Reviewerly/exHarmony huggingface dataset:
IOPE: Interactive Ontology Population and Enrichment Guided by Ontological Constraints

Specialized ontologies are constructed to capture the skills of experienced experts, with the goal of sharing them with a larger community of trainees and less experienced experts in the domain. A challenging task in the engineering pipeline of specialized ontologies is ontology updating, which encompasses enrichment and population. Domain experts require an understanding of the RDF notation and OWL semantics to update their ontologies, which is not often the case. In this paper, we present IOPE, an interactive framework for the automatic construction of a Graphical User Interface (GUI) to support the controlled update process, by enabling the experts to easily interact with their ontology. We contribute a set of “mapping rules” to transform the ontological constraints into interactive widgets organized in the form of pre-filled Web pages in the GUI, and a set of “binding rules” to transform the expert interactions into RDF graphs, and hence perform the updates. In an extensive set of experiments, we illustrate the efficacy of IOPE in empowering medical experts to update their specialized ontology.
Hybrid Instance-Aware Temporal Fusion for Online Video Instance Segmentation

Recently, transformer-based image segmentation methods have achieved notable success against previous solutions. While for video domains, how to effectively model temporal context with the attention of object instances across frames remains an open problem. In this paper, we propose an online video instance segmentation framework with a novel instance-aware temporal fusion method. We first leverage the representation, \ie, a latent code in the global context (instance code) and CNN feature maps to represent instance- and pixel-level features. Based on this representation, we introduce a cropping-free temporal fusion approach to model the temporal consistency between video frames. Specifically, we encode global instance-specific information in the instance code and build up inter-frame contextual fusion with hybrid attentions between the instance codes and CNN feature maps. Inter-frame consistency between the instance codes is further enforced with order constraints. By leveraging the learned hybrid temporal consistency, we are able to directly retrieve and maintain instance identities across frames, eliminating the complicated frame-wise instance matching in prior methods. Extensive experiments have been conducted on popular VIS datasets, i.e. Youtube-VIS-19/21. Our model achieves the best performance among all online VIS methods. Notably, our model also eclipses all offline methods when using the ResNet-50 backbone.
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation to be off-policy regardless of the fact that these off-policy extensions could either change the original objective or involve complicated optimization. We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our formulation capitalizes on two key insights: (1) the similarity between the Bellman equation and the stationary state-action distribution equation allows us to derive a novel temporal difference (TD) learning approach; and (2) the use of a deterministic policy simplifies the TD learning. Combined, these insights yield a practical algorithm, Deterministic and Discriminative Imitation (D2-Imitation), which oper- ates by first partitioning samples into two replay buffers and then learning a deterministic policy via off-policy reinforcement learning. Our empirical results show that D2-Imitation is effective in achieving good sample efficiency, outperforming several off-policy extension approaches of adversarial imitation on many control tasks.
Rainbow on Cortex-M4

We present the first Cortex-M4 implementation of the NISTPQC signature finalist Rainbow. We target the Giant Gecko EFM32GG11B which comes with 512 kB of RAM which can easily accommodate the keys of RainbowI.We present fast constant-time bitsliced F16 multiplication allowing multiplication of 32 field elements in 32 clock cycles. Additionally, we introduce a new way of computing the public map P in the verification procedure allowing vastly faster signature verification.Both the signing and verification procedures of our implementation are by far the fastest among the NISTPQC signature finalists. Signing of rainbowIclassic requires roughly 957 000 clock cycles which is 4× faster than the state of the art Dilithium2 implementation and 45× faster than Falcon-512. Verification needs about 239 000 cycles which is 5× and 2× faster respectively. The cost of signing can be further decreased by 20% when storing the secret key in a bitsliced representation.
Detection of Stego Images with Adaptively Embedded Data by Component Analysis Methods

Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing stego- to-cover ratio. The modern paradigm of image calibration is based on usage of enormous set of high-pass filters. However, selection of filter(s) that maximizes the probability of stego images detection is non-trivial task, especially in case of limited a prior knowledge about embedding methods. For solving this task, we proposed to use component analysis methods for image calibration, namely principal components analysis. Results of comparative analysis of novel maxSRMd2 cover rich model and proposed solution showed that principal component analysis allows increasing detection accuracy up to 1.5% even in the most difficult cases (low cover image payload and absence of cover- stego images pairs in training set).
Pengembangan Bahasa Anak Usia Dini Melalui Metode Bercerita Di Lembaga Paud Meraje Gune

Early Childhood Language Development Through Storytelling Method at Meraje Gune PAUD Institution. This paper will explore the methods used in learning to develop early childhood language skills at the Meraje Gune PAUD Institution. The focus of this research will be on how to develop language at an early age by using the storytelling method in improving language skills at an early age. The study uses descriptive qualitative research where one of the studies that describes the findings or results in the field, observes and analyzes what is in the research. The results of this study indicate that, early childhood language learning using methods that can develop the potential of early childhood language. In fact, the influence of learning methods in developing children's language skills is by opening access to improve children's language skills, seeing from starting to speak, never feeling shy in asking questions, being active in various fields, namely storytelling, singing and being a leader in class.
Finding Our Voice: Highly Flexible ED for the HyFlex World

The COVID-19 pandemic brought about unprecedented changes in our approach to delivering educational development (ED) programming. In this article we discuss how our dual ED centers pivoted during the sudden switch to online learning, highlighting how we overcame challenges such as a small staff, tight timelines, and faculty anxieties. Particularly, we explore how we adapted to the university’s investment in technologically advanced Hybrid-Flexible (HyFlex) classroom spaces and utilized a multi-pronged team approach to provide effective and timely ED to faculty. By identifying key faculty leaders, identifying multiple sources of data, and using multiple modalities, we supported the faculty in their mission to effectively serve their students during this difficult and stressful time. In pivoting from a triage approach to more tactically focused development, the two ED centers discovered that they could more effectively serve faculty (and by extension students) by shattering the structural silos that had previously defined them and instead working as a unified entity.
Private Data Harvesting on Alexa Using Third-Party Skills

We are currently seeing an increase in the use of voice assistants which are used for various purposes. These assistants have a wide range of inbuilt functionalities with the possibility of installing third-party applications. In this work, we will focus on analyzing and identifying vulnerabilities that are introduced by these third-party applications. In particular, we will build third-party applications (called Skills) for Alexa, the voice assistant developed by Amazon. We will analyze existing exploits, identify accessible data and propose an adversarial framework that deceives users into disclosing private information. For this purpose, we developed four different malicious Skills that harvest different pieces of private information from users. We perform a usability analysis on the Skills and feasibility analysis on the publishing pipeline for one of the Skills.
Improvement of time series forecasting quality by means of multiple models prediction averaging

Construction of time series models is usually based on Akaike and Bayes information criteria.Requirement of model simplicity is built inside the information criteria structure and the best fitted models aren't always best in terms of criteria values.Often there are a few best models that fit investigated time series well and there's problem of choice between them.Usually information criteria values allow to choose among them.But if one needs the best model by forecasts quality, best fitted models, or there are other thoughts a research has to choose and test models manually.At the same time when a few models are chosen it's possible to construct their combination.The simplest way is to count mean value of their forecasts and to use it as a combined prediction.Practical researches confirm that forecast error gets lower in this approach.Also, more complex construction than averaging of forecasts can be used (for example, weighted voting that is widely used in bagging technique in solution of classification problems).But this approach hasn't got enough theoretical base.From theoretical point of view confidence intervals of time series forecasts (usually here they're considered as prediction intervals) is also very complex task.Intervals tend to be very wide even for models with good prediction quality in terms of mean forecast errors.Thus, prediction intervals are rare in use.In this paper a few time series models are constructed for wage and income indices of Russian macroeconomic time series.Their predictions are combined into one forecast and it's quality is compared to individual ones.Transformation of forecast variance and prediction intervals in case of simple (moving averages MA(q) and autoregressions AR(p) of low order) models is also considered but is a part of further work.
Faktor-Faktor Yang Mempengaruhi Penyelesaian Tugas Akhir Mahasiswa Program Studi Akuntansi Universitas Kristen Satya Wacana

The purpose of this study was to investigate the Impact of the motivation of on-time graduation, availability of learning resources, the quality of research consultation, and peer environment to the completion of students’ research. This study was an ex-post facto research with the quantitative approach. The respondents of this study are sixty students of the Accounting Program in the Faculty of Economy of Universitas Kristen Satya Wacana who were taking the research course in the second semester of 2020/2021 period. To collect the data, questionnaires were spread in the form of google form and to analyze the data, this study used multiple regression. The result of the study showed that the motivation of on-time graduation, availability of learning resources, the quality of research consultation, and peer environment did not give positive impacts towards the completion of students’ research.
Graph Neural Networks: Self-supervised Learning

Although deep learning has achieved state-of-the-art performance across numerous domains, these models generally require large annotated datasets to reach their full potential and avoid overfitting. However, obtaining such datasets can have high associated costs or even be impossible to procure. Self-supervised learning (SSL) seeks to create and utilize specific pretext tasks on unlabeled data to aid in alleviating this fundamental limitation of deep learning models. Although initially applied in the image and text domains, recent interest has been in leveraging SSL in the graph domain to improve the performance of graph neural networks (GNNs). For node-level tasks, GNNs can inherently incorporate unlabeled node data through the neighborhood aggregation unlike in the image or text domains; but they can still benefit by applying novel pretext tasks to encode richer information and numerous such methods have recently been developed. For GNNs solving graph-level tasks, applying SSL methods is more aligned with other traditional domains, but still presents unique challenges and has been the focus of a few works. In this chapter, we summarize recent developments in applying SSL to GNNs categorizing them via the different training strategies and types of data used to construct their pretext tasks, and finally discuss open challenges for future directions.
Classification on Digital Pathological Images of Breast Cancer Based on Deep Features of Different Levels

Breast cancer is one of the primary causes of cancer death in the world and has a great impact on women’s health. Generally, the majority of classification methods rely on the high-level feature. However, different levels of features may not be positively correlated for the final results of classification. Inspired by the recent widespread use of deep learning, this study proposes a novel method for classifying benign cancer and malignant breast cancer based on deep features. First, we design Sliding + Random and Sliding + Class Balance Random window slicing strategies for data preprocessing. The two strategies enhance the generalization of model and improve classification performance on minority classes. Second, feature extraction is based on the AlexNet model. We also discuss the influence of intermediate- and high-level features on classification results. Third, different levels of features are input into different machine-learning models for classification, and then, the best combination is chosen. The experimental results show that the data preprocessing of the Sliding + Class Balance Random window slicing strategy produces decent effectiveness on the BreaKHis dataset. The classification accuracy ranges from 83.57% to 88.69% at different magnifications. On this basis, combining intermediate- and high-level features with SVM has the best classification effect. The classification accuracy ranges from 85.30% to 88.76% at different magnifications. Compared with the latest results of F. A. Spanhol’s team who provide BreaKHis data, the presented method shows better classification performance on image-level accuracy. We believe that the proposed method has promising good practical value and research significance.
Secure Quantum Pattern Communication

We propose a multimode modulation scheme for continuous-variable (CV) quantum communications, which we call quantum pattern encoding. In this setting, classical information can be encoded into multimode patterns of discretely modulated coherent states, which form instances of a communicable image space. Communicators can devise arbitrarily complex encoding schemes that are degenerate and highly nonuniform, such that communication is likened to the task of pattern recognition. We explore initial communication schemes that exploit these techniques and that lead to an increased encoding complexity. We discuss the impact that this has on the role of a near-term quantum eavesdropper; formulating new realistic classes of attacks and secure communication rates.Received 12 April 2021Revised 22 October 2021Accepted 16 December 2021DOI:https://doi.org/10.1103/PRXQuantum.3.010311Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningOptical quantum information processingQuantum communicationQuantum measurementsQuantum metrologyQuantum Information
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks

We study different aspects of active learning with deep neural networks in a consistent and unified way.i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training.ii) We study active learning w.r.t. the model configurations such as the number of epochs and neurons as well as the choice of batch size.iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures.iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning.v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.
Image forgery detection review

With the wide spread of digital document use in administrations, fabrication and use of forged documents have become a serious problem.This paper presents a study and classification of the most important works on image and document forgery detection.The classification is based on documents type, forgery type, detection method, validation dataset, evaluation metrics and obtained results.Most of existing forgery detection works are dealing with images and few of them analyze administrative documents and go deeper to analyze their contents.
GMH: A General Multi-hop Reasoning Model for KG Completion

Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing.This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning.However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities.To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios.We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop.Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching.The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.
Some Properties of Membership Functions Composed of Triangle Functions and Piecewise Linear Functions

Summary . IF-THEN rules in fuzzy inference is composed of multiple fuzzy sets (membership functions). IF-THEN rules can therefore be considered as a pair of membership functions [7]. The evaluation function of fuzzy control is composite function with fuzzy approximate reasoning and is functional on the set of membership functions. We obtained continuity of the evaluation function and compactness of the set of membership functions [12]. Therefore, we proved the existence of pair of membership functions, which maximizes (minimizes) evaluation function and is considered IF-THEN rules, in the set of membership functions by using extreme value theorem. The set of membership functions (fuzzy sets) is defined in this article to verifier our proofs before by Mizar [9], [10], [4]. Membership functions composed of triangle function, piecewise linear function and Gaussian function used in practice are formalized using existing functions. On the other hand, not only curve membership functions mentioned above but also membership functions composed of straight lines (piecewise linear function) like triangular and trapezoidal functions are formalized. Moreover, different from the definition in [3] formalizations of triangular and trapezoidal function composed of two straight lines, minimum function and maximum functions are proposed. We prove, using the Mizar [2], [1] formalism, some properties of membership functions such as continuity and periodicity [13], [8].
Fuzzy Identification-Based Encryption for healthcare user face authentication

Background: Internet of Medical Things (IOMT) has the potential to monitor health continuously and in real-time. One of the main issues that arise in IOMT is how securely the data can be transmitted to the clinical team. In this project, biometric Identity-based encryption was utilized using the Fuzzy-IBE (Identity-based encryption) scheme that uses face features of the clinicians to create the public key. Figure 1 shows the testbed setup designed to improve the privacy and security of the patients’ healthcare data. Methods: The testbed comprises an ESP32 platform sensing and encrypting data, the Nvidia Jetson Nano for data collection and decryption, and the Thingsboard online platform for vital information visualization. Fuzzy Identity-Based Encryption (FIBE)1–3 uses legitimate users’ facial features. The encrypted vital information is transmitted to the Edge-device (Jetson Nano) through BLE/Wi-Fi. On the edge-device of the healthcare system, the face authentication mechanism verifies the user&apos;s (clinician) legitimacy to assess the data. Upon user authentication, their facial features will be used to generate a private decryption key that can decrypt the received encrypted data. The data is further sent to the core cloud (Thingsboard) for storage and visualization. To secure the data on the cloud, we deployed an Intrusion Detection System (IDS) model using deep learning to identify the inter-domain stream of malicious traffic. Results: The face authentication testing using Fuzzy Identity-based Cryptography relied on a public data set. The execution time was calculated for Encryption (time to encrypt the patient&apos;s vital data using a public key of health physician&apos;s facial features) and decryption (time to match at least d components of the ciphertext and perform message decryption). The experimental results are reported in Table 1. Conclusion: In today&apos;s age of advanced telecommunication technology, cyber security is a very important factor. The designed testbed setup in this work showcases how healthcare data can be secured against malicious attacks.