Reviewerly/exHarmony huggingface dataset:
Specification and Verification of Communication Paradigms for CBSE in Event B

The development of distributed computing systems and of their usage in domains such as the Internet of Things, Big Data, etc., raises numerous questions on the tools available to model the complexity of such systems. Non-formal modeling methods fail to create a rigorous way to describe and analyze these systems. In this paper, we propose an approach to model and analyze the structural and behavioural aspect of these systems using formal techniques. As a prerequisite, we build reusable model libraries to specify and verify communication paradigms for modeling software architectures of distributed systems in a component-based system engineering (CBSE) context. First, we describe high-level concepts for system architecture in a component-port-connector fashion as a metamodel. Then, we develop an Event-B interpretation of the metamodel adding communication characteristics such as buffering, FIFO and synchronicity. To validate our work, we studied the two well-known communication paradigms, namely message passing and remote procedure call.
Accelerating Distributed ML Training via Selective Synchronization (Poster Abstract)

In Bulk-synchronous parallel (BSP) or simply synchronous training, deep neural networks (DNNs) are launched across multiple workers concurrently and aggregate their local updates either by Parameter server (PS) [1] or via decentralized AllReduce [2] . Thus, aggregation step on every iteration is blocking, i.e., all workers must wait for reduction phase to complete before proceeding to the next step. ML accelerators like GPUs and TPUs have reduced computation times, but communication cost continues to increase with the growing size of DNNs. Even with weak scaling and Gustafson’s law [3] , distributed training does not scale linearly with the number of workers due to high synchronization overhead.
Private set intersection using RSA subgroups with constant-size encryptions

Private set intersection (PSI) evaluation is a well-known secure multiparty computation problem that has received much attention. Authors generally tend to state improved performance as a goal and justification for their proposed PSI schemes. In this paper, we propose an original and new approach to two-party PSI evaluation for small sets that is based on small RSA subgroups. Contrary to previous approaches that at best incur linear communication and computational complexity to the set size, our scheme is the first to achieve constant communication and computational complexity incurring only 2 group elements and 5 exponentiations. An extended PSI scheme handling larger sets is also proposed.
Privacy-Preserving Reputation System Against Dishonest Queries

Reputation systems are helpful for our decision making when we have to deal with unfamiliar service providers. However, some of these systems do not preserve privacy of the users, e.g., rating from an individual is obviously shown. Therefore, users may provide dishonest feedback due to the fear of retaliation. Although, some privacy-preserving reputation systems are proposed in the literature, most of them cannot withstand some specific attacks. These attacks include "dishonest queries to similar sets of users" and "collusion between the dishonest querier and all-but-one users". As a result, rating from an individual may be leaked.As the contribution of this work, we propose a novel privacy-preserving reputation system that addresses the aforementioned attacks. The idea is that, the querier firstly specifies a set of users, then the average rating is computed from a random subset of that set. Since the members of the random subset are not known to anyone, the attacks become difficult. We construct our scheme based on a secret sharing technique, specifically ABY3 framework (Mohassel and Rindal, CCS’18) that supports conversion between different data representations. Correctness, security, privacy, and complexity are then discussed, and possible improvements are also suggested. The experiments show that the accuracy of our scheme is similar to the case where privacy is not preserved.
Course-Correct to DeFi Lacking Default Deficiency

This paper explores methods of assessing risk on-chain by efficiently integrating off-chain identity- and attribute-verification and on-chain transaction activity. While decentralized finance (DeFi) is a well-suited application of permissionless blockchains, it is severely constrained in its ability to reconcile real-world identities and, as a result, to quantify their associated transaction risk using solely on-chain data, limiting its realistic applications. Opening the ecosystem to a broader range of use cases requires consistent pseudonymity and quantifiable reputation. Traditional finance quantifies risk by collecting and vetting reputation information for an individual, such as credit scores or payment history. We explore a mechanism to incorporate these traditional trust factors into the DeFi ecosystem while both preserving individual privacy within a competitive and fair environment and retaining compatibility with existing platforms such as Ethereum. Even though blockchains are inherently public, our solution gives users control over the release of information that pertains to them. Consequently, our contribution focuses on customized methods that balance the level of disclosure of verfiable, provably-sourced user information against the likelihood of the user successfully gaining access to a desired resource, such as a loan under mutually-agreeable terms. Our solution is consistent with the zero-trust model in that it imports explicit trust from recognized sources through relevant, continuously updated metrics.
A TCP-based Covert Channel with Integrity Check and Retransmission

We propose a covert channel and its implementation in Windows OS. This storage channel uses the Initial Sequence Number of TCP to hide four characters of text, and the identification field to "sign" the message and thus understand if it has been altered during the transmission. The secret is sent in the first SYN packet to open a connection, and an ACK-RST response acknowledges the receipt. Designed error-correction codes make the protocol more robust and able to handle (IP) packet drops and transmission errors. In this paper, we provide a detailed discussion of the implementation and an evaluation of the stealthiness of the proposed channel.
Formal alignment of requirements models with high-level architecture models

The quality of a system depends on the degree to which it fulfills its requirements. Requirements traceability is broadly recognized as a crucial element of any rigorous system development process, especially for the design of critical complex systems. The paper proposes a model-based approach to support traceability by defining several kinds of alignment links between requirements models and high-level architecture models. We have already defined these models using a combination of graphical notations based on SysML and the formal method Event-B, hence alignment links are first graphically specified by refining the Dependency concept of SysML. Then they are translated into Event-B specifications. The main idea is to use the formal refinement concept to prove their correctness. As the semantics of this refinement is not the same as the standard Event-B refinement, we have defined new proof obligations to express it, which can be discharged using Atelier B. Finally we illustrate our approach on a case study from the railway domain.
A Trust-Based Approach for Data Sharing in the MQTT Environment

Internet of Things (IoT) is considered as a giant network of connected devices who collect data and share them with each other.There has been extensive developments on IoT standards and protocols that enable IoT devices to exchange data in a structured and meaningful way.Message Queuing Telemetry Transport (MQTT) is one of such developments receiving widely adoption for industrial applications.It is designed as a lightweight messaging protocol based on the publish-subscribe model by which clients publish messages to a broker who is responsible for distributing the messages to subscribed clients.MQTT is often deployed in a hostile environment in which IoT devices and brokers are vulnerable to attacks.While security for MQTT has received great attention, it does not adequately address the authorisation issues within a decentralised MQTT environment.Existing work adopts policy-based approaches to regulate data sharing across multiple brokers, which we believe, are unlikely to scale well.In this paper we propose a trust-based approach that can be easily incorporated into the existing implementation of MQTT broker.We introduce a way of computing trust rating of brokers and develop two means of using the trust ratings to control data flow across multiple broker domains.Our approach is capable of detecting and blocking malicious clients and brokers from sending false or malicious messages into the system.
Layered Security Analysis for Container Images: Expanding Lightweight Pre-Deployment Scanning

Containerization using tools such as Docker has transformed the way applications are deployed and managed in various organizations. However, the use of containers also presents new security challenges due to the potential vulnerabilities that may be present in the container images. To address this, various vulnerability detection tools that use static analysis have been developed that focus more on OS packages, and fail to detect known package vulnerabilities. In this paper, we propose a pre-deployment methodology that detects vulnerabilities in container images targeting both the OS and application packages using a layered approach, where static tools generally fail to detect vulnerabilities in those images. Our solution offers a high degree of customizability and control over its performance in detecting vulnerabilities in images. Users can choose a specific scan profile that is tailored to their particular needs, enabling the detection of vulnerabilities that are either more common, more unique, or a balance of the two. This adaptability makes our solution more flexible and better suited to meet the specific needs of our users. We evaluate our proposed framework against 111 both official and community images collected from Docker Hub to demonstrate its effectiveness compared to other popular static analysis tools for containers.
Few‐shot learning for word‐level scene text script identification

Abstract Script identification of text in scene images has attracted massive attention recently. However, the existing techniques primarily emphasize on scripts where data are available abundantly, such as English, European, or East Asian. Although these methods are robust in dealing with high‐resource data, how these techniques will work on low‐resource scripts has yet to be discovered. For example, in India, there is a disparity among the text scripts across the country's demographic. To bridge the research gap for resource‐constraint script identification, we present a few‐shot learning network called the TextScriptFSLNet. This network does not require huge training data while achieving state‐of‐the‐art performance on benchmark datasets. Our proposed method acts in accordance with a ‐way ‐shot paradigm by splitting the train set as support and query set, respectively. The support set learns representative knowledge of each class and creates its prototypes. We use multi‐kernel spatial attention fused 2‐layer convolutional neural network and averaging technique to generate the prototype of each class. This spatial attention aids in grasping important information in an image and enriches the feature representation. To the best of our knowledge, the proposed work is the first of its kind in the scene text understanding domain. Additionally, we created a dataset called Indic‐FSL2023 comprising 10 of the 22 officially recognized Indian scripts. The proposed method achieves the highest accuracy among the tested methods on the newly created Indic‐FSL2023. Experiments are also conducted on MLe2e to demonstrate its versatility. Furthermore, we also showed how our proposed model performed concerning illumination changes and blur on scene text script images.
Video Denoising Using Cascaded Skip Connection Feedforward UNets

Quality video services have already gained high technical and commercial importance. The published work so far in this domain proposed mathematically and computationally complex algorithms, followed by the recent training-greedy deep learning-based denoising algorithms. This work proposes a video-denoising algorithm based on multiple UNet networks. The proposed video-denoising algorithm uses multiple encoder-decoder networks for video noise residual frame estimation, un-like the single encoder-decoder used by the published denoising algorithms. Using multiple skip connection UNets, we increase the residual noise modeling accuracy while restricting the signal features, which helps to improve denoising performance. The proposed network is trained end-to-end without motion compensation to reduce its complexity. The proposed network outperforms all the video denoising algorithms in terms of SSIM metric while it yields comparable performance in terms of PSNR.
Camera Image Dehazing and Target Detection for Autonomous Vehicles

This paper describes a simple target detection system for autonomous vehicles to perform an emergency stop. We especially focus on the system only with a digital camera as the perception unit for the situation. The detecting performance of the imaging sensors, including the digital cameras, often suffer from “haze” due to atmospheric obscuration. In this paper, we review the image “dehazing” algorithms aiming for the in-vehicle usage, and we propose a system configuration for the safety measure with applying a selected algorithm. A series of experimental results depicted the feasibility of the system configuration and its applicability to real-time processing.
A Cost-Sensitive and Simple Masking Design for Side-Channels

Conventional countermeasures against Power Analysis Attacks ignore area overhead for implementation since it only pursuit perfect tamper-resistance. While the countermeasures achieve high tamper-resistance, unacceptable area overhead is required. Thus, the design of the countermeasures cannot be applied for IoT edge devices, which are provided as cheaper products. In this paper, first, we show the unacceptable area overhead by the conventional countermeasures, then propose a lightweight and fundamental VLSI design method against Power Analysis Attack. Masked Wave Flip-Flop (MW-FF), proposed design method achieves higher tamper-resistance than Wave Dynamic Differential Logic (WDDL) that is one of the conventional countermeasures with small and acceptable area overhead. Our evaluations show that MW-FF requires only 12.51% and 5.89% of are overhead in FPGA and ASIC respectively, which is 114.03% and 21.82% saved comparing with WDDL.
Implementation of Transformer-Based Model for Acute Lymphoblastic Leukemia Segmentation

The examination of peripheral blood smear images for acute lymphoblastic cells represents a diagnostic approach for leukemia. The utilization of semantic segmentation of acute lymphoblastic cells can be employed in the development of a computer-aided analysis system. In the realm of peripheral blood smear analysis, deep learning methods, particularly convolutional neural networks, are commonly utilized. Currently, transformer-based models have emerged as the state-of-the-art approach for semantic segmentation tasks. In this study, Seg-Former, a transformer-based model for semantic segmentation, was utilized to segment and classify acute lymphoblastic cells using four distinct training strategies. The optimal outcome was achieved with a mean intersection-over-union (IoU) of 0.821 and a mean accuracy of 0.917.
Exploring the Impact of Frequency Components on Adversarial Patch Attacks Against an Image Classifier Model

Exceptional advancements in various computer vision tasks, such as identifying and categorizing objects, have been realized through the use of deep learning models, with a particular emphasis on convolutional neural networks (CNNs). Yet, while these models deliver outstanding results, they remain vulnerable to adversarial examples, thereby raising questions about their safety and dependability. In this paper, we investigate the influence of the image characteristics on the efficacy of adversarial patch attack against an image classifier model. We analyzed such characteristics in the frequency domain, where the frequencies indicate the periodicity and information density that contribute to the efficacy of adversarial patches. Our results showed that low-frequency components had significant contribution to the effectiveness of adversarial patch attacks.
Enhancing Student Engagement in Engineering and Education Through Virtual Reality: A Survey-Based Analysis

This paper investigates the impact of virtual reality (VR) on student engagement in engineering education and their potential in enhancing the student learning experience through technology-led learning. VR technology has shown to improve student engagement in different educational sectors. As a result, this study evaluated the efficacy of VR technology as an immersive learning tool focusing on engineering education. The survey collects data from a diverse sample of students who experienced the use of VR-based flight simulator as a part of their continuous assessment, providing valuable insights. Conditional results indicate that by using VR headsets 70% of students reported improved learning outcomes for the module, while 100% students agreed that VR technology offered a more immersive learning experience. The survey results indicate and emphasize the potential benefits that integrating VR headsets into engineering education could bring to the student learning experience through enhanced student engagement and the acquisition of practical skills in simulated immersive environments. The paper also makes recommendations for further research and implementation of VR technology in other fields of STEM education.
Measuring Credibility Level of E-commerce Sellers with Structural Equation Modeling and Naive Bayes on Tweets of Service Quality

In digital development 4.0, store brands are very important. The problem in this research is the lack of consumer trust to buy quality goods in e-commerce store accounts so that it affects consumer satisfaction. This study aims to address this question feedback from the problems of the customer, then on the other hand a questionnaire with the PLS-SEM (Partial Least Squares Structural Equation Modeling) model to determine the dimensions of the variables selected according to customer experience. To achieve this aim, both negative and positive customer comments were compiled to assess customer satisfaction, employing a comparative analysis method through Naive Bayes algorithm. The overarching goal was to achieve optimal results and extract valuable insights regarding the determinants that influenced customer satisfaction within the domain of online transactions. This research also has an impact on buyers so they can have an understanding of the factors that support trust in customer satisfaction, so that individuals do not hesitate in making purchasing decisions to shop online. The results showed that the algorithm initially recorded a modest accuracy score of 0.37. Meanwhile, after implementing hyperparameter tuning, the accuracy increased significantly to 0.62. In the aspect of Smart PLS questionnaire analysis, a standardized Normed Fit Index (NFI) of 0.707 was recorded, which was slightly below the established threshold of 0.90. The standardized root mean square residual (SRMR) was measured at 0.071, falling below the specified value of 0.08, indicating a commendable model fit. However, the RMS theta value at 0.240 exceeded the threshold of 0.102.
Compressed Image Super Resolution using Convolutional Neural Network

Image compression is a topic of significant interest as it reduces file sizes in stored data. In this paper, we propose a model that achieves multiple levels of compression, thereby minimizing the storage space required for images, which typically consume substantial amounts of data due to their size and resolution. We combine an image downscaling and upscaling model with an image compression model. By leveraging convolutional techniques to identify image features, we can effectively reduce the size of the image through downscaling and subsequently upscaling it. Additionally, we employ entropy image compression and arithmetic encoding to compress and reconstruct the image while preserving its lossless data. Through experimentation with the Kodak dataset, we observed that our proposed model achieved a compression rate of 96.92%, significantly reducing the data needed for file storage. Moreover, our reconstructed images attained a standardized measure with a signal-to-noise ratio of 33.10 dB and a structural similarity of 0.9219. Notably, the perceptual quality of the images, including intricate details, remained intact to the human eye.
Knowledge Graph for Deriving Insights on The Thai Government Dataset

Natural language processing (NLP) is mandatory in working with text. There are many tools and applications that are based on it. However, most of those tools are often operated in, or only, the English language. In recent years, there has been a continuing development for NLP tools to support other languages or creating a specific tool for certain language for simple NLP tasks, but for some of the advanced tasks, the advancement is still behind that of the English language, Thai language is also one of them. So, in this research, the capabilities of the currently existing Thai NLP tools are explored and evaluated, with the tasks of extracting text from the Thai government dataset (eMENSCR) and creating the knowledge graph from it to improve data interpretability and gain more insight from the data, by utilizing queries that are exclusive, or less complex to execute, when the data is stored in the graph database such as performing a path traversal or relationship counting on the data. Natural language processing's part of speech tagging and named entity tagging is used to perform entity and relation extraction after filtering the unneeded data fields. Then the extracted information will be formulated into the format of “triple”, which is in the form of (head, relation, tail). After the process of triple construction is finished, The triples are evaluated by Precision, recall, and F1 in order to measure the pipeline's performance and import to the Neo4j for query testing. The obtained results show that there is still room for improvement for both the tools and the methodology itself.
A Multi-View Skeleton Data Fusion Method Based on BP Neural Network

In recent years, human skeleton tracking technology has attracted a lot of attention in the fields of virtual reality, human-computer interaction and medical rehabilitation. Human skeleton tracking technology is the basis for building human models in virtual reality scenarios. Among them, Kinect camera is widely used as a motion tracking sensor for virtual reality human-computer interaction. However, many current studies on skeleton point tracking are limited to single or dual camera systems, which leads to problems such as occlusion, missing skeleton data and errors. To solve the problems of limited capture range and data occlusion of a single Kinect camera, this paper proposes a skeleton point tracking method based on multiple Kinect cameras. The method uses multiple Kinect cameras to track the 3D coordinates of 32 body joints simultaneously, and unifies the joint coordinates captured by multiple Kinect cameras in different viewpoints into the same world coordinate system through coordinate transformation. The BP (Back Propagation) neural network is used to train the skeleton data from multiple viewpoints, thus generating a reliable user skeleton position in real time. By this method, the problems of the existing methods for obtaining skeleton points in a single camera view are solved.