Reviewerly/exHarmony huggingface dataset:
ESA-UNet for assisted diagnosis of cardiac magnetic resonance image based on the semantic segmentation of the heart

Cardiovascular diseases have become the number one disease affecting human health in today's society. In the diagnosis of cardiac diseases, magnetic resonance image (MRI) technology is the most widely used one. However, in clinical diagnosis, the analysis of MRI relies on manual work, which is laborious and time-consuming, and also easily influenced by the subjective experience of doctors.In this article, we propose an artificial intelligence-aided diagnosis system for cardiac MRI with image segmentation as the main component to assist in the diagnosis of cardiovascular diseases. We first performed adequate pre-processing of MRI. The pre-processing steps include the detection of regions of interest of cardiac MRI data, as well as data normalization and data enhancement, and then we input the images after data pre-processing into the deep learning network module of ESA-Unet for the identification of the aorta in order to obtain preliminary segmentation results, and finally, the boundaries of the segmentation results are further optimized using conditional random fields. For ROI detection, we first use standard deviation filters for filtering to find regions in the heart cycle image sequence where pixel intensity varies strongly with time and then use Canny edge detection and Hough transform techniques to find the region of interest containing the heart. The ESA-Unet proposed in this article, moreover, is jointly designed with a self-attentive mechanism and multi-scale jump connection based on convolutional networks.The experimental dataset used in this article is from the Department of CT/MRI at the Second Affiliated Hospital of Fujian Medical University. Experiments compare other convolution-based methods, such as UNet, FCN, FPN, and PSPNet, and the results show that our model achieves the best results on Acc, Pr, ReCall, DSC, and IoU metrics. After comparative analysis, the experimental results show that the ESA-UNet network segmentation model designed in this article has higher accuracy, intuitiveness, and more application value than traditional image segmentation algorithms.With the continuous application of nuclear magnetic resonance technology in clinical diagnosis, the method in this article is expected to become a tool that can effectively improve the efficiency of doctors' diagnoses.
An Automatic IP Autoconfiguration Scheme using Harmony Search Algorithm Optimized Hybrid Extreme Learning Machine Model

IP autoconfiguration is the process of allocating unique network settings, flows, policies, and controls. Each node in the network must be configured to connect with other nodes in the system. The centralized DHCP server can accomplish it. Meanwhile, in the mobile ad hoc network (MANET), there are no centralized servers; hence, the autoconfiguration of nodes to the network is arduous. It can be achieved by distributed address autoconfiguration approach, and we proposed a novel Harmony Search Algorithm (HSA) based hybrid structure-adaptive radial basis function-extreme learning machine (HSARBF-ELM) (HSHSAELM) approach for MANET. The proposed HSA is used to detect the duplicate address node. An HSARBF-ELM classifier effectively assigns a reserved address to the new node by classifying the nodes. The proposed HSHSAELM approach effectively achieves the address allocation and duplicate address nodes. Further, it is also used to minimize the communication overhead and ensures unique address allocation to the new nodes. Experimental analysis of our proposed method with state-of-art works shows that our proposed method outperforms all the other approaches.
Drivers Affecting Bitcoin Adoption as a Payment Mechanism in the Tourism Industry

While travelers' desire to visit the world's most remote places has grown, the inefficiency of global payments indicates a significant barrier to tourism growth. As an emerging, decentralized, and borderless digital innovation, Bitcoin technology seems to have the ability to serve as a payment alternative and address such fundamental inefficiencies. On the other hand, bitcoin adoption can only happen when tourists and business owners choose to operate bitcoin simultaneously. The study has developed a novel Bitcoin Collaborative Network and Tourism Collaborative Network model to examine Bitcoin adoption factors. Then a fuzzy DEMATEL method was applied to the factors influencing the adoption domain, as identified based on an extensive literature review, in-depth interviews, and an international Delphi process. The study offered a model for the heterogeneous collaborative network of Bitcoin and Tourism (BCN and TCN), revealing that Perceived Usefulness is the most influencing criterion and the most prominent variable in Bitcoin Adoption. Bitcoin Technological Complexity, Government Regulatory, and Bitcoin Awareness are the factors that give the highest impacts. Also, Bitcoin's Technological Complexity is the most significant factor in bitcoin adoption. The findings might assist businesses in adopting a new market expansion strategy and benefiting from technological spillover, while government officials can explore new supporting legislation.
Performance Analysis of Hybrid Deep Learning Approach for Human Actions Recognition

Automatic recognition of human activities, for example, HAR, i.e., Human actions recognition has now developed as a cutting-edge area in assistive learning, mobile computing, Context-aware computing, human computer interaction and many more. CNN are one of the best deep learning methods now-a-days as they are showing remarkable results particularly in recognition problems. In this paper, a comparative analysis has been performed on three different datasets using a hybrid of random forest and 2D CNN approach. The essential goal of this hybrid approach is to obtain the data set and apply preprocessing and feature extraction on the images that are available in the dataset. Then, in the first stage, the activities are classified based on their action type by using random forest algorithm. After that, in the second stage, different action activities are classified into various activity classes using 2D CNN. Three public datasets have been used and the results obtained from three different datasets have been compared. The accuracy rates were 93.33.
Zero-Shot Multi-Modal Artist-Controlled Retrieval and Exploration of 3D Object Sets

When creating 3D content, highly specialized skills are generally needed to design and generate models of objects and other assets by hand. We address this problem through high-quality 3D asset retrieval from multi-modal inputs, including 2D sketches, images and text. We use CLIP as it provides a bridge to higher-level latent features. We use these features to perform a multi-modality fusion to address the lack of artistic control that affects common data-driven approaches. Our approach allows for multi-modal conditional feature-driven retrieval through a 3D asset database, by utilizing a combination of input latent embeddings. We explore the effects of different combinations of feature embeddings across different input types and weighting methods.
Learning Syntactic Structures from String Input

This chapter addresses a series of interrelated questions about the origin of syntactic structures: How do language learners generalise from the linguistic stimulus with which they are presented? To what extent does linguistic cognition recruit domain-general (i.e., not language-specific) processes and representations? And to what extent are rules and generalisations about linguistic structure separate from rules and generalisations about linguistic meaning? We address these questions by asking what syntactic generalisations can be acquired by a domain-general learner from string input alone. The learning algorithm we deploy is a neural-network based Language Model (GPT-2; Radford et al., 2019), which has been trained to provide probability distributions over strings of text. We assess its linguistic capabilities by treating it like a human subject in a psycholinguistics experiment, and inspect behaviour in controlled, factorised tests that are designed to reveal the learning outcomes for one particular syntactic generalisation. The tests presented in this chapter focus on a variety of syntactic phenomena in two broad categories: rules about the structure of the sentence and rules about the relationships between smaller lexical units, including scope and binding. Results indicate that our target model has learned many subtle syntactic generalisations, yet it still falls short of humanlike grammatical competence in some areas, notably for cases of parasitic gaps (e.g., “I know what you burned __ after reading __ yesterday”). We discuss the implications of these results under three interpretive frameworks, which view the model as (a) a counter-argument against claims of linguistic innateness, (b) a positive example of syntactic emergentism, and (c) a fully articulated model of grammatical competence.
Understanding Hierarchical Processes

Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks. They allow, for instance, networks of probability vectors to be used in general statistical modelling, intrinsically supporting information sharing through the network. This paper presents a general theory of hierarchical stochastic processes and illustrates its use on the gamma process and the generalised gamma process. In general, most of the convenient properties of hierarchical Dirichlet processes extend to the broader family. The main construction for this corresponds to estimating the moments of an infinitely divisible distribution based on its cumulants. Various equivalences and relationships can then be applied to networks of hierarchical processes. Examples given demonstrate the duplication in non-parametric research, and presents plots of the Pitman–Yor distribution.
CEOs' psychological trait and firms' adoption of blockchain technology: The role of hometown identity

The impact of CEOs' psychological traits on firms' decision-making has been explored by many psychological researchers. What's more, the CEO hometown identity, as one of the most fundamental psychological traits, has drawn increasing attention from the psychological literature. Firms' adoption of blockchain technology plays an innovative and efficient role in firms' strategic transformation. Thus, it is necessary to investigate the relationship between CEOs' psychological traits and firms' adoptions of blockchain technology from the perspective of hometown identity. To examine the impact of the CEO hometown identity on firms' adoption of blockchain technology, this paper manually collects information about the CEO hometown identity and constructs the index of firms' adoption of blockchain technology based on the textual analysis of firms' annual reports. Based on the theory about the psychology of identity, this paper constructs the theoretical hypothesis about the relationship between CEO hometown identity and firms' adoption of blockchain technology. Then, this paper uses a two-way fixed effect regression model to estimate the impact of the CEO hometown identity on firms' adoption of blockchain technology based on the panel data of Chinese A-share non-financial listed firms during 2008–2019. The research results show that: (1) the CEO hometown identity has a positive effect on firms' adoption of blockchain technology. (2) For firms with severe financing constraints and State-owned enterprises (SOEs), the positive effect of the CEO hometown identity on firms' adoption of blockchain technology is more prominent. (3) Our benchmark results still hold after a series of robustness checks, including altering the measurement of the CEO hometown identity, altering the sample, adding CEO-specific control variables, and altering the logit regression model. Based on the above-mentioned findings, this paper not only sheds new light on the power of CEOs' psychological trait but also deepens the understanding about theories of the psychology of identity.
Agile project portfolio management as a strategic enabler in the Brazilian marketplace: five case studies and one substantive theory

Purpose The purpose of this study is to present an empirical framework for changes, communication and team restructuring developed through a substantive theory that defines the relationships between Agile adoption and organizational strategies in the Brazilian marketplace, providing assertive propositions. Design/methodology/approach The research analysed five case studies of private companies in the Brazilian market, adopting the grounded theory (GT) method to examine the relationships between the categories. There was consistency in the reality of 22 participatory interviews with experts in Agile in the five case studies. The excellent integration between the chosen methodological approach and the organizations' characteristics reinforces a strategy focused on mixed methods. Findings As a result, the authors deploy an empirical framework, displaying new strategies that generate structural changes, obtaining Agile information technology (IT) project portfolio management (PPM) practices and strategies with superior performance. The necessary responses through organizational structural changes are observed, making it possible to notice changes in routines and contingencies. Research limitations/implications Some limitations should be pointed out for this study. The case studies were carried out in private companies in Brazil, and cultural aspects must be considered if one wants to generalize. Furthermore, to underline the effects of time, a longitudinal study would have to be employed to improve the interpretation of the results. Another limitation applies to the framework proposed in our study and its reality-simplifying nature. Models and theories with these visible generic characteristics compromise understanding specific situations. Practical implications The authors strongly recommend that teams focus on communication among stakeholders to increase the ability to adopt Agile and create valuable knowledge inside the organizations, architecting process innovation. Originality/value The forged strategic Agile substantive theory contributes to the competitive Brazilian IT company departments. The need for velocity in organizing teams, accomplishing changes and efficient communication challenges connecting value creation with project results.
Effects of Connectivity on Narrative Temporal Processing in Structured Reservoir Computing

Computational models of language are having an increasing impact in understanding the neural bases of language processing in humans. A recent model of cortical dynamics based on reservoir computing was able to account for temporal aspects of human narrative processing as revealed by fMRI. In this context the current research introduces a form of structured reservoir computing, where network dynamics are further constrained by the connectivity architecture in order to begin to explain large scale hierarchical network properties of human cortical activity during narrative comprehension. Cortical processing takes place at different time scales depending on the position in a “hierarchy” from posterior sensory input areas to higher level associative frontal cortical areas. This phenomena is likely related to the cortical connectivity architecture. Recent studies have identified heterogeneity in this posterior-anterior hierarchy, with certain frontal associative areas displaying a faster narrative integration response than much more posterior areas. We hypothesize that these discontinuities can be due to white matter connectivity that would create shortcuts from fast sensory areas to distant frontal areas. To test this hypothesis, we analysed the white matter connectivity of these areas and discovered clear connectivity patterns in accord with our hypotheses. Based on these observations we performed simulations using reservoir networks with connectivity patterns structured with an exponential distance rule, yielding the sensory-associative hierarchy. We then introduce connectivity short-cuts corresponding to those observed in human anatomy, resulting in frontal areas with unusually fast narrative processing. Using structured reservoir computing we confirmed the hypothesis that topographic position in a cortical hierarchy can be dominated by long distance connections that can bring frontal areas closer to the sensory periphery.
Exploring the Use of an Electronic Competency Assessment Document Using iPad Minis to Assess Clinical Practice Competency in a Preregistration Nursing Program

The aim of this study was to explore the feasibility of using iPad minis as a method of completing competency assessment in clinical practice. Digital technology helps to revolutionize all aspects of our lives. The use of digital technologies in clinical practice can facilitate a move toward a more flexible learning environment and enable students to adapt in a rapidly changing, interconnected world. The introduction of electronic clinical booklets in practice placements could facilitate the sharing of clinical information through connected healthcare systems, thus improving the student experience. A cross-sectional design was used in this study. A sample of BSc nursing students (n = 53) and clinically based healthcare professionals (n = 27) (preceptors and clinical placement coordinators) participated in this study. Data were collected using a modified version of Garrett and colleagues' instrument, the System Usability Scale, a demographics questionnaire, and three open-ended questions. There was a significant difference between the students and preceptors/clinical placement coordinators across all items with higher proportions of students (ranging from 66% to 75.5%), indicating that they agreed or strongly agreed with the reliability, ease of use, and effectiveness of the device in assessing their clinical competency (ranging from 11.1% to 40.7%). In addition, the iPad minis were found to be smaller, lighter, and easier to carry than paper-based booklets and encouraged students to access timely online learning resources during placement to help augment their learning. The use of iPad mini to complete the electronic competency assessment document as a replacement for a paper-based system to assess clinical practice is feasible. The introduction of electronic assessment documents should incorporate a robust training plan and standard operating procedures.
Lang-n-Prove: A DSL for Language Proofs

Proofs of language properties often follow a schema that does not apply just to one language but, rather, applies to many languages of a certain class.
An Automatic Ultrasonic Segmentation Method by Two-stage Semi-supervised Learning Strategy

Medical image segmentation plays an important role in computer aided diagnosis. This requires a lot of labeling, and the cost is significant. As a result, a new deep learning model has been proposed to improve the workload of radiologists' labeling. This method contains two stage: 1) Supervised learning network to frame the region of interest to narrow the segmentation scope for the next step; 2) Unsupervised learning is used to accurately segment the prostate. Especially, only in the first part is a rough notation needed. In addition, we use two modalities containing B-mode and elastography to validate the segmentation network and adopt multi-center data to guarantee the robustness of the model. In the end, the network is compared with other advanced semi-supervised DL methods such as DAN and ASDNet according to several quantitative indicators, and achieve the state-of-the-art performance.
Adaptive Contrast Enhancement of Cardiac Ultrasound Images using a Deep Unfolded Many-Body Quantum Algorithm

Speckle has a considerable impact on medical ultrasound (US) imaging due to its its intrinsic random nature and spatially correlated behavior that severely reduces image contrast. In this paper, leveraging the quantum many-body theory, we propose a deep-learning architecture recasting a baseline denoising algorithm for adaptive contrast enhancement of US images. The proposed deep neural network integrates quantum mechanical concepts based on Schrödinger equations which makes our model robust for US image restoration. The potential of the proposed deep unfolded network is illustrated on simulated data and clinical cardiac US images. Both results show enhanced images with improved contrast and resolution while preserving underlying structures and significantly reducing the speckle noise.
A Framework for the Design of Secure and Efficient Proofs of Retrievability

Proofs of Retrievability (PoR) protocols ensure that a client can fully retrieve a large outsourced file from an untrusted server. Good PoRs should have low communication complexity, small storage overhead and clear security guarantees with tight security bounds. The focus of this work is to design good PoR schemes with simple security proofs. To this end, we propose a framework for the design of secure and efficient PoR schemes that is based on Locally Correctable Codes, and whose security is phrased in the Constructive Cryptography model by Maurer. We give a first instantiation of our framework using the high rate lifted codes introduced by Guo et al. This yields an infinite family of good PoRs. We assert their security by solving a finite geometry problem, giving an explicit formula for the probability of an adversary to fool the client. Moreover, we show that the security of a PoR of Lavauzelle and Levy-dit-Vehel was overestimated and propose new secure parameters for it. Finally, using the local correctability properties of Tanner codes, we get another instantiation of our framework and derive an analogous formula for the success probability of the audit.
Exploring Factors Influencing Student's Learning Difficulties During Pandemic in Indonesia: A Structural Equation Modelling

&lt;p style="text-align:justify"&gt;The pandemic era has caused changes in the learning system. The situation demanded online learning and triggered students to have learning difficulties. The research aims to examine the impact of social media, social environment, and student learning potential on student learning difficulties. This study utilizes a quantitative approach. The respondents were university students experiencing online learning in West Java, Indonesia. A questionnaire validated by four experts was distributed to 539 of them. Accommodating structural equation modeling (SEM) by evaluating the measurement and structural models was used in data analysis. This study revealed that the instrument had good construct validity and reliability. A good instrument will produce a good measurement process so that the instrument can get the data needed. Hypothesis testing shows that high media activity can inhibit students from learning. However, social media activity directly influences learning difficulties. Meanwhile, the variables of learning potential and social environment positively influence student learning difficulties directly or indirectly. There are a few things to note on learning potential, social environment, and social media to delve into their influences on students' learning difficulties.&lt;/p&gt;