Reviewerly/exHarmony huggingface dataset:
Transformational leadership and organizational performance of Omani SMEs: the role of market orientation

Purpose This study proposed and tested a model on the relationship between transformational leadership (TL) and organizational performance (OP) with a possible intervening effect of market orientation (MO) at Omani small and medium enterprises (SMEs). Design/methodology/approach The study followed a quantitative research design and the deductive approach. Data were collected from 259 employees working in Omani SMEs and were analyzed using structural equation modeling SEM AMOS V24. Findings The findings supported positive direct effects of transformational leadership on Omani SMEs' business performance and market orientation. The results also supported the effect of market orientation on business performance. Further analysis showed that market orientation partly mediates the relationship between transformational leadership and business performance. Practical implications The results motivate Omani SMEs' leaders to use intangible capabilities (i.e., market orientation practices) to improve the different aspects of organizational performance. It also suggests that developing SMEs leaders' transformational skills on envisioning the future, adopting open communication styles, motivating employees and stimulating their creativity influences Omani SMEs' performance. Originality/value Due to the mixed results regarding the TL–OP relationship and the recommendations for further exploring this relationship, this study contributes to theory and practice. It is among the few studies to explore the mediating role of MO between TL and OP in a developing country's SMEs environment.
The Emergence and Future of Public Health Data Science

Data science is a newly‐formed and, as yet, loosely‐defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term “data science,” particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective.
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models

A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one.However, CSC models may fail to correct spelling errors covered by the confusion sets, and also will encounter unseen ones.We propose a method, which continually identifies the weak spots of a model to generate more valuable training instances, and apply a task-specific pre-training strategy to enhance the model.The generated adversarial examples are gradually added to the training set.Experimental results show that such an adversarial training method combined with the pretraining strategy can improve both the generalization and robustness of multiple CSC models across three different datasets, achieving stateof-the-art performance for CSC task.
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval

Recent pretrained language models "solved" many reading comprehension benchmarks, where questions are written with the access to the evidence document.However, datasets containing information-seeking queries where evidence documents are provided after the queries are written independently remain challenging.We analyze why answering information-seeking queries is more challenging and where their prevalent unanswerabilities arise, on Natural Questions and TyDi QA.Our controlled experiments suggest two headrooms -paragraph selection and answerability prediction, i.e. whether the paired evidence document contains the answer to the query or not.When provided with a gold paragraph and knowing when to abstain from answering, existing models easily outperform a human annotator.However, predicting answerability itself remains challenging.We manually annotate 800 unanswerable examples across six languages on what makes them challenging to answer.With this new data, we conduct percategory answerability prediction, revealing issues in the current dataset collection as well as task formulation.Together, our study points to avenues for future research in informationseeking question answering, both for dataset creation and model development. 1
UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2.

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre-trained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system response of every dialog turn. Additionally, UBAR is evaluated in a more realistic setting, where its dialog context has access to user utterances and all content it generated such as belief states, system acts, and system responses. Experimental results on the MultiWOZ datasets show that UBAR achieves state-of-the-art performances in multiple settings, improving the combined score of response generation, policy optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively. Thorough analyses demonstrate that the session-level training sequence formulation and the generated dialog context are essential for UBAR to operate as a fully end-to-end task-oriented dialog system in real life. We also examine the transfer ability of UBAR to new domains with limited data and provide visualization and a case study to illustrate the advantages of UBAR in modeling on a dialog session level.
EduChain: CIA-Compliant Blockchain for Intelligent Cyber Defense of Microservices in Education Industry 4.0

Massive data handling requirement in education Industry 4.0 has attracted interests in the research of microservice architectures due to their scalability, resilience, and elasticity characteristics. This development has been challenged by extensive data exchange required by a set of independent microservices to build a complete application, which could result in increasing risks and exposure to the security and privacy breaches of the data. It is imperative to see that educational data are highly sensitive, critical for ascertaining educational attainment and facilitating credentials for qualification verifications. This article puts forward a new proposal of devising a security and privacy-preserving design mechanism of data transactions in educational microservices leveraging the blockchain technology. The design comprises three phases, namely the blockchain framework, data sending–receiving, and confidentiality-integrity-availability over a secured platform with each phase having detailed mechanisms for algorithm implementation. The proposal is shown to exhibit favorable performance in terms of time cost of publishing, throughput, and latency, and shown to have high survey acceptance in terms of confidentiality, integrity, and availability with approximately 10% improvement from prior blockchain adoption.
What’s on the Horizon? An In-Depth Forensic Analysis of Android and iOS Applications

In the digital forensics discipline, the lack of comprehensive research that addresses investigative challenges and opportunities for newer mobile Operating Systems (OSs) such as Android and iOS keeps continuing. These two OSs are currently widely operated by millions of smartphones and used by millions of users; therefore, forensic investigators need to be prepared to analyze these OSs during an investigation giving consideration to mobile app updates. The current research efforts focus on the forensic analysis of individual applications of certain OSs. In this study, we conducted a detailed forensic investigation of both Android and iOS OSs to (1) elucidate their structures for investigators, (2) identify pertinent forensic artifacts, (3) highlight any privacy and security concerns in popular applications present on both OSs, and (4) validate the forensic investigation on the selected tools for reproducibility and verification purposes. This work aims to analyze 27 Android and 33 iOS mobile applications comprehensively.
The Markets in Crypto-Assets regulation (MiCA) and the EU digital finance strategy

Global stablecoins (GSCs) have attracted significant global political, regulatory and supervisory attention following Facebook’s proposal for the creation of Libra (now renamed ‘Diem’)—a combination of a private cryptocurrency fixed in value to major fiat currencies mated with its own and other global electronic payment and identification systems.1 Beyond the high-profile example of Libra and other potential GSCs, the question of regulatory treatment of crypto-assets has been a major focus of regulators and market participants across the world since the launch of Bitcoin in 2009 and in particular in the course of the crypto bubble of 2018.2 Not only across the world, but also in the context of the EU, legal uncertainty remains, resulting in divergent approaches to crypto-assets, token offerings and stablecoins. Instead of merely addressing issues and challenges of digital assets, the European Commission took a broader approach to the future development of digital finance and...
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization

Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not provide sufficient privacy protection, and it is desirable to facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP mechanisms would introduce random noise with magnitude proportional to the model size, which can be quite large in deep neural networks. In this paper, we propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee. Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost. We rigorously analyze the convergence of our approach and utilize Renyi DP to tightly account the end-to-end DP guarantee. Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency.
Synchronization using quantum photons for satellite-to-ground quantum key distribution

Time synchronization is crucial for quantum key distribution (QKD) systems. In order to compensate for the time drift caused by the Doppler effect and adapt to the unstable optical link in satellite-to-ground QKD, previous demonstrations generally adopted synchronization methods requiring additional hardware. In this paper, we present a novel synchronization method based on the detected quantum photons, thus simplifying additional hardware and reducing the complexity and cost. This method adopts target frequency scanning to realize fast frequency recovery, utilizes polynomial fitting to compensate for the Doppler effect, and takes use of the vacuum state in the decoy-state BB84 protocol to recover the time offset. This method can avoid the influence of synchronization light jitter, thus improving the synchronization precision and the secure keys as well. Successful satellite-to-ground QKD based on this new synchronization scheme has been conducted to demonstrate its feasibility and performance. The presented scheme provides an effective synchronization solution for quantum communication applications.
Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases

Making co-existent and convergent the need for efficiency of relational query processing over Clouds and the security of data themselves is figuring-out how one of the most challenging research problems in the Big Data era. Indeed, in actual analytics-oriented engines, such as Google Analytics and Amazon S3, where key–value storage-representation and efficient-management models are employed as to cope with the simultaneous processing of billions of transactions, querying encrypted data is becoming one of the most annoying problem, which has also attracted a great deal of attention from the research community. While this issue has been applied to a large variety of data formats, e.g. relational, RDF and multidimensional data, very few initiatives have pointed-out skyline query processing over encrypted data, which is, indeed, relevant for database analytics. In order to fulfill this methodological and technological gap, in this paper we introduce an innovative algorithm for effectively and efficiently supporting skyline query processing over encrypted data in Cloud-enabled databases, named as Attribute-Order-Preserving-Free-SFS (AOPF-SFS), a suitable extension of the well-known Sort-Filter-Skyline (SFS) algorithm. The proposed algorithm enables the processing of skyline queries over encrypted data, even without preserving the order on each attribute as order-preserving encryption would do. We also present eSkyline, a prototype system that embeds AOPF-SFS equipped with a suitable query interface comprising an encryption scheme that facilitates the evaluation of domination relationships, hence allows for state-of-the-art skyline processing algorithms to be used. In order to prove the effectiveness and the reliability of our system, we also provide the details of the underlying encryption scheme, plus a suitable GUI that allows a user to interact with a server, and showcases the efficiency of computing skyline queries and decrypting the results.
A Comparative Study of Regression Models and Meteorological Parameters to Estimate the Global Solar Radiation on a Horizontal Surface for Baghdad City, Iraq

In this study, data of the monthly average of daily global solar radiation falling on a horizontal surface, relative humidity, maximum temperature, and duration of sunshine for the city of Baghdad were collected through two sources. First, from the Iraqi Meteorological Authority (IMA) for a period extending from 1961 to 2016. The second is from NASA, for the period from 1984 to 2004. Then, four linear regression models, two single and two polynomials were formulated to calculate the values of the monthly average of daily global horizontal solar radiation (GHSR) incidents. The models calculated the monthly average of daily extraterrestrial radiation and day length, using some data provided by NASA and the IMA. To ensure the validity of the used models, a statistical test was performed for the performance of the proposed models, using the indicators mean bias Error (MBE), root mean square error (RMSE) as well as mean percentage error (MPE). The validation shows the relationship between the measured and computed values (through the analysis of the results), where a great convergence was found between the measured and calculated values. This means that the proposed models can be adapted to predict global solar radiation. The highest values of measured solar radiation were during the month of June, which were 28.555 and 27.280 MJ/m2/day from the IMA and NASA, respectively. The same applies to the radiation calculated using the four empirical models. The month of June was the highest in terms of solar radiation values. The radiation values were 28.947, 26.315, 29.699, and 26.716 MJ/m2/day for the first, second, third, and fourth models, respectively. The lowest values of measured and calculated radiation were during the month of December. Always, radiation measured by the IMA was greater than those of NASA, as well as the values of radiation calculated in the two IMA-based models were greater than the other two NSA-based models. In the absence of a method for measuring the diffuse and direct (beam) solar radiations, as well as the lack of such values by meteorological authorities, and its paramount importance, they were reported to mathematically calculate them in this study. The values of statistical indicators RMSE; MJ/m2/day, MBE; MJ/m2/day and MPE% were (0.4769, 0.0164, 0.2207), (0.8641, 0.1773, -0.9680), (0.6420, 0.3996, -1.1487), (0.9604, 0.218, -1.0225) for the first, second, third and fourth models, respectively. According to the results of the statistical test, it can be indicated that the single linear regression model, based on the IMA’s data (model No.1), is the most accurate to calculate global solar radiation for Baghdad City.
Determining the heat transfer coefficient during the continuous casting process using stochastic particle swarm optimization

In recent years, the continuous casting process has started to play a key role in the casting region. To obtain high quality steel, the temperature field within the steel needs to be identified. Thus, a method is needed for accurately determining the heat transfer coefficient h in the cooling regions during the casting process. In this study, we built an inverse heat transfer problem (IHTP) model. Based on this model, we then employed stochastic particle swarm optimization (SPSO) to solve the problem. The results of the numerical experiments showed that the relative errors obtained using our inverse method were all less than 2%, which indicates high accuracy. During the solving process, we found that this is a multimodal problem; thus, we examined the causes of the multimodal property. Finally, we developed a preliminary strategy for eliminating the infeasible solutions derived using our method. Our findings are significant for accurately estimating the heat transfer coefficient in the cooling region and reducing the effects of the multimodal property during the solving process.
Research on digital copyright protection based on the hyperledger fabric blockchain network technology

With the recent development in network technology over a few years, digital works can be easily published online. One of the main issues in the field of digital technology is the infringement of digital works, which can seriously damage the data owners’ rights and affects the enthusiasm of the owners to create original work. Thus, more attention is required for the protection of digital copyright as it has a great impact on the development of society. Many digital copyright protection techniques were developed in the past, but still, there are many loopholes in the protection systems to be covered. The protection means are still relatively weak, timeliness is poor, infringement is frequent, a right determination is cumbersome, and the results are not ideal. Aiming at the mentioned problems, this paper proposes a protection technique, which can realize the automatic management of the complete digital rights life cycle on the blockchain using fabric's smart contract technology. The proposed system is based on blockchain technology, which leverages the distributed, tamper-proof and traceable characteristics of blockchain. The system uses smart contracts to manage the full life cycle of digital copyright. The test results show that the proposed system provides effective protection of the digital copyright system and can efficiently confirm the rights of digital copyright.
Serial memory: Putting chains and position codes in context.

From the beginning of research on serial memory, chaining theories and position coding theories have been pitted against each other. The central question is whether items are associated with each other or with a set of position codes that are independent of the items. Around the turn of this century, the debate focused on serial recall tasks and patterns of error data that chaining models could not accommodate. Consequently, theories based on other ideas flourished and position coding models became prominent. We present an analysis of a retrieved context model that integrates chains and position codes. Under some parameter values, it produces classic chains. Under most parameter values, it produces context representations that contain information sufficient to specify the position codes in position coding theories. We suggest three ways to extract position codes from context representations and show the codes they produce are mathematically equivalent to the codes in position coding models. The extracted position codes can be substituted for the position codes in position coding models and run through their machinery to mimic their predictions exactly. We suggest that chains, position codes, and retrieved contexts may reflect different strategies for extracting desired information from a common set of memory representations, and we emphasize the value of considering item-dependent context representations that are made from fading traces of past items encoded or retrieved. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Transformer in Transformer

Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (e.g., 16$\times$16) as visual and present to further divide them into smaller patches (e.g., 4$\times$4) as visual words. The attention of each word will be calculated with other words in the given sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture, e.g., we achieve an $81.5%$ top-1 accuracy on the ImageNet, which is about $1.7%$ higher than that of the state-of-the-art transformer with similar computational cost. The PyTorch code is available at this https URL, and the MindSpore code is at this https URL.