Reviewerly/exHarmony huggingface dataset:
PCaSM: Text-Guided Composed Image Retrieval with Parallel Content and Style Modules

The query for text-guided image retrieval references two parts: the first part is the image, and the second part is the text describing the part of the image that needs to be modified. By composing the features of the text and the image, the goal is to retrieve a target image that complies with the demands of the modification text and resembles the reference image. modification text Currently, some image retrieval software fails to take into account the rich content and style information contained in images. To solve this problem, we propose a novel architecture designed by the image-text synthesis model, namely PCaSM, which consists of three modules: a content module, a style module, and a combination module. First, to ensure the stability of each module training, we normalize the reference image features using instance normalization and then implement content modification by using decomposed multi-modal non-local blocks. Next, the style module is implemented with the remaining stacks and skip links. Finally, the content and style modules are fed into the combination module outputs in parallel to fuse the local content features with the global style features. Therefore, the final combination features obtained are similar to the target image features. Adequate experiments show that PCaSM demonstrates good performance on the three datasets: FashionIQ, Fashion200K, and CIRR, compared with the latest proposed models such as CoSMo and RTIC.
The Challenges of Augmented Reality for Higher Education in Spanish-speaking Countries

This systematic review of augmented reality in higher education answers the following question: What are the challenges of augmented reality in higher education in Spanish-speaking countries? The general objective is to identify the current challenges of augmented reality in higher education in Spanish-speaking countries. A bibliographic research method was carried out using the following keywords: augmented reality, higher education, AR; as well as Boolean search operators AND and OR. A total of 96 sources published in Scielo, Scopus, and WOS-indexed journals between 2018 and 2022 in English and Spanish were analyzed. It is concluded that augmented reality contributes to the achievement of learning outcomes and consequently, to the improvement of the quality of educational processes in higher education. However, there are different challenges, such as curricular, technological infrastructure, financial, and training challenges, among others, that require a change in institutional stance in order to improve teaching and learning processes in universities.
Policy Integrated Blockchain to Automate HIPAA Part 2 Compliance

Healthcare organizations exchange sensitive health records, including behavioral health data, across peer-to-peer networks, and it is challenging to find and fix compliance issues proactively. The Healthcare industry anticipates a growing need to audit substance use disorder patient data, commonly referred to as Part 2 data, having been shared without a release of information signed by the patient. To address this need, we developed and evaluated a novel methodology to detect Part 2 data exchanged between organizations that integrates Blockchain technologies with knowledge graphs. We detect substance use disorder data in patient encounters exchanged using clinical terminology based upon the value sets provided by the National Institutes of Health for the Substance Abuse and Mental Health Services Administration. Generally, we consider sharing Part 2 data without consent as Byzantine medical faults, as they represent data shared between known and trusted network participants, that is valid, but is not relevant, and sharing it causes a breach. In this paper, we present our methodology in detail along with the experiment results. We model a medical network of hospitals based upon the most recent healthcare legislation, TEFCA, and generate synthetic patient encounter data dynamically in HL7 format. We convert exchanged encounter data into a knowledge graph data model so that we can use SNOMED-CT for identifying Part 2 data. For cohorts of 1,000 patients, we detect Part 2 data in a subset of their encounter data shared between organizations and log that securely on an Ethereum-based blockchain.
Feature-Guided Machine-Centric Image Coding for Downstream Tasks

Video coding, a process of compressing and decompressing digital video content, has traditionally been optimized for human visual systems by reducing its size while maintaining the human perceptual quality. However, with the remarkable progress of artificial intelligence (AI) technology, the need for machine-centric coding has rapidly been increasing in recent years. In response to these trends, international standardization organizations such as MPEG are actively working to develop and launch new standards on coding technologies for machines, called video coding for machines (VCM). In this paper, we present a novel feature-guided block-wise image blending method for image compression, which is suitable for machine applications such as object detection and segmentation. For this, we use a gradient map of the feature loss using the pretrained encoder part of a task-specific network as guide for input degradation, so that the degraded input images can be effectively compressed for machine-centric tasks. Our method is simple but effective because additional training is not required by utilizing the pretrained encoder parts of networks for targeted tasks. Experimental results show that BD-rate gains can be obtained by applying our proposed method with averages 11% and 8% for object detection and instance segmentation tasks, respectively, compared to the image anchor results of MPEG-VCM reference software v0.4.
Cross-Chain Swaps with Preferences

Extreme valuation and volatility of cryptocurrencies require investors to diversify often which demands secure exchange protocols. A cross-chain swap protocol allows distrusting parties to securely exchange their assets. However, the current models and protocols assume predefined user preferences for acceptable outcomes. This paper presents a generalized model of swaps that allows each party to specify its preferences on the subsets of its incoming and outgoing assets. It shows that the existing swap protocols are not necessarily a strong Nash equilibrium in this model. It characterizes the class of swap graphs that have protocols that are safe, live and a strong Nash equilibrium, and presents such a protocol for this class. Further, it shows that deciding whether a swap is in this class is NP-hard through a reduction from 3SAT, and further is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Sigma_{2}^{\mathsf{P}}$</tex> -complete through a reduction from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\exists\forall \mathsf{DNF}$</tex> .
Real-Time End-to-End Portrait and In-Hand Object Segmentation with Background Fusion

Portrait segmentation has become increasingly important for video conferencing software. However, existing methods fail to produce consistent and accurate segmentation of in-hand objects due to the weak connection in pixel distribution between humans and the in-hand objects. In this paper, we propose a fast and lightweight end-to-end model that performs portrait and in-hand object segmentation simultaneously. Our design takes spatial and temporal information into account by using a model containing a Convolution-Transformer hybrid encoder and a recurrent decoder, followed by a background fusion refinement module to produce accurate and consistent segmentation results. Our experimental results show improvements both visually and quantitatively compared to existing real-time video portrait segmentation with respect to accuracy, consistency, and speed.
IoV Edge Data Integrity Audit Method Based on Blockchain

With the quick development of Internet of Vehicles (IoV) technology, a lot of private data of vehicle users has been generated. An increasing amount of vehicle data is being collected in the cloud storage providers (CSP) and the edge node (EN). These data involve user privacy and vehicle driving data, and once they are damaged or lost, they wm lead to serious traffic accidents. Therefore, safeguarding the security and integrity of IoV data has become a hot issue. However, due to the distributed nature of the IoV data system and the difficulty of edge node management, the traditional data integrity auditing methods can no longer satisfy the needs in the IoV cloud-edge collaboration scenario. Therefore, we propose a IoV edge data integrity audit method based on blockchain First, our design a novel cloud-edge collaborative storage model that not only allows both cloud and edge to participate in the audit, but also enables the data user (DU) to quickly access the IoV data and reduce the pressure of data transmission. Second, during the audit phase, we use the third-party auditor (TPA) to design a low-overhead audit algorithm and ensure that the privacy of the IoV data is not compromised. Finally, the TPA records audit transactions on the blockchain, so that the DU can re-review the TPA’s audit results to find out whether the TPA is delaying and falsifying the audit result. Theoretical analysis shows that our method can protect the privacy of IoV data. Meanwhile, the experimental results show that our method has a low time overhead and the storage model is suitable for the IoV environment.
The Evaluation Method of Science and Technology Collaborative Innovation Based on Blockchain Technology

Blockchain technology has significantly applicable value in technology collaborative innovation evaluation for its decentralization, tamper resistance, traceability, concealment, and openness. It forms the evaluation pattern at the theoretical framework level, builds an evaluation technical platform with blockchain technology, and establishes case demonstrations, effectively solving the bottleneck problems in information source timeliness, authenticity, and accuracy, which is encountered by traditional methods.
Research on the System of Blockchain Data Sharing and Early-warning Decision for Public Health Emergency

The key of early prevention and control of infectious diseases is to use the early warning technology and system to monitor the abnormal occurrence and trend of infectious diseases. Based on the hospital data, this paper analyzes the patient demographics, syndromic features, and other sensitive medical characteristics, proposes an infectious disease risk warning algorithm based on dynamic adaptive modeling of event evolution, and constructs an infectious disease risk early warning and data sharing system based on blockchain technology. The proposed system realizes the safe storage and sharing of data throuth the blockchain technology, and combines smart contracts and federal learning to realize distributed risk warning and consensus decision making. The experiments demonstrate that the proposed risk warning model can improve infectious disease risk warning and fusion decision making by taking into account the requirements of warning accuracy and timeliness while protecting patient privacy.
A Hybrid Neural Network Model-based Approach for Detecting Smart Contract Vulnerabilities

Smart contract vulnerabilities have become a common source of security incidents in the blockchain network in recent years. To mitigate the impact of such vulnerabilities, scholars have been exploring more effective and dependable methods for detecting them. However, existing smart contract vulnerability detection methods suffer from issues like a high rate of false positives and omissions, limited scalability, and reliance on expert knowledge, among others. To address these challenges, this study proposes a hybrid neural network model-based approach to smart contract vulnerability detection that leverages the strengths of different neural networks. By incorporating global context alongside local feature extraction, the method significantly enhances feature extraction rates. Experimental results demonstrate that the proposed method is highly efficient and accurate, making it a suitable solution for detecting smart contract vulnerabilities.
Blockchain-based ciphertext access control for data sharing using key encapsulation mechanism

The article presents a plan for distributing access control and data sharing through CP-ABE. This plan allows for provable security and privacy protection without requiring authorization agencies. The main key of the CP-ABE algorithm is managed in a distributed manner based on blockchain through proxy key encapsulation technology, and a blockchain dual-chain model is adopted to improve system performance and security. At the same time, the block structure and data format specifications are designed to achieve on-chain supervision with traceable management and operational behavior of the PKEM-CPABE algorithm. Experimental simulation analysis shows that the proposed algorithm achieves an efficient and secure sharing mechanism while protecting data privacy.
VulScan: A Vulnerability Detection Model Based on Deep Learning

Ensuring software security requires effective identification of vulnerabilities in the source code, as they can make software systems susceptible to attacks. In recent years, deep learning techniques have emerged as promising approaches for vulnerability detection in source code. This paper introduces VulScan, a model that combines Graph Convolutional Network (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) to identify vulnerabilities. By representing the source code as an Abstract Syntax Tree (AST) and applying GCN to extract structural features, VulScan captures both the graph-based relationships and sequential information using BiLSTM. Comparative evaluations against baseline techniques commonly used in vulnerability detection demonstrate VulScan’s superior performance, leading to improved detection accuracy.
Trusted Searchable Cryptographic Authentication Model in Edge Computing

Edge computing is a new paradigm in which end users’ access to computing resources is constrained by their physical size. And where edge nodes in edge computing networks generate income by offering services like storage, computing resources, etc. Although end users and edge nodes desire more benefits with fewer resources, edge computing operates in a trustless environment. The current options either use an honest third party or a blockchain with high computational overhead authentication. To create a committee of witnesses to verify outsourced file search services, we propose a validation model that makes use of game theory and smart contracts. The witnesses are encouraged to provide truthful responses in the verification service by a payoff function created by the Nash equilibrium principle. To prevent collusion and a single point of failure, unbiased witness selection is also carried out using a witness randomization technique. Besides, a reputation mechanism for the witness’s behavior is created, and it will suffer if the witness acts sluggishly or irregularly. When its reputation value is below a set threshold, it is ineligible to participate in the witness selection process. The approach is put into practice using the Ethereum blockchain’s smart contracts, and experimental results show that it can work.
Research on the Reasoning Credibility of Privacy Infringement Risk Monitoring System Based on Blockchain

As privacy protection policies continue to be strengthened, regulators are imposing harsher penalties for privacy infringement incidents. Driven by the profit factor, companies naturally wish to conceal and not report personal privacy infringements in their organizations. Therefore, it is crucial to ensure the credibility of the evidential data obtained by the privacy infringement risk monitoring system and the models and rules of the inference framework employed. In this paper, we analyze various factors affecting the inference trustworthiness of the privacy infringement risk monitoring system in cross-domain scenarios, propose improvement measures and methods to improve the inference trustworthiness of the system, define quantitative indicators and technical requirements for the design of the inference trustworthiness of the system based on the CF model, and verify the effectiveness of the inference trustworthiness design method of the privacy infringement risk monitoring system in cross-domain scenarios.
Exploring the Impact of Non-IID on Federated Learning

In the past few years, the growing problem of data silos has given rise to an emerging framework for distributed deep learning: Federated learning. The framework enables the construction of global models across multiple participants without the need for directly sharing raw data. However, because the environments oriented towards Federated learning are usually complex and heterogeneous, resulting in varying degrees of data distribution among individual local devices, the distribution of data usually does not satisfy the assumption of non-independent and identically distributed. In this paper, several different types of non-independent and identically distributed datasets are obtained by dividing the MNIST dataset using various methods. Each dataset is then trained using the FedAvg algorithm to explore the effects of different datasets on the training of Federated learning models. It is finally concluded that in the process of Federated learning training, when the non-independent and identically distributed dataset contains partially independent and identically distributed data, a better training model can be obtained by appropriately adjusting the training parameters of the model. This paper also introduces the process of parameter debugging and analyzes the model training results under different parameters to improve the efficiency of Federated learning training models.
Enhancing IoT Security and Privacy through Blockchain Technology, Reinforcement Learning, and Constitutional AI

The rapid growth of the Internet of Things (IoT) has brought significant benefits to various industries, but it also poses new challenges in terms of security and privacy protection. In this paper, we propose a novel approach to enhance IoT security and privacy by combining blockchain technology with reinforcement learning algorithms and Constitutional Artificial Intelligence (CAI). This approach enables secure data storage and access control, adaptive data provenance and integrity, and adaptive identity management in IoT networks. We introduce Reinforcement Learning from Artificial Intelligence Feedback (RLAIF) and CAI to make the system adaptive and intelligent. RLAIF allows the system to learn from AI-generated feedback signals and make adjustments to its security mechanisms, while CAI ensures that the system adheres to the principles and guidelines set forth in a constitution, prioritizing user privacy and security. Experimental results demonstrate the effectiveness of the proposed method, paving the way for more secure and privacy-preserving IoT networks.
Secure voting system based on blockchain and threshold cryptography

Traditional electronic voting schemes based on blind signature often rely on fully trusted signature authorities, tally authorities, and face security threats. In this paper, threshold technology and privacy computing are used to realize decentralized data computing, decentralize the power of signature authorities and tally authorities; The blockchain technology is used to realize the function of electronic bulletin board and complete decentralized data storage, so as to ensure the openness and transparency of the whole election process. Furthermore, the proposed scheme is based on elliptic curve. Compared with the similar schemes based on bilinear pair, its computational complexity is reduced by an order of magnitude and has higher efficiency. Finally, this paper analyzes the security attributes and performance of the proposed scheme, and the experimental results of Python show that the proposed scheme has good performance in the optimized environment.
A Multi-Party Collaboration Framework for Regulatory Compliance for Smart Contract Systems

The increasing adoption of blockchain (BC) and smart contracts (SCs) in the financial sector necessitates effective regulatory compliance solutions that align with the decentralized nature of these systems. Existing centralized approaches to regulatory compliance face challenges in keeping pace with the rapidly evolving technology, creating potential gaps in enforcement. This paper proposes a MultiParty Collaboration Framework (MPCF) for Regulatory Compliance for SC Systems, a novel decentralized approach to integrating regulatory compliance directly into BC-based financial systems. This framework enables multi-party collaboration and enforces compliance with various regulatory requirements while preserving the core principles of decentralization. We present the design principles of the framework, its implementation and use cases. We also discuss the challenges, limitations, potential extensions, and future research directions. Our work contributes to the ongoing efforts to develop more robust and adaptable regulatory frameworks for BC-based financial systems, fostering innovation while ensuring compliance.
Feature Engineering for Predicting Frags in Tactical Games

In tactical games players command troops of soldiers to fight against human or AI opponents in a variety of 2/3D maps involving specialized units and weapons. In turn-based games one of the key objective of each turn is to score a frag i.e. kill at least one unit of the opposing player. Efficient prediction of frags could be used to design a high quality AI-controlled agents that moderate and fine-tune the game in real-time to make it challenging, immersive and attractive to the human players. In the context of ICME 2023 Grand Challenge to predict frags in Anthracite Shift game turns, we have engineered efficient and robust families of predictive features that combined with state-of-the-art tree-based boosting algorithms deliver highly robust classification models that can be retrained in seconds and make accurate frag predictions in real-time. The feature families are derived from health-points (HP), distances (DS) and line-of-sight (LS) obstruction among opposing player units, consistently sorted by HP/LS to properly correlate various relational properties of all pairs of opposing units. With just over 70 fine-tuned features our shallow and actively diversified ensemble of boosting model variants achieves the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> highest preliminary leader-board AUC score of 0.8533 and the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> place in the final evaluation.