Reviewerly/exHarmony huggingface dataset:
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer

Massively multilingual models are promising for transfer learning across tasks and languages.However, existing methods are unable to fully leverage training data when it is available in different task-language combinations.To exploit such heterogeneous supervision, we propose Hyper-X, a single hypernetwork that unifies multi-task and multilingual learning with efficient adaptation.This model generates weights for adapter modules conditioned on both tasks and language embeddings.By learning to combine task and language-specific knowledge, our model enables zero-shot transfer for unseen languages and task-language combinations.Our experiments on a diverse set of languages demonstrate that Hyper-X achieves the best or competitive gain when a mixture of multiple resources is available, while being on par with strong baselines in the standard scenario.Hyper-X is also considerably more efficient in terms of parameters and resources compared to methods that train separate adapters.Finally, Hyper-X consistently produces strong results in few-shot scenarios for new languages, showing the versatility of our approach beyond zero-shot transfer.
Multitasking via baseline control in recurrent neural networks

Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a recurrent neural network with random couplings. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncovered a number of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. We identified several phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across a phase boundary retain the memory of the preceding phase. In each bistable phase, the reservoir performs a different binary decision-making task. Fast switching between different tasks can be controlled by adjusting the baseline input mean and variance. Moreover, we found that the reservoir network achieves optimal memory performance at any first-order phase boundary. In summary, baseline control enables multitasking without any optimization of the network couplings, opening directions for brain-inspired artificial intelligence and providing an interpretation for the ubiquitously observed behavioral modulations of cortical activity.
Motif-Guided Time Series Counterfactual Explanations

With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI) field has emerged. The XAI paradigm is bifurcated into two main categories: feature attribution and counterfactual explanation methods. While feature attribution methods are based on explaining the reason behind a model decision, counterfactual explanation methods discover the smallest input changes that will result in a different decision. In this paper, we aim at building trust and transparency in time series models by using motifs to generate counterfactual explanations. We propose Motif-Guided Counterfactual Explanation (MG-CF), a novel model that generates intuitive post-hoc counterfactual explanations that make full use of important motifs to provide interpretive information in decision-making processes. To the best of our knowledge, this is the first effort that leverages motifs to guide the counterfactual explanation generation. We validated our model using five real-world time-series datasets from the UCR repository. Our experimental results show the superiority of MG-CF in balancing all the desirable counterfactual explanations properties in comparison with other competing state-of-the-art baselines.
Characterizing the qIND-qCPA (In)security of the CBC, CFB, OFB and CTR Modes of Operation

We fully characterize the post-quantum security of the CBC, CFB, OFB and CTR modes of operation by considering all the possible notions of qIND-qCPA security defined by Carstens, Ebrahimi, Tabia and Unruh (TCC 2021), thus extending the work performed by Anand, Targhi, Tabia and Unruh (PQCrypto 2016). We show that the results obtained by Anand et al. for the qIND-qCPA-P6 security of these modes carry on to the other IND-qCPA notions, namely the qIND-qCPA-P10 and qIND-qCPA-P11 ones. We also show that all of these modes are insecure according to all of the other notions, regardless of the block cipher they are used with. We also provide two general results concerning the insecurity of commonly used properties of block ciphers, namely those preserving the length of their input and those using the XOR operation as a way to randomize the encryption. Finally, we use these results to highlight the need for new quantum semantic security notions.
NerCo: A Contrastive Learning Based Two-Stage Chinese NER Method

Sequence labeling serves as the most commonly used scheme for Chinese named entity recognition(NER). However, traditional sequence labeling methods classify tokens within an entity into different classes according to their positions. As a result, different tokens in the same entity may be learned with representations that are isolated and unrelated in target representation space, which could finally negatively affect the subsequent performance of token classification. In this paper, we point out and define this problem as Entity Representation Segmentation in Label-semantics. And then we present NerCo: Named entity recognition with Contrastive learning, a novel NER framework which can better exploit labeled data and avoid the above problem. Following the pretrain-finetune paradigm, NerCo firstly guides the encoder to learn powerful label-semantics based representations by gathering the encoded token representations of the same Semantic Class while pushing apart that of different. Subsequently, NerCo finetunes the learned encoder for final entity prediction. Extensive experiments on several datasets demonstrate that our framework can consistently improve the baseline and achieve state-of-the-art performance.
Requirements quality research: a harmonized theory, evaluation, and roadmap

Abstract High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap.
The Changing Landscape of Privacy– Countermeasures in the Era of the COVID-19 Pandemic

The COVID-19 pandemic has changed the landscape of data privacy, and many countermeasures have been developed to lower privacy risks from an epidemic handling system (EHS), e.g., from contact-tracing apps, proximity detection systems, and mobility modeling frameworks. This study discusses the changes in the privacy landscape and highlights the diverse factors causing such changes amid the COVID-19 pandemic. It pinpoints various countermeasures that were recently developed, discusses key lessons that can be derived from this pandemic with respect to privacy, and highlights the actions that can be taken to improve data privacy while using digital tools in pandemic times. This will allow the development of privacy-friendly EHSs to help our society. Our findings can guide the privacy and database community in creating appropriate strategies for data governance without losing guarantees of privacy in COVID-19-like pandemics.
Are We Writing for Bots or Humans?

Use chatbots with caution as they are only as good as the information they are fed.
GriTS: Grid Table Similarity Metric for Table Structure Recognition

In this paper, we propose a new class of metric for table structure recognition (TSR) evaluation, called grid table similarity (GriTS). Unlike prior metrics, GriTS evaluates the correctness of a predicted table directly in its natural form as a matrix. To create a similarity measure between matrices, we generalize the two-dimensional largest common substructure (2D-LCS) problem, which is NP-hard, to the 2D most similar substructures (2D-MSS) problem and propose a polynomial-time heuristic for solving it. This algorithm produces both an upper and a lower bound on the true similarity between matrices. We show using evaluation on a large real-world dataset that in practice there is almost no difference between these bounds. We compare GriTS to other metrics and empirically validate that matrix similarity exhibits more desirable behavior than alternatives for TSR performance evaluation. Finally, GriTS unifies all three subtasks of cell topology recognition, cell location recognition, and cell content recognition within the same framework, which simplifies the evaluation and enables more meaningful comparisons across different types of TSR approaches. Code will be released at https://github.com/microsoft/table-transformer .
Blockchain based trusted execution environment architecture analysis for multi - source data fusion scenario

Abstract Multi-source data fusion techniques are widely applied in dynamic target detection scenarios, such as target situational awareness, radar signal resolution, and feature fusion labeling. Currently, techniques including clustering, neural networks, Bayesian analysis, and machine learning have been applied to improve the success rate of multi-source data fusion in terms of interference data noise reduction. The research on data tampering prevention of multiple data sources is mainly based on the data distributed authentication technology. The research on performing data fusion process in a trusted execution environment is mainly based on cryptography and codec technology. This paper focuses on the technical application architecture that can effectively improve the comprehensive efficiency of multi-source data fusion processing under the constraints of business scenarios. Accordingly, this paper proposes a trusted execution environment architecture based on blockchain technology for multi-source data fusion scenarios. It integrates the strategy of trusted data source data verification in blockchain smart contracts into the typical multi-source data fusion application architecture. After comparison tests in a simulation environment, the trusted execution environment architecture based on blockchain technology has shown considerable improvements in fusion success rate with limited performance cost.
Belief revision and incongruity: is it a joke?*

Incongruity often makes people laugh. You have to be smart to say stupid things. It requires to be even smarter for understanding them. This paper is a shameless attempt to formalize this intelligent behavior in the case of an agent listening to a joke. All this is a matter of revision of beliefs, surprise and violation of norms.
Comprehensive study of moiré deflectometry based on diffraction order filtering of fringes

This paper presents a detailed investigation of the theory of moiré deflectometry through a comprehensive analysis of the higher-order diffraction of moiré fringes. We demonstrate the possibility of achieving always-high-resolution moiré fringes regardless of Ronchi grating positions and Talbot distance by filtering out the even diffraction orders of moiré fringes. Our theoretical studies lead to a comprehensive formulation of this technique, revealing the high potential of moiré deflectometry towards more precise measurement.
Masked Embedding Modeling with Rapid Domain Adjustment for Few-Shot Image Classification

In few-shot classification, performing well on a testing dataset is a challenging task due to the restricted amount of labelled data available and the unknown distribution. Many previously proposed techniques rely on prototypical representations of the support set in order to classify a query set. Although this approach works well with a large, in-domain support set, accuracy suffers when transitioning to an out-of-domain setting, especially when using small support sets. To address out-of-domain performance degradation with small support sets, we propose Masked Embedding Modeling for Few-Shot Learning (MEM-FS), a novel, self-supervised, generative technique that reinforces few-shot-classification accuracy for a prototypical backbone model. MEM-FS leverages the data completion capabilities of a masked autoencoder to expand a given embedded support set. To further increase out-of-domain performance, we also introduce Rapid Domain Adjustment (RDA), a novel, self-supervised process for quickly conditioning MEM-FS to a new domain. We show that masked support embeddings generated by MEM-FS+RDA can significantly improve backbone performance on both out-of-domain and in-domain datasets. Our experiments demonstrate that applying the proposed technique to an inductive classifier achieves state-of-the-art performance on mini-imagenet, the CVPR L2ID Classification Challenge, and a newly proposed dataset, IKEA-FS. We provide code for this work at https://github.com/Brikwerk/MEM-FS.
The time course of visual foraging in the lifespan: Spatial scanning, organization search, and target processing

Abstract Visual foraging is a variant of visual search, consisting of searching for an undetermined number of targets among distractors (e.g., looking for various LEGO pieces in a box). Under non-exhaustive tasks, the observer scans the display, picking those targets needed, not necessarily all of them, before leaving the search. To understand how the organization of such natural foraging tasks works, several measures of spatial scanning and organization have been proposed in the exhaustive foraging literature: best-r, intertarget distances, PAO, and target intersections. In the present study, we apply these measures and new Bayesian indexes to determine how the time course of visual foraging is organized in a dynamic non-exhaustive paradigm. In a large sample of observers (279 participants, 4–25 years old), we compare feature and conjunction foraging and explore how factors like set size and time course, not previously tested in exhaustive foraging, might affect search organization in non-exhaustive dynamic tasks. The results replicate previous findings showing younger observers’ searching being less organized, feature conditions being more organized than conjunction conditions, and organization leading to a more effective search. Interestingly, observers tend to be less organized as set size increases, and search is less organized within a patch as it advances in time: Search organization decreases when search termination is coming, suggesting organization measures as potential clues to understand quitting rules in search. Our results highlight the importance of studying search organization in foraging as a critical source of understanding complex cognitive processes in visual search.
Anomaly classification by inserting prior knowledge into a max-tree based method for divertor hot spot characterization on WEST tokamak

The divertor of WEST (W Environment in Steady-state Tokamak) is the main component for plasma control and exhaust. It receives high heat fluxes, which can cause damage to plasma facing units above the allowable heat flux. Improving the operation safety on the actively cooled tungsten divertor is being researched in place at WEST, toward providing divertor monitoring solution for ITER. Divertor operation safety relies on detecting, monitoring, and classifying all hot spots on the divertor surface using infrared (IR) cameras. In this paper, a method based on max-tree representation and attributes of IR images is used to classify normal from abnormal strikelines on the divertor. The proposed method requires only high-level prior knowledge of abnormal temperatures and divertor structure but does not require any labeled data, unlike existing methods, such as support vector machines (SVMs) or convolutional neural networks (CNNs). The max-tree classifier method is tested on real IR images from the WEST tokamak and shows that 88% of hot spots are accurately classified with a small enough calculation duration that can be performed between two pulses.
Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance

We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good trade-off in terms of quality, accuracy, speed, and resource utilization.
Deep Learning-Based Action Classification Using One-Shot Object Detection

Deep learning-based action classification technology has been applied to various fields, such as social safety, medical services, and sports. Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions. There are various related studies on the real-time classification of actions in an image. However, existing deep learning-based action classification models have prolonged response speeds, so there is a limit to real-time analysis. In addition, it has low accuracy of action of each object if multiple objects appear in the image. Also, it needs to be improved since it has a memory overhead in processing image data. Deep learning-based action classification usingone-shot object detection is proposed to overcome the limitations of multi-frame-based analysis technology. The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image. Then, a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector. Compared to the existing studies, the constructed model shows higher accuracy of 74.95%, and in terms of speed, it offered better performance than the current studies at 0.234 s per frame. The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network. Therefore, it is expected to contribute significantly to commercializing realistic streaming data analysis technologies, such as CCTV.
Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically optimized. This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system. The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch. Each scenario in this dataset is classified and labeled as a fall or a non-fall. In comparison to the three machine learning models—K-nearest neighbors (KNN), decision tree (DT), and standard random forest (SRF), the proposed ensemble classifier outperformed the other models and achieved 98.4% accuracy. The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.
CARE: Confidence-Rich Autonomous Robot Exploration Using Bayesian Kernel Inference and Optimization

In this paper, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel inference and optimization (BKIO), achieving an approximate logarithmic complexity without the need for training. BKIO can also infer the CRMI and generate the best action using BO with bounded cumulative regret, which ensures its comparable accuracy to GPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments. We also provide our open-source implementation code at https://github.com/Shepherd-Gregory/BKIO-Exploration.