ethicalabs/Research-Intent-Collab huggingface dataset:
Available Wireless Sensor Network and Internet of Things testbed facilities: dataset [version 2; peer review: 2 approved, 1 approved with reservations]

The availability of data is an important aspect of any research as it determines the likelihood of the study’s commencement, completion, and success. The Internet of Things and Wireless Sensor Networks technologies have been attracting a huge amount of researchers for more than two decades, without having a consolidated or unified source that identifies and describes available Internet of Things and Wireless Sensor Network testbed facilities. In this paper, a dataset including 41 distinct testbed facilities is described. These testbed facilities are classified according to their key features such as Device Under Test (DUT) type, mobility, access level, facility count, connection/interaction interfaces, and other criteria. The systematic review process resulting in the gathered data set consisted of three filtering phases applied to relevant articles published between the years 2011 and 2021 as obtained from the Web of Science and SCOPUS databases.
Digital Phenotyping for Mental Health: Reviewing the Challenges of Using Data to Monitor and Predict Mental Health Problems

We review recent developments within digital phenotyping for mental health, a field dedicated to using digital data for diagnosing, predicting, and monitoring mental health problems. We especially focus on recent critiques and challenges to digital phenotyping from within the social sciences.Three significant strands of criticism against digital phenotyping for mental health have been developed within the social sciences. This literature problematizes the idea that digital data can be objective, that it can be unbiased, and argues that it has multiple ethical and practical challenges. Digital phenotyping for mental health is a rapidly growing and developing field, but with considerable challenges that are not easily solvable. This includes when, and if, data from digital phenotyping is actionable in practice; the involvement of user and patient perspectives in digital phenotyping research; the possibility of biased data; and challenges to the idea that digital phenotyping can be more objective than other forms of psychiatric assessment.
Current Situation and Countermeasures of Adolescent Mental Health in the Context of Social Media

In contemporary society, social media serves as both a means for students to connect and a source of entertainment and relaxation. Nevertheless, despite its many advantages, it can also lead to adverse psychological effects. The digital environment and the vast array of information available online significantly influence students' mental well-being. This article seeks to raise awareness about the negative consequences that social media has on students, the potential psychological issues they may encounter, and various recommendations from multiple perspectives. This includes strategies for students facing mental health challenges to find healing and resolution, as well as how educational institutions can support student mental wellness in relation to social media usage. In conclusion, the psychological ramifications of social media on students are significant; it is not only addictive but can also trigger various mental health concerns. Schools should organize more activities aimed at reducing addiction to virtual platforms. Additionally, teachers and parents need to be vigilant regarding students' mental health and offer timely advice and appropriate interventions when issues arise.
Global standards on judicial independence and removal: grappling with vetting and fresh appointment

An independent, professionally skilled and widely trusted judiciary is indispensable to the rule of law. It is a great feat for national judiciaries to attain this level of functioning and some fall far short. This article asks whether international standards are sufficiently attentive to the challenge of reforming problematic judiciaries in post-conflict or post-authoritarian transitions, focusing on situations in which the removal of judges is sought. It discusses global standards that have some form of UN endorsement and approaches in the European system, concentrating on norms relating to the tenure, conduct and accountability of judges. In situations where judicial shortcomings are thought to be widespread, transitional societies have been willing to qualify judicial tenure and introduce exceptionnal, time-limited accountability processes such as fresh appointments to judicial posts, or systematic, individualised vetting. The article charts the growing understanding over the past 15 years that a rigid adherence to security of tenure may not always strengthen the rule of law and that exceptional measures may be necessary and considers the approaches that are emerging, particularly in the European system, for determining when exceptional removal processes are acceptable. It argues that the international norms require revision to deal with these situations and that the preferable approach is not to treat them flexibly, as currently in the European system, but to revise them.
Weighted Multi-view Deep Neural Networks for Weather Forecasting

In multi-view regression the information from multiple representations of the input data is combined to improve the prediction. Inspired by the success of deep learning, this paper proposes a novel model called Weighted Multi-view Deep Neural Networks (MV-DNN) regression. The objective function used is a weighted version of the primal formulation of the existing Multi-View Least Squares Support Vector Machines method, where both the objectives from all different views, as well as the coupling term, are weighted. This work is motivated by the challenging application of weather forecasting. To predict the temperature, the weather variables from several previous days are taken into account. Each feature vector belonging to a previous day (delay) is regarded as a different view. Experimental results on the minimum and maximum temperature prediction in Brussels, reveal the merit of the weighting and show promising results when compared to existing the state-of-the-art methods in weather prediction.
The design and implementation of a prototype collaborative filtering-based recommendation system within the national library of the Kingdom of Morocco

The search for information is an activity whose objective is to enable the user to obtain relevant documents, so that it meets his information needs. Indeed, the corpus composed of documents, subject of an analysis during the indexing phase which produces representations of the documents interpretable by a computer system. While the user carries his need for information and formulates it in a natural language in the form of a request, the latter is in turn interpreted during the indexing of the corpus in order to obtain a representation of the request close to that of the documents. In our work, we aspire to design a model recommendation system to optimize the selection and access of users to documentary resources, where information is an inevitable instrument for the academic path and the production scientist.
The influence of social media and digital technologies on adolescent mental health – A literature review

Media społecznościowe stały się integralną częścią życia młodzieży, oferując nowe możliwości nawiązywania relacji, wyrażania siebie i uzyskiwania wsparcia. Jednocześnie rosną obawy dotyczące ich potencjalnego negatywnego wpływu na zdrowie psychiczne. Celem artykułu była analiza powiązań między korzystaniem z mediów społecznościowych a dobrostanem psychicznym młodych osób, uwzględniając zarówno korzyści, jak i zagrożenia. Artykuł opiera się na aktualnej literaturze, analizując wpływ cyfrowego zaangażowania na zdrowie psychiczne nastolatków i młodych dorosłych. Skupiono się na takich kwestiach, jak uzależnienie od mediów społecznościowych, emocjonalność, wizerunek ciała oraz rola społeczności internetowych. Nadmierne korzystanie z mediów społecznościowych wiąże się ze zwiększonym ryzykiem wystąpienia lęku, depresji, niskiej samooceny i problemów ze snem, często wynikającymi z porównywania się z innymi i lęku przed wykluczeniem. Z drugiej strony media społecznościowe mogą pełnić pozytywną rolę – umożliwiają nawiązywanie relacji, wsparcie rówieśnicze i kształtowanie tożsamości, szczególnie w przypadku grup marginalizowanych. Problem uzależnienia od tych platform pozostaje jednak dużym wyzwaniem. Wpływ mediów społecznościowych na zdrowie psychiczne młodzieży jest złożony i wielowymiarowy. Choć zagrożenia są dobrze udokumentowane, nie należy pomijać potencjału mediów. Promowanie świadomego i zrównoważonego korzystania z internetu, reagowanie na problematyczne zachowania i wspieranie kompetencji cyfrowych może pomóc młodym ludziom poruszać się w przestrzeni internetowej w zdrowszy i bardziej świadomy sposób.
A Natural Language Processing Approach to Identify Negative Keywords in Electronic Health Records for Maternal Care (Preprint)

BACKGROUND                         Maternal harm is a major crisis that disproportionately affects Black women. Unstructured clinical notes from electronic health records (EHR) data may contain insights for unsafe maternal care delivery. Studies have analyzed unstructured clinical notes in the EHR using natural language processing for tone and sentiment and discovered that tone and sentiment contributes to preventable patient safety events.                                                                       OBJECTIVE                         We analyzed potentially negative keywords in the clinical notes in EHR data recorded for women who experienced severe maternal morbidity or had postpartum readmission within 42 days of delivery at one of two large, birthing hospitals in Washington, DC.                                                                       METHODS                         Design: Retrospective cohort study. Setting: Using a predefined list of negative keywords (i.e., nonadherent, aggressive, agitated, angry, challenging, combative, noncompliant, confront, noncooperative/uncooperative, defensive, exaggerate, hysterical, unpleasant, refuse, and resist), we applied natural language processing and machine learning techniques to detect negative keywords in unstructured EHR clinical notes.  Participants: The cohort was defined as female patients who had delivery encounter at one of two large, birthing hospitals in Washington, DC from January 1st, 2016, to March 31st, 2020.                                                                       RESULTS                         Negative keywords were more frequently used for 30-44 years old patients. The adjusted odds of having negative keywords in EHR clinical notes during delivery encounters were 1.08, 1, and 0.77 for Black, White, and Other patients, respectively. Patients with commercial insurance type had lower adjusted odds of having a negative keyword (commercial = 1 vs. self pay = 1.13 and Medicare/Medicaid = 1.14).                                                                       CONCLUSIONS                         Our findings indicated healthcare providers’ potential implicit racial biases in documenting EHR clinical notes for Black patients during delivery encounters.
A review of adipose-derived mesenchymal stem cells‘ impacts and challenges: metabolic regulation, tumor modulation, immunomodulation, regenerative medicine and genetic engineering therapies

Adipose-derived mesenchymal stem cells (ADSCs), multipotent stromal cells abundant in adipose tissue, exhibit remarkable plasticity in modulating systemic metabolism, inflammatory responses, and immune homeostasis. Their bidirectional interactions with the tumor microenvironment (TME) position them as both accomplices and antagonists in cancer progression, offering unique therapeutic opportunities. ADSCs also hold significant potential for clinical application in the fields of regenerative medicine, tissue engineering, and gene engineering. This review synthesizes the impacts and challenges of ADSCs involving  metabolic regulation, tumor modulation, immunomodulation, regenerative medicine and genetic engineering therapies—while elucidating underlying molecular mechanisms and signaling pathways, clinical studies, applications and challenges.
The Precautionary Principle: Scientific Uncertainty and Omitted Research in the Context of GMO Use and Release

Commercialization of genetically modified organisms (GMOs) have sparked profound controversies concerning adequate approaches to risk regulation. Scientific uncertainty and ambiguity, omitted research areas, and lack of basic knowledge crucial to risk assessmentshave become apparent. The objective of this article is to discuss the policy and practical implementation of the Precautionary Principle. A major conclusion is that the void in scientific understanding concerning risks posed by secondary effects and the complexity ofcause-effect relations warrant further research. Initiatives to approach the acceptance or rejection of a number of risk-associated hypotheses is badly needed. Further, since scientific advice plays a key role in GMOregulations, scientists have a responsibility to address and communicate uncertainty to policy makers and the public. Hence, the acceptance of uncertainty is not only a scientific issue, but is related to public policy and involves an ethical dimension.
A Benchmark and Evaluation for Real-World Out-of-Distribution Detection Using Vision-Language Models

Out-of-distribution (OOD) detection is a task that detects OOD samples during inference to ensure the safety of deployed models. However, conventional benchmarks have reached performance saturation, making it difficult to compare recent OOD detection methods. To address this challenge, we introduce three novel OOD detection benchmarks that enable a deeper understanding of method characteristics and reflect real-world conditions. First, we present ImageNet-X, designed to evaluate performance under challenging semantic shifts. Second, we propose ImageNet-FS-X for full-spectrum OOD detection, assessing robustness to covariate shifts (feature distribution shifts). Finally, we propose Wilds-FS-X, which extends these evaluations to real-world datasets, offering a more comprehensive testbed. Our experiments reveal that recent CLIP-based OOD detection methods struggle to varying degrees across the three proposed benchmarks, and none of them consistently outperforms the others. We hope the community goes beyond specific benchmarks and includes more challenging conditions reflecting real-world scenarios. The code is https://github.com/hoshi23/OOD-X-Benchmarks.
Prediction of non-linear pharmacokinetics in humans of an antibody-drug conjugate (ADC) when evaluation of higher doses in animals is limited by tolerability: Case study with an anti-CD33 ADC

For antibody-drug conjugates (ADCs) that carry a cytotoxic drug, doses that can be administered in preclinical studies are typically limited by tolerability, leading to a narrow dose range that can be tested. For molecules with non-linear pharmacokinetics (PK), this limited dose range may be insufficient to fully characterize the PK of the ADC and limits translation to humans. Mathematical PK models are frequently used for molecule selection during preclinical drug development and for translational predictions to guide clinical study design. Here, we present a practical approach that uses limited PK and receptor occupancy (RO) data of the corresponding unconjugated antibody to predict ADC PK when conjugation does not alter the non-specific clearance or the antibody-target interaction. We used a 2-compartment model incorporating non-specific and specific (target mediated) clearances, where the latter is a function of RO, to describe the PK of anti-CD33 ADC with dose-limiting neutropenia in cynomolgus monkeys. We tested our model by comparing PK predictions based on the unconjugated antibody to observed ADC PK data that was not utilized for model development. Prospective prediction of human PK was performed by incorporating in vitro binding affinity differences between species for varying levels of CD33 target expression. Additionally, this approach was used to predict human PK of other previously tested anti-CD33 molecules with published clinical data. The findings showed that, for a cytotoxic ADC with non-linear PK and limited preclinical PK data, incorporating RO in the PK model and using data from the corresponding unconjugated antibody at higher doses allowed the identification of parameters to characterize monkey PK and enabled human PK predictions.
Feedback Coding for Efficient Interactive Machine Learning.

When training machine learning systems, the most basic scenario consists of the learning algorithm operating on a fixed batch of data, provided in its entirety before training. However, there are a large number of applications where there lies a choice in which data points are selected for labeling, and where this choice can be made “on the fly” after each selected data point is labeled. In such interactive machine learning (IML) systems, it is possible to train a model with far fewer labels than would be required with random sampling. In this thesis, we identify and model query structures in IML to develop direct information maximization solutions as well as approximations that allow for computationally efficient query selection. To do so, we frame IML as a feedback communications problem and directly apply principles and tools from coding theory to design and analyze new interaction selection algorithms. First, we directly apply a recently developed feedback coding scheme to sequential human-computer interaction systems. We then identify simplifying query structures to develop approximate methods for efficient, informative query selection in interactive ordinal embedding construction and preference learning systems. Finally, we combine the direct application of feedback coding with approximate information maximization to design and analyze a general active learning algorithm, which we study in detail for logistic regression. ; Ph.D.
Comparison of a Natural Language Processing Model and Large Language                     Models for Extracting Incidental Lung Nodule Data

A task-specific natural language processing model demonstrated higher accuracy and reliability when compared with seven general-purpose large language models for extracting incidental lung nodule data from unstructured chest CT radiology reports.
Collaborative filtering recommendation system: Comparison study

Tavsiye Sistemleri (TS) kullanıcılara potansiyel olarak ilgilerini çekebilecek seçenekleri önererek bilgi aşırı bilgi yüklenmesi ile ilgili problemlerin çözümü konusunda büyük bir ilgi görmektedir. Tavsiye sistemleri genellikle belirli tekniklerden birinde belli sayıda öneri üretir. Tavsiye Sistemleri üç türe ayrılır: İçerik Temelli Filtreleme, İşbirliğine Dayalı Filtreleme ve Hibrit Tavsiye Sistemi. İşbirliğine Dayalı Filtreleme (İDF) en yaygın kullanılan tavsiye tekniğidir ve birçok ticarî alana yaygın bir biçimde uyarlanmıştır. Bununla birlikte, İDF herhangi bir ek bilgiyi değerlendirmez ve bu yüzden soğuk başlatma ve veri seyrekliği problemlerinin çözümünü zorlaştırır. Çoğu bilgi için de söz konusu olduğu şekilde, Tavsiye Sistemi&apos;nin de soğuk başlatma, veri seyrekliği ve ölçeklenebilirlik gibi bazı sorunları vardır. Bu sorunların çözülmesi ve tahmin doğruluğunun arttırılması için birçok araştırma yapılmaktadır. Bu araştırma, Tavsiye Sistemleri&apos;ndeki farklı algoritmalar olan KEYK (k-En Yakın Komşuluk), TDA (Tekil Değer Ayrışması) ve Naïve Bayes arasında karşılaştırmalar üreterek bunların en iyi performanslarının kontrol edilmesini sağlayacaktır. İki farklı veri seti boyutu (% 80 ve % 20 ile % 60 ve % 40) uygulanmıştır ve her bir boyut eğitim ve test içeriğine sahiptir. Dahası, performans da üç ölçü (OMH (Ortalama Mutlak Hata), KOKH (Kök Ortalama Kare Hatası) ve zaman) ile hesaplanmıştır. Elde edilen sonuçlar, her iki veri seti için de gerek OMH gerekse KOKH&apos;nda en yüksek doğruluk derecesinin Naïve Bayes ile sağlandığını, zaman açısından ise sonuçların veri setinin boyutuna ve algoritma tekniğine bağlı olarak farklılık gösterdiğini ortaya koymuştur.
The evolution of European Union law: A new data set on the                     <i>Acquis Communautaire</i>

The European Union legal system is one of the most complex and sophisticated in the world. This article models the Acquis Communautaire (i.e. the corpus of European Union law) as a network and introduces the Evolution of European Union Law data set, which tracks connections between European Union primary law, European Union secondary law, European Union case law, national case law that applies European Union law, and national law that implements European Union law. It is the largest, most comprehensive data set on European Union law to date. It covers the entire history of the European Union (1951–2015), contains over 365,000 documents, and records over 900,000 connections between them. Legislative and judicial scholars can use this data set to study legislative override of the Court of Justice of the European Union, the implementation of European Union law, and other important topics. As an illustration, I use the data set to provide empirical evidence consistent with legislative override.
Analytics of Learning and Educational Neurosciences: challenges in technological integration

The aim of learning analytics (LA) is to understand and optimize learning and the environments in which it occurs. Given the complex nature of learning, it has become necessary to use tools from various fields of research to obtain, describe, analyze and interpret data about students, their learning processes and contexts. Rather than taking isolated methods or techniques for the study of this process, learning analytics is beginning to integrate the perspectives of other fields to achieve a research of learning with a transdisciplinary vision. One of these fields is neurosciences, specifically those related to learning or educational neurosciences. The objective of this research is to explore the implications and challenges of the use of EEG technologies (traditionally used in neurological studies) in conjunction with learning analytics for the study of students learning processes and their context. This research focus on the study of the brain waves of a single sample of 6 students during a class session. This standpoint, framed within the educational neurosciences, constitutes a tool of analysis that can be used to achieve a quality teaching.