Reviewerly/exHarmony huggingface dataset:
Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Enterprise data warehouses (EDWs) serve as foundational infrastructure in a modern learning health system, housing clinical and other system-wide data and making it available for research, strategic, and quality improvement purposes. Building on a longstanding partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), an end-to-end clinical research data management (cRDM) program was created to enhance clinical data workforce capacity and further expand related library-based services for the campus.The training program covers topics such as clinical database architecture, clinical coding standards, and translation of research questions into queries for proper data extraction. Here we describe this program, including partners and motivations, technical and social components, integration of FAIR principles into clinical data research workflows, and the long-term implications for this work to serve as a blueprint of best practice workflows for clinical research to support library and EDW partnerships at other institutions.This training program has enhanced the partnership between our institution's health sciences library and clinical data warehouse to provide support services for researchers, resulting in more efficient training workflows. Through instruction on best practices for preserving and sharing outputs, researchers are given the tools to improve the reproducibility and reusability of their work, which has positive effects for the researchers as well as for the university. All training resources have been made publicly available so that those who support this critical need at other institutions can build on our efforts.Library-based partnerships to support training and consultation offer an important vehicle for clinical data science capacity building in learning health systems. The cRDM program launched by Galter Library and the NMEDW is an example of this type of partnership and builds on a strong foundation of past collaboration, expanding the scope of clinical data support services and training on campus.
Variance–covariance from a metropolis chain on a curved, singular manifold

We consider estimation of variance and covariance from a point cloud that are draws from a posterior distribution that lie on a curved, singular manifold. The motivating application is Bayesian inference regarding a likelihood subject to overidentified moment equations using MCMC (Markov Chain Monte Carlo). The MCMC draws lie on a singular manifold that typically is curved. Variance and covariance are Euclidean concepts. A curved, singular manifold is not typically a Euclidean space. We explore some suggestions on how to adapt a Euclidean concept to a non-Euclidean space then build on them to propose and illustrate appropriate methods.
Formally verified bundling and appraisal of evidence for layered attestations

Remote attestation is a technology for establishing trust in a remote computing system. Copland is a domain-specific language for specifying attestation protocols that operate in diverse, layered measurement topologies. In this work, we formally define and verify the Copland Virtual Machine for executing Copland protocols alongside a dual generalized appraisal procedure. Together these components provide a principled pipeline to execute and bundle arbitrary Copland-based attestations, then unbundle and evaluate the resulting evidence for measurement content and cryptographic integrity. All artifacts are implemented as monadic, functional programs in the Coq proof assistant and verified with respect to the Copland reference semantics. Finally, we leverage formal properties of component implementations and their surrounding security architecture to aid in the design and analysis of attestation scenarios in the context of an active adversary attempting to subvert attestation. These components lay the foundation for a verified end-to-end attestation stack.
Innovation in Management Digital-Based Paper Research of Bachelor Student in Information Engineering Study Program Faculty of Computer Science Uniku

Kuningan University is one of the universities located in Kuningan.To become a superior university, it is not only supported by facilities and infrastructure but also supported by other supports such as the use of information technology in various activities.Currently, the use of technology allows for easy data processing, can produce the required information accurately, streamline time and costs so that it is more efficient.One of the obstacles faced by the Faculty of Computer Science, especially the Informatics Engineering study program, is managing thesis data which is done manually.At this time, the management of thesis data in the study program, especially in Informatics Engineering at the FKOM UNIKU level, is still done manually starting from submitting titles and processing thesis data, where the title submission goes to the supervisor and study program, the thesis file is not neatly arranged, and Borrowed research reports of bachelor student are not returned.This is seen as the need for an application so that some activities that take a long time can use an application faster.To overcome these problems, in this study an application will be designed to manage thesis data.The research method used is library research, observation, interviews, and stages of Rapid Application Development (RAD) as a system development method.The application is designed using UML and several tools for application development such as XAMPP, Macromedia Dreamweaver, and PHP as a programming language.
ABIPA: ARIMA-Based Integration of Accelerometer-Based Physical Activity for Adolescent Weight Status Prediction

Obesity is a global health concern associated with various demographic and lifestyle factors including physical activity (PA). Research studies generally used self-reported PA data or, when accelerometer-based activity trackers were used, highly aggregated data (e.g., daily average). This suggests that the rich potential of detailed activity tracker data is largely under-exploited and that deeper analyses may help better understand such relationships. This is particularly true in children and adolescents who are distinct and engage more in bursts of PA. This article presents ABIPA, a machine learning-based methodology that integrates various aspects of accelerometer-based PA data into weight status prediction for adolescents. We propose a method to derive features regarding the structure of different PA time series using Auto-Regressive Integrated Moving Average (ARIMA). The ARIMA-based PA features are combined with other individual attributes to predict weight status and the importance of these features is further unveiled. We apply ABIPA to a dataset about young adolescents (N = 206) containing, for each participant, a 7-day continuous accelerometer dataset (60 Hz, GENEActiv tracker from ActivInsights) and a range of their socio-demographic, anthropometric, and lifestyle information. The results indicate that our method provides a practical approach for integrating accelerometer-based PA patterns into weight status prediction and paves the way for validating their importance in understanding obesity factors.
Supercanonical coordinates for orthosymplectic evolution operators

A time-dependent self-adjoint even Hamiltonian is defined by a linear combination of generators of the semidirect sum osp(m′/n′)⊕sne(n′+1), of the orthosymplectic plus the even Heisenberg algebra by computing the supercommutator of odd binary forms Π, given as linear combinations of odd bilinear generators of the odd Heisenberg algebra no(m′+1) elements times ne(n′+1) elements, establishing a relationship between entangled boson systems and entangled fermion systems. This approach leads to the concept of intertwining, defined through the resulting quadratic Hamiltonians of bosons and, separately, of fermions with coefficients given in terms of the same coefficients of Π. Intertwining is invariant under transformations of Π, which leave certain binary forms of the coefficients of Π in the Hamiltonian unchanged. Alternatively, the coefficients can be interpreted as simultaneous time-dependent (super-) control parameters for both spin-statistics. Time-dependent inhomogeneous linear supercanonical transformations of wave vectors leave invariant the Heisenberg superalgebra n(m′/n′) and belong to the semidirect product Osp(m′/n′) ⋉ Ne(n′ + 1) of the orthosymplectic supergroup with the even Heisenberg group. The unitary time evolution operator is constructed using the adjoint map in canonical coordinates determined by the supercanonical transformation. The method is a generalization of an Inönu–Wigner contraction procedure and a Wei–Norman method for superalgebras with a selection of subalgebras associated with the root space decomposition of the Lie superalgebra. Analogously, this is a separation of variables method for quantum mechanical problems in systems with bosons and fermions. The standard Floquet theory leads to new results concerning stability for locally periodic coefficients. The lowest dimensional cases are explicitly computed. The intertwining of boson and fermions systems and the Hamiltonians considered here are of interest in quantum control theory for systems including fermions and bosons, in quantum optics, and quantum computation.
Fast estimation and choice of confidence interval methods for step regression

In this paper we propose a new fast grid search algorithm for finding the least square estimators of a step regression model. This algorithm makes it practical to compute resampling-based confidence intervals for step regression models. We introduce five data generating models, including one where the mean model is a step model (model correctly specified) and four where the mean models are not step models (model misspecified), and use them to study the coverage probabilities of two new types of resampling-based confidence intervals for step regression: symmetric percentile bootstrap confidence intervals and subsampling confidence intervals using a new set of rules-of-thumb to select block size. Our results show that when the model is correctly specified, the symmetric percentile Efron bootstrap confidence intervals provide close-to-nominal coverage and have shorter intervals than the subsampling methods; when the model is misspecified, the subsampling method using the rules-of-thumb provides good coverage and shorter confidence intervals than the symmetric percentile Efron bootstrap method and the subsampling method using a double bootstrap-like procedure for block size selection. Finally, we apply the proposed methods to a real world environmental dataset on the relationship between grassland productivity, soil moisture anomalies and other hydro-climatic and land use variables to provide inference for the threshold in soil moisture anomalies, across which there is a jump in grassland productivity.
Introduction to the Special Issue on Software Tools for Quantum Computing: Part 1

introduction Share on Introduction to the Special Issue on Software Tools for Quantum Computing: Part 1 Editors: Yuri Alexeev View Profile , Alex McCaskey View Profile , Wibe De Jong View Profile Authors Info & Claims ACM Transactions on Quantum ComputingVolume 3Issue 306 September 2022Article No.: 11pp 1–3https://doi.org/10.1145/3532179Published:06 September 2022Publication History 0citation108DownloadsMetricsTotal Citations0Total Downloads108Last 12 Months108Last 6 weeks6 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
Digital Modeling of a Domain Ontology for Hospital Information Systems

Hospital information systems need to rely on a well-defined, coordinated ontological infrastructure. We use conceptual metamodeling to capture and communicate an ontology for hospital operations, which is designated for use by an actual information system under development. We base our metamodel on the system requirements specification. We demonstrate how this ontology evolves throughout development iterations, as well as how it can be used for threat modeling in the early stages of the system development. We discuss the practical and theoretical implications and potential of our approach.
Mask RCNN Methods for Eyes Modelling

Object detection is one of Deep Learning section in Computer Vision. The application of computer vision is divided into image classification and object detection. Object detection have target to find specific object from an image. The application of object detection for security are face recognition, and face detection. Face detection have been developed for medical application to identify emotion from faces. In this research, we proposed an eye modelling by using Mask RCNN. The eye model was applied in real time detection combined with OpenCV. The dataset was created from online dataset and image from webcam. The model was trained with 4 epochs and 131 iterations. The final model was successfully detected eye from real-time application.
Amharic Semantic Information Retrieval System

Amharic is the official language of Ethiopia, currently having a population of over 118 million. Developing effective information retrieval (IR) system for Amharic has been a challenging task due to limited resources coupled with complex morphology of the language. This paper presents the development of Amharic semantic IR system using query expansion based on deep neural learning model and WordNet. In order to optimize the retrieval result, we propose Amharic text representation using root forms of words applied for stopword identification, indexing, term matching and query expansion. Comparisons are made with the conventional stem-based text representation for information retrieval, and we show that using the root forms of words is better for both resource construction and system development. The effectiveness of the proposed Amharic semantic IR system is evaluated on Amharic Adhoc Information Retrieval Test Collection (2AIRTC).
A Novel Semi-supervised Clustering Algorithm: CoExDBSCAN

Cluster analysis helps to better understand the inherent structure of data by grouping similar data points together. Typically this similarity is expressed in terms of distance between data points, either in full value space or value subspaces or in terms of correlations among attributes. However, distance-based clustering algorithms suffer the curse of dimensionality, where points tend to become equidistant from one another as the dimensionality increases. Subspace and correlation clustering algorithms overcome these issues but still face challenges when data points have complex relations or clusters overlap. Semi-supervised clustering algorithms can significantly improve the results of any cluster analysis that encounters such problems by incorporating a priori knowledge into the clustering process. This paper presents a novel semi-supervised clustering algorithm, CoExDBSCAN, that is based on the original DBSCAN algorithm and combines traditional, density-based clustering with techniques from subspace, correlation and constrained clustering methods. The correctness and usefulness of the algorithm, specifically for spatio-temporal data, is demonstrated on a synthetic dataset and a real-world dataset in the domain of climatology.
Vision based formability testing of sheet metal using portable Erichsen cupping tester

The main objective of this work is to design and fabricate a portable Erichsen cupping tester to perform the formability studies of non-ferrous sheet metals on the optical workbench and machine vision system. The formability of sheet metal is identified by measuring Erichsen cupping index (IE) using a probe in a machine vision system. The loading setup is modular so that it can be easily lodged and dislodged during testing on an optical bench and machine vision. The major components of an Erichsen cupping tester are top die, bottom die, and punch. The components are designed in SolidWorks.The formability analysis of aluminium, copper and brass sheet metals each of 2 mm thickness is also carried out in the ABAQUS CAE software earlier and able to test it practically on a scaled-down model of 0.2 mm thickness due to constraints in the loading setup. The IE numbers for all three sheet metals obtained from the simulation and the experimental results depict the formability order of aluminium, and copper, followed by brass, indicating the highest formability with brass compared to the other two metals.
Hierarchical framework for mobile robots to effectively and autonomously explore unknown environments

Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods.
An auxiliary <scp>Part‐of‐Speech</scp> tagger for blog and microblog cyber‐slang

Abstract The increasing impact of Web 2.0 involves a growing usage of slang, abbreviations, and emphasized words, which limit the performance of traditional natural language processing models. The state‐of‐the‐art Part‐of‐Speech (POS) taggers are often unable to assign a meaningful POS tag to all the words in a Web 2.0 text. To solve this limitation, we are proposing an auxiliary POS tagger that assigns the POS tag to a given token based on the information deriving from a sequence of preceding and following POS tags. The main advantage of the proposed auxiliary POS tagger is its ability to overcome the need of tokens' information since it only relies on the sequences of existing POS tags. This tagger is called auxiliary because it requires an initial POS tagging procedure that might be performed using online dictionaries (e.g., Wikidictionary) or other POS tagging algorithms. The auxiliary POS tagger relies on a Bayesian network that uses information about preceding and following POS tags. It was evaluated on the Brown Corpus, which is a general linguistics corpus, on the modern ARK dataset composed by Twitter messages, and on a corpus of manually labeled Web 2.0 data.
The impact of GDPR infringement fines on the market value of firms

Purpose This paper aims to investigate the impact of the General Data Protection Regulation (GDPR) infringement fine announcements on the market value of mostly European publicly listed companies with a view to reinforcing the importance of data privacy compliance, thereby informing cyber security investment strategies for organisations. Design/methodology/approach Previous studies have shown (varying degrees of) evidence of a negative impact of data breach announcements on the share price of publicly listed companies. Following on from this research, further studies have been carried out in assessing the economic impact of the introduction of legislation in this area to encourage firms to invest in cyber security and protect the privacy of data subjects. Existing research has been predominantly US centric. Findings Using event study techniques, a data set of 25 GDPR fine announcement events was analysed, and statistically significant cumulative abnormal returns of around 1% on average up to three days after the event were identified. In almost all cases, this negative economic impact on market value far outweighed the monetary value of the fine itself, and relatively minor fines could result in major market valuation losses for companies, even those having large market capitalisations. Originality/value This research would be of benefit to business management, practitioners of cyber security, investors and shareholders as well as researchers in cyber security or related fields (pointers to future research are given). Data protection authorities may also find this work of interest.
Experimental platform for testing solar tracking and energy conversion strategies

Improve the conversion efficiency of solar technology, based on solar tracking systems, has two fundamental challenges, increasing the precision in the solar tracking process and reducing the energy consumption due to the tracking action. There are developments that make the comparison of energy saving strategies, considering the number of axes of the system, the monitoring method, and the configuration of the tracker. Also, there are developments focused on reducing the tracking error, implementing various tracking control strategies. However, it is not possible to make comparisons of these developments under the same weather conditions since multiple tracking systems would be required. The present proposal develops an experimental platform from a concurrent approach. It is composed by three open kinematic chain solar tracking systems, each robot has two axes, the hardware has an open architecture, it has electric power sensors and solar pointing sensors. For the verification of the experimental platform, two control strategies were simulated, and three embedded processing boards were used in the physical systems for the experimental test. The PD controller had a mixed error of 0.0593° and the PID controller a mixed error of 0.0173°. The STM32 Nucleo-64 board had the best performance with a consumption of 1.2418Wh. The experimental platform allows the implementation and testing of different tracking and energy strategies under the same environmental conditions.