Reviewerly/exHarmony huggingface dataset:
An Efficient and Lightweight Commitment Scheme for IoT Data Streams

Data integrity is a crucial property for offering Internet of Things (IoT) services. Usually, this is achieved by assuming a high level of trust in the service provider, a practice that induces negative implications. In the present paper, an efficient and lightweight commitment scheme for IoT data streams is presented. The proposed scheme combines KZG polynomial commitments and a blockchain smart contract to enable full data integrity while requiring trust only at the IoT device level. Given that the IoT device operates properly, no tampering can happen at any other point of the IoT workflow, without being noticed, while the protocol offers the option for automated settlement of cases where the operator tampers with users' data. A number of experiments have been conducted to assess the computation/gas fees requirements of the scheme and validate its real-world applicability.
Skeleton-Based Adaptive Graph Convolutional Networks for Cockpit Sign Language Classification

To solve the problem of barrier-free communication for deaf-mute people in traffic travel, the problem of cockpit sign language classification is proposed. On this issue, this paper proposes an adaptive graph convolutional network based on skeleton features (NA-AGCN), which is a way to improve the ST-GCN model. First of all, a lightweight normalized attention module NAM is added, which allows the network to suppress those insignificant weights, thereby improving network efficiency; then, an adaptive graph convolution module is proposed, which breaks the original graph The constraints of the convolutional adjacency matrix divide the matrix into three subgraphs, realize the extraction of global features, and increase the flexibility of the model construction graph. The model has achieved a good classification effect on the self-made cockpit sign language data set, and can accurately obtain the sign language expression results of the people in the video. This model has very good practical significance and can enable deaf-mute people to better enter society. Integrate into society and realize the effect of barrier-free communication between deaf and mute people in transportation and travel.
Simulation of Computer Communication Network Flow Distribution Model Based on Improved Ant Colony Optimization

How to distribute and balance the flow in computer networks has always been a difficult problem to give an answer. In the design of computer communication network, the average delay of the network is an important factor to be considered. In this paper, the simulation research of computer communication network flow distribution model based on improved ACO(ant colony optimization) algorithm is carried out. ACO breaks the limitation of traditional network, and controls the path of data flow in the network according to the state information of the network, thus achieving the purpose of controlling network flow. In this paper, ACO is used in computer communication network flow distribution, and GA(genetic algorithm) is used to update pheromones through path crossover and mutation operations, which can optimize the global situation and avoid stagnation, so as to improve the convergence speed and global search ability of the algorithm. The research results show that with the increase of the unit packet delay cost, the fixed cost of the link is reduced from 70% to about 60%, while the variable cost has no obvious fluctuation, which fully reflects the superiority of the improved ACO in the flow distribution of computer communication networks.
Path Planning of Aerospace Simulator Based on Improved Wolf Pack Algorithm

In order to deal with the complex environmental factors in microgravity test system, an improved wolf swarm algorithm (IWPA) was proposed and applied to the path planning of space simulator in micro gravity environment. Different from the traditional wolf colony algorithm, this paper uses Tent mapping to initialize the population; Introduce adaptive weight coefficient; And improve the fierce wolf's attack strategy; The wolves are updated in the iteration process by introducing the wolves' updating scale factor and reverse learning strategy. Considering the dynamic characteristics of the simulator, the position points obtained from the algorithm are used as control points, and the curve is fitted by B-spline, and the traffic cost of the fitted curve is used as the evaluation standard of the path. The performance of IWPA and WPA is compared by several benchmark functions, and the results show that IWPA is better than WPA. Finally, the algorithm is applied to the path planning of the simulator in the microgravity environment model. The experimental results show that IWPA has better path planning performance than WPA.
Small Objects Recognition by Exploiting an Improved YOLOv5 Algorithm on the UAV Platform

The aerial imagery captured by unmanned aerial vehicles (UAVs) exhibits several challenging characteristics such as large variations in object size and complex backgrounds, which make it difficult for existing detectors to detect small objects in such imagery. To address the problem of false or missed detections of small objects in UAV imagery, the BHF-YOLOv5 detection model is proposed. Firstly, the Biformer module is added to the backbone network, which employs sparse sampling to retain fine-grained details in the image while reducing computation, thereby enhancing attention to small objects and effectively improving the problem of small object detection. Secondly, a high-resolution detection head is added to the detection layer, which effectively increases the network's sensitivity to small objects. By combining multi-scale detection and multi-level information fusion, the network's representation ability is significantly enhanced. Furthermore, the Focal EIoU Loss optimization loss function is used to address the problem of imbalanced samples, thereby optimizing the network's training efficiency. Multiple experimental results on the VisDrone dataset show that our proposed BHF-YOLOv5 model improves the mAP metric by 3.19% compared to the original YOLOv5 model. Compared with other mainstream models, our proposed model achieves better detection performance for small objects in UAV imagery and can effectively complete the small object detection task of UAVs.
A Novel Dynamic Motion Planning Based on Error Tolerance Batch Informed Tree*

In a static environment for robotic manipulators, many present motion planning algorithms can generate collision-free paths effectively. But in a human-robot work scenario or a unstructured environment, these algorithms may fail or difficultly avoid obstacles in real-time planning due to missing updating environment model. To realize dynamic obstacle avoidance, a novel dynamic motion planning algorithm, called error tolerance batch informed tree (et-BIT*) is proposed. Experiments showed that the et-BIT* planner can avoid a suddenly appeared obstacle and bypass it by choosing a candidate motion path.
(POSTER) Unsupervised and Condition Invariant Anomaly Detection for The Constrained Edge

Deep Learning models in the context of retrofitting are significantly constrained by the impracticality of data collection. Acquiring faulty, labelled and heterogeneous data is difficult and expensive. While the non-availability of faulty and labelled data mandates the usage of unsupervised anomaly detection methods, the lack of heterogeneity in the gathered samples forces these models to be generalizable. On the other hand, if the intended model implementation platform is a micro-controller, these deep data-driven models further exacerbate the issue of model selection. Most of the existing unsupervised anomaly detection techniques neglect the distribution shift in streaming data. Additionally, they fall short of maintaining a modest level of model complexity. To address this gap in the literature, we proposed a latent representation of Convolutional Auto-encoder and Non-linear State Estimation based unsupervised anomaly detection. We empirically show using four public datasets that it is condition-invariant, requires below 135 kB active memory and has 11 ms inference latency on a typical low resource Internet of Things device.
Cloud Computing Adoption in the Banking Sector: A Comparative Analysis of Three Major CSPs

Cloud computing has emerged as a transformative technology with significant implications for various industries. While numerous sectors have already embraced cloud computing to revolutionize their business operations, the banking sector has exhibited hesitancy in adopting this technology, primarily due to concerns related to regulatory compliance, security, and reliability. This research aims to address these reservations by conducting a comprehensive examination of the potential benefits that cloud computing can offer to the banking sector. By employing a comparative analysis approach, this study seeks to explore and assess the diverse functionalities and services provided by three leading cloud service providers: AWS, Microsoft Azure, and Google Cloud. Through this analysis, the research endeavours to identify specific use cases within the banking sector where cloud computing can deliver tangible advantages. By understanding the capabilities and offerings of these platforms, banks can gain valuable insights to inform their decision-making processes regarding cloud adoption. Furthermore, this research also seeks to shed light on the potential challenges associated with the implementation of cloud computing in the banking sector. These challenges encompass critical aspects such as security concerns, vendor longevity and reliability, and management issues.
Scalable Evaluation Pipeline of CNN-Based Perception for Robotic Sensor Data Under Different Environment Conditions

Deep learning impacted a wide variety of perception applications for autonomous mobile robots. In classic computer vision benchmark tests, new algorithms keep appearing that outperform each other. However, these benchmark tests cannot be generalized, so that the specific application must be considered for the selection of sensors and algorithms. Especially in the agricultural domain, environmental conditions like weather and vegetation significantly influence the reliability of sensor systems. Therefore, it is essential to test different sensor modalities and algorithms in the operational design domain. This motivates the need for an evaluation framework which has the flexibility to compare and validate various perception algorithms, sensors suites, and data samples with a focus on different conditions. This paper proposes a pipeline combining a test environment (AI-TEST-FIELD), a semantic environment representation (SEEREP), and an inference server (Triton) for an automatic evaluation of different CNN-based perception algorithms under various environment conditions. Recurring and comparable recordings of raw sensor data with identical scenarios and objects can be performed on the test field, with the only difference being the environmental conditions. The inference results are inferred once and stored alongside the sensor data in SEEREP. Thus, they can be queried efficiently based on the environment conditions to generate (partially overlapping) subsets of the whole dataset. It is demonstrated how this pipeline can be used to apply the CNN-inference just once on the data, and how the queried subsets can subsequently be used to evaluate the performance in different environment conditions.
Improved AlexNet-Based Network for Face Expression Recognition

With the development of technology and equipment, biometric recognition technology has been widely used in surveillance and law enforcement systems. Face recognition, the most widely used biometric technology, has had a positive impact on the development of artificial intelligence. As the most intuitive way to map the inner world of human emotions, expressions are an important way to analyse emotions in human communication and exchange. Face expression recognition has also become a hot research topic in recent years. Traditional expression recognition methods rely on manual extraction of features, which are too complex and have low recognition accuracy, and are no longer suitable for the development needs of current applications. In this paper, we study and improve the face expression recognition method based on convolutional neural network, and design a face expression recognition system based on the improved algorithm.
A Sequential Recommendation Model Combining Contrastive Learning and Self-Attention

Sequential recommendation dynamically model user interests for item recommendations based on the user's historical interaction behavior with the item. Existing sequential recommendation algorithms usually consider only the order of user actions and ignore the information of time interval between user actions; And the data sparsity problem when training the model leads to limited recommendation performance. For this purpose this work proposes a sequential recommendation model that combines contrastive learning and self-attention(CLSSRec). First, the recommendation performance is improved by capturing the relationship patterns between items and interaction sequential time intervals through the embedded self-attention module; Second, the contrastive learning view is constructed based on node representations using a neuron masking model augmentation method to alleviate the data sparsity issue; Finally, our model experiments on both Home and Sports datasets, outperforming the baseline model on the recommendation performance metrics of Recall@k and NDCG@k, demonstrating the effectiveness of the CLSSRec model.
Research on Dynamic Gesture Recognition with Low-Pixel ToF-Sensors

Low-pixel time-of-flight (ToF) sensors offer advantages in low power consumption, small size, and low cost. For gesture recognition, there are two advantages. Firstly, they are robust to changes in lighting conditions. Secondly, they could protect privacy. This paper addresses the challenge of utilizing an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8}\times \mathbf{8}$</tex> pixel ToF sensor with extremely low resolution by proposing a signal processing system. The system includes pre-processing the data through non-global mean filtering to calibrate outliers. Additionally, we design a frames difference sliding window to ensure the integrity of gesture sequences and use Lagrangian interpolation to norm the fixed length. Finally, we design a tiny parallel convolutional network (TPC-Net) to recognize dynamic gestures. Experimental results demonstrate that the proposed system achieves an average accuracy of 98.47% over 6 dynamic gestures. Furthermore, our system achieves better performance with less model complexity compared to existing methods using low-pixel ToF sensors. These results illustrate the effectiveness of our system.
A Retrieval Method in Heterogeneous Domains Based on Orthogonal Softmax

Most of the existing retrieval methods are based on homogeneity domains, however, a retrieval method in heterogeneous domains was proposed in this paper. The similarity of different classes was discussed, that was, a pair of classes was similar in one domain, but it may be different in another domain. To solve this problem, orthogonal softmax was introduced, which made the decision axis of softmax have consistent similarity. Thus, it was a simple linear transformation from one feature space into another space through cross-domain to share the same feature space structure. The results of three data sets in heterogeneous domains showed the validity of this method.
Masked Face Recognition with Qaudruplet Loss

Since the outbreak of COVID-19 pandemic, people wear masks to guide against virus, which occlude important facial features that many face recognition models rely on to extract features, making it difficult for these models to accurately recognize masked faces. We propose a Quadruplet Loss model to address this problem, which makes model to generates closed embedding vectors for both masked and non-masked face images. This model is based on triplet loss and incorporates synthesized masked face images to create a quadruplet. Our quadruplet loss function combines triplet loss with the similarity between the anchor image and its masked counterpart. Our experiment results demonstrate that our Quadruplet Loss model outperforms the Triplet Loss model in recognizing masked faces while also preserving performance on non-masked face recognition.
Research on Blockchain-Based Data Sharing and Access Control Model

To address the issues of privacy protection and user access control in electronic health record (EHR) sharing process, this research proposes a blockchain-based secure EHR sharing and access control model to improve the security of EHR and enhance the management of user access rights. This model is built on Hyperledger Fabric (abbreviated as Fabric) to obtain performance optimization and scalable applications. Using edge computing nodes to store large amounts of medical data and several access control policies, the ciphertext policy attribute-based encryption mechanism (CP-ABE) provides flexible and stable encryption protection for the data in the model; the eXtensible access control markup language (XACML) provides fine-grained access control for the model. We also introduce Ether Universe, a hybrid cross-chain technology that combines a sidechain model and a notary mechanism, into the model to facilitate information interaction among independent blockchains. Finally, we show through extensive experimental tests that the model has good operational stability and high data processing performance, and can provide reasonable record and permission control during user access to EHR data.
Hash-based Digital Signatures- A tutorial review

There has been great interest currently in investigating various techniques for realizing public key encryption as well digital signatures. A promising techniques for digital signatures use well-investigated secure Hash functions. The state of the art has evolved from Lamport’s pioneering work of one tie signatures today’s few time signatures as well as stateless signatures. IETF has two RFCs on Extended Merkle signature system (XMSS) and Leighton-Micali Signature system (LMS). NIST competition on Post-Quantum Cryptography for KEM (Key Encapsulation Module) and digital signatures in the third round has considered SPHINCS+ as a potential candidate. In this tutorial paper, we describe from basics the digital signatures based on hash functions and trace the efforts made in hardware implementations as well.
Generating Imperceptible Adversarial Patch Based on Vulnerable Targeted Attack

The vulnerability and sensitivity of deep neural networks to adversarial examples have attracted researchers' attention towards the security application of deep model. Current methods mainly focus on the aggressiveness and size of the patches, while ignoring the imperceptibility and concealment of the patches. In this paper, we propose a two-step attack algorithm to generate imperceptible adversarial patches. Specifically, the first step of our algorithm is to obtain the vulnerable target category that is most likely to be misclassified; and the second step is to perform targeted attack using the vulnerable target coming from the first step. The proposed algorithm is very robust and can be generalized to other untarget attack approaches, such as Shadow attack and IFGSM. Extensive experiments on widely used classifiers demonstrate the availability and efficiency of the proposed algorithm.
On the Accuracy-Energy Tradeoff for Hierarchical Federated Learning via Satisfaction Equilibrium

Hierarchical Federated Learning (HFL) has recently emerged as a promising method to overcome the limitations of conventional Federated Learning (FL) in terms of communication inability of the end users with the cloud and increased backhaul network traffic when considering implementations over wireless networks. Nevertheless, to reap the benefits of HFL, proper user association with the different edge servers and wireless resource allocation is required. Unlike existing works - that in their majority treat unilaterally a single objective using centralized optimization techniques - in this paper, we particularly aim to explore the tradeoff induced between the users' local model training accuracy and personal energy consumption in a distributed manner. The joint problem of user-to-edge-server association and uplink power allocation for transmitting the users' local model parameters to the edge is formulated and solved as a game in satisfaction form. Each user autonomously seeks to achieve a target accuracy-energy ratio by selecting their edge association and uplink transmission power level, while their cumulative decisions result in a desired Satisfaction Equilibrium (SE) point. Specifically, to determine the respective SE of the formulated game, a Reinforcement Learning (RL) algorithm is utilized. Numerical results obtained via modeling and simulation demonstrate the operational and performance characteristics of the proposed framework in terms of achieving the users' personally desired tradeoff value.
Employing Recursive Neural Networks in Voice Question-Answering Systems: A Novel Approach for Sequence Processing

In this study, we propose and validate a novel Recurrent Neural Network (RNN) model designed for handling sequential data in voice question-answering systems. In our system, the RNN effectively processes speech sequences of various lengths and captures valuable dependencies within. Our model undergoes rigorous performance evaluation on standard TIMIT and LibriSpeech datasets, including Word Error Rate (WER), Answer Correct Rate (ARA), and Speech Synthesis Quality (SSQ). The results demonstrate the excellent performance of our model in dealing with speech sequences of varying lengths and practical applications. Through future research, our model will be further improved and can play its value in a broader scenario.
Data Marketplaces: Best Practices, Challenges, and Advancements for Embedded Finance

Data leads to knowledge, being considered crucial for running a successful business. Its production is dramatically increasing, generating challenges into identifying what data is worth extracting and what is not. Complementary to the data value, data marketplaces are becoming increasingly popular serving as the sources for additional data. They offer purchasing and selling capabilities for external data, simplifying data sourcing, while enabling users to navigate today's complex data world. Data marketplaces are continuously developed, serving the requirements of individuals and businesses in industries such as healthcare or telecommunications, with the top ones covering individual online activities and financial information. Above all, bank transactions have altered due to digitalization, with most consumers currently employing Embedded Finance (EmFi) applications. Given that the latter can benefit from accessing data from federated data providers in different sectors and transparent value driven approaches, the existence of EmFi data marketplaces is vital. In this manuscript, the FAME project is introduced aiming to launch a federated EmFi data marketplace. It goes beyond current data marketplaces since it supports the EmFi domain that currently lacks dedicated marketplaces, also offering: (i) a federated data catalogue, (ii) a single-entry point authorization/authentication infrastructure, (iii) a decentralized trading and monetization capability, and (iv) a set of EmFi-related trusted and energy-efficient analytics. All the capabilities of the FAME data marketplace are analysed, depicting the current market status and FAME's advancements. The manuscript concludes into the overall vision of FAME, depicting its applicability and future evaluation processes.