ethicalabs/Research-Intent-Collab huggingface dataset:
The Indonesian Constitutional Court’s position in keeping good governance through judicial review of legislation

The creation of Constitutional Court by Indonesian Constitution of 1945 corrected state policy at the New Order era. Constitutional Court is authorized to review legislation when it in contrary to the Constitution, in principle of fairness, or general principle of truth. This paper aims to determine role and contribution of the Constitutional Court in maintaining good governance in Indonesia. This is normative legal research in relation to rule of law contained in legislation. Summing up the analysis, the authors conclude that good governance must be implemented on the basis of rule of law, and a new legal act must go through material tests by Constitutional Court. It can be concluded that Constitutional Court has an important role in maintaining good governance.
AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling

This paper describes the organization and findings of AXOLOTL'24, the first multilingual explainable semantic change modeling shared task. We present new sense-annotated diachronic semantic change datasets for Finnish and Russian which were employed in the shared task, along with a surprise test-only German dataset borrowed from an existing source. The setup of AXOLOTL'24 is new to the semantic change modeling field, and involves subtasks of identifying unknown (novel) senses and providing dictionary-like definitions to these senses. The methods of the winning teams are described and compared, thus paving a path towards explainability in computational approaches to historical change of meaning.
Natural Language Processing for Breast Imaging: A Systematic Review

Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field.
Learning Deep Convolutional Features for MRI Based Alzheimer's Disease Classification

Effective and accurate diagnosis of Alzheimer's disease (AD) or mild cognitive impairment (MCI) can be critical for early treatment and thus has attracted more and more attention nowadays. Since first introduced, machine learning methods have been gaining increasing popularity for AD related research. Among the various identified biomarkers, magnetic resonance imaging (MRI) are widely used for the prediction of AD or MCI. However, before a machine learning algorithm can be applied, image features need to be extracted to represent the MRI images. While good representations can be pivotal to the classification performance, almost all the previous studies typically rely on human labelling to find the regions of interest (ROI) which may be correlated to AD, such as hippocampus, amygdala, precuneus, etc. This procedure requires domain knowledge and is costly and tedious.   Instead of relying on extraction of ROI features, it is more promising to remove manual ROI labelling from the pipeline and directly work on the raw MRI images. In other words, we can let the machine learning methods to figure out these informative and discriminative image structures for AD classification. In this work, we propose to learn deep convolutional image features using unsupervised and supervised learning. Deep learning has emerged as a powerful tool in the machine learning community and has been successfully applied to various tasks. We thus propose to exploit deep features of MRI images based on a pre-trained large convolutional neural network (CNN) for AD and MCI classification, which spares the effort of manual ROI annotation process.
How to exploit China’s AI-powered platforms for Korean-Chinese translation/interpreting education

Abstract                   In the wake of the Fourth Industrial Revolution, artificial intelligence (AI) is rapidly transforming human lives at an unprecedented rate. As this new era begins and technological advancements continue to accelerate, there appears to be a parallel need for corresponding changes and reforms in the field of translation and interpretation education. Indeed, many interpreters and translators now incorporate automated translation tools in their work, and a significant number of researchers are advocating for the application of AI platforms in translation and interpretation education, proposing innovative teaching methods. Among these innovations, various platforms developed specifically for interpreter training can be categorized into training-based platforms, data storage-based platforms, and interpreter material storage-based platforms. This paper delves into the impact of such platforms on translation and interpretation education, with a particular focus on the neighboring country of China, which extensively utilizes Learning Management System (LMS)-based smart cloud platforms, AI platforms, and voice recognition applications in this educational field. Firstly, the analysis of classroom systems based on LMS, such as the iSmart smart educational cloud platform, the SHIYIBAO smart translation and interpretation education platform, and Oia developed in collaboration with Shanghai International Studies University, reveals their usage patterns. Secondly, experiments with applications capable of voice recognition, such as iFLYTEK, are examined. Th dly, the impact of on- screen subtitles displayed on computer monitors on interpreters is considered. These case studies demonstrate that AI platforms can enhance the quality of translation and interpretation, and also significantly alleviate the fear and burden associated with interpreting practice for students. This positive effect, noted during their interpreting exercises, confirms that platform systems incorporating voice recognition and other AI technologies positively influence interpreter education and the quality of interpretation. Additionally, these findings highlight the pressing need for South Korea to actively adopt such platforms in its translation and interpretation education moving forward.
On-Policy Robot Imitation Learning from a Converging Supervisor

Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during policy learning, such as a human performing a novel task or an improving algorithmic controller. We formalize imitation learning from a "converging supervisor" and provide sublinear static and dynamic regret guarantees against the best policy in hindsight with labels from the converged supervisor, even when labels during learning are only from intermediate supervisors. We then show that this framework is closely connected to a class of reinforcement learning (RL) algorithms known as dual policy iteration (DPI), which alternate between training a reactive learner with imitation learning and a model-based supervisor with data from the learner. Experiments suggest that when this framework is applied with the state-of-the-art deep model-based RL algorithm PETS as an improving supervisor, it outperforms deep RL baselines on continuous control tasks and provides up to an 80-fold speedup in policy evaluation.
(Invited) Multi-Material Additive Manufacturing (3D/4D printing)

Like any other manufacturing technology, Additive Manufacturing (AM) suffers from certain limitations. Grand challenges that hinder the use of AM in the field of sensors, actuators and energy are:                                                lack of materials available for AM                                                                     multi-material AM                                                                     feature size and accuracy                                                                            In order to overcome the grand challenges that hinder the use of AM in the field of sensors, actuators and energy, we have developed a universal multi-material 3D/4D printer which is capable of printing metals, thermoplastics, gels, pastes, elastomers and nanocomposites [1, 2]. The developed 3D printer is unique in that sense that it employs a screw-based extrusion system. The materials to be 3D printed (also known as feedstock) are fed into the hopper, single screw extrusion system mixes and melts the material, remove any air bubbles, extrudes the material through a nozzle of 100 micron, ensuring 3D/4D printing without any defect [3]. By using the developed universal multi- material 3D printer we have been able to 3D/4D print Steel, Aluminium, Silver, Nickle, Cobalt, Copper, Carbon fibre based nanocomposites, Ionic gels, Titanium dioxide, various thermoplastics, Shape memory materials, Wood and Epoxies as shown in figure 1. Such type of single screw base extruder 3D/4D printer will be a new addition to the additive manufacturing process.                 References:                                                Shiblee, Md Nahin Islam, Kumkum Ahmed, Ajit Khosla, Masaru Kawakami, and Hidemitsu Furukawa. "3D printing of shape memory hydrogels with tunable mechanical properties." Soft matter (2018). DOI:10.1039/C8SM01156G                                                                     Khosla, Ajit, et al. "Carbon fiber doped thermosetting elastomer for flexible sensors: physical properties and microfabrication." Scientific reports 8.1 (2018): 12313. https://doi.org/10.1038/s41598-018-30846-3                                                                     Shah, Shreyas, et al. "3D printing of electrically conductive hybrid organic–inorganic composite materials." Microsystem Technologies 24.10 (2018): 4341-4345. https://doi.org/10.1007/s00542-018-3781-x                                                                                                                                                                                        Figure 1
Secure and Privacy-Preserving Authentication for Data Subject Rights Enforcement

In light of the GDPR, data controllers (DC) need to allow data subjects (DS) to exercise certain data subject rights. A key requirement here is that DCs can reliably authenticate a DS. Due to a lack of clear technical specifications, this has been realized in different ways, such as by requesting copies of ID documents or by email address verification. However, previous research has shown that this is associated with various security and privacy risks and that identifying DSs can be a non-trivial task. In this paper, we review different authentication schemes and propose an architecture that enables DCs to authenticate DSs with the help of independent Identity Providers in a secure and privacy-preserving manner by utilizing attribute-based credentials and eIDs. Our work contributes to a more standardized and privacy-preserving way of authenticating DSs, which will benefit both DCs and DSs.
ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

Accepted to AIMedHealth 10 pages, 5 figures
Exploring COVID-19 vaccine uptake, confidence and hesitancy among people experiencing homelessness in Toronto, Canada: protocol for the                     <i>Ku-gaa-gii pimitizi-win</i>                     qualitative study

Introduction                     People experiencing homelessness are at high risk for COVID-19 and poor outcomes if infected. Vaccination offers protection against serious illness, and people experiencing homelessness have been prioritised in the vaccine roll-out in Toronto, Canada. Yet, current COVID-19 vaccination rates among people experiencing homelessness are lower than the general population. This study aims to characterise reasons for COVID-19 vaccine uptake and hesitancy among people experiencing homelessness, to identify strategies to overcome hesitancy and provide public health decision-makers with information to improve vaccine confidence and uptake in this priority population.                                                           Methods and analysis                                            The                       Ku-gaa-gii pimitizi-win                       qualitative study (formerly the COVENANT study) will recruit up to 40 participants in Toronto who are identified as experiencing homelessness at the time of recruitment. Semistructured interviews with participants will explore general experiences during the COVID-19 pandemic (eg, loss of housing, social connectedness), perceptions of the COVID-19 vaccine, factors shaping vaccine uptake and strategies for supporting enablers, addressing challenges and building vaccine confidence.                                                                                Ethics and dissemination                     Approval for this study was granted by Unity Health Toronto Research Ethics Board. Findings will be communicated to groups organising vaccination efforts in shelters, community groups and the City of Toronto to construct more targeted interventions that address reasons for vaccine hesitancy among people experiencing homelessness. Key outputs will include a community report, academic publications, presentations at conferences and a Town Hall that will bring together people with lived expertise of homelessness, shelter staff, leading scholars, community experts and public health partners.
Pengembangan Data Warehouse dan Online Analytical Processing (OLAP) untuk Data Artikel pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)

Saat ini, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) telah terakreditasi dengan peringkat 2. Dengan JTIIK yang telah terakreditasi, maka peminat peneliti untuk mengirimkan artikel ke JTIIK semakin tinggi. Namun, proses untuk menerbitkan suatu artikel memerlukan waktu lebih dari satu tahun. Agar proses penerbitan tidak memerlukan waktu yang terlalu lama, mulai pada tahun 2018 JTIIK menerbitkan jurnal sebanyak enam kali dimana penerbitan mengalami peningkatan dari tahun sebelumnya. Dari peningkatan penerbitan ini, data yang disimpan juga akan semakin bertambah. Sebuah sistem diperlukan untuk mengelola data yang besar dari beberapa sumber dan melakukan analisis yang dapat menjadi bahan pertimbangan dalam pengambilan keputusan. Pada penelitian ini dilakukan pengembangan data warehouse dan Online Analytical Processing (OLAP) sebagai langkah penyelesaian untuk masalah dalam mengelola dan menganalisis banyaknya data dari beberapa sumber. Dalam penelitian ini dilakukan analisis sebagai tahapan pertama. Berdasarkan hasil analisis didapatkan 2 information package yaitu information package data artikel dan information package data penulis. Penelitian dilanjutkan dengan perancangan data warehouse dengan menggunakan snowflake schema yang menghasilkan 2 tabel fakta dan 4 tabel dimensi. Selanjutnya dilakukan implementasi ETL dan OLAP. Kemudian dilakukan 2 jenis pengujian. Hasil pengujian pertama yaitu validasi kebutuhan menunjukkan bahwa data warehouse yang dibangun telah sesuai dengan kebutuhan yang dirancang. Hasil pengujian kedua yaitu performansi proses ETL dari segi waktu menunjukkan bahwa hasil rata-rata waktu yang dibutuhkan untuk eksekusi proses ETL adalah 14,5 detik.
Strengthening Systems and Realising Human Rights: Strategies to Progress Water, Sanitation and Hygiene (WASH)

Abstract                This book explores two frameworks for progressing structural change that supports safe, universal and equitable water, sanitation and hygiene (WASH): systems strengthening and the human rights to water and sanitation. As such, it elaborates and defines the intersection between these two areas. Whilst inherently interconnected, the two also represent different entry points for change, with opportunity for increased intentional integration to improve progress towards Sustainable Development Goal 6: Clean water and sanitation.                Systems strengthening is widely recognised as foundational for ensuring access to sustainable, equitable WASH services in low- and middle-income countries, leaving no-one behind. Such systems comprise clear sector policy and strategy, effective institutional arrangements, sufficient sector financing, regular cycles of planning, monitoring and review and capacity development across the sector; as well as the people, interactions, power relations and behaviours that shape these elements.                Equally, the human rights to water and sanitation provide numerous entry points for change, both in supporting people to claim and realise their rights and in supporting and advocating for governments at all levels to respond.                Effective approaches to strengthen systems and realise the rights depend on inclusive processes that meaningfully involve people and advance equality, including gender equality. They facilitate mutual learning, capacity building and increased accountability, promote informed choice and place development in the hands of those it concerns.                In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.
Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs

In today's data-driven world, the ability to extract meaningful information from data is becoming essential for businesses, organizations and researchers alike. For that purpose, a wide range of tools and systems exist addressing data-related tasks, from data integration, preprocessing and modeling, to the interpretation and evaluation of the results. As data continues to grow in volume, variety, and complexity, there is an increasing need for advanced but user-friendly tools, such as intelligent discovery assistants (IDAs) or automated machine learning (AutoML) systems, that facilitate the user's interaction with the data. This enables non-expert users, such as citizen data scientists, to leverage powerful data analytics techniques effectively. The assistance offered by IDAs or AutoML tools should not be guided only by the analytical problem's data but should also be tailored to each individual user. To this end, this work explores the usage of Knowledge Graphs (KG) as a basic framework for capturing in a human-centered manner complex analytics workflows, by storing information not only about the workflow's components, datasets and algorithms but also about the users, their intents and their feedback, among others. The data stored in the generated KG can then be exploited to provide assistance (e.g., recommendations) to the users interacting with these systems. To accomplish this objective, two methods are explored in this work. Initially, the usage of query templates to extract relevant information from the KG is studied. However, upon identifying its main limitations, the usage of link prediction with knowledge graph embeddings is explored, which enhances flexibility and allows leveraging the entire structure and components of the graph. The experiments show that the proposed method is able to capture the graph's structure and to produce sensible suggestions.
A Peano-Like Theorem for Stochastic Differential Equations with Nonlocal Sample Dependence

The existence of a not-necessarily-unique strong solution for a stochastic differential equations with nonlocal sample dependence is established under the assumption that the coefficients satisfy an asymptotically local boundedness condition in addition to continuity. The proof is by an Euler-like construction of approximations. These equations include mean-field stochastic differential equations, but the nonlocal sample dependence can be more general than just the dependence on moments of the solution.
The International Criminal Tribunal for Rwanda as Theater: The Social Negotiation of the Moral Authority of International Law

Maya Steinitz, The International Criminal Tribunal for Rwanda as Theater: The Social Negotiation of the Moral Authority of International Law, 5 J. Int'l L. &amp; Pol'y 1:1 (2007).
Striking a Balance between Biodiversity Conservation and Socioeconomic Viability in the Design of Marine Protected Areas

Abstract:                      The establishment of marine protected areas is often viewed as a conflict between conservation and fishing. We considered consumptive and nonconsumptive interests of multiple stakeholders (i.e., fishers, scuba divers, conservationists, managers, scientists) in the systematic design of a network of marine protected areas along California's central coast in the context of the Marine Life Protection Act Initiative. With advice from managers, administrators, and scientists, a representative group of stakeholders defined biodiversity conservation and socioeconomic goals that accommodated social needs and conserved marine ecosystems, consistent with legal requirements. To satisfy biodiversity goals, we targeted 11 marine habitats across 5 depth zones, areas of high species diversity, and areas containing species of special status. We minimized adverse socioeconomic impacts by minimizing negative effects on fishers. We included fine‐scale fishing data from the recreational and commercial fishing sectors across 24 fisheries. Protected areas designed with consideration of commercial and recreational fisheries reduced potential impact to the fisheries approximately 21% more than protected areas designed without consideration of fishing effort and resulted in a small increase in the total area protected (approximately 3.4%). We incorporated confidential fishing data without revealing the identity of specific fisheries or individual fishing grounds. We sited a portion of the protected areas near land parks, marine laboratories, and scientific monitoring sites to address nonconsumptive socioeconomic goals. Our results show that a stakeholder‐driven design process can use systematic conservation‐planning methods to successfully produce options for network design that satisfy multiple conservation and socioeconomic objectives.                     Marine protected areas that incorporate multiple stakeholder interests without compromising biodiversity conservation goals are more likely to protect marine ecosystems.
TRIPS and the PHILIPPINES : Pharmaceutical patents and the right to health on access to essential medicines

This thesis discusses about the key provisions of the TRIPS Agreement and their effects on the realization of the right to health on access to essential medicines. It explores also the relationship between the trade right to intellectual property and the fundamental human rights to health on access to affordable medicines and how can such be reconciled.The Philippines' experience with regard to property rights is used in the illustration.