Reviewerly/exHarmony huggingface dataset:
Refinement and Verification for IoT Service Composition

Internet of Things (IoT) is a finite set of interconnected devices that can cooperate and interact with each other through the Internet. As the number of IoT devices have increased, the number of services increased as well, further complicating the process of service composition. In this paper, an Event-B formal model is presented to verify the correctness of the IoT service composition (IoTSC) system. In addition, the proposed model satisfies some functional and non-functional properties such as compatibility and availability to fulfil the requirements of the IoTSC. Our model is developed incrementally from abstract level to target level by using the refinement mechanism. A Fire Alarm Detection System is used as a case study for our model. Finally, we use proof obligations and the Rodin platform to validate and proof the correctness of the proposed formal model.
Exploiting Emercoin Blockchain and Trusted Computing for IoT Scenarios: A Practical Approach

Traditional Public Key Infrastructures (PKIs) seem not adequate for some Internet of Things (IoT) environments asking for fast, flexible, and secure solutions. Alternatively, IoT devices could generate asymmetric key pairs on their own, and store the relative public keys or X.509 certificates into a blockchain, e.g., Emercoin. Nevertheless, in some contexts, reliable device identification is still required. We extended an Emercoin-based decentralized PKI solution for IoT scenarios, by integrating the device's identification with a TPM (Trusted Platform Module) 2.0 to a specific trusted node in an IoT network named Device Manager (DM). Through experimental tests performed with a TPM 2.0-equipped Raspberry Pi 4 device, we evaluated the time spent registering the IoT devices into the blockchain, or establishing secure (TLS) channels. Even though the Emercoin-based TLS handshake time is higher than the standard one, the proposed solution remains a viable alternative in scenarios requiring flexibility and device identification.
Revisiting Data Poisoning Attacks on Deep Learning Based Recommender Systems

Deep learning based recommender systems(DLRS) as one of the up-and-coming recommender systems, and their robustness is crucial for building trustworthy recommender systems. However, recent studies have demonstrated that DLRS are vulnerable to data poisoning attacks. Specifically, an unpopular item can be promoted to regular users by injecting well-crafted fake user profiles into the victim recommender systems. In this paper, we revisit the data poisoning attacks on DLRS and find that state-of-the-art attacks suffer from two issues: user-agnostic and fake-user-unitary or target-item-agnostic, reducing the effectiveness of promotion attacks. To gap these two limitations, we proposed our improved method Generate Targeted Attacks(GTA), to implement targeted attacks on vulnerable users defined by user intent and sensitivity. We initialize the fake users by adding seed items to address the cold start problems of fake users so that we can implement targeted attacks. Our extensive experiments on two real-world datasets demonstrate the effectiveness of GTA.
Facing Unknown: Open-World Encrypted Traffic Classification Based on Contrastive Pre-Training

Traditional Encrypted Traffic Classification (ETC) methods face a significant challenge in classifying large volumes of encrypted traffic in the open-world assumption, i.e., simultaneously classifying the known applications and detecting unknown applications. We propose a novel Open-World Contrastive Pre-training (OWCP) framework for this. OWCP performs contrastive pre-training to obtain a robust feature representation. Based on this, we determine the spherical mapping space to find the marginal flows for each known class, which are used to train GANs to synthesize new flows similar to the known parts but do not belong to any class. These synthetic flows are assigned to Softmax's unknown node to modify the classifier, effectively enhancing sensitivity towards known flows and significantly suppressing unknown ones. Extensive experiments on three datasets show that OWCP significantly outperforms existing ETC and generic open-world classification methods. Furthermore, we conduct comprehensive ablation studies and sensitivity analyses to validate each integral component of OWCP.
Human Face Detection Improvement using Subclass Learning and Low Variance Directions

In order to increase the face detection rate in complicated images, a novel approach is presented in this work. The suggested method seeks to improve accuracy by utilizing low-variance directions for data projection and one-class subclass learning. Previous studies have demonstrated that taking into account the data carried by low-variance directions enhances the performance of models in one-class classification. For dispersion data, subclass learning is extremely successful. To evaluate the effectiveness of our subclass method, we conducted a comparison between our proposed approach and other one-class classifiers on multiple face detection datasets. Results reveal that the suggested method performs better than other methods, demonstrating its potential to develop face identification technologies.
AlgoID: A Blockchain Reliant Self-Sovereign Identity Framework on Algorand

The Self-Sovereign Identity (SSI) is a novel paradigm aimed at giving back users sovereignty over their digital identities. Adopting the SSI approach prevents users to have a distinct identity for each service they use, instead, use a unique decentralised identity for all the services they need to access. However, to really benefit from the SSI advantages, an actual decentralised implementation is needed to fit the specific requirements and limits of decentralised architectures, such as blockchain. To this aim, this paper proposes Algorand Identity (AlgoID), a new SSI framework for the Algorand blockchain which differs from the already existing one, because it is fully blockchain based, i.e., it exploits Algorand itself for the storage of the data identity and as the registry location. The proposed framework has been completely implemented and validated through experiments, showing that the time required to execute the framework operations is acceptably low in realistic use cases.
FedEF: Federated Learning for Heterogeneous and Class Imbalance Data

Federated learning (FL) is a scheme that enables multiple participants to cooperate to train a high-performance machine learning model in a way that data cannot be exported. FL effectively protects the data privacy of all participants and reduces communication costs. However, a key challenge for federated learning is the data heterogeneity across clients. In addition, in real FL applications, the class distribution of data is usually unbalanced. Although many researches have been conducted to solve the problem of data heterogeneity, class imbalance problem usually arises along with the heterogeneity data, resulting in the poor performance of the global model. In this paper, a novel FL method (we call it FedEF) is designed for heterogeneous data and local class imbalance problem via optimize feature extractors and classifiers. FedEF optimizes the local feature extractor representation of individual clients through contrastive learning to maximize the consistency of the feature extractor representation trained by the local client and the central server to handle heterogeneous data. Meanwhile, we modified the cross entropy loss in the model, assigned different loss weights to different classes of data, paid more attention to the class with fewer samples in the training process, and corrected the biased classifier to alleviate the problem of class imbalance, thus can improve the performance of the global model. Experiments show that FedEF is an effective solution to FL model obtained under heterogeneous and local class imbalance.
RVDSE: Efficient Result Verifiable Searchable Encryption in Redactable Blockchain

To solve the inefficiencies, inflexibility in updates, and high storage costs associated with current result verifiable searchable encryption schemes, we propose an efficient scheme that result verifiable dynamic searchable encryption in a redactable blockchain (RVDSE). By dividing the inverted index into blocks, uploading corresponding verification tags to the blockchain, and using smart contracts to verify query results, we improve query and verification performance. Additionally, we employ blockchain rewriting technology to update tags in the result checklist, thereby improving blockchain data update performance and scalability while maintaining constant storage overhead. Security analysis confirms that our solution guarantees query result accuracy and completeness. Experimental results demonstrate that our approach enhances query and result verification efficiency, even with low-speed blockchain data scale growth, particularly as data collection scales increase.
GCFL: Blockchain-based Efficient Federated Learning for Heterogeneous Devices

Federated Learning has emerged as a promising machine learning paradigm to protect data privacy. However, the differences between heterogeneous clients and the performance bottleneck of central server limit the efficiency of FL. As a typical decentralized solution, the combination of blockchain and FL has been studied in recent years. However, the use of single-chain blockchain and traditional consensus algorithms in these studies have drawbacks such as high resource consumption, low TPS and low scalability. This paper proposes an efficient solution that combines a DAG blockchain and FL, called GCFL(Graph with Coordinator Federated Learning). GCFL introduces a new block structure that reduces data redundancy. For DAG blockchains, we proposed a two-phase tips selection consensus algorithm that can reduce resource consumption and tolerate a certain proportion of malicious nodes. Simulation experiments show that GCFL has higher stability and fast convergence time for targeted accuracy compared to traditional on-device FL systems.
Performance Analysis of Meta-Learning Based Bayesian Deep Kernel Transfer Methods for Regression Tasks

Meta-learning aims to apply existing models on new tasks where the goal is “learning to learn” so that learning from a limited amount of labeled data or learning in a short amount of time is possible. Deep Kernel Transfer (DKT) is a recently proposed meta-learning approach based on Bayesian framework. DKT's performance depends on the used kernel functions and it has two implementations, namely DKT and GPNet. In this paper, we use a large set of kernel functions on both DKT and GPNet implementations for two regression tasks to study their performances and train them under different optimizers. Furthermore, we compare the training time of both implementations to clarify the ambiguity in terms of which algorithm runs faster for the regression based tasks.
Video Copy Detection Based on Blockchain

In recent years, the number of pirated videos has soared, which has brought great damage to the interests of the original. Video copy detection technology can solve such problems to some extent, but the detection process of traditional video copy detection technology is not open, so that the detection results are not convincing. Based on this, this paper proposes an open, transparent and verifiable video copy detection method based on blockchain technology to ensure the transparency and openness of the results. The smart contract is called on the chain for automatic copyright detection, and the video characteristic value is permanently immutable saved on the block chain to ensure the originality of copyright works and the verifiability of detection results. In similarity comparison, the method of slice comparison can greatly shorten the comparison time, which is 1/50 of the traditional comparison time. Experimental results show that this method is very effective in retrieving similar images and detecting the similarity between original and pirated videos.
Multi-Head Self-Attention Network for Multi-View Deep Clustering

Multi-view clustering comprehensively processes data objects by studying different views or modalities, as well as the differences or complementary information between different views, then classifies the data objects into different groups by unsupervised learning. However, most of the work these days fuses the information of multiple views only after rough processing, which ignores the difference of information value among different data features in the process of deep fusion representation. In this paper, we propose a Multi-head Self-attention network for Deep Clustering (MSDC) to highlight valuable information features and improve the effect of multi-view clustering. Comprehensive experiments have been carried out on real world datasets and results show that MSDC outperforms other advanced baselines, demonstrating that MSDC provides an excellent approach for multi-view clustering.
Implications of Node Selection in Decentralized Federated Learning

Decentralized Federated Learning (DFL) offers a fully distributed alternative to Federated Learning (FL). However, the lack of global information in a highly heterogeneous environment negatively impacts its performance. Node selection in FL has been suggested to improve both communication efficiency and convergence rate. In order to assess its impact on DFL performance, this work evaluates node selection using performance metrics. It also proposes and evaluates a time-varying parameterized node selection method for DFL employing validation accuracy and its per-round change. The mentioned criteria are evaluated using both hard and stochastic/soft selection on sparse networks. The results indicate that the bias associated with node selection adversely impacts performance as training progresses. Furthermore, under extreme conditions, soft selection is observed to result in higher diversity for better generalization, while a completely random selection is shown to be preferable with very limited participation.
CheckMate: English Grammatical Error Correction for Native Turkish Speakers

The covid-19 outbreak left many countries have no choice but turn to online education. Turkish students, who were among those who were affected, faced difficulties in improving their English as the opportunity to have face-to-face feedback was not available. In this work, we build and open-source a dataset for grammatical error correction composed of essays written by Turkish students from different universities capturing the errors Turkish native speakers tend to make. We utilize the dataset and build a model, which we deploy along with a web interface.
Swin Transformer based Siamese Network for Thermal and Optical Image Registration

The process of multi-modal image registration is fundamental in remote sensing and visual navigation applications. However, existing image registration methods that are designed for single modality images do not provide satisfactory results when applied to multi-modal image registration. In this research, our objective is to achieve highly accurate alignment of both infrared and optical (visible range) images. To accomplish this goal, we explore the effectiveness of the Swin Transformer encoder and cosine loss in enhancing the keypoint-based image registration process. Simulation results show the improvement achieved in multi-modal registration by using a transformer based Siamese network.
An Empirical Study on Scrum Deviations in Organizations with Software Development

Scrum is one of the most popular agile software development frameworks. As a guideline to help development teams to work effectively to achieve development goals, Scrum Guide is provided. In practice, many organizations do not strictly follow the Scrum Guide, causing Scrum deviations. The objective of this research is to gather Scrum deviations data and analyze the impact and factors that cause Scrum deviations in Scrum Teams. Via the interviews with the Scrum Teams from IT departments of different sizes and types of software development and in different organizations in Thailand, 19 Scrum deviations were identified. These deviations were then used in an online questionnaire to collect the impact of Scrum deviations on Scrum Teams. The results show that the deviations do not necessarily have a negative impact on the Scrum Teams and their organizations, as certain deviations are even beneficial. However, there is no statistical difference in the deviating behavior of the Scrum Teams from IT departments of different sizes and different types of software development. Also, the major cause of the deviating behavior is the Scrum Teams themselves, as the teams tend to adjust Scrum activities to better suit team practices.
Fed-SIc: Selecting Important Clients for Federated Learning

Federated learning (FL) is a decentralized training that aims to produce a global model which is an updated model aggregated from clients without accessing their local data. There will be a challenge when malicious clients involve in FL training. The common FL such as Federated Averaging (FedAvg) lacks the mechanism to perform client selections for avoiding malicious clients. The traditional approach of client selection uses Shapley Value (SV) to filter out malicious clients. However, SV requires server-labeled data for filtering malicious clients which is hard to fulfill in real-world situations. This paper introduces the Fed-SIc mechanism that does not need server-labeled data requirements. The server selects evaluators among clients according to the average F1-score when other clients evaluate their model. The other clients outside of evaluators become candidates. Next, the server filters out candidates into participants by selection probability and selects honest participants according to evaluators' evaluations. Using evaluators and honest participants models that are selected by evaluators, the server performs aggregation to update the global model. Experiments using popular benchmark datasets such as MNIST show the proposed clients selection mechanism outperforms vanilla FL when malicious clients present during training.
The Character Recognition Method Based on OCR

The application scenarios of using digital documents for information exchange in modern society are gradually increasing with the rapid development of new technologies. Manually completing the classification and entry of book texts, book lending certificates and other information will also consume a lot of manpower, material resources and time costs. OCR technology based on deep learning processes data efficiently and with high accuracy which can help users better manage and process information. Therefore, this paper proposes an optical character recognition method based on deep neural network, integrating the DB Net model and the CRNN model. The main work is as follows: (1) We investigate and compare different deep learning-based text detection and text recognition algorithms. (2) Based on the features of random text position and directional orientation in text images under natural scenes, the text recognition method of DB Net and CRNN with CTC is proposed. DB Net Model outperforms other text detection models in terms of accuracy and speed and the accuracy of CRNN model can meet the requirements of real business scenarios. (3) We use the dataset to train the algorithm model and expand the method structure combined with the front-end framework. The method meets the task requirements of users to identify images and text accurately and provides a reference value for the study of the same type of methods.
Classification of Red Blood Cells Randomly Selected from Dataset of National Institutes of Health

Millions of people worldwide suffer from malaria, a potentially fatal disease. Early and precise diagnosis is essential for the medical condition to be successfully treated and managed. This paper employs three computer aided methods to determine percentages of red blood cells that are either parasitic or uninfected given test set(s) randomly obtained from National Institutes of Health (NIH) dataset. The three methods employed are traditional image processing, Support Vector Machine (SVM), and Convolutional Neural Networks based Deep Learning (CNN-DL). The simulations were performed using a dataset that had 27,558 images of red blood cells. The traditional image processing method achieves an accuracy of 91.97%. SVM classifier using Histogram of Oriented Gradients (HOG) features had accuracy of 88.6% and with features extracted using Local Binary Patterns (LBP) accuracy had improved to 92.5%. The two previous methods were proved to be inferior when compared with the CNN- DL classification that gave an accuracy of 95.7%.
A Genetic Algorithm based Parallel Task Planning Method for Cooperative MRS

With in the trend of intelligence, task cooperation has been emerging as one main feature for Multiple Robot Systems (MRS) and applications, typically Multi-UAV (MUAV) system. For such swarm cooperative systems, how to plan and map their tasks to proper members within systems optimally and fast has been one vital issue that affects the efficiency of cooperation. So, in this article, a two-level task model is firstly established, which is the foundation of automatic task parsing and planning. Further, a Genetic Algorithm (GA) based Parallel Task Planning algorithm PTP-GA is proposed and designed, the goal of which is to improve the performance via cluster related tasks together and separating the dividing the solution space. Particularly, several constrained operators are designed to warrantee both the randomness and elitist individuals or segments. Finally, this proposed method is verified within a simulated scenario, and it shows that the performance of this parallel algorithm is indeed improved.
Research on Quality Model for Quantum Simulator Software

In recent years, the demand for arithmetic power from all walks of life is growing, especially the increasing popularity of AIGC, which makes people realize that huge technological progress cannot be made without the support of large arithmetic power. Quantum computing may bring a leap in computing power and become a ”trigger” and “catalyst” to accelerate the development of science and technology innovation, and its technical development trend and application prospects have become a hot issue of concern, and quantum simulators are a very important part of the development process. The development of quantum simulators is a very important part of the process. However, due to the lack of systematic evaluation theories and methods, it is difficult to quantify and analyze the quality of the increasing number of simulators on the market, which may hinder researchers from practicing their quantum algorithms objectively or developing quantum technologies. For these reasons, this paper proposes a quality model for quantum simulator software, which reflects the quality characteristics of simulators in seven aspects, such as functionality and performance efficiency, and selects some simulators for experimental validation of the proposed model to prove its rationality.
Object Oriented BDD and Executable Human-Language Module Specification

This paper presents an approach to software development which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-language module specification and a tool to support it, which has been used successfully for human-language UI test automation. We anticipate further development of this approach to enable complex software to be programmed in human language, allowing for more intuitive and efficient software development.