Reviewerly/exHarmony huggingface dataset:
De-identification of Free Text Data containing Personal Health Information: A Scoping Review of Reviews

IntroductionUsing data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data. MethodsWe adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence. ResultsThe initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning. ConclusionOur review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.
Constructing a Nondegenerate 2D Integer-Domain Hyperchaotic Map Over GF(2n) with Application in Parallel Hashing

To solve the problem of finite precision effect of existing chaotic maps on digital platform, first, a nondegenerate 2D integer-domain hyperchaotic map (2D-IDHCM) over GF([Formula: see text]) is constructed. Then, the proof that 2D-IDHCM satisfies Devaney’s definition of chaos and the proof of boundedness of Lyapunov exponents are given. The analytic results of dynamic behaviors demonstrate that 2D-IDHCM has ergodicity and large Lyapunov exponents within a certain parameter range, and without dynamic degradation. Finally, to verify the practicality of 2D-IDHCM, a keyed hash function based on 2D-IDHCM is designed, which can absorb variable-length message and generates 256, 512, 1024-bit or longer hash values in parallel. The experimental results demonstrate that 2D-IDHCM has better dynamic behaviors, and can be used in practical applications.
Pembelajaran Blending Asynchronous-Synchronous Learning Berbasis Relevan terhadap Hasil Belajar Koqnitif Peserta Didik Sekolah Dasar pada Masa Pandemi

Abstrak: Penelitian strategi pembelajaran berbasis relevan dengan metode blending asynchronous dan synchronous terhadap hasil belajar kognitif peserta didik kelas 5 Sekolah Dasar bertujuan untuk mengetahui dampak pembelajaran online terhadap kognitif peserta didik. Dalam riset ini kelas kontrol dan kelas eksperimen mendapat media pembelajaran yang sama, asynchronous learning menggunakan aplikasi Google Classrom sehingga peserta didik dapat mengirim materi, dapat mengumpulkan tugas dan dapat berkomunikasi secara tidak langsung dengan guru. Sedangkan synchronous learning menggunakan aplikasi zoom, dimana peserta didik dapat berinteraksi dan berkomuniasi dengan guru dan teman secara langsung. Kelas control tidak menggunakan pembelajaran berbasis relevan berjumlah 24 anak dan kelas eksperimen, menggunakan pembelajaran berbasis relevan berjumlah 44 anak. Hasil penelitian diperoleh rata- rata pretest kelas control sebesar 54,38 serta kelas eksperimen 55,94 sebaliknya hasil rata- rata posttest kelas control sebesar 63,54 serta kelas eksperimen sebesar 83,50. Hasil ini menampilkan perbedaan yang signifikan, setelah itu dilanjutkan dengan uji statistic sampai pada tahap uji korelasi dan memperoleh hasil yang signifikan sebesar 0.743 serta sig 0.000. Kontribusi pembelajaran berbasis relevan sebesar 55.1% terhadap hasil belajar kognitif siswa. Abstract: Research on blending relevance based asynchronous and synchronous learning across the cognitive result of 5th grade elementary school students. The aim is to determine the impact of online learning on cognitive students. In this research, the control class and the experimental class received the same learning media, asynchronous learning using the Google Classroom application so that students could send materials, collect assignments and communicate indirectly with the teacher. Meanwhile, synchronous learning uses a zoom application, where students can interact and communicate with teachers and friends directly. The control class did not use relevant-based learning with a total of 24 children and the experimental class, which used relevant-based learning, amounted to 44 children. The results showed that the average pretest for the control class was 54.38 and the experimental class was 55.94. On the other hand, the average posttest for the control class was 63.54 and the experimental class was 83.50. These results show a significant difference, then continued with statistical tests to the correlation test stage and obtained significant results of 0.743 and 0.000 sig. The contribution of relevant-based learning is 55.1% to students' cognitive learning outcomes.
Communication-efficient Personalized Federated Edge Learning for Massive MIMO CSI Feedback

Deep learning (DL)-based channel state information (CSI) feedback has garnered significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by transmitting CSI datasets during the training process. In this study, we introduce a federated edge learning (FEEL)-based training framework for DL-based CSI feedback. This approach differs from the conventional centralized learning (CL)-based framework, where the CSI datasets are collected at the base station (BS) before training. Instead, each user equipment (UE) trains a local autoencoder network and exchanges model parameters with the BS. This approach provides better protection for data privacy compared to CL. To further reduce communication overhead in FEEL, we quantize the uplink and downlink model transmission into different bits based on their influence on feedback performance. Additionally, since the heterogeneity of CSI datasets among different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to enhance feedback performance. This strategy allows for local fine-tuning to adapt the global model to the channel characteristics of each UE. Simulation results indicate that the proposed personalized FEEL-based training framework can significantly improve the performance of DL-based CSI feedback while reducing communication overhead.
Analysis of Feature Selection methods in Software Defect Prediction Models

Improving software quality by proactively detecting potential defects during development is a major goal of software engineering. Software defect prediction plays a central role in achieving this goal. The power of data analytics and machine learning allows us to focus our efforts where they are needed most. A key factor in the success of software fault prediction is selecting relevant features and reducing data dimensionality. Feature selection methods contribute by filtering out the most critical attributes from a plethora of potential features. These methods have the potential to significantly improve the accuracy and efficiency of fault prediction models. However, the field of feature selection in the context of software fault prediction is vast and constantly evolving, with a variety of techniques and tools available. Based on these considerations, our systematic literature review conducts a comprehensive investigation of feature selection methods used in the context of software fault prediction. The research uses a refined search strategy involving four reputable digital libraries, including IEEE Explore, Science Direct, ACM Digital Library, and Springer Link, to provide a comprehensive and exhaustive review through a rigorous analysis of 49 selected primary studies from 2014. The results highlight several important issues. First, there is a prevalence of filtering and hybrid feature selection methods. Second, single classifiers such as Naïve Bayes, Support Vector Machine, and Decision Tree, as well as ensemble classifiers such as Random Forest, Bagging, and AdaBoost are commonly used. Third, evaluation metrics such as area under the curve, accuracy, and F-measure are commonly used for performance evaluation. Finally, there is a clear preference for tools such as WEKA, MATLAB, and Python. By providing insights into current trends and practices in the field, this study offers valuable guidance to researchers and practitioners to make informed decisions to improve software fault prediction models and contribute to the overall improvement of software quality.
The Impact of Cloud-Based Enterprise Resource Planning System on Blockchain Adoption, with the Presence of Cloud Auditing as an Intermediary Variable in Jordanian Commercial Banks

The study aimed to determine the impact of a cloud-based Enterprise Resource Planning (ERP) system on the adoption of blockchain technology, with the presence of cloud auditing as an intermediary variable, in Jordanian commercial banks. The study utilized a descriptive-analytical approach to describe and analyze the phenomenon under investigation, which revolves around cloud-based ERP systems and their influence on blockchain technology adoption, along with the mediating role of cloud auditing. The study's population consisted of Jordanian commercial banks listed on the Amman Stock Exchange, with a total of 12 banks by the end of the year 2022. The study utilized the Statistical Package for Social Sciences (SPSS) and Amos software to analyze the study's questionnaire. The study yielded several key findings, one of which is the presence of an impact of cloud-based Enterprise Resource Planning (ERP) systems on the adoption of blockchain technology, mediated by cloud auditing, in Jordanian commercial banks. As for the study's recommendations, they emphasize the necessity for Jordanian commercial banks to provide appropriate training and awareness programs for their auditing teams regarding cloud auditing. This initiative aims to enhance their understanding of cloud technologies, associated tools, and cloud auditing skills. Furthermore, it's important for these banks to fulfill all requirements and procedures ensuring compliance with laws and regulations related to financial technology and blockchain.
Explainable hierarchical clustering for patient subtyping and risk prediction

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK’s National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.
Differentiated Teaching Module for Indonesian Language Based on Graphological Analysis of Letter I, R, G

There are many ways that can be done to find out the individual characteristics of each student. The purpose of this study was to determine the internal potential of 10th Grade “Halimah” Students by using graphological analysis based on general characteristics, which is the slope of the writing direction, the slope of the writing lines, the spacing between words, and the spacing between lines, and based on the general characteristics, which are the letters "I", "R", and "G". This study uses a qualitative descriptive research method. This method is used to describe the general description of the research, which are the characteristics of handwriting with the internal potential of students and to interpret graphology in several aspects. Then, this type of descriptive research is also capable of solving practical problems in the development of science, especially for graphological research. The stages of this research are handwriting analysis, triangulation through interviews, grouping results. The results of the analysis show that the internal potential of students is variative, so that appropriate learning methods or approaches are needed in order to achieve individual student learning goals
Neural Text Generators in Enterprise Modeling: Can Chatgpt be Used as Proxy Domain Expert?

Enterprise modeling is concerned with the systematic development of a comprehensive and holistic representation of an enterprise (an enterprise model) to support organisational initiatives. Domain experts have an essential role in enterprise modeling projects (EM), as they provide the required domain knowledge or specifics of the organisation under consideration. The paper investigates if neural text generators (large language models) can reduce the dependency on domain experts for certain tasks in enterprise modeling. The main contributions of this paper are (1) a systematic literature analysis on neural text generator use in EM, (2) the identification of potential for applying large language models in EM, and (3) findings from quasi-experiments comparing output of ChatGPT and domain experts for the same EM task.
Spatial attention modulation of the brain network involved in mental time travel.

The ability to mental time travel (MTT) consists in moving along a cognitive and spatially oriented representation of time, that is, an ideal mental time line, where past and future events are, respectively, located on the left and on the right portion of such a line. A shift of spatial attention by prismatic adaptation (PA) influences this spatial coding of time, thus affecting MTT. Here, we investigated the neural correlates of such a spatial modulation on MTT in a functional Magnetic Resonance Imaging protocol.To study MTT ability, participants were asked to indicate if a series of events took place before or after (Self-Reference component) an imagined self-location in time (Past, Present or Future; Self-Projection component), where they had to project themselves. The MTT task was performed before and after PA inducing a leftward shift of spatial attention, which is supposed to move toward the left portion of mental time line (MTL), where Past is represented.Following PA, we observed a facilitation in responding to past as compared to future events when participants projected themselves to the Past projection. As a functional counterpart of this behavioral finding, we propose a model of the brain activity modulations following the PA effects on MTT.As a result of the shift of spatial attention toward the left, the facilitation in having access to past events is associated with the inhibition of superior frontal gyrus in the left hemisphere, whereas the facilitation in projecting toward the Past may result from the activity modulation in right and left inferior parietal lobule. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
On the Adversarial Robustness of Multi-Modal Foundation Models

Multi-modal foundation models combining vision and language models such as Flamingo or GPT-4 have recently gained enormous interest. Alignment of foundation models is used to prevent models from providing toxic or harmful output. While malicious users have successfully tried to jailbreak foundation models, an equally important question is if honest users could be harmed by malicious third-party content. In this paper we show that imperceivable attacks on images $\left({{\varepsilon _\infty } = 1/255}\right)$ in order to change the caption output of a multi-modal foundation model can be used by malicious content providers to harm honest users e.g. by guiding them to malicious websites or broadcast fake information. This indicates that countermeasures to adversarial attacks should be used by any deployed multi-modal foundation model. Note: This paper contains fake information to illustrate the outcome of our attacks. It does not reflect the opinion of the authors.
Federify: A Verifiable Federated Learning Scheme based on zkSNARKs and Blockchain

Federated learning (FL) has emerged as an alternative to traditional machine learning in scenarios where training data is sensitive. In federated learning, training is held at end devices, and thus data does not need to leave users devices. However, most approaches to federated learning rely on a central server to coordinate the learning process which, in turn, introduces its own security and privacy problems. We propose Federify, a decentralized federated learning framework based on blockchain which employs homomorphic encryption and zero knowledge proofs to provide security, privacy, and transparency. The scheme preserves the confidentiality of both the data used for training and the local models using homomorphic encryption. zkSNARKs are used to provide security by verifying the contributions from the different agents, and transparency of both the learning process and the incentive mechanism is achieved by delegating coordination into a smart contract in a public blockchain. We have also implemented, deployed, and evaluated a proof of concept of our framework, to demonstrate its viability both in terms of computational resources needed and cost to train in a public generic blockchain such as Ethereum.
Organisational digital capability: a cross-country review of guidance

Purpose Digital transformation continues to rapidly progress in higher education globally, spanning all aspects of higher education operations, values and culture. Despite expanding literature, guidance remains focussed on emergency application during pandemic lockdowns and/or on single organisational case studies. Digital transformation frameworks that move beyond these foci are heavily criticised for being commercially contextualised (outside of higher education) and are often too narrowly conceptualised. The purpose of this paper is to review the most common framework currently used in the UK, which takes a strategic and organisational perspective on digital transformation. Design/methodology/approach This is a technical review article that summarises key guidance for organisational digital capability and then reflects on its application in the UK (a developed economy and higher education system) and in Vietnam (one of the fastest-growing economies with a developing higher education committed to digital transformation) as an initial attempt to explore its applicability beyond the UK context. Vietnam has been chosen as a reference context, given its significant current digital transformation policy reform at the national level and as a collaboration partner with the UK in its digital transformation. Findings The guidance highlights six core areas to consider for digital capability: information and communication technology (ICT) infrastructure, content and information, research and innovation, communication, learning, teaching and assessment and organisational digital culture. Although the framework is normative, findings suggest it is sufficiently open-ended to enable its users to determine practical steps to drive digital transformation. However, complementary tools are suggested to deal with the rapidly developing digital transformation policy context of Vietnam. Originality/value This is the first time a review has been conducted from the perspective of different countries, with a view to supporting leaders, managers and policymakers in the UK, Vietnam and other Association of South Eastern Nations (ASEAN) networks in their own digital transformation transitions.
Local Predictive Optimization of Globally Planned Motions for Truck-Trailer Systems

Logistics tasks often require the precise maneuvering of truck-trailer systems to a target position. For example, the rear of the trailer must be steered to a docking position for unloading. Global path planning algorithms usually generate suboptimal solutions. In an additional planning step, these solutions can be used as initialization for a local optimizer. In this article, a model predictive controller is presented, which performs a local optimization for suboptimal reference trajectories. Allowing to deviate temporarily from the previously generated solution allows to eliminate redundant direction changes, to increase obstacle clearance, and in the end, to generate more efficient solutions. To provide numerical robustness, it is proposed to constrain the terminal state to the reference path of the global planner solution. Simulation results for a one-trailer system highlight the benefits of the optimized solution in a particularly tight planning scenario. The applicability of the generated solutions is demonstrated in an experimental model scale setup. Furthermore, the method allows to switch between trajectory optimization (TO) and trajectory tracking (TT) mode to adapt to different driving situations, which is demonstrated in multiple simulation examples for a two-trailer system.