Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and Smart Contracts for Smart Sourcing Process in Automotive Supply Chain

The sourcing process can be a stressful task for the procurement team as it requires a lot of efforts, time and costs to be efficiently performed. The situation becomes more complex when dealing with a large supply chain (SC) like automotive SC. Hence, to handle such complexity, the digitalization of this process becomes a necessary. As, blockchain technology helps lighten-up the human intervention and encrypts information to save data integrity, it has been considered by different researchers as a solution for the public procurement area. Therefore, the aim of this paper is first to examine the sourcing process in a non-public area such as the automotive SC. Second, a framework based on the blockchain technology & Smart contract is presented to answer the identified issues. Third, a practical solution for the identified issues is presented for the automotive SC. This solution clears the sourcing team from tasks (i.e., transferring data, time planning...etc), and help to digitally perform a transparent and traceable process. These findings give an opportunity for researchers and practitioners to have an overview about how to digitize the sourcing process using blockchain technology & smart contracts to benefit from its advantages and features.
Blockchain Technology for Supply Chain Traceability: Case Study of Pharmaceutical Supply Chain

Pharmaceutical drugs are a part of high-value goods that tricking in it can generate annually thousands of deaths and billions of dollars of losses in revenue globally. Blockchain technology (BT) can digitize the tracking of physical assets from production to delivery or until customer. It provides greater product history and transparency. This creates a decentralized and immutable record of all transactions, which provides more visibility to both businesses and consumers into the product that they use. In this paper, we will present a formal assessment of the literature on blockchain focusing on the exploitation of blockchain technology in the pharmaceutical supply chain (PSC). Besides, we will propose a blockchain-based smart contract solution implemented on Ethereum for PSC. This can bring researchers more areas to conduct scientific research in other supply chains. Moreover, it could help managers maintain more traceability over the supply chain.
Artificial Intelligence for Work Measurement: A Promising Approach to Improving Productivity and Reducing Waste

Work measurement is a critical aspect of industrial engineering, where various methods are employed to analyze work and optimize productivity. One of the primary tools used by industrial engineers is cycle time, which determines the time taken to complete tasks within a company. However, traditional human analysis of work can be challenging and time-consuming, requiring significant effort to measure and analyze work through direct observations. This paper proposes a new methodology that uses artificial intelligence algorithms, combining ConvLSTM and Faster R-CNN, to identify and track the actions and items manipulated by operators while computing important metrics like cycle time and total wastes. Our approach enables the analysis of human-object interaction from an egocentric perspective, providing an alternative to traditional methods that involve human analysts. We tested our method on the Meccano Dataset and our own dataset, and the results are promising. The proposed methodology is robust, cost-effective, and useful for measuring standard time and identifying waste in various industrial environments. This research demonstrates the potential of artificial intelligence in work measurement and analysis and can lead to significant improvements in productivity and efficiency.
Trustworthy Semantic Communications for the Metaverse Relying on Federated Learning

As an evolving successor to the mobile Internet, the Metaverse creates the impression of an immersive environment, integrating the virtual and real world. In contrast to the traditional mobile Internet based on servers, the Metaverse is constructed by billions of cooperating users by harnessing their smart edge devices and having limited communication and computation resources. In this immersive environment, an unprecedented amount of multi-modal data has to be processed. To circumvent this impending bottleneck, low-rate semantic communication might be harnessed in support of the Metaverse. However, given that private multi-modal data is exchanged in the Metaverse, we have to guard against security breaches and privacy invasions. Hence, we conceive a trust-worthy semantic communication system for the Metaverse based on a federated learning architecture by exploiting its distributed decision-making and privacy-preserving capability. We conclude by identifying a suite of promising research directions and open issues.
SCAResNet: A ResNet Variant Optimized for Tiny Object Detection in Transmission and Distribution Towers

Traditional deep learning-based object detection networks often resize images during the data preprocessing stage to achieve a uniform size and scale in the feature map. Resizing is done to facilitate model propagation and fully connected classification. However, resizing inevitably leads to object deformation and loss of valuable information in the images. This drawback becomes particularly pronounced for tiny objects like distribution towers with linear shapes and few pixels. To address this issue, we propose abandoning the resizing operation. Instead, we introduce Positional-Encoding Multi-head Criss-Cross Attention. This allows the model to capture contextual information and learn from multiple representation subspaces, effectively enriching the semantics of distribution towers. Additionally, we enhance Spatial Pyramid Pooling by reshaping three pooled feature maps into a new unified one while also reducing the computational burden. This approach allows images of different sizes and scales to generate feature maps with uniform dimensions and can be employed in feature map propagation. Our SCAResNet incorporates these aforementioned improvements into the backbone network ResNet. We evaluated our SCAResNet using the Electric Transmission and Distribution Infrastructure Imagery dataset from Duke University. Without any additional tricks, we employed various object detection models with Gaussian Receptive Field based Label Assignment as the baseline. When incorporating the SCAResNet into the baseline model, we achieved a 2.1% improvement in mAPs. This demonstrates the advantages of our SCAResNet in detecting transmission and distribution towers and its value in tiny object detection. The source code is available at https://github.com/LisavilaLee/SCAResNet_mmdet.
SMTN: Multidimensional Fusion and Time-Domain Coding for Object Tracking in Satellite Videos

Object tracking in satellite videos contains various small targets, such as cars and ships. However, since small targets always lack salient texture features, and have low contrast to the background, it is difficult to detect targets, and distinguish different target instances, which results in tracking failure. In this letter, we propose a Siamese multidimensional fusion and time domain coding network (SMTN) with an efficient attention-based multidimensional information fusion (MDF) module and a time domain information fusion (TDF) module. The MDF module fuses multi-scale template map and search map information to make the network focus on the target, thus improving the target discriminability. In the TDF module, the aggregated temporal information of previous frames is used to adjust the current frame response map through a local-sensing Metaformer module, which suppresses the response of similar interferences. Different from other temporal fusion methods in object tracking in satellite videos, the TDF enables video satellite trackers to be optimized end-to-end, and improves the tracking success rate. Experiments demonstrate that the proposed method outperforms state-of-the-art trackers.
From the Web to the Mathematics Classroom: Investigating Internet Phenomena as Educational Resources in Mathematics

Twenty-first-century learners access and share information and knowledge differently from previous generations. This difference depends greatly on the technological changes that nurtured the birth of websites hosting user-generated content, including digital objects that span across a variety of themes and purposes. Some objects are created and shared with explicit educational aims, and others populate the Web for mere recreation. Among recreational user-generated digital objects, some reveal unplanned educational potentialities in many fields, as is the case of mathematical memes, and aggregate interest-driven online communities that become informal learning environments. Despite these educational potentialities, these objects are immersed in the culture that generated them, and therefore, they are not yet ready to become traditional educational resources. To profit from them for mathematics education, we need knowledge about how to investigate and transform them into usable resources for teaching mathematics. This chapter focuses on recreational user-generated digital objects with mathematical content with a two-fold purpose: to show that these objects are worth exploring as examples of out-of-school mathematics that can inspire the design of new teaching materials, and to provide a theoretical understanding and a consistent method for the investigation. This understanding may guide further researchers interested in exploring Internet phenomena in general with an educational purpose, in the broader research perspective of addressing the technical and cultural discontinuity between informal out-of-school learning contexts and traditional school-based ones.
MadFed: Enhancing Federated Learning With Marginal-Data Model Fusion

As the demand for intelligent applications at the network edge grows, so does the need for effective federated learning (FL) techniques.However, FL often relies on non-identically and nonindependently distributed local datasets across end devices, which could result in considerable performance degradation.Prior solutions, such as model-driven approaches based on knowledge distillation, metalearning, and transfer learning, have provided some reprieve.However, their performance suffers under heterogeneous local datasets and highly skewed data distributions.To address these challenges, this study introduces the MArginal Data fusion FEDerated Learning (MadFed) approach, a groundbreaking fusion of model-and data-driven methodologies.By utilizing marginal data, MadFed mitigates data distribution skewness, improves the maximum achievable accuracy, and reduces communication costs.Furthermore, the study demonstrates that the fusion of marginal data can significantly improve performance even with minimal data entries, such as a single entry.For instance, it provides up to a 15.4% accuracy increase and 70.4% communication cost savings when combined with established model-driven methodologies.Conversely, relying solely on these model-driven methodologies can result in poor performance, especially with highly skewed datasets.Significantly, MadFed extends its effectiveness across various FL algorithms and offers a unique method to augment label sets of end devices, thereby enhancing the utility and applicability of federated learning in real-world scenarios.The proposed approach is not only efficient but also adaptable and versatile, promising broader application and potential for widespread adoption in the field.
Design of an Expressive Robotic Guitarist

We introduce a newly developed robotic musician designed to play an acoustic guitar in a rich and expressive manner. Unlike previous robotic guitarists, our Expressive Robotic Guitarist (ERG) is designed to play a commercial acoustic guitar while controlling a wide dynamic range, millisecond-level note generation, and a variety of playing techniques such as strumming, picking, overtones, and hammer-ons. To allow for these functionalities, we recorded and modeled human guitarists and implemented our findings in hardware and software design. The ERG's final construction includes a robotic cobot arm for strumming and plucking, as well as a 12-motor fret-shifting module that can move across the guitar bridge. We evaluated the ERG dynamic range, speed, microtiming control, and guitar techniques using both objective and subjectivemethods. The objective benchmarks and subjective ratings demonstrate that the ERG can perform with human-level expressivity.
Open Source Solutions for Vulnerability Assessment: A Comparative Analysis

As software applications continue to become more complex and attractive to cyber-attackers, enhancing resilience against cyber threats becomes essential. Aiming to provide more robust solutions, different approaches were proposed for vulnerability detection in different stages of the application life-cycle. This article explores three main approaches to application security: Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Software Composition Analysis (SCA). The analysis conducted in this work is focused on open-source solutions while considering commercial solutions to show contrast in the approaches taken and to better illustrate the different options available. It proposes a baseline comparison model to help evaluate and select the best solutions, using comparison criteria that are based on community standards. This work also identifies future opportunities for application security, highlighting some of the key challenges that still need to be addressed in order to fully protect against emerging threats, and proposes a workflow that combines the identified tools to be used for vulnerability assessments.
Differentially Private Denoise Diffusion Probability Models

Diffusion models and their variants have achieved high-quality image generation without adversarial training. These algorithms provide new ideas for data shortages in some fields. But the diffusion model also faces the same problem as other generative models: the learned probability density function will retain the characteristics of the training samples, which means that the high complexity of the deep network will make the model easily remember the training samples. When a diffusion model is applied to sensitive datasets, the distribution the model focuses on may reveal private information, and the security concerns described above become more pronounced. To address this challenge, this paper proposes a privacy diffusion model named DPDM (Differentially Private Denoise Diffusion Probability Models) that satisfies differential privacy by adding appropriate noise to the gradient during the training. Besides, this paper adopts a series of optimization strategies to improve model performance and training speed such as adaptive gradient clipping threshold and dynamic decay learning rate. Through the evaluation and analysis of the benchmark dataset, it is found that the attempt in this paper has promising usability, and the synthetic data has better performance.
A Conceptual Model Framework for XAI Requirement Elicitation of Application Domain System

The use of data analytics and Machine Learning (ML) branches of AI for predictive and analytic knowledge retrieval has surged significantly in various industries (e.g., health, finance, business, and manufacturing). However, the acceptance of AI has been hindered by opaque models that lack transparency. Explainability in AI (XAI) has gained significant prominence owing to its focus on introducing avenues of accountability in AI. XAI acknowledges the importance of human factors and strives to incorporate them into the design process, recognising that the cognitive effort involved in understanding explanations is a key aspect. Mental Models play a crucial role in the XAI evaluative premise, but their current utility is limited. By intentionally designing explanations that align with users’ mental models, their experiences can be significantly enhanced, leading to improved understanding, satisfaction, trust, and performance. This study proposes using Mental Models to elicit explainability requirements and to develop an Ontology-Driven Conceptual Model to facilitate the learning process for a better understanding of explanations.
PWR: Path Weighted Random Scheme in Multipath Traffic Splitting Against Website Fingerprinting

With the continuous evolution of network attack methods, traditional security mechanisms based solely on data content encryption are no longer sufficient to ensure data security and privacy. Traffic analysis techniques, such as website fingerprinting (WFP) attacks, can effectively classify and extract information without cracking encrypted content, which enables the analysis of user behavior. The splitting of traffic in heterogeneous network resources has emerged as a new defense mechanism to disrupt traffic characteristics, however current research has not achieved a balance between security and efficiency. To address this challenge, we propose a path-weighted random (PWR) scheduling mechanism based on path quality to construct a time-varying path selection probability distribution function, that disrupts traffic characteristics and achieves resistance to traffic analysis. Furthermore, we incorporate order scheduling based on data arrival estimation to balance the transmission efficiency in heterogeneous networks. Our proposed solution was experimentally validated in real-world settings, demonstrating superior performance in terms of both security and efficiency.
Toward Understanding Efficient Privacy-Preserving Homomorphic Comparison

The security issues that arise in public cloud environments raise several concerns about privacy-preserving. Conventional security practices successfully protect stored and transmitted data by encryption, but not during data processing where the data value extraction requires decryption. It creates critical exposure points for sensitive sectors like healthcare, pharmaceutical, genomics, government, and financial, among many others that cause hesitation to use these third-party services and prevent widespread practical adoption of cloud solutions. Homomorphic Encryption (HE) emerges as a mechanism for expanding the scope of public cloud services for sensitive data processing. However, high-demand solutions such as artificial intelligence and machine learning require efficient operations beyond HE additions and multiplications. In this paper, we analyze the current homomorphic comparison methods across their strengths and weaknesses and present theoretical concepts, state-of-the-art techniques, challenges, opportunities, and open problems. We theoretically prove the limits of the representability of sign and comparison functions in polynomial forms for HE schemes. We show that both functions can be represented as polynomials over the Galois field and cannot be represented over a residue ring with zero divisors. We compare the efficiency, accuracy, and computational complexity of different homomorphic comparison approaches. The experimental results demonstrate that Newton-Raphson is the fastest method for generating polynomial approximations and evaluating comparisons, and the Fourier method is the most accurate considering the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> norms and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> measure. The bi-objective analysis presents the performance compromise between complexity and accuracy.
Moth Flame Optimization With Hybrid Deep Learning Based Sentiment Classification Toward ChatGPT on Twitter

ChatGPT, developed by OpenAI, is an advanced language model that excels at generating human-like text responses in conversational settings. As ChatGPT interacts with the user, it creates a range of sentiments from them, involving neutral, positive, or negative expressions. Sentiment analysis (SA), also called opinion mining, is a branch of natural language processing (NLP) that defines the emotional tone or sentiment conveyed in textual data. Sentiment analysis (SA) plays a major role in understanding how people respond and perceive different entities, involving services, products, brands, or artificial intelligence (AI) models GPT. Analyzing the sentiment toward ChatGPT gives valuable insight into, user experience, areas, and satisfaction for development. The study presents a moth flame optimization with hybrid deep learning-based sentiment analysis (MFOHDL-SA) on ChatGPT. The major aim of the MFOHDL-SA method is to design an automated AI model to properly classify the tweets as positive, negative, or neutral in sentiment towards ChatGPT. To accomplish this, the MFOHDL-SA technique initially pre-processes the tweets in different stages. Next, the TF-IDF model is used for the word embedding process. Moreover, the HDL method comprising a convolutional neural network with long short-term memory (CNN-LSTM) method was utilized for sentiment classification. To improve the classifier results of the HDL model, the MFO algorithm is used for hyperparameter tuning. The simulation results of the MFOHDL-SA technique are validated on the Twitter dataset from the Kaggle repository. The obtained experimental outcomes stated the betterment of the MFOHDL-SA approach over other existing techniques in terms of different measures. This provides a valued understanding of public sentiment towards ChatGPT on Twitter, allowing improved understanding and assessment of its impact and perception among users.
Feature Map Activation Analysis for Object Key-Point Detection

Determining object location information in an image can enable more accurate CNN classification. Several extended CNN models have been developed to include both object location and classification into a unified model, at a cost of increasing compute complexity, increasing the number of parameters, and typically having a lower number of classification categories. We show that key-points within classifiable objects can be identified in early layer feature maps of a simple CNN without dependence on deeper layer processing or classification predictions. A statistical analysis of early feature maps is used to create a method for identifying and locating key-points that, with high probability, correspond to object locations in the image. This method uses only the forward pass of the simple CNN and requires no additional training. The method is tested on an image data set with known ground truth object locations, and results for object locations derived from key-points compare favorably to results obtained from R-CNN.