Reviewerly/exHarmony huggingface dataset:
Structurally-controlled Gold Mineralization in the Southern Zuru Schist Belt NW Nigeria: Application of Remote Sensing and Geophysical Methods

A significant number of gold occurrences are hosted by hydrothermal alteration zones in the Zuru Schist Belt, Northwestern Nigeria. The aim of this study is to identify the spatial correlation between faults, sheared, hydrothermally altered zones, and gold mineralized areas using geophysical and remote sensing data. The main structural trends of the schist belt as interpreted from the application of total gradient amplitude and local wavenumber techniques on the aeromagnetic data are in the E-W and NE-SW directions. These trends align with the locations of gold mineralization and key pathfinders such as faults in the Anka-Yauri-Iseyin shear zone. In the radiometric data, magnetic faults occurred in areas with elevated potassium enrichment within the Anka-Yauri-Iseyin shear zone and present evidence for structurally controlled hydrothermal processes. The mapped Anka-Yauri-Iseyin shear zone from the aeromagnetic data further coincides with morphological features such as ridges, valleys, and highlands, signifying that the shear zone is represented by topographical linear indicators. Importantly, the integrated approach and results presented in this work show that the Southern Zuru Schist Belt is fragmented by a NE-SW oriented AYI shear complex. Faults and hydrothermally altered zones in the AYI shear complex are new target areas for direct exploration of gold in the study area.
Securing Electronic Health Record System in Cloud Environment Using Blockchain Technology

Current online healthcare services Electronic Health Records/Electronic Medical Records (EHR/EMR) play an important role in storing, sharing, and maintaining patients' medical records. Implementing the EHR system in a cloud environment offers many options, including ubiquitous access, elastic computing resources, high-grade fault tolerance, and differentiation with other systems. Cloud storage solves the problem of storing complex medical data in leisure mode, while cloud service providers in transit are treated as non-covered units. Therefore, the cloud service provider has no obligation to ensure privacy and adequate access to the EHR. Consequently, security and privacy issues have become major issues for optimizing EHR systems for cloud environments. To provide security to the cloud environment the people use different cryptographic techniques. Cloud users use Trusted Third Party (TTP) for authentication and auditing, protecting unauthorized users, and authorized confidential data. Trust becomes a major barrier to sharing electronic data in cloud environments. If a third party is compromised, then there is a chance to hack the data because the client does not aware of the location information of storage. So, the trust issue is a challenging task in the cloud environment. To overcome these problems, we have introduced blockchain technology in cloud environments to securely transfer EHR data between authorized entities. Blockchain technology is important in such cases because it provides data-ledger-based features that can be distributed to all companies on the network. This approach is a tamper-proof mechanism because every health transaction information in the blockchain is stored as hash values. This approach provides tamper-proof, trust, confidentiality, integrity, authentication, availability, and easy access control to the cloud user data at the time of data transfer and rest modes.
Social trust and firm innovation*

We investigate whether social trust improves corporate innovation. Based on a comprehensive firm-level panel dataset of patents filed by companies from 43 countries over 1995–2015, we find that compared with firms in low-trust countries, firms in high-trust countries have higher innovation output as measured by number of patents and patent forward citations. We also find that greater trust is associated with i) higher levels of innovation investment, ii) less volatile innovation investment over time, and iii) weaker relation between innovation investment and internally generated cash flow. Finally, we document that greater trust is associated with higher market valuation of innovation investment and more efficient conversion of innovation investment into future sales. Overall, our firm-level evidence indicates that greater social trust improves corporate innovation and its outcomes.
TCB: Accelerating Transformer Inference Services with Request Concatenation

Transformer has dominated the field of natural language processing because of its strong capability in learning from sequential input data. In recent years, various computing and networking optimizations have been proposed for improving transformer training efficiency. However, transformer inference, as the core of many AI services, has been seldom studied. A key challenge of transformer inference is variable-length input. In order to align these input, existing work has proposed batching schemes by padding zeros, which unfortunately introduces significant computational redundancy. Moreover, existing transformer inference studies are separated from the whole serving system, where both request batching and request scheduling are critical and they have complex interaction. To fill the research gap, we propose TCB, a Transformer inference system with a novel ConcatBatching scheme as well as a jointly designed online scheduling algorithm. ConcatBatching minimizes computational redundancy by concatenating multiple requests, so that batch rows can be aligned with reduced padded zeros. Moreover, we conduct a systemic study by designing an online request scheduling algorithm aware of ConcatBatching. This scheduling algorithm needs no future request information and has provable theoretical guarantee. Experimental results show that TCB can significantly outperform state-of-the-art.
Flexible and fast calibration method for uni-directional multi-line structured light system

Multi-line Shift Profilometry (MLSP) is widely used to measure the high-reflectivity object by projecting high-contrast binary stripe image. However, in the calibration of a MLSP system, the lens distortion of the projector makes the most existing calibration methods fail in obtaining high accurate result. Therefore, to avoid the influence of lens distortion, we propose a novel flexible and fast calibration method for MLSP, which requiring only uni-directional patterns. In this method, for each bending light plane, lens distortion is avoided by establishing a polynomial model between three-dimensional (3D) coordinates and camera sub-pixel coordinates. Because the position of calibration planar targets is placed freely without any constraint, this method is flexible and convenient to operate. Experimental results demonstrate that the proposed method achieves high-precision 3D reconstruction.
Quantifying Degrees of E-admissibility in Decision Making with Imprecise Probabilities

This paper is concerned with decision making using imprecise probabilities and looks at extensions and aspects of the criterion of E-admissibility, as introduced by Levi and extensively studied and advocated by Teddy Seidenfeld. In the first part, we introduce a decision criterion that allows for explicitly modeling how far maximal decisions in Walley’s sense are accepted to deviate from E-admissibility. We also provide an efficient and simple algorithm based on linear programming theory for this criterion. In the second part of the paper, we propose two measures for quantifying what we call the extent of E-admissibility of an E-admissible act, i.e. the size of the set of measures for which the corresponding act maximizes expected utility. The first measure is the maximal diameter of this set, while the second one relates to the maximal barycentric cube that can be inscribed into it. Also here, for both measures, we give linear programming algorithms capable to deal with them. Finally, we discuss some ideas in the context of ordinal decision theory. The paper concludes with a stylized application example illustrating all introduced concepts.
Counting Vampires: From Univariate Sumcheck to Updatable ZK-SNARK

We propose a univariate sumcheck argument $$\mathfrak {Count}$$ of essentially optimal communication efficiency of one group element. While the previously most efficient univariate sumcheck argument of Aurora is based on polynomial commitments, $$\mathfrak {Count}$$ is based on inner-product commitments. We use $$\mathfrak {Count}$$ to construct a new pairing-based updatable and universal zk-SNARK $$\mathfrak {Vampire}$$ with the shortest known argument length (four group and two finite field elements) for $$\textsf{NP}$$ . In addition, $$\mathfrak {Vampire}$$ uses the aggregated polynomial commitment scheme of Boneh et al.
Latent profile analysis – An emerging advanced statistical approach to subgroup identification

Latent profile analysis (LPA) is emerging as an advanced statistical clustering approach. It is a type of mixture modeling that uses a person-centred approach to classify individuals from a heterogeneous population into homogenous subgroups. LPA identifies the distinct patterns of responses to a set of observed continuous variables in a sample of individuals, and these response patterns are known as latent profiles. This article presents an overview of LPA with key assumptions, sample size considerations, advantages, and limitations. Using an example of LPA application in research, the article also presents the process of conducting LPA and its implications for nursing research. LPA has valuable potential in nursing and could provide new insights into a particular research concept and offer more nuanced information regarding patterns of responses. Further, researchers could examine the impact of targeted assessment and interventions, identify predictors of subgroup membership and explore differences in outcomes across the profiles.
Hierarchical attention vision transformer for fine-grained visual classification

Recently, vision transformer has gained a breakthrough in image recognition. Its self-attention mechanism (MSA) can extract discriminative tokens information from different patches to improve image classification accuracy. However, the classification token in its deep layer ignore the local features between layers. In addition, the patch embedding layer feeds fixed-size patches into the network, which inevitably introduces additional image noise. Therefore, we propose a hierarchical attention vision transformer (HAVT) based on the transformer framework. We present a data augmentation method for attention cropping to crop and drop image noise and force the network to learn key features. Second, the hierarchical attention selection (HAS) module is proposed, which improves the network's ability to learn discriminative tokens between layers by filtering and fusing tokens between layers. Experimental results show that the proposed HAVT outperforms state-of-the-art approaches and significantly improves the accuracy to 91.8% and 91.0% on CUB-200–2011 and Stanford Dogs, respectively. We have released our source code on GitHub https://github.com/OhJackHu/HAVT.git.
Be flexible! learn to debias by sampling and prompting for robust visual question answering

Recent studies point out that VQA models tend to rely on the language prior in the training data to answer the questions, which prevents the VQA model from generalization on the out-of-distribution test data. To address this problem, approaches are designed to reduce the language distribution prior effect by constructing negative image–question pairs, while they cannot provide the proper visual reason for answering the question. In this paper, we present a new debiasing framework for VQA by Learning to Sample paired image–question and Prompt for given question (LSP). Specifically, we construct the negative image–question pairs with certain sampling rate to prevent the model from overly relying on the visual shortcut content. Notably, question types provide a strong hint for answering the questions. We utilize question type to constrain the sampling process for negative question–image pairs, and further learn the question type-guided prompt for better question comprehension. Extensive experiments on two public benchmarks, VQA-CP v2 and VQA v2, demonstrate that our model achieves new state-of-the-art results in overall accuracy, i.e., 61.95% and 65.26%.
Shuffled Shepherd Deer Hunting Optimization based Deep Neural Network for Breast Cancer Classification using Breast Histopathology Images

Accurate classification of breast cancer from the histopathology images poses a difficult task because of various benign breast tissue proliferative lesions and heterogeneity of abnormal cell growth. Various breast cancer classification methods are adopted in recent decades to categorize the breast cancer from histopathology images, but generating accurate classification result poses a complex task in medical image analysis system. Therefore, an accurate breast cancer classification method named Shuffled Shepherd Deer Hunting Optimization-based Deep Neural Network (SSDHO-based DNN) is designed for classifying the breast tumor images into six different classes, like non-tubule, non-tumor nuclei, tubule, apoptosis, tumor nuclei, and mitosis. The proposed algorithm named SSDHO is modelled by merging the Shuffled Shepherd Optimization Algorithm (SSOA) and Deer Hunting Optimization Algorithm (DHOA). Here, the feature, like statistical features, shape features and Convolutional Neural Network (CNN) features are effectively mined from the segmented blood cells such that these extracted features make the classification process more effective using DNN. By employing DNN, the breast cancer classification process is achieved more efficiently with their associated hidden neurons and generates the classification result more accurately. The devised approach obtained maximum results in terms of accuracy, precision, sensitivity, and specificity by acquiring the values of 0.9561, 0.8232, 0.7903, and 0.9426, respectively.
Seeking at-home long-term autonomy of assistive mobile robots through the integration with an IoT-based monitoring system

In this paper, we propose a system that stems from the integration of an autonomous mobile robot with an IoT-based monitoring system to provide monitoring, assistance, and stimulation to older adults living alone in their own houses. The creation of an Internet of Robotics Things (IoRT) based on the interplay between pervasive smart objects and autonomous robotic systems is claimed to enable the creation of innovative services conceived for assisting the final user, especially in elderly care. The synergy between IoT and a Socially Assistive Robot (SAR) was conceived to offer robustness, reconfiguration, heterogeneity, and scalability, by bringing a strong added value to both the current SAR and IoT technologies. First, we propose a method to achieve the synergy and integration between the IoT system and the robot; then, we show how our method increases the performance and effectiveness of both to provide long-term support to the older adults. To do so, we present a case-study, where we focus on the detection of signs of the frailty syndrome, a set of vulnerabilities typically conveyed by a cognitive and physical decline in older people that concur in amplifying the risks of major diseases hindering the capabilities of independent living. Experimental evaluation is performed in both controlled settings and in a long-term real-world pilot study with 9 older adults in their own apartments, where the system was deployed autonomously for, on average, 12 weeks.
How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence

Artificial intelligence (AI) has become part of our everyday lives, and its presence and influence are expected to grow exponentially. Regardless of its expanding impact, the perplexing algorithms and processes that drive AI's decision and output can lead to decreased trust, and thus impede the adoption of future AI services. Explainable AI (XAI) in recommender systems has surfaced as a solution that can help users understand how and why an AI recommended a specific product or service. However, there is no standardized explanation method that satisfies users' preferences and needs. Therefore, the main objective of this study is to explore a unified explanation method that centers around human perspective. This study examines the preference for AI interfaces by investigating the components of user-centered explainability, including scope (global and local) and format (text and visualization). A mixed logit model is used to analyze data collected by a conjoint survey. Results show that local explanation and visualization are preferred, and users dislike lengthy textual interfaces. Our findings incorporate the extraction of monetary value from each attribute.
FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework

Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are the high precision and easy convergence of the model. However, this synchronous communication strategy has the risk of the straggler effect. Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash. In this paper, we propose a model discrepancy-aware semi-asynchronous clustered FL framework, <i>FedMDS</i> , which alleviates the straggler effect by 1) a clustered strategy based on the delay and direction of the model update and 2) a synchronous trigger mechanism that limits the model staleness. <i>FedMDS</i> leverages the clustered algorithm to reschedule the clients. Each group of clients performs asynchronous updates until the synchronous update mechanism based on the model discrepancy is triggered. We evaluate <i>FedMDS</i> based on four typical federated datasets in a non-IID setting and compare <i>FedMDS</i> to the baselines. The experimental results show that <i>FedMDS</i> significantly improves average test accuracy by more than <inline-formula><tex-math notation="LaTeX">$+9.2\%$</tex-math></inline-formula> on the four datasets compared to <i>TA-FedAvg</i> . In particular, <i>FedMDS</i> improves absolute Top-1 test accuracy by <inline-formula><tex-math notation="LaTeX">$+37.6\%$</tex-math></inline-formula> on FEMNIST compared to <i>TA-FedAvg</i> . The frequency of the average synchronization waiting time of <i>FedMDS</i> is significantly lower than that of <i>TA-FedAvg</i> on all datasets. Moreover, <i>FedMDS</i> can improve the accuracy and alleviate the straggler effect.
Can robots recover a service using interactional justice as employees do? A literature review-based assessment

Interactional justice (e.g., empathy) plays a crucial role in service recovery. It relies on human social skills that would prevent it from automation. However, several considerations challenge this view. Interactional justice is not always necessary to recover service, and progress in social robotics enables service robots to handle social interactions. This paper reviews service recovery and social robotics literature and addresses whether service robots can use interactional justice as frontline employees do during service recovery. Results show service robots can replicate interactional justice norms, although with some considerations. Accordingly, we propose a research agenda for future studies.
TPACK–UotI: the validation of an assessment instrument for elementary school teachers

Abstract Technological pedagogical content knowledge (TPACK) has evolved as a valuable model for understanding teacher performances in technology use in education to support pedagogy with proper content knowledge for the success of teaching and learning. Many self-report questionnaires have been developed for TPACK. However, limited studies assessed the extension of the questionnaires for a specific context. This study aims to develop and validate a questionnaire scale to assess elementary school teachers’ TPACK and the Use of the Internet (UotI). We used the scale to report TPACK’s internal relationships and TPACK with UotI. The study measured whether the model reflects transformative perspectives concerning how the domains (TPACK and UotI) interact. The initial scale consisted of 43 items, validated and assessed for reliability through some procedures; face and content validity, pilot study, and measurement model in the partial least-square structural equation modeling (PLS-SEM). The scale was distributed to 1100 elementary school teachers. Findings inform that 40 scale items are valid and reliable for assessing teachers’ TPACK and UotI. The significant relationships of all factors support a transformative view of the scale.
Each discipline is different: teacher capabilities for future-focussed digitally infused undergraduate programmes

Disciplines in Higher Education have their own interpretations of what is essential knowledge that influences what is taught, how teaching occurs, and the role of digital tools. Disciplinary culture is dynamic and evolving, informed by disciplinary research and technology improvement. During the COVID-19 pandemic, digital solutions enabled ongoing teaching when undergraduate courses could not be taught on campus, in lecture theatres, seminar rooms, laboratories, or in the field. Using digital tools and changes in teaching practices has created a context where Higher Education teachers must consider how future learning and teaching should occur. To explore this, a cross-discipline team used appreciative inquiry framed in complexity theory to examine how teaching in undergraduate programmes is changing in the digital age and implications for Higher Education teachers. The research identifies how digital technologies influence undergraduate programmes in Applied Statistics, Computer Science, Critical Indigenous Studies, Geography, and Information Systems. Analysis of the case studies identified how disciplinary culture, context, and technology combine to influence pedagogical practice and digital capabilities needed to teach in undergraduate programmes. We conclude that Higher Education teachers require capability in appropriate pedagogical practice that aligns with disciplinary culture and the technologies available.