Reviewerly/exHarmony huggingface dataset:
Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval

Sketch based image retrieval (SBIR), which uses free-hand sketches to search the images containing similar objects/scenes, is attracting more and more attentions as sketches could be got more easily with the development of touch devices. However, this task is difficult as the huge differences between sketches and images. In this paper, we propose a cross-domain representation learning framework to reduce these differences for SBIR. This framework aims to transfer sketches to images with the information learned both in the sketch domain and image domain by the proposed domain migration generative adversarial network (DMGAN). Furthermore, to reduce the representation gap between the generated images and natural images, a similarity learning network (SLN) is also proposed with the new designed loss function incorporating semantic information. Extensive experiments have been done from different aspects, including comparison with state-of-the-art methods. The results show that the proposed DMGAN and SLN really work for SBIR.
Key-Recycling Strategies in Quantum-Key-Distribution Networks

Quantum-key-distribution (QKD) networks can provide absolutely secure keys for the entire communication system in theory. At present, the key-distribution rate is relatively low, and the key-distribution rate decreases exponentially as the distribution distance increases. The trusted-relay scheme commonly used in existing QKD networks achieves the purpose of extending the security distance by consuming additional keys. Since the channel is unreliable, the key-relay process will accumulate system errors to a certain extent, increasing the probability of key-relay failure. In some high-bit-error-rate network scenarios such as wireless networks and disaster environments, the channel-error rate is as high as 30–50%. And in these scenarios, there are usually a large number of confidential messages that need to be delivered. However, the key-management mechanism of the current QKD system does not consider the scenario of key-relay failure. If the key is not successfully relayed, all the keys participating in the relay process will be destroyed, including the key that has been successfully relayed before. This situation causes the key to be wasted and reduces the encryption capability of the system. In this paper, we proposed the quantum-key-recycling (QKR) mechanism to increase the number of keys available in the network and introduced a secure service grading mechanism to properly reuse the recycled keys. The QKR mechanism can be regarded as a key-management mechanism acting on the point-to-point QKD system, and the mechanism is designed for a classical channel to reuse the key resources. A post-processing method for recycled keys is proposed to improve the security of the keys. Simulation results show that the QKD network using the key-recycling strategy is about 20% higher in key-utilization rate than the traditional QKD network without the QKR mechanism, and about 10% higher in-service security coverage.
Humanization of education in digital era

А. В. Шуталева, Ю
<i>ROPPSA</i>: TV Program Recommendation Based on Personality and Social Awareness

The rapid growth of mobile television (TV), smart TV, and Internet Protocol Television (IPTV) content due to the convergence of broadcasting and the Internet requires effective recommendation methods to select appropriate TV programs/channels. Many previous methods have been proposed to address this issue. However, imperative factors such as the utilization of personality traits and social properties to recommend programs for TV viewers remain a challenge. Consequently, in this paper, we propose a recommender algorithm called Recommendation of Programs via Personality and Social Awareness ( ROPPSA ) for TV viewers. ROPPSA utilizes normalization and folksonomy procedures to generate group recommendations for TV viewers who have common similarities in terms of personality traits and tie strength with a Target TV Viewer ( TTV ). Therefore, ROPPSA improves TV viewer cold-start and data sparsity situations by utilizing their personality traits and tie strengths. We conducted extensive experiments on a relevant dataset using standard evaluation metrics to substantiate our ROPPSA recommendation method. Results of our experimentation procedure depict the advantage, recommendation accuracy, and outperformance of ROPPSA in comparison with other contemporary methods in terms of precision, recall, f -measure ( F 1), and arithmetic mean (AM).
REST: A Thread Embedding Approach for Identifying and Classifying User-Specified Information in Security Forums

How can we extract useful information from a security forum? We focus on identifying threads of interest to a security professional: (a) alerts of worrisome events, such as attacks, (b) offering of malicious services and products, (c) hacking information to perform malicious acts, and (d) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Novel approaches are needed to address the challenges in this domain: (a) the difficulty in specifying the “topics” of interest efficiently, and (b) the unstructured and informal nature of the text. We propose, REST, a systematic methodology to: (a) identify threads of interest based on a, possibly incomplete, bag of words, and (b) classify them into one of the four classes above. The key novelty of the work is a multi-step weighted embedding approach: we project words, threads and classes in appropriate embedding spaces and establish relevance and similarity there. We evaluate our method with real data from three security forums with a total of 164k posts and 21K threads. First, REST robustness to initial keyword selection can extend the user-provided keyword set and thus, it can recover from missing keywords. Second, REST categorizes the threads into the classes of interest with superior accuracy compared to five other methods: REST exhibits an accuracy between 63.3-76.9%. We see our approach as a first step for harnessing the wealth of information of online forums in a user-friendly way, since the user can loosely specify her keywords of interest.
Back up: can users sue platforms to reinstate deleted content?

A private order of public communication has emerged.Today, social network services fulfill important communicative functions.A lot has been written about the failings of companies in deleting problematic content.This paper flips the question and asks under which conditions users can sue to reinstate content and under which circumstances courts have recognised 'must carry' obligations for social network services.Our analysis, an initial comparative analysis of case law on the reinstatement of user-generated content, will point to a larger issue of systemic relevance, namely the differences in treatment of states and private companies as threats to and/or guarantors of fundamental rights in the United States and in Germany.It is a contribution to the important debate on the interaction of states and platforms in governing online content.
PENINGKATAN KEMAMPUAN MEMBACA PEMAHAMAN SISWA MELALUI MODEL PEMBELAJARAN COOPERATIVE INTEGRATED READING AND COMPOTION (CIRC)

The purpose of this study was to describe the improvement in reading comprehension results using CIRC type cooperative learning in class V SDN 04 Padang Ganting. This study uses a qualitative approach to the classroom action research design. The design of this research includes (1) preliminary studies, preparation of action plans, (2) research implementation activities consisting of the stages of implementing actions, observations and reflection stages. This action research was carried out in two cycles. The researcher acts as a presenter / practitioner in the learning process, while the observer is a colleague on duty at the research site. The results showed that the use of the CIRC type cooperative approach in learning to read comprehension was effective for improving reading comprehension skills. This can be seen from the value of students obtained from the first cycle which is an average student score of 7.09, while in the second cycle the average value of students is 8.55. From the results of this study it can be concluded that learning to read comprehension by using a CIRC type cooperative approach can improve student learning outcomes.
[Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system].

Morphological analysis of the blood smear is an essential element of diagnosing a disease hematologically and has been performed by conventional manual light microscopy for several decades. Although this method is the gold standard, it is labor-intensive, requires continuous training of the personnel, and is subject to relatively large interobserver variability. The artificial intelligence (AI)-based automated methods for the digital morphological analysis of blood smears have recently been developed. In this review, our recently developed convolutional neural network (CNN)-based digital morphology hematology analysis system is introduced. AI-based digital morphology hematology analysis system is firstly needed to incorporate digital imaging of blood cells into the analysis system. It is essential to establish a digital platform, which was already established in the radiological diagnosis, for the dissemination of CNN-based automated digital morphology hematology analyzer in the near future.
ENROLLMENT FORECASTING BASED ON LINGUISTIC TIME SERIES

Dealing with the time series forecasting problem attracts much attention from the fuzzy community. Many models and methods have been proposed in the literature since the publication of the study by Song and Chissom in 1993, in which they proposed fuzzy time series together with its fuzzy forecasting model for time series data and the fuzzy formalism to handle their uncertainty. Unfortunately, the proposed method to calculate this fuzzy model was very complex. Then, in 1996, Chen proposed an efficient method to reduce the computational complexity of the mentioned formalism. Hwang et al. in 1998 proposed a new fuzzy time series forecasting model, which deals with the variations of historical data instead of these historical data themselves. Though fuzzy sets are concepts inspired by fuzzy linguistic information, there is no formal bridge to connect the fuzzy sets and the inherent quantitative semantics of linguistic words. This study proposes the so-called linguistic time series, in which words with their own semantics are used instead of fuzzy sets. By this, forecasting linguistic logical relationships can be established based on the time series variations and this is clearly useful for human users. The effect of the proposed model is justified by applying the proposed model to forecast student enrollment historical data.
Domain adaptation based on domain-invariant and class-distinguishable feature learning using multiple adversarial networks

Adversarial networks have been used to learn transferable representations in many domain adaptation methods. However, there is no theoretical guarantee that two distributions are identical, even if the discriminator is fully confused. Therefore, a more elaborate domain adversarial method to better align distributions is desirable. In this paper, we propose two groups of multiple adversarial networks for domain-invariant and class-distinguishable feature learning: i) class-wise domain adversarial networks based on sample locations and ii) sample adversarial networks. We analyze the impact of the sample’s intra-class distribution on transfer learning and reveal that the distance between a sample and its cluster center affects the role of that sample in transfer learning. Domain adversarial learning is conducted separately on samples located near the cluster center (central samples) and samples located far away from cluster center (non-central samples) in order to align the distributions of the source domain and target domain better. However, separate domain adversarial learning on central and non-central samples ignores the relationship between them, because all these samples belong to the same class. We therefore propose a method called sample adversarial learning to convert the distribution of non-central samples to the distribution of central samples. In this way, the relationship between central samples and non-central samples is rebuilt. Sample adversarial learning solves the problem that arises in the sperate domain adversarial learning. Sample adversarial learning also enables us to obtain a class-distinguishable feature representation because of the reduction in intra-class distance. Experimental results show that our method extracts more transferable and class-distinguishable features than existing methods and achieves start-of-the-art results on several datasets. The significant contribution of this paper is to show how separate domain adversarial learning based on sample locations and sample adversarial learning together enhance positive transfer by maximally matching the multimodal structures underlying the data distributions across domains.
CP-NAS: Child-Parent Neural Architecture Search for 1-bit CNNs

Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architectures, which are still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS by taking advantage of the strengths of each in a uniﬁed framework. To this end, a Child-Parent model is introduced to a differentiable NAS to search the binarized architecture(Child) under the supervision of a full-precision model (Parent). In the search stage, the Child-Parent model uses an indicator generated by the parent and child model accuracy to evaluate the performance and abandon operations with less potential. In the training stage, a kernel level CP loss is introduced to optimize the binarized network. Extensive experiments demonstrate that the proposed CP-NAS achieves a comparable accuracy with traditional NAS on both the CIFAR and ImageNet databases. It achieves an accuracy of 95.27% on CIFAR-10, 64.3% on ImageNet with binarized weights and activations, and a 30% faster search than prior arts.
Differential Privacy for Stackelberg Games

This paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets. This coordination represents a classic Stackelberg game and relies on the exchange of sensitive information between the system agents. The paper is motivated by the observation that the perturbation introduced by traditional DP mechanisms fundamentally changes the underlying optimization problem and even leads to unsatisfiable instances. To remedy such limitation, the paper introduces the Privacy-Preserving Stackelberg Mechanism (PPSM), a framework that enforces the notions of feasibility and fidelity (i.e. near-optimality) of the privacy-preserving information to the original problem objective. PPSM complies with the notion of differential privacy and ensures that the outcomes of the privacy-preserving coordination mechanism are close-to-optimality for each agent. Experimental results on several gas and electricity market benchmarks based on a real case study demonstrate the effectiveness of the proposed approach. A full version of this paper [Fioretto et al., 2020b] contains complete proofs and additional discussion on the motivating application.
Intent Preference Decoupling for User Representation on Online Recommender System

Accurately characterizing the user's current interest is the core of recommender systems. However, users' interests are dynamic and affected by intent factors and preference factors. The intent factors imply users' current needs and change among different visits. The preference factors are relatively stable and learned continuously over time. Existing works either resort to the sequential recommendation to model the current browsing intent and historical preference separately or just mix up these two factors during online learning. In this paper, we propose a novel learning strategy named FLIP to decouple the learning of intent and preference under the online settings. The learning of the intent is considered as a meta-learning task and fast adaptive to the current browsing; the learning of the preference is based on the calibrated user intent and constantly updated over time. We conducted experiments on two public datasets and a real-world recommender system. When equipping it with modern recommendation methods, significant improvements are demonstrated over strong baselines.
Nonlocal patches based Gaussian mixture model for image inpainting

We consider the inpainting problem for noisy images. It is very challenge to suppress noise when image inpainting is processed. An image patches based nonlocal variational method is proposed to simultaneously inpainting and denoising in this paper. Our approach is developed on an assumption that the small image patches should be obeyed a distribution which can be described by a high dimension Gaussian Mixture Model. By a maximum a posteriori (MAP) estimation, we formulate a new regularization term according to the log-likelihood function of the mixture model. To optimize this regularization term efficiently, we adopt the idea of the Expectation Maximization (EM) algorithm. In which, the expectation step can give an adaptive weighting function which can be regarded as a nonlocal connections among pixels. Using this fact, we built a framework for non-local image inpainting under noise. Moreover, we mathematically prove the existence of minimizer for the proposed inpainting model. By using a splitting algorithm, the proposed model are able to realize image inpainting and denoising simultaneously. Numerical results show that the proposed method can produce impressive reconstructed results when the inpainting region is rather large.
A Multi-Player Minimax Game for Generative Adversarial Networks

While multi-discriminators have been recently exploited to enhance the discriminability and diversity of Generative Adversarial Networks (GANs), these independent discriminators may not collaborate harmoniously to learn diverse and complementary decision boundaries. This paper extends the original two-player adversarial game of GANs by introducing a new multi-player objective named Discriminator Discrepancy Loss (DDL) for diversifying the multi-discriminators. Besides the competition between the generator and each discriminator, there are also competitions between the discriminators: 1) When training multi-discriminators, we simultaneously minimize the original GAN loss and maximize DDL, seeking a good trade-off between the accuracy and diversity. This yields diversified multi-discriminators that fit the generated data distribution to the real data distribution from more comprehensive perspectives. 2) When training the generator, we minimize DDL to encourage the generator to confuse all discriminators. This enhances the diversity of the generated data distribution. Further, we propose a layer-sharing network architecture for the multi-discriminators, which allows them to learn from distinct perspectives about the shared low-level features through better collaboration. It also makes our model more lightweight than existing multi-discriminators approaches. Our DDL-GAN remarkably outperforms other GANs over five standard datasets for image generation tasks.
JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks

Existing deep learning-based image de-blocking methods use only pixel-level loss functions to guide network training. The JPEG compression factor, which reflects the degradation degree, has not been fully utilized. However, due to the non-differentiability, the compression factor cannot be directly utilized to train deep networks. To solve this problem, we propose compression quality ranker-guided networks for this specific JPEG artifacts removal. We first design a quality ranker to measure the compression degree, which is highly correlated with the JPEG quality. Based on this differentiable ranker, we then propose one quality-related loss and one feature matching loss to guide de-blocking and perceptual quality optimization. In addition, we utilize dilated convolutions to extract multi-scale features, which enables our single model to handle multiple compression quality factors. Our method can implicitly use the information contained in the compression factors to produce better results. Experiments demonstrate that our model can achieve comparable or even better performance in both quantitative and qualitative measurements.
A Relaxed Matching Procedure for Unsupervised BLI

Recently unsupervised Bilingual Lexicon Induction(BLI) without any parallel corpus has attracted much research interest.One of the crucial parts in methods for the BLI task is the matching procedure.Previous works impose a too strong constraint on the matching and lead to many counterintuitive translation pairings.Thus, We propose a relaxed matching procedure to find a more precise matching between two languages.We also find that aligning source and target language embedding space bidirectionally will bring significant improvement.We follow the previous iterative framework to conduct experiments.Results on standard benchmark demonstrate the effectiveness of our proposed method, which substantially outperforms previous unsupervised methods.
Robust Data Programming with Precision-guided Labeling Functions

Scarcity of labeled data is a bottleneck for supervised learning models. A paradigm that has evolved for dealing with this problem is data programming. An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions (LF) that output possibly noisy labels to input instances and a generative model for consolidating the weak labels. We enhance and generalize this paradigm by supporting functions that output a continuous score (instead of a hard label) that noisily correlates with labels. We show across five applications that continuous LFs are more natural to program and lead to improved recall. We also show that accuracy of existing generative models is unstable with respect to initialization, training epochs, and learning rates. We give control to the data programmer to guide the training process by providing intuitive quality guides with each LF. We propose an elegant method of incorporating these guides into the generative model. Our overall method, called CAGE, makes the data programming paradigm more reliable than other tricks based on initialization, sign-penalties, or soft-accuracy constraints.