Reviewerly/exHarmony huggingface dataset:
Lightweight YOLOv5 model based small target detection in power engineering

Deep learning architectures have yielded a significant leap in target detection performance. However, the high cost of deep learning impedes real-world applications, especially for UAV and UGV platforms. Moreover, detecting small targets is still of lower accuracy in contrast to the large ones. Aiming to comprehensively handle these two issues, a novel SP-CBAM-YOLOv5 architecture is proposed. The main novelty of our hybrid model lies in the cooperation of the attention mechanism and the typical YOLOv5 architecture, which can largely improve the performance of the small target detection. Moreover, the depth convolution and knowledge distillation are jointly introduced for lightening the model architecture. To evaluate the performance of our proposed SP-CBAM-YOLOv5, we built a novel dataset containing challenging scenes of power engineering. Experimental results on this benchmark demonstrate that our proposed SP-CBAM-YOLOv5 achieves a competitive performance in contrast to the other YOLO architectures. Besides, our lightweight YOLOv5 has more than 70% decrease of parameters. Moreover, the ablation study is conducted to demonstrate the compact architecture of SP-CBAM-YOLOv5.
The critical success factors for Security Education, Training and Awareness (SETA) program effectiveness: a lifecycle model

Purpose This study explores the critical success factors (CSFs) for Security Education, Training and Awareness (SETA) program effectiveness. The questionable effectiveness of SETA programs at changing employee behavior and an absence of empirical studies on the CSFs for SETA program effectiveness is the key motivation for this study. Design/methodology/approach This exploratory study follows a systematic inductive approach to concept development. The methodology adopts the “key informant” approach to give voice to practitioners with SETA program expertise. Data are gathered using semi-structured interviews with 20 key informants from various geographic locations including the Gulf nations, Middle East, USA, UK and Ireland. Findings In this study, the analysis of these key informant interviews, following an inductive open, axial and selective coding approach, produces 11 CSFs for SETA program effectiveness. These CSFs are mapped along the phases of a SETA program lifecycle (design, development, implementation and evaluation) and nine relationships identified between the CSFs (within and across the lifecycle phases) are highlighted. The CSFs and CSFs' relationships are visualized in a Lifecycle Model of CSFs for SETA program effectiveness. Originality/value This research advances the first comprehensive conceptualization of the CSFs for SETA program effectiveness. The Lifecycle Model of CSFs for SETA program effectiveness provides valuable insights into the process of introducing and sustaining an effective SETA program in practice. The Lifecycle Model contributes to both theory and practice and lays the foundation for future studies.
Secure Multi-Level Privacy-Protection Scheme for Securing Private Data over 5G-Enabled Hybrid Cloud IoT Networks

The hybrid cloud is a secure alternative for enterprises to exploit the benefits of cloud computing to overcome the privacy and security concerns of data in IoT networks. However, in hybrid cloud IoT, sensitive items such as keys in the private cloud can become compromised due to internal attacks. Once these keys are compromised, the encrypted data in the public cloud are no longer secure. This work proposes a secure multilevel privacy-protection scheme based on Generative Adversarial Networks (GAN) for hybrid cloud IoT. The scheme secures sensitive information in the private cloud against internal compromises. GAN is used to generate a mask with the input of sensory data-transformation values and a trapdoor key. GAN’s effectiveness is thoroughly assessed using Peak Signal-to-Noise Ratio (PSNR), computation time, retrieval time, and storage overhead frameworks. The obtained results reveal that the security scheme being proposed is found to require a negligible storage overhead and a 4% overhead for upload/retrieval compared to the existing works.
The 11th IJCAR automated theorem proving system competition – CASC-J11

The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic, Automated Theorem Proving (ATP) systems. CASC-J11 was the twenty-seventh competition in the CASC series. Twenty-four ATP systems competed in the various competition divisions. This paper presents an outline of the competition design and a commentated summary of the results.
Comprehensive sentimental analysis of tweets towards COVID-19 in Pakistan: a study on governmental preventive measures

Sentiments are the key factors that lead to influence our behavior. Sentiment analysis is a technique that analyzes people's behaviors, attitudes, and emotions toward a service, product, topic, or event. Since 2020, no country has remained untouched by COVID-19, and the governing bodies of most countries have been applying several anti-pandemic countermeasures to combat it. In this regard, it becomes tremendously important to analyze people's sentiments when tackling infectious diseases similar to COVID-19. The countermeasures taken by any country to control the pandemic leave a direct and crucial impact on each sector of public life, and every individual reacts to them differently. It is necessary to consider these reactions to implement appropriate messaging and decisive policies. Pakistan has done enough to control this virus's spread like every other country. This research aims to perform a sentimental analysis on the famous microblogging social platform, Twitter, to get insights into public sentiments and the attitudes displayed towards the precautionary steps taken by the Government of Pakistan in the years 2020 and 2021. These steps or countermeasures include the closure of educational institutes, suspension of flight operations, lockdown of business activities, enforcement of several standard operating procedures (SOPs), and the commencement of the vaccination program. We implemented four approaches for the analysis, including the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, Flair, and Bidirectional Encoder Representations from Transformers (BERT). The first two techniques are lexicon-based. Flair is a pre-trained embedding-based approach, whereas BERT is a transformer-based model. BERT was fine-tuned and trained on a labeled dataset, achieving a validation accuracy of 92%. We observed that the polarity score kept varying from month to month in both years for all countermeasures. This score was analyzed with real-time events occurring in the country, which helped understand the public's sentiment and led to the possible formation of a notable conclusion. All implemented approaches showed independent performances. However, we noticed from the classification results of both TextBlob and the fine-tuned BERT model that neutral sentiment was dominant in the data, followed by positive sentiment.
Transformer guided progressive fusion network for 3D pancreas and pancreatic mass segmentation

Pancreatic masses are diverse in type, often making their clinical management challenging. This study aims to address the task of various types of pancreatic mass segmentation and detection while accurately segmenting the pancreas. Although convolution operation performs well at extracting local details, it experiences difficulty capturing global representations. To alleviate this limitation, we propose a transformer guided progressive fusion network (TGPFN) that utilizes the global representation captured by the transformer to supplement long-range dependencies lost by convolution operations at different resolutions. TGPFN is built on a branch-integrated network structure, where the convolutional neural network and transformer branches first perform separate feature extraction in the encoder, and then the local and global features are progressively fused in the decoder. To effectively integrate the information of the two branches, we design a transformer guidance flow to ensure feature consistency, and present a cross-network attention module to capture the channel dependencies. Extensive experiments with nnUNet (3D) show that TGPFN improves the mass segmentation (Dice: 73.93% vs. 69.40%) and detection accuracy (detection rate: 91.71% vs. 84.97%) on 416 private CTs, and also obtains performance improvements of mass segmentation (Dice: 43.86% vs. 42.07%) and detection (detection rate: 83.33% vs. 71.74%) on 419 public CTs.
An improved fuzzy C-means clustering algorithm using Euclidean distance function

The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties.
Heterogeneous Datasets for Federated Survival Analysis Simulation

Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In this context, federated learning can be exploited to tremendously improve the quality of the models trained on distributed data while preserving user privacy. However, federated survival analysis is still in its early development, and there is no common benchmarking dataset to test federated survival models. This work provides a novel technique for constructing realistic heterogeneous datasets by starting from existing non-federated datasets in a reproducible way. Specifically, we propose two dataset-splitting algorithms based on the Dirichlet distribution to assign each data sample to a carefully chosen client: quantity-skewed splitting and label-skewed splitting. Furthermore, these algorithms allow for obtaining different levels of heterogeneity by changing a single hyperparameter. Finally, numerical experiments provide a quantitative evaluation of the heterogeneity level using log-rank tests and a qualitative analysis of the generated splits. The implementation of the proposed methods is publicly available in favor of reproducibility and to encourage common practices to simulate federated environments for survival analysis.
A Case of Multi-Resource Fairness for Serverless Workflows (Work In Progress Paper)

Serverless platforms have exploded in popularity in recent years, but, today, these platforms are still unsuitable for large classes of applications. They perform well for batch-oriented workloads that perform coarse transformations over data asynchronously, but their lack of clear service level agreements (SLAs), high per-invocation overheads, and interference make deploying online applications with stringent response time demands impractical.
Parametric technology is used for the design of weaving products

Traditional craft is an important part of cultural confidence in the national cultural strategy, and cultural design is an industry with great development potential in the future. With the arrival of artificial intelligence, virtual reality technology and a new round of information technology on the Internet, the design industry has ushered in the information age, and the traditional processes are endowed with more diversified, scale and open attributes. In order to break the one-sided structure of the traditional craft products and give the modern bamboo weaving more three-dimensional space possibilities, the parametric technology can provide the changing logical structure of the new form construction of the bamboo weaving products through the parameters. This paper uses the digital transformation of traditional bamboo weaving technology to carry out the new innovative design thinking process of bamboo weaving process, Use the 3 D Rhino software combined with Grasshopper to adjust the parameters of the bamboo weaving product weaving patterns and bamboo weaving derivative products, and then fine-tune according to the specific requirements of the curve surface shape. Compared with the traditional bamboo weaving method, the parameter modeling method can make the bamboo weaving form more intelligent, and the constructed parametric structure can be more flexible and efficient for the bamboo weaving derivative and design, which provides a new design method and ideas for the development of bamboo weaving products.
GNN Model Based On Node Classification Forecasting in Social Network

In the time of ever-growing technology, engineering, and deep learning methods, one thing that has caught the attention of people is the invention of Neural Networks, also known as Artificial Neural Networks [1]. These are the subset of machine learning and are at the core of deep learning. Their structure and nomenclature are modeled after the human brain, mimicking the communication between biological neurons [2].This work is presented and explained by ANN, Graphical Neural Network [3], which is a type of NN that works on graphs. In today’s world, one can see various real-life applications of GNNs like those in various social networks, prediction of molecules, and drug preparation in medical sciences, road traffic, etc.The article deals with the application of the GNN showing how can a GNN helps in forecasting information about a person in a social network based on various given datasets. In the end, one can easily forecast the information of a person using various tools like Pytorch, etc.
TransU<sup>2</sup>-Net: A Hybrid Transformer Architecture for Image Splicing Forgery Detection

In recent years, various convolutional neural network (CNN) based frameworks have been presented to detect forged regions in images. However, most of the existing models can not obtain satisfactory performance due to tampered areas with various sizes, especially for objects with large-scale. In order to obtain an accurate object-level forgery localization result, we propose a novel hybrid transformer architecture, which exhibits both advantages of spatial dependencies and contextual information from different scales, namely, TransU <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net. Specifically, long-range semantic dependencies are captured by the last block of encoder to locate large-scale tampered areas more completely . Meanwhile, non-semantic features are filtered out by enhancing low-level features under the guidance of high-level semantic information in the skip connections to achieve more refined spatial recovery. Therefore, our hybrid model can locate spliced forgeries with various sizes without requiring large data set pre-training. In comparison with other existing CNN-based methods, our framework achieves better performance over state-of-the-art methods.
Blockchain Approach for Implementing Access Control In IOT

Block chain is now attracting a lot of interest from scientists and experts for a variety of reasons, including wireless communication decentralization, remote access, digital security, and confidentiality. Although peer-to-peer networking, confidentiality, improved storage, and extra security are all advantages of crypto currencies, its irreversible nature is the primary factor in its top ranking. Block chain may be utilized as a crucial technique to do away with the need for a trustworthy third person in networks that are linked to one another because of how it is dispersed. The most well-known block chains that may be used for installation are Public block chain Fiber, IBM Bit coin, Eth, Eris, R3 Corda, and multichain. The abovementioned review study presents and evaluates the current proof - of - stake security methods for distribution chain, medicine, and IoT access controls. The thorough analysis of applications of block chain will function as the most recent state-of-the-art for academics to conduct cutting-edge research in the pursuit of block chain - based across diverse disciplines.
Classification and Contrast of Supervised Machine Learning Algorithms

Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for Data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.
An Image Copy-Move Forgery Detection based on SURF and Fourier-Mellin Transforms

Image forgery is widespread nowadays on social media. The problem worsened with advanced editing software, making forgery very hard to detect. A natural image consists of different features. During forgery detection, these features are extracted to find any manipulation in the image. Two main approaches under copy-move forgery detection are block-based and key-based techniques. The paper proposes exploiting a combined approach based on block-based and key-point techniques such as speed-up robust feature (SURF) and Fourier-Mellin transform (FMT). The image is first categorized into smooth and textured parts. Surf is applied to textural areas of the image, while FMT coefficients are exploited from smooth regions. Dense linear fitting (DLF) and random sampling consensus (RANSAC) are used separately to eliminate the false matching points and outliers. Finally, mathematical morphology is adapted to generate the binary map for both parts of the image to locate the forgery area. Experimental results prove that the suggested model is robust against blurring, scaling, and compression attacks.
Blockchain Integrated with Industrial IOT Towards Industry 4.0

Cryptographic possibilities have received a lot of attention in art and the journalism, particularly in the banking and payment sectors. At the company level, one very new trend is the use of cryptocurrency as the foundation for irreversibility and security software. Industry 4.0 and Smart manufacturing are examples of emerging application fields (IIoT). Therefore, we thoroughly examine current block chain possibilities in Ir 4.0 and IIoT scenarios in this study. We specifically outline the latest research developments in all of the important industrial areas, along with examples of blockchain - based being successfully used in business. We also talk about the difficulties that each sector will face while using cryptocurrency. We also cover more recent potential uses and ongoing problems with Business 4.0's use of blockchains. Our findings should enable and facilitate more study in this area, as well as help decision-makers embrace blockchain technology and make investments in the Ir 4.0 and IIoT field.
Sensitivity Analysis of LSTM Networks for Fall Detection Wearable Sensors

Falling detection concerns identifying and alerting when a person falls or experiences a sudden loss of balance. It is an important safety feature, particularly for elderly or vulnerable individuals, as falls can lead to severe injuries. One applicable technique is employing Long Short-Term Memory (LSTM) networks that can be used for falling detection by processing data from sensors, such as accelerometers or gyroscopes, to identify patterns associated with falls. This paper demonstrates that a single LSTM layer can be used for this purpose. Still, multiple LSTM layers can improve the system’s performance with less processing time, reaching 99.13% of accuracy with 4.35% of loss values. Furthermore, this work thoroughly studies the sensitivity of the size of an LSTM cell, demonstrating that it is not necessary to obtain a size close to the timestamp of the features and thus save hardware resources.
Depressive and Non-depressive Tweets Classification using a Sequential Deep Learning Model

Nowadays, depression is one of the most predominant mental health ailments, even causing demise of many lives, especially among the youngsters and middle-aged persons. Use of social media and posting individual expression on it is very much use to for the people. So, social media posts are very useful for detecting a person is in depression or not. In this paper, we have considered depressive and non-depressive tweets classification. We have proposed a sequential Deep learning model with three layers: Embedded, lD-Convolutional and LSTM Layer. The performance of the proposed model has been compared with three traditional Machine Learning models Naïve Bayes, KNN, Random Forest for depressive and non-depressive tweets classification. Our proposed approach of sequential Deep Learning model has achieved 98.47% accuracy, which is better than other models we have compared with.
Using Blockchain to Reduce Multi-Server Edge Computing Latencies for Supervised Learning

This research investigates a novel issue as the block chain federated learning (BFL). In this system paradigm, block mining and machine learning (ML) model training are handled concurrently by in communication through a group named as a edges servers (ESs). We create the offloading techniques that enable the MD to send the required data to the linked ESs in order to aid in the ML training for the resource and constrained MDs. Then, based on a consensus method to create peer-to-peer (P2P)-based blockchain communications, we suggest a new decentralized approach at the edge layer. In order to reduce system latency, we propose that takes into account, MD transmits the required amount of power, channel bandwidth allocation for MD data offloading, MD computational allocation, and hash power allocation. With a parameterized advantage actor critic method, we offer a unique deep reinforcement learning scheme given the mixed action space of discrete offloading and continuous allocation variables. We theoretically define the aggregation latency, mini-batch size, and number of P2P communication rounds as the convergence properties of BFL. In terms of model training effectiveness, convergence rate, system latency, and robustness against model poisoning attempts, our numerical evaluation shows that our suggested approach outperforms baselines.