Reviewerly/exHarmony huggingface dataset:
Federated Learning-Based Detection of Invasive Carcinoma of No Special Type with Histopathological Images

Invasive carcinoma of no special type (IC-NST) is known to be one of the most prevalent kinds of breast cancer, hence the growing research interest in studying automated systems that can detect the presence of breast tumors and appropriately classify them into subtypes. Machine learning (ML) and, more specifically, deep learning (DL) techniques have been used to approach this problem. However, such techniques usually require massive amounts of data to obtain competitive results. This requirement makes their application in specific areas such as health problematic as privacy concerns regarding the release of patients' data publicly result in a limited number of publicly available datasets for the research community. This paper proposes an approach that leverages federated learning (FL) to securely train mathematical models over multiple clients with local IC-NST images partitioned from the breast histopathology image (BHI) dataset to obtain a global model. First, we used residual neural networks for automatic feature extraction. Then, we proposed a second network consisting of Gabor kernels to extract another set of features from the IC-NST dataset. After that, we performed a late fusion of the two sets of features and passed the output through a custom classifier. Experiments were conducted for the federated learning (FL) and centralized learning (CL) scenarios, and the results were compared. Competitive results were obtained, indicating the positive prospects of adopting FL for IC-NST detection. Additionally, fusing the Gabor features with the residual neural network features resulted in the best performance in terms of accuracy, F1 score, and area under the receiver operation curve (AUC-ROC). The models show good generalization by performing well on another domain dataset, the breast cancer histopathological (BreakHis) image dataset. Our method also outperformed other methods from the literature.
Trends of Augmented Reality Applications in Science Education

The current study was intended to conduct a systematic literature review in the field of augmented reality (AR) in science education. In this review, we considered 319 refereed articles from the Scopus database over the last 15 years (2007-2022). For this review, trends of AR applications in science education were proposed from various aspects, such as annual scientific growth, the number of authors, most active countries, most prolific journals, most cited articles, and most preferred research methods. The results indicated that: (1) research on AR applications has steadily increased since 2007 and peaked in 2020 and 2021; (2) the majority of publications have two authors followed by three authors and four authors; (3) the countries of the first authors of the AR studies were mostly from the US followed by Taiwan and Turkey; (4) the majority of articles were published by the Journal of Chemical Education and Computers and Education with 19 and 11 papers, respectively; (5) the most cited papers were written by Dunleavy, Dede, and Mitchell (638 citations), Potkonjak and colleagues (339 citations), and Squire and Jan (300 citations); and (6) more than one-third of the documents employed quantitative methods followed by mixed and qualitative methods. Based on the findings, discussion and suggestions are presented for future studies.
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments

Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separately is expensive and cannot be accomplished in real time given limited computing and battery capabilities on a mobile platform. In this paper, we propose an efficient multi-task approach for RGB-D scene analysis~(EMSANet) that simultaneously performs semantic and instance segmentation~(panoptic segmentation), instance orientation estimation, and scene classification. We show that all tasks can be accomplished using a single neural network in real time on a mobile platform without diminishing performance - by contrast, the individual tasks are able to benefit from each other. In order to evaluate our multi-task approach, we extend the annotations of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance segmentation and orientation estimation. To the best of our knowledge, we are the first to provide results in such a comprehensive multi-task setting for indoor scene analysis on NYUv2 and SUNRGB-D.
A Multi-VTOL Modular Aspect Ratio Reconfigurable Aerial Robot

This work presents a novel Aspect Ratio-Modular Vertical Take-Off and Landing (ARM-VTOL) aerial robot, which is a meta-aircraft composed of two or more TiltRotor hybrid aircraft systems capable of magnetically being coupled during hovering flight, and of executing VTOL / Fixed-Wing hybrid missions once combined. The proposed meta-aircraft system carries the advantage of improved aerodynamic efficiency due its increased cumulative planform aspect ratio, which can be leveraged to achieve prolonged flight times in collaborative multi-vehicle flight. We propose an extendable methodology for its control which relies on the multi-body equivalent dynamics, and we present the coupling mechanism design that facilitates its experimental demonstration. We accompany these contributions with a field test-driven evaluation study conducted with a bi-vehicle ARM-VTOL prototype. The presented sequence includes vehicle-to-vehicle magnetic coupling during hovering flight, and is followed by a combined-vehicle mission comprising vertical climb, VTOL-forward transition, fixed-wing flight and maneuvering, and reverse-transition to VTOL and landing.
From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology

Informal social interaction is the primordial home of human language.Linguistically diverse conversational corpora are an important and largely untapped resource for computational linguistics and language technology.Through the efforts of a worldwide language documentation movement, such corpora are increasingly becoming available.We show how interactional data from 63 languages (26 families) harbours insights about turn-taking, timing, sequential structure and social action, with implications for language technology, natural language understanding, and the design of conversational interfaces.Harnessing linguistically diverse conversational corpora will provide the empirical foundations for flexible, localizable, humane language technologies of the future.
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages

Cross-lingual transfer learning with large multilingual pre-trained models can be an effective approach for low-resource languages with no labeled training data.Existing evaluations of zero-shot cross-lingual generalisability of large pre-trained models use datasets with English training data, and test data in a selection of target languages.We explore a more extensive transfer learning setup with 65 different source languages and 105 target languages for part-of-speech tagging.Through our analysis, we show that pre-training of both source and target language, as well as matching language families, writing systems, word order systems, and lexical-phonetic distance significantly impact cross-lingual performance.The findings described in this paper can be used as indicators of which factors are important for effective zero-shot cross-lingual transfer to zero-and low-resource languages.
Generative Steganography Based on Long Readable Text Generation

Text steganography has received a lot of attention in the application of covert communication. How to ensure desirable capacity and imperceptibility has become a key issue in text steganography. There are two typical approaches, i.e., text-selection-based steganography and text-generation-based steganography. However, the text-selection-based approaches generally have the very low hidden capacity and are not applicable in practical scenarios. Although the text-generation-based approaches can embed secret messages with higher capacity during text generation, they are prone to semantic incoherence and semantic errors when generating long texts. To address the abovementioned issues, this article proposes a novel text steganography based on long readable text generation. It first determines the topic of the stego-text according to the scenarios of the communication parties. Then, the plug and play language model (PPLM) is explored to generate the long readable stego-text conforming to the topic with semantic coherency. A given secret message is hidden during text generation by selecting proper words in an established embeddable candidate word pool (ECWP). Establishing the ECWP prevents the language model (LM) from selecting words with low probability in the text generation, thereby avoiding the generation of low-quality or even grammatically incorrect stego-text. Experimental results show that the proposed approach significantly increases hidden capacity while maintaining good imperceptibility compared with the existing approaches.
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation

End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, making synthetic data augmentation by back-translation or knowledge distillation a necessary ingredient of end-to-end training. In this paper, we present a novel approach to data augmentation that leverages audio alignments, linguistic properties, and translation. First, we augment a transcription by sampling from a suffix memory that stores text and audio data. Second, we translate the augmented transcript. Finally, we recombine concatenated audio segments and the generated translation. Besides training an MT-system, we only use basic off-the-shelf components without fine-tuning. While having similar resource demands as knowledge distillation, adding our method delivers consistent improvements of up to 0.9 and 1.1 BLEU points on five language pairs on CoVoST 2 and on two language pairs on Europarl-ST, respectively.
On the Sensitivity and Stability of Model Interpretations in NLP

Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions.Despite the surge of new interpretation methods, it remains an open problem how to define and quantitatively measure the faithfulness of interpretations, i.e., to what extent interpretations reflect the reasoning process by a model.We propose two new criteria, sensitivity and stability, that provide complementary notions of faithfulness to the existed removal-based criteria.Our results show that the conclusion for how faithful interpretations are could vary substantially based on different notions.Motivated by the desiderata of sensitivity and stability, we introduce a new class of interpretation methods that adopt techniques from adversarial robustness.Empirical results show that our proposed methods are effective under the new criteria and overcome limitations of gradient-based methods on removal-based criteria.Besides text classification, we also apply interpretation methods and metrics to dependency parsing.Our results shed light on understanding the diverse set of interpretations.
CNN and HEVC Video Coding Features for Static Video Summarization

This study proposes a novel solution for the detection of keyframes for static video summarization. We preprocessed the well-known video datasets by coding them using the HEVC video coding standard. During coding, 64 proposed features were generated from the coder for each frame. Additionally, we converted the original YUVs of the raw videos into RGB images and fed them into pretrained CNN networks for feature extraction. These include GoogleNet, AlexNet, Inception-ResNet-v2, and VGG16. The modified datasets are made publicly available to the research community. Before detecting keyframes in a video, it is important to identify and eliminate duplicate or similar video frames. A subset of the proposed HEVC feature set was used to identify these frames and eliminate them from the video. We also propose an elimination solution based on the sum of the absolute differences between a frame and its motion-compensated predecessor. The proposed solutions are compared with existing works based on an SIFT flow algorithm that uses CNN features. Subsequently, an optional dimensionality reduction based on stepwise regression was applied to the feature vectors prior to detecting key frames. The proposed solution is compared with existing studies that use sparse autoencoders with CNN features for dimensionality reduction. The accuracy of the proposed key-frame detection system was assessed using the positive predictive values, sensitivity, and F-scores. Combining the proposed solution with Multi-CNN features and using a random forest classifier, it was shown that the proposed solution achieved an average F-score of 0.98.
A Hybrid Machine-Learning Ensemble for Anomaly Detection in Real-Time Industry 4.0 Systems

Detecting faults and anomalies in real-time industrial systems is a challenge due to the difficulty of sufficiently covering an industrial system's complexity.Today, Industry 4.0 makes it possible to tackle these problems through emerging technologies such as the Internet of Things and Machine Learning.This paper proposes a hybrid machine-learning ensemble real-time anomaly-detection pipeline that combines three Machine Learning models -Local Outlier Factor, One-Class Support Vector Machine, and Autoencoder-, through a weighted average to improve anomaly detection.The ensemble model was tested with three air-blowing machines obtaining a F 1 -score value of 0.904, 0.890, and 0.887, respectively.The results of the ensemble model showed improved performance metrics concerning the individual metrics.A novelty of this model is that it consists of two stages inspired by a standard industrial system: i) a manufacturing stage and ii) an operation stage.
Saliency as Evidence: Event Detection with Trigger Saliency Attribution

Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts.Despite significant advances in ED, existing methods typically follow a "one model fits all types" approach, which sees no differences between event types and often results in a quite skewed performance.Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem.This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events.On this foundation, we develop a new training mechanism for ED, which can distinguish between triggerdependent and context-dependent types and achieve promising performance on two benchmarks.Finally, by highlighting many distinct characteristics of trigger-dependent and context-dependent types, our work may promote more research into this problem.
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings

In order to equip NLP systems with 'selective prediction' capability, several task-specific approaches have been proposed.However, which approaches work best across tasks or even if they consistently outperform the simplest baseline MaxProb remains to be explored.To this end, we systematically study selective prediction in a large-scale setup of 17 datasets across several NLP tasks.Through comprehensive experiments under in-domain (IID), out-ofdomain (OOD), and adversarial (ADV) settings, we show that despite leveraging additional resources (held-out data/computation), none of the existing approaches consistently and considerably outperforms MaxProb in all three settings.Furthermore, their performance does not translate well across tasks.For instance, Monte-Carlo Dropout outperforms all other approaches on Duplicate Detection datasets but does not fare well on NLI datasets, especially in the OOD setting.Thus, we recommend that future selective prediction approaches should be evaluated across tasks and settings for reliable estimation of their capabilities.
Are you there? Presence in collaborative distance work

Already before the pandemic, digitally mediated collaborative work and communication were perceived as challenging. We investigate the attitudes towards emerging technologies and for transforming practises in workplaces. The focus lies on understanding the readiness for appropriating emotional tracking on presence and support for collaboration. The research-based design framework allowed to combine the various perspectives of the transdisciplinary team. Methods included participatory design, design thinking, contextual inquiry and prototype testing for enhancing presence while working with shared objects in video conferencing to explore the appropriation of tools. The findings revealed four indications: 1) awareness of interlocutors’ presence during synchronous communication is crucial. 2) Emotion and behaviour tracking raises concerns about privacy and personal control over what is displayed to others, and technology could be simpler non-distracting the work at hand. 3) The prototype was found to enhance the feeling of presence without disturbing work at hand, and 4) appropriation requires a step-by-step approach.
Visual tracking control of SCARA robot system based on deep learning and Kalman prediction method

When the workpiece moves, the existing industrial machine vision tracking system has some problems, such as low accuracy, poor recognition effect and so on. In this paper, optimising convolution neural network based on stochastic gradient descent is used for motion foreground segmentation and tracking object, to realise high-speed and high precision of recognition of the moving object. The hard triggered capture system ensures the time interval of the image sequence. And an improved image sequence speed detection algorithm is proposed, combined with the Kalman prediction equation to predict the coordinate position of the workpiece in the nonlinear motion state in order to compensate the time delay error of the visual servo system. Finally, the experiment is carried out on visual tracking robot system with the belt conveyor. The results show that the maximum tracking speed can reach 90 mm/s, and the tracking accuracy error is less than 1 mm. Although facing sudden speed changes in conveying blet, the robot system can be stable in several milliseconds which prove that this method has the characteristics of high reliability and good tracking accuracy.
Deployed measurement-device independent quantum key distribution and Bell-state measurements coexisting with standard internet data and networking equipment

Abstract The forthcoming quantum Internet is poised to allow new applications not possible with the conventional Internet. The ability for both quantum and conventional networking equipment to coexist on the same fiber network would facilitate the deployment and adoption of coming quantum technology. Most quantum networking tasks, like quantum repeaters and the connection of quantum processors, require nodes for multi-qubit quantum measurements (often Bell-State measurements), and their real-world coexistence with the conventional Internet has yet to be shown. Here we field deploy a Measurement-Device Independent Quantum Key Distribution (MDI-QKD) system, containing a Bell-State measurement node, over the same fiber connection as multiple standard Internet Protocol (IP) data networks, between three nearby cities in the Netherlands. We demonstrate over 10 Gb/s classical data communication rates simultaneously with our next-generation QKD system, and estimate 200 GB/s of classical data transmission would be easily achievable without significantly affecting QKD performance. Moreover, as the system ran autonomously for two weeks, this shows an important step towards the coexistence and integration of quantum networking into the existing telecommunication infrastructure.
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms require models of identical architecture to be deployed across the clients and server, making it infeasible to train large models due to clients' limited system resources. In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server. Unlike in conventional ensemble learning, in FL the ensemble can be trained on clients' highly heterogeneous data. Cognizant of this property, Fed-ET uses a weighted consensus distillation scheme with diversity regularization that efficiently extracts reliable consensus from the ensemble while improving generalization by exploiting the diversity within the ensemble. We show the generalization bound for the ensemble of weighted models trained on heterogeneous datasets that supports the intuition of Fed-ET. Our experiments on image and language tasks show that Fed-ET significantly outperforms other state-of-the-art FL algorithms with fewer communicated parameters, and is also robust against high data-heterogeneity.
Search-Based Testing of Reinforcement Learning

Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments make testing the behavior of deep RL agents difficult. We present a search-based testing framework that enables a wide range of novel analysis capabilities for evaluating the safety and performance of deep RL agents. For safety testing, our framework utilizes a search algorithm that searches for a reference trace that solves the RL task. The backtracking states of the search, called boundary states, pose safety-critical situations. We create safety test-suites that evaluate how well the RL agent escapes safety-critical situations near these boundary states. For robust performance testing, we create a diverse set of traces via fuzz testing. These fuzz traces are used to bring the agent into a wide variety of potentially unknown states from which the average performance of the agent is compared to the average performance of the fuzz traces. We apply our search-based testing approach on RL for Nintendo's Super Mario Bros.