Reviewerly/exHarmony huggingface dataset:
BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis

As neurodegenerative disease pathological hallmarks have been reported in both grey matter (GM) and white matter (WM) with different density distributions, automating the segmentation process of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing a scalable analysis on pathology images. This paper proposes an automated segmentation pipeline (BrainSec) combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues. The final output generates XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, BrainSec, to classify the GM/WM/background regions. We demonstrate BrainSec is robust and has reliable performance by testing it on over 180 WSIs that incorporate numerous unique cases as well as distinct neuroanatomic brain regions. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights. In addition, we have integrated BrainSec with an existing Amyloid-β pathology classification model into a unified framework (without incurring significant computation complexity) to identify pathologies, visualize their distributions, and quantify each type of pathologies in segmented GM/WM regions, respectively.
Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)

The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet.Cloud security represents an important research area.Confidentiality, integrity, and availability are the main cloud security characteristics addressed.Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability.Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system's state.Reactive cloud fault tolerance is activated for system process failures after failure effectively happens.Reactive cloud fault tolerance handles failure after the fault has occurred.Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approaches in a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques.This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types, frameworks, and future directions.
An Implementation of Hybrid CNN-LSTM Model for Human Activity Recognition

Human Activity Recognition (HAR) is the process of predicting the activity of a person based on a trace of their movement using sensors. Wearable smart devices such as body-worn sensors and smart phones are currently used to detect human activity recognition. By wearing these devices, it is easy to collect data and perform further analysis to obtain necessary information on human activities. The main goal of this project is to recognize patterns from the raw data and extract useful information about the daily activities of the human. Existing strategies, like deep learning, made progress for explicit recognition, yet the recognition of the transition from one activity to the other is poor. In this project, the data corresponding to human activity recognition is presented to convolutional neural network (CNN) model and transitions between activities is presented to the long short-term memory (LSTM) model. The combined model of CNN and LSTM is implemented to improve the HAR accuracy. The HAPT and HAR datasets are used to train the model. The performance analysis of the model is evaluated by tuning the model hyperparameters, such as number of neurons, batch size, learning rate, batch normalization, and dropout. Further the evaluation metrics namely, precision, F1-score and recall are computed. It is observed from the results that better HAR accuracy is obtained with optimized hyperparameter values.
Multiple Forgery Detection in Video Using Convolution Neural Network

With the growth of digital media data manipulation in today’s era due to the availability of readily handy tampering software, the authenticity of records is at high risk, especially in video. There is a dire need to detect such problem and do the necessary actions. In this work, we propose an approach to detect the interframe video forgery utilizing the deep features obtained from the parallel deep neural network model and thorough analytical computations. The proposed approach only uses the deep features extracted from the CNN model and then applies the conventional mathematical approach to these features to find the forgery in the video. This work calculates the correlation coefficient from the deep features of the adjacent frames rather than calculating directly from the frames. We divide the procedure of forgery detection into two phases–video forgery detection and video forgery classification. In video forgery detection, this approach detect input video is original or tampered. If the video is not original, then the video is checked in the next phase, which is video forgery classification. In the video forgery classification, method review the forged video for insertion forgery, deletion forgery, and also again check for originality. The proposed work is generalized and it is tested on two different datasets. The experimental results of our proposed model show that our approach can detect the forgery with the accuracy of 91% on VIFFD dataset, 90% in TDTV dataset and classify the type of forgery–insertion and deletion with the accuracy of 82% on VIFFD dataset, 86% on TDTV dataset. This work can helps in the analysis of original and tempered video in various domain.
Prerequisite Relations among Knowledge Units: A Case Study of Computer Science Domain

The importance of prerequisites for education has recently become a promising research direction. This work proposes a statistical model for measuring dependencies in learning resources between knowledge units. Instructors are expected to present knowledge units in a semantically well-organized manner to facilitate students’ understanding of the material. The proposed model reveals how inner concepts of a knowledge unit are dependent on each other and on concepts not in the knowledge unit. To help understand the complexity of the inner concepts themselves, WordNet is included as an external knowledge base in this model. The goal is to develop a model that will enable instructors to evaluate whether or not a learning regime has hidden relationships which might hinder students’ ability to understand the material. The evaluation, employing three textbooks, shows that the proposed model succeeds in discovering hidden relationships among knowledge units in learning resources and in exposing the knowledge gaps in some knowledge units.
Certificateless Authentication and Consensus for the Blockchain-Based Smart Grid

Authentication and key agreement scheme facilitates secure two-way communication in the smart grid (SG) and enables each entity to verify whether the received message comes from a legitimate sender. Also, it assists in establishing the shared key that will be used to secure subsequent communication. Several schemes of authentication and key agreement have been proposed over the last few years, but many of them are not efficient in terms of communication overhead and computational overhead. In addition to that, they do not adequately adhere to the fundamental security of the smart grid authentication, such as forward secrecy. In this paper, we suggest a certificateless authentication and consensus for a blockchain-based smart grid. The proposed protocol has a lightweight communication overhead and puts less computation cost on the smart meter. Furthermore, it satisfies the fundamental security requirements of the smart grid, plus consensus in the blockchain. Besides, the security of the proposed protocol is rooted in the intractability assumption of the elliptic curve discrete logarithm (ECDL) problem, and it is proved using the random oracle model (ROM).
Exploring Opportunities and Challenges of Using WhatsApp in Teaching Reading: A Malaysian Rural Primary School Context

Through the lens of COVID-19, the loss of learning in Malaysia was apparent as the rest of the countries in the world.In an effort to mitigate the loss, Malaysia announced the Movement Control Order (MCO) which helped to facilitate the home-based learning (HBL) system.As the schooling system migrates to online environments, teachers face significant barriers such as inavailability or lagging internet connectivity especially in the rural areas.Thus, asynchronous learning via WhatsApp was done throughout the MCO period.Cognizant to the foregoing issue, the aim of this study is to identify the technical and pedagogical opportunities as well as challenges of using WhatsApp features in teaching reading.Adopting survey research, this study examined 123 Malaysian rural primary English as a Second Language (ESL) teachers during the COVID-19 school closure.Descriptive analysis revealed that What-sApp is advantageous as teachers are already using it and it can be used anytime and anywhere.It was also revealed that the lack of handphones and inactive students provide novel challenges in teaching and learning of reading via WhatsApp.This study contributes toward understanding the challenges teachers face in asynchronous ESL teaching and learning, besides discussing the implementation of WhatsApp as a means for instructional reading skills during the pandemic.The use of WhatsApp before and during the pandemic in ESL teaching can be explored as well.As asynchronous learning is implemented widely in areas with limited access to the internet during MCO, this study is pertinent to understand teaching reading via WhatsApp is possible and enables students to learn reading successfully.*A survey research paper to explore the technical and pedagogical opportunities as well as challenges of teaching reading via WhatsApp throughout the implementation of Home-Based Learning (HBL) during the COVID-19 pandemic: A Malaysian primary school context.
Hy-Fi: Hybrid Five-Dimensional Parallel DNN Training on High-Performance GPU Clusters

Recent advances in High Performance Computing (HPC) enable Deep Learning (DL) models to achieve state-of-the-art performance by exploiting multiple processors. Data parallelism is a strategy that replicates the DL model on each processor, which is impossible for models like AmoebaNet on NVIDIA GPUs. Layer parallelism avoids this limitation by placing one or more layers on each GPU, but still cannot train models like AmoebaNet on high-resolution images. We propose Hy-Fi: Hybrid Five-Dimensional Parallelism; a system that takes advantage of five parallelism dimensions—data, model, spatial, pipeline, and bi-directional parallelism—which enables efficient distributed training of out-of-core models and layers. Hy-Fi also proposes communication-level optimizations to integrate these dimensions. We report up to $$2.67{\times }$$ and $$1.68{\times }$$ speedups over layer and pipeline parallelism, respectively. We demonstrate Hy-Fi on up to 2, 048 GPUs on AmoebaNet and ResNet models. Further, we use Hy-Fi to enable DNN training on high-resolution images, including 8,192 $$\times $$ 8,192 and 16,384 $$\times $$ 16,384.
Investigating Bidimensional Downsampling in Vision Transformer Models

Vision Transformers (ViT) and other Transformer-based architectures for image classification have achieved promising performances in the last two years. However, ViT-based models require large datasets, memory, and computational power to obtain state-of-the-art results compared to more traditional architectures. The generic ViT model, indeed, maintains a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. With the goal of increasing the efficiency of Transformer-based models, we explore the application of a 2D max-pooling operator on the outputs of Transformer encoders. We conduct extensive experiments on the CIFAR-100 dataset and the large ImageNet dataset and consider both accuracy and efficiency metrics, with the final goal of reducing the token sequence length without affecting the classification performance. Experimental results show that bidimensional downsampling can outperform previous classification approaches while requiring relatively limited computation resources.
The Economics of Sharing Unclassified Cyber Threat Intelligence by Government Agencies and Departments

This paper extends the literature on the economics of sharing cybersecurity information by and among profit-seeking firms by modeling the case where a government agency or department publicly shares unclassified cyber threat information with all organizations. In prior cybersecurity information sharing models a common element was reciprocity—i.e., firms receiving shared information are also asked to share their private cybersecurity information with all other firms (via an information sharing arrangement). In contrast, sharing of unclassified cyber threat intelligence (CTI) by a government agency or department is not based on reciprocal sharing by the recipient organizations. After considering the government's cost of preparing and disseminating CTI, as well as the benefits to the recipients of the CTI, we provide sufficient conditions for sharing of CTI to result in an increase in social welfare. Under a broad set of general conditions, sharing of CTI will increase social welfare gross of the costs to the government agency or department sharing the information. Thus, if the entity can keep the sharing costs low, sharing cybersecurity information will result in an increase in net social welfare.
Evaluating Mixture-of-Experts Architectures for Network Aggregation

Abstract The mixture-of-experts (MoE) architecture is an approach to aggregate several expert components via an additional gating module, which learns to predict the most suitable distribution of the expert’s outputs for each input. An MoE thus not only relies on redundancy for increased robustness—we also demonstrate how this architecture can provide additional interpretability, while retaining performance similar to a standalone network. As an example, we train expert networks to perform semantic segmentation of the traffic scenes and combine them into an MoE with an additional gating network. Our experiments with two different expert model architectures ( and ) reveal that the MoE is able to reach, and for certain data subsets even surpass, the baseline performance and also outperforms a simple aggregation via ensembling. A further advantage of an MoE is the increased interpretability—a comparison of pixel-wise predictions of the whole MoE model and the participating experts’ help to identify regions of high uncertainty in an input.
Algorithm/Accelerator Co-Design and Co-Search for Edge AI

The world has seen the great success of deep neural networks (DNNs) in a massive number of artificial intelligence (AI) applications. However, developing high-quality AI services to satisfy diverse real-life edge scenarios still encounters many difficulties. As DNNs become more compute- and memory-intensive, it is challenging for edge devices to accommodate them with limited computation/memory resources, tight power budgets, and small form-factors. Challenges also come from the demanding requirements of edge AI, requesting real-time responses, high-throughput performance, and reliable inference accuracy. To address these challenges, we propose a series of efficient design methods to perform algorithm/accelerator co-design and co-search for optimized edge AI solutions. We demonstrate our proposed methods on popular edge AI applications (object detection and image classification) and achieve significant improvements than prior designs.
The Requirement Cube

Requirements engineering activities are carried out to come up with right and suitable quality requirements. However, problems in requirements engineering remain even though there is a vast amount of requirement elicitation and writing methods. Methods are either too specific for industry application or focus on a specific part of requirements engineering. This paper proposes a new requirement writing template, the 5W1H requirement cube. The template consists of the answers to six questions (Why, Who, When, Where, What, and How) and links business, user, and functional requirements in a hierarchy within the enterprise business architecture. The template was developed in a prominent Islamic Bank in Turkey and tested on a case study. Authors have rewritten three software requirement specification (SRS) documents with new approach and compared them, showing that new approach has more brief and well-organized documents. BOA Cube software has been developed to implement the 5W1H requirement cube approach within the organization.
Industrial Anomaly Detection with Skip Autoencoder and Deep Feature Extractor

Over recent years, with the advances in image recognition technology for in-depth learning, researchers have devoted continued efforts toward importing anomaly detection technology into the production line of automatic optical detection. Although the features of unsupervised learning help overcome the high costs associated with labeling, further improvements in the anomaly detection ability remain possible. Accordingly, this paper proposes a novel deep learning model for anomaly detection to overcome this bottleneck. Leveraging a powerful pre-trained feature extractor and the skip connection, the proposed method achieves better feature extraction and image restoration capabilities. Additionally, the effects of different feature extractors and training with limited samples are discussed herein. Results reveal that the areas under the curve (AUC) for the proposed method are higher than those of previous anomaly detection models for 16 out of 17 categories. Further, even with limited training samples or the replacement of lighter feature extractors, the proposed method features good capability in terms of detecting anomalies. This indicates that the proposed method can realize the most appropriate adjustments to the needs of production lines, in order to maximize economic benefits.
Live Twitter Sentiment Analysis Using Streamlit Framework

Sentiment analysis as per the textual description suggests that, it provides the sentiments or emotions on any form of given data in real life. As the social media information, now-a-days is flooded with various kinds of data from Facebook, Instagram, Twitter, and Whatsapp and so on. It is the need to handle data intelligently and classify as malicious and genuine data. The resultant growth in the area of social media entices tremendous challenges to the researchers in the field of social media analytics and deep learning. This paper contributes a user-friendly web application on sentiment analysis of live twitter data’ using the keyword or handle, built on TextBlob library available in Python and Streamlit framework. The input data on the web application is firstly preprocessed with data cleaning, feature extraction and unstructured dataset view is filtered. Pre-processed data is further analyzed to collect the sentiments from a given twitter posts and predict into three classes viz positive, negative and neutral. The results presented in the work highlights the analysis of sentiments extracted from live tweets using keyword 'Russian Ukraine World War' and classify the opinions as true or false tweets with positive, negative or neutral opinion by the use of Cat Boost Classifier.
A Modular Runtime Enforcement Model Using Multi-traces

Runtime enforcement seeks to provide a valid replacement to any misbehaving sequence of events of a running system so that the correct sequence complies with a user-defined security policy. However, depending on the capabilities of the enforcement mechanism, multiple possible replacement sequences may be available, and the current literature is silent on the question of how to choose the optimal one. In this paper, we propose a new model of enforcement monitors, that allows the comparison between multiple alternative corrective enforcement actions and the selection of the optimal one, with respect to an objective user-defined gradation, separate from the security policy. These concepts are implemented using the event stream processor BeepBeep and a use case is presented. Experimental evaluation shows that our proposed framework can dynamically select enforcement actions at runtime, without the need to manually define an enforcement monitor.
Continuous Safety Control of Mobile Robots in Cluttered Environments

This letter studies the safety control problem for mobile robots working in cluttered environments. A compact set is employed to represent the obstacles, and a direction-distance function is used to describe the obstacle-measurement model. The major contribution is a nontrivial modification of the quadratic programming (QP) approach for continuous safety control of integrator-modeled mobile robots. In particular, a refinement of the Moreau-Yosida method is proposed to regularize the measurement model while retaining feasibility and safety. The second contribution is the development of a new feasible set shaping technique with a positive basis for a QP-based continuous safety controller. Physical experiments are employed to verify the proposed method.
Requirements and Secure Serialization for Selective Disclosure Verifiable Credentials

The emergence of the Verifiable Credentials recommendation from W3C allows the adoption of credential systems in a much wider range of user-centric applications and use cases. With this shift to user-centric credential systems, Selective Disclosure has been proposed and used to cryptographically secure user privacy. Although much work has been undertaken in creating selective disclosure supporting cryptographic protocols, those schemas are not directly applicable for credentials. Implementations rely on canonicalization algorithms to transform a credential to the necessary data format, which will be used by the cryptographic layer. Those algorithms are often used without the necessary cryptographic and security considerations, leading to insecure implementations. In this work we define three necessary security properties for the canonicalization algorithms. We also propose a mathematical model for JSON credentials, which we use to prove the security of a proposed canonicalization algorithm.