Reviewerly/exHarmony huggingface dataset:
Efficient Multi-Party Private Set Intersection Against Malicious Adversaries

Private set intersection (PSI) enables parties to compute the intersection of their inputs without leaking any additional information. Recently, there have been significant advances in the two-party settings with malicious security, making two-party PSI truly practical even compared with the naive insecure method. However, efficient PSI solutions in the more general case (multi-party) are only known for the semi-honest setting. How to construct a practical multi-party solution (especially for large inputs) in the malicious setting remains an important open question in this research line.
Parallel Deep Learning Detection Network in the MIMO Channel

For deep learning detection networks in the multiple-input-multiple-output (MIMO) channel, deepening the network does not significantly improve performance beyond a certain number of layers. In this letter, we propose a parallel detection network (PDN) that consists of several deep learning detection networks in parallel without connection. By designing a specific loss function and reducing similarity between detection networks, the PDN obtains a considerable diversity effect. The performance of the PDN improves significantly as the number of parallel detection networks increases in time-varying MIMO channels. This is superior to the existing deep learning detection networks, in both performance and complexity.
A Blockchain System for E-Learning Assessment and Certification

Nowadays, the applications of blockchain technology spring up rapidly, providing opportunities for solving the weaknesses in online education field, such as the complexity of e-learning assessment, the lack of a unified e-learning assessment standard and the insecurity of digital education certificates. In this paper, we propose a blockchain system for e-learning assessment and certification, which includes an entirely new network structure on the basis of the combination of the public and private blockchains, as well as four specific smart contract schemes for the realization of the e-learning assessment and credit exchange, the digital certificate issuance and secure storage, the digital certificate verification and the e-learning voucher allocation, respectively. The proposed system has been demonstrated as a promising candidate solution to establish a fairer, healthier and more open e-learning and online education environment.
Time-Series Aware Precision and Recall for Anomaly Detection

We proposetime-series aware precision andrecall, which are appropriate for evaluating anomaly detection methods in time-series data. In time-series data, an anomaly corresponds toa series of instances. The conventional metrics, however, overlook this characteristic, so they suffer from a problem of giving a high score to the method that only detects a long anomaly. To overcome the problem, our metrics consider thevariety of the detected anomalies to be more important through two scoring strategies,detection scoring (\ie, how many anomalies are detected) andportion scoring (\ie, how precisely each anomaly is detected). Moreover, our metrics concernambiguous instances, which indicate the instances labeled as 'normal' although they are affected by their precedent anomaly. Our metrics give smaller scores to those instances as they are likely to be anomalous. We demonstrate that our metrics are more suitable for time-series data compared to existing metrics by evaluations using a real-world dataset as well as several examples.\footnoteOur code and the detection results are available at: https://github.com/saurf4ng/TaPR.
CPDA: A Confidentiality-Preserving Deduplication Cloud Storage With Public Cloud Auditing

With growing popularity of cloud storage, the number of users of outsourcing data to cloud servers has increased dramatically.On the one hand, the rapidly increasing volume of data in the cloud is accompanied by a lot of data duplication.On the other hand, the cloud server stores only a unique copy of outsourced data in deduplication cloud storage system and the corruption or missing of the unique copy may bring immeasurable loss.Therefore, the file deduplication and integrity auditing are very important and how to securely and efficiently achieve them simultaneously needs to be settled urgently in academia and industry.In this paper, we propose a confidentiality-preserving deduplication cloud storage with public cloud auditing (CPDA).Firstly, our CPDA scheme achieves secure file deduplication on encrypted file, which supports public integrity auditing for the unique copy in the deduplication cloud storage system.Particularly, our CPDA scheme also realizes secure authentication tag deduplication.Secondly, our CPDA scheme utilizes the convergent encryption and random masking techniques to ensure data confidentiality during the file deduplication and integrity auditing process.Thirdly, our scheme not only supports each data owner to independently launch the integrity auditing of their own files, but also supports cloud server to periodically delegate the third party auditor to concurrently handle multiple auditing tasks to ensure the integrity of the outsourced files.Finally, the security of our scheme is formally proved and its performance is confirmed by numerical analyses and simulation experiments.
28 Blinks Later

In this work we address three overlooked practical challenges of continuous authentication systems based on eye movement biometrics: (i) changes in lighting conditions, (ii) task dependent features and the (iii) need for an accurate calibration phase. We collect eye movement data from 22 participants. To measure the effect of the three challenges, we collect data while varying the experimental conditions: users perform four different tasks, lighting conditions change over the course of the session and we collect data related to both accurate (user-specific) and inaccurate (generic) calibrations. To address changing lighting conditions, we identify the two main sources of light, i.e., screen brightness and ambient light, and we propose a pupil diameter correction mechanism based on these. We find that such mechanism can accurately adjust for the pupil shrinking or expanding in relation to the varying amount of light reaching the eye. To account for inaccurate calibrations, we augment the previously known feature set with new features based on binocular tracking, where the left and the right eye are tracked separately. We show that these features can be extremely distinctive even when using a generic calibration. We further apply a cross-task mapping function based on population data which systematically accounts for the dependency of features to tasks (e.g., reading a text and browsing a website lead to different eye movement dynamics). Using these enhancements, even while relaxing assumptions about the experimental conditions, we show that our system achieves significantly lower error rates compared to previous work. For intra-task authentication, without user-specific calibration and in variable screen brightness and ambient lighting, we achieve an equal error rate of 3.93% with only two minutes of training data. For the same setup but with constant screen brightness (e.g., as for a reading task) we can achieve equal error rates as low as of 1.88%.
GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.
Large Scale Unsupervised Domain Adaptation of Segmentation Networks with Adversarial Learning

Most current state-of-the-art methods for semantic segmentation on remote sensing imagery require large labeled data, which is scarcely available. Due to the distribution shifting phenomenon inherent in remote sensing imagery, the reuse of pre-trained models on new areas of interest rarely yield satisfactory results. In this paper, we approach this problem from an adversarial learning perspective toward unsupervised domain adaptation. The core concept is to infuse fully convolutional neural networks and adversarial networks for semantic segmentation assuming the structures in the scene and objects of interest are similar in two set of images. Models are trained on a source dataset where ground truth is available and adapted to new target dataset iteratively via a adversarial loss on unlabeled samples. We use two real large scale datasets to validate the framework: 1) cross city road extraction and 2) cross country building extraction. The preliminary results show the usefulness of considering adversarial learning for indirect re-use of the pre-trained models. Experimental validation suggests significant benefits over models without adaptation.
Document rectification and illumination correction using a patch-based CNN

We propose a novel learning method to rectify document images with various distortion types from a single input image. As opposed to previous learning-based methods, our approach seeks to first learn the distortion flow on input image patches rather than the entire image. We then present a robust technique to stitch the patch results into the rectified document by processing in the gradient domain. Furthermore, we propose a second network to correct the uneven illumination, further improving the readability and OCR accuracy. Due to the less complex distortion present on the smaller image patches, our patch-based approach followed by stitching and illumination correction can significantly improve the overall accuracy in both the synthetic and real datasets.
Evaluating Museum Virtual Tours: The Case Study of Italy

Virtual tours in museums are an ideal solution for those that are not able to visit a museum or those who want to have a small taste of what is presented in the museum before their visit. However, these tours often encounter severe problems while users interact with them. In order to check the status of virtual tours of museums, we present the implementation of an evaluation experiment that uses a combination of two multi-criteria decision making theories, namely the analytic hierarchy process (AHP) and the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS). AHP has been used for the estimation of the weights of the heuristics and fuzzy TOPSIS has been used for the evaluation of virtual tours of museums. This paper presents the exact steps that have to be followed in order to implement such an experiment and run an example experiment for virtual tours of Italian museums.
Multiple stream deep learning model for human action recognition

Human action recognition is one of the most important and challenging topic in the fields of image processing. Unlike object recognition, action recognition requires motion feature modeling which contains not only spatial but also temporal information. In this paper, we use multiple models to characterize both global and local motion features. Global motion patterns are represented efficiently by the depth-based 3-channel motion history images (MHIs). Meanwhile, the local spatial and temporal patterns are extracted from the skeleton graph. The decisions of these two streams are fused. At the end, the domain knowledge, which is the object/action dependency is considered. The proposed framework is evaluated on two RGB-D datasets. The experimental results show the effectiveness of our proposed approach. The performance is comparable with the state-of-the-art.
Animating landscape

Automatic generation of a high-quality video from a single image remains a challenging task despite the recent advances in deep generative models. This paper proposes a method that can create a high-resolution, long-term animation using convolutional neural networks (CNNs) from a single landscape image where we mainly focus on skies and waters. Our key observation is that the motion (e.g., moving clouds) and appearance (e.g., time-varying colors in the sky) in natural scenes have different time scales. We thus learn them separately and predict them with decoupled control while handling future uncertainty in both predictions by introducing latent codes. Unlike previous methods that infer output frames directly, our CNNs predict spatially-smooth intermediate data, i.e., for motion, flow fields for warping, and for appearance, color transfer maps, via self-supervised learning, i.e., without explicitly-provided ground truth. These intermediate data are applied not to each previous output frame, but to the input image only once for each output frame. This design is crucial to alleviate error accumulation in long-term predictions, which is the essential problem in previous recurrent approaches. The output frames can be looped like cinemagraph, and also be controlled directly by specifying latent codes or indirectly via visual annotations. We demonstrate the effectiveness of our method through comparisons with the state-of-the-arts on video prediction as well as appearance manipulation. Resultant videos, codes, and datasets will be available at http://www.cgg.cs.tsukuba.ac.jp/~endo/projects/AnimatingLandscape.
Improving performances of Top-N recommendations with co-clustering method

Collaborative filtering has been widely used in many applications. The typical idea is to identify preferences of users by utilizing their interaction data over the whole items. However, this is sometimes inaccurate since users might share different preferences with different sets of items. In this paper, we put forward a new recommendation method based on collaborative filtering called User-Item Community Detection based Recommendation (UICDR) method. The new parameter-free and scalable community detection method is modified from our previous work. We derive a unipartite form of bipartite modularity and put forward a new network representation. By constructing a bipartite network with user-item interaction data, we first partitions users and items into several subgroups. After getting clusters with tightly linked users and items, traditional collaborative filtering models can be trained for each cluster. The results on four real-world data sets show that, the proposed UICDR significantly improves the performances of Top-N recommendations of several traditional collaborative filtering methods. In addition, UICDR is helpful to cold-start problem.
Dynamic Deep Pixel Distribution Learning for Background Subtraction

Previous approaches to background subtraction usually approximate the distribution of pixels with artificial models. In this paper, we focus on automatically learning the distribution, using a novel background subtraction model named Dynamic Deep Pixel Distribution Learning (D-DPDL). In our D-DPDL model, a distribution descriptor named Random Permutation of Temporal Pixels (RPoTP) is dynamically generated as the input to a convolutional neural network for learning the statistical distribution, and a Bayesian refinement model is tailored to handle the random noise introduced by the random permutation. Because the temporal pixels are randomly permutated to guarantee that only statistical information is retained in RPoTP features, the network is forced to learn the pixel distribution. Moreover, since the noise is random, the Bayesian theorem is naturally selected to propose an empirical model as a compensation based on the similarity between pixels. Evaluations using standard benchmark demonstrates the superiority of the proposed approach compared with the state-of-the-art, including traditional methods as well as deep learning methods.
Bubble regime identification in an attention-based model for Bitcoin and Ethereum price dynamics

In this paper we extend the model in Cretarola, Figà-Talamanca, "Detecting bubbles in Bitcoin price dynamics via market exuberance", Annals of Operations Research (2019), by allowing for a state-dependent correlation parameter between asset returns and market attention. We assume that the change of state is described by a continuous time latent Markov chain and we propose an estimation procedure based on the conditional maximum likelihood and on the Hamilton filter. Finally, model parameters, as well as Markov chain transition probabilities, are estimated on Bitcoin and Ethereum returns in case market attention is measured via the Google Search Volume Index for the keywords "bitcoin" and "ethereum", respectively; up to four regimes are considered in the empirical application. The empirical outcomes show that the model is not only capable of identifying bubble and non-bubble regimes but also enables the interpretation of the correlation between cryptocurrencies and their market attention as a tuning to define the speed at which a bubble boosts.
SCSCN: A Separated Channel-Spatial Convolution Net With Attention for Single-View Reconstruction

Three-dimensional (3-D) object reconstruction is a challenging problem in computer vision, especially the single-view reconstruction. In this article, we propose a new 3-D reconstruction network, termed as separated channel-spatial convolution net with attention (SCSCN), which can reconstruct the 3-D shape of objects by given a two-dimensional (2-D) image from any viewpoint. Our method is a simple encoder-decoder structure, where the encoder uses separated channel-spatial convolution and separated channel-spatial attention to extract features from the input image, and the decoder recovers 3-D shapes from the features. The separated channel-spatial convolution can obtain channel information and spatial information through the channel path and spatial path separately. At the same time, in order to select a more reasonable combination of features according to the degree of contribution to the reconstruction task, channel attention and spatial attention are relevantly inserted into these two paths. As a result, the encoder can extract a strong representation of object. Quantitative experiments show that our SCSCN has a weak dependence on 3-D supervision and achieves high-quality reconstruction just under 2-D supervision, which proves the effectiveness of the encoder. In addition, we conduct the qualitative visualization experiment to confirm the rationality of the attention blocks in the feature extraction process.
ENF Signal Enhancement in Audio Recordings

In electric network frequency (ENF) based audio forensics, the ENF signal captured in a questioned audio recording is estimated and analyzed for authentication purposes. However, the captured ENF signal is usually contaminated by very strong noise and interference. In this paper, we propose a robust filtering algorithm (RFA) for ENF signal enhancement in audio recordings, which could effectively suppress the additive noise and facilitate subsequent ENF estimation, especially in practical low signal-to-noise ratio (SNR) situations. The proposed algorithm encodes the time domain expression of the preprocessed audio signal (ENF signal plus noise) as the instantaneous frequencies (IFs) of an analytical sinusoidal frequency modulated (SFM) signal. Then, a kernel function is utilized to generate a sinusoidal time-frequency distribution (STFD) whose peaks correspond to the IFs of the analytical signal, i.e., the denoised ENF signal. It is then proven that finding the STFD peaks is equivalent to finding the averaged phases of the kernel function if the additive noise is a zero mean wide sense stationary (WSS) process. The RFA serves as a noise reduction mechanism yielding improved SNR at the filter output. Combined with generic frequency estimation methods, ENF extraction accuracy could be substantially improved with the use of the proposed RFA than without using it. Both synthetic and experimental results are provided to illustrate the effectiveness of our proposal. Reliable ENF extraction could be achieved under noise level down to -20 dB SNR, and the RFA is suitable for a wide range of ENF-based forensic applications.
Explaining digital humanities by aligning images and textual descriptions

Replicating the human ability to connect Vision and Language has recently been gaining a lot of attention in the Computer Vision and the Natural Language Processing communities. This research effort has resulted in algorithms that can retrieve images from textual descriptions and vice versa, when realistic images and sentences with simple semantics are employed and when paired training data is provided. In this paper, we go beyond these limitations and tackle the design of visual-semantic algorithms in the domain of the Digital Humanities. This setting not only advertises more complex visual and semantic structures but also features a significant lack of training data which makes the use of fully-supervised approaches infeasible. With this aim, we propose a joint visual-semantic embedding that can automatically align illustrations and textual elements without paired supervision. This is achieved by transferring the knowledge learned on ordinary visual-semantic datasets to the artistic domain. Experiments, performed on two datasets specifically designed for this domain, validate the proposed strategies and quantify the domain shift between natural images and artworks.
On the polytomous generalization of knowledge space theory

One of the core assumptions of knowledge space theory (KST) is that the answer of a subject to an item can be dichotomously classified as correct or incorrect. Schrepp (1997) provided a very first attempt to generalize the main KST concepts to items with more than two response alternatives, but his work has not had a strong impact on the subsequent research on KST. The aim of the present article is to introduce a new formulation of the polytomous KST, starting from the work of Schrepp and broadening it to a wider extent. Schrepp's generalization is revisited, and the fundamental closure conditions are reformulated and decomposed into a necessary and sufficient set of four independent properties of polytomous knowledge structures. Among them, two special properties emerge in the polytomous case that in the dichotomous one are neither testable nor immediately visible, since necessarily true. These properties allow for a straight generalization of Birkhoff's Theorem with respect to quasi-ordinal knowledge spaces, and Doignon and Falmagne's Theorem for knowledge spaces. Such findings open the field to a systematic generalization of many KST concepts to the polytomous case.