Reviewerly/exHarmony huggingface dataset:
Scalable Distributed Data Anonymization for Large Datasets

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa $</tex-math></inline-formula> -Anonymity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell $</tex-math></inline-formula> -diversity are two well-known privacy metrics that guarantee protection of the respondents of a dataset by obfuscating information that can disclose their identities and sensitive information. Existing solutions for enforcing them implicitly assume to operate in a centralized scenario, since they require complete visibility over the dataset to be anonymized, and can therefore have limited applicability in anonymizing large datasets. In this paper, we propose a solution that extends Mondrian (an efficient and effective approach designed for achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa $</tex-math></inline-formula> -anonymity) for enforcing both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa $</tex-math></inline-formula> -anonymity and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell $</tex-math></inline-formula> -diversity over large datasets in a distributed manner, leveraging the parallel computation of multiple workers. Our approach efficiently distributes the computation among the workers, without requiring visibility over the dataset in its entirety. Our data partitioning limits the need for workers to exchange data, so that each worker can independently anonymize a portion of the dataset. We implemented our approach providing parallel execution on a dynamically chosen number of workers. The experimental evaluation shows that our solution provides scalability, while not affecting the quality of the resulting anonymization.
ZeroProKeS: A Secure Zeroconf Key Establishment Protocol for Large-Scale Low-Cost Applications

Traditional approaches to authenticated key establishment include the use of PKI or trusted third parties. While certificate deployment is sub-optimal for large-scale, low-cost applications, the use of trusted third parties is subject to human error and leaked credentials. For this context, co-location can be a valuable resource, and it is often exploited through common randomness harvesting techniques, but these, in turn, suffer from low achievable rates and usually from restrictive assumptions about the environment. Recent techniques for exploiting co-location are based on the notion of quality time and rely on sophisticated throttled clue-issuing mechanisms that allow a device with enough time to spend in the vicinity of the transmitter to find a secret key by collecting enough consecutive clues. By contrast, attackers are afforded only limited time to listen to, or interact with, the clue transmitter. Previous work in this direction deals solely with passive attackers and uses high-overhead information throttling mechanisms. This paper introduces the active attacker model for the quality-time paradigm and proposes a simple solution, a Zeroconf Key Establishment Protocol (ZeroProKeS). Additionally, the paper shows how to efficiently expand the proposed protocol to adhere to any customized information transfer function between legitimate users.
Structure-Enriched Topology Learning For Cross-Domain Multi-Person Pose Estimation

Human pose estimation has been widely studied with much focus on supervised learning. However, in real applications, a pretrained pose estimation model usually needs be adapted to a novel domain without labels or with sparse labels. Existing domain adaptation methods cannot well deal with it since poses have flexible topological structures and need fine-grained local features. Aiming at the characteristics of human pose, we propose a novel domain adaptation method for multi-person pose estimation (MPPE) to alleviate the human-level shift. Firstly, the training samples of human poses are clustered into groups according to the posture similarity. Within the clustered space, we conduct three adaptation modules: Cross-Attentive Feature Alignment (CAFA), Intra-domain Structure Adaptation (ISA) and Adaptive Human-Topology Adaptation (AHTA). The CAFA adopts a bidirectional spatial attention mechanism to explore fine-grained local feature correlation between two humans, and thus to adaptively aggregate consistent features for adaptation. ISA only works in semi-supervised domain adaptation (SSDA) to exploit semantic relationship of corresponding keypoints for reducing the intra-domain bias. Importantly, we creatively propose an AHTA to enrich human topological knowledge for reducing the inter-domain discrepancy. Specifically, the pose structure and the cross-instance topological relations are modeled via graph networks. This flexible topology learning benefits the occluded or extreme pose inference. Extensive experiments are conducted on two popular benchmarks and additional two challenging datasets. Results demonstrate the competency of our method, which works in unsupervised or semi-supervised modes, compared with the existing supervised approaches.
A Nonblind Deconvolution Method by Bias Correction for Inaccurate Blur Kernel Estimation in Image Deblurring

The blur kernel estimated by a blind deblurring algorithm is hardly to be error-free. The blur kernel error is usually ignored in the nonblind deconvolution stage and may result in severe artifacts or other negative effects. In addition, the bias hidden in the blurry image formation model has not been found and investigated due to ignoring the existence of the blur kernel errors. To this end, we develop a nonblind deconvolution method by bias correction for inaccurate blur kernels in this article, which are constructed on the basis of the classic errors-in-variables (EIVs) model. First we analyze in detail the bias caused by errors of inaccurate blur kernel from blurry image formation model. Next, the latent sparsity property of jointed latent image and errors of inaccurate blur kernel is counted statistically, which is imposed as a new regularization term. Then, the objective function of the new nonblind method is established, and an alternative minimization algorithm is derived and employed to estimate the latent clear image. Furthermore, a filtering method is utilized to modify the bias term, which is added to amend intermediate image in each iteration. Finally, extensive experiments with benchmark datasets and blurred micro-nano satellite remote sensing images are carried out to evaluate the proposed method. Experimental results demonstrate that the proposed method can obtain high-quality restored images, and it is comparable to or even better than some state-of-the-art nonblind deconvolution methods.
A Generative Adversarial Network for Multistyle Unsupervised Image Transfer Based on Dynamic Position Normalization

Image transfer is a technology that changes the image effect by processing the image’s color, contour, line, and other information. To stylize the visual appearance of the output will be adapted to the subject of the original image, this paper proposes an image feature transfer method called Cycle-DPN-GAN, based on the Cycle-Consistent Adversarial Networks. Firstly, Positional Normalization-Dynamic Moment Shortcut (PONO-DMS) module is introduced to learn more structural information from the input image, and the edge blurring and object losing are efficiently alleviated. In addition, the Multi-Scale-Structural Similarity Index (MS-SSIM) loss is added to the reconstruction loss, which improves visual perceptions and enhances the constraints on the reconstructed image in terms of image brightness, color contrast and structure. In this model, to verify the feasibility and superiority of the proposed method, the data sets of monet2photo, vangogh2photo, ukiyoe2photo and cezanne2photo are performed in the experiments, and the Inception Score and Fréchet Inception Distance evaluation index are improved. In addition, ablation studies are performed to demonstrate the validity of each proposed component. In this paper, the results of the quantitative evaluation are consistent with the qualitative evaluation. It can be demonstrated that the images generated by Cycle-DPN-GAN have higher visual quality.
Towards Impact of Chunk-Level Characteristics on Mobile Live Streaming Performance

Today, mobile live streaming is gaining a rapid growth in use, which refers to watching the media content recorded and broadcast in real time on mobile devices. In live streaming process, each video segment must go through recording, encoding, uploading, transcoding, publishing, downloading, decoding before playback. The ingest algorithm inside the streamer decides the upload bitrate, while the adaptive bitrate (ABR) algorithm inside the player determines the download bitrate. Thanks to the chunked CMAF standard, each segment is split into smaller chunks that can be independently encoded, transferred, decoded and played. It is of great help to quantify the impact of chunk-level characteristics on mobile live streaming performance. In this paper, we establish a tandem queuing model to describe the whole streaming system. Based on the model, we respectively characterize rebuffering probability, rebuffering count, streaming latency, and average bitrate, analyzing the impact of chunk upload and download rates, upload and download time variances, startup threshold and chunk length on them. From analysis results, we propose insights and recommendations for bitrate adaptation in mobile live streaming and design simple heuristic ingest and ABR algorithms leveraging them. Extensive simulations verify the insights as well as effectiveness of designed algorithms.
Analyzing Preference Data With Local Privacy: Optimal Utility and Enhanced Robustness

Online service providers benefit from collecting and analyzing preference data from users, including both implicit preference data (e.g., watched videos of a user) and explicit preference data (e.g., ranking data over candidates). However, it brings ethical and legal issues of data privacy at the same time. In this paper, we study the problem of aggregating individual's preference data in the local differential privacy (LDP) setting. One naive approach is to add Laplace random noises, which however suffers from low statistical utility and is fragile to LDP-specific poisoning attacks. Therefore, we propose a novel mechanism to improve the utility and the robustness simultaneously: the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">additive mechanism</i> . The additive mechanism randomly outputs a subset of candidates with a probability proportional to their total scores. For preference data with Borda rule over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> items, its mean squared error bound is optimized from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\frac{d^{5}}{n\epsilon ^{2}})$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\frac{d^{4}}{n\epsilon ^{2}})$</tex-math></inline-formula> , and its maximum poisoning risk bound is reduced from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$+\infty$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\frac{d^{2}}{n\epsilon })$</tex-math></inline-formula> . We also theoretically investigate minimax lower bounds of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -LDP preference data aggregation, and prove the error rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\frac{d^{4}}{n\epsilon ^{2}})$</tex-math></inline-formula> is optimal for the Borda rule. Experimental results validate that our proposed approaches averagely reduce estimation error by 50% and are more robust to adversarial poisoning attacks.
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps

ARM TrustZone is widely deployed on commercial-off-the-shelf mobile devices for secure execution. However, many Apps cannot enjoy this feature because it brings many constraints to App developers. Previous works have been proposed to build a secure execution environment for developers on top of TrustZone. Unfortunately, these works are still not fully-fledged solutions for mobile Apps, especially for emerging intelligent Apps. To this end, we propose LEAP, which is a lightweight developer-friendly TEE solution for mobile Apps. LEAP enables isolated codes to execute in parallel and access peripheral (e.g., mobile GPUs) with ease, flexibly manages system resources upon different workloads, and offers the auto DevOps tool to help developers prepare the codes running on it. We implement the LEAP prototype on the off-the-shelf ARM platform and conduct extensive experiments on it. The experimental results show that Apps can be adapted to run with LEAP easily and efficiently. Compared to the state-of-the-art work along this research line, LEAP can achieve an average 3.57x speedup in supporting intelligent Apps using mobile GPU acceleration.
Extending Ontology Engineering Practices to Facilitate Application Development

Ontologies define data organization and meaning in Knowledge Graphs (KGs). However, ontologies have generally not been taken into account when designing and generating Application Programming Interfaces (APIs) to allow developers to consume KG data in a developer-friendly way. To fill this gap, this work proposes a method for API generation based on the artefacts generated during the ontology development process. This method is described as part of a new phase, called ontology exploitation, that may be included in the last stages of the traditional ontology development methodologies. Moreover, to support some of the tasks of the proposed method, we developed OATAPI, a tool that generates APIs from two ontology artefacts: the competency questions and the ontology serialization. The conclusions of this work reflect that the limitations found in the state-of-the-art have been addressed both at the methodological and tooling levels for the generation of APIs based on ontology artefacts. Finally, the lines of future work present several challenges that need to be addressed so that the potential of KGs and ontologies can be more easily exploited by application developers.
Wavelet-Based Saliency and Ensemble Classifier for Pedestrian Detection in Infrared Images

Night vision systems designed for intelligent transportation system ensuring pedestrian safety are the need of the hour. The pedestrian detection system should be real time and precise during day and night-time and should alert the driver with an alarm or automatically apply brake to avoid collision. In this chapter, a robust approach using wavelet-based saliency for region of interest detection and local denary signature for pedestrian representation in thermal infrared imagery is suggested. Unlike the state-of-the-art approaches that rely on the intensity thresholding for pedestrian detection, this framework depends on wavelet-based saliency map generated by both approximation and detailed sub-bands for prospective regions where pedestrians are more likely to be found. There may be other regions that have similar thermal characteristics of the pedestrian in infrared image, raising false alarm. Thus, an effective representation with ten intensity levels termed as local denary pattern, and the descriptor based on edge orientation histogram precisely describes the pedestrian characteristics. Local denary pattern is a robust and discriminative representation as it accomplishes a better tolerance against greyscale variation. Finally, Logitboost ensemble classifier is used to classify pedestrians from background. The efficacy in detecting pedestrians is demonstrated by experimental findings on a publically available OSU Thermal Pedestrian Database.
First-Order Constrained Optimization: Non-smooth Dynamical System Viewpoint

In a recent paper, Muehlebach and Jordan (2021a) proposed a novel algorithm for constrained optimization that uses original ideals from nonsmooth dynamical systems. In this work, we extend Muehlebach and Jordan (2021a) in several important directions: (i) we provide existence and convergence results for continuous-time trajectories under general conditions, and (ii) we provide a convergence guarantee for a perturbed version of the discrete-time version of the algorithm (covering stochastic gradient updates), for nonconvex and nonsmooth objective functions. Our analysis framework rationalizes the continuous-time and discrete-time cases, which not only provides an important intuition but could also enable convergence proofs for accelerated or Newton-like versions of our algorithm.
Investigating Text Complexity of Reading in National Matriculation English Test

This study is based on a small-scale corpus of 15 reading texts of the National Matriculation English Test in 2021, one of the largest-scale and the most influential high-stakes tests in China, which includes three types of nationwide version examination papers and one local version examination paper (i.e., Zhejiang Province version). A variety of natural language processing tools were used to carry out an in-depth investigation into text complexity of NMET reading from fore aspects, namely length, vocabulary, syntax and cohesion. Results suggest that these texts are similar in the examined quantitative indices and in a reasonable range. Pedagogical implications of the study were thereafter discussed.
Side-Channel Attack on Deep Reinforcement Learning of AES

In the research field of side-channel attacks, label-based modeling attacks based on machine learning have been successfully applied to block cipher AES. Compared with non-modeling attacks, modeling attacks have a large workload of data collection and processing in the early stage, but their advantage lies in the high reusability of the model. Once the model is established successfully, the key can be recovered with less traces. First, implement the AES algorithm on the Chipwhisperer platform and collect the energy traces of the process, borrow the CPA technology to extract the points of interest, and the obtained points of interest are the data set for model training. This paper applies deep reinforcement learning to the research of symmetric cipher AES, and realizes a label-free training model. Experiments show that deep reinforcement learning technology can be successfully applied to side-channel attacks. The model can predict that the correct rate of both synchronous and asynchronous energy trajectories is as high as 91% or more. This data shows that the model can attack the correct key with an energy trace.
Improved Divide-and-Conquer Approach to Abstractive Summarization of Scientific Papers

Recently, a simple and effective method called Divide ANd ConquER (DANCER) is proposed for automated abstractive summarization of scientific papers. However, we point out that DANCER still has several weaknesses. The most prominent one is that DANCER relies on ROUGE tool for the Section-to-Summary (SEC2SuMM) alignment. We address the weaknesses and propose a novel framework to improve DANCER. Our method includes a delicate training objective, which learns the SEC2SuMM alignment, and the section-level summarizer in a joint way. We investigate our framework on two publicly available scientific paper summarization benchmarks: PubMed and arXiv. Our method achieves significant improvements over DANCER and several competitive baselines.
Class Embedding: A BERT-Whitening and Attention-Based Approach to Text Classification

Text classification tasks often encounter setbacks when the amount of data is insufficient. At this moment, the model will enter a state of overfitting, so that it will get a disappointing result because it does not have a generalization ability in the performance of the testset. We propose a concept of class embedding method, which needn’t to train the model but a little attention network with only one-fiftieth of the total number in training sets. By using Bert-Whitening to reduce dimension and improved formula Attention-Euclidean to measure the distance between sentence and each class embedding, we have achieved satisfactory results on Chinese data sets. We propose a method with simple concepts and frameworks to be applied to text classification, and effective results are obtained with only a small number of samples. A comparative experiment on the Chinese text dataset shows that the model has a higher accuracy rate, which verifies the effectiveness of the method.
Creating a Japanese Dialogue Corpus with Multi-level Topic Analysis

The study of generative dialogue systems has become a hotspot with the recently well-studied natural language understanding and generation techniques. However, most works have been focusing on those widely used languages, such as English and Chinese, with huge dialogue corpora being collected and thoroughly analyzed for training the dialogue generation models. In addition, retaining the human-like diversity and spontaneity of speech in these corpora is challenging for building dialogue systems. In this paper, we propose a method to build a lage Japanese dialogue corpus by using the conversations posted on Twitter and to annotate the dialogue- and utterance-level topic labels and the corresponding probabilistic scores automatically by analyzing the similarity between the word clusters and the dialogue clusters of the corpus in the same semantic space. The advantage of this method is that it does not require the expensive time and effort of human workers for mimicking dialogues and annotating labels, which is specifically useful for those less widely used languages, such as Japanese. We compare four filtering settings with respect to the lower-bound of utterance length for corpus creation and topic annotation and report the effect of the utterance length to the quality of our dialogue corpus, based on a manual evaluation. Based on this corpus, we further propose two topic-based dialogue generation tasks, that is, the next-response-topic prediction task and the next-topic-based response generation task. The Japanese dialogue corpus is available on GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Language Model Adaptation for Downstream Tasks using Text Selection

Previous research shows that the domain of the training data has a large impact on the performance of the downstream tasks. Selecting data from an appropriate domain leads to improvements on the performance. Using text classification can help discriminate the data which belong to different domains. In this paper, we use a text classification method to select data from a particular domain (task-specific target domain). We experiment with different sizes of target domain corpus to explore the effect of the method. A pretrained RoBERTa model is adapted to the target domain corpus using the selected data prior to training the model on the downstream tasks. Our experiments show that using a simple domain classifier to select a small dataset to adapt the model can help stabilize the performance of downstream tasks.