Reviewerly/exHarmony huggingface dataset:
Padding Aware Neurons

Convolutional layers are a fundamental component of most image-related models. These layers often implement by default a static padding policy (e.g. zero padding), to control the scale of the internal representations, and to allow kernel activations centered on the border regions. In this work we identify Padding Aware Neurons (PANs), a type of filter that is found in most (if not all) convolutional models trained with static padding. PANs focus on the characterization and recognition of input border location, introducing a spatial inductive bias into the model (e.g. how close to the input’s border a pattern typically is). We propose a method to identify PANs through their activations, and explore their presence in several popular pre-trained models, finding PANs on all models explored, from dozens to hundreds. We discuss and illustrate different types of PANs, their kernels and behaviour. To understand their relevance, we test their impact on model performance, and find padding and PANs to induce strong and characteristic biases in the data. Finally, we discuss whether or not PANs are desirable, as well as the potential side effects of their presence in the context of model performance, generalisation, efficiency and safety.
Histological Grading of Breast Cancer Malignancy using Automated Image Analysis and Subsequent Machine Learning

Aim: The objective of this study was to determine the histological degree of breast cancer malignancy using the automated principle of machine learning with the free access computer programs Cell Profiler and Tanagra. Methods and results: Digital photographs of neoplastic tissue histological slides were obtained from 224 women with breast cancer. The digitized images were transferred to the Cell Profiler software and treated according to a predetermined algorithm, resulting in a database exported to the Tanagra software for further automated classification of the histological degree of malignancy. The Kappa index of agreement between the medical pathologist and the automated analysis performed in the Tanagra software was 0.91 for the tubular score, 0.55 for the nuclear score, and 0.49 for the mitotic index score.
Linear Dependency Segments in Foreign Language Acquisition: Syntactic Complexity Analysis in Czech Learners’ Texts

Abstract The paper discusses a new way to measure syntactic complexity in foreign language acquisition. It is based on a recently proposed syntactic unit called linear dependency segment (LDS), the longest possible sequence of words belonging to the same clause where all linear neighbours are also syntactic neighbours. The dataset comprises 5,721 Czech texts from the CzeSL-SGT learner corpus covering five CEFR proficiency levels (A1–C1). The study covers two analyses. First, the development of the average clause length in terms of LDS and the average LDS length in the number of words across the latter language proficiency levels. Second, we consider the differences between Slavic and non-Slavic speakers. The results show an increasing tendency of the average clause length measured in LDS while the average clause length measured in words is decreasing. Results also show statistically significant differences between Slavic and non-Slavic speakers in most cases. Our results indicate that using LDS may be a useful unit of syntactic complexity measure in foreign language acquisition research.
Spanish Synonyms as Part of a Multilingual Event-Type Ontology

Abstract This paper presents an ongoing work on the multilingual event-type ontology SynSemClass, where multilingual verbal synonymy is formalized in terms of syntactic and semantic properties. In the ontology, verbs are grouped into synonym classes, both monolingually and cross-lingually. Specifically, verbs are considered to belong to the same class if they both express the same meaning in a specific context, and their valency frame can be mapped to the set of roles defined for a particular class. SynSemClass is built following a bottom-up approach where translational equivalents are automatically extracted from parallel corpora and annotated by human annotators. The task of the annotators consists in mapping the valency frame of a particular verb with the set of roles defined for the class where the verb is included as a potential class member, establishing links to external resources, and selecting relevant examples. The Spanish part of the ontology currently contains 257 classes enriched with Spanish synonyms. The resulting resource provides finegrained syntactic and semantic information on multilingual verbal synonyms and links to other existing monolingual and multilingual resources.
SText-DETR: End-to-End Arbitrary-Shaped Text Detection with Scalable Query in Transformer

Recently, Detection Transformer has become a trendy paradigm in object detection by virtual of eliminating complicated post-processing procedures. Some previous literatures have already explored DETR in scene text detection. However, arbitrary-shaped texts in the wild vary greatly in scale, predicting control points of text instances directly might achieve sub-optimal training efficiency and performance. To solve this problem, this paper proposes Scalable Text Detection Transformer (SText-DETR), a concise DETR framework using scalable query and content prior to improve detection performance and boost training process. The whole pipeline is built upon the two-stage variant of Deformable-DETR. In particular, we present a Scalable Query Module in the decoder stage to modulate position query with text’s width and height, making each text instance more sensitive to its scale. Moreover, Content Prior is presented as auxiliary information to offer better prior and speed up the training process. We conduct extensive experiments on three curved text benchmarks Total-Text, CTW1500, and ICDAR19 ArT, respectively. Results show that our proposed SText-DETR surpasses most existing methods and achieves comparable performance to the state-of-art method.
A feasibility study to explore the use of real‐time caregiver reports for people living with dementia at home to label multi‐modal sensor data

Abstract Background Multi‐modal sensor systems, consisting of sensors throughout the home, are able to collect data on the person living with dementia’s (PlwD’s) activities. However, caregivers may have challenges interpreting the data, leading to a lack of actionable information. It’s important to get real‐time reports from caregivers to label the sensor data to build relevant machine learning algorithms; yet, this has not been done with PlwD. The aim of this feasibility study was to explore if the use of a multi‐modal sensor system in conjunction with real‐time caregiver reports can provide valuable insights into the daily lives of PlwD. Method We developed the MAISON (Multimodal AI‐based Sensor platform for Older Individuals) system and deployed it into the homes of dyads (PlwD and their family caregiver) for 8‐weeks. MAISON includes: a smartphone, a smartwatch to continuously collect heart rate, step, and accelerometer data to infer walking, and running, as well as a smart mattress that can collect sleep time and quality. A digital diary was added to the MAISON system that automatically sends the caregiver 2‐3 pre‐set questions every day about their loved one. Caregivers could also report any significant occurrences throughout the day, as they see fit, through the app. Result Two dyads were recruited to date. Only about 15% of data was missing from the sensor system by the end of the study. Caregivers were providing their reports with high adherence. The caregivers’ reports were used to label the sensor data successfully, and upon analysis, we found that caregivers’ reports were reflected in the sensor data and the labels derived from their reports were accurate. Results indicate this study methodology is an innovative approach to utilize the family caregiver’s reports. Conclusion This is the first longitudinal study to demonstrate the feasibility of a multi‐modal sensor system used by caregivers and PwlD. Findings show that caregivers' reports can be useful sources of data for labeling sensor data. The conjunction of caregiver reports and sensor data will provide valuable information to build accurate machine learning models about behaviors of PlwD which is a foundational step to support dyads at home using technology.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS WEB GOOGLE SITES PADA PEMBELAJARAN IPA SEKOLAH DASAR MATERI SIFAT-SIFAT CAHAYA KELAS V

This research aims to produce a product in the form of learning media, namely web-based learning media with Google Sites as a site creation tool. A validation test was carried out to assess the feasibility and quality of the Google Sites web-based learning media for grade V elementary school science learning. This type of research is development research (R&amp;D) with the ADDIE model, consisting of five steps: analysis, design, development, implementation and evaluation. Data collection techniques involve observation or observations, and questionnaires. The results of the validation test by two validators, namely the media expert validator obtained an average percentage value of 67.5%, interpreted into the feasible category, and the material expert validator obtained an average percentage value of 100% in the very feasible category. Students' responses to the media achieved 100% results in the very appropriate category. And the teacher's response to learning media received a percentage score of 100% in the very appropriate category. Based on the data that has been obtained, it can be concluded that the Google Sites web-based learning media for grade V elementary school science learning is suitable for use in learning activities.
Cognitive reserve and resilience of cognitive networks in ageing

Abstract Background In the ageing process, different cognitive functions decline at different time points and at different rates. Additionally, the inter‐individual variability in cognitive decline could partially depend on cognitive reserve, which represents a resilience factor against age‐related cognitive decline. These complex relationships can be captured with advanced multivariate methods such as graph theory, that can be applied to investigate cognitive networks. The present study aimed to examine the association between cognitive reserve and cognitive networks across age. Method A total of 334 cognitively healthy participants were stratified into early‐middle‐age (37‐50 years; n = 110), late‐middle‐age (51‐64 years; n = 106), and elderly (65‐78 years; n = 118) groups. Within each age group, individuals were further subdivided into two cognitive reserve levels (high, low) based on a crystallized intelligence proxy. For each subgroup, cognitive networks were constructed based on correlations between 47 z‐scored cognitive variables. Applying graph theory, different global and nodal network measures were compared between subgroups. Results Graph‐theoretical analyses revealed that individuals with high cognitive reserve were characterized by a stable cognitive network across age groups. The only consistent difference between the high cognitive reserve groups emerged in modularity with the late‐middle‐age group showing decreased modularity compared to early‐middle‐age and elderly groups. In contrast, individuals with low cognitive reserve showed a marked reconfiguration of cognitive networks across age groups with a pronounced dedifferentiation in late‐middle‐age. This was reflected as a decrease in global efficiency and increase in mean network strength and small‐worldness in the late‐middle‐age group compared to the early‐middle‐age group. The elderly group had a higher global efficiency and lower mean network strength and small‐worldness compared to the late‐middle‐age group. Conclusion A high cognitive reserve was associated with the resilience of the cognitive connectome during the ageing process, while individuals with low cognitive reserve showed prominent changes in their cognitive connectome with increasing age. Gaining further insights into these mechanisms will contribute to our understanding of age‐related cognitive decline and guide the development of strategies to preserve cognitive function in ageing.
A Project to Purify Education of the Superficial and Return to the Roots. Review of the Joint Monograph “Modern Russian Education: Challenges and Answers”

The monograph provides an analysis of the condition of the education system in Russia and the process of its renewal. The authors discuss issues of for-profit education, the training of professionals in the higher education system, the development of values, thinking and worldview of a person during the educational process in detail and consider the problems of reforming school education. They highlight the qualitative analysis of the state of the higher education system before and after Russia’s ratification the Bologna Declaration, outline the functional foundations of education in the Soviet period and formulate the results of the reform efforts of recent decades. The authors substantiate the conclusion that there has been no unified reform of Russian education – a well-targeted and working for the interests of the nation state and society – for the last thirty years. And there is still no such reform. There are only a number of reform trends coming from various initiators, and there are vague public proposals for what the future of Russian education should be. The authors predict that it will be developed as an independent structure in the global education services market under conditions of many determining factors.
Development of Android-Based Learning Application Using Articulate Storyline 3

This research aims to create an Android-based learning application assisted by Articulate Storyline 3 software for class VIII students of SMP Negeri 13 Kupang that is valid, practical, and effective. The method used is Research and Development (R&amp;D) with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. Data collection techniques and instruments include interviews, questionnaires, and tests. The data analysis technique uses qualitative and quantitative descriptive analysis techniques. This research produces an Android-based application product named "Relations and Functions" and has met the criteria for validity, practicality, and effectiveness. For media validity, the average assessment by 3 material experts was 4.37, and the average assessment by 3 media experts was 4.38, so it has the "valid" category. For the practicality of the media in small-scale trials, the student response was 4.72, and the effectiveness of the media through the percentage of learning completion was 100%. Meanwhile, in the large-scale trial, the subject teacher's assessment response was 4.5 and the student response was 4.63, so it was in the "practical" category. For media effectiveness, the learning completion percentage was 83.33% and was categorized as "very effective". Thus, the learning application developed is valid, practical, effective, and suitable for use in learning activities.
High dimensional Longitudinal Assessment of Ambient Home Environments and Their Association with Behaviors of People with Cognitive Decline

Abstract Background Troubling behavioral symptoms such as mood changes or agitation are highly prevalent during the course of Alzheimer’s disease and related dementias (ADRD). Effective management is dependent upon understanding the causes and precipitants of behavioral symptoms. The objective of our studies is to evaluate the role of the physical ambient environment in instigating symptoms. Method Participants are from two studies: 1) MOnitoring DEmentia‐Related Agitation Using Technology Evaluation (MODERATE) which focuses on agitation in people with later‐stage dementia, and 2) Monitoring Apathy, Depression and Anxiety Using Technology Evaluation (ADA) which focuses on apathy, depression and anxiety in older adults with or without cognitive decline. A commercially available, multifunction environmental sensor, Awair Omni, was deployed in the room where the participant sleeps, measuring levels of temperature, humidity, carbon dioxide (CO2), total volatile organic compounds (VOC), particulate matter 2.5 (PM2.5), noise and light every 5 minutes. Additionally, a multi‐sensor array (bed pressure mats, motion sensors, and wearables) is deployed in homes to objectively measure participants’ behaviors. Caregivers in MODERATE report agitation level/event experienced by their partners while participants in ADA self‐report their level of symptoms via weekly online surveys. Result To date, 9 dyads (persons with cognitive decline and their caregivers) have enrolled in MODERATE and 9 participants are enrolled in ADA. 3135 days of data have been collected for MODERATE while 849 days of data have been collected for ADA. Mean participant characteristics: age = 75±8 years; 48% women; Blind MoCA = 13.2±7.96. For 4 weeks from 12/19/2022 to 1/15/2023, ambient environmental conditions identified (mean±SD): temperature = 21 ±2.0°C; humidity = 41±7.6%; VOC = 195±263 ppm; CO2 = 651±225 ppm; particulate matter 2.5 = 2.4±13 µg/m3; noise = 52±4.3 dBA; light = 9.5±40 Lux. The home environments are individually heterogeneous with high variability over time across multiple environmental conditions. Reports of behavioral symptoms show high week‐to‐week variability as well. Proposed methods of analyzing such data include generalized linear mixed model. Conclusion This in situ environmental sensing approach provides more objective and timely data to enable identifying the relationship between physical environments and the behaviors of older adults at risk of or with ADRD. The knowledge generated will help prevent or mediate symptoms by prospectively modifying environmental conditions.
First insights exploring Activities of Daily Living performance captured through activity sensors in a Smart Home Environment as an indicator of cognitive decline: A cross‐sectional Analysis

Abstract Background Smart homes offer a unique potential not only for supporting various users’ needs but also for monitoring their activities. Herein the findings of the RADAR‐AD Smart Home study are presented. Using data from activity sensors installed in a smart home environment simulating real‐life conditions, we aim to detect deficits while performing Activities of Daily Living (ADLs) in three known groups of people in different stages of cognitive decline related to Alzheimer’s Disease (AD). Method In a fully equipped Smart Home (https://smarthome.iti.gr/), Fibaro smart plugs (to monitor power consumption), motion‐, door‐, flood‐sensors and panic buttons were installed. During their 24 hour visit, participants [13 Healthy Control (HC), 14 Preclinical AD (PreAD), 13 Prodromal AD (ProAD)] (Table 1) followed a protocol listing a number of ADLs (e.g., meal/beverage preparation) (Table 1). Data collection, feature extraction (i.e., activity duration, number of repetitions) and visualization of ADLs were performed through the CARL data collection and analysis platform for assisted living ( https://carl.iti.gr/ ). (Figure 1). Two‐Way ANOVA and Mann‐Whitney were used to test the statistical significance between the groups. Result Comparing Activity Duration for different ADLs and groups (Figure 2), HC executed the ADLs in less time compared to PreAD and ProAD, while PreAD showed no difference in the majority of the tasks compared to ProAD. Three activities were completed by 50% of the HC, 36% of the PreAD and only 17% of the ProAD. Two‐way ANOVA revealed a statistically significant interaction between the effects of cognitive decline and Activity Duration F(3,106) = 3.504, p = 0.034. Mann‐Whitney analysis for the complex task “Meal Preparation” showed decreased duration for HC compared to PreAD (U = 33.00, p = 0.042) and ProAD (U = 17.00, p = 0.052). Conclusion The RADAR‐AD Smart Home study provides a proof‐of‐concept for the use of home‐based sensors for investigating ADLs in patients with cognitive decline. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein .
AI‐guided patient stratification for neurodegenerative disorders using unsupervised trajectory modelling

Abstract Background Predicting neurodegenerative disorders early has major implications for timely clinical management and patient outcomes. Despite advances in medical technology, we still lack tools to precisely stratify patients for targeted interventions. Here, we propose an unsupervised modelling approach based on mixtures of state space models that learns from longitudinal multimodal (brain imaging, cognitive) data to stratify patients into clusters with different clinical outcomes. Our approach has the potential to support dementia prediction based on cost‐effective, non‐invasive digital measures alone. Method We trained (with cross‐validation, 4 components selected with elbow method) a mixture of linear Gaussian state space models on 571 trajectories (2‐4 assessments) from ADNI comprising a) state variables: neuroimaging biomarkers (temporal lobe gray matter density, β‐amyloid) and b) observations: cognitive tests (ADNI‐Mem, ADNI‐EF, MoCA, &amp; ADAS‐13) collected at regular 2‐year intervals. The model clustered individuals based on longitudinal changes in biomarkers and the relationship between biomarkers and cognitive scores (figure 1). We validated model‐derived clusters against clinical outcomes (unseen by the model) based on each individual’s final assessment. Result Our unsupervised modelling approach effectively stratifies individuals by clinical outcome. Cluster A comprises 65% healthy controls and 33% stable MCI patients (individuals with MCI who do not convert to AD in a 3‐year period) while cluster D comprises 77% Alzheimer’s patients (table 1). Profiling these clusters using MMSE score validates our approach: individuals in cluster A maintain high memory scores over 6 years whereas individuals in cluster D decline (figure 2). Further, we find that our model maintains good stratification when tested a) with a single rather than multiple assessments and b) with cognitive data only (i.e. without biomarkers). These results enhance the clinical utility of our approach, as collecting longitudinal and biomarker data may prove challenging in clinical settings. Conclusion Our modelling approach robustly stratifies patients by clinical outcome when trained on longitudinal multimodal data and tested using cognitive scores alone. Our approach has strong potential to generalize to other non‐invasive and cost‐effective data (e.g. digital markers from wearable technologies), enhancing the translational impact of our AI‐guided tool for early and precise patient stratification in clinical practice.