be fruitful, but we also agree that the tyranny of dynamic stimuli is real, and that links between eye movements and cognition are highly dependent on task and stimulus properties.
Towards User Generated AR Experiences

Communication with a customer or future user during the planning and design phase is crucial in applications such as interior design and furniture retailing. Augmented Reality (AR) has the potential to make these communication processes highly effective and provide a better experience for the customer. Current AR authoring solutions are quite complex and require manually creating scenes or rely on objects prepared with even more complex applications such as CAD tools. However, both design experts and their customers often lack the IT skills to use these tools. In addition, many practical cases involve changing reality rather than just adding to it, thus requiring the use of Diminished Reality (DR) technologies. This paper presents a comprehensive analysis of the requirements of both professionals and consumers (gathered using user surveys and individual interviews) for a lightweight and automated authoring process of AR and DR experiences, deriving a set of requirements that can be aligned with state of the art technologies and identifying a number of challenges for AR research.
Noise-Suppressing Deep Tracking

In visual tracking, it is challenging to distinguish the target from similar objects called noises in the background. As deep trackers use convolutional neural networks for image classification as feature extractors, the extracted features are insensitive to different instances in the same class, which is prone to make prediction models confuse the target and the similar noises in the background. To this end, we propose a noise-suppressing algorithm to learn the discriminative representation for distinguishing the target from the noises in the background. First, we learn polynomial kernels for a search patch under the semantic guidance to increase the difference between representations of the target and the noises in the background. Second, we formulate the online foreground-background functions for the target and the noises in the background to learn an adaptive kernel, which suppresses the features positive for the noises and promotes the features positive for the target. We evaluate the proposed method on seven public datasets including OTB-2013, OTB-2015, VOT-2018, LaSOT, TrackingNet, GOT10k, and NFS. The comprehensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods, while running at real-time speed.
Boml: A Modularized Bilevel Optimization Library In Python For Meta Learning

Meta-learning (a.k.a. learning to learn) has recently emerged as a promising paradigm for a variety of applications. There are now many meta-learning methods, each focusing on different modeling aspects of base and meta learners, but all can be (re)formulated as specific bilevel optimization problems. This work presents BOML, a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework. It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods, such as meta-feature-based and meta-initialization-based formulations. The library is written in Python and is available at https://github.com/dutmedia-lab/BOML.
Bitwise Neural Network Acceleration Using Silicon Photonics

Hardware accelerators provide significant speedup and improve energy efficiency for several demanding deep neural network (DNN) applications. DNNs have several hidden layers that perform concurrent matrix-vector multiplications (MVMs) between the network weights and input features. As MVMs are critical to the performance of DNNs, previous research has optimized the performance and energy efficiency of MVMs at both the architecture and algorithm levels. In this paper, we propose to use emerging silicon photonics technology to improve parallelism, speed and overall efficiency with the goal of providing real-time inference and fast training of neural nets. We use microring resonators (MRRs) and Mach-Zehnder interferometers (MZIs) to design two versions (all-optical and partial-optical) of hybrid matrix multiplications for DNNs. Our results indicate that our partial optical design gave the best performance in both energy efficiency and latency, with a reduction of 33.1% for energy-delay product (EDP) with conservative estimates and a 76.4% reduction for EDP with aggressive estimates.
Touch Gesture Recognition Using Spatiotemporal Fusion Features

The touch gesture is one of the most essential and effective means to transfer affective feelings and intents in humans’ communication. For an intelligent agent or a robot, the ability to automatically detect and recognize human touch can realize efficient and natural human–robot interaction. To this end, a novel spatiotemporal fusion feature extraction method is proposed for touch gesture classification tasks. The proposed method extracts time-frequency features from wavelet coefficients based on discrete wavelet transforms. Then, the feature array of space and frequency bands is constructed to extract the spatiotemporal fusion features. A publicly available touch gesture dataset called CoST is used to perform the touch gesture recognition. The recognition result of 14 gesture classes using a user-independent model yields an accuracy of up to 64.17%. Experimental results show that this method outperforms the state-of-the-art ones and that the spatiotemporal fusion features effectively boost the performance of touch gesture recognition.
Comparative Analysis of Machine Learning and Deep Learning Algorithms for Detection of Online Hate Speech

In the day and age of social media, users have become prone to online hate speech. Several attempts have been made to classify hate speech using machine learning, but the state-of-the-art models are not robust enough for practical applications. This is attributed to the use of primitive NLP feature engineering techniques. In this paper, we explored various feature engineering techniques ranging from different embeddings to conventional NLP algorithms. We also experimented with combinations of different features. From our experimentation, we realized that robustly optimized BERT approach (RoBERTa)-based sentence embeddings classified using decision trees gives the best results of 0.9998 F1 score. In our paper, we concluded that BERT based embeddings give the most useful features for this problem and have the capacity to be made into a practical robust model.
LNGate

Bitcoin has emerged as a revolutionary payment system with its decentralized ledger concept however it has significant problems such as high transaction fees and long confirmation times. Lightning Network (LN), which was introduced much later, solves most of these problems with an innovative concept called off-chain payments. With this advancement, Bitcoin has become an attractive venue to perform micro-payments which can also be adopted in many IoT applications (e.g. toll payments). Nevertheless, it is not feasible to host LN and Bitcoin on IoT devices due to the storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN through an untrusted gateway node. The gateway hosts LN and Bitcoin nodes and can open & close LN channels, send LN payments on behalf of the IoT device. This delegation approach is powered by a (2,2)-threshold scheme that requires the IoT device and the LN gateway to jointly perform all LN operations which in turn secures both parties' funds. Specifically, we propose to thresholdize LN's Bitcoin public and private keys as well as its commitment points. With these and several other protocol level changes, IoT device is protected against revoked state broadcast, collusion, and ransom attacks. We implemented the proposed protocol by changing LN's source code and thoroughly evaluated its performance using a Raspberry Pi. Our evaluation results show that computational and communication delays associated with the protocol are negligible. To the best of our knowledge, this is the first work that implemented threshold cryptography in LN.
AR Smart Navigation System

Augmented Reality has started becoming a booming industry nowadays. Augmented reality is used for educational purposes, businesses and now in navigation of huge multi-complexes too. Augmented Reality makes work easier and more interesting with its various features, and a smaller number of hardware components at the same time. Augmented Reality when used in navigation, can provide a user with augmented directions from the user's current position to his/her desired location, in an undetermined surrounding. The aim of our project is to look for all feasible technology in terms of Indoor Navigation and present a modest prototype as a solution. For Indoor Navigation, Augmented Reality uses its inbuilt characteristics to determine a person's position and provide the necessary navigation directive. The area of the project's prototype is a huge Campus, that would provide students and guests with usability advantages and location finding features in the indoors of the college itself, particularly where the target is located. This project makes use of technologies like Unity, ARCore and Navmesh etc.
Analysis of Dynamic Interference Constraints in Cognitive Radio Cloud Networks

To analyze the security performance of a cloud network integrated with a cognitive radio. Cognitive Radio Cloud Network (CRCN) have a resilient potential for dynamic operation for energy saving. Integrating this Cognitive Radio Network (CRN) along with that of a cloud network produces great network exposure, spectrum usage and reduced power consumption. The problem arises for secure transmission and safe storage of huge amount of data in the cloud-based Cognitive Radio (CR) network. This paper, shows a study on preventing the CRCN from some advanced jamming techniques. An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented. A cognitive transmitter uses a pre-trained classifier to predict the current channel status based on recent sensing results and decides whether to transmit or not, and then uses a Generative Adversarial Network (GAN), for generating synthetic data or false data, thus misleading the jammer to sense false data. The transmitter systematically selects when to take wrong actions and adapts the level of defense to mislead the jammer into making prediction errors and consequently increase its throughput. And to use the conventional cryptographic protocols for protecting the stored database in the cloud network from unauthorized users.
Getting There? Together. Cultural Framing of Augmented and Virtual Reality for Art Education

Informal learning contexts of creating and exploring artefacts through Augmented Reality (AR) and Virtual Reality (VR) are increasingly common. Nevertheless, insights on art educators' perspectives on potential for conceptualization and classroom implementation are scarce. In this study, twenty art educators with diverse backgrounds (elementary and secondary school, special education) were exploring a selection of AR/VR applications during online workshops. In addition, six media artists working on AR/VR were sharing experiences in educational contexts. Both art educators as well was artists are pointing out ways of integrating a variety of AR/VR applications in art education teaching practise, from creating in multiplayer mode to exploring artefacts and loci in SocialVR. Grounded Theory Methodology, specifically Situational Analysis as well as Visual Grounded Theory Methodology was applied. Core insights are differing media cultural perspectives in proximity and distance to AR/VR related “worlds”, resulting in different journeys from first encounter to conceptualization and implementation in educational practice.