Reviewerly/exHarmony huggingface dataset:
AUDIT SISTEM INFORMASI SAS PADA BPK PENABUR GADING SERPONG MENGGUNAKAN KERANGKA COBIT 4.1

This study aims to determine whether: (1) The research carried out makes it easier for the teachers of the BPK PENABUR Gading Serpong area to apply for tickets at SAS in the IT menu, to the Ticketing SIM menu. (2) Notification of ticket information to IT Areas can be grouped by area of the BPK PPENABUR Jakarta school complex, especially the IT Area of the BPK PENABUR GadingSerpong complex. The sampling technique used the COBIT 4.1 framework. Data can be obtained by distributing questionnaires using Google Form to active users of SAS BPK PENABUR GadingSerpong, namely 95 users. Researchers took 2 Domains in COBIT 4.1, namely DS and ME. Fromthe DS domain, researchers took 2 processes, namely DS7 and DS8, and from the ME domain, researchers took 1 process, namely ME1. From the questionnaire, the researcher obtained data from the DS and ME domains was 2.48 and the target and the maturity level of the system (maturity level) was 3. Then the gap was 0.52. Based on these results, SAS BPK PENABUR Gading Serpong needs to be improved both in terms of appearance and menus to make it easier for teachers to use SAS, especially on the IT menu, in the Ticketing SIM menu about ticket applications.
Analyticо-Synthetic Approach in Pedagogical Research: Stages of Realization

This study examines the execution of the scientific methods of analysis and synthesis, as they concern problems that are common for the philosophy of science, methodology of science, and methodology of pedagogy. The author’s point of view is based оn two fundamental statements. First – analysis and synthesis are used in almost every scientific field, and, in their application, both general and specific characteristics are noticed. The methodology of science treats the former, and the private methodologies examine the latter. Second – in the field of scientific research, analysis and synthesis are always executed together in an indivisible unity called “analytico-synthetic approach”. On this theoretical basis, the author maintains and proves the thesis that it is possible to conceptualize a procedure for the analytico-synthetic approach. It can be applied in different scientific areas, including in pedagogy. The procedure includes five stages, each of which contains specific activities. The object, subject, and goal of the research are focused respectively on peculiar characteristics of the methods of analysis and synthesis, and on application features of the analytico-synthetic approach in scientific studies, especially in the field of pedagogy.
Aspects of Practical Vocational Training of Future Primary Teachers. Research Through the Interview Method

The main goal of the article is to study the current state of the organization, implementation, and quality of the practical vocational training of future primary teachers. For this purpose, it presents the opinions and assessment of students studying for the profession of primary teacher in various full-time and part-time Bachelor’s and Master’s degree programmes in pedagogy, of young primary teachers with no more than three years of experience, as well as of primary school principals. The article analyzes the results of a research conducted using the interview method, and it proposes summaries, conclusions, and recommendations regarding the overall organization and optimization of the practical vocational training of students.
A Three-Camera Digital Image Correlation System For Full-Field 3D Shape and Motion Measurement

A cluster-approach-based three-camera digital image correlation (DIC) system is introduced for full-field 3D shape and motion measurement. In this system, three cameras are employed to measure the same specimen area at different viewing angles. Data points within the region of interest can be evaluated by arbitrary camera pairs as a stereo DIC system so that data points with the smallest 3D residuum are selected and mapped into one common coordinate system. Two stationary shape measurements and one out-of-plane motion measurement were carried out with the three-camera DIC system. Test results were analyzed based on the same image series, projection calibration, and correlation parameters, but compared using different camera combinations (i.e., three-camera and two-camera data). Three-camera test results show not only an improved surface coverage due to the additional camera viewing angle for uneven specimen surfaces, but also a smaller and more homogenous distributed measurement uncertainty compared to the two-camera test results. The selection of data points with the smallest 3D residuum evaluated from any arbitrary camera pairs enables a better tolerance of the three-camera DIC system against various measurement error sources such as limited depth of field, lens distortion, and speckle pattern distortion due to tilted camera viewing angles.
Indonesian Literary Genres Reading Preference of Students from Polytechnic University of the Philippines, Department of English Foreign Languages and Linguistics as a Tool and Aid in Learning Bahasa Indonesia

Developing the cognitive process of learning foreign languages, language learners are encouraged to seek for ways or tools to make their learning convenient and inspiring, perceiving that the benefits of language learners derives from applying literary text, especially, if the language is embedded deeply in the culture of the society. This study aims to seek literary genre preferences as reading interest of foreign students from the Polytechnic University of the Philippines to aid in an active process of continuous learning Bahasa Indonesia, as it cognitively, continuously affects each foreign student in learning the language, in which it helps every learner to easily retain, remember, and apply lessons and expressions of Bahasa Indonesia, specially, constructing sentence structure, utilizing traditional expressions, and cultural awareness.
A novel face recognition approach based on strings of minimum values and several distance metrics

This paper proposes a novel approach to face recognition using string of minimum values (SMV) as a new face feature extractor for face representation. Unlike most of the face representative methods, which focus only on micro-structures information in image analysis, by surrounding the treated pixel with a mask. The proposed descriptor uses the chains of unit vectors in four directions, by moving from the current pixel to the next one, from which to a new next pixel, and so on, in order to encode also the global appearance of the face image. Furthermore, seven distance metrics from the nearest neighbour classifier are evaluated in the classification stage. The experimental results show which metrics perform well and demonstrate the efficiency of the proposed approach in terms of recognition rate compared to the existing face recognition methods.
The threats to Jupyter Notebook

The popular data practitioners’ tool, Jupyter Notebook, has become the victim of a Python-based ransomware attack for the first time. The popular open-source platform is vulnerable to misconfiguration, which could leave your servers wide open to attack.
A 2.47 μJ/sample QR-Decomposition-based Extreme Learning Machine Engine Supporting Online Class Incremental Learning for ECG-based User Identification

In the Industrial Internet of Things (IIoT) applications, smart devices generate massive data during monitoring machine status. Meanwhile, authorized access to these IIoT data and machines becomes crucial where biometrics comes in handy [1]. Among all bio-signals, Electrocardiography (ECG) signal has two key advantages: 1) ECG signals indicates electrical activity of the heart, providing intrinsic proof of liveness. 2) ECG signals cannot be easily eavesdropped without physical contact [2]. These anti-spoofing features make ECG an attractive biometric modality in the IIoT scenario. One challenge in such user identification system is how to identify a “newly enrolled user” efficiently without retraining the entire system as shown in Fig. 1. That is, the classifier of the system should “incrementally” learn the new authorized users without “forgetting” the previously learned/trained ones, called “online class incremental learning (O-CIL).” It involves both “online learning (OL)” and “class incremental learning (CIL).” However, most back-propagation-based neural networks (BP-NN) suffers from the notorious “catastrophic forgetting,” which results in severe accuracy degradation due to the recency learning bias towards new classes [3]. To deal with the issue of catastrophic forgetting, the scalable extreme learning machine (S-ELM) [4] provides an analytical solution by online updating the classifier with recursive least squares (RLS). However, the matrix-inversion operations of conventional RLS are very computation-intensive, which is not suitable for direct VLSI implementation. Moreover, most existing ELM chips only support inferencing [5], lacking the O-CIL capability.
WingCore: Pipeline Convolutional Neural Network Accelerator for Object Detection Using the Winograd Algorithm

Convolutional Neural Network (CNN) has been widely used in computer vision problems in recent years. Because of their excellent performance, energy economy, and versatility, FPGAs have been intensively investigated for use in accelerating CNNs. In this paper, we present WingCore, which uses the fast Winograd method to improve FPGA-based neural network accelerators. We use the weight stationary method and the addition process after element-wise multiplication to improve accelerator performance. We evaluate the hardware architecture on PYNQ Z2 platform. When compared to the CPU, our design achieves a $216.16\times$ speedup on Yolov3-Tiny layer 1.
Classification of Handball Shot through Image Analysis

The gathering and use of play data in professional sports, such as baseball and soccer, has recently gained pace. In handball, the European Federation has started collecting play data during a game using wearable terminals, such as KINEXON. However, these gadgets are highly costly, and it is difficult to introduce them to Japan’s amateur handball sector. Additionally, there is an increasing need to use IT tools to enhance player evaluation and training with the goal of everyone improving regardless of the setting or the coach’s skill. Therefore, the purpose of this study is to categorize shooting by gathering joint coordinates and categorizing them using publicly available footage of shooting scenes in handball games.We specifically used the open-source program CenterNet to extract joint coordinates from the video. We categorized the shooters and discussed their traits based on these values.
Reducing chronic disease may just be a walk in the park

Wearable technology allows the collection of real-world granular data at scales that would be impossible using traditional collection methods. Master et al. demonstrate the power of this technology to estimate the risk of disease based on daily step counts.1.
Azra Bihorac

Azra Bihorac is an internationally recognized physician-scientist expert in medical AI, data sciences, informatics, and translational research in acute and critical illnesses at the University of Florida. Her research is driven by the vision for intelligent human-centered health care. In this Q&A, she shares some details on current projects and comments on the future of AI in medicine.
Loss‐of‐set and strategy application on the Menu Task: An exploratory study.

Abstract Background Performance‐based tests of functional cognition (FC) use simulated instrumental activities of daily living (IADL) performed under rule constraint to evaluate ability to perform actual IADL. Because the test characteristics and the test‐takers daily activities are similar, the test‐takers need to avoid becoming “derailed” by habits or personal preferences that are not relevant to the test, a phenomenon referred to as loss of set (Burgess, 2005; Schneider, 1984). The Menu Task, a sensitive FC screening test, was designed to provoke loss of set by having the format of a food selection activity, in which test‐takers own food preferences may disrupt actual task requirements. However, there has until now been no examination of whether test‐takers approach and strategies to maintain set are associated with cognitive abilities and FC abilities. Method A convenience sample of 93 adults living independently in the community were administered a battery of cognitive screening measures, a self‐report IADL measure, and assessments of FC including the Menu Task, and the After Menu Task Interview. Responses to the After Menu Task Interviews were qualitatively categorized according to 1. loss of set 2. calorie counting as a strategy, or 3. self‐reported planning. Independent t‐tests were used to compare the categorized group performance on cognitive, FC, and IADL measures. Result Individuals who lost set on the Menu Task performed significantly poorer on the MoCA ( p = .012), the ADCS‐MCI ( p &lt; .001), and FC assessments ( p &lt; .01) than those who did not lose set. Those who reported counting calories as a strategy scored significantly better on the MoCA ( p = .012), TMT A ( p = .023), TMT B ( p = .012), the ADCS‐MCI ( p = .044), and FC assessments ( p &lt; .05). No significant group differences were observed for those who reported planning compared to those who did not report planning. Conclusion Clinicians using the Menu Task may wish to incorporate an After Task Interview focusing on the client’s reported way of approaching the Menu Task. Individuals who focus on their own food preferences or show other indications of loss of set while completing the Menu Task may benefit from further cognitive or FC assessment.
Unsupervised learning on electronic health records to uncover heterogeneity in Alzheimer’s disease and related dementias

Abstract Background Alzheimer’s Disease (AD) is a heterogenous disease with distinct etiologies, disease courses, and/or age of onset, which may be associated with different clinical features. Here, we aim to use unsupervised learning techniques on electronic health records (EHRs) from a group of memory clinic patients to characterize heterogeneity in this disease population. Method We identify a patient dataset (N = 9,249) within the Mass General Brigham (MGB) system who had at least two encounters at the memory clinic, and were 50 years or older at their last visit. We also define a subset (N = 3,638) of these patients with at least one substantial visit note by an MGB memory specialist. Pre‐trained embeddings for associated ICD codes are used to encode patient EHRs in the first dataset, and transformer‐derived Clinical BERT embeddings of free text are used to encode the patient notes dataset. 1,2 For each dataset, we cluster these patient representations using hierarchical clustering and test for enrichment of ICD codes within clusters using chi‐squared tests. Significant codes following Bonferroni correction are retained, and clusters are then characterized based on their most significantly enriched codes. Result We identify four clusters within the ICD dataset and three clusters within the patient notes dataset. In the ICD dataset, clusters of patients are identified with increased prevalence of type 2 diabetes and kidney disease (N = 205), chronic pain (N = 669), cardiovascular and other age‐related diseases (N = 2,149), as well as a cluster mostly defined by focal neurological diseases (N = 6,226). In the notes dataset, clusters were identified by enrichment of codes for hydrocephalus and Lewy body disease (N = 1,565), and again with cardiovascular and other age‐related diseases (N = 336), and focal neurological diseases (N = 1,737). Conclusion In this study, we demonstrate the potential to leverage pre‐trained embeddings from both structured and unstructured data in EHRs to characterize patient heterogeneity in a group of patients with Alzheimer’s Disease and related dementias. In the future, we plan to further characterize these clusters in terms of disease course and underlying mechanism. 1. Choi et al., Learning low‐dimensional representations of medical concepts. AMIA Summits. Transl. Sci. Proc. 2016, 41 (2016). 2. Alsentzer et al., Publicly available clinical BERT embeddings. arXiv:1904.03323 (2019).
LANGaware: Robust Multimodal Machine Learning Methods for the Early Detection of Neurodegenerative and Psychiatric Diseases

Abstract Background Multiple neurodegenerative and psychiatric diseases can affect speech, manifesting as subtle changes in spoken language. We perform a rigorous evaluation of a Machine Learning (ML) pipeline on a large‐scale multiaxial experimental setting, to investigate its capacity for detecting early signs of cognitive decline as manifested in speech. Using a diverse dataset of speech samples spanning different languages, patient cohorts and cognitive status diagnoses, we propose that the developed pipeline showcases robust performance, facilitating efficient decision support for early cognitive decline detection. Method We build upon previous research efforts on data‐driven cognitive decline prediction. Random forest models are constructed from a dataset of more than 3000 audio recordings and ASR‐generated transcripts, constituting cognitive assessment test tasks (e.g. picture description, everyday activity recounting, etc.). A multimodal NLP and audio analysis procedure derives sophisticated predictive biomarkers, using three methods for feature selection, feature decorrelation preprocessing and rigorous, grid search‐based hyperparameter optimization. We introduce novel experimental axes of investigation, including the spoken language (English and Greek), single and multi‐task verbal task settings, and different neurodegenerative diseases for the pathological cohorts (Alzheimer’s Disease or Dementia, along with age and education‐matched healthy cohorts). Four different NLP analysis tools for tokenization, syntax and dependency extraction are considered, using both ML and Deep Learning (DL) techniques. Model stability and generalization is supported via 5‐fold cross‐validation and the use of a separate test set. Result Results are validated against medical expert diagnosis. Αverage performance across all investigative axes reaches high scores for accuracy (0.73), sensitivity (0.72), specificity (0.74), precision (0.77), F1 (0.74), and AUC (0.78). Additional findings suggest that single‐task models outperform multitask ones (e.g. reaching AUC scores of 0.85). Moreover, cognitive decline detection is more efficient for pathological cohorts of Alzheimer's, versus patients with Dementias. Conclusion The broad empirical evaluation verifies the effectiveness of the deployed ML system, adding evidence in favor of multimodal AI solutions for predicting early cognitive decline and showcasing their robustness in diverse evaluation settings. Future work includes leveraging DL methods for classification and evaluation on differential diagnosis discrimination tasks.
Brain age estimation by structural MRI in superagers: a BrainAGE approach in elderly people with youthful episodic memory

Abstract Background Age‐related episodic memory decline is not the only trajectory of healthy ageing. Superagers are the living proof that an 80yo can have the memory of a 50‐60yo. We aim to estimate the biological brain age of superagers using the BrainAGE approach to understand the underlying ageing process. In the context of normal ageing, resistance refers to the avoidance of ageing effects and will translate into a deceleration of the ageing process, whereas resilience is the successful coping with age‐related changes and will translate into a similar estimated brain age and chronological age. We tested these hypotheses in this study. Method A sample of 63 superagers (mean age 81.8 years ± 1.8 std, 60.3% women) and 55 age‐matched controls (mean age 82.4 years ± 1.9 std, 63.6% women) from the Vallecas Project cohort in Madrid, Spain, were selected according to their episodic memory score in the Free and Cued Selective Reminding Test. Superagers scored at or above the mean value of a 50‐56yo person, whereas controls scored within one standard deviation for their age. BrainAGE (“Brain Age Gap Estimation”) aggregates to a single value the complex multidimensional aging pattern within the whole brain by applying established kernel regression methods to anatomical brain magnetic resonance images (Franke and Gaser 2019). The BrainAGE score was calculated for both groups as the difference between chronological age and estimated brain age. Positive values indicate an acceleration of the ageing process and negative values indicate a deceleration of the ageing process. Result The BrainAGE score of superagers was ‐0.5 years, showing that their estimated brain age is lower than their chronological age whereas the estimated brain age of the control group is similar to their chronological age. The difference in BrainAGE between the superager and the control group is significant ( p = 1x10 ‐6 ). This result indicates a deceleration in the brain ageing process of superagers compared to age‐matched controls. Conclusion Superagers showed a slowing of the structural brain age‐related changes compared to the control group. These results suggest that superagers, selected for their youthful episodic memory, show whole‐brain resistance to the ageing process.
Efforts To Improve Village Administration Services Through The Digital Mail Information System

The development of technology is currently growing so rapidly and is able to affect all aspects of human life, one of which is the administration of the Kambingan Village which must provide effective and efficient services to the community. This service activity aims to provide convenience for the Kambingan village community so that the management of correspondence can be faster and more precise. The service method used is using a qualitative approach with data collection techniques through observation, interviews and documentation studies. Based on the results of observations and interviews, it is known that the correspondence technology used so far is considered to be less effective and efficient where the processing of correspondence takes a long time. Therefore, as an effort to improve village administration services, a Macro Vba Excel-based correspondence administration information system was developed which can provide faster correspondence services than the technology used previously. The resulting product has proven to be effective in facilitating the work of village officials, especially in the process of managing correspondence. In addition, village officials do not have to worry about filling in the wrong data because the resident's biodata is filled in automatically.
Information Protection of Engineering Cost Information System based on IP Anti- Counterfeiting Technology

Based on IP anti-counterfeiting technology, this paper analyzes the current status of personal information protection, and proposes a personal information management mechanism and a personal information protection mechanism based on the relevant ideas and methods of hierarchical protection and combined with the actual situation, put forward a targeted institutional guarantee system to provide an effective guarantee for the construction effect of project cost informatization. Combined with relevant standards to implement promotion value enhancement. In this paper, based on the system analysis ideas of engineering construction, combined with the obstacle elements and the requirements of informatization construction goals, this study will analyze the ways to reduce obstacles, optimize steps, and build a completely standard system.