Reviewerly/exHarmony huggingface dataset:
A Test of Protection Motivation Theory in the Information Security Literature: A Meta-Analytic Structural Equation Modeling Approach in Search Advertising

Information security is one of the important domains of information systems research today, with protection motivation theory (PMT) one of its most influential theoretical lenses. However, empirical findings based on PMT are often inconsistent and inconclusive. To reconcile these inconsistent findings, we conducted a meta-analysis to investigate the relationships among PMT constructs while also considering additional contextual constructs that are not specified in PMT. Ninety-two published studies were meta-analyzed and estimated using structural equation modeling. Our results provide support for three of the five predictors of security motivation intention, as postulated by PMT, mixed support for perceived vulnerability, and no support for response cost. We found that coping appraisal variables of response efficacy and self-efficacy have the largest average effects on security behavior. In addition, cultural attributes of collectivism and individualism moderated some of the pairwise correlations, PMT-theoretic relationships were generally stronger in personal contexts than in workplace contexts, and the intention-behavior relationship was strongest in workplace and compliance settings. Our results contribute to the information security literature by providing guidance for future PMT-related research and by demonstrating how meta-analysis and structural equation modeling can be combined to test theories in information systems research.
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. In order to improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream vision-language tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR$^2$, ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-of-the-art, while enjoying faster inference speed. Code and pre-trained models are available at this https URL.
The Internet of Federated Things (IoFT)

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.
Performance Evaluation of Data Transactions in Blockchain

Blockchain is an emerging technology, with a decentralized infrastructure avoiding third party dependency. Smartcontracts are one of the features of Ethereum blockchain, capable of running distributed applications in unreliableenvironments, enabling process automation and being one of the most sought technologies due to the highcustomization added to transactions. However, little is known about predicting the cost and execution time behavior of blockchain-based system transactions. This work aims to evaluate the performance of an Ethereum network through an application designed to analyze the cost and time of transactions that store characters in the blockchain. To meet the proposed objective, we designed an application for performing transactions with data inclusion and query on a blockchain, collecting time and cost data. As main conclusions of this work we have: the Ethereum platform proved to be inconstant in relation to the processing time of transactions on the blockchain and the application developed based on blockchain can provide a mechanism to evaluate text-type operations on Ethereum network.
Machine Learning–Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal

In the United States, national guidelines suggest that aggressive cancer care should be avoided in the final months of life. However, guideline compliance currently requires clinicians to make judgments based on their experience as to when a patient is nearing the end of their life. Machine learning (ML) algorithms may facilitate improved end-of-life care provision for patients with cancer by identifying patients at risk of short-term mortality.This study aims to summarize the evidence for applying ML in ≤1-year cancer mortality prediction to assist with the transition to end-of-life care for patients with cancer.We searched MEDLINE, Embase, Scopus, Web of Science, and IEEE to identify relevant articles. We included studies describing ML algorithms predicting ≤1-year mortality in patients of oncology. We used the prediction model risk of bias assessment tool to assess the quality of the included studies.We included 15 articles involving 110,058 patients in the final synthesis. Of the 15 studies, 12 (80%) had a high or unclear risk of bias. The model performance was good: the area under the receiver operating characteristic curve ranged from 0.72 to 0.92. We identified common issues leading to biased models, including using a single performance metric, incomplete reporting of or inappropriate modeling practice, and small sample size.We found encouraging signs of ML performance in predicting short-term cancer mortality. Nevertheless, no included ML algorithms are suitable for clinical practice at the current stage because of the high risk of bias and uncertainty regarding real-world performance. Further research is needed to develop ML models using the modern standards of algorithm development and reporting.
Symmetric Image Encryption Algorithm Based on a New Product Trigonometric Chaotic Map

In the present work, a neotype chaotic product trigonometric map (PTM) system is proposed. We demonstrate the chaotic characteristics of a PTM system by using a series of complexity criteria, such as bifurcation diagrams, Lyapunov exponents, approximate entropy, permutation entropy, time-series diagrams, cobweb graphs, and NIST tests. It is proved that the PTM system has a wider chaotic parameter interval and more complex chaotic performance than the existing sine map system. In addition, a novel PTM based symmetric image encryption scheme is proposed, in which the key is related to the hash value of the image. The algorithm realizes the encryption strategy of one-graph-one-key, which can resist plaintext attack. A two-dimensional coordinate traversal matrix for image scrambling and a one-dimensional integer traversal sequence for image pixel value transformation encryption are generated by the pseudo-random integer generator (PRING). Security analysis and various simulation test results show that the proposed image encryption scheme has good cryptographic performance and high time efficiency.
Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time.
Federated Learning for IoMT Applications: A Standardization and Benchmarking Framework of Intrusion Detection Systems

Efficient evaluation for machine learning (ML)-based intrusion detection systems (IDSs) for federated learning (FL) in the Internet of Medical Things (IoMTs) environment falls under the standardisation and multicriteria decision-making (MCDM) problems. Thus, this study is developing an MCDM framework for standardising and benchmarking the ML-based IDSs used in the FL architecture of IoMT applications. In the methodology, firstly, the evaluation criteria of ML-based IDSs are standardised using the fuzzy Delphi method (FDM). Secondly, the evaluation decision matrix (DM) is formulated based on the intersection of standardised evaluation criteria and a list of ML-based IDSs. Such formulation is achieved using a dataset with 125,973 records, and each record comprises 41 features. Thirdly, the integration of MCDM methods is formulated to determine the importance weights of the main and sub standardised security and performance criteria, followed by benchmarking and selecting the optimal ML-based IDSs. In this phase, the Borda voting method is used to unify the different ranks and perform a group benchmarking context. The following results are confirmed. (1) Using FDM, 17 out of 20 evaluation criteria (14 for security and 3 for performance) reach the consensus of experts. (2) The area under curve criterion has the lowest set of weights, whilst the CPU time criterion has the highest one. (3) VIKOR group ranking shows that the BayesNet is a best classifier, whilst SVM is the last choice. For evaluation, three assessments, namely, systematic ranking, computational cost and comparative analysis, are used.
A new median-average round Robin scheduling algorithm: An optimal approach for reducing turnaround and waiting time

A variety of algorithms handles processes on the CPU. The round-robin algorithm is an efficient CPU scheduling mechanism for a time-sharing operating system. The system processes the methods based on the time slice; however, determining the time slice has proven highly challenging for the researchers. As a result, a variety of dynamic time quantum scheduling techniques are presented by various academics to address this challenge. This study aims to determine how to best schedule resources to maximize efficiency. It is important to note that this scheduling mechanism rotates between the processes after the static quantum time is complete. However, the quantum decision affects how effectively and efficiently the procedures may be scheduled. Additionally, the quantum decision has an impact on the scheduling of processes. The average waiting time, turnaround time, and context switch times of the Round Robin scheduling algorithm are high enough to influence the system's performance. To get over the round-drawbacks, robin's the authors in this study suggest using the improved algorithm Median-Average Round Robin (MARR). Using the median and average of the burst time of each process, the author proposes a dynamic time quantum for the system. The authors compared the proposed model with four other scheduling algorithms. The results vividly depict that the proposed algorithms successfully give effective results with reduced average turnaround time and waiting time. In the future, cost and RAM utilization will be considered to enhance the algorithm.
Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms

Cloud computing seems to be the result of advancements in distributed computing, parallel computing, and network computing. The management and allocation of cloud resources have emerged as a central research direction. An intelligent resource allocation system can significantly minimize the costs and wasting of resources. In this paper, we present a task scheduling technique based on the advanced Phasmatodea Population Evolution (APPE) algorithm in a heterogeneous cloud environment. The algorithm accelerates up the time taken for finding solutions by improving the convergent evolution of the nearest optimal solutions. It then adds a restart strategy to prevent the algorithm from entering local optimization and balance its exploration and development capabilities. Furthermore, the evaluation function is meant to find the best solutions by considering the makespan, resource cost, and load balancing degree. The results of the APPE algorithm being tested on 30 benchmark functions show that it outperforms similar algorithms. Simultaneously, the algorithm solves the task scheduling problem in the cloud computing environment. This method has a faster convergence time and greater resource usage when compared to other algorithms.
A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources

As an important way to alleviate information overload, a recommender system aims to filter out irrelevant information for users and provides them items that they may be interested in. In recent years, an increasing amount of works have been proposed to introduce auxiliary information in recommender systems to alleviate data sparsity and cold-start problems. Among them, heterogeneous information networks (HIN)-based recommender systems provide a unified approach to fuse various auxiliary information, which can be combined with mainstream recommendation algorithms to effectively enhance the performance and interpretability of models, and thus have been applied in many kinds of recommendation tasks. This paper provides a comprehensive and systematic survey of HIN-based recommender systems, including four aspects: concepts, methods, applications, and resources. Specifically, we firstly introduce the concepts related to recommender systems, heterogeneous information networks and HIN-based recommendation. Secondly, we present more than 70 methods categorized according to models or application scenarios, and describe representative methods symbolically. Thirdly, we summarize the benchmark datasets and open source code. Finally, we discuss several potential research directions and conclude our survey.
NFT, copyright and intellectual property commercialization

Abstract The invention of disruptive technologies broadens the horizon of opportunities for intellectual property owners. The very idea of selling copyright works in a digital space and using the same to form a digital currency is disruptive. This is one opportunity Non-Fungible Token (NFT) offers. But that disruptiveness raises certain questions and provoke the consciousness to wonder if NFTs are a form of intellectual property, or whether NFT would shift the paradigm of copyright law as we know it. Governments through her agencies are also caught in the unrestlessness of deciphering what NFT means and whether it holds any value for intellectual property. This article will address the relationship between NFT and copyright, the forseeable problems and solutions, and how NFTs are channels for intellectual property commercialisation.
Improving Conversational Recommender Systems via Transformer-based Sequential Modelling

In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the development of a dialogue. In other words, potential sequential dependencies exist in conversations. However, most of the existing CRSs neglect these potential sequential dependencies. In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. We represent conversations by items and entities, and construct user sequences to discover user preferences by considering both mentioned items and entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines.
Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists’ mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists’ models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.
Fostering positive customer attitudes and usage intentions for scheduling services via chatbots

Purpose This article draws on a reasoned action perspective and the two fundamental dimensions (i.e. warmth and competence) of the Stereotype Content Model (SCM) to analyze customers' chatbot-related attitudes and usage intentions in service retailing. The authors investigate how chatbot, customer, and contextual characteristics, along with perceptions of chatbot warmth and competence, shape customers' chatbot-related attitudes. Furthermore, the authors analyze whether the customer generation or the service context moderates the relationship between chatbot-related attitudes and usage intentions. Design/methodology/approach The results are based on two studies ( N = 807). Study 1 relies on a 2 (chatbot communication style: high vs low social orientation) × 2 (customer generation: generation X [GenX] vs generation Z [GenZ]) × 2 (service context: restaurant vs medical) between-subjects design. Study 2 relies on a similar number of respondents from GenX and GenZ who answered questions on scheduling a service with either the dentist or the favorite restaurant of the respondents. Findings GenZ shows more positive attitudes toward chatbots than GenX, due to higher perceptions of warmth and competence. While GenZ has similar attitudes toward chatbots with a communication style that is high or low in social orientation, GenX perceives chatbots with a high social orientation as warmer and has more favorable attitudes toward chatbots. Furthermore, the positive effect of chatbot-related attitudes on usage intentions is stronger for GenX than GenZ. These effects do not significantly differ between the considered contexts. Originality/value This research formulates future directions to stimulate debate on factors that service retailers should consider when employing chatbots.
Figure Heads in HPSG

Figure heads are words without autonomous content, such as the copula and the infinitival to. They pose a challenge for the head-driven semantics of HPSG-94, since the latter requires the CONTENTINUCLEUS value of a head-complement combination to be identical to the one of its head daughter. As a consequence, if the head is semantically vacuous, the combination is —erroneously— predicted to be vacuous as well. In order to repair this, HPSG-94 stipulates that the CONTENT value of a vacuous word is identified with the one of its complement. Technically, this gives the right result, but it leaves a number of issues unresolved. First, if the copula is vacuous, then what happens to the semantic contribution of its tense? Second, what are the criteria for identifying vacuous words? Third, are there any alternatives for the figure head treatment, and if so, what is the kind of evidence that can be used to motivate a choice? In dealing with these questions, I will integrate a DRT style treatment of the tenses in the HPSG framework (section 3), I will define a number of criteria for identifying vacuous verbs (section 4) and I will provide some empirical evidence against the figure head treatment, replacing it with an alternative analysis in which vacuous verbs have no CONTENT feature at all (section 5).