Reviewerly/exHarmony huggingface dataset:
Joint Feature Learning for Cell Segmentation Based on Multi-scale Convolutional U-Net

A major challenge in the analysis of tissue imaging data is cell segmentation, the task of identifying precisely the boundary of each cell in a microscopic image. The cell segmentation task is still challenging due to the variable shapes, large size differences, uneven grayscale, and dense distribution of biological cells in microscopic images. In this paper, we propose a joint feature learning method that integrates the density and boundary branch into a multi-scale convolutional U-Net (MC-Unet). To enhance the supervision of cell density and boundary detection, the density and boundary loss is constructed to guide the joint learning of multiple features, where the density loss branch can address the challenges posed by high density, while the boundary loss branch can address the problems of unclear cell boundaries and partial cell occlusion. A series of experiments on different cell datasets show that two auxiliary branches improve the learning of features on cell density and cell boundaries and that the proposed method is effective on different segmentation models. The code is available at: https://github.com/HuHaigen/Joint-Feature-Learning-for-Cell-Segmentation.
Development of Vision Screening Tool in Virtual Reality: A Usability Assessment Study

In recent years, rapid advancements in technologies have opened new paradigms for vision screening and vision therapy by incorporating serious games (SGs) and eye trackers (ETs). The head-mounted display (HMD) with built-in ET makes it possible for researchers to study novel approaches for interaction and gaze movements in a 3-dimensional (3D) virtual world. While there are validated vision screening applications on desktop computers, these are limited by the size of the screen. Several spatial functional vision problems cannot be identified only using a laptop surfaces. In this study, we propose a vision screening application for VR devices with ET. The developed SG tasks for vision screening in VR are inspired by a vision screening application on laptops. The data of ET is stored in the database, and finally, the replays of tasks are prepared and displayed in VR. The usability of this solution was evaluated by seven participants using a user experience questionnaire (UEQ), observations and interviews. The results indicate the attractiveness of the VR application compared to laptop screens. However, further studies are needed to determine the validity of VR-based screening instruments.
Preva: Protecting Inference Privacy through Policy-based Video-frame Transformation

Real-time edge-cloud video analytics systems have been widely used to support such applications as traffic counting, surveillance, autonomous driving, Metaverse, etc. In such a system, the edge and the cloud cooperatively conduct model inference of the video frames captured by the camera of the edge, using a trained DNN model of the video analytics application. The edge conducts initial analytics on the video frames to a split layer of the DNN model; and then sends intermediate results to the cloud for follow-up analytics. In this paper, we show that an attacker can perform reconstruction attacks to the intermediate results; and private information of the raw video frames, e.g., a plate number of a car, can be leaked. In this paper, we present Preva, a new Privacy preserving Real-time Edge-cloud Video Analytics system. The core idea of Preva is to conduct image transformation on the video frames, as preprocessing, prior to the video frames starting the edge-cloud video analytics process, so that during edge-cloud video analytics, the intermediate results will not leak private information under attack. We design a policy-based video-frame transformation scheme. Given the resource constraints of the edge, Preva ensures high accuracy in the final video analytics results and minimizes privacy leakage in any split layer. We present a formal privacy analysis and we show that Preva can guarantee privacy leakage under the reconstruction attacks of both outsider attackers and insider attackers. We evaluate Preva through three video analytics applications and we show that Preva outperforms existing systems for 64.4% in analytics accuracy and 59.2% in privacy leakage.
Few-shot Learning Framework Based on Adaptive Subspace for Skin Disease Classification

Skin disease classification from images is crucial to dermatological diagnosis. It is an important task to develop Computer Aided Detection(CAD) systems that can help dermatologists improve classification performances. One challenge limits the adoption of such systems so far: traditional CAD can’t handle new emerging diseases. The reason is that standard deep learning models can only identify the categories that appear in the training set. However, this limitation can be addressed with Few-shot learning, a machine learning paradigm where a classifier has to generalize to a new category not seen in training, only given a few examples of this category. However, because of the complexity of lesions, most Few-shot learning methods do not work well in medical tasks. We find that most methods assume a single similarity measure and only obtain a single feature space. Motivated by this, we propose a three-stage learning paradigm. In the second stage, we introduce the subspace method to construct a symmetric function. In the third stage, we propose a metric module that consists of two similarity measures. In this way, the model enables to learn more discriminative features from few shots of skin disease images and has better generalization ability. The results demonstrate that our method produces a substantial improvement on the ISIC-2019 dataset.
Information System for Leisure Time-Management in Quarantine Conditions

The research is devoted to the study of the problem of planning leisure time during quarantine periods (forced staying at home) using information technology tools. The need for adaptation and modification of the usual forms of leisure activity to the new format has been determined. The methods of providing recommendations were studied. Using the Analytical Hierarchy Method, the optimal type of system for the implementation of the proposed solution was chosen – a recommendation system. The algorithm of the recommendation system, which offers alternatives for spending time during periods of forced staying at home, is described. A weighted hybrid mechanism was used to provide recommendations. The recommendations feature of the developed prototype of the information system is the provision of offers that contain, in addition to passive types of leisure, also active ones that take into account the characteristics of each of its users.
Attention-based Memory Fusion Network for Clinical Outcome Prediction using Electronic Medical Records

Recent methods of patient clinical outcome prediction focus on embedding the temporal time-series data by sequential data encoders without considering the dependency between the different variables and the static demographics data. To solve this problem and achieve better patient outcome prediction, we propose an attention-based memory fusion (AMF) network with Gated Recurrent Unit (GRU) (called GRU-AMFN) to model the dependency between the different time-series and static demographic data and extract effective personalized representation about the patient’s clinical health status. We evaluate our proposed GRU-AMFN method on eICU, a publicly available dataset, to validate its effectiveness for the in-hospital mortality prediction task. Experimental results demonstrate that our proposed method outperforms several state-of-the-art models for the in-hospital mortality prediction task. Ablation studies show the effectiveness of the proposed attention-based memory fusion module and the adaptive fusion module. Besides, our proposed method finds several static demographic and time-series features that are important for mortality prediction.
Application of the Cognitive Approach for IT Project Management and Implementation

The article describes the principles and methods of the cognitive approach to project management. Cognitive analysis and its content are analyzed. The essence of the cognitive system is considered, and developed cognitive map is chosen and described as a cognitive model, the method of its construction and application. The advances and main tools of cognitive modeling are described for IT project management and implementation. Cognitive map of project implementation is considered as a useful method for IT projects.
Post-pandemic adopted learning approach to promote architectural education: statistical approach

Purpose Online learning has many limitations in studio-based courses, such as architectural design courses, considering the challenges during post-pandemic. Therefore, this study aims to propose a post-pandemic adopted learning approach, which integrates flipped classrooms (FC) with project-based learning (PBL). In addition, this study evaluates the perceptions of students based on and the effects of the proposed learning approach in architectural design. Design/methodology/approach This study provides a mixed methodology based on a literature review on the topic to bridge the gaps in previous studies regarding the FC and PBL. In addition, a case study survey including semi-structured interviews, observations questionnaire recruited undergraduate students to generate both qualitative and quantitative data to investigate the perceptions of students based on post-pandemic adopted learning approach. Findings This study has highlighted the significance of post-pandemic adopted hybrid learning method, especially in architectural education, whereas the research finds that FC is an appropriate solution to improve design courses with online technologies. Practical implications The proposed approach provides specialists to develop and integrate proposals and strategies to enable a better online practice for students and instructors. The proposed approach can enhance the students' interpersonal skills, hence active online learning related architectural design projects. Originality/value Recognizing the significance of e-learning in response to the post-pandemic scenario, this study developed and assessed new learning technique that combines online learning with traditional design studios via hybrid learning method.
Quantum-Classical Hybrid Algorithms

At the moment, quantum computers are extremely noisy. Yet, one would still like to harness the advantage of existing quantum computational device if possible. The conviction comes from our understanding of quantum mechanics.
Quantum Algorithm for Option Pricing

One of the main areas for the application of quantum algorithms is in the pricing of options. An introductory text cannot do justice to this fast-evolving subject, and hence, the discussion will focus on only the pricing of a European call or a put option.
Phase Estimation and quantum Fourier Transform (qFT)

Phase estimation and the quantum Fourier transform (qFT) are the inverse of each other. From a pedagogical point of view, to start the discussion with phase estimation provides greater clarity. The reason being, as will become clear in the sections below, that phase estimation requires the removal of phase factors from a qubit, whereas the qFT requires the addition of phase factors. Removing a phase factor from a qubit has a transparent representation in terms of the quantum gates, and hence, we start with phase estimation; the quantum Fourier transform is the inverse of the transformation for phase estimation.
Bin-scanning: Segmentation of X-ray CT volume of binned parts using Morse skeleton graph of distance transform

Abstract X-ray CT scanners, due to the transmissive nature of X-rays, have enabled the non-destructive evaluation of industrial products, even inside their bodies. In light of its effectiveness, this study introduces a new approach to accelerate the inspection of many mechanical parts with the same shape in a bin. The input to this problem is a volumetric image (i.e., CT volume) of many parts obtained by a single CT scan. We need to segment the parts in the volume to inspect each of them; however, random postures and dense contacts of the parts prohibit part segmentation using traditional template matching. To address this problem, we convert both the scanned volumetric images of the template and the binned parts to simpler graph structures and solve a subgraph matching problem to segment the parts. We perform a distance transform to convert the CT volume into a distance field. Then, we construct a graph based on Morse theory, in which graph nodes are located at the extremum points of the distance field. The experimental evaluation demonstrates that our fully automatic approach can detect target parts appropriately, even for a heap of 50 parts. Moreover, the overall computation can be performed in approximately 30 min for a large CT volume of approximately 2000×2000×1000 voxels.
Positioning Augmented Reality in Oil and Gas Maintenance Support

High-risk, hazardous, and harsh environments are synonymous with the oil and gas industry. Accidents are inevitable without proper maintenance for equipment installed in any asset. Therefore, maintenance is a critical activity required to operate oil and gas facilities. Existing methods consist of corrective and proactive maintenance, where operators are betting a bottom dollar on original equipment manufacturer (OEM) specialists to assess the functionality of the equipment. Nevertheless, beginning of 2020, the Coronavirus disease (COVID-19) pandemic has hindered most of the yearly turn around (TA) to maintain the assets due to travel restrictions and mandatory quarantine regulations. Therefore, this paper aims to develop a strategic plan during the COVID-19 pandemic to position augmented reality (AR) in the maintenance support of the oil and gas industry. This proposal used literature reviews related to AR and maintenance support as its research methodology. The strategies encourage remote support via suitable AR technology to reduce cost and shorter mean time to repair (MTTR).
VOICE ASSISTANT USING PYTHON

The primary goal of Artificial Intelligence is to make human interaction with computers and other electronic devices considerably easier and more practical. Personal assistant that can carry out activities for everyday needs with just a simple phrase is a rapidly increasing field nowadays. Many firms have leveraged dialogue systems technology to create Virtual Personal Assistants (VPAs) based on their applications and regions, such as Google Assistant, Siri by Apple, Bixby by Samsung, and many others. However, in this idea, we've taken a different method for lowering the personal assistant's error rate. It uses both text and audio data to figure out what the individual is saying.
The effectiveness of Women 4IR Cyber 3A #Aware, Avoid, Act Program in Malaysia

Developing a sustainable country needs to consider the cyber well-being of its people, especially in empowering women who are often victims of cybercrime. There are various cybercrime issues, such as cyber harassment, cyberbullying, cyberstalking, cyber violence, and cyberbullying online, which mostly involve women as the main victims. Malaysian women's culture that is lacking in awareness, and the existing cybercrime protection learning module is still less comprehensive and effective in recognizing, preventing, practicing a safe culture, and acting when faced with the threat of cybercrime, prompting the creation of a form of a training program that focuses on gender equality and women's empowerment in cyber security. Therefore, this study discusses the results of the cyber security training program named “Women IR4.0 Cyber 3A #Aware, Avoid, Act Program”. It aims to raise general awareness about cybercrime among Malaysian women as unwitting victims of crime containing four Modules: Threats and Cyber Crime, Best Practices in Handling Cyber Crime, Cyber Crime Prevention Methods, and Cyber Crime Free Reflection Activities. Module content is designed using the Analysis, Development, Implementation, and Evaluation (ADDIE) approach. Approximately 100 women attended this Cyber 3A workshop. The analysis carried out on those participants before and after the workshop found that the workshop's success can be seen by increasing the knowledge and awareness of the participants about cyber security.
Intelligent Hand Gesture Recognition System Empowered With CNN

Communication gap among deafened and dumb communal, general public sign language recognition is a significant milestone, so we come with sign language translator that convert given gestures into textual form (alphabets and digits). It makes speech recognition of textual form and enable the user to listen about the gestures they passed. In this study a dataset of 44 gestures that include alphabets and digits is used and proposed an intelligent hand gesture recognition system empowered with CNN. Proposed model is used for preprocessing of input image and then make use of threshold to eliminate noise from image and smoothen the photo. Region filling is applied to fill holes in the object of interest. The training of data collected is done through CNN keras model using TensorFlow as a backend. After training data is classified. Testing of data is done using keras model. After testing is accomplished gesture recognition took place as user pass the gesture and window display a textual form of given gesture as well as convert it into speech form.
Influencing Factors of E-Learning Towards E-Learner's Satisfaction

Tremendous revolution brings in the world economy due to innovative innovations in information technology. Every aspect of human life is modified pleasantly and effectively through such a revolution, along with introducing a shift in the traditional education system named E-learning. Because of the rapid advancements in technology, education and training are shifting away from traditional classroom learning and toward Information Communication Technology (ICT)-based learning, which necessitates self-organized, collaborative learning with flexible timings and a community of facilitators and learners and experts. This paper aims to study the factors responsible for satisfaction in E-learners. Variables of interest are Willingness, technology aspect and design aspect, and their influence on learner's satisfaction. This is explorative research considering all virtual campuses of universities around the globe. The acceptance of E-learning for higher education in small nations is a construct that is further explored in this study.
AGIC: Approximate Gradient Inversion Attack on Federated Learning

Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model.Depending on the aggregation method used, the local updates are either the gradients or the weights of local learning models, e.g., FedAvg aggregates model weights.Unfortunately, recent reconstruction attacks apply a gradient inversion optimization on the gradient update of a single minibatch to reconstruct the private data used by clients during training.As the state-of-the-art reconstruction attacks solely focus on single update, realistic adversarial scenarios are overlooked, such as observation across multiple updates and updates trained from multiple mini-batches.A few studies consider a more challenging adversarial scenario where only model updates based on multiple mini-batches are observable, and resort to computationally expensive simulation to untangle the underlying samples for each local step.In this paper, we propose AGIC, a novel Approximate Gradient Inversion Attack that efficiently and effectively reconstructs images from both model or gradient updates, and across multiple epochs.In a nutshell, AGIC (i) approximates gradient updates of used training samples from model updates to avoid costly simulation procedures, (ii) leverages gradient/model updates collected from multiple epochs, and (iii) assigns increasing weights to layers with respect to the neural network structure for reconstruction quality.We extensively evaluate AGIC on three datasets, namely CIFAR-10, CIFAR-100 and ImageNet.Our results show that AGIC increases the peak signal-to-noise ratio (PSNR) by up to 50% compared to two representative state-of-the-art gradient inversion attacks.Furthermore, AGIC is faster than the state-of-the-art simulationbased attack, e.g., it is 5x faster when attacking FedAvg with 8 local steps in between model updates.
DeFi: A risky business or silver bullet for SMEs?

Small and Medium Enterprises (SMEs) contribute significantly to the society and the economy however, most of their financing needs throughout their life cycle remains unmet by traditional financial channels. Fintech solutions help to fulfil this gap through advanced digital economic tools such as Decentralized Finance (DeFi) that are powered through the underlying blockchain technology. While this might seem to be a silver bullet for most financing problems faced by SMEs, there are issues pertaining to cyber security when SMEs adopt such technologies. SMEs are already seen as lucrative targets for cyber-attacks given their poor cyber hygiene practices, lack of investments coupled with huge skills gap in implementing cyber best practices for improving their resilience. Adoption of DeFi without completely understanding the underlying ecosystem might further exacerbate the cyber threats they face. This calls for further research and implementation of socio-technical solutions that will help SMEs understand better the DeFi ecosystem and implement effective solutions. In this paper, the unique challenges that SMEs face pertaining to security, risk and vulnerability management and potential mitigation strategies to improve their resilience are outlined in the UK context.
Genetic Algorithm and Machine Learning

Genetic algorithm is based on the natural search process, which mimics natural growth and employs approaches inspired by natural evolution to solve optimization problems, employing bequest, mutation, and miscellany, as well as intersect. Its actual meaning is a competent, concurrent, and universal search approach that continuously obtains and builds up knowledge about search space and command management search space in order to alter the best search result. The traditional multilevel association rules mining techniques generate a large number of candidate items and compare them to the whole database. Nonetheless, the majority of mining procedures are in vain, since they guide crucial costs associated with computing. The inherited algorithms provide a novel technique for tackling these sorts of problems.