Reviewerly/exHarmony huggingface dataset:
Multiple criteria ABC classification: an accelerated hybrid ELECTRE-PSO method

ABC classification analysis categorises inventory items into predefined classes namely A, B and C. The limitation of the ABC system is that only one criterion is considered, however, as emphasised in the literature, the inventory classification is multi-criteria problem. So, this paper proposed a multiple criteria ABC inventory classification (MCIC) method integrating ELECTRE TRI with particle swarm optimisation (PSO) algorithm. Since, the application of ELECTRE TRI method requires to determine the preferences of decision makers (DMs) as parameter values, the solution process is very complex and time-consuming especially in large-scale problems. Tackling these difficulties, all ELECTRE TRI parameters are inferred from training data through a procedure using hybrid PSO algorithm, for accelerating the PSO, the variable position (VP) model is also proposed as an exploitation and variable exploration. Finally, the model applied to six inventory datasets and the results revealed high applicability of the proposed model to inventory classification problems.
A Text-Independent Forced Alignment Method for Automatic Phoneme Segmentation

Phoneme segmentation is important for many healthcare applications, such as the diagnosis and monitoring of children with speech sound disorders (SSDs). This is usually addressed by performing forced alignment (FA), which essentially annotates an audio file to provide information on what has been uttered and where. While many FA tools exist, very few can work automatically without the assistance of a transcription. This work aims at providing a novel text-independent FA tool by using two models, namely wav2vec 2.0 and an unsupervised segmentor known as UnsupSeg. To provide labels to the segments, the class regions that are obtained by nearest-neighbour classification with wav2vec 2.0 labels pre-CTC collapse as the reference points. Maximal overlap between the class regions and the segments determines class label. Additional post-processing steps, such as over-fitting cleaning and application of voice activity detection, are also performed to further improve the segmentation performance. All the models used to create the tool are self-supervised, and thus can leverage great amounts of unlabelled data to reduce the need for labelled data. When evaluated on the TIMIT dataset, our implementation achieved a harmonic mean score of 76.88%, competitive against other alternatives.
Entrepreneurial Mindfulness Website: Entrepreneur Profile Survey through Artificial Intelligence

One of the breakthrough solutions in the development of the latest website is the use of artificial intelligence (AI) technology. Therefore, AI is used in the development of entrepreneurial mindfulness websites. Entrepreneurial mindfulness website is a website that was developed by utilizing the development of AI to conduct a survey of entrepreneurs in order to obtain a profiling of entrepreneurs based on their entrepreneurial mindfulness capacity. This capacity can be seen from the conditions related to entrepreneurial engagement capacity, entrepreneurial attitude capacity, entrepreneurial strategic capacity, entrepreneurial mindset, and their cognitive and affective mindfulness. The AI-based website was developed using the Neuroresearch research method and the waterfall method. The Neuroresearch method is carried out by combining qualitative and quantitative research so that it is included in the mixed method. The waterfall method is applied to website development whose process starts from the stages of needs analysis, design development, website development, testing and maintenance. The result of the research is the Artificial Neural Network (ANN) process as part of AI development which produces the ANN structure to explain the entire survey process to the results obtained from the AI-based entrepreneurial mindfulness website.
Digital transformation of education with the support of the national project IT Academy

One of the IT Academy national project aims was to support the digital transformation of schools. Digital transformation is an inevitable condition for innovation of the educational system, as well as the needs of the digital society. The paper presents training and support of the newly established role - the school digital coordinator. The training and subsequent support contents were developed, and conducted, to sustain the role of digital coordinators at their schools.
Discontinued virtual reality systems: the future becoming the past

Virtual Reality is commonly thought of as new technology. However, technology by its very nature is always changing. When technology advances, many systems can be left behind, this includes virtual reality. This paper focuses on discontinued virtual reality systems and how the technology has changed in recent years.
Impact Analysis of Different Effective Loss Functions by Using Deep Convolutional Neural Network for Face Recognition

Smart Library Automation System is increasingly attractive as an effective digital library system. Adapting automation in the library helps reduce the duplication of work, time-saving, and boosts work efficiency. One such significant feature is face recognition integrated. With this application, the system can use face recognition to enter and get the details of an end user. In recent years, face recognition has achieved many prodigious accomplishments based on Deep Convolutional Neural Networks (DCNN). In addition to constructing large face datasets, designing new effective DCNN architectures and loss functions are two trends to improve the performance of face recognition systems. Therefore, many studies have been published in state-of-the-art methods, making high-accuracy face recognition systems more possible than days in the past. However, it is still difficult for all research communities to train robust face recognition models because it depends heavily on their resources. This paper investigates and analyzes the effect of several effective loss functions based on softmax. Moreover, we also evaluate how hyper-parameter settings can impact the optimization process as well as the final recognition performance of the model trained by re-implementing these methods. The results of our experiments achieve state-of-the-art figures, which show the proposed method’s massive potential in improving face recognition performance.
Event Management System for Webinars and Survey

Webinars and surveys have been widely used. Popular applications such as ezTalks, Zoom, Demio, Google Meet, LiveStorm have been used in seminars and online meetings. For every webinar activity, the organizers often carry out survey activities to see feedback from participants, google forms applications, survey monkeys, typeforms, zoho surveys, and gizmo surveys are used to see responses from all participants. In addition, several organizers also gave appreciation in the form of certificates. However, the management of many webinars is often an obstacle to seeing the results of each activity and the participants who take part in the activity. Although the use of google forms has been very good and effective, the lack of features in the management of webinar activities is a problem in itself in making reports in a short time. Therefore, we need a service application that can assist in the management of webinar activities and has survey features from registration, attendance to certificates. Where later application users (organizations, committees) can fill out webinar activities and can propose surveys for each activity carried out. In software development, Agile Development Methods (ASD) are used, Event Management System for Webinars and Survey applications that the researchers built using CodeIgniter and React-Js. In the test, we plan the data analysis to be divided into three parts, namely Demographic Analysis Results, Reliability Test Results Analysis, Validity, and Data Processing Analysis. Analysis Data processing includes Importance-Performance Analysis (IPA). So far, webinar and survey management applications have completely fulfilled needs such as making a number of questions in the form of multimedia and sharing research data, but webinar management applications with survey services in an application have not been carried out. It is hoped that the development of research on webinar and survey management applications will produce more visualitative applications and be able to manage webinar activities and reporting related to surveys, reporting participants for each activity carried out. Based on the results of the Importance Performance Analysis, it is known that the average value of the level of expectation or importance for usability is 3.97, and the average value for the level of performance is 3.68. the average value of the level of expectation or importance for information quality is 4.07, and the average value for the level of performance is 3.70. the average value of the level of expectation or importance for service interaction is 3.94, and the average value for the level of performance is 3.71.
Improving Handwriting Recognition for Historical Documents Using Synthetic Text Lines

Automatic handwriting recognition for historical documents is a key element for making our cultural heritage available to researchers and the general public. However, current approaches based on machine learning require a considerable amount of annotated learning samples to read ancient scripts and languages. Producing such ground truth is a laborious and time-consuming task that often requires human experts. In this paper, to cope with a limited amount of learning samples, we explore the impact of using synthetic text line images to support the training of handwriting recognition systems. For generating text lines, we consider lineGen, a recent GAN-based approach, and for handwriting recognition, we consider HTR-Flor, a state-of-the-art recognition system. Different meta-learning strategies are explored that schedule the addition of synthetic text line images to the existing real samples. In an experimental evaluation on the well-known Bentham dataset as well as the newly introduced Bullinger dataset, we demonstrate a significant improvement of the recognition performance when combining real and synthetic samples.
Similarity-based Personalized Risk Calculation

In patient monitoring systems, personalized risk calculation is of vital importance. Instead of the generalized medical recommendations defined in literature, patient-specific limits should be applied, taking into account the specific medical history of the given user. To follow up and analyse the patient's condition, the measured values have to be compared to the specified recommendations. During this comparison it is insufficient to examine the instantaneous value, it is necessary to take into account the measurement statistics for longer periods. In this paper statistics-based fuzzy sets are evaluated using similarity measures. The basic idea of the proposed method is to create a fuzzy set representing the current state of the patient based on the histogram of the measured values. The authors propose a Simplified Centre of Gravity method-based similarity measure to compare the current state to the personal medical recommendations. The aim is to assess patient state progress, based on which the improvement or deterioration of the overall health condition can be monitored.
A Unified Analysis of Masked Face Recognition &amp; Social Distancing Detection

Facial recognition is an important application in today’s world but given the importance of wearing face masks due to the COVID-19 pandemic, it is essential that masked face recognition performs well. It is also equally important to ensure that protective measures such as wearing face masks and social distancing are being followed. Hence, this paper introduces an approach unifying masked face recognition, mask detection, and social distancing detection into a single system. The proposed method uses RetinaFace for face detection, VGG-19 for image classification, as well as VGGFace and SVC for facial recognition. As part of experimental analysis, CNN models such as ResNet50 and Inceptionv3 were used to evaluate mask detection while FaceNet and ArcFace were used to evaluate masked face recognition. The entire system was evaluated using accuracy, precision, recall, and F1-score. The obtained results indicate that the proposed system performs efficiently.
A Framework for Crime Detection and Diminution in Digital Forensics (CD3F)

Cyber-attacks have become one of the world's most serious issues.Every day, they wreak serious financial harm to governments and people.As cyber-attacks become more common, so does cyber-crime.Identifying cyber-crime perpetrators and understanding attack tactics are critical in the battle against crime and criminals.Cyber-attack detection and prevention are difficult undertakings.Researchers have lately developed security models and made forecasts using artificial intelligence technologies to solve these concerns.In the literature, the authors explained numerous ways of predicting crime.They, on the other hand, have a problem forecasting cyber-crime and cyber-attack strategies.Here, in this paper author proposed a digital forensic investigation procedure that deals with cybercrime.In this investigation, the process author explains digital forensics techniques for ensuring that digital evidence is located, collected, preserved, evaluated, and reported in such a way that the evidence's integrity is preserved.These sequential digital forensic stages affect a standard and accepted digital forensic investigation procedure, and each phase is influenced by sequential occurrences, with each event relying on tasks.Digital forensics investigation is a technique for ensuring that digital evidence is handled in such a way that the evidence's integrity is preserved.Sequential digital forensic stages affect a standard and accepted digital forensic investigation procedure, and each phase is influenced by sequential occurrences, with each event relying on tasks.
Efficient and Fine-Grained Sharing of Signed Healthcare Data in Smart Healthcare

Smart healthcare, as an examplar domain, is empowered by the remarkable miniaturization of sensors and the proliferation of smart devices, which lead to the production of massive amounts of healthcare data. Smart healthcare in the future is expected as a health service system that uses wearable devices, IoT, and mobile internet to dynamically collect and deliver healthcare data and then intelligently provide healthcare services. However, security is increasingly being challenged due to the highly valuable data transformed in such a healthcare network that might be tampered with by malicious users or disclosed without considering the privacy of patients. These pose new challenges for improving healthcare services’ safety, quality, and efficiency. In this paper, we propose a redactable signature scheme (RSS) with a short signature and fine-grained sharing control for authenticating the integrity and authenticity of healthcare data while preserving the privacy of patients in the smart healthcare system. The security analysis of our construction shows that it achieves unforgeability and unlinkability. Finally, the efficiency comparison with other related solutions indicates that our scheme could solve the security issues for healthcare data sharing in the smart healthcare system with less computational and communication overhead.
Neural Electromagnetic Transients Program

This paper devises a neural electromagnetic transients program (NeuEMTP), an unsupervised, physics-informed learning approach to numerical-integration-free EMTP solutions. The main contributions lie in: (1) a learning-based NeuEMTP architecture to simultaneously generate the electromagnetic states at all desired time steps, making the step-by-step integration unnecessary; (2) an unsupervised, physics-informed training procedure to realize the NeuEMTP functionality without requiring any EMTP trajectories beforehand; (3) an EMTP-oriented-neural-network (EMTPNet) accompanied with a novel activation function Act_mix to enable efficient extrapolations of diverse oscillation modes under arbitrary frequencies. Case studies sys-tematically verify that NeuEMTP generates high-fidelity EMTP trajectories without involving any numerical integration before or during the training process, and is promising to achieve faster-than-real-time EMTP simulations on the off-the-shelf computers.
CurT: End-to-End Text Line Detection in Historical Documents with Transformers

We present the curve transformer (CurT), a novel method of direct baseline detection that models document text line detection as set prediction of cubic Bézier curves, simplifying the layout analysis pipeline by removing the need for the laboriously hand-crafted postprocessing algorithms that are necessary with the current state of the art. CurT combines multiple appealing features: direct prediction enabling processing of material that is ill-suited for the prevailing methods adapting semantic segmentation backbones, a conceptually simple Transformer-based encoder-decoder architecture that can be extended to additional tasks beyond baseline detection, and increased computational efficiency in comparison to older approaches. In addition, we demonstrate that CurT achieves metrics that are competitive with methods based on semantic segmentation. Training and inference code is available under Apache 2.0 license at https://github.com/mittagessen/curt .
Parallelizable Delegation from LWE

We present the first non-interactive delegation scheme for $$\textsf{P} $$ with time-tight parallel prover efficiency based on standard hardness assumptions. More precisely, in a time-tight delegation scheme—which we refer to as a SPARG (succinct parallelizable argument)—the prover’s parallel running time is $$t + \textrm{polylog} (t)$$ , while using only $$\textrm{polylog} (t)$$ processors and where t is the length of the computation. (In other words, the proof is computed essentially in parallel with the computation, with only some minimal additive overhead in terms of time). Our main results show the existence of a publicly-verifiable, non-interactive, SPARG for $$\textsf{P} $$ assuming polynomial hardness of LWE. Our SPARG construction relies on the elegant recent delegation construction of Choudhuri, Jain, and Jin (FOCS’21) and combines it with techniques from Ephraim et al. (EuroCrypt’20). We next demonstrate how to make our SPARG time-independent—where the prover and verifier do not need to known the running-time t in advance; as far as we know, this yields the first construction of a time-tight delegation scheme with time-independence based on any hardness assumption. We finally present applications of SPARGs to the constructions of VDFs (Boneh et al., Crypto’18), resulting in the first VDF construction from standard polynomial hardness assumptions (namely LWE and the minimal assumption of a sequentially hard function).
Semantic Relationship-Based Image Retrieval Using KD-Tree Structure

The semantic relationship of visual objects plays an important role in determining the context and semantics of an image. In this paper, a method of classifying semantic relationships between objects on image is proposed and applied to a semantic-based image retrieval system. Firstly, the visual objects on an input image are extracted and classified using the R-CNN network model. Secondly, a semantic description of the image is determined by the KD-Tree structure. From that, a model of classifying semantic relationships and extracting semantic descriptions for an input image is proposed to retrieve a set of similar images by semantics. To prove the correctness of the proposed theoretical basis, an experiment was built on the COCO and Flickr image data sets with an average image retrieval performance of 0.6972, and 0.7794, respectively. Experimental results are compared with other works on the same data set to demonstrate the effectiveness of our proposed method and can be applied to multi-object image data sets.
Relevant Cybersecurity: Curriculum Guidance for the South African Context

Cybersecurity is vital to most organisations, and it is important that Higher Education institutions ensure the relevance of degrees to meet industry needs. It is desirable to minimise any gap in the skills graduates obtain compared to what organisations expect when hiring a cybersecurity professional. This research examines the cybersecurity tasks, knowledge, and skills that are sought by South African organisations. Content analysis was used to compare job advertisements from five well-known online job portals with the knowledge areas, units, and topics from the Cybersecurity Curricula 2017. The results indicate that knowledge in organisational security is most needed, specifically relating to identifying and protecting organisations against risk. Some areas are shown to be less relevant in the sample of advertisements, such as component security. The results of the analysis can be used by Higher Education institutions, as well as other training providers, to ensure an industry-relevant cybersecurity curricula in the South African context. The research also demonstrates a replicable approach that can be used to ensure cybersecurity education-industry alignment in the future.
Map matching navigation method based on scene information fusion

In the process of large topological map creation, the odometer track reckoning can meet the calculation requirements in the system with limited computing ability, but the error of the odometer will lead to the cumulative error in the process of map creation. This paper proposes multi-robot cooperative exploration based on geometric-topological scene map and proposes a new representation method of topological map, which adds the scene information. This method uses the SuperPoint network structure to extract the local feature point information in the image and uses this information for scene matching and node localisation to provide more reliable support for map creation. Considering the spatial relationship between a single node and different nodes, we use the hidden Markov model (HMM) to locate nodes accurately and use the geometrical-topological feature scene matching method to perform map fusion. Finally, the effectiveness and feasibility of the proposed algorithm are verified by experiments.