Reviewerly/exHarmony huggingface dataset:
Studying the Visual Representation of Microgestures

The representations of microgestures are essentials for researchers presenting their results through academic papers and system designers proposing tutorials to novice users. However, those representations remain disparate and inconsistent. As a first attempt to investigate how to best graphically represent microgestures, we created 21 designs, each depicting static and dynamic versions of 4 commonly used microgestures (tap, swipe, flex and hold). We first studied these designs in a quantitative online experiment with 45 participants. We then conducted a qualitative laboratory experiment in Augmented Reality with 16 participants. Based on the results, we provide design guidelines on which elements of a microgesture should be represented and how. In particular, it is recommended to represent the actuator and the trajectory of a microgesture. Also, although preferred by users, dynamic representations are not considered better than their static counterparts for depicting a microgesture and do not necessarily result in a better user recognition.
Use of subword tokenization for domain generation algorithm classification

Abstract Domain name generation algorithm (DGA) classification is an essential but challenging problem. Both feature-extracting machine learning (ML) methods and deep learning (DL) models such as convolutional neural networks and long short-term memory have been developed. However, the performance of these approaches varies with different types of DGAs. Most features in the ML methods can characterize random-looking DGAs better than word-looking DGAs. To improve the classification performance on word-looking DGAs, subword tokenization is employed for the DL models. Our experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking DGAs. We then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the DGAs. Results show that the integrated scheme outperformed existing ML and DL methods, and also the subword DL methods.
Scene-adaptive pattern coding-based fringe projection profilometry: diffuse surfaces identification and 3-D reconstruction in cluttered scenes

Fringe projection profilometry (FPP) is one of the most widely used optical three-dimensional (3-D) perceiving techniques. However, when applied to cluttered scenes, acquiring accurate 3-D shapes is difficult because of the influences of indirect light caused by non-diffuse surfaces. In this paper, we first theoretically analyze and model the influences of indirect light in FPP, and then propose a scene-adaptive pattern coding-based method, which can design projection patterns based on the reflective properties of the scene's surfaces, to achieve accurate 3-D perceiving in cluttered scenes. Specifically, the scene confidence analysis method is first proposed to identify the reflective properties of various surfaces and localize the camera pixels of the diffuse surface. The illumination status (i.e., "0" or "1") of each projector pixel can be determined according to the camera-projection coordinate mapping and spatial pattern coding, where only diffuse surfaces can be illuminated, thus fundamentally preventing the influences of indirect light from the point of view of the light source. The 3-D shapes of diffuse surfaces can be accurately reconstructed in cluttered scenes. Different from traditional reflective properties change or light separation solutions, the proposed method can achieve accurate 3-D perceiving of cluttered scenes without additional hardware or expensive calculation. Extensive experiments verify that the proposed method outperforms the traditional methods in terms of accuracy and robustness.
The Effect of Disclosing the Social, Environmental and Economic Dimensions on the Profitability of the Services Sector Companies Listed on the Amman Stock Exchange

Purpose: Examine the influence of disclosing social, environmental, and economic activities on profitability in the services sector on the Amman Stock Exchange.&#x0D; &#x0D; Theoretical Framework: Explore theoretical underpinnings connecting disclosure of social, environmental, and economic activities to business profitability, drawing from corporate responsibility, sustainability, and financial theories.&#x0D; &#x0D; Methodology: Utilized a questionnaire distributed to Jordanian service sector firms' auditors and financial managers, employing simple random sampling. Focused on variables related to social, environmental, and economic disclosure.&#x0D; &#x0D; Findings: Positive link between social disclosure and profitability, attributed to fair service distribution and indirect employee support. Environmental disclosure positively affects profits through resource optimization and reduced pollution. Economic disclosure correlates positively with profitability by influencing pricing and product design.&#x0D; &#x0D; Conclusion: Disclosing social, environmental, and economic activities significantly impacts profitability in Amman Stock Exchange-listed service sector firms, emphasizing equitable services, resource efficiency, and strategic economic disclosure.
Hardware VM Isolation in the Cloud

Confidential computing is a security model that fits well with the public cloud. It enables customers to rent VMs while enjoying hardware-based isolation that ensures that a cloud provider cannot purposefully or accidentally see or corrupt their data. SEV-SNP was the first commercially available x86 technology to offer VM isolation for the cloud and is deployed in Microsoft Azure, AWS, and Google Cloud. As confidential computing technologies such as SEV-SNP develop, confidential computing is likely to simply become the default trust model for the cloud.
Towards General and Efficient Online Tuning for Spark

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning techniques to solve this problem but suffer from three issues: limited functionality, high overhead, and inefficient search. In this paper, we present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. First, we introduce a generalized tuning formulation, which can support multiple tuning goals and constraints conveniently, and a Bayesian optimization (BO) based solution to solve this generalized optimization problem. Second, to avoid high overhead from additional offline evaluations in existing methods, we propose to tune parameters along with the actual periodic executions of each job (i.e., online evaluations). To ensure safety during online job executions, we design a safe configuration acquisition method that models the safe region. Finally, three innovative techniques are leveraged to further accelerate the search process: adaptive sub-space generation, approximate gradient descent, and meta-learning method. We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent. The empirical results on both public benchmarks and large-scale production tasks demonstrate its superiority in terms of practicality, generality, and efficiency. Notably, this service saves an average of 57.00% memory cost and 34.93% CPU cost on 25K in-production tasks within 20 iterations, respectively.
PLACES: Prompting Language Models for Social Conversation Synthesis

Collecting high quality conversational data can be very expensive for most applications and infeasible for others due to privacy, ethical, or similar concerns. A promising direction to tackle this problem is to generate synthetic dialogues by prompting large language models. In this work, we use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations. This includes various dimensions of conversation quality with human evaluation directly on the synthesized conversations, and interactive human evaluation of chatbots fine-tuned on the synthetically generated dataset. We additionally demonstrate that this prompting approach is generalizable to multi-party conversations, providing potential to create new synthetic data for multi-party tasks. Our synthetic multi-party conversations were rated more favorably across all measured dimensions compared to conversation excerpts sampled from a human-collected multi-party dataset.
Synthesizing Human Gaze Feedback for Improved NLP Performance

Integrating human feedback in models can improve the performance of natural language processing (NLP) models. Feedback can be either explicit (e.g. ranking used in training language models) or implicit (e.g. using human cognitive signals in the form of eyetracking). Prior eye tracking and NLP research reveal that cognitive processes, such as human scanpaths, gleaned from human gaze patterns aid in the understanding and performance of NLP models. However, the collection of real eyetracking data for NLP tasks is challenging due to the requirement of expensive and precise equipment coupled with privacy invasion issues. To address this challenge, we propose ScanTextGAN, a novel model for generating human scanpaths over text. We show that ScanTextGAN-generated scanpaths can approximate meaningful cognitive signals in human gaze patterns. We include synthetically generated scanpaths in four popular NLP tasks spanning six different datasets as proof of concept and show that the models augmented with generated scanpaths improve the performance of all downstream NLP tasks.
Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning

Chunyang Jiang, Tianchen Zhu, Haoyi Zhou, Chang Liu, Ting Deng, Chunming Hu, Jianxin Li. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset

In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world. In this work, we annotate Instruction Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in a multimodal collaborative dialogue game. We show that it contains lexically and semantically diverse iCRs being produced self-motivatedly by players deciding to clarify in order to solve the task successfully. With 8.8k iCRs found in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus, making it a valuable resource for data-driven research on clarification in dialogue. We then formalise and provide baseline models for two tasks: Determining when to make an iCR and how to recognise them, in order to investigate to what extent these tasks are learnable from data.
Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation

Task-oriented Dialogue (ToD) agents are mostly limited to a few widely-spoken languages, mainly due to the high cost of acquiring training data for each language. Existing low-cost approaches that rely on cross-lingual embeddings or naive machine translation sacrifice a lot of accuracy for data efficiency, and largely fail in creating a usable dialogue agent.We propose automatic methods that use ToD training data in a source language to build a high-quality functioning dialogue agent in another target language that has no training data (i.e. zero-shot) or a small training set (i.e. few-shot). Unlike most prior work in cross-lingual ToD that only focuses on Dialogue State Tracking (DST), we build an end-to-end agent.We show that our approach closes the accuracy gap between few-shot and existing full-shot methods for ToD agents.We achieve this by (1) improving the dialogue data representation, (2) improving entity-aware machine translation, and (3) automatic filtering of noisy translations.We evaluate our approach on the recent bilingual dialogue dataset BiToD.In Chinese to English transfer, in the zero-shot setting, our method achieves 46.7% and 22.0% in Task Success Rate (TSR) and Dialogue Success Rate (DSR) respectively. In the few-shot setting where 10% of the data in the target language is used, we improve the state-of-the-art by 15.2% and 14.0%, coming within 5% of full-shot training.
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models

We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge bases and generate free-flow textual explanations about various health-related conditions across several dimensions. We collect and present an associated dataset, CHARDat, consisting of explanations about 52 health conditions across three clinical dimensions. We conduct extensive experiments using BART and T5 along with data augmentation, and perform automatic, human, and qualitative analyses. We show that while our models can perform decently, CHARD is very challenging with strong potential for further exploration.
Transfer Learning with Kernel Methods

Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop scalable kernel-based transfer learning methods across general source and target tasks with possibly differing label dimensions. In this work, we propose a transfer learning framework for kernel methods by projecting and translating the source model to the target task. We demonstrate the effectiveness of our framework in applications to image classification and virtual drug screening. For both applications, we identify simple scaling laws that characterize the performance of transfer-learned kernels as a function of the number of target examples. We explain this phenomenon in a simplified linear setting, where we are able to derive the exact scaling laws.
CPNet: Continuity Preservation Network for infrared video colorization

Infrared video colorization can significantly improve perceptual quality by predicting reasonable colors and restoring vivid details, especially in harsh environments. However, as an inconspicuous computer vision task, there is no specialized method. Besides, directly applying current grayscale colorization methods may generate structurally obscure and temporally inconsistent frames. In this paper, we design an infrared video colorization network CPNet aims to generate visually plausible and spatial–temporal consistent colorized videos. To achieve this, a feature fusion module and a hierarchical colorizer are designed to learn the importance of each consecutive frame and the local and global correlation of the integrated features, respectively. In addition, to consolidate the temporal consistency at a fine-grained level, we further introduce a composite loss function to narrow the distance between high-level feature representations while retaining pixel-wise correspondence. Moreover, a new metric named Mean Temporal Variation Similarity (MTVS) is proposed for effectively evaluating the degree of video continuity. Comprehensive experiments conducted on the KAIST dataset demonstrate the superiority of CPNet to produce more authentic colorized videos than state-of-the-art colorization methods. In terms of quantitative comparison, CPNet achieves improvements of at least 0.89 dB on PSNR, 0.016 on SSIM, and a significant promotion on MTVS. In addition, experiments conducted on the DAVIS dataset also prove the applicability of CPNet for grayscale video colorization task.
LSR: A Light-Weight Super-Resolution Method

A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work. LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. To lower the computational complexity, LSR does not adopt the end-to-end optimization deep networks. It consists of three modules: 1) generation of a pool of rich and diversified representations in the neighborhood of a target pixel via unsupervised learning, 2) selecting a subset from the representation pool that is most relevant to the underlying super-resolution task automatically via supervised learning, 3) predicting the residual of the target pixel via regression. LSR has low computational complexity and reasonable model size so that it can be implemented on mobile/edge platforms conveniently. Besides, it offers better visual quality than classical exemplar-based methods in terms of PSNR/SSIM measures.
Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research.
Inverse design of deformed Sb<sub>2</sub>Se<sub>3</sub> stripes in silicon waveguide for reconfigurable mode converters

Reconfigurable photonic devices integrated with silicon waveguides are important building blocks for future on-chip photonic circuits. In this paper, we focus on the mode order conversion in silicon waveguides with non-volatile reconfigurable capability. Deformed phase change material Sb 2 Se 3 (antimony triselenide) stripes are introduced at the edges of the functional region to provide the refractive index difference required by mode conversions. The shapes of stripes are inversely designed by a gradient-based iterative optimization strategy with 57 (19) iterations for TE0-to-TE1 (TE0-to-TE2) mode converter. The footprint of the functional region is as compact as square center wavelength. In the crystalline phase, TE0-to-TE1 and TE0-to-TE2 mode conversions are realized with conversion efficiencies of 98.5% and 96.3% at a center wavelength of 1550 nm, respectively. While in the amorphous phase, the input TE0 mode directly passes through the functional region with efficiencies of 93.0% and 92.4%, respectively. The output mode can be reconfigured by changing the phase of Sb 2 Se 3 stripes. Moreover, after introducing ±10 nm geometrical deviations to the perfect Sb 2 Se 3 stripe design, corresponding red and blue shifts of conversion efficiency spectra can be observed, and the simulation results reflect the reasonable robustness of the proposed mode converters.
De la presencialidad a la virtualidad: Implicaciones para la población estudiantil de la Universidad Nacional de Costa Rica, ante la pandemia del COVID 19

This article shows the impact of methodological shift in the learning process, from face-to-face to virtual teaching as result from the Pandemic, on the student body at the Campus Liberia, Chorotega Regional Section of the National University of Costa Rica. 373 questionnaires were applied in all the Campus carriers. The study describes the difficulties related to the different aspects of the technology employed. As result of this investigation, it was found that students perceive that they have had to invest much more time in their coursework, and the learning process has been harder compared to face-to-face classes. Students reports that virtual classes are confusing, too long, or excessive homework. Its noteworthy the perception by students of a rise in anxiety due to an excessive academic coursework. The research shows that students consider that this change in teaching modality has negatively affected their academic performance. Thus, the student body believes, as well as professors, that they should be adequately trained in the appropriate use of the instruments for virtual teaching/learning.
ActiveSelfHAR: Incorporating Self-Training Into Active Learning to Improve Cross-Subject Human Activity Recognition

Deep learning-based human activity recognition (HAR) methods have shown promise in the applications of health internet of things (IoT) and wireless body sensor networks (BSN). However, adapting these methods to new users in real-world scenarios is challenging due to the cross-subject issue. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning’s benefit of sparsely acquiring informative samples with actual labels and self-training’s benefit of effectively utilizing unlabeled data to adapt the HAR model to the target domain, i.e., the new users. ActiveSelfHAR consists of several key steps. First, we utilize the model from the source domain to select and label the domain invariant samples, forming a self-training set. Second, we leverage the distribution information of the self-training set to identify and annotate samples located around the class boundaries, forming a core set. Third, we augment the core set by considering the spatiotemporal relationships among the samples in the non-self-training set. Finally, we combine the self-training set and augmented core set to construct a diverse training set in the target domain and fine-tune the HAR model. Through leave-one-subject-out validation on three IMU-based datasets and one EMG-based dataset, our method achieves mean HAR accuracies of 95.20%, 82.06%, 89.52%, and 92.82%, respectively. Our method demonstrates similar HAR accuracies to the upper bound, i.e., fine-tuning framework with approximately 1% labeled data of the target dataset, while significantly improving data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into health IoT and BSN.
Flexible reconfigurable entanglement-based quantum key distribution network

Abstract Recently, we implemented a basic star-shaped entanglement-based quantum key distribution network without trusted nodes. It enables simultaneous pairwise exchange of quantum keys between multiple users. In this paper, we demonstrate its flexibility by focusing on several aspects. We show continuous system operation over a partially deployed 100 km fiber link for multiple days as well as dynamic reconfiguration of the communication partners. The photon pair source enables type-II or type-0 photon pair generation by SPDC as well as various demultiplexing strategies and repetition rates.