Reviewerly/exHarmony huggingface dataset:
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation

Meaning Representation (AMR) research is relatively limited and challenging for languages other than English.Upon the availability of English AMR dataset and English-to-X parallel datasets, in this paper we propose a novel cross-lingual pre-training approach via multi-task learning (MTL) for both zeroshot AMR parsing and AMR-to-text generation.Specifically, we consider three types of relevant tasks, including AMR parsing, AMR-to-text generation, and machine translation.We hope that knowledge gained while learning for English AMR parsing and text generation can be transferred to the counterparts of other languages.With properly pretrained models, we explore four different finetuning methods, i.e., vanilla fine-tuning with a single task, one-for-all MTL fine-tuning, targeted MTL fine-tuning, and teacher-studentbased MTL fine-tuning.Experimental results on AMR parsing and text generation of multiple non-English languages demonstrate that our approach significantly outperforms a strong baseline of pre-training approach, and greatly advances the state of the art.In detail, on LDC2020T07 we have achieved 70.45%, 71.76%, and 70.80% in Smatch F1 for AMR parsing of German, Spanish, and Italian, respectively, while for AMR-to-text generation of the languages, we have obtained 25.69, 31.36, and 28.42 in BLEU respectively.We make our code available on github https:// github.com/xdqkid/XLPT-AMR.
Rethinking Bi-Level Optimization in Neural Architecture Search: A Gibbs Sampling Perspective

One-Shot architecture search, which aims to explore all possible operations jointly based on a single model, has been an active direction of Neural Architecture Search (NAS). As a well-known one-shot solution, Differentiable Architecture Search (DARTS) performs continuous relaxation on the architecture's importance and results in a bi-level optimization problem. However, as many recent studies have shown, DARTS cannot always work robustly for new tasks, which is mainly due to the approximate solution of the bi-level optimization. In this paper, one-shot neural architecture search is addressed by adopting a directed probabilistic graphical model to represent the joint probability distribution over data and model. Then, neural architectures are searched for and optimized by Gibbs sampling. We rethink the bi-level optimization problem as the task of Gibbs sampling from the posterior distribution, which expresses the preferences for different models given the observed dataset. We evaluate our proposed NAS method -- GibbsNAS on the search space used in DARTS/ENAS and the search space of NAS-Bench-201. Experimental results on multiple search space show the efficacy and stability of our approach.
Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

Almost all image sensors measure only one color per pixel through the color filter array.Missing pixels are estimated using a demosaicing process.For this reason, a demosaiced image leaves a particular trace.When an image is manipulated or tampered, the demosaicing trace can be changed.This change can serve as a basic clue for detecting or localizing image tampering.Demosaicing pattern-based tampering localization algorithms require a re-interpolation process, and the prediction residue between the given image and the re-interpolated image is commonly used to localize tampered regions.However, the prediction residue is not always valid because the demosaicing interpolation kernel cannot be known, which deteriorates the localization performance.This paper presents an effective re-interpolation process using singular value decomposition for an unknown demosaicing method.First, the green channel of the given image is decomposed into four sub-images according to the Bayer pattern.For a small block of each sub-image, the singular value decomposition is performed.The prediction residue is obtained by reconstructing the image block after removing the largest singular value.The feature to localize the forged regions is extracted by the logarithm ratio of the prediction residue variance.The proposed method does not require any statistical model for the extracted feature, because the prediction residue is more accurate than that of conventional methods.We perform intensive experiments for three test datasets and compare the proposed method with state-of-the-art tampering localization methods, the results of which indicate that the proposed scheme outperforms existing approaches.
GIF Thumbnails: Attract More Clicks to Your Videos

With the rapid increase of mobile devices and online media, more and more people prefer posting/viewing videos online. Generally, these videos are presented on video streaming sites with image thumbnails and text titles. While facing huge amounts of videos, a viewer clicks through a certain video with high probability because of its eye-catching thumbnail. However, current video thumbnails are created manually, which is time-consuming and quality-unguaranteed. And static image thumbnails contain very limited information of the corresponding videos, which prevents users from successfully clicking what they really want to view. In this paper, we address a novel problem, namely GIF thumbnail generation, which aims to automatically generate GIF thumbnails for videos and consequently boost their Click-Through-Rate (CTR). Here, a GIF thumbnail is an animated GIF file consisting of multiple segments from the video, containing more information of the target video than a static image thumbnail. To support this study, we build the first GIF thumbnails benchmark dataset that consists of 1070 videos covering a total duration of 69.1 hours, and 5394 corresponding manually-annotated GIFs. To solve this problem, we propose a learning-based automatic GIF thumbnail generation model, which is called Generative Variational Dual-Encoder (GEVADEN). As not relying on any user interaction information (e.g. time-sync comments and real-time view counts), this model is applicable to newly-uploaded/rarely-viewed videos. Experiments on our built dataset show that GEVADEN significantly outperforms several baselines, including video-summarization and highlight-detection based ones. Furthermore, we develop a pilot application of the proposed model on an online video platform with 9814 videos covering 1231 hours, which shows that our model achieves a 37.5% CTR improvement over traditional image thumbnails. This further validates the effectiveness of the proposed model and the promising application prospect of GIF thumbnails.
Improving logistic regression on the imbalanced data by a novel penalized log-likelihood function

Logistic regression is estimated by maximizing the log-likelihood objective function formulated under the assumption of maximizing the overall accuracy. That does not apply to the imbalanced data. The resulting models tend to be biased towards the majority class (i.e. non-event), which can bring great loss in practice. One strategy for mitigating such bias is to penalize the misclassification costs of observations differently in the log-likelihood function. Existing solutions require either hard hyperparameter estimating or high computational complexity. We propose a novel penalized log-likelihood function by including penalty weights as decision variables for observations in the minority class (i.e. event) and learning them from data along with model coefficients. In the experiments, the proposed logistic regression model is compared with the existing ones on the statistics of area under receiver operating characteristics (ROC) curve from 10 public datasets and 16 simulated datasets, as well as the training time. A detailed analysis is conducted on an imbalanced credit dataset to examine the estimated probability distributions, additional performance measurements (i.e. type I error and type II error) and model coefficients. The results demonstrate that both the discrimination ability and computation efficiency of logistic regression models are improved using the proposed log-likelihood function as the learning objective.
Work-in-Progress: Syntactic Code Similarity Detection in Strongly Directed Assessments

When checking student programs for plagiarism and collusion, many similarity detectors aim to capture semantic similarity. However, they are not particularly effective for strongly directed assessments, in which the student programs are expected to be semantically similar. A detector focusing on syntactic similarity might be useful, and this paper reports its effectiveness on programming assessment tasks collected from algorithms and data structures courses in one academic semester. Our study shows that syntactic similarity detection is more effective than its semantic counterpart in strongly directed assessments, with some irregular similarity patterns being useful for raising suspicion. We also tested whether take-home assessments have higher similarity than in-class assessments, and confirmed that hypothesis. Consistency of the findings will be further validated on other courses with strongly directed assessments, and a syntactic similarity detector specifically tailored for strongly directed assessments will be proposed.
EMBODIED DIGITAL TWINS FOR ENVIRONMENTAL APPLICATIONS

Abstract. The synergies of advances in environmental sensing and modeling and the mainstreaming of immersive technologies lay the foundation for a theoretical grounding of embodied digital twins. Embodied digital twins draw on an established understanding of the importance of place for environmental sciences as well as a paradigmatic shift in the cognitive sciences toward embodied cognition. Nonetheless, the excitement of realizing embodied experiences through immersive technologies such as augmented and virtual reality stands in stark contrast to a lack of consistent terminology and empirical research. In this vision paper, we are proposing to draw more deeply on the theoretical basis of embodied cognition and to establish research frameworks for advancing embodied digital twins. We discuss several examples for turning environmentally sensed and modeled information into high-fidelity immersive experiences and provide a discussion and outlook on critical topics to address.
Recommender Systems: Challenges and Opportunities in the Age of Big Data and Artificial Intelligence

Modern recommendation systems require access to and understanding of the big data built on top of large data islands. This is important as the growing enhancement in interconnection, storage, as well as data management has made it possible to connect to a data deluge from big data—which in turn, can lead to making intelligent and accurate personalization and recommendations. Despite the effectiveness of the presented methods, in the times of big data and AI, the need for more advanced methods has been a strong force to build intelligent systems for quick, accurate, and personalized recommendations tailored to each customer’s needs and preferences. In this chapter, we provide an overview of different types of real-world recommender systems, along with challenges and opportunities in the age of big data and AI. We will discuss how recent growth in cognitive technology, together with advancement in areas such as AI (plus ML, DL and NLP) as well as knowledge representation, interaction, and personalization, have resulted in the substantial enhancement in the research of recommender systems.
MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification

We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the labels are obtained from an existing curated database, and the actual contents are extracted from papers associated with each of the records in the database. We benchmark various state-of-the-art NLP and computer vision models, including unimodal models which only take either caption texts or images as inputs, and multimodal models. Extensive experiments and analysis show that multimodal models, despite outperforming unimodal ones, still need improvements especially on a less-supervised way of grounding visual concepts with languages, and better transferability to low resource domains. We release our dataset and the benchmarks to facilitate future research in multimodal learning, especially to motivate targeted improvements for applications in scientific domains.
Norms and Open Systems in Open Science

Through a review of studies of open science and open behaviors (data sharing, open access publishing, open source software development) and editorial writing that promotes open science, we identify two themes prominent in the advocacy of open science: normative (Mertonian) scientific values and the importance of open systems. We report examples of these themes and suggest that open science advocates understand the movement as a value-driven ethos pursuing improved science through the use of technology. We contend that a belief in the open ethos is distinct from participation in open behaviors and that, consequently, open systems are used by two ideologically distinct user groups. We conclude by discussing the implications of this characterization of open science, focusing on the consequences of different user groups using the same technological systems.
Probabilistic Intuitionistic Fuzzy Decision Making Algorithms

In this paper, the probabilistic version of intuitionistic fuzzy decision making methods is introduced to show the duality present within probabilistic distance. Here, two types of methods, namely, probabilistic intuitionistic fuzzy TOPSIS (PI-TOPSIS) algorithm and probabilistic intuitionistic fuzzy face identification (PIFI) algorithm are proposed. The PI-TOPSIS is utilizing probabilistic distance as the separation measure; for its justification rankings and reduced information loss of the multi criteria decision making problems are compared. Our other proposed decision making method called PIFI algorithm handles Face Identification Problem. It is exemplified by better benchmark indexes of PIFI in comparison to support vector machine, naive bais classifier, fuzzy support vector machine algorithms, that, the probabilistic distance can be used as a similarity measure. The two well-known feature extraction techniques, called Local binary pattern (LBP) and Angular radial transformation (ART) are employed to extract the features in the face images. Further, it is concluded from the experimental findings, that, the proposed algorithms are adaptive in nature.
Principal Component Analysis Using CKKS Homomorphic Scheme

Principal component analysis (PCA) is one of the most popular linear dimensionality reduction techniques in machine learning. In this paper, we try to present a method for performing PCA on encrypted data using a homomorphic encryption scheme. In a client-server model where the server performs computations on the encrypted data, it (server) does not require to perform any matrix operations like multiplication, inversion, etc. on the encrypted data. This reduces the number of computations significantly since matrix operations on encrypted data are very computationally expensive. For our purpose, we used the CKKS homomorphic encryption scheme since it is most suitable for machine learning tasks allowing approximate computations on real numbers. We also present the experimental results of our proposed Homomorphic PCA (HPCA) algorithm on a few datasets. We measure the R2 score on the reconstructed data and use it as an evaluation metric for our HPCA algorithm.
Estimation of Traffic Occupancy using Image Segmentation

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.
To hop or not, that is the question: Towards effective multi-hop reasoning over knowledge graphs

Abstract With the proliferation of large-scale knowledge graphs (KGs), multi-hop knowledge graph reasoning has been a capstone that enables machines to be able to handle intelligent tasks, especially where some explicit reasoning path is appreciated for decision making. To train a KG reasoner, supervised learning-based methods suffer from false-negative issues, i.e., unseen paths during training are not to be found in prediction; in contrast, reinforcement learning (RL)-based methods do not require labeled paths, and can explore to cover many appropriate reasoning paths. In this connection, efforts have been dedicated to investigating several RL formulations for multi-hop KG reasoning. Particularly, current RL-based methods generate rewards at the very end of the reasoning process, due to which short paths of hops less than a given threshold are likely to be overlooked, and the overall performance is impaired. To address the problem, we propose , a revised RL formulation of multi-hop KG reasoning that is characterized by two novel designs—the stop signal and the worth-trying signal. The stop signal instructs the agent of RL to stay at the entity after finding the answer, preventing from hopping further even if the threshold is not reached; meanwhile, the worth-trying signal encourages the agent to try to learn some partial patterns from the paths that fail to lead to the answer. To validate the design of our model , comprehensive experiments are carried out on three benchmark knowledge graphs, and the results and analysis suggest the superiority of over state-of-the-art methods.
Complex Deep Neural Network Based Intelligent Signal Detection Methods for OFDM-IM Systems

Advanced signal detectors pose a lot of technical challenges for designing signal detection methods in orthogonal frequency division multiplexing (OFDM) with index modulation (IM). Traditional signal detection methods such as maximum likelihood have an excessive complexity, and existing deep learning (DL) based detection methods can reduce the complexity significantly. To further improve the detection performance, in this paper, we propose a complex deep neural network (C-DNN) and a complex convolution neural network (C-CNN) based intelligent signal detection method for OFDM-IM. Specifically, the proposed intelligent signal detection method is designed by C-DNN and C-CNN. The proposed signal detection methods for OFDM-IM use pilots to achieve semi-blind channel estimation, and to reconstruct the transmitted symbols based on channel state information (CSI). Simulation results are given to confirm the performance of the proposed signal detection method in terms of bit error rate and convergence speed.
Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment

In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.
Narrowband photonic quantum entanglement with counterpropagating domain engineering

Narrowband photonic entanglement is a crucial resource for long-distance quantum communication and quantum information processing, including quantum memories. We demonstrate the first polarization entanglement with 7.1 GHz inherent bandwidth by counterpropagating domain engineering, which is also confirmed by Hong–Ou–Mandel interference with 155-ps base-to-base dip width and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mo stretchy="false">(</mml:mo> <mml:mn>97.1</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.59</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mi>%</mml:mi> </mml:math> high visibility. The entanglement is harnessed with 18.5-standard-deviations Bell inequality violation, and further characterized with state tomography of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>95.71</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.61</mml:mn> <mml:mo stretchy="false">)</mml:mo> <mml:mi>%</mml:mi> </mml:mrow> </mml:math> fidelity. Such narrowband entanglement sets a cornerstone for practical quantum information applications.
CrowdNotifier: Decentralized Privacy-Preserving Presence Tracing

Abstract There is growing evidence that SARS-CoV-2 can be transmitted beyond close proximity contacts, in particular in closed and crowded environments with insufficient ventilation. To help mitigation efforts, contact tracers need a way to notify those who were present in such environments at the same time as infected individuals. Neither traditional human-based contact tracing powered by handwritten or electronic lists, nor Bluetooth-enabled proximity tracing can handle this problem efficiently. In this paper, we propose CrowdNotifier, a protocol that can complement manual contact tracing by efficiently notifying visitors of venues and events with SARS-CoV-2-positive attendees. We prove that CrowdNotifier provides strong privacy and abuse-resistance, and show that it can scale to handle notification at a national scale.
New Set of Invariant Quaternion Krawtchouk Moments for Color Image Representation and Recognition

One of the most often used techniques to represent color images is quaternion algebra. This study introduces the quaternion Krawtchouk moments, QKrMs, as a new set of moments to represent color images. Krawtchouk moments (KrMs) represent one type of discrete moments. QKrMs use traditional Krawtchouk moments of each color channel to describe color images. This new set of moments is defined by using orthogonal polynomials called the Krawtchouk polynomials. The stability against the translation, rotation, and scaling transformations for QKrMs is discussed. The performance of the proposed QKrMs is evaluated against other discrete quaternion moments for image reconstruction capability, toughness against various types of noise, invariance to similarity transformations, color face image recognition, and CPU elapsed times.