Reviewerly/exHarmony huggingface dataset:
Joint Channel and Layer Search Strategy Pruning Algorithm for Accelerating CNNs

Channel pruning aims to remove redundant channel subsets from deep models without compromising performance. Previous work mainly focused on reducing the floating-point operations (FLOPs) and parameters due to the high storage cost in the past. However, channel pruning still faces the following problems: 1) Memory access cost (MAC) significantly impacts the inference speed of the model. Methods ignoring MAC may lead to slower inference. 2) Networks with identical FLOPs can exhibit large inference speed differences due to discrepancies in parallelism. Pruning reduces parallelism, making it difficult to improve inference speed effectively. Therefore, we propose a joint channel and layer pruning method based on batch normalization (BN) scaling factors to address this issue. In the proposed method, the importance of channels and layers is measured by a trainable score. The scores are optimized by sparse training to search a good-performance pruned structure. After the training process, we derive the significance of channels and layers. Finally, we remove these redundant parts to obtain a compact model. We apply this method to YOLOv3 and YOLOv4 and evaluate performance by Ship dataset. Experimental results show that our proposed method achieves up to 95% reduction in model storage space and 1.76x acceleration without significantly compromising model accuracy.
A Study on Virtual Machine Placement, its Parameters and Challenges

In real-world scenarios, cloud computing data centres house hundreds of thousands of virtual machines (VMs).Computing resources are provisioned as metered on-demand services across networks, and may be promptly allocated and released with low administration effort, thanks to the rise of cloud computing. The virtual machine is one of the most often used resource carriers in the cloud computing paradigm for encapsulating business services. In this regard, Virtual Machine Placement (VMP) is one of the most difficult problems in cloud infrastructure management, given the enormous number of alternative optimization criteria and differences in cloud infrastructure management.
Mutual Education Guidance by using AI

Well, we all know Artificial intelligence is blooming everywhere so, we tried to use this wonderful Technology to enlight every student's life. Our software is an ultimate combo of AI and Assistance which gonna bring a revolution in educational field. This could be an alternative for the conventional educational system. We using the application form(login) in online for students based on the marks by using AI concept it used to analyze the student face of interest.The main scope of this project is reduce the student time and it is used to create flexible study schedule based on the time given by the user in recent technology. We used here “Buddy” concept with AI to growth of student study.
On-Orbit Mission Planning of SAR Satellite Based on Timeline Planning Repair

Aiming at the high-priority target screening and fast response requirements of SAR satellite imaging, an on-orbit mission planning method of SAR satellite based on temporal constraint repair is proposed by analyzing the characteristics of load imaging process. Considering the target priority as the evaluation weight of the algorithm, the solving steps of imaging time window, side-looking state and working mode are designed, and the SAR load boot task is arranged. The task conflict resolution is completed by timeline planning repair, and the imaging wave position calculation is completed by target fusion. The simulation results show that under the condition of limited on-orbit computing resources, the SAR satellite on-orbit mission planning method based on timeline planning repair can quickly generate effective SAR imaging tasks, and has the advantages of fast planning speed, short response time and high load utilization.
Students Perspectives on the Guidance and Counselling Units in the School System

This study aims to investigate the student's perspectives towards the School Guidance and Counselling Unit. School Guidance and Counselling Units were established in all government schools with the assistance of the Ministry of Education through the applications recommended by circulars no 16/2011and 6/2013. Due to the students' emerging psychological and behavioral problems, there was a growing interest in enhancing the School Guidance and Counselling Service. The research objectives were to investigate the student's awareness about the School Guidance and Counselling Unit, student's attitudes towards the School Guidance and Counselling Unit, perception of the students towards the School Guidance and Counselling Unit, and identify the steps to increase the function of the unit. A questionnaire survey was administered under the mixed- method research design, and semi-structured interviews were conducted with ten purposively selected students. Data analysis was done by using quantitative and qualitative methods. Findings indicate that most of the students are unaware of the services provided by the School Guidance and Counselling Unit. Students positively perceive that seeking help from the unit is beneficial. However, most students believe that seeking guidance and counselling services consumes much time, and the unit does not encourage one to seek counselling services. It was evident that whether schools have implemented the recommendations addressed via circulars, the function of the school guidance and counselling service is not up to the desired level. Therefore, it is essential to circulate a proper monitoring system towards the school guidance and the counselling center. Needed utilities and resources should provide for the proper function of the Student Guidance and Counselling Unit within the school level with the assistance of the ministry of education.
Applications, Restrictions, and Evaluation of Quantum Cryptography for Secure Unmanned Aerial Vehicle Communication

Because of the abrupt increase in demand for security, researchers have developed immediate safety solutions that outperform state-of-the-art solutions. Data security was first sought for during the Spartan period. These days, efforts are being made to broaden this area of study by challenging the status quo and creating novel algorithms that outperform their weaker predecessors. Unmanned aerial vehicles, or UAVs, are widely used in a variety of industries, including agriculture, the military, healthcare, monitoring and surveillance, and many more, because of their svelte designs and adaptable mobility. In this post, we go over the significance of drone technology as well as its growth and demand. The study also discusses the current state of security concerns in real-time settings and how quantum cryptography outperforms conventional methods in protecting data. This inspires us to offer an overview of quantum cryptography's significance, function, and advantages in protecting UAV communications that go beyond 5G networks. Additionally, a unique layered architectural approach based on quantum cryptography is suggested to ensure excellent data security and effective transmission. A case study on the Internet of Military Things' implementation in the battlefield is also included in this presentation. The latency, security, and dependability of the suggested case study system are taken into account while assessing its performance.
OPEN-AMZPRE : Optimized Preprocessing with Ensemble Classification for Amazon Product Reviews Sentiment Prediction

Customer feedback plays a vital role in helping consumers make informed purchasing decisions. Understanding customer opinions and preferences through sentiment analysis is crucial. However, existing sentiment analysis methods face challenges when dealing with noisy, unstructured text data, leading to limitations in accuracy, precision, recall, and F1-score. To address these limitations, this paper introduces OPEN-AMZPRE, an innovative solution for sentiment prediction in Amazon Product Reviews. Unlike traditional approaches that rely on standard techniques like tokenization and stopword removal, OPEN-AMZPRE utilizes a comprehensive preprocessing pipeline. This pipeline includes various steps such as text normalization, lowercasing, handling rare words, expanding contractions, removing HTML tags, tokenization, removing stopwords, replacing slang words, removing digits, stemming, lemmatization, punctuation and special character removal, white space removal, spell checking and correction, and removal of duplicate text. Additionally, the proposed algorithm employs an ensemble classification approach by combining optimized versions of K-Nearest Neighbors (KNN), Naive Bayes, J48 (C4.5 decision tree), and Random Forest classifiers. The hyperparameters of each classifier are tuned to achieve optimal accuracy and performance. By combining the outputs of these classifiers, the algorithm produces robust sentiment predictions. The methods of accuracy, precision, recall, and F1-score are utilized to improve sentiment prediction and provide valuable insights for both consumers and businesses in relation to Amazon Product Reviews.
The Future of Democracy: Exploring the Potential of Block chain-Based Digital Voting Systems

A key component of democratic governance in modern countries is the election process. But due to worries about things like polling booth capturing, data manipulation, and vote rigging, a general mistrust in the electoral process has evolved. Because they put election data under the authority of outside organizations, both the traditional and computerized voting systems now in use lack the required transparency. Voters have few options to verify that election administrators will carefully and accurately count their votes due to a lack of openness. To create an electronic voting (e-voting) system that upholds the ideals of fairness and security, it is imperative to take advantage of developing technology, particularly blockchain. When correctly applied, blockchain technology's public distributed ledger holds the potential to make tampering almost impossible. In this regard, our research suggests a decentralized electronic voting system that makes use of blockchain technology as a remedy to deal with the aforementioned issues. Through the elimination of the possibility of centralized election control, this approach seeks to reduce the dangers connected with conventional election procedures and increase voter confidence. The suggested method offers a tamper-proof, transparent, verifiable, economical, and reliable voting process through the distribution of control across several governing and non-governing bodies. This paper examines the development and implementation of such a blockchain-based electronic voting system, shedding insight on how it may enhance the openness and accessibility of democratic elections in contemporary societies.
Transforming Digital Dispute Resolution in India

The era of digital reality offers and shapes new dispute resolution mechanisms, imposes high demands and sets its own rules to be complied with. The introduction of information and communication technologies and artificial intelligence into judicial and out-of-court dispute resolution proceedings in India has shown innovative results quite rapidly. Transformation of digital modes of dispute resolution continues with new research and development of modern technologies that strive for excellence. The purpose of this study is to analyze transformation of digital dispute resolution methods in India, considering the historical evolutionism that led from the Vedic era to the global digital revolution, formation of a hybrid model that combines several modes of dispute resolution - offline courts, online courts and online dispute resolution using digital technologies and alternative dispute resolution methods. However, the digital system is far from being perfect; it has significant drawbacks associated with the risks of cyber-attacks and leakage of personal data containing sensitive confidential and personal information. Besides, the digital literacy of the Indian population located on the periphery is quite low, which greatly reduces the level of trust in electronic courts and online dispute resolution.
The Performance of Small and Medium Sized Enterprises (SMEs) in Punjab: A Moderating Role of Corporate Image

The main purpose of this study is to check the factors affecting the performance of Small and Medium Sized Enterprises (SMEs) in Punjab. These factors include quality certification, open innovation, organizational culture and corporate philanthropy. Moreover, to check the moderating effect of corporate image between corporate philanthropy and SMEs’ performance. Quantitative research approach is used in this study because it is based on primary quantitative data which is collected by adapted questionnaire. Managerial level employees of SMEs located in Lahore were selected as population. Simple Random Sampling technique was used to collect data. Reliability and validity of model was checked and hypotheses were tested by using SMART PLS-SEM. The results of this study revealed that quality certification, open innovation, organizational culture and corporate philanthropy positively and significantly related to SMEs’ performance. Moreover, moderating variable, corporate image significantly moderates the relationship between corporate philanthropy and SMEs’ performance. Above mentioned practices play key role in boosting the performance of SMEs in Punjab, Pakistan. The results of this study are significant for the strategists, policy makers, managers and owners of SMEs located in Punjab, Pakistan or elsewhere. Results suggested them to incorporate such practices for enhancing their businesses’ performance. It will ultimately enhance the contribution of SMEs in the growth of economy.
Concept of Improved UAV Pilots Training

Unmanned aerial vehicles (UAVs) are becoming increasingly popular in a wide range of applications. To ensure the smooth and, above all, safe operation of air traffic despite the increasing number of UAV users, there is an effort to create suitable conditions for their theoretical and practical training. To this end, several software solutions have been created and tested that are being integrated into the education process. This paper presents three systems, which test the UAV remote pilots in different scenarios, starting with basic stick movements, through simulated flights to flights with a real UAV indoors, along predetermined trajectories. The results of these measurements are analyzed end evaluated using the parameters of the human model.
Securing the Digital Sky: a scenario-driven study on the enhancement of cybersecurity in location-independent aerodrome control systems

The rapid advancement of technology in aviation business management, notably through the implementation of location-independent aerodrome control systems, is reshaping service efficiency and cost-effectiveness. However, this emphasis on operational enhancements has resulted in a notable gap in cybersecurity incident management proficiency. This study addresses the escalating sophistication of the cybersecurity threat landscape, where malicious actors target critical safety information, posing risks from disruptions to potential catastrophic incidents. The paper employs a specialized conceptualization technique, derived from prior research, to analyze the interplays between malicious software and degraded modes operations in location-independent aerodrome control systems. Rather than predicting attack trajectories, this approach prioritizes the development of training paradigms to rigorously evaluate expertise across engineering, operational, and administrative levels in air traffic management domain. This strategy offers a proactive framework to safeguard critical infrastructures, ensuring uninterrupted, reliable services, and fortifying resilience against potential threats. This methodology promises to cultivate a more secure and adept environment for aerodrome control operations, mitigating vulnerabilities associated with malicious interventions.
Study on the Application of Graph Theory Algorithms and Attack Graphs in Cybersecurity Assessment

Postulating the behavior of attackers is important in the design of cybersecurity protection measures. Attack graph is a technique employed for this purpose, which aids in identifying and modeling the potential attack paths an attacker could take to gain unauthorized access to a cyber network, exploit vulnerabilities, and compromise the system's confidentiality, integrity, and availability. In this study, we propose a framework aimed at identifying potential attack paths and determining the shortest path with the highest probability of a successful attack. Meanwhile, based on the attack graph determine the minimum patch sets with the most severity to protect the network security. Common Vulnerability Scoring System (CVSS) is utilized to quantify the exploitability and severity of each vulnerability. The Dijkstra algorithm is utilized to calculate the shortest path with the highest probability, and the Stoer-Wagner algorithm is utilized to calculate the minimum patch sets with the most severity. To demonstrate the proposed framework, we apply it to a simplified SCADA system within a corporate network susceptible to cyber attacks.
Verifiable Certificateless Signcryption Scheme for Smart Grids

Smart Grids (SG) are potential replacements for older power grids, capable of adapting and distributing energy based on demand. The recent technological advances and the increased dependence on electrical energy are all factors in the increased need for reliable and efficient energy systems. The complexity of the smart grid arises from using various components and the high requirements for real-time reliable two-way communication. Accordingly, this represents additional security and privacy challenges. In this paper, we proposed a security and efficient certificateless signcryption scheme with flexible verifiability properties using equality test techniques for Advanced Metering Infrastructure (AMI) within the SG; abbreviated CL-SG. The proposed scheme provides privacy-preserving, confidentiality, data integrity, verifiability, and reduces the level of trust in the third party (e.g., central unit, substation, data concentrator, etc.). The performance evaluation regarding security and complexity analysis shows that the proposed scheme is secure and efficient with good functionality to be fully adopted and implemented within SG. Also, an extended future work is presented to add more flexibility and security properties and features to the current proposed model.
Potential of selected Industry 4.0 tools in the field of airport security screening

In the article, the authors address the possibility of using augmented reality and its potential together with artificial intelligence in air transport, especially in the context of airport security screening. In the introduction, the authors describe what augmented reality is and how it is used. The second chapter describes in detail how this tool works and the individual types available on the market with their brief features. The third chapter deals directly with the concrete use of these technologies at international airports around the world. In the end, the authors summarise the results and describe the advantages and disadvantages of the technologies discussed in the article.
A Novel Method for Computing Minimal Cut Sets of Variant-Rich Systems in Fault Tree Analysis

Railway vehicles are considered one of the most important transportation systems for which fault tree analysis can be leveraged to ensure safety and reliability. Computing minimal cut sets of a fault tree in a system is one of the industry's most widely used techniques. Most studies in the field have only focused on specific variants. However, product line engineering and variant management has become an important topic and successful variant management is essential for providing reusability in manufacturing similar products. An essential aspect of variation is the numerous number of variants which increases the required time, space, and complexity for the design and implementation of safe variant-rich systems. This paper proposes a new method for computing minimal cut sets in different variants by considering a 150% model for a set of variants and then removing corresponding basic events in the MCS of the 150% model. The introduced method has been simulated and tested by the FT-MCS library for fault trees with different variables. The results indicate that the presented method computes minimal cut sets for variants efficiently. We have also examined our proposed method in a small case study in railway vehicles. This case study indicates that for calculating MCS of variant products in the industry it is not necessary to compute MCS for each of them and the MCS of the 150% model can be used.
Using Sensitivity Analysis and Search-Based Testing in the Verification of a Computer Vision Function

For operating safely, autonomous robots and automated driving applications need to reliably detect objects in their environment under a huge variety of external conditions. Previous works introduced the usage of semantic domain models for capturing important domain parameters that have an impact on detection performance of perception systems. In these approaches, rating the importance of domain parameters is usually left to the intuition and experience of a test engineer. In this paper, we present a framework utilizing global sensitivity analysis (GSA) for determining the impact of domain parameters on detection performance and exploiting search-based testing (SBT) technology for generating difficult input images for testing a computer vision function based on the domain parameters identified by GSA. In addition, we discuss the benefits and lessons learned of the interplay of GSA and SBT for testing a perception function. In our evaluation, we apply our framework on a pedestrian detection use case from the automated driving domain. Our evaluation results show that SBT finds critical test images faster than random testing and that selecting the right domain parameters based on GSA is crucial for good test results.
Investigating Lipschitz Constants in Neural Ensemble Models to Improve Adversarial Robustness

This work investigates the relationship between adversarial robustness and the local Lipschitz constant in ensemble neural network frameworks, namely bagging and stacking. Capitalising on this, we introduce an ensemble neural network design that improves both accuracy and adversarial resilience. We theoretically obtain the local Lipschitz constants for both ensembles, offering insights into their susceptibility to adversarial attacks and identifying architectures optimal for adversarial defense. Notably, our approach negates the need for specific adversarial attack and accommodates any number of pre-trained networks for an ensemble architecture. Evaluations on the MNIST and CIFAR-10 datasets against white-box attacks, specifically FGSM and PGD, show our approach is adversarially robust compared to standalone networks and vanilla ensemble architectures.