Reviewerly/exHarmony huggingface dataset:
Clouds Coalition Mitigation for Business Process Outsourcing

Organizations managing complex business processes (BP) would have outsourced their processes in order to enjoy of benefits of cloud computing. However, as such processes express their know-how and because of the nature of the cloud, they are still reluctant to do so. Although some solutions already exist and consist in splitting the model into a collaboration of BP fragments for a deployment in a multi-cloud context, none of them takes into account the possibility of collusion between the clouds executing the different fragments. To address this issue, we propose in this paper an approach that introduces decoy processes, having the same structure of the real one with decision and synthesis strategy making that are completely different of those of the real process. The objective being to hide the execution of the real process even if a coalition actually happens. The approach is validated against an introduced metric. The obtained results show that our approach generates BP fragment with a high obfuscation level, contributing to protect BP know-how even in a case of a coalition.
Semantic Evaluation of Privacy Policy Compliance in Medical Applications

Modern technologies are improving the way how human beings monitor the surrounding environment and, in fact, their own health conditions. This paper proposes an innovative user-centric framework to quantify the privacy level offered by medical awareness devices. Trying to address the well-known privacy issue about personal data collection without user understanding the real risks, the framework proposes a privacy quantification based on: user preferences (users have the right to decide about their privacy); context (scenario and system analysis); and privacy policy analysis. By employing Natural Language Processing (NLP) semantic analysis, our proof-of-concept was able to outperform state-of-the-art models for classification of Privacy Policies, within a well known dataset. The main benefits of the proposed framework are (i) improved informed consent, (ii) users’ possibility to define privacy preferences, and (iii) a system analysis not only focused on privacy policies.
Ball Detection for a Lightweight Mobile Platform

While convolutional neural networks have excelled in recent years in the domain of object detection, many of the benchmark datasets focus on large objects in static frames and neglect the importance of small moving object detection. Furthermore, most state-of-the-art detectors are comprised of many millions of parameters, making them too large to be efficiently deployed onto a computationally constrained mobile platform. To address the challenge of detecting small moving targets from a robotic platform, we introduce a lightweight temporally-aware ball tracker, with a reduced computational footprint and enhanced motion understanding. To better understand how different model variants utilise motion, we analyse the detection performance of different architectures under varying forms of motion information and find that multiple-input-multiple-output models learn to favour motion information in sequences more strongly than multiple-input-single-output models.
Semantic Interaction Fusion Framework for Multimodal Sentiment Recognition

Multimodal sentiment recognition has gained considerable attention for its relevance to various applications. To improve performance, it is critical to extract semantic information and fuse multimodal features. However, most current methods either emphasize single-modal semantic extraction and representation or lack semantic integration at a deep level. In this paper, we propose a Semantic Interaction Fusion Framework (SIFF) extracting the semantic information that evokes the specific sentiment from multiple modalities and integrating multimodal semantic information using a gate attention fusion module. The gate attention fusion module fuses multimodal semantic information adaptively, eliminating the influence of conflicting information and strengthening the emotional cues interaction between multiple modalities. We perform experiments on two benchmark datasets, CMU-MOSI and CMU-MOSEI. Our proposed method on both datasets achieves the accuracy of 87.2% and 86.5%, respectively, which is a 1.1 % and 1.8% absolute improvement over the current state-of-the-art.
AI Enhanced Resistance Training: Segmentation and Velocity Tracking Using Computer Vision

Analysis and assessment of human movement are crucial for enhancing athletic performance, preventing injuries, and aiding rehabilitation. This paper applies the most recent iteration of the You Only Look Once (YOLO) family of algorithms (version 8) to resistance training, segmenting humans and gym equipment in real time. Velocity metrics are calculated directly from segmentation masks, adding to the growing body of research concerned with velocity-based training (VBT), an emerging alternative to traditional percentage-based training regimes. Detection performance is assessed in terms of mask mean average precision ($\text{mAP}_{50:5:95}$), and MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is used to calculate two important velocity metrics: Peak velocity (PV) and mean velocity (MV). Our trained model achieves $71.7\%\text{mAP}_{50:5:95},87.7\%\text{mAP}_{50}$ and $\mathbf{8 5}\%$ F1 score at 0.492 confidence threshold, with an inference speed of 45 fps. Range of motion (RoM), MV, and PV for squat, bench, and deadlift were successfully extracted, demonstrating the potential of pure computer vision methods without requiring costly or inaccessible sensor hardware as in traditional approaches. Our work provides a proof-of-concept for applications of object detection within professional and recreational sports, as well as in rehabilitation settings.
Object Detection under Finger Occlusion in AR Geography Assisted Teaching System by Using PCCNet

Augmented reality(AR) plays an important role in geography teaching for creating interactive and immersive experiences. Combining object detection algorithms with AR can identify the specified content quickly and thus overlay digital content to the real world. However, finger occlusion in AR interactions has a bad influence on object detection, which will affect the users' experience. In this paper, we focus on the detection of country regions on a globe, and aim to improve the performance of object detection in practical AR development. Firstly, we propose a geographic region recognition approach based on region missing-completion. Specifically, we design a supplementary algorithm PCCNet to infer the obscured country by utilizing the invariance of relative position between countries. Moreover, to reduce manual annotation and enrich the virtual dataset, we design a scalable automatic annotation system based on the Unreal Engine and construct a virtual globe dataset named DGAR. Finally, we build an AR geography-assisted teaching system to recognize the area pointed and play multi-media materials. Experiment results show that our proposed approach effectively improves the recognition accuracy from 88.5% to 94%. The practical significance and value of the proposed recognition approach have been confirmed based on the positive user experience with the AR system, highlighting its efficacy in real world scenarios.
Bengali News Abstractive Summarization: T5 Transformer and Hybrid Approach

In today’s fast-paced world, everyone wants things to happen quickly. Thanks to the internet, news spreads super fast. But not all news is important. News summarization helps by giving a short version of each news story, so readers can easily figure out what type of news they want to read. There are two main types of summarization: Abstractive Text Summarization and Extractive Text Summarization. The process of abstractive text summarization is much more complex than that of extractive text summarization. This study proposes a model for generating extractive summaries, which are then utilized as input to generate abstractive summaries. The model uses the Bengali Text Summarization (BenSumm) model for extractive summarization and the Bangla Text-to-Text Transfer Transformer (BanlaT5) for abstractive summarization. The research also compares summarization acquired straight from the BanglaT5 model with summarization obtained via the proposed model. Abstractive summarization in the Bengali language has been accomplished using the Text-to-Text Transfer Transformer(T5) in this research. Although abstractive summarization of the Bengali language has been accomplished over the years using a variety of techniques, the field of using T5 in this field has only recently been discovered, and there is still a wide range of opportunities to be explored. The study has achieved promising results.
Transformers Are All You Need to Generate Automatic Report from Chest X-ray Images

Chest X-ray imaging plays a vital role in diagnosing and treating thoracic diseases in clinical and research settings. However, the process of examining and creating reports for chest X-ray images can pose challenges due to the scarcity of experienced radiologists and time-consuming report generation, leading to reduced clinical efficacy. To address this issue and make progress in clinical automation, there has been significant research focused on developing automated systems for radiology report generation. However, existing systems have limitations, such as considering clinical workflow, neglecting clinical context, and providing explainability. This paper presents the development of a fully transformer-based automatic chest X-ray report generation network named TransXpainNet that focuses on clinical accuracy and enhances other text generation metrics. The model utilizes a domain-knowledge-based vision transformer, DeiT-CXR, to extract image features. Supportive documents like clinical history are incorporated to enrich the report generation process. The model is trained and tested on two X-ray report generation datasets, IU X-ray and MIMIC-CXR, achieving promising results on word overlap, clinical accuracy, and semantic similarity-based metrics. Qualitative results with Grad-CAM demonstrate disease location for radiologists’ understanding. Our proposed model embraces the radiologists’ workflow to improve explainability, transparency, and trustworthiness for radiologists.
The Effectiveness of DKIM and SPF in Strengthening Email Security

Email security is of utmost importance to organizations, and to enhance it, frameworks such as Sender Policy Framework (SPF) and DomainKeys Identified Mail (DKIM) are utilized for authentication. In this research work, we conducted a comparative analysis of the efficacy of SPF and DKIM in authenticating emails. Both frameworks offer a certain level of protection against email threats. However, our research indicates that DKIM is more effective as it adopts a more comprehensive approach. Based on our findings, we recommend that businesses prioritize the implementation of DKIM and the Domain-based Message Authentication, Reporting, and Conformance (DMARC) framework to enhance email security and resilience against email impersonation.
Investigating the Challenges Companies in Rwanda Face when Implementing Zero-Trust Network

Increased security measures are essential due to the rising frequency of cyber threats and attacks, especially in small and medium-sized organizations (SMEs). By continually monitoring and authenticating every user and device seeking to access network resources, the Zero Trust Network (ZTN) has developed into a reliable security paradigm that offers increased protection. There have been many solutions developed, yet the adoption and efficiency of Zero-Trust Network in Rwandan SMEs have not been properly researched, and the research that has been done does not clearly indicate what could be of assistance to a country like Rwanda in terms of assets and expertise. The objective of this research is to close that gap through investigating Zero-Trust Network adoption and effectiveness in Rwandan SMEs. SME managers and IT experts were polled as part of the study to get insights about their existing security practices and perceptions about ZTN. The survey’s findings indicate that SMEs face financial and expertise issues when using ZTN. In spite of having onsite and remote networks, over 75% of businesses still rely on perimeter-based security and have little experience with ZTN. However, its application could assist SMEs strengthen their security posture and reduce cyber risks. In addition to outlining the networking protocols needed for ZTN, our paper suggests an economical approach that SMEs can utilize to set up a low-cost Zero-Trust Network. By utilizing open-source and widely accessible technologies, this design makes deployment simple for SMEs. Finally, the findings of our analysis indicate that firms can prevent up to 94% of potential cyberattacks by employing the suggested solution.
Semantic Segmentation Based on Vision Transformer via Interactive Attention

Semantic segmentation is a fundamental task in the computer vision community that aims to achieve pixel-wise classification of images. Convolutional Neural Networks (CNNs) have been the backbone of typical semantic segmentation methods. However, the recent success of the Transformer architecture in natural language processing has led to its application in the field of image semantic segmentation. These methods mainly focus on learning more effective information through the encoder, while paying less attention to the decoder. In this paper, we propose a novel attention-based decoder module called the Attention In Attention (AIA) module. This module employs interactive attention to extract spatial and channel information and dynamically determine feature importance. Additionally, we propose the Feature Position Offset Estimation Module (FPOEM) to mitigate feature misalignment when features of different scales are fused. Experiments on two datasets, Cityscapes and ADE20K, show that the method proposed in this paper achieves state-of-the-art performance.
A Study on Base Station Traffic Classification Based on K-means and Improved KNN Models

In this paper, time series data features are extracted for classification based on HTRN Dataset, and 30,000 data objects are randomly selected as the training test sample set for preliminary cluster analysis. Then, based on the improved KNN algorithm, the remaining samples are categorized into the categories with the closest distance. The original data are preprocessed and randomly sampled to obtain the test samples to divide the dataset; the tsfresh tool is used to extract the statistical features, entropy features and segmentation features of the time series as the corresponding feature vectors for clustering, and the feature vector F is obtained; at the same time, the importance of the individual features of the constituent feature vectors is analyzed based on the Random Forest method. The optimal number of clusters is obtained according to the contour coefficient and the elbow rule, and then the k-means method is used for feature-based cluster analysis; the improved KNN model is applied to classify the non-tested sample plots. The final results show that the above method can effectively improve the processing efficiency and accuracy, and has certain practical feasibility, in order to provide certain reference for research in other fields.
POA: Passable Obstacles Aware Path-Planning Algorithm for Navigation of a Two-Wheeled Robot in Highly Cluttered Environments

This paper focuses on Passable Obstacles Aware (POA) planner - a novel navigation method for two-wheeled robots in a highly cluttered environment. The navigation algorithm detects and classifies objects to distinguish two types of obstacles - passable and unpassable. Our algorithm allows two-wheeled robots to find a path through passable obstacles. Such a solution helps the robot working in areas inaccessible to standard path planners and find optimal trajectories in scenarios with a high number of objects in the robot's vicinity. The POA planner can be embedded into other planning algorithms and enables them to build a path through obstacles. Our method decreases path length and the total travel time to the final destination up to 43 % and 39 %, respectively, comparing to standard path planners such as GVD, A*, and RRT*.
Anomaly Detection for Industrial Sensors Using Transformers

Anomaly detection is a critical task in many domains, including the Internet of Things (IoT), where large volumes of sensor data are generated from various devices. Traditional methods based on domain knowledge often fail to capture the complex correlations among the variables and time series. Therefore, there is a need for robust and efficient anomaly detection methods that can learn from data. Recently, deep learning models have shown promising results in detecting anomalies in multivariate time-series data. In particular, transformer-based models and convolutional networks have gained popularity due to their ability to capture long-term dependencies and extract meaningful features from high-dimensional data. In this study, we propose a transformer-based framework for anomaly detection in IoT systems. Our model employs dynamic graph attention to capture the complex correlations among variables and time series, and a transformer architecture with parallel processing ability to extract multidimensional features for downstream prediction tasks. We evaluate our model on several publicly available datasets and demonstrate its superior performance compared to state-of-the-art methods. In summary, our proposed framework is a promising approach for anomaly detection in sensor data for industry predictive maintenance, which can potentially reduce downtime, increase efficiency, and save costs.
ChartEye: A Deep Learning Framework for Chart Information Extraction

The widespread use of charts and infographics as a means of data visualization in various domains has inspired recent research in automated chart understanding. However, information extraction from chart images is a complex multi-tasked process due to style variations and, as a consequence, it is challenging to design an end-to-end system. In this study, we propose a deep learning-based framework that provides a solution for key steps in the chart information extraction pipeline. The proposed framework utilizes hierarchal vision transformers for the tasks of chart-type and text-role classification, while YOLOv7 for text detection. The detected text is then enhanced using Super Resolution Generative Adversarial Networks to improve the recognition output of the OCR. Experimental results on a benchmark dataset show that our proposed framework achieves excellent performance at every stage with F1-scores of 0.97 for chart-type classification, 0.91 for text-role classification, and a mean Average Precision of 0.95 for text detection.
Enhanced Experience Replay for Class Incremental Continual Learning

Continual learning aims to construct a machine model that learns multiple tasks sequentially. However, it may face the significant challenge of catastrophic forgetting, where the model minimizes the loss on the current data and forgets the previously learned tasks. A common approach to mitigate catastrophic forgetting is replay-based continual learning, which stores data points from previous tasks in a buffer and revisits them periodically. However, not all data points in a task contribute the same significance for learning. Hence, coreset selection is crucial for continual learning with imbalanced and noisy datasets. In this paper, we introduce Enhanced Experience Replay (EER), a simple yet effective method that selects the most representative and informative coreset for training at each iteration. EER not only optimizes the model’s adaptability to the current dataset but also prioritizes samples exhibiting a high affinity with previous tasks. Evaluated on CIFAR-10 and CIFAR-100 datasets, our coreset selection mechanism significantly enhances task adaptability and prevent catastrophic forgetting. The proposed method achieves state-of-the-art performance on the two benchmark datasets.
Blockchain Security Encryption to Preserve Data Privacy and Integrity in Cloud Environment

Blockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest.
Self-sovereign Identity-based Access Control Management in Forestry 4.0

Today’s forest-based sector has been perceived as an industrial branch that slowly accommodates digital transformation toward Industry 4.0. The integration of Internet of Things (IoT) technologies in this sector offers numerous benefits and opportunities for enhanced efficiency, sustainability, and decision-making processes. In Forestry 4.0, which is characterized by its decentralized and heterogeneous nature, enabling transparent, secure, and self-sovereign interconnection and data exchange becomes especially crucial. In this context, authentication and authorization have become vital pillars for ensuring the security of IoT systems. Self-sovereign identities (SSI) enable individuals to have full control over their identities and related information. They can be created, managed, and verified without dependency on a centralized authority. The integration of SSIs into authentication and authorization systems has proven to be robust and secure for communication in various domains such as smart cities and health care. The adoption of SSIs is hypothesized to be the crucial factor in establishing decentralized communication while maintaining tamper-evident and trustworthy systems. From this perspective, we propose our SSI-based generic access control management system in this paper. Concretely, we structure the system based on XACML and integrate DIDs/VCs to enable authentication and authorization processes in a formalized way. Finally, a proof-of-concept prototype is implemented to demonstrate the feasibility of our proposed system in Forestry 4.0 with the passability use case.