Reviewerly/exHarmony huggingface dataset:
W3C data recommendations -- there are many!

The World Wide Web Consortium (W3C) publishes a range of specifications and guidelines which help move web standards forward. However, even when restricting scope to the Latest version of specifications with the status Recommendation and with the tag Data, there are currently 77 of them: https://www.w3.org/TR/?tag=data&amp;amp;status=REC&amp;amp;version=latest!
Why Is There Not Just One Metadata Format for All Kinds of Research Data?

Why is there not just one metadata format for all kinds of research / data? – asked on fairdataforum.org Metadata modeling and formatting are separate concerns. It is reasonable that different scientific domains and studies within domains may have widely varying modeling concerns. Controlled vocabulary terms, validity constraints, and other metadata elements will surely vary and evolve over time.
FAIR Principle R1.1: Meta(data) are released with a clear and accessible data usage license

I’ve been recording introductions to each of the 15 FAIR Principles and releasing them as episodes of my Machine-Centric Science podcast (https://podcast.polyneme.xyz/). I just released the 13th one, featuring an overview of various data and code licenses. Listen here. Full transcript below (but also linked to via the episode landing page): ====== Hello, and welcome to Machine-Centric Science.
"Straightening Out" Circular Causality

We often seek to “straighten out” a maze-like, loop-containing situation. We try to find a “path” through “causal” explanations that go in only one direction. Why? There are countless different types of networks that contain loops. But all networks that contain no loops are basically the same: each has the form of a simple chain. Any directed acyclic graph (DAG) can be linearized, i.e. topologically sorted.
¬ consistent ⇒ ¬ valid ⇒ ¬ accurate

If it’s not consistent, it can’t be valid. If it’s not valid, it can’t be accurate. If it’s not accurate, who cares if it’s timely?
Migrating Conflicts Between Agents to Higher Levels

Many children not only like to build, they also like to knock things down – to hear the complicated noises and watch so many things move at once. Let’s imagine a sibling agent to &lt;em&gt;Builder&lt;/em&gt; called &lt;em&gt;Wrecker&lt;/em&gt;, whose specialty is knocking things down: Suppose &lt;em&gt;Wrecker&lt;/em&gt; gets aroused, but there’s nothing in sight to smash. Then &lt;em&gt;Wrecker&lt;/em&gt; will have to get some help – by putting &lt;em&gt;Builder&lt;/em&gt; to work, for example.
GUIs and APIs Are Both Human Interfaces

GUIs and APIs are both human interfaces. They both frame perspectives on data/operation service offerings so that human beings can navigate and consume them. The human being in the case of APIs is the application programmer, a subset of users. GUIs are applications, so it is natural to expect an API’s capabilities to be a superset of a corresponding GUI’s — application programmers program the GUI using the API.
The World of Blocks

Imagine a child playing with blocks. Imagine the child’s mind contains a host of smaller minds - “agents”. Imagine an agent called &lt;em&gt;Builder&lt;/em&gt; in control. &lt;em&gt;Builder&lt;/em&gt; makes towers from blocks: &lt;em&gt;Builder&lt;/em&gt; is a simple agent.
DIKW Bases and Products

“Self-describing” data products include/contain/link to any “knowledge products” (schema, ontology, etc.) they were informed by. In the sense of the data-information-knowledge-wisdom (DIKW) conceptualization, are there &lt;em&gt;information products&lt;/em&gt;? Perhaps self-describing &lt;em&gt;information products&lt;/em&gt; include/contain/link to any “wisdom products” (usage context!
Easy Things Are Hard

In general, we’re least aware of what our minds do best. It’s mainly when systems start to fail that we engage the special agencies involved with what we call “consciousness.” Accordingly, we’re more aware of simple processes that don’t work well than of complex ones that work flawlessly. This phenomenon helps to explain the poor performance of many so-called &lt;em&gt;expert systems&lt;/em&gt; in the 1980s.
The Agents of a FAIR Research Platform

To explain a FAIR research platform, we have to show how they are built from mindless stuff, smaller and simpler than anything we’d consider smart. What could these simpler particles be – the “agents” that compose the platform? There are many questions to answer: Function: How do agents work? Embodiment: What are they made of? Interaction: How do they communicate? Learning: How do we make new agents and change old ones?
Hierarchies, Heterarchies, and Agent Memory

In a hierarchy, each agent only acts on behalf of one other agent: from rdflib import Graph from rdflib.namespace import RDF, PROV def hierarchy(graph): return all( len(set(graph.objects(agent, PROV.actedOnBehalfOf))) &amp;lt;= 1 for agent in graph.subjects(RDF.type, PROV.Agent) ) hierarchy(Graph().parse(data=""" @prefix prov: . @prefix : . :doc a prov:Agent; . :sleepy a prov:Agent; prov:actedOnBehalfOf
Let Traits Accrete

How can it be that complex, dynamic objects can be described by short and simple strings and words? We often seek:&lt;sup&gt;1&lt;/sup&gt; &lt;em&gt;Selectivity&lt;/em&gt; – Our images are often falsely clear. We may think of an object’s “personality” in terms of that which we can easily describe. We may set aside the rest for now as though it simply weren’t there.
Governance of Conceptual Knowledge versus Policy/Reference Knowledge

What do knowledge governance systems look like in practice? By knowledge governance, I don’t just mean governing ontologies. By knowledge, I mean categories/sets/types of things – what a thing “is,” or at least what it is described to be in the information system – and the relationships between these types, as opposed to specific recorded facts.
Cool LUIs Dont Change

After my last note on identifiers, Leo Talirz pointed me to a great riff&lt;sup&gt;1&lt;/sup&gt; on Tim Berners-Lee’s classic note&lt;sup&gt;2&lt;/sup&gt; on “cool URIs”. In the “Cool DOIs” article, Fenner breaks down a DOI into three parts: proxy, prefix, and suffix. A proxy is a server that maintains a map from prefixes to registrants. Example proxies are https://doi.org/ and https://hdl.handle.net/. An example prefix is 10.5281.
Java Collections Framework and Their Applications in Software Development

Abstract: Incorporating a variety of classes and interfaces, the Java Collections Framework provides a centralized and efficient means of managing and manipulating collections of objects within the Java programming language. In pursuit of thoroughly investigating the expansive array of data structures and algorithms housed within the Java Collections Framework, we craft this thoroughly researched paper. Accessing official records, scholarly works, and real-world instances, researchers employ a comprehensive approach. In software development, the Java Collections Framework (JCF) is a valuable resource for efficiently managing and manipulating collections of data. It offers a set of well-designed classes and interfaces that simplify the process. The research outcome suggests that the Java Collections Framework is an effective toolset for application developers to manage data with efficiency and accuracy.
Wholes and Parts, in FAIR and Mind

It is the nature of the mind that makes individuals kin, and the differences in the shape, form, or manner of the material atoms out of whose intricate relationships that mind is built are altogether trivial. – Isaac Asimov It is the nature of FAIR that can make digital resources kin – interoperable in fugues of machine action.
Determining Causes of Deviant Behavior in Students

The deviant behavior of students is a cause of concern for our society. Media sources signal daily the omnipresence of this social problem that must be researched from several perspectives. The way the student behaves is a reflection of his inner self. The inner balance determines the outer balance in relationships with others, in approaching problems, in the way of life. It is very important to understand the reasons, the causes that condition deviant behavior. Most often it is formed during the formation of the personality, as a result of a defective education, educational deficiencies, lack of trust and respect between the educator and the educated. That is why in order to understand the causes that determine deviant behavior in students, it is necessary to analyze the internal structure of the person, to take into account his needs, his life experience, the attitude he has about himself and others, his beliefs. Students who do not accept themselves unconditionally often have a distorted view of themselves, thus ending up living an illusion and exhibiting deviant behavior. Success in overcoming this problem is conditioned by the way the individual thinks about himself and the world around him, his attitudes towards life in general, his self-image and unconditional self-acceptance of his own person because this is a first step towards change.
Pulse Generation and Memory in Opto-Electronic Neurons with Time-Delayed Feedback

We study a neuromorphic circuit composed of a nano resonant tunneling diode (RTD) operated in the excitable regime, where the diode generates, when triggered, an all-or-nothing electrical response pulse. This pulse is fed into a nano laser diode (LD), which in turn emits an optical pulse that is re-injected with time delay back into the RTD, as depicted in Fig. 1 (a).
Detecting Sensitive Data with GANs and Fully Convolutional Networks

The article presents a method of document anonymization using generative adversarial neural networks. Unlike other anonymization methods, in the presented work, the anonymization concerns sensitive data in the form of images placed in text documents. Specifically, it is based on the CycleGAN idea and uses the U-Net model as a generator. To train the model we built a dataset with text documents with embedded real-life images, and medical images. The method is characterized by a very high efficiency, which enables the detection of 99.8% of areas where the sensitive image is located.
BSL: Sustainable Collaborative Inference in Intelligent Transportation Systems

As the recent rise of intelligent transportation systems (ITS), the sensing capability of vehicles has become crucial in realizing sophisticated intelligent transportation services. Collaborative sensing, an important approach to extend the sensing coverage of individual vehicles, has become an essential component of connected vehicle systems. However, due to challenges such as privacy concerns, frequent communication interruptions, customized models, and limited available data, the application of collaborative sensing in current ITS systems is still limited. In this paper, we propose BSL, a novel multi-exit split learning-based collaborative inference system. The key innovation of BSL is the introduction of multi-exit to the split network, enabling network training and collaborative inference between distributed device nodes and the cloud in a split manner. Specifically, BSL allows the device node to dynamically collaborate with the cloud by introducing the edge mode and collaboration mode, ensuring that intelligent services provided to the device will be sustained even if the communication is interrupted, which is crucial in ITS systems. We have implemented the system and evaluated it with public dataset on different embedded devices. The results demonstrate the promising performance of BSL.
CDLI catalogue growth over time

Since Google Summer of Code 2020 I have been contributing code to the new framework of the Cuneiform Digital Library Initiative (CDLI), a digital repository for metadata, transliterations, images, and other data of cuneiform inscriptions, as well as tools to work with that data. One of the features of the new CDLI framework is improved editing of artifact metadata, as well as inscriptions.
Exploratory Behavior With Generate-and-Test

One powerful mechanism of robustness is exploratory behavior, for which the desired outcome is produced by a generate-and-test mechanism. This organization allows the generator to work and be developed independently of the tester that accepts or rejects a particular result. One can make an analogy to biological evolution, where the generator is random mutation and the tester is natural selection.
Value Lattices for Data Collaboration

Values like “3” are typically considered separately from &lt;em&gt;constraints&lt;/em&gt; like “&amp;lt; 10” or &lt;em&gt;types&lt;/em&gt; like “number”. What if these were all on equal footing as values in a hierarchy?
Data Reduction for Science

Earlier this week, I wrote that In sharing scientific research data, the goal is often to provide data reductions to the extent possible without loss – the output is, in a strong sense, equivalent to the input. Any further reduction that may be necessary to support decisions and policy can be done as fusion beyond unification.
Resource Description (Ontology/Schema) Versus Resource Layout (API)

Resource description refers to defining concepts and relationships that represent the content and structure of some subject matter (ontology) or a database (schema) in a formal language. The relationship between ontology and database schema is nuanced – Uschold provides a nice comparison.&lt;sup&gt;1&lt;/sup&gt; You can formally describe resources using the resource description framework (RDF), SQL’s data definition language (DDL), etc.
"Lets Not Reinvent the Wheel"

I was reading about hidden costs of “packaged” software solutions – that is, using existing software to solve problems – and came across this sentence:&lt;sup&gt;1&lt;/sup&gt; While the packaged solution saved some money on development, they more than made up for it in implementation. Huh? I typically do not distinguish &lt;em&gt;development&lt;/em&gt; from &lt;em&gt;implementation&lt;/em&gt;. What McComb is calling “implementation” I just call “installation”. Weird.
How Is Data Unification Different Than Data Fusion?

In response to this note, a reader asked How is data unification different than data fusion? I re-read the Stonebraker whitepaper I had linked to in that note, and what it describes does &lt;em&gt;not&lt;/em&gt; seem meaningfully different than data fusion. I propose that data unification is more conservative than data fusion – it stops short of the lossy reduction often required for decision-support systems.
The QUDT System for Dimensional Analysis and Unit Conversions

In order to integrate quantitative data, you need to know (a) if units are commensurate, and (b) if so, how to do conversions. The Quantities, Units, Dimensions, and Types (QUDT) ontology serves three major purposes. First, it provides a global reference for units via URIs; this helps avoid tacit conventions that are prone to misinterpretation.
Stability Analysis of Photonic Ising Machines

Photonic Ising machines are a promising tool for solving NP-hard optimisation problems, claiming both a smaller time-to-solution and energy consumption than CPUs used today [1]. They are based on the principle that a natural system automatically settles in the lowest energy state. When the cost function of a certain optimisation problem can be mapped to the energy of a natural system, the ground state of that system can be translated to the optimal solution to the problem. This ground state however, is not always reached as the system can get stuck in local minima, resulting in sub-optimal solutions. It turns out that the success rate of finding the optimal solution is heavily dependent on the problem at hand [2]. Understanding why the Ising machine fails to find the ground state for certain problems and how to avoid these difficulties, is essential to increase their efficiency. To tackle these questions, we perform an analytical stability analysis of the fixed points and use it to predict regions in parameter space with higher success rate.
A Distributed and Secure Self-Sovereign-Based Framework for Systems of Systems

Security and privacy are among the main challenges in the systems of systems. The distributed ledger technology and self-sovereign identity pave the way to empower systems and users’ security and privacy. By utilizing both technologies, this paper proposes a distributed and self-sovereign-based framework for systems of systems to increase the security of such a system and maintain users’ privacy. We conducted an extensive security analysis of the proposed framework using a threat model based on the STRIDE framework, highlighting the mitigation provided by the proposed framework compared to the traditional SoS security. The analysis shows the feasibility of the proposed framework, affirming its capability to establish a secure and privacy-preserving identity management system for systems of systems.
The Effect of Illusion in Test Score Prediction and Learning Strategy Judgment on Academic Achievement

이 연구의 목적은 교양과목을 수강하는 대학생 75명을 대상으로 시험점수를 예측하는 상황과 학습전략의 사용과 효과를 판단하는 상황에서 학생들이 겪는 착각현상을 확인하고, 두 종류 착각현상의 관련성과 실제점수에 미치는 영향은 어떠한지를 분석하는 것이다. 연구참여자에게 가장 자주 사용하는 학습전략 3개와 가장 효과있을 것으로 판단하는 전략 3가지를 선정하게 하고, 각 전략에 대해 사용비율과 효과비율을 명시하도록 했다. 사용정도와 효과정도는 각 전략의 유용성 수준과 비율을 동시에 고려하여 산정했다. 시험점수 예측상황에서 겪는 대표적인 착각현상인 더닝-크루거 효과가 시험석차를 예측하는 상황에서도 발생했음을 확인하였다. 학습전략의 사용정도와 효과정도에서 차이를 정량적으로 확인한 결과, 사용을 많이 하는 전략과 효과가 높다고 판단하는 전략이 상이함이 밝혀져 착각현상이 있음이 입증되었다. 시험수행 착각점수와 학습전략 착각점수 간 유의한 부적(-) 상관관계를 확인했는데, 이는 시험상황에서 착각이 심한 학생이 사용전략보다는 효과전략 선정에서 유용성 더 높은 전략을 많이 포함했음을 의미한다. 즉, 유용성 낮은 전략을 많이 사용하면서 효과 판단에서는 유용성 높은 전략을 많이 포함하는 모순된 현상을 보이고 있었다. 시험수행착각점수와 학습전략착각점수가 실제점수에 미치는 영향을 중다회귀분석을 통해 확인한 결과, 시험수행착각점수는 실제점수에 유의한 부적 영향을 미치지만 학습전략착각점수는 아무런 영향을 끼치지 않았다. 학습전략의 사용과 효과에서 확인된 착각현상이 실제점수에 아무런 영향을 끼치지 않는 이유에 대해 논의했다.The purpose of this study is to identify the illusion that students experience in predicting test scores for liberal arts course and judging the use and effect of learning strategies, and to analyze the relationship between the two types of illusion and their effects on actual scores. Seventy five college students were asked to select the 3 most frequently used and the 3 most effective strategies, and the use and effect ratio were specified for each strategy. The degree of use and effectiveness was calculated by considering the ratio and the level of usefulness of each strategy at the same time. It was confirmed that the Dunning-Kruger effect, also occurred in the situation of predicting the test score. As a result of quantitatively confirming the difference in the degree of use and effectiveness of the learning strategy, it was found that the strategies used a lot and the strategies judged to be highly effective were different, proving that there was an illusion. A significant negative correlation between the test performance illusion score and the learning strategy illusion score was confirmed, which means that students who were highly mistaken in the test situation included more useful strategies in selecting effective strategies than use strategies. While using a lot of low utility strategies, there was a contradictory phenomenon that included a lot of high utility strategies in determining the effect. As a result of confirming the effect of the test performance illusion score and the learning strategy illusion score on the actual score through multiple regression analysis, the test performance illusion score had a significant negative effect on the actual score, but the learning strategy illusion score had no effect. The study discussed why the illusion identified in the use and effect of learning strategies does not affect actual grades.
Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review

BACKGROUND: Schizophrenia is a severe psychiatric disorder associated with a significant negative impact. Early diagnosis and treatment of schizophrenia has a favorable effect on the clinical outcome and patients quality of life. In this context, machine learning techniques open up new opportunities for a more accurate diagnosis and prediction of the clinical features of this illness.&#x0D; AIM: This literature review is aimed to search for information on the use of machine learning techniques in the prediction and diagnosis of schizophrenia and the determination of its clinical features.&#x0D; METHODS: The Google Scholar, PubMed, and eLIBRARY.ru databases were used to search for relevant data. The review included articles that had been published not earlier than January 1, 2010, and not later than March 31, 2023. Combinations of the following keywords were applied for search queries: machine learning, deep learning, schizophrenia, neural network, predictors, artificial intelligence, diagnostics, suicide, depressive, insomnia, and cognitive. Original articles regardless of their design were included in the review. Descriptive analysis was used to summarize the retrieved data.&#x0D; RESULTS: Machine learning techniques are widely used in the functional assessment of patients with schizophrenia. They are used for interpretation of MRI, EEG, and actigraphy findings. Also, models created using machine learning algorithms can analyze speech, behavior, and the creativity of people and these data can be used for the diagnosis of psychiatric disorders. It has been found that different machine learning-based models can help specialists predict and diagnose schizophrenia based on medical history and genetic data, as well as epigenetic information. Machine learning techniques can also be used to build effective models that can help specialists diagnose and predict clinical manifestations and complications of schizophrenia, such as insomnia, depressive symptoms, suicide risk, aggressive behavior, and changes in cognitive functions over time.&#x0D; CONCLUSION: Machine learning techniques play an important role in psychiatry, as they have been used in models that help specialists in the diagnosis of schizophrenia and determination of its clinical features. The use of machine learning algorithms is one of the most promising direction in psychiatry, and it can significantly improve the effectiveness of the diagnosis and treatment of schizophrenia.
Adversarial Resilient Cyber Effects for Decision Dominance Collaborative Research Program (DOD)

Federal Grants & ContractsVolume 47, Issue 19 p. 3-4 Grants Alerts Adversarial Resilient Cyber Effects for Decision Dominance Collaborative Research Program (DOD) First published: 04 September 2023 https://doi.org/10.1002/fgc.33223Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue197 September 2023Pages 3-4 RelatedInformation