Reviewerly/exHarmony huggingface dataset:
An power and bound-aware optimised scheduler for virtualized cloud computing

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task’s quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm’s exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance.
Analysis of the trade-offs between parametric and non-parametric classifiers for classification of faults in pneumatic actuator. Case: DAMADICS

This paper disseminates an extra dimension of substantial analysis demonstrating the trade-offs between the performance of Parametric (P) and Non-Parametric (NP) classification algorithms when applied to classify faults occurring in pneumatic actuators. Owing to the criticality of the actuator failures, classifying faults accurately may lead to robust fault tolerant models. In most cases, when applying machine learning, the choice of existing classifier algorithms for an application is random. This work, addresses the issue and quantitatively supports the selection of appropriate algorithm for non-parametric datasets. For the case study, popular parametric classification algorithms namely: Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Perceptron (PER) and non-parametric algorithms namely: Multi-Layer Perceptron (MLP), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) are implemented over a non-parametric, imbalanced synthetic dataset of a benchmark actuator process. Upon using parametric classifiers, severe adultery in results is witnessed which misleads the interpretation towards the accuracy of the model. Experimentally, about 20% improvement in accuracy is obtained on using non-parametric classifiers over the parametric ones. The robustness of the models is evaluated by inducing label noise varying between 5% to 20%. Triptych analysis is applied to discuss the interpretability of each machine learning model. The trade-offs in choice and performance of algorithms and the evaluating metrics for each estimator are analyzed both quantitatively and qualitatively. For a more cogent reasoning through validation, the results obtained for the synthetic dataset are compared against the industrial dataset of the pneumatic actuator of the sugar refinery, Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS). The efficiency of non-parametric classifiers for the pneumatic actuator dataset is well proved.
MVCformer: A transformer-based multi-view clustering method

In recent years, multi-view graph-based clustering methods have received great attention due to the ability to integrate complementary features from multiple views to partition samples into the corresponding clusters. However, most existing graph-based approaches belong to shallow models, which cannot extract latent information from complex multi-view data. Inspired by the success of self-attention, this study proposes a Transformer-based multi-view clustering method named MVCformer, which learns a deep non-negative spectral embedding as an indicator matrix for one-stage cluster assignment. In addition, a simple but effective optimization framework, which combines the reconstruction loss from the viewpoint of similarity graph reconstruction and the orthogonal loss to make the learned non-negative embedding column orthogonal, is designed. The proposed method is verified by extensive experiments on nine real-world multi-view datasets. The experimental results demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
Integrating Blockchain Technology into a University Graduation System

The integration of blockchain technology in educational institutions is gaining momentum due to its promise to improve the graduation system and the process of verifying educational qualifications. However, little research exists on the social aspects of incorporating blockchain technology in an educational environment. This study aims to evaluate the social impact of blockchain technology on internal and external stakeholders of a higher education institution, including educational leaders, management, graduates, and external organisations. Using the Social Impact Assessment methodology, this study reveals that the integration of blockchain technology can have an overall positive impact on industrial and alumni relations and resource optimization. This study also contributes to the literature on Social Impact Assessment by presenting a methodological model for the integration of blockchain technology in the context of a higher education environment.
Part-of-speech Tagger for Assamese Using Ensembling Approach

Ensemble system for part-of-speech (POS) tagging is beneficial for many resource-poor languages that do not have enough annotated training data to train Deep Learning (DL, also named Deep Neural Network)-based POS taggers. An Ensemble system is a better choice to incorporate the linguistic features of a language and leverage the benefits of various types of POS taggers. In this work, we present our experiment of developing an ensemble tagger for Assamese, a low-resource, morphologically rich scheduled language of India, spoken by more than 15 million people. Despite the success of modern neural-network-based models in sequence tagging tasks, it has yet to receive attention in developing tasks such as POS in a resource-poor language such as Assamese. We develop a POS tagging model based on the BiLSTM-CRF architecture with a corpus of 404k tokens. We cover several word embeddings during training. Among all the experiments, the top two POS tagging models achieve tagging F1 scores of 0.746 and 0.745. We observe that the DL-based taggers are not able to achieve decent accuracy. It may be due to the inability to capture the linguistic features of the language or due to comparatively less annotated data. So, we build another POS tagger using a rule-based approach considering several morphological phenomena of the language and get an F1 score of 0.85. Subsequently, we integrate the top two DL-based taggers with the rule-based ones and develop a new POS tagger using an ensemble approach, of which we get an improved F1 score of 0.925. Performance improvement of our new ensemble POS taggers over the baseline taggers suggests that integration of the taggers combines the qualities of all taggers in the new tagger. Therefore, this study also states ensemble taggers are more suitable for highly inflectional, morphologically rich resource-poor languages.
Design and Modeling Atelier: Interaction of Physical and Virtual Models for Augmented Design Experiences

The study discusses the outcomes of the Atelier Design and Modeling course in the third-year Bachelor of Studies in Architecture at the Polytechnic of Turin. The course, a collaboration between Drawing and Architectural and Urban Composition disciplines, focuses on designing for an area in the Cenisia district in Turin. Emphasizing the connection between conception and representation, students are introduced to architectural and urban composition through physical and digital modeling, integrating AR technologies. The paper examines the hybridization between models, referencing similar educational experiences from other universities. Then delves into the relationship between physical and digital models, highlighting the interdisciplinary educational approach and the potential of different modeling techniques.
Explanation Paradigms Leveraging Analytic Intuition (ExPLAIn)

Abstract In this paper, we present the envisioned style and scope of the new topic “Explanation Paradigms Leveraging Analytic Intuition” (ExPLAIn) with the International Journal on Software Tools for Technology Transfer (STTT). Intention behind this new topic is to (1) explicitly address all aspects and issues that arise when trying to, if possible, reveal and then confirm hidden properties of black-box systems, or (2) to enforce vital properties by embedding them into appropriate system contexts. Machine-learned systems, such as Deep Neural Networks, are particularly challenging black-box systems, and there is a wealth of formal methods for analysis and verification waiting to be adapted and applied. The selection of papers of this first Special Section of ExPLAIn, most of which were co-authored by editorial board members, is an illustrative example of the style and scope envisioned: In addition to methodological papers on verification, explanation, and their scalability, case studies, tool papers, literature reviews, and position papers are also welcome.
Digital Transformation of the Higher Education System: Directions and Risks

Purpose of research. The higher education system is undergoing changes under the influence of an increasing number of IT solutions used. Transformation changes take place at the organizational, technological, legal, and regulatory levels of management. Each of the directions affects the features of the functioning and development of the higher education system. In the process of their implementation, there are also deviations, risks that need to be, if not eliminated, then at least minimized. The article describes four main directions of development: technical, technological, instrumental, and educational. The types of risks associated with each of the described areas are also highlighted. Materials and methods. A set of methods was used in the paper: bibliographic (selection of articles by keywords); bibliometric (quantitative characteristics by time parameters); content analysis (method of studying the content of articles); evaluation of keyword queries using Internet services. Results. An analysis of queries by keywords showed that interest in the issues of digitalization and digital transformation of higher education arose later than in the system of general secondary education. There is a tendency to adapt successful models of digitalization of secondary education and business areas to the activities of the higher education system. Without considering the peculiarities of the functioning and development of the higher education system, we can get negative consequences expressed in different types of risks. The paper highlights financial, form-major, technological, operational, strategic, cognitive, and social risks. Conclusion. One of the key problems highlighted in the process of analyzing developments in the field of digital transformation of the education system is the consideration of digitalization as means, and not as a catalyst for systemic changes in all areas of activity. Point solutions will not allow you to fully realize the potential of digital solutions. When considering the problems of digitalization and digital transformation, higher education systems are often guided by successful models in the field of secondary general education and / or business environment, which can contribute to the formation of negative consequences when adapting approaches without considering their own specifics.
Multi-temporal Sequential Recommendation Model Based on the Fused Learning Preferences

Abstract Since sequential recommendation can effectively capture the user’s dynamic interaction pattern according to the user’s historical interaction, it plays one of the important roles in the field of recommendation systems. Nowadays, the sequential recommendation models based on deep learning approaches have achieved good results in reality due to their excellent performance in information retrieval and filtering. However, there are still some challenges that still should be overcome of them. A major disadvantage of them is that they are only considering users’ short-time interests while ignoring their long-term preferences. Moreover, they are incapable of considering the influence of time information in the original interaction sequence, which could be helpful to fully extract various patterns via the different temporal embedding forms. Therefore, this paper proposes a novel model named multi-temporal sequential recommendation model based on the fused learning preference named MTSR-FLP in short. In the proposed framework, this paper adopts an MLP-based state generation module to consider the user’s long-term preferences and the short-time interests simultaneously. In particular, the proposed MTSR-FLP model designs a global representation learning approach to obtain the user’s global preference, and a local representation learning approach to capture the users’ local preference via its historical information. Moreover, the proposed model develops a multiple temporal embedding scheme to encode the positions of user–item interactions of a sequence, in which multiple kernels are utilized for the absolute or relative timestamps to establish unique embedding matrices. Finally, compared with other advanced sequence recommendation models on five public real-world datasets, the experimental results show that the proposed MTSR-FLP model has improved the performance of HR@10 from the 6.68% through 31.10% and NDCG@10 from the 8.60% through 42.54%.
A novel continuous authentication method using biometrics for IOT devices

In this paper, we examine continuous authentication for IoT devices using real-time biometrics of a person's electrocardiogram (ECG) and electromyography (EMG). ECG is mainly used as a biometric identifier because it has specific features such as mathematical, morphological, and wavelet characteristics. EMG is a bio-signal defining a hand gesture of a person. Our authentication system would require no human interaction as it will have a continuous authentication schema. As soon as the user leaves a specific perimeter, the session will be killed by the system. In this paper, we propose a challenging and integrated methodology for developing, prototyping, and evaluating a continuous authentication scheme to ensure that currently insecure IoT networks are improved to have a high level of security with two layers of biometric security with continuous authentication to perform authentication in an automated manner. We used the dataset from PhysioNet for ECG, which contains samples of around 12 K for 298 people. We also used the EMG dataset available on the geostatic python library containing 150 K samples. In this experiment, we concluded that it is viable to use our continuous authentication for IoT devices with the lowest performance consumption and power consumption. The experimentation also demonstrates that the training model on two bio-signals helps obtain higher accuracy on continuous authentication within an average of 99.6%-99.99%. Our authentication schema can be implemented and integrated on any IoT device with having at least one wireless frequency that can receive and send a signal to the sender/authenticator.
Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset.
Portrait Sketch Generative Model for Misaligned Photo-to-Sketch Dataset

A deep-learning-based model for generating line-based portrait sketches from portrait photos is proposed in this paper. The misalignment problem is addressed by the introduction of a novel loss term, designed to tolerate misalignments between Ground Truth sketches and generated sketches. Artists’ sketching strategies are mimicked by dividing the portrait into face and hair regions, with separate models trained for each region, and the outcomes subsequently combined. Our contributions include the resolution of misalignment between photos and artist-created sketches, and high-quality sketch results via region-based model training. The experimental results show the effectiveness of our approach in generating convincing portrait sketches, with both quantitative and visual comparisons to State-of-the-Art techniques. The quantitative comparisons demonstrate that our method preserves the identity of the input portrait photos, while applying the style of Ground Truth sketch.
Attention-based Image Captioning Assisted Visual Question Answering

Abstract Visual question answering has received increasing attention as a multimodal task whose goal is to be able to answer natural language-related questions by reasoning about a given picture. The SOTA model in the current mainstream visual question answering usually uses language bias in the training data to predict the question, resulting in the VQA model often lacking an image foundation. Based on the attention mechanism, this paper designs a method to use image description to assist the training of a visual question-answering model. The model is problem-oriented and encodes images and image descriptions based on a collaborative attention mechanism, which enhances the feature representation of the model and the ability to learn image information, making the model more robust and generalizable. The experimental results show that the accuracy of this method on the dataset VQA v2 has increased by 3.89% compared with the baseline, which proves that the method is effective.
Theory and Verification of Projection Phase Profile Measurement in Strong Salt and Pepper Noise Environment

Abstract In recent years, 3D profile measurement has been widely used in the high-precision manufacturing industry. However, the unavoidable salt and pepper noise will greatly reduce the accuracy of the measurement system. To measure the surface profile with variable curvature more accurately, a 3D measurement system is built based on fringe projection technology and Spearman Correlation Analysis. Experimental results show that the error caused by pepper and salt noise is low and the system performance is good.
Defending against Adversarial Attacks using Digital Image Processing

Abstract The rapidly maturing deep neural networks are used in self-driving cars, facial recognition payment, security, and other aspects, and are crucial to how we conduct our daily lives. However, past research has shown that adversarial attacks pose a significant danger to deep learning. This paper proposes a defense based on digital image processing to protect the handwritten digit recognition model from adversarial attacks. This method can defend against adversarial attacks simply and effectively using spatial filtering and thresholding for image pre-processing to remove adversarial perturbations. The experimental findings on the MNIST data set show that this technique can increase the average accuracy of the model against adversarial samples to 91.20%.
SISTEM INFORMASI WEB PROFIL DESA LUBUK JERING KECAMATAN AIR HITAM KABUPATEN SAROLANGUN PROVINSI JAMBI

Desa Lubuk Jering is one of the villages in Air Hitam District, Sarolangun Regency, Jambi Province. Most of their lives work in the agricultural and business sectors. Recognizing the magnitude of the benefits of currently developing information technology, a web-based system is needed that aims to make it easier for the community to find information in the village at any time, without having to be limited in the working hours of village officials to obtain this information. The input needs are in the form of admin data, year data, official data, pkk data, neighborhood association data, work program data, achievement data, announcement data, activity data, and message data. Process requirements are announcement pages, activity pages and other processes, while output requirements are activity data reports and announcement data reports. With this web-based information system, village communities can more easily get information and can provide advice to the village. So that the expected results by building this system can motivate people to become more familiar with technology and take advantage of technological developments, especially for the village youth.