Reviewerly/exHarmony huggingface dataset:
A Chaotic Image Encryption Scheme Based on Hénon–Chebyshev Modulation Map and Genetic Operations

Thanks to the complex characteristics of ergodicity, pseudo-randomness and sensitivity in initial conditions, chaotic systems have been widely applied in the field of cryptography. By cascading the Hénon map and the Chebyshev map, a new two-dimensional Hénon–Chebyshev modulation map (2D-HCMM) is proposed in this paper. Several methods of objective assessment, including phase diagrams, bifurcation diagrams, Lyapunov exponents and information entropy, are utilized to analyze the dynamics of the 2D-HCMM. The results show that the 2D-HCMM possesses better ergodicity and unpredictability, with larger chaotic ranges, compared with the original chaotic maps. By using the proposed map and the essential principles of genetic recombination and genetic mutation, a new image encryption scheme is proposed. In this scheme, the bit planes of image are substituted by genetic recombination operation, and the pixel values are scrambled randomly by genetic mutation operation. The simulation results and security analysis demonstrate that the proposed scheme not only can resist various conventional attacks, but also possesses a fast speed, achieving a good trade-off between security and efficiency.
Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars

The need to recognise long-term dependencies in sequential data such as video streams has made Long Short-Term Memory (LSTM) networks a prominent Artificial Intelligence model for many emerging applications.However, the high computational and memory demands of LSTMs introduce challenges in their deployment on latency-critical systems such as self-driving cars which are equipped with limited computational resources on-board.In this paper we introduce a progressive inference computing scheme that combines model pruning and computation restructuring leading to the best possible approximation of the result given the available latency budget of the target application.The proposed methodology enables mission-critical systems to make informed decisions even in early stages of the computation, based on approximate LSTM inference, meeting their specifications on safety and robustness.Our experiments on a state-of-the-art driving model for autonomous vehicle navigation demonstrate that the proposed approach can yield outputs with similar quality of result compared to a faithful LSTM baseline, up to 415× faster (198× on average, 76× geo.mean).
VQA With No Questions-Answers Training

Methods for teaching machines to answer visual questions have made significant progress in recent years, but current methods still lack important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answers and handling new domains without explicit examples. We propose a novel method that consists of two main parts: generating a question graph representation, and an answering procedure, guided by the abstract structure of the question graph to invoke an extendable set of visual estimators. Training is performed for the language part and the visual part on their own, but unlike existing schemes, the method does not require any training using images with associated questions and answers. This approach is able to handle novel domains (extended question types and new object classes, properties and relations) as long as corresponding visual estimators are available. In addition, it can provide explanations to its answers and suggest alternatives when questions are not grounded in the image. We demonstrate that this approach achieves both high performance and domain extensibility without any questions-answers training.
Linguistic variation in the discourse of corporate annual reports: A multi-dimensional analysis

Combining the frameworks of multi-dimensional (MD) analysis and rhetorical structure theory (RST), this study examines the linguistic co-occurrence patterns in the discourse of corporate annual reports (CARs) and interprets their underlying functional dimensions. Our corpus consists of texts of corporate 10K reports from firms listed on New York Stock Exchange (NYSE; N of texts = 642, totally 14,674,047 tokens). Five functional dimensions are quantitatively extracted and qualitatively interpreted: (1) expression of direct persuasion; (2) expression of impersonal stance; (3) subjective versus objective positioning; (4) integrative expression of stance versus fragmented expression; and (5) expression of reliability. All these dimensions contribute to the communicative function of persuasion. The analysis of the rhetorical structures of excerpts with high concentrations of co-occurring linguistic features on each dimension further indicates the communicative strategy of persuasion. The proposed MD model is then applied to analyze the effect of firm performance on the linguistic variation in CAR discourse. We found that firm performance can significantly affect the linguistic variation in CAR discourse. CAR discourse from firms with good performance is more reliable. The result reveals managements’ use of concealment strategy in impression management. This study has implications for MD analysis, business discourse analysis, language pedagogy and accounting research.
Multi-Gradient Convolutional Neural Network Based In-Loop Filter For Vvc

While recent researches on convolutional neural network (CNN) based in-loop filters for High Efficiency Video Coding (HEVC) have achieved great success, the performance of these models on the new standard Versatile Video Coding (VVC) may degrade due to many novel adopted techniques which make the compression process more fine and capture more image details. In this work, the performances on VVC of two CNN based in-loop filters proposed for HEVC are investigated and a multi-gradient convolutional neural network based in-loop filter (MGNLF) for VVC is proposed. The proposed model exploits the divergence and second derivative of frame, which contain much potential image structural information, like contour information, to restore more detail information to further improve the quality of frames. Experimental results demonstrate our approach can significantly improve the coding performance. On average, 3.29% BD-Rate reduction is achieved on luma component under all intra configuration compared with the original VVC with DBF and SAO enabled, which also outperforms other state-of-the-art approaches for VVC.
The road to DNS privacy

With the rapid evolution of the Internet, it has become a significant issue to further enhance the security of traditional protocols which were lack of comprehensive security considerations when originally designed. Among them, the Domain Name System (DNS) as the most important infrastructure is a typical one. To deal with the DNS security risks (e.g., Distributed Denial-of-Service (DDoS), DNS hijack and etc.), multiple extension protocols have been developed. The DNS management architecture has also been optimized from various aspects. Nowadays, as individual privacy on the Internet is attracting more and more attention, it is becoming one of the most important tasks for both DNS itself and its related applications to guarantee the privacy of end-user. This paper mainly studies the problem of DNS privacy leakage and analyzes some promising solutions. However, these solutions may affect the performance of some Internet services related to the DNS like the “Butterfly Effect”. It is highly advised that the whole community should pay more attention to the issue of adding DNS privacy, while maintaining the stability and backward compatibility
Probabilistic Pixel-Adaptive Refinement Networks

Encoder-decoder networks have found widespread use in various dense prediction tasks. However, the strong reduction of spatial resolution in the encoder leads to a loss of location information as well as boundary artifacts. To address this, image-adaptive post-processing methods have shown beneficial by leveraging the high-resolution input image(s) as guidance data. We extend such approaches by considering an important orthogonal source of information: the network's confidence in its own predictions. We introduce probabilistic pixel-adaptive convolutions (PPACs), which not only depend on image guidance data for filtering, but also respect the reliability of per-pixel predictions. As such, PPACs allow for image-adaptive smoothing and simultaneously propagating pixels of high confidence into less reliable regions, while respecting object boundaries. We demonstrate their utility in refinement networks for optical flow and semantic segmentation, where PPACs lead to a clear reduction in boundary artifacts. Moreover, our proposed refinement step is able to substantially improve the accuracy on various widely used benchmarks.
Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget

We explore the behavior of a standard convolutional neural net in a continual-learning setting that introduces visual classification tasks sequentially and requires the net to master new tasks while preserving mastery of previously learned tasks. This setting corresponds to that which human learners face as they acquire domain expertise serially, for example, as an individual studies a textbook. Through simulations involving sequences of ten related visual tasks, we find reason for optimism that nets will scale well as they advance from having a single skill to becoming multi-skill domain experts. We observe two key phenomena. First, \emph{forward facilitation}---the accelerated learning of task n+1 having learned n previous tasks---grows with n. Second, \emph{backward interference}---the forgetting of the n previous tasks when learning task n+1 ---diminishes with n. Amplifying forward facilitation is the goal of research on metalearning, and attenuating backward interference is the goal of research on catastrophic forgetting. We find that both of these goals are attained simply through broader exposure to a domain.
Neural Unified Review Recommendation with Cross Attention

There are two main paradigms to exploit review information for recommendation. One is to concatenate all reviews of a user/item into a long document, which may neglect the different usefulness of reviews. The other paradigm is review-level i.e., analyzing each review separately to learn user/item features. In fact, the two paradigms are complementary, and fusing them together has the potential to learn more comprehensive features of users/items. Hence, we propose a unified framework to jointly learn document- and review-level representations of users/items. We design a document encoder to learn document-level features of users/items. Then, we use a review encoder to learn representations of reviews from words, and a user/item encoder to learn review-level features of users/items. Besides, different reviews from the same user may have different importance for different target items due to different item characteristics. We propose a cross attention model for user representation learning whose query vector is the embedding of target item ID, and apply it to the above three encoders to select different informative words and reviews for different target items. Extensive experiments validate the effectiveness of our method.
Let Me Choose: From Verbal Context to Font Selection

In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to. Compared to related work leveraging the surrounding visual context, we choose to focus only on the input text, which can enable new applications for which the text is the only visual element in the document. We introduce a new dataset, containing examples of different topics in social media posts and ads, labeled through crowd-sourcing. Due to the subjective nature of the task, multiple fonts might be perceived as acceptable for an input text, which makes this problem challenging. To this end, we investigate different end-to-end models to learn label distributions on crowd-sourced data, to capture inter-subjectivity across all annotations.
A nonparametric approach to high-dimensional k-sample comparison problems

Summary High-dimensional $k$-sample comparison is a common task in applications. We construct a class of easy-to-implement distribution-free tests based on new nonparametric tools and unexplored connections with spectral graph theory. The test is shown to have various desirable properties and a characteristic exploratory flavour that has practical consequences for statistical modelling. Numerical examples show that the proposed method works surprisingly well across a broad range of realistic situations.
Geographies of youth, mobile phones, and the urban hustle

Geographers have shown how mobile phones are transforming urban economies in Africa by altering the temporal and spatial nature of commercial transactions. Less well documented is how young people in Africa are using mobile phones to navigate the interplay between personal hopes, social expectations, and financial uncertainty associated with urban life. Drawing on qualitative data from youth in two Ghanaian cities, and insights from literature on youth geographies, hustling, and everyday urbanism, this paper explores how young people use mobile phones to conjure up and sustain social relations and income‐generating opportunities in urban settings. Three significant thematic findings emerge from our empirical data: the role of mobile phones in shaping young people’s social status and responsibilities; the use of mobile phones as instruments for surveillance and relationships; and the use of mobile phone technologies as assets for realising income‐generating opportunities. Conceptually, our exploration of mobile phone usage furthers geographical scholarship by providing new insights on the multidimensional nature of constraints faced by youth and their strategies to overcome them. Furthermore, the incorporation of a comparative element within the study illustrates the need for a more spatially nuanced understanding of young people’s uptake and use of mobile phone technologies.
Classification Constrained Discriminator For Domain Adaptive Semantic Segmentation

Unsupervised domain adaptation for semantic segmentation aims to transfer knowledge from label-rich synthetic datasets to real-world images without any annotation. The traditional adversarial learning methods for domain adaptation learn to extract domain-invariant feature representations by aligning the feature distributions of both domains. However, these methods suffer from an imbalance in adversarial training and feature distortion. In this work, we propose a classification constrained discriminator to alleviate these problems. Specifically, we first propose to balance the adversarial training by eliminating any pooling layers or strided convolutions in the discriminator. Then, we propose to constrain the discriminator with an auxiliary classification loss to help the feature generator extract the domain-invariant features that are useful for segmentation rather than just ambiguous features to fool the domain discriminator. Extensive experiments demonstrate the superiority of our proposed approach. The source code and models have been made available at https://github.com/NUSTMachine-Intelligence-Laboratory/ccd.
Solving Jigsaw Puzzles With Eroded Boundaries

Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem-solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the boundaries. Nevertheless, this variant is important since erosion and missing data often occur at the boundaries. The key idea of our proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as ”neighbors or not”. An interesting feature of our architecture is that the same GAN discriminator is used for both inpainting and classification; training of the second task is simply a continuation of the training of the first, beginning from the point it left off. We show that our approach outperforms other SOTA methods.
Neural Mathematical Solver with Enhanced Formula Structure

Automatically answering mathematical problems is a challenging task since it requires not only the ability of linguistic understanding but also mathematical comprehension. Existing studies usually explore solutions on the elementary math word problems that aim to understand the questions described in natural language narratives, which are not capable of solving more general problems containing structural formulas. To this end, in this paper, we propose a novel Neural Mathematical Solver (NMS) with enhanced formula structures. Specifically, we first frame the formulas in a certain problem as a TeX dependency graph to preserve formula-enriched structures. Then, we design a formula graph network (FGN) to capture its mathematical relations. Next, we develop a novel architecture with two GRU models, connecting tokens from both word space and formula space together, to learn the linguistic semantics for the answers. Extensive experiments on a large-scale dataset demonstrate that NMS not only achieves better answer prediction but also visualizes reasonable mathematical representations of problems.
Automatic optimization of outlier detection ensembles using a limited number of outlier examples

Abstract In data analysis, outliers are deviating and unexpected observations. Outlier detection is important, because outliers can contain critical and interesting information. We propose an approach for optimizing outlier detection ensembles using a limited number of outlier examples. In our work, a limited number of outlier examples are defined as from 1 to 10% of the available outliers. The optimized outlier detection ensembles consist of outlier detection algorithms, which provide an outlier score and utilize adjustable parameters. The automatic optimization determines the parameter values, which enhance the discrimination of inliers and outliers. This increases the efficiency of the outlier detection. Outliers are rare by definition, which makes the optimization with a few examples beneficial. Obtaining examples of outliers can be prohibitively challenging, and the outlier examples should be used efficiently.
FinTech for Consumers and Retail Investors: Opportunities and Risks of Digital Payment and Investment Services

Payments and investments are part of consumers' everyday life and vital to society and its economic and social systems. As the evolution of FinTech has laid the foundation for the next generation of technical innovation in the financial service sectors, this chapter discusses examples of FinTech innovation and analyzes their opportunities as well as their ecological, societal, and technological risks from consumer and retail investor perspectives. While FinTech innovation has the potential to make financial services easier, cheaper, and better available, all payment and investment services discussed in this chapter have in common the technological risks arising from the possibility to (mis)use consumers' and retail investors' data for price discrimination or identity theft. Cryptocurrencies additionally pose ecological risks due to their enormous energy demand, while the new investment services may help retail investors to decrease the ecological risk of their portfolios. The societal risks of FinTech overlap or coincide with the technological risks, for example, when consumer data is used to discriminate groups of consumers. In addition, the high degree of anonymity in cryptocurrency networks and the lack of centralized supervision can provide an ideal playing field for criminal activities such as money laundering and tax evasion that threaten consumer and retail investor welfare.
LSM: Learning Subspace Minimization for Low-Level Vision

We study the energy minimization problem in low-level vision tasks from a novel perspective. We replace the heuristic regularization term with a data-driven learnable subspace constraint, and preserve the data term to exploit domain knowledge derived from the first principles of a task. This learning subspace minimization (LSM) framework unifies the network structures and the parameters for many different low-level vision tasks, which allows us to train a single network for multiple tasks simultaneously with shared parameters, and even generalizes the trained network to an unseen task as long as the data term can be formulated. We validate our LSM frame on four low-level tasks including edge detection, interactive segmentation, stereo matching, and optical flow, and validate the network on various datasets. The experiments demonstrate that the proposed LSM generates state-of-the-art results with smaller model size, faster training convergence, and real-time inference.
SenCAPTCHA

CAPTCHAs are used to distinguish between human- and computer-generated (i.e., bot) online traffic. As there is an ever-increasing amount of online traffic from mobile devices, it is necessary to design CAPTCHAs that work well on mobile devices. In this paper, we present SenCAPTCHA, a mobile-first CAPTCHA that leverages the device's orientation sensors. SenCAPTCHA works by showing users an image of an animal and asking them to tilt their device to guide a red ball into the center of that animal's eye. SenCAPTCHA is especially useful for devices with small screen sizes (e.g., smartphones, smartwatches). In this paper, we describe the design of SenCAPTCHA and demonstrate that it is resilient to various machine learning based attacks. We also report on two usability studies of SenCAPTCHA involving a total of 472 participants; our results show that SenCAPTCHA is viewed as an "enjoyable" CAPTCHA and that it is preferred by over half of the participants to other existing CAPTCHA systems.
Non-Adversarial Video Synthesis with Learned Priors

Most of the existing works in video synthesis focus on generating videos using adversarial learning. Despite their success, these methods often require input reference frame or fail to generate diverse videos from the given data distribution, with little to no uniformity in the quality of videos that can be generated. Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames. To this end, we develop a novel approach that jointly optimizes the input latent space, the weights of a recurrent neural network and a generator through non-adversarial learning. Optimizing for the input latent space along with the network weights allows us to generate videos in a controlled environment, i.e., we can faithfully generate all videos the model has seen during the learning process as well as new unseen videos. Extensive experiments on three challenging and diverse datasets well demonstrate that our proposed approach generates superior quality videos compared to the existing state-of-the-art methods.