Reviewerly/exHarmony huggingface dataset:
eX-ViT: A Novel explainable vision transformer for weakly supervised semantic segmentation

Recently vision transformer models have become prominent models for a multitude of vision tasks. These models, however, are usually opaque with weak feature interpretability, making their predictions inaccessible to the users. While there has been a surge of interest in the development of post-hoc solutions that explain model decisions, these methods can not be broadly applied to different transformer architectures, as rules for interpretability have to change accordingly based on the heterogeneity of data and model structures. Moreover, there is no method currently built for an intrinsically interpretable transformer, which is able to explain its reasoning process and provide a faithful explanation. To close these crucial gaps, we propose a novel vision transformer dubbed the eXplainable Vision Transformer (eX-ViT), an intrinsically interpretable transformer model that is able to jointly discover robust interpretable features and perform the prediction. Specifically, eX-ViT is composed of the Explainable Multi-Head Attention (E-MHA) module, the Attribute-guided Explainer (AttE) module with the self-supervised attribute-guided loss. The E-MHA tailors explainable attention weights that are able to learn semantically interpretable representations from tokens in terms of model decisions with noise robustness. Meanwhile, AttE is proposed to encode discriminative attribute features for the target object through diverse attribute discovery, which constitutes faithful evidence for the model predictions. Additionally, we have developed a self-supervised attribute-guided loss for our eX-ViT architecture, which utilizes both the attribute discriminability mechanism and the attribute diversity mechanism to enhance the quality of learned representations. As a result, the proposed eX-ViT model can produce faithful and robust interpretations with a variety of learned attributes. To verify and evaluate our method, we apply the eX-ViT to several weakly supervised semantic segmentation (WSSS) tasks, since these tasks typically rely on accurate visual explanations to extract object localization maps. Particularly, the explanation results obtained via eX-ViT are regarded as pseudo segmentation labels to train WSSS models. Comprehensive simulation results illustrate that our proposed eX-ViT model achieves comparable performance to supervised baselines, while surpassing the accuracy and interpretability of state-of-the-art black-box methods using only image-level labels.
Toward Forward and Backward Private Dynamic Searchable Symmetric Encryption Supporting Data Deduplication and Conjunctive Queries

In Internet of Things (IoT) application scenarios, such as intelligent logistics, secure data access control, and sharing based on dynamic searchable symmetric encryption (DSSE) has become a research hotspot in recent years. DSSE is an encryption technology which gratifies the above requirements, while protecting the sensitive data during the operation. The existing DSSE schemes do not achieve data deduplication and conjunctive queries concurrently, and exist problems, such as complex update process, inflexible query method, and privacy disclosure. For this purpose, this article proposes DSSE for data deduplication and conjunctive queries (DSSE-DC), and constructs a concrete DSSE-DC scheme. Our scheme uses symmetric encryption with homomorphic addition and bitmap index to develop a secure and reliable search architecture, and updates through modular addition. Meanwhile, we add a deduplication mechanism to realize data deduplication which avoids the waste of cloud storage resources. Furthermore, we introduce the idea of inner product matching to achieve efficient conjunctive queries. The adaptive security of the DSSE-DC scheme is proved in the random oracle model. While our scheme satisfies forward and backward privacy. In comparison to the existing DSSE schemes for conjunctive queries, our scheme has advantage in update and search performances, which is applicable to IoT applications like intelligent logistics.
Bridging the gap – the impact of ChatGPT on financial research

In March 2023, the release of GPT-4 and its application, Copilot, astounds the world and thrusts AI into the spotlight in industry, and academia. The incredible superiority of GPT-4 is demonstrated by its ability to achieve high scores on almost all mainstream academic and professional standard exams, Copilot’s capability to accomplish nearly all repetitive office work, and the rapid spread of its applications across massive areas of human society within weeks of its launch. These changes lead to the belief on the emergence of GPT-4 having a significant impact on academic research in the finance and accounting fields by establishing a consensus on the psychological acceptance of AI and rapidly eliminating technical barriers of using it. This paper presents practical examples to demonstrate GPT-4’s effectiveness in sentiment analysis, ESG analysis, corporate culture analysis, and Federal Reserve opinion analysis, and provides instructive recommendations for applying it in these subject areas.
Impact of irritation and negative emotions on the performance of voice assistants: Netting dissatisfied customers’ perspectives

With the rising popularity of voice assistants like Alexa, Siri, etc., understanding the service gaps experienced by customers has become imperative. In unison, limited studies are focusing on the perspectives of dissatisfied customers. This research aims to address this gap by suggesting the antecedents of technology irritation and its impact on the overall performance of voice assistants. The study uses a mixed-methods approach by analyzing the data gathered from user interviews, online reviews from users, and also empirical surveys. The qualitative study, capturing both etic and emic perspectives, found problems related to responsiveness, usability, platform, connectivity, and compatibility of VAs. While the empirical study revealed that utility gratification and service quality have a significant impact on technology irritation. The findings also show that customers from negative high and low arousal emotion groups have varying perceptions about utility gratifications.
Bias-tailored quantum LDPC codes

Bias-tailoring allows quantum error correction codes to exploit qubit noise asymmetry. Recently, it was shown that a modified form of the surface code, the XZZX code, exhibits considerably improved performance under biased noise. In this work, we demonstrate that quantum low density parity check codes can be similarly bias-tailored. We introduce a bias-tailored lifted product code construction that provides the framework to expand bias-tailoring methods beyond the family of 2D topological codes. We present examples of bias-tailored lifted product codes based on classical quasi-cyclic codes and numerically assess their performance using a belief propagation plus ordered statistics decoder. Our Monte Carlo simulations, performed under asymmetric noise, show that bias-tailored codes achieve several orders of magnitude improvement in their error suppression relative to depolarising noise.
User privacy prevention model using supervised federated learning‐based block chain approach for internet of Medical Things

Abstract This research focuses on addressing the privacy issues in healthcare advancement monitoring with the rapid establishment of the decentralised communication system in the Internet of Medical Things (IoMT). An integrated blockchain homomorphic encryption standard with an in‐build supervised learning‐based smart contract is designed to improvise personal data prevention. The Internet of Medical Things (IoMT) has advanced in healthcare with the rapid establishment of decentralised communication systems. Distributed ledgers have resource constraints to leverage public, private, and hybrid blockchain transactions to facilitate heterogeneous operations. The authors propose a supervised learning strategy in healthcare to mitigate learning health‐related issues, improvise clinical monitoring, and ensure secure communication. The proposed approach handles the vast IoMT data by adopting blockchain for IoMT as (BIoMT) to preserve sensitive clinical information. It incorporates hybrid encryption techniques to improve patient and health records' privacy protection. BIoMT also maintains secured and sustainable supply chain management with a highly confidential decentralised framework using blockchain‐based smart contracts, which minimises data loss. Moreover, a framework is designed with a hybrid hashing that integrates a homomorphically encrypted algorithm to support a smart contract for decentralised applicability. The BIoMT approach is tested and compared with the relevant prevention mechanisms. The evaluation shows that the effects observed from the result analysis noted that the proposed method outperforms reliable prevention mechanisms compared to the existing approaches.
SFRNet: Fine-Grained Oriented Object Recognition via Separate Feature Refinement

Fine-grained oriented object recognition (FGO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> R) is a practical need for intellectually interpreting remote sensing images. It aims at realizing fine-grained classification and precise localization with oriented bounding boxes, simultaneously. Our considerations for the task are general but decisive: (i) the extraction of subtle differences carries a big weight in differentiating fine-grained classes, and (ii) oriented localization prefers rotation-sensitive features. In this article, we propose a network with separate feature refinement (SFRNet), in which two transformer-based branches are designed to perform function-specific feature refinement for fine-grained classification and oriented localization, separately. To highlight the discriminative information advantageous to fine-grained classification, we propose a spatial and channel transformer (SC-Former) to capture both the long-range spatial interactions and the key correlations hidden in the feature channels. Besides, we design a Multi-RoI loss (MRL) following the protocol of deep metric learning to enhance the separability of fine-grained classes further. For oriented localization, we integrate the oriented response convolution with the transformer structure (namely, OR-Former) to assist in encoding rotation information during regression. Extensive experimental results validate the effectiveness and robustness of our SFRNet. Without bells and whistles, our SFRNet achieves state-of-the-art performance on the large-scale FAIR1M datasets (FAIR1M-1.0 and FAIR1M-2.0). Code will be available at https://github.com/Ranchosky/SFRNet.
Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives

The success of machine learning (ML) techniques in the formerly difficult areas of data analysis and pattern extraction has led to their widespread incorporation into various aspects of human life. This success is due in part to the increasing computational power of computers and in part to the improved ability of ML algorithms to process large amounts of data in various forms. Despite these improvements, certain issues, such as privacy, continue to hinder the development of this field. In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to clients to be trained locally, and then only the models themselves, without associated data, are sent back to the server to combine the different locally trained models into a single global model. In this respect, the aggregation algorithms play a crucial role in the federated learning process, as they are responsible for integrating the knowledge of the participating clients, by integrating the locally trained models to train a global one. To this end, this paper explores and investigates several federated learning aggregation strategies and algorithms. At the beginning, a brief summary of federated learning is given so that the context of an aggregation algorithm within a FL system can be understood. This is followed by an explanation of aggregation strategies and a discussion of current aggregation algorithms implementations, highlighting the unique value that each brings to the knowledge. Finally, limitations and possible future directions are described to help future researchers determine the best place to begin their own investigations.
CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization algorithms, allowing the algorithm to jump out of the local optimum, maintain population diversity, and improve global search ability. This paper presents a chaotic-based tumbleweed optimization algorithm (CTOA) that incorporates chaotic maps into the optimization process of the TOA. By using 12 common chaotic maps, the proposed CTOA aims to improve population diversity and global exploration and to prevent the algorithm from falling into local optima. The performance of CTOA is tested using 28 benchmark functions from CEC2013, and the results show that the circle map is the most effective in improving the accuracy and convergence speed of CTOA, especially in 50D.
Personalized Feedback Enhanced by Natural Language Processing in Intelligent Tutoring Systems

This paper proposes a novel approach for enhancing feedback in intelligent tutoring systems (ITSs) for Java programming using natural language processing (NLP). The proposed approach overcomes the limitations of traditional rule-based feedback generation systems and provides more personalized and relevant feedback to learners. The architecture includes three main components: a natural language parser (that takes as input comments and/or questions of the user that can be inserted through a text box in the user interface.), a feedback generator, and a feedback evaluator. The natural language parser is responsible for converting the unstructured text input of the learner into structured data, which can be analyzed for generating feedback. The feedback generator component then processes this data and generates personalized feedback for the learner based on their specific needs. Finally, the feedback evaluator component assesses the quality of the generated feedback and determines its helpfulness to the learner. The evaluation results are promising, indicating that using NLP techniques can improve the overall performance of intelligent tutoring systems and provide a more personalized learning experience for students.
An In-Depth Analysis of the Role That ML and Big Data Play in Driving Digital Marketing’s Paradigm Shift

Machine learning (ML) is an artificial neural network (ANN) that helps developers improve their software’s predictive abilities before they have all the data they need. Because information is so priceless, progress toward fully autonomous agents requires better methods for managing the omnipresent content infrastructures that exist today. All sorts of fields have benefited from advancements in computer vision and AI, from medical diagnosis to data presentation and operations to scientific study, and so on. Learning from polluted or erroneous data may be expensive, much as training for a sport can be dangerous to those who are vulnerable to injury. An organization will incur costs rather than see benefits if its algorithms are improperly taught, as explained in Approaching Data Science. Organizations need to be able to verify the quality and consistency of any large datasets, as well as their sources, to ensure the efficacy of any algorithm.
Simulating Open Quantum System Dynamics on NISQ Computers with Generalized Quantum Master Equations

We present a quantum algorithm based on the Generalized Quantum Master Equation (GQME) approach to simulate open quantum system dynamics on noisy intermediate-scale quantum (NISQ) computers. This approach overcomes the limitations of the Lindblad equation, which assumes weak system-bath coupling and Markovity, by providing a rigorous derivation of the equations of motion for any subset of elements of the reduced density matrix. The memory kernel resulting from the effect of the remaining degrees of freedom is used as input to calculate the corresponding non-unitary propagator. We demonstrate how the Sz.-Nagy dilation theorem can be employed to transform the non-unitary propagator into a unitary one in a higher-dimensional Hilbert space, which can then be implemented on quantum circuits of NISQ computers. We validate our quantum algorithm as applied to the spin-boson benchmark model by analyzing the impact of the quantum circuit depth on the accuracy of the results when the subset is limited to the diagonal elements of the reduced density matrix. Our findings demonstrate that our approach yields reliable results on NISQ IBM computers.
Implementation of IoT Security System by Incorporating Block Chain Technology

Internet of Things (IoT) allows both physical and virtual objects to communicate with each other over a network. The services provided with IoT helps in easing the day-to-day activities. IoT has numerous advantages like scalability and ease of access but the drawback of the IoT system is that a centralized cloud is required for data storage and ensure the security and privacy of the users. In order to eliminate the drawback of IoT, recent studies have highlighted the integration of IoT systems with the distributed ledger technologies like Blockchain. The Blockchain technology would provide military grade security to the data. This article presents a comprehensive literature review for the Internet of Things (IoT) and Blockchain protocols.
A Mathematical Interpretation of Autoregressive Generative Pre-Trained Transformer and Self-Supervised Learning

In this paper, we present a rigorous mathematical examination of generative pre-trained transformer (GPT) models and their autoregressive self-supervised learning mechanisms. We begin by defining natural language space and knowledge space, which are two key concepts for understanding the dimensionality reduction process in GPT-based large language models (LLMs). By exploring projection functions and their inverses, we establish a framework for analyzing the language generation capabilities of these models. We then investigate the GPT representation space, examining its implications for the models’ approximation properties. Finally, we discuss the limitations and challenges of GPT models and their learning mechanisms, considering trade-offs between complexity and generalization, as well as the implications of incomplete inverse projection functions. Our findings demonstrate that GPT models possess the capability to encode knowledge into low-dimensional vectors through their autoregressive self-supervised learning mechanism. This comprehensive analysis provides a solid mathematical foundation for future advancements in GPT-based LLMs, promising advancements in natural language processing tasks such as language translation, text summarization, and question answering due to improved understanding and optimization of model training and performance.
Bringing trust and transparency to the opaque world of waste management with blockchain: A Polkadot parathread application

The majority of countries are currently struggling with unsustainable levels of waste production and low levels of recycling, particularly relating to household waste, and this area is in urgent need of new solutions. In general, the waste management sector has struggled with low consumer trust, fraud, manipulation, significant manual processes, and low levels of information and control. Recent events relating to the COVID-19 pandemic have highlighted, in particular, the role of trust in effective public policy making and consumer behavioural change. Here we propose a hybrid blockchain solution called a Polkadot parachain. Polkadot is a blockchain technology that connects a network of blockchains, each called a parachain, that can be customised to the business needs of a given application. This solution provides the cost benefits, scalability, and control of a permissioned or private blockchain while providing the security, verifiability, and trust of a public blockchain. The solution is developed with a design science approach and combines three typically separate blockchain use cases: supply chain tracking, incentivisation through a payment system, and gamification to achieve a complete solution for waste management. We provide a detailed discussion on the design of this blockchain solution with the use of blockchain functionality assessed against the criteria and development approaches found in the literature. Finally, we demonstrate how such a blockchain can be implemented with the Substrate blockchain development framework.
CAM-VT: A Weakly supervised cervical cancer nest image identification approach using conjugated attention mechanism and visual transformer

Cervical cancer is the fourth most common cancer among women, and cytopathological images are often used to screen for this cancer. However, manual examination is very troublesome and the misdiagnosis rate is high. In addition, cervical cancer nest cells are denser and more complex, with high overlap and opacity, increasing the difficulty of identification. The appearance of the computer aided automatic diagnosis system solves this problem. In this paper, a weakly supervised cervical cancer nest image identification approach using Conjugated Attention Mechanism and Visual Transformer (CAM-VT), which can analyze pap slides quickly and accurately. CAM-VT proposes conjugated attention mechanism and visual transformer modules for local and global feature extraction respectively, and then designs an ensemble learning module to further improve the identification capability. In order to determine a reasonable interpretation, comparative experiments are conducted on our datasets. The average accuracy of the validation set of three repeated experiments using CAM-VT framework is 88.92%, which is higher than the optimal result of 22 well-known deep learning models. Moreover, we conduct ablation experiments and extended experiments on Hematoxylin and Eosin stained gastric histopathological image datasets to verify the ability and generalization ability of the framework. Finally, the top 5 and top 10 positive probability values of cervical nests are 97.36% and 96.84%, which have important clinical and practical significance. The experimental results show that the proposed CAM-VT framework has excellent performance in potential cervical cancer nest image identification tasks for practical clinical work.