Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Authentication based on DCT transform watermarking

Digital image can easily be tampered and destroyed by some people with malice. Therefore, the protection of digital information transmitted on a network has become an important research topic in recent years. The major goal of image authentication is to detect the tampered regions to achieve the authenticity and integrity. In this paper one watermark bit will be embedded in each DCT block by shifting a randomly selected coefficient to have a mapped value, in a coefficient-binary-mapping function that is identical to the watermark bit. The method was tested under different attacks and was found to provide good detection performance while maintaining the quality of the watermarked image. INTRODUCTION Digital image watermarking has received increasing attention in the few last years due to rapid growth in the internet traffic. It is gaining popularity due its significance in content authentication and copyright protection for digital multimedia data [1]. Watermarking applications include copyright protection, authentication, embedded and hidden information. Firstly, watermarking systems that are intended for copyright protection require a very high degree of robustness while watermarking process for authentication belongs to the fragile class of schemes. Slightest change in the image completely destroys the watermark. Image Authentication based on DCT transform watermarking
Theory of Unpleasant Symptoms and Concept of Nursing Support.

Theory of unpleasant symptoms – TOUS – has been developing since the 1990s. TOUS is a theory in healthcare developed by healthcare workers and intended to be applied by them. According to this theory, multiple symptoms may occur simultaneously, interact with each other and be multiplicative. Several symptoms have been presented, including also psychological, physiological and cognitive aspects of a patient’s personality. Symptom models described in the TOUS are the nociceptive model of dyspnea, symptom interpretation model, model of chronic dyspnea and symptom management model. TOUS is the first model to describe multiple, mutually related symptoms. By studying this theory, it can be concluded that controlling a single symptom is beneficial for controlling other symptoms. TOUS views a set of occurring symptoms without isolating any of the symptoms, but allowing for each symptom to be monitored separately. The theory helps in planning nursing interventions and facilitates healthcare delivery in general. The purpose of this theory is to help healthcare workers understand the symptoms and the methods used for their management. TOUS has been developed through clinical research; it establishes a logical connection between the symptoms and extends theoretical knowledge by taking into account previous factors, experiences of single or multiple symptoms and their impact on performance. (Miksic S, Bosnjakovic M, Simic I, Stanic B, Stankovic M, Stojkov Ž. Theory of Unpleasant Symptoms and Concept of Nursing Support. SEEMEDJ 2018; 2(2); 17-22) KEYWORDS: theory, unpleasant symptoms, pain, dyspnea, healthcare
Query Expansion for Patent Searching using Word Embedding and Professional Crowdsourcing

The patent examination process includes a search of previous work to verify that a patent application describes a novel invention. Patent examiners primarily use keyword-based searches to uncover prior art. A critical part of keyword searching is query expansion, which is the process of including alternate terms such as synonyms and other related words, since the same concepts are often described differently in the literature. Patent terminology is often domain specific. By curating technology-specific corpora and training word embedding models based on these corpora, we are able to automatically identify the most relevant expansions of a given word or phrase. We compare the performance of several automated query expansion techniques against expert specified expansions. Furthermore, we explore a novel mechanism to extract related terms not just based on one input term but several terms in conjunction by computing their centroid and identifying the nearest neighbors to this centroid. Highly skilled patent examiners are often the best and most reliable source of identifying related terms. By designing a user interface that allows examiners to interact with the word embedding suggestions, we are able to use these interactions to power crowdsourced modes of related terms. Learning from users allows us to overcome several challenges such as identifying words that are bleeding edge and have not been published in the corpus yet. This paper studies the effectiveness of word embedding and crowdsourced models across 11 disparate technical areas.
Inverse-Transform AutoEncoder for Anomaly Detection

Reconstruction-based methods have recently shown great promise for anomaly detection. We here propose a new transform-based framework for anomaly detection. A selected set of transformations based on human priors is used to erase certain targeted information from input data. An inverse-transform autoencoder is trained with the normal data only to embed corresponding erased information during the restoration of the original data. The normal and anomalous data are thus expected to be differentiable based on restoration errors. Extensive experiments have demonstrated that the proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets, especially on ImageNet, increasing the AUROC of the top-performing baseline by 10.1%. We also evaluate our method on a real-world anomaly detection dataset MVTec AD and a video anomaly detection dataset ShanghaiTech to validate the effectiveness of the method in real-world environments.
Deformed Hermitian Yang–Mills connections, extended gauge group and scalar curvature

The deformed Hermitian Yang‐Mills (dHYM) equation is a special Lagrangian type condition in complex geometry. It requires the complex analogue of the Lagrangian phase, defined for Chern connections on holomorphic line bundles using a background Kähler metric, to be constant. In this paper, we introduce and study dHYM equations with variable Kähler metric. These are coupled equations involving both the Lagrangian phase and the radius function, at the same time. They are obtained by using the extended gauge group to couple the moment map interpretation of dHYM connections, due to Collins–Yau and mirror to Thomas' moment map for special Lagrangians, to the Donaldson–Fujiki picture of scalar curvature as a moment map. As a consequence, one expects that solutions should satisfy a mixture of K‐stability and Bridgeland‐type stability. In special limits, or in special cases, we recover the Kähler–Yang–Mills system of Álvarez–Cónsul, Garcia–Fernandez and García–Prada, and the coupled Kähler–Einstein equations of Hultgren–Witt Nyström. After establishing several general results, we focus on the equations and their large/small radius limits on abelian varieties, with a source term, following ideas of Feng and Székelyhidi.
Constrained Linear Data-feature Mapping for Image Classification

In this paper, we propose a constrained linear data-feature mapping model as an interpretable mathematical model for image classification using convolutional neural network (CNN) such as the ResNet. From this viewpoint, we establish the detailed connections in a technical level between the traditional iterative schemes for constrained linear system and the architecture for the basic blocks of ResNet. Under these connections, we propose some natural modifications of ResNet type models which will have less parameters but still maintain almost the same accuracy as these corresponding original models. Some numerical experiments are shown to demonstrate the validity of this constrained learning data-feature mapping assumption.
Non-autoregressive Transformer by Position Learning

Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling is an essential problem in non-autoregressive text generation. In this study, we propose PNAT, which incorporates positions as a latent variable into the text generative process. Experimental results show that PNAT achieves top results on machine translation and paraphrase generation tasks, outperforming several strong baselines.
Contextuality of general probabilistic theories and the power of a single resource

Generalized contextuality refers to our inability of explaining measurement statistics using a context-independent probabilistic model. On the other hand, measurement statistics can also be modeled most generally within the framework of general probabilistic theories. Here, starting from a construction of general probabilistic theories based on a Gleason-type theorem, we show that for any such theory the three insistence of (i) the no-restriction hypothesis, (ii) the ontological noncontextuality, and (iii) multiple nonrefinable measurements for any fixed number of outcomes are incompatible. In other words, requiring any two of these properties for a general probabilistic theory implies the violation of the third one. We extensively discuss general probabilistic theories for which (ii) and (iii) are met while (i) is violated, exploring the role of the no-restriction hypothesis as a sufficient condition that enforces ontological contextuality on nonsimplicial general probabilistic theories. Finally, we establish as a corollary the necessary and sufficient condition for a single resourceful measurement or state to promote an ontologically noncontextual (i.e. classical) general probabilistic theory to an ontologically contextual (i.e. nonclassical) one under the no-restriction hypothesis.
Safe Reinforcement Learning via Probabilistic Shields

This paper targets the efficient construction of a safety shield for decision making in scenarios that incorporate uncertainty. Markov decision processes (MDPs) are prominent models to capture such planning problems. Reinforcement learning (RL) is a machine learning technique to determine near-optimal policies in MDPs that may be unknown prior to exploring the model. However, during exploration, RL is prone to induce behavior that is undesirable or not allowed in safety- or mission-critical contexts. We introduce the concept of a probabilistic shield that enables decision-making to adhere to safety constraints with high probability. In a separation of concerns, we employ formal verification to efficiently compute the probabilities of critical decisions within a safety-relevant fragment of the MDP. We use these results to realize a shield that is applied to an RL algorithm which then optimizes the actual performance objective. We discuss tradeoffs between sufficient progress in exploration of the environment and ensuring safety. In our experiments, we demonstrate on the arcade game PAC-MAN and on a case study involving service robots that the learning efficiency increases as the learning needs orders of magnitude fewer episodes.
Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference

Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN - a novel framework for solving the inference problem in GANs, which involves training an encoder network capable of inverting a pre-trained generator network without access to any training data. Under mild assumptions, we theoretically show that using InvGAN, we can approximately invert the generations of any latent code of a trained GAN model. Furthermore, we empirically demonstrate the superiority of our inference scheme by quantitative and qualitative comparisons with other methods that perform a similar task. We also show the effectiveness of our framework in the problem of adversarial defenses where InvGAN can successfully be used as a projection-based defense mechanism. Additionally, we show how InvGAN can be used to implement reparameterization white-box attacks on projection-based defense mechanisms. Experimental validation on several benchmark datasets demonstrate the efficacy of our method in achieving improved performance on several white-box and black-box attacks. Our code is available at this https URL.
Real-time Analysis of Privacy-(un)aware IoT Applications

Abstract Abstract: Users trust IoT apps to control and automate their smart devices. These apps necessarily have access to sensitive data to implement their functionality. However, users lack visibility into how their sensitive data is used, and often blindly trust the app developers. In this paper, we present IoTWATcH, a dynamic analysis tool that uncovers the privacy risks of IoT apps in real-time. We have designed and built IoTWATcH through a comprehensive IoT privacy survey addressing the privacy needs of users. IoTWATCH operates in four phases: (a) it provides users with an interface to specify their privacy preferences at app install time, (b) it adds extra logic to an app’s source code to collect both IoT data and their recipients at runtime, (c) it uses Natural Language Processing (NLP) techniques to construct a model that classifies IoT app data into intuitive privacy labels, and (d) it informs the users when their preferences do not match the privacy labels, exposing sensitive data leaks to users. We implemented and evaluated IoTWATcH on real IoT applications. Specifically, we analyzed 540 IoT apps to train the NLP model and evaluate its effectiveness. IoTWATcH yields an average 94.25% accuracy in classifying IoT app data into privacy labels with only 105 ms additional latency to an app’s execution.
A Stochastic Tensor Method for Non-convex Optimization

We present a stochastic optimization method that uses a fourth-order regularized model to find local minima of smooth and potentially non-convex objective functions. This algorithm uses sub-sampled derivatives instead of exact quantities and its implementation relies on tensor-vector products only. The proposed approach is shown to find an $(\epsilon_1,\epsilon_2)$-second-order critical point in at most $\bigO\left(\max\left(\epsilon_1^{-4/3}, \epsilon_2^{-2}\right)\right)$ iterations, thereby matching the rate of deterministic approaches. Furthermore, we discuss a practical implementation of this approach for objective functions with a finite-sum structure, as well as characterize the total computational complexity, for both sampling with and without replacement. Finally, we identify promising directions of future research to further improve the complexity of the discussed algorithm.
Peak-to-Average Power Ratio Analysis for OFDM-Based Mixed-Numerology Transmissions

In this paper, the probability distribution of the peak to average power ratio (PAPR) is analyzed for the mixed numerologies transmission based on orthogonal frequency division multiplexing (OFDM). State of the art theoretical analysis implicitly assumes continuous and symmetric frequency spectrum of OFDM signals. Thus, it is difficult to be applied to the mixed-numerology system due to its complication. By comprehensively considering system parameters, including numerology, bandwidth and power level of each subband, we propose a generic analytical distribution function of PAPR for continuous-time signals based on level-crossing theory. The proposed approach can be applied to both conventional single numerology and mixed-numerology systems. In addition, it also ensures the validity for the noncontinuous-OFDM (NC-OFDM). Given the derived distribution expression, we further investigate the effect of power allocation between different numerologies on PAPR. Simulations are presented and show the good match of the proposed theoretical results.
Kernel-Based Inverse Optimization: Application to the Power Forecasting and Bidding of a Fleet of Electric Vehicles

This paper considers an aggregator of Electric Vehicles (EVs) who aims to learn the aggregate power of his/her fleet while also participating in the electricity market. The proposed approach is based on a data-driven inverse optimization (IO) method, which is highly nonlinear. To overcome such a caveat, we use a two-step estimation procedure which requires solving two convex programs. Both programs depend on penalty parameters that can be adjusted by using grid search. In addition, we propose the use of kernel regression to account for the nonlinear relationship between the behaviour of the pool of EVs and the explanatory variables, i.e., the past electricity prices and EV fleet's driving patterns. Unlike any other forecasting method, the proposed IO framework also allows the aggregator to derive a bid/offer curve, i.e. the tuple of price-quantity to be submitted to the electricity market, according to the market rules. We show the benefits of the proposed method against the machine-learning techniques that are reported to exhibit the best forecasting performance for this application in the technical literature.
The Circumstellar Environment around the Embedded Protostar EC 53

EC 53 is an embedded protostar with quasi-periodic emission in the near-IR and submillimeter. We use Atacama Large Millimeter/submillimeter Array (ALMA) high-resolution observations of continuum and molecular line emission to describe the circumstellar environment of EC 53. The continuum image reveals a disk with a flux that suggests a mass of 0.075 M⊙, much less than the estimated mass in the envelope, and an in-band spectral index that indicates grain growth to centimeter sizes. Molecular lines trace the outflow cavity walls, infalling and rotating envelope, and/or the Keplerian disk. The rotation profile of the C17O 3–2 line emission cannot isolate the Keplerian motion clearly, although the lower limit of the protostellar mass can be calculated as 0.3 ± 0.1 M⊙ if the Keplerian motion is adopted. The weak CH3OH emission, which is anticorrelated with the H13CO+ 4–3 line emission, indicates that the water snow line is more extended than what expected from the current luminosity, attesting to bygone outburst events. The extended snow line may persist for longer at the disk surface because the lower density increases the freeze-out timescale of methanol and water.
Latent space conditioning for improved classification and anomaly detection

We propose a variational autoencoder to perform improved pre-processing for clustering and anomaly detection on data with a given label. Anomalies however are not known or labeled. We call our method conditioned variational autonencoder since it separates the latent space by conditioning on information within the data. The method fits one prior distribution to each class in the dataset, effectively expanding the prior distribution to include a Gaussian mixture model. Our approach is compared against the capabilities of a typical variational autoencoder by measuring their V-score during cluster formation with respect to the $k$-means and EM algorithms. For anomaly detection, we use a new metric composed of the mass-volume and excess-mass curves which can work in an unsupervised setting. We compare the results between established methods such as as isolation forest, local outlier factor and one-class support vector machine.
ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition

Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96%) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification.
Deep Visual Waterline Detection within Inland Marine Environment

Waterline usually plays as an important visual cue for maritime applications. However, the visual complexity of inland waterline presents a significant challenge for the development of highly efficient computer vision algorithms tailored for waterline detection in a complicated inland water environment. This paper attempts to find a solution to guarantee the effectiveness of waterline detection for inland maritime applications with general digital camera sensor. To this end, a general deep-learning-based paradigm applicable in variable inland waters, named DeepWL, is proposed, which concerns the efficiency of waterline detection simultaneously. Specifically, there are two novel deep network models, named WLdetectNet and WLgenerateNet respectively, cooperating in the paradigm that afford a continuous waterline image-map estimation from a single captured video stream. Experimental results demonstrate the effectiveness and superiority of the proposed approach via qualitative and quantitative assessment on the concerned performances. Moreover, due to its own generality, the proposed approach has the potential to be applied to the waterline detection tasks of other water areas such as coastal waters.
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.