Reviewerly/exHarmony huggingface dataset:
A conceptual analysis of the use of systems-psychodynamics as an organisation development intervention: A neuroscientific perspective

Orientation: Systems-psychodynamics as a consulting stance offers learning experiences that not only have links with the first organisation development interventions but also remains a popular approach for organisational consultation. Here, the argument is made that neuroscientific principles, as embedded in neuropsychotherapy, offer a lens for evaluating and improving the effectiveness of systems-psychodynamic interventions.Research purpose: The purpose of this study was to hypothesise about the effectiveness of systems-psychodynamic interventions, and to offer propositions for improvement.Motivation for the study: Studies on the effectiveness of systems-psychodynamic interventions from outside the same network of science-practitioners, are limited. Furthermore, no evidence of a similar study using a neuroscientific framework could be found in the English literature.Research approach/design and method: This was a conceptual analysis with theory adaption as an approach. Systems-psychodynamics was chosen as domain theory and was discussed first, followed by neuropsychotherapy as method theory.Main findings: It was hypothesised that, using the lens of neuropsychotherapy, systems-psychodynamics – with its focus on insight into unconscious processes – would most likely enhance fear-based learning. To facilitate transformational learning, the experience could be augmented through a better alignment with neuroscientific principles.Practical/managerial implications: There is a need to augment the role of the consultant as science-practitioner with the skills of a reflective practitioner. This will enable consultants to continuously critique and adapt preferred interventions, by integrating new neuroscience-related knowledge in those interventions.Contribution/value-add: This study contributes to the literature on organisation development interventions, and the reflective practice of the science-practitioner.
CURRICULAR SYNCOPATIONS: What is the payoff?

column Share on CURRICULAR SYNCOPATIONS: What is the payoff? Author: Henry M. Walker Grinnell College Grinnell CollegeView Profile Authors Info & Claims ACM InroadsVolume 13Issue 1March 2022 pp 10–14https://doi.org/10.1145/3505561Online:14 February 2022Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Emotional Analysis of Clothing Product Reviews Based on Machine Learning

Emotional analysis of product reviews is a hot spot in current data mining research. Whether it is in academic or economic fields, text emotional analysis of e-commerce product reviews has great research value. This article used machine learning to conduct sentiment analysis for clothing e-commerce product reviews. Here, we used the data collection program to collect comment data. Then we performed preprocessing, word segmentation and sentiment labeling on the data. By setting different parameters to train the data, we build a sentiment classifier to automatically classify unknown data. We show experimentally that using machine learning methods to perform sentiment classification on product reviews can achieve good results.
On the relationship between corpus frequency and acceptability

Abstract Chapter 5 starts by observing that linguists from different theoretical traditions tend to express different views on the relative importance of judgment data and corpus data for developing theories of grammatical knowledge. Similarly, although everyone recognizes frequency of use as a factor to which language users are sensitive, there is disagreement as to how it relates to acceptability. Proponents of usage-based and probabilistic theories have tended to emphasize the close correlations between frequency and acceptability, while proponents of traditional generative theories have tended to emphasize cases in which acceptability judgments differ among low-frequency or zero-frequency structures. This chapter discusses a few studies which have directly examined the relationship between corpus frequency and acceptability and considers their implications for these different theoretical approaches. Alongside more traditional corpus studies, machine learning studies are also considered.
Based on Harris corner of Drones vehicle target image matching method

For acquiring vehicle top feature data what algorithm is used to study and its matching effect and other issues. Based on this, key recognition features such as vehicle top geometry features, especially sunroof, cab roof light and bus rearview mirror, are fully utilized to improve the UAV image target matching accuracy. In this paper, I use UAV vertical observation true-color image data, based on Harris and SIFT feature descriptors combined with the NNDR matching method, focusing on corner point feature extraction of top features of vehicles in images, and determine thresholds to match different types of known samples with images to be matched. The experimental data results show that the top lights of cabs are rich in corner point features, and their matching accuracy can reach more than 85%, which is higher than that of small cars with relatively few corner point features, both corner points are particularly critical features in the matching process. The method significantly improves matching for vehicle types rich in corner point features, and the top features make more use of geometric feature information, which is especially suitable for image data with higher spatial resolution and better image grayscale contrast.
Digital Transformation and Skill Acquisition

The chapter brings a review of literature on primary and secondary data analysing the aspect of digital transformation from the point of view of human resources (HR). It describes the perspective of employees and their digital skills acquisition, as well as the perspective of employers and their readiness for digital transformation. It also introduces barriers to digital skills acquisition. The primary data used in this chapter is presented on a sample of Czech companies and employees in order to illustrate the current situation in this field. The authors concentrate on answers to the following research questions: What digital competencies were identified as those mainly needed for current employees and how they acquire them? What are the main barriers to closing digital skills gaps at work? How has the Covid-19 pandemic changed the requirements for employees' digital skills? The literature review results are supported by global secondary data analysis on demand for skills at the workplace in European countries.
Anonymization Technique For Privacy Preservation In Social Networks

Anonymization is a valuable privacy protection strategy utilized to protect highly sensitive data from third-party access in a variety of technology fields, including data mining, cloud storage and big data. Data protection against all threats is becoming increasingly important as the importance and quantity of data in today&#x2019;s world grows. The primary goal of this study is to present concise review of data anonymization and differential privacy approaches. In order to address problem of privacy protection, a new k-anonymous solution has been proposed which differs from the traditional k-anonymous solution. In this paper, new algorithm for attaining k-anonymity by more effective clustering is proposed. The majority of clustering algorithms need more computation to process data; however, if the detected initial centers that are consistent with the data setup, then it will obtain a better cluster array. Our research proposes a Dissimilarity Treebased method for identifying a better initial centroid and a somewhat more accurate cluster with less computation time, as well as NCP. According to graphical findings, the anonymised dataset&#x2019;s total information loss is about 20&#x0025; lower on average than that of other approaches. It&#x2019;s also capable of handling categorical and numerical characteristics.
Learning to triage by learning to reconstruct: a generative self-supervised approach for prostate cancer based on axial T2w MRI

Prostate cancer (PCa) is the second most commonly diagnosed cancer worldwide among men. In spite of it, its current diagnostic pathway is substantially hampered by over-diagnosis of indolent lesions and under-detection of aggressive ones. Imaging techniques like magnetic resonance imaging (MRI) have proven to add additional value to the current diagnostic practices, but they rely on specialized training and can be time-intensive. Deep learning (DL) has arisen as an alternative to automatize tasks such as MRI analysis. Nevertheless, its success relies on large amounts of annotated data which are rarely available in the medical domain. Existing work tackling data scarcity commonly relies on ImageNet pre-training, which is sub-optimal due to the existing gap between the training and the task domain. We propose a generative self-supervised learning (SSL) approach to alleviate such issues. We show that by making use of an auto-encoder architecture and by applying different patch-level transformations such as pixel intensity or occlusion transformations to T2w MRI slices and then trying to recover the original T2w slice we are able to learn robust medical visual representations that are domain-specific. Furthermore, we show the usefulness of our approach by making use of the representations as an initialization method for PCa lesion classification downstream task. Following, we show how our method outperforms ImageNet initialization and how the performance gap increases as the amount of the available labeled data decreases. Furthermore, we provide a detailed sensitivity analysis of the different pixel manipulation transformations and their effect on the downstream task performance.
Library, Information Science, and Archives Doctoral Research Trends in Australia

Research in the fields of the LIS, Archives and Recordkeeping sectors faces challenges as a result of course closures, which could have an impact on the number of future “academic-research-trained LIS professionals” and consequently the volume and quality of future research in Australia. This short paper aims to introduce a project currently underway in Australia to identify emerging trends in related PhD research, identify community needs and provide a summary of relevant background literature.
Exception Handling Recommendation Based on Self-Attention Network

Exception handling is widely applied to prevent unexpected runtime errors in software development. However, developers, especially novices, may not be able to write proper exception handling code, causing program crashes and reducing system reliability. To assist users to handle exceptions properly, we propose EHRSAN, a novel exception handling recommendation approach. It adopts the self-attention network to predict potential runtime exceptions in the programming context and recommend relevant code to handle the exceptions.
Virtual Reality Environments as a Support in Elementary School

Virtual reality is an emerging technology that can support the teaching and learning process by allowing the user to acquire knowledge in an immersive and interactive way. Virtual environments can help students to build their learning through experiences abstracted from reality by representing different concepts and interacting with well-defined elements within virtual scenarios. This work proposes the production and use of virtual reality environments as a support for the acquisition of skills within the teaching-learning process of children in basic education, including children with special educational needs. A proposed model is presented under the instructional design that considers technological platforms, multidisciplinary teams for the production of virtual reality environments according to the needs of users, and thus offers both teachers, parents, and students solutions that can promote the acquisition of learning skills through the execution of various tasks designed to encourage the user to the acquisition of learning skills in an immersive, safe, and fun way.
Visual Tracking with Layer-Wise Transformer Fusion

Current popular Siamese-based trackers widely depend on cross-correlation for object prediction. However, there’s still an accuracy and success gap compared with state-of-the-art algorithms because cross-correlation operation is a simple and local fusion manner and it can’t take advantage of deeper semantic information. To address this issue, we propose a novel layer-wise transformer tracking network to gain global and deeper contextual interdependencies. The new architecture includes a self-context augment model (SCA) to learn strong long-distance context information and a cross-augment fusion model (CAF) to learn global interaction information. Moreover, the new architecture aggregates the layer-wise SCA-CAF outputs to further improve the tracking accuracy. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed module. In addition, we also extensively evaluate our tracker on UAV123, GOT-10k, LaSOT and TrackingNet. The results show that our tracker performs favorably against the state-of-the-art algorithms.
Real Time Static and Dynamic Hand Gestures Cognizance for Human Computer Interaction

Human-Computer Interaction (HCI) has become ubiquitous. Hand gestures are integral part of HCI. Static and dynamic hand gestures used for HCI in real time systems are area of vital analysis, with manifold feasible applications. Vision based hand gesture recognition techniques had proven many advantages compared with hardware devices, as it provides end user an elementary and more perceptive method of communication between user and system. Although a wide range of recognition systems based on gesture is available, they are expensive and complex. Some existing gesture recognition methods are intended for usage with either statics or dynamic gestures. In this paper, a low-cost system for recognition of both static and dynamic hand gestures, based on computer vision is proposed. The application of vision based technology for managing the devices by gestures, dwindle the required work space.
Application of the Novel Parallel QUasi-Affine TRansformation Evolution in WSN Coverage Optimization

With the development of wireless sensor network (WSN) technology, the coverage optimization of sensor nodes has gradually become the focus of researchers. Therefore, how to maximize the network coverage of WSN is worth studying. In this paper, Parallel QUasi-Affine TRansformation Evolution (P-QUATRE) is proposed based on the traditional QUATRE, which improves the performance of the algorithm. Next, a 3D WSN network layout model is proposed. Finally, the proposed P-QUATRE is applied to the model for simulation experiments to improve the network coverage. Through the analysis of the experimental results, compared with Particle Swarm Optimization (PSO), Gases Brownian Motion Optimization (GBMO) and Tunicate Swarm Algorithm (TSA), P-QUATRE shows better performance in this field.
Development of Digital Skills Oriented to Kids in the Post-Pandemic Vulnerability Situation

The objective of the present study was to reflect on psychosocial intervention programs for the development of digital skills aimed at kids between the seventh and tenth grade of schooling in situations of post-pandemic vulnerability. The research methodology was non-experimental, documentary, descriptive, and reflective. The information was collected from multinational organisms and scientific articles from indexed magazines and web platforms. Obtained results indicate that the COVID-19 pandemic aggravated the vulnerability conditions in children living in multidimensional poverty conditions. For this reason, international organisms—UN, World Bank, ILO—have proposed the creation of programs that promote resilience among the vulnerable population, based on the use of virtual platforms. In this context, this investigation proposes the implementation of psychosocial intervention programs for the development of digital skills aimed at kids in situations of post-pandemic vulnerability.
A Decentralized Network to Secure Smart Grid Transactions Using Ethereum on RISC-V

Increasing demand of renewable energy is driving the energy producers towards secured and decentralized network for energy trading. A decentralized framework could mitigate security threats in smart grid. Blockchain based smart contracts could improve the need of interaction producer and consumer by implementing reliable distributed transactions. This help to reduce interaction of third parties which causes security vulnerability. A decentralized infrastructure using Ethereum is proposed in this study. Ethereum keeps the track of transactions taken place from the consumers to the supply industries using crypto algorithms. Creating private node using Ethereum framework facilities the decentralization, anonymity, transparency and secure in the network. A private node using RISC-V platform is demonstrated in this paper. Smart contracts for the energy trading is written on solidity remix simulator and further validated on U540 RISC-V platform. End to end energy trading in smart grid using private Ethereum blockchain is demonstrated. Performance of the transaction, encryption and reliability of the application is validated over the platform.
Underwater image stitching using globally optimal local homographies with application to seafloor mosaicing

We present in this paper a stitching approach for large-scale mosaicing of underwater images. The method we propose adopts a local warp model for the underwater case, which improves the alignment and minimizes local distortions when compared to the more common global wrap approach. This we then improve by adding a global constraint that allows generating optimal warps for all images in a single optimization. This way, the distortions are also minimized globally and linearly in the least-squares sense. A comprehensive evaluation shows that the proposed method generates accurate alignments and provides natural-looking mosaics. Additionally, we propose a sea floor mosaicing pipeline that is based on our stitching method and demonstrate how it yields accurate results even when applied on large datasets.
Synthetic Data Guided Breast Cancer Diagnosis and Prognosis Using Integrated Deep Framework

Breast cancer is the world's second-largest cause of cancer mortality among women. Due to the progress of Artificial Intelligence (AI) in healthcare, the survival rate of breast cancer patients has risen in recent years by early diagnosis and effective prognosis. However, substantial AI research necessitates a large quantity of high-quality data for performing credible state-of-the-art research. In this concern, we investigated the potentiality of deep generative models including Tabular Variational Autoencoder (TVAE) and Conditional Generative Adversarial Network (CTGAN) to generate high-quality synthetic tabular data of breast cancer to support the breast cancer diagnosis and prognosis. Additionally, we proposed an integrated interpretable deep learning framework that includes the synthetic generation process of breast cancer data leading to the classification of breast cancer with interpretable deep attention-based model TabNet by adopting the domain of breast cancer research at every stage of our framework. Our proposed findings were justified using benchmark datasets of breast cancer. After rigorous investigation, we found the Tabular Variational Autoencoder (TVAE) model to be outperforming in the synthetic generation process of breast tumor data with a CSTest score of 0.916 (prognosis) and 0.964 (diagnosis), and also KSTest score of 0.887 (prognosis) and 0.928 (diagnosis). In the classification stage, despite being trained with solely synthetically generated data, the interpretable TabNet architecture outperformed all other machine learning and deep learning classifiers, with an Accuracy of 96.66 % in diagnosis and 82.83 % in prognosis.
Examining Data Mining Classification Techniques for Predicting Early Childhood Development in Nigeria

Early childhood is a critical part of a child’s development as it involves physical, cognitive, and psychological development. In the educational domain, especially early childhood education, there is rich data available that we could leverage to determine the development stage of a child and hidden patterns of a child’s learning ability or disability. This study investigates which data mining classification technique will be most suitable in building a predictive model that can identify the social, cognitive, and emotional stages of a child. The authors compared J48, Naïve Bayes, random forest, support vector machines (SVM), and k-nearest neighbors (KNN) classifiers using performance measures like Kappa statistics, receiver operating characteristic (ROC), root-mean-square error (RMSE), and mean absolute error (MAE) using a data mining analytical tool called WEKA. The authors also compared the accuracy measures like true positive (TP) rate, false positive (FP) rate, precision, recall, and F-measure. The results indicate that the J48 classifier has a better classification accuracy and prediction rating over other tested algorithms using the early childhood dataset.
The Presence, Performance, and Publics of Online Interactions

Abstract Symbolic interactionists have long focused on how context and settings influence interaction dynamics. While developments in digital media have transformed mediated interaction, the key components of social interaction persist, and symbolic interactionism continues to have implications. This chapter discusses how symbolic interactionism elucidates three main aspects of mediated interaction: 1) perception of the presence of others, 2) online presentations of the self, and 3) the ambiguous public and subsequent unique intersubjectivities of online interactions. The growing multimodality of digital media increases cues in mediated interaction and varies the degree of presence online, thus changing how interactants perceive others and themselves. Consequently, new strategies of online self-presentation are used to balance self-idealization and anticipated authenticity. Meanwhile, the invisible audience, context collapse, and blurred boundaries between public and private domains present new challenges.
WBMatrix: An Optimized Matrix Library for White-Box Block Cipher Implementations

White-box block cipher (WBC) has been proposed by Chow \textit{et al.} to prevent the secret key to be extracted from its implementation in an untrusted context. A pivotal technique behind WBC is to convert the iterated round functions into a series of look-up tables (LUTs) with encodings. The construction of encoded LUTs consists of matrix operations, such as multiplication and inversion. The widely-used matrix libraries in applications, such as open-source NTL and M4RI, are primarily designed for large dimensional matrix operations. Therefore, they might not be suitable for WBC implementations which are mainly based on small-scale matrices and vectors. In this paper, we propose a new matrix library named WBMatrix for the optimization of WBC implementations. WBMatrix reduces the operating steps of multiplication and simultaneously generates pairwise invertible matrices as encodings. The performance comparison supports that WBMatrix improves the table construction and encryption phases on Intel x86 and ARMv8 platforms. Moreover, WBMatrix also boosts the initialization and encryption phases of LowMC/LowMC-M block ciphers and enhances the performance for the generation of key-dependent Sbox.