Reviewerly/exHarmony huggingface dataset:
Managing Cold-start in the Serverless Cloud

Managing cold-start challenges in the serverless cloud environment is crucial for ensuring optimal performance and resource efficiency. This paper presents a comprehensive approach to address these challenges by integrating Temporal Convolutional Networks (TCNs) and Ensemble Policies, aiming to revolutionize the management of serverless cloud environments. The proposed framework leverages predictive models to anticipate infrastructure demands and function instance arrivals, enabling proactive resource provisioning and code optimization. A critical analysis, literature review, and methodological evaluation highlight the robustness and adaptability of the integrated approach. The ensemble policy’s parallel paths provide a versatile and scalable mechanism for addressing both infrastructure-level and function-level cold-start issues, resulting in improved resource allocation and minimized delays. This research significantly contributes to the advancement of cloud infrastructure management, offering valuable insights into optimizing serverless computing performance under varying workload conditions. Furthermore, the implementation analysis emphasizes the practical applicability of the proposed approach, demonstrating its potential to enhance overall system efficiency and responsiveness in dynamic and resource-constrained cloud environments.
Security metrics and tradeoff of cyber-physical systems subject to false data injection attacks

This paper focuses on the vulnerable and stealthy regions of the intricate cyber attackers pertaining to state-of-the-art cyber-physical systems from the perspective of control-theoretic metrics. Using the minimum-energy attack signals and the L 2-gain of output signals as vulnerability and stealthiness, respectively, these two predominant concerns of attackers and the corresponding vulnerable subspaces and stealthy subspaces are interpreted by the eigenvalues and eigenvectors of controllability Gramian matrix and observability Gramian matrix. It is also shown that the principal angles of these two subspaces indicate the tradeoff between the vulnerability and stealthiness of subspaces. A model of moving vehicles validates the effectiveness of theoretical results.
Toxicity and Sentiment Analysis About Digital Bounty on Social Media

The presence of social media has a positive impact but social media is also a means for negative things to happen, such as cyberbullying, data theft, and sexual crimes. Digital bounty or competition to find perpetrators digitally is one of the activities of cyberbullying and digital vigilantism (doxing). Digital bounties have negative impacts such as psychological and socio-economic impacts on perpetrators (bounty targets) and encourage reduced public trust in applicable legal institutions. The number of opinions on social media requires sentiment classification and toxicity measurements. To get the best results, a classification method is searched for the appropriate dataset by comparison, using k-fold validation (KNN, NBC, SVM, DT), in the classification process using the selected method. Give positive opinion results, (supporting digital bounty posts of 1540 and negative of 439, and in toxicity, analysis using Communalytic, most comments in the dataset scored 0 – 0.4.
Automated English Essay Scoring Based on Machine Learning Algorithms

Many English learners need to improve their English writing skills because of the lack of writing practice, which is a common phenomenon, especially in China. However, it is impractical for English teachers to grade and provide comprehensive feedback for essays due to the limited time. In this paper, an automated essay scoring method is proposed to solve this issue. The 3000 features are ordered by term frequency from the whole Kaggle dataset. Furthermore, 10 handcrafted features of evaluating an essay are selected based on experience, and then 2-degree polynomial form of the handcrafted features is prepared for comparison. After data preprocessing, Natural Language Processing (NLP) and other machine learning models are considered in this paper. By using the mean-square error and mean average percentage error as the criteria, the model comparison is conducted in the base 3000 features, 10 handcrafted features with base 3000 features, and 66 polynomial features with base 3000 features. Then, Linear Regression, Ridge Regression, Gradient Boosting Regression, Random Forest Regression or XGboost Regression with the best performance can be found. The results demonstrate that XGboost and Ridge Regression provide the top two performance among the algorithms used on this dataset. The polynomial feature importance can be obtained from the algorithm with the best performance. These polynomial features can be the guidance for students to improve their writing skills. In conclusion, the proposed methods can be effective for English essay scoring.
Towards Adaptive Environment Perception and Understanding for Autonomous Mobile Robots

Autonomous mobile robots (AMRs) are increasingly used in various applications like intralogistics, hospitality and agriculture. Though, their software and abilities are still mainly designed and implemented for specific environments and tasks, impeding an even more widespread deployment of AMRs. Their environment perception and understanding lacks automated adaption to specific operating environments and conditions.In this paper, we propose a software architecture that allows the automated adaption of AMRs’ environment perception and understanding to different operating environments. Further, we provide a concise review of state-of-the-art approaches and technologies needed. We present an exemplary implementation and evaluate the proposed architecture. On this basis, advantages and open research questions of the architecture are derived.
Contrast Stretching For Automatic Road Marking Detection at Night With YOLOv5

The ability to recognize and keep track of road signs is one of the most significant obligations that a visual driver assistance system must fulfill. The study of deep learning and recognition of road markings has made tremendous progress in recent years thanks to advances in technology. Road surface markings cover a wide range of features, including crossings, directional arrows, zebra crossings, and other signs. The surface of the road is immediately painted with these marks. In this study, to detect the road marking sign, we implement Contrast Stretching (CS), as well as YOLOv5s. Often referred to as "normalization," contrast stretching is a straightforward method for boosting an image's contrast by artificially expanding its intensity range. We build the dataset by ourselves and focus on Taiwan road marking sign dataset at night (TRMSDN). Our results show that when compared to YOLOv5s trained on the original image, YOLOv5s trained on the CS image performs better in both training and testing. Furthermore, YOLOv5s exhibits 87.25% mAP during training and 83.83% mAP in testing phase.
Design of Computer Intelligent Translation System Based on Natural Language Processing

With the rapid development of social economy and the increasing frequency of global trade, translation services are crucial to international trade. However, the traditional translation system supports a single language, which cannot meet the translation efficiency and accuracy required by the users. The realization of multilingual mutual translation has become the bottleneck of the development of international trade. The application of computer intelligent translation system in international trade has become a research hotspot. This paper studies the computer intelligent translation system, combines natural language processing, machine translation and neural network technology, and designs a computer intelligent translation system based on natural language processing. The architecture of the whole system includes functional modules, system structure and hardware structure. In this paper, the system is divided into functional modules and uses this model to identify and analyze the results of machine translation. The experimental results show that the computer intelligent translation system designed here has high accuracy without manual intervention. Compared with traditional machine translation methods, this system shows obvious advantages in both translation accuracy and time cost. By combining natural language processing, machine translation and neural network technology, the system can better meet the needs of multilingual mutual translation and improve the accuracy and efficiency of translation. It can effectively improve the accuracy of translation and reduce the time cost while ensuring the quality of translation.
On Runtime Reduction in 3D Extended Object Tracking by Measurement Downsampling

In 3D extended object tracking (EOT), well-established models exist for tracking the object extent using various shape priors. A single update, however, has to be performed for every measurement using these models leading to a high computational runtime for high-resolution sensors. In this paper, we address this problem by using various model-independent downsampling schemes based on distance heuristics and random sampling as pre-processing before the update. We investigate the methods in a simulated and real-world tracking scenario using two different measurement models with measurements gathered from a LiDAR sensor. We found that there is a huge potential for speeding up 3D EOT by dropping up to 95% of the measurements in our investigated scenarios when using random sampling. Since random sampling, however, can also result in a subset that does not represent the total set very well, leading to a poor tracking performance, there is still a high demand for further research.
Unknown Forgery Method Recognition: Beyond Classification

N owadays, deep learning-based generative models are capable of producing high-quality spoofed speech. This presents new challenges for audio forensics, such as detecting spoofed speech and understanding how it is created. Current work is either limited to true-or-false detection or classification, or simply groups all samples from forgery classes unseen during training to an unknown class. In this paper, beyond classifying all samples from unseen classes as unknown, and considering the correlations between different classes, a cluster analysis-based embedding extractor training method is proposed to learn representations of speech forgery methods, which has good generalization ability to unknown speech forgery classes. In this method, a merged speech forgery recognition dataset is firstly constructed with more categories and more speech samples based on manually created labels. Based on this dataset, an embedding extractor is trained by finetuning a pre-trained Transformer. Subsequently, clustering is conducted with the embedding representations to label each sample as a cluster index. The pre-trained transformer with the same network structure is again finetuned on the new dataset with the cluster indexes as labels. The finetuned transformer acts as the final embedding extractor. Finally, the extractor is used to obtain the embedding representations and predict the number of classes of unknown data based on clustering analysis. Extensive experiments are performed on publicly available datasets. From the experimental results, the following two conclusions can be drawn: (1) Embedding extractors trained on clustering-based merged datasets have a better ability on traceability generalization to unknown forgery methods compared to embedding extractors trained on label-based merged datasets. (2) As the number of forgery method classes in the training set increases, the trained embedding extractor has better traceability generalization to unknown forgery methods.
Efficient Adversarial Input Generation via Neural Net Patching

The generation of adversarial inputs has become a crucial issue in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. However, the problem poses multiple practical challenges, including scalability issues owing to large-sized networks, and the generation of adversarial inputs that lack important qualities such as naturalness and output-impartiality. This problem shares its end goal with the task of patching neural nets where small changes in some of the network’s weights need to be discovered so that upon applying these changes, the modified net produces the desirable output for a given set of inputs. We exploit this connection by proposing to obtain an adversarial input from a patch, with the underlying observation that the effect of changing the weights can also be brought about by changing the inputs instead. Thus, this paper presents a novel way to generate input perturbations that are adversarial for a given network by using an efficient network patching technique. We note that the proposed method is significantly more effective than the prior state-of-the-art techniques.
Optimal DNA Encoding with Secret Sharing Scheme for Medical Image Encryption

Currently, the demand for digital images from various intellectual sensors and devices has intensely augmented in smart healthcare. Medical applications like telediagnosis need information exchange on insecure networks. Cryptographic processes, particularly secret sharing (SS), with certain extraordinary capacities and qualities, will be considered for handling confidential data. Therefore, this study develops an Optimal DNA Encoding with Secret Sharing Scheme for Medical Image Encryption (ODNAE-S3MIE) approach. The presented ODNAE-S3MIE algorithm concentrations on the accomplishment of security in the medical image transmission process. The ODNAE-S3MIE approach follows a three stage procedure such as optimal key generation, share creation, and share encryption. Primarily, the ODNAE-S3MIE technique enables to generation of a group of N shares for the input medical image. Next, encryption of shares takes place using the DNA encoding scheme. Finally, oppositional jellyfish search optimization (OJSO) algorithm is employed for effectively choosing the encryption keys. A comprehensive experimental results analysis ensured the improved efficacy of the ODNAE-S3MIE system with maximum PSNR and minimum MSE values.
idMotif: An Interactive Motif Identification in Protein Sequences

This article presents a visual analytics framework, idMotif, to support domain experts in identifying motifs in protein sequences. A motif is a short sequence of amino acids usually associated with distinct functions of a protein, and identifying similar motifs in protein sequences helps to predict certain types of disease or infection. idMotif can be used to explore, analyze, and visualize such motifs in protein sequences. We introduce a deep-learning-based method for grouping protein sequences and allow users to discover motif candidates of protein groups based on local explanations of the decision of a deep-learning model. idMotif provides several interactive linked views for between and within protein cluster/group and sequence analysis. Through a case study and experts' feedback, we demonstrate how the framework helps domain experts analyze protein sequences and motif identification.
A Safeguard and Safety in E-Health Records: A Committed Clinch

Mobile healthcare and medical services have become quite popular thanks to an influx of medical gadgets and enhanced usability of remote health services. The patient’ accessible interest with their own care and their accessible moral outrage over it combine with these. As a result, a tone of medical data is produced. These data sets need to be transferred, archived, and accessed securely. Through the use of a very effective encryption system, we have suggested a practical method for maintaining the identity and safeguarding the privacy of clinical data in this study. We have also talked about an authorization framework that makes use of different levels of access. Various entities frequently have access to medical records with differing levels of authorization. We have also talked about an authorisation framework that makes use of different levels of access. Various entities frequently have access to medical records with differing levels of authorization. In this study, we managed the access control mechanism of patient data according to the AI scheme. The framework for access control is created using the ML access model. The main justification for utilising this architecture is the ML-based restriction on accessing the data through AI. Additionally, for the proper data access regulation, the encryption of medical data utilising a variety of authorisation approaches has become necessary. It could affect user confidence in the e-health paradigm and, as a result, improve usability on a large scale.
Computer Vision Based Face Mask Recognition in Religious Mass Gatherings and COVID-19 Infection

Religious mass gathering at religious places presents a great difficulty in crowd management, providing the necessities of basic amenities, and addressing their healthcare requirements. Certain ritual practices may increase the risk of transferring respiratory pathogens. The COVID19 pandemic was an unexpected healthcare crisis with impacts reflected in every sphere of human lives. To limit the spread of the virus, the World Health Organization (WHO) declared wearing face masks is imperative manner. But manual assessment, whether an individual wears face masks or not in a public place, will be a difficult task. The need to monitor people wearing face masks is required to construct an automatic method. This study introduces a Computer Vision based Face Mask Recognition in Religious Mass Gatherings and COVID-19 Infection, named the CVFMR-RMG technique. The presented CVFMR-RMG technique aims to recognize the faces with and without masks via CV and image processing techniques. Initially, the input images are pre-processed via the adaptive Weiner filtering (WF) model. In the presented CVFMR-RMG technique, the face detection process takes place using Faster Region Convolutional Neural Network (Faster RCNN) approach. For facemask detection and classification, the sparse stacked auto encoder (SSAE) technique was employed. To improve the facemask detection performance of the SSAE model, a modified artificial fish swarm algorithm (MAFSA) is used for hyperparameter tuning. The performance assessment of the CVFMR-RMG system can be tested using the facemask database. The simulation outcomes illustrate the improved performance of the CVFMR-RMG system over other models.
Enhancing Facial Emotion Recognition through Deep Learning and Image Pre-processing

Humans frequently communicate effectively using their faces in addition to their words. The use of computer vision techniques to categorize face emotion has been extensively studied. Given the difficulties and restrictions of databases, such as the use of static data or facial capture in artificial contexts, these have had different degrees of success. As a result, we think that novel preprocessing methods are necessary to increase the precision of facial expression detection models. In this study, we suggest a novel, yet straightforward, approach to improving the precision of face emotion identification. Our research was done using the FER2013 dataset, which includes static facial photos of 7 basic emotions. Resizing and Normalization image preprocessing techniques were applied on the dataset prior to the splitting into train and test sets. The training images were split into train and test sets in the ratios of 75% and 25% respectively. The facial expressions in the photos were classified using Convolution Neural Networks [CNNs], with an accuracy of 76.75% on the test set.
Digital Mystery: Enhanced Active Citizens’ Participation Through Information Communications Technology in Community Policing

The ubiquitous proliferation of mobile phones has made digital surveillance and monitoring both accessible and desirable. Citizens have actively engaged in managing their neighborhood security. The study aims to analyze participatory policing practices that citizens do in their neighbourhood security management and apply Information Communication Technology. To achieve the research objective, the study used mixed methods exploratory sequential design. Qualitative purposive sampling targeted four focus group discussions and one key informant guided by an interview guide. The household survey used randomly sampled 432 respondents using a structured questionnaire. Data collected was analyzed using descriptive statistics in SPSS version 20. Qualitative data analysis follows transcribing, coding, and grouping into sub-themes, themes answering to research objectives. Results show citizens engage neighborhood patrols and they feel responsible and are satisfied with level of participation. Almost every person counts and this includes every gender in securing neighborhoods. Citizens use SMS seconded by WhatsApp and least Telephone calls for participatory activities, have revolutionized communication for (i) reporting and requesting transport on security information to police and CPF members, (ii) for organizing patrols and (iii) mobilizing help during participatory activities in the neighborhoods. ICTs have greatly enhanced citizens' participation in managing security within their neighbourhoods because they accorded real-time updates, real-time responses, and real-time capacity to access prompt assistance through virtual context. Virtual platform accorded wide formal and non-formal communication with authorities and increased social cohesion, collective efficacy and community guardianship improving prevention of crime and the arresting of suspects.
I am What I Have and I Have What I am, What am I?

The term homeomerosity refers to when a whole and its parts are the same kind of thing. For instance, a computer and its processor can both be classified as machines. Homeomerosity is a prerequisite for meaningful addition and subtraction. For example, adding the area sizes of two independent regions gives another area size, but adding an area size and a number of hours yields a number with a peculiar unit. In earlier work, homeomerosity has been formalized with respect to mereological parthood, but not in concurrence with a notion of class subsumption. Both are essential to homeomerosity, as a part can only be observed to be of the same kind as the whole if they are observed to be of some kinds in the first place. In this work, we use formal concept analysis to organize conceptual representations of parts and wholes in a shared contextual model. In our doing so, we show wholes and parts can be represented by sub-concepts of a concept with respect to which they are homeomerous.
A Quest for Identity Criteria in Computational Ontologies

The notion of identity criteria marked the dawn of contemporary formal ontology. Despite a number of issues this notion has raised, the quest for them still seems to be worthwhile, in particular in the case of a formal ontology built in the context of information systems. In the current paper I investigate the benefits and costs of using automatic theorem provers in the task of identifying such criteria for formal ontologies that are expressed in a prover-processable language. To this end two detailed case studies were performed – each concerned an upper-level ontology presented in a recent volume of the Applied Ontology journal. The identity criteria found by the process described in this paper turned out to be not particularly illuminating. The respective theorems that define them are rather direct consequences of the axioms, so proofs and models provided by the prover do not provide any new insights into the actual conceptual contents of the formal ontologies.