Reviewerly/exHarmony huggingface dataset:
Performance assessment of ontology matching systems for FAIR data

Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision.We included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings' classes belonged to top-level classes that matched.Our findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem.
An IoV-PBFT Consensus-Based Blockchain for Collaborative Congestion Avoidance and Simulation Test

This paper proposes a novel Internet of Vehicles (IoV) practical Byzantine fault tolerance (PBFT) consensus mechanism suitable for the distributed vehicle-to-vehicle (V2V) scenario and the V2V collaborative congestion avoidance mechanism based on blockchain. Compared with the traditional PBFT consensus mechanism, the proposed IoV-PBFT consensus mechanism has been improved in three aspects. (1) The authenticated vehicles are divided into three types including common nodes, production nodes, and verification nodes. The vehicles in the same congestion road section are used as the consensus participation nodes, which are divided into the production node and verification node. Since the vehicles in this section are moving slowly or even stopping and the number of newly added and departing nodes are less, the identity of the previous nodes is verified before each round of consensus, which ensures the dynamic tolerance with less additional delay. (2) The verification nodes are grouped based on path loss, and the nodes with good channel quality are selected as the leader nodes. (3) The main node in traditional PBFT is omitted, and the two-stage process is used instead of the traditional three-stage process, which effectively reduces the consensus delay and communication complexity. The theoretical derivation and simulation results show that the proposed IoV-PBFT has greatly improved the V2V link quality compared with traditional PBFT, and the success ratio of links between nodes can reach more than 93%, which has met the requirements of establishing blockchain in IoV.
AQT: Adversarial Query Transformers for Domain Adaptive Object Detection

Adversarial feature alignment is widely used in domain adaptive object detection. Despite the effectiveness on CNN-based detectors, its applicability to transformer-based detectors is less studied. In this paper, we present AQT (adversarial query transformers) to integrate adversarial feature alignment into detection transformers. The generator is a detection transformer which yields a sequence of feature tokens, and the discriminator consists of a novel adversarial token and a stack of cross-attention layers. The cross-attention layers take the adversarial token as the query and the feature tokens from the generator as the key-value pairs. Through adversarial learning, the adversarial token in the discriminator attends to the domain-specific feature tokens, while the generator produces domain-invariant features, especially on the attended tokens, hence realizing adversarial feature alignment on transformers. Thorough experiments over several domain adaptive object detection benchmarks demonstrate that our approach performs favorably against the state-of-the-art methods. Source code is available at https://github.com/weii41392/AQT.
Tradformer: A Transformer Model of Traditional Music Transcriptions

We explore the transformer neural network architecture for modeling music, specifically Irish and Swedish traditional dance music. Given the repetitive structures of these kinds of music, the transformer should be as successful with fewer parameters and complexity as the hitherto most successful model, a vanilla long short-term memory network. We find that achieving good performance with the transformer is not straightforward, and careful consideration is needed for the sampling strategy, evaluating intermediate outputs in relation to engineering choices, and finally analyzing what the model learns. We discuss these points with several illustrations, providing reusable insights for engineering other music generation systems. We also report the high performance of our final transformer model in a competition of music generation systems focused on a type of Swedish dance.
Together about Dementia

We present ``Together about Dementia'', a mobile health app that aims to help persons with dementia when they get lost through the help of caregivers, relatives, and volunteering citizens. When the app detects that a person with dementia is disoriented, wandering, and getting lost, it triggers an alarm that activates a relative and possibly a volunteer in the proximity. A backend system based on a microservice and serverless architecture performs the detection of wandering and the subsequent coordination of users that are put on a mission to the rescue of the person with dementia. The backend system implements an AI technique for spatio-temporal anomaly detection based on location data recorded by the frontend system installed on the portable device of the person with dementia.
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks

A fundamental problem in adversarial machine learning is to quantify how much training data is needed in the presence of evasion attacks. In this paper we address this issue within the framework of PAC learning, focusing on the class of decision lists. Given that distributional assumptions are essential in the adversarial setting, we work with probability distributions on the input data that satisfy a Lipschitz condition: nearby points have similar probability. Our key results illustrate that the adversary's budget (that is, the number of bits it can perturb on each input) is a fundamental quantity in determining the sample complexity of robust learning. Our first main result is a sample-complexity lower bound: the class of monotone conjunctions (essentially the simplest non-trivial hypothesis class on the Boolean hypercube) and any superclass has sample complexity at least exponential in the adversary's budget. Our second main result is a corresponding upper bound: for every fixed k the class of k-decision lists has polynomial sample complexity against a log(n)-bounded adversary. This sheds further light on the question of whether an efficient PAC learning algorithm can always be used as an efficient log(n)-robust learning algorithm under the uniform distribution.
AMICA: An Argumentative Search Engine for COVID-19 Literature

AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to Covid-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers' argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews.
Automatic Noisy Label Correction for Fine-Grained Entity Typing

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weaklysupervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance of the FET task. Although previous studies have made great success in automatically identifying the noisy labels in FET, they usually rely on some auxiliary resources which may be unavailable in real-world applications (e.g., pre-defined hierarchical type structures, humanannotated subsets). In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel candidate noisy labels by training a robust model over the remaining clean labels. Experiments on two popular benchmarks prove the effectiveness of our method. Our source code can be obtained from https://github.com/CCIIPLab/DenoiseFET.
Effect of Mobile-Based Video Learning on Clinical Nursing Students Learning Outcomes

Mobile device is a valuable form of technology that can be used with videoclips in nursing education to promote nursing students' learning motivation and confidence inlearning a clinical nursing skill.
Investigating and Explaining the Frequency Bias in Image Classification

CNNs exhibit many behaviors different from humans, one of which is the capability of employing high-frequency components. This paper discusses the frequency bias phenomenon in image classification tasks: the high-frequency components are actually much less exploited than the low- and mid- frequency components. We first investigate the frequency bias phenomenon by presenting two observations on feature discrimination and learning priority. Furthermore, we hypothesize that (1) the spectral density, (2) class consistency directly affect the frequency bias. Specifically, our investigations verify that the spectral density of datasets mainly affects the learning priority, while the class consistency mainly affects the feature discrimination.
On Quantifying Literals in Boolean Logic and its Applications to Explainable AI (Extended Abstract)

Quantified Boolean logic results from adding operators to Boolean logic for existentially and universally quantifying variables. This extends the reach of Boolean logic by enabling a variety of applications that have been explored over the decades. The existential quantification of literals (variable states) and its applications have also been studied in the literature. We complement this by studying universal literal quantification and its applications, particularly to explainable AI. We also provide a novel semantics for quantification and discuss the interplay between variable/literal and existential/universal quantification. We further identify classes of Boolean formulas and circuits that allow efficient quantification. Literal quantification is more fine-grained than variable quantification, which leads to a refinement of quantified Boolean logic with literal quantification as its primitive.
Scalable Anytime Planning for Multi-Agent MDPs (Extended Abstract)

We present a scalable planning algorithm for multi-agent sequential decision problems that require dynamic collaboration. Teams of agents need to coordinate decisions in many domains, but naive approaches fail due to the exponential growth of the joint action space with the number of agents. We circumvent this complexity through an anytime approach that allows us to trade computation for approximation quality and also dynamically coordinate actions. Our algorithm comprises three elements: online planning with Monte Carlo Tree Search (MCTS), factorizing local agent interactions with coordination graphs, and selecting optimal joint actions with the Max-Plus method. On the benchmark SysAdmin domain with static coordination graphs, our approach achieves comparable performance with much lower computation cost than the MCTS baselines. We also introduce a multi-drone delivery domain with dynamic, i.e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods.
Shared Autonomy Systems with Stochastic Operator Models

We consider shared autonomy systems where multiple operators (AI and human), can interact with the environment, e.g. by controlling a robot. The decision problem for the shared autonomy system is to select which operator takes control at each timestep, such that a reward specifying the intended system behaviour is maximised. The performance of the human operator is influenced by unobserved factors, such as fatigue or skill level. Therefore, the system must reason over stochastic models of operator performance. We present a framework for stochastic operators in shared autonomy systems (SO-SAS), where we represent operators using rich, partially observable models. We formalise SO-SAS as a mixed-observability Markov decision process, where environment states are fully observable and internal operator states are hidden. We test SO-SAS on a simulated domain and a computer game, empirically showing it results in better performance compared to traditional formulations of shared autonomy systems.
On Preferences and Priority Rules in Abstract Argumentation

Dung's abstract Argumentation Framework (AF) has emerged as a central formalism for argumentation in AI. Preferences in AF allow to represent the comparative strength of arguments in a simple yet expressive way. In this paper we first investigate the complexity of the verification as well as credulous and skeptical acceptance problems in Preference-based AF (PAF) that extends AF with preferences over arguments. Next, after introducing new semantics for AF where extensions are selected using cardinality (instead of set inclusion) criteria and investigating their complexity, we introduce a framework called AF with Priority rules (AFP) that extends AF with sequences of priority rules. AFP generalizes AF with classical set-inclusion and cardinality based semantics, suggesting that argumentation semantics can be viewed as ways to express priorities among extensions. Finally, we extend AFP by proposing AF with Priority rules and Preferences (AFP^2), where also preferences over arguments can be used to define priority rules, and study the complexity of the above-mentioned problems.
THE BENEFITS OF SKIMMING TECHNIQUE IN READING COMPREHENSION FOR THE SECOND SEMESTER STUDENTS OF SANATA DHARMA UNIVERSITY

Reading is a process to make sense written ideas through a meaningful interpretation and interaction with the language (Heilman Blair, 1981). One of the techniques that can be used in reading process is the skimming technique. Skimming is used when a reader wants to cover the main idea of the text in a hurry. Basic Reading II class was chosen as the object of the research because reading techniques are covered in this semester. This research is aimed to answer a research question, namely: How does the skimming technique influence students reading comprehension? To answer the research question, the researcher used a quantitative method. Questionnaire was considered as the instrument. The researcher distributed the questionnaire to 28 students in Basic Reading II class C of ELESP of Sanata Dharma University to collect data. The results showed that most of students positively responded that the skimming technique gave them benefits in their reading activity, such as increases their interest in learning English, saves their time, eases students to predict the content, helps students to find the main idea, and keeps them informed about the text.
Digital Literacy via Readersâ€™ Responses towards Humor Presented in â€œ9GAG Memesâ€

Language education by using digital literacy via reader response approaches towards humor presented in social media will attract the student attention during learning process. The purpose of this research is to analyze the text produced by learners in order to explain how they provide responses towards humorous, fearing, annoying, pitiful, and confusing memes.This research is a qualitative research. The approach of the research is descriptive approach. This research was a readers response analysis focusing on descriptive qualitative. The data collecting done by observation, documentation and questionnaires. The subject of the study are students of SMA Negeri 1 Pecangaan Jepara. The method applied in this research is General Theory of Verbal Humor and Reader â€˜s Response Theory as the approach in which the researcher related the contexts of the data to the data analyzed in order to make replicable and valid inferences from data to its context. The result of the research shows digital literacy skill of the XII grade of SMA Negeri 1 Pecangaan Jepara students is quite good. The reader accepts the aspects of humor through their current contribution on their response inside their peer group.
Observations and Implications of Diurnal Climatology and Trends in Direct and Diffuse Solar Radiation Over China

Abstract Solar radiation received at the Earth's surface ( R s ) is comprised of two components, the direct radiation ( R d ) and the diffuse radiation ( R f ). R d , the direct beam from the sun, is essential for concentrated solar power generation. R f , scattered by atmospheric molecules, aerosols, or cloud droplets, has a fertilization effect on plant photosynthesis. But how R d and R f change diurnally is largely unknown owing to the lack of long‐term measurements. Taking advantage of 22 years of homogeneous hourly surface observations over China, this study documents the climatological means and evolutions in the diurnal cycles of R d and R f since 1993, with an emphasis on their implications for solar power and agricultural production. Over the solar energy resource region, we observe a loss of R d which is relatively large near sunrise and sunset at low solar elevation angles when the sunrays pass through the atmosphere on a longer pathway. However, the concentrated R d energy covering an average 10‐hr period around noon during a day is relatively unaffected. Over the agricultural crop resource region, the large amounts of clouds and aerosols scattering more of the incoming light result in R f taking the main proportion of R s during the whole day. R f resources and their fertilization effect in the main crop region of China further enhances since 1993 over almost all hours of the day.
Discerning Art Works through Active Machine Learning

Scene classification is a popular and important question in computer vision and has been developed in different areas. Applying computer vision to artworks has become a popular topic in recent years. However, the traditional random sampling to identify the artworks through machine learning requires a large data set and, therefore, a higher cost to get a solid result. This paper compares random sampling and active learning (uncertainty sampling) performance using a data set (8446 paintings) of the 50 most influential painters in Europe from the 13th to the 20th century. and then propose that the active learning strategy can build a stronger model that requires smaller data sets. The active learning model can be further improved through training in larger data sets and applied in the artwork recognition for artificial intelligence..
Smart Teacher - An AI-Based Self-Learning Platform for Grade 10 ICT Students

E-learning is a method of learning that uses electronic resources but is based on institutionalized education. While education can take place in or outside of the classroom, E-learning relies heavily on computers and the Internet. The smart Teacher is a self-learning platform for ICT students that is powered by artificial intelligence. The study's purpose is to enhance teaching and learning functions by allowing for the creation and storage of learning materials, as well as their availability and accessibility. We concentrated our research on the Grade 10 ICT topic. We created a web application that Grade 10 students can use to self- We created a web application that Grade 10 students can use to self-teach and self-assess their ICT skills, and it will be free. It will result in a higher pass rate for fundamental ICT knowledge.Students can ask questions on the main page (Image). We assigned ratings to the responses provided by different users and automatically ordered the responses based on the ratings. We'll offer real-time cautions about unrelated questions. Simultaneously, we added a chatbot that will provide related site recommendations as well as real-time website instructions. It will listen to the students' queries (voice or sound) and then propose relevant questions in text form that the students can utilize.
Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM

The significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integrate services or products such as music distribution, content management, audiobooks, streaming, and so on, which require users to demonstrate and guarantee their audio ownership. The use of acoustic fingerprint technology has emerged as a solution that is widely used to secure audio applications. This technique extracts and analyzes certain information that identifies the inherent properties of a partial or complete audio file. In this paper, we introduce two audio fingerprinting hardware architectures with a feature extraction system based on spectrogram saliency maps (SSM) and a brute-force search. The first of these conducts a search in 33 saliency maps of 32 × 32 pixels in size. After analyzing the first algorithm, a second architecture is proposed, in which the saliency map is reduced to 27 × 25 pixels, requiring 75.67% fewer hardware resources, lowering the power consumption by 64.58%, and improving the efficiency by 3.19 times via a throughput reduction of 22.29%.