Reviewerly/exHarmony huggingface dataset:
Prompt text classifications with transformer models! An exemplary introduction to prompt-based learning with large language models

This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without sophisticated programming skills and (2) make use of artificial intelligence without fine-tuning models with large amounts of labeled training data. We apply this novel method to perform an experiment using so-called zero-shot classification as a baseline model and a few-shot approach for classification. For comparison, we also fine-tune a language model on the given classification task and conducted a second independent human rating to compare it with the given human ratings from the original study. The used dataset consists of 2,088 email responses to a domain-specific problem-solving task that were manually labeled for their professional communication style. With the novel prompt-based learning approach, we achieved a Cohen’s kappa of .40, while the fine-tuning approach yields a kappa of .59, and the new human rating achieved a kappa of .58 with the original human ratings. However, the classifications from the machine learning models have the advantage that each prediction is provided with a reliability estimate allowing us to identify responses that are difficult to score. We, therefore, argue that response ratings should be based on a reciprocal workflow of machine raters and human raters, where the machine rates easy-to-classify responses and the human raters focus and agree on the responses that are difficult to classify. Further, we believe that this new, more intuitive, prompt-based learning approach will enable more people to use artificial intelligence.
The enabling and constraining connections between trust and digitalisation in incumbent value chains

Digitalisation is a disruptive socio-technical process that goes beyond digital technologies and their use within an organisation, and involves besides (in many cases radical) technological change, social, institutional and economic change. This creates uncertainties for value chain actors and the trust relationships between them. In this paper we aim to understand the connections between trust and digitalisation. We investigate how trust relations affect digitalisation, and how digitalisation affects trust relations among value chain actors, using the Dutch flower sector as a case study. Our findings show that the sector has a high level of interpersonal trust, but limited institutional trust, as the relationships between companies are highly competitive and transactional. In this context, limited trust hinders digitalisation in multiple and mutually reinforcing ways, inducing a vicious cycle whereby existing distrust or limited trust results in limited digitalisation, which in turn causes more distrust due to uncertainties around the digitalisation process, further increased by existing (technological) path dependencies. Hence there is a need for 1) awareness of mutually reinforcing (dis)trust dynamics and vicious (or virtuous) cycles in relation to digitalisation are needed; 2) higher levels of understanding of what digitalisation entails and 3) joint strategy building and foresighting in the value chain.
Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images

Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.
Laplacian Lp norm least squares twin support vector machine

Semi-supervised learning has become a hot learning framework, where large amounts of unlabeled data and small amounts of labeled data are available during the training process. The recently proposed Laplacian least squares twin support vector machine (Lap-LSTSVM) is an excellent tool to solve the semi-supervised classification problem. Motivated by the success of Lap-LSTSVM, in this paper, we propose a novel Laplacian Lp norm least squares twin support vector machine (Lap-LpLSTSVM). There are several advantages of our proposed method: (1) The performance of our proposed Lap-LpLSTSVM can be improved by the adjustability of the value of p. (2) The introduced Lp norm graph regularization term can efficiently exploit the geometric information embedded in the data. (3) An efficient iterative strategy is employed to solve the optimization problem. Besides, to demonstrate that our proposed method can make use of unlabeled data effectively, least squares twin support vector machine (LSTSVM) which only uses the same labeled data is used to compare with our proposed method. The experimental results on both synthetic and real-world datasets show that our proposed method outperforms other state-of-the-art methods and can also deal with noisy datasets.
Cyber-Physical-Social System and İncident Management

The issue of security is a global problem and manifests itself in many areas. One of these areas is the cyber-physical-social system. Physical and virtual threats are always in the spotlight. The main security issues here are related to network threats. Here is a theoretical overview of the concept of the cyber-physical system, the cyber-physical-social system and its components, and other technologies related to CPS. The issues of incident management and incident response plan are also considered separately.
Design of privacy preserving model based on clustering involved anonymization along with feature selection

Healthcare services has become a hug transformation due to the number of disease emerging at presently. Accordingly, enormous amount of data is generated regarding on the patient data. Hence, preserving privacy of patient data is the major concern to meet privacy requirements and to prevent healthcare data from numerous privacy attacks. To protect the information from privacy attack, various studies were performed to safeguard the patient's data but, these methods have several issues when preserving privacy that increases proportionally as the data dimension increased in multiple approaches, which produces less data quality, more information loss and more execution time.Thus, selecting relevant attributes plays an important factor for improving the efficiency of any preserving algorithm. In order to overcome these concerns, in this paper proposed a privacy preserving model based on clustering involved anonymization along with feature selection approaches. The proposed model consist of two phases such as, feature selection and anonymization. In the initial phase, the relevant features are selected using Symmetrical Uncertainty (SU) and the redundant features present in the dataset are removed utilizing Spearman's Correlation Coefficient. In the second phase, the privacy preservation is performed using Utility Preserved Anonymization (UPA) algorithm. Also, the proposed algorithm reduces the dimensionality of data to ease the process of forming clusters for anonymization. The experimental analysis using real time datasets to verifies the effectiveness of the proposed method. The results show high sensitivity (up to 98.63%) and high accuracy (up to 98%), allowing us to claim efficient attribute selection for anonymization. Hence proved the proposed method reduces clustering's complexity by removing the irrelevant attributes effectively.
Comparative study of metaheuristic optimization algorithms for image steganography based on discrete Fourier transform domain

Image steganography and watermarking have demonstrated their efficiency in creating a covert communication channel and in digital image authentication respectively. The importance of cybersecurity tasks sets high efficiency requirements for information embedding methods. Many studies in this area focus on finding new ways to improve the embedding quality. One of them is to use metaheuristic optimization algorithms. The use of classical metaheuristics is common enough for a data hiding area. However, a lot of new metaheuristics have been proposed over the past few years. Their applicability has not yet been evaluated in the field of steganography and digital watermarking. In this paper, we present a comparative study on the efficiency of seven metaheuristic optimization algorithms for finding the best embedding options in the phase spectrum of the Discrete Fourier Transform (DFT). One of the contributions of our research is an improved DFT-based data hiding algorithm, which is a development of the error-free embedding algorithm we obtained earlier. The new algorithm also provides error-free extraction of embedded data, and it is distinguished by the high invisibility of embedding through the use of metaheuristic optimization. The use of metaheuristic optimization led to an increase in the PSNR value by an average of 2%–6% and an increase in the capacity value by an average of 16%–25%. Another important contribution of our research is the original formulation of the optimization problem for different classes of metaheuristics. The experimental results demonstrate the efficiency of metaheuristic optimization for solving data hiding problem. The Differential Evolution and the Particle Swarm Optimization showed the best values of embedding indicators among the classical metaheuristic optimization algorithms. The Gradient-Based Optimizer showed the highest efficiency among modern algorithms. Thus, our research indicates the relevance of further studies of other modern optimization algorithms for a data hiding area.
Ontology-based semantic retrieval of documents using Word2vec model

Semantic retrieval of engineering knowledge is crucial in various engineering activities, such as process development and product model planning. To make a solution for this issue, previously, a few word-based semantic enabling existing approaches such as Lexical Retrieval Model with Semantic Residual Embedding's (LR-SRE), Document Retrieval Model through Semantic Linking (DR-MTS), Semantic Term Matching in Axiomatic Approaches to Information Retrieval (STM-IR) and Hybrid Ontology for Semantic Information Retrieval Model using Keyword Matching Indexing System (HOS-IR) were utilized. But, they all have shown less query-based accuracy results than the required value. In our proposed Information Retrieval (IR) design, the semantic knowledge-based retrieval scheme has been implemented. For query, entered by a user and processed for finding the dominated word. Word is then compared for its similarity equations, and similarity values are then computed to give output. Highly similar values are obtained as the class value. From class, the respective clusters are selected. Then, documents in that cluster are retrieved and ranked according to the relevance of the user. To support the accuracy level performance of this IR system, a word2vec model has been employed with the benefits of Horse Herd Optimization (HHO), which helps to extract vectors as features for classification. These results are stored as a .csv file for further retrieval. By implementing the proposed IR-word2vec model, the results showed that it outperforms other existing techniques by improved similarity index and accuracy for query results in an execution time of 1.7 s.
Blockchain Based Offloading Strategy: Incentive, Effectiveness and Security

To securely integrate Mobile Edge Computing (MEC) into Wireless Blockchain Network (WBN), this paper proposes a framework for blockchain based offloading strategy, where blockchain nodes are categorized as blockchain users and blockchain miners from a motivation perspective. Particularly, aiming at improving the motivation ability, a block generation process is first designed for blockchain miners’ short transaction processing time lower bound. Then, to further maximize the utilities of both blockchain users and blockchain miners, an optimization problem is formulated to determine an optimal strategy which involves a trade-off between the fast transaction confirmation rate required by blockchain users and the transaction fees obtained by blockchain miners. A Stackelberg game is introduced to model the interaction between the blockchain users and miners. Meanwhile, a distributed algorithm is designed to converge this strategy in an iterative manner based on the buyer-seller negotiation. Additionally, double-spending attack and selfish mining attack are analysed to examine their impact on the system performance in terms of confirmation delay and throughput while guaranteeing the high security level. Finally, extensive experiments have been conducted to show the rightness and effectiveness of the proposed equilibrium-based strategy and mathematical analysis, and some insights are discussed for the further guide as well.
Compact Learning Model for Dynamic Off-Chain Routing in Blockchain-Based IoT

Dynamic off-chain routing in payment channel network (PCN)-based Internet of Things (IoT) is attracting increasing research attention. However, there are two major issues in dynamic routing in PCN-based IoT with resource-limited devices. The first issue is how to achieve high long-term transaction efficiency in PCN with dynamic channel capacities. The second issue is how to achieve a lightweight routing algorithm deployed on IoT devices while achieving high transaction efficiency, i.e., successful payment amount and success ratio. Therefore, in this paper, we propose a compact deep reinforcement learning (DRL) algorithm to learn the joint dynamic and lightweight routing policy for maximizing long-term transaction efficiency. To obtain optimal performance in dynamic routing problems for off-chain systems, a proximal policy optimization algorithm is employed to create an actor–critic learning structure for training the teacher DRL model. To obtain a compact and efficient student DRL model, an adaptive pruning technique is utilized for pruning unnecessary parameters of networks in the teacher model adaptively without affecting its learning ability. Furthermore, knowledge distillation is leveraged to improve the performance of the student network. Thus, a compact and efficient student DRL model can be developed and implemented to maximize the long-term transaction efficiency in off-chain systems on resource-limited IoT devices. The simulation results demonstrate that the proposed DRL algorithm outperforms the other baseline algorithms in PCN transaction efficiency while requiring only 10% of the computation and storage resources compared with that of the original teacher model.
Meepo: Multiple Execution Environments per Organization in Sharded Consortium Blockchain

Blockchain performance cannot meet the requirement nowadays. One of the crucial ways to improve performance is sharding. However, most blockchain sharding research focuses on the public blockchain. As for consortium blockchain, previous studies cannot support high cross-shard efficiency, multiple-shard contract calling, strict transaction atomicity, and shard availability, which are essential requirements but also challenges in consortium blockchain systems. Facing these challenges, we propose Meepo, a systematic study on sharded consortium blockchain. Meepo enhances cross-shard efficiency via the cross-epoch and cross-call. Moreover, a partial cross-call merging strategy is designed to handle the multi-state dependency in contract calls, achieving flexible multiple-shard contract calling. Meepo employs a replay-epoch to ensure strict transaction atomicity, and it also uses a backup algorithm called shadow shard based recovery to improve the shard robustness. On a test-bed of 128 AliCloud servers, setting 32 shards and 4 consortium members, Meepo-OpenEtheruem can achieve more than 140,000 cross-shard TPS under the workload of 100,000,000 asset transactions. It also shows more than 50,000 TPS under the transactions of real-world shopping behaviors.
AESM<sup>2</sup> Attribute-Based Encrypted Search for Multi-Owner and Multi-User Distributed Systems

With the rapid development of cloud computing, it is popular for data owners to outsource massive data to the cloud server for data sharing. To protect the privacy of sensitive data, many searchable encryption schemes are proposed. However, most of the existing studies focus on the single-owner model. In practice, users need to query data from distributed owners one by one, which inevitably brings great communication and computation overheads. Moreover, it lacks a secure scheme that realizes the access control requirements of individual owners. In this article, we propose AESM <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , a new attribute-based encrypted search with ownership enhancement scheme for multi-owner and multi-user distributed systems. Our design enables users to search data from authorized owners with only one trapdoor. Owners can enforce owner level permission on users and encrypt their data individually with fine-grained attribute level permission. For practical consideration, we further devise an efficient revocation method of the owner level permission for users, where ciphertexts do not need to be updated. We formally define and prove the security of our design. Moreover, we implement a system prototype and analyze the performance from theoretical and experimental aspects. The evaluation results demonstrate that our scheme is effective and efficient.
Waste heat reutilization and integrated demand response for decentralized optimization of data centers

In recent years, the energy consumption of data centers (DCs) has increased rapidly. Since the demand response (DR) capability of DCs is considerable, DCs play an important role in DR programs. In this work, we further focus on the DCs' integrated demand response (IDR) capability which owes to its temporally and spatially shiftable electricity and heat. To make full use of waste heat retained in the coolant, we design a waste heat recovery process for waste heat reutilization. Moreover, we propose a decentralized optimization algorithm based on the alternating direction multiplier method (ADMM) to protect the inner sensitive data of DCs. Numerical results show that energy purchase costs can be effectively reduced for DCs with IDR capability. It is also proved that the reutilized waste heat constitutes an important part of the heat supply. Besides, the convergence performance of the proposed algorithm is verified based on large quantities of simulations.
Adaptive minimum adjustment consensus model for large-scale group decision making under social networks and its application in Integrated Care of Older People

Elderly care has become a vital livelihood issue as the aging population continues to grow. Generally, integrated care of older people (ICOPE) involves doctors, nurses, and family members. The service quality evaluation of integrated care can be modeled by social network large-scale group decision making (SN-LSGDM), and linguistic distribution assessment (LDA) is a powerful means to express evaluation information. However, it lacks the associated research. This paper proposes an adaptive minimum adjustment consensus model for SN-LSGDM with LDA and then applies it to evaluate elderly care modes. Firstly, trust relationships are updated according to the opinion similarity and the trust deviation. Then, a new clustering method based on trust density and opinion similarity is proposed to cluster DMs. Further, the expert and subgroup consensus levels are measured by considering the distance from the collective opinion. Moreover, adaptive consensus models are constructed in terms of the minimum adjustment of subgroups and experts. Finally, a case study of the service quality of ICOPE demonstrates the effectiveness of the new method, and comparison and sensitivity analysis reflect the advantages of the new approach.
Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments

Faults in photovoltaic arrays are known to cause severe energy losses. Data-driven models based on machine learning have been developed to automatically detect and diagnose such faults. A majority of the models proposed in the literature are based on artificial neural networks, which unfortunately represent black-boxes, hindering user interpretation of the models' results. Since the energy sector is a critical infrastructure, the security of energy supply could be threatened by the deployment of such models. This study implements explainable artificial intelligence (XAI) techniques to extract explanations from a multi-layer perceptron (MLP) model for photovoltaic fault detection, with the aim of shedding some light on the behavior of XAI techniques in this context. Three techniques were implemented: Shapley Additive Explanations (SHAP), Anchors and Diverse Counterfactual Explanations (DiCE), each representing a distinct class of local explainability techniques used to explain predictions. For a model with 99.11% accuracy, results show that SHAP explanations are largely in line with domain knowledge, demonstrating their usefulness to generate valuable insights on model behavior which could potentially increase user trust in the model. Compared to Anchors and DiCE, SHAP demonstrated a higher degree of stability and consistency.
2C2S: A two-channel and two-stream transformer based framework for offline signature verification

Recently, with the outstanding performance of the transformer in NLP, approaches that employ the transformer to address vision problem is becoming a research focus. However, transformer-based research rarely focuses on signature verification. To fill this gap, this paper proposes a two-channel and two-stream transformer approach (2C2S) to cope with the signature verification problem. 2C2S is composed of original and central streams. The original stream receives the original signature pair as input, and the central stream receives the signature pair generated by cropping the central at the original pair as input. In order to establish the associations among feature channels, a squeeze-and-excitation operation is applied between two standard Swin Transformer blocks. Moreover, an up-sampling enhancement module directly steers the model to focus on useful information. The verification accuracy of 2C2S on SUES-SiG and several publically available datasets: CEDAR, BHSig-B, and BHSig-H, reaches 93.25%, 90.68%, 100%, and 72.22%, respectively. Extensive experiments illustrate that the proposed framework is competitive with the existing techniques for offline handwritten signature verification.