Reviewerly/exHarmony huggingface dataset:
Noise Distribution Adaptive Self-Supervised Image Denoising using  Tweedie Distribution and Score Matching

Tweedie distributions are a special case of exponential dispersion models, which are often used in classical statistics as distributions for generalized linear models. Here, we reveal that Tweedie distributions also play key roles in modern deep learning era, leading to a distribution independent self-supervised image denoising formula without clean reference images. Specifically, by combining with the recent Noise2Score self-supervised image denoising approach and the saddle point approximation of Tweedie distribution, we can provide a general closed-form denoising formula that can be used for large classes of noise distributions without ever knowing the underlying noise distribution. Similar to the original Noise2Score, the new approach is composed of two successive steps: score matching using perturbed noisy images, followed by a closed form image denoising formula via distribution-independent Tweedie's formula. This also suggests a systematic algorithm to estimate the noise model and noise parameters for a given noisy image data set. Through extensive experiments, we demonstrate that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset.
Current Methods of Privacy Protection in IoTs

In this chapter, we present the mainstream research of current privacy preservation in IoTs built upon the literature review we have done in recent years [1–3].
Zero-shot meta-learning for small-scale data from human subjects

While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of-sample subjects. Instead, models must make predictions on test data that may be drawn from a different distribution, a problem known as \textit{zero-shot learning}. To address this challenge, we develop an end-to-end framework using a meta-learning approach, which enables the model to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. We use three real-world small-scale human subjects datasets (two randomized control studies and one observational study), for which we predict treatment outcomes for held-out treatment groups. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multi-task predictions. We show that our model performs the best holistically for each held-out group and especially when the test group is distinctly different from the training group. Our model has implications for improved generalization of small-size human studies to the wider population.
Signal processing of Internet of Vehicles based on intelligent interference

Abstract With the rapid development of in-vehicle electronic technology and artificial intelligence, Internet of Vehicles (IoV) technology, as an effective integration of the two, greatly reduces the probability of road traffic accidents. However, the current IoV system is not perfect for the control process of abnormal vehicles. Therefore, to strengthen the management and control of abnormal vehicles in the IoV, it is extremely necessary to propose a method for interfering with IoV signals. Among the current popular intelligent interference methods, most of them rely on the prior knowledge of the signal. However, prior knowledge is difficult to obtain in practical applications. Therefore, in view of the shortcomings of the current communication interference technology, this study introduces intelligent interference into the signal processing technology of the IoV. When the service provider identifies abnormal signals, the intelligent interference method can be used to achieve precise interference for a single target and then realize the control of the IoV signals. This study proposes an interference waveform generation technology based on convolutional autoencoders. The convolutional autoencoder was used to change the features on the fully connected layer to generate an interference waveform that is very similar to the received signal waveform. The simulation results show that the interference waveform generation technology proposed in this study can make the bit error rate (BER) reach 38.4% within the signal-to-interference ratio (SIR) from − 10 to − 15 dB.
PhishMatch: A Layered Approach for Effective Detection of Phishing URLs

Phishing attacks continue to be a significant threat on the Internet. Prior studies show that it is possible to determine whether a website is phishing or not just by analyzing its URL more carefully. A major advantage of the URL based approach is that it can identify a phishing website even before the web page is rendered in the browser, thus avoiding other potential problems such as cryptojacking and drive-by downloads. However, traditional URL based approaches have their limitations. Blacklist based approaches are prone to zero-hour phishing attacks, advanced machine learning based approaches consume high resources, and other approaches send the URL to a remote server which compromises user's privacy. In this paper, we present a layered anti-phishing defense, PhishMatch, which is robust, accurate, inexpensive, and client-side. We design a space-time efficient Aho-Corasick algorithm for exact string matching and n-gram based indexing technique for approximate string matching to detect various cybersquatting techniques in the phishing URL. To reduce false positives, we use a global whitelist and personalized user whitelists. We also determine the context in which the URL is visited and use that information to classify the input URL more accurately. The last component of PhishMatch involves a machine learning model and controlled search engine queries to classify the URL. A prototype plugin of PhishMatch, developed for the Chrome browser, was found to be fast and lightweight. Our evaluation shows that PhishMatch is both efficient and effective.
Quantum Parameterized Complexity

Parameterized complexity theory was developed in the 1990s to enrich the complexity-theoretic analysis of problems that depend on a range of parameters. In this paper we establish a quantum equivalent of classical parameterized complexity theory, motivated by the need for new tools for the classifications of the complexity of real-world problems. We introduce the quantum analogues of a range of parameterized complexity classes and examine the relationship between these classes, their classical counterparts, and well-studied problems. This framework exposes a rich classification of the complexity of parameterized versions of QMA-hard problems, demonstrating, for example, a clear separation between the Quantum Circuit Satisfiability problem and the Local Hamiltonian problem.
Design of inverse permutations derived by compositions of permutations

In block encryption algorithms, random permutations are often employed for nonlinear transformation called a substitution box (S-box). Because an S-box is the only nonlinear portion of a block cipher, it accounts for most of implementation costs. For decryption, the circuit implementation of the corresponding inverse permutation is also required. To reduce the circuit size of the inverse permutation, we derive a method to generate inverse permutations based on the compositions of permutations. The effectiveness of the proposed method in terms of the complexity of Boolean functions is demonstrated through its application to the S-box used in PRESENT and to the optimum S-boxes.
GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction

Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting diverse attributes over multiple categories, 2) modeling attributes-category dependency, 3) capturing both global and local scene context, and 4) predicting attributes of objects with low pixel-count. To address these issues, we propose a novel multi-category attribute prediction deep architecture named GlideNet, which contains three distinct feature extractors. A global feature extractor recognizes what objects are present in a scene, whereas a local one focuses on the area surrounding the object of interest. Meanwhile, an intrinsic feature extractor uses an extension of standard convolution dubbed Informed Convolution to retrieve features of objects with low pixel-count. GlideNet uses gating mechanisms with binary masks and its self-learned category embedding to combine the dense embeddings. Collectively, the Global-Local-Intrinsic blocks comprehend the scene's global context while attending to the characteristics of the local object of interest. Finally, using the combined features, an interpreter predicts the attributes, and the length of the output is determined by the category, thereby removing unnecessary attributes. GlideNet can achieve compelling results on two recent and challenging datasets -- VAW and CAR -- for large-scale attribute prediction. For instance, it obtains more than 5\% gain over state of the art in the mean recall (mR) metric. GlideNet's advantages are especially apparent when predicting attributes of objects with low pixel counts as well as attributes that demand global context understanding. Finally, we show that GlideNet excels in training starved real-world scenarios.
Practical implementation of the algorithm for designing additional professional education programs using the competence-based approach

The paper defines the methodology for designing professional retraining programs, taking into account the requirements of professional standards and applying the “one discipline—one competence” approach as its basis. The use of the described design methodology makes it possible to formalize the designing process itself and reduce it to coordinating the requirements of the professional standard with the customer’s requirements to the contents of the disciplines included in the professional retraining program.
What Artificial Intelligence Can Do and What It Cannot Do

Artificial Intelligence (AI) is not the solution to everything. It is important to understand that computational models in general and AI in particular face some limitations. Overcoming these limitations requires a lot of human expertise. As a consequence, AI systems are largely made of human intelligence. This chapter gives an overview of how AI programs work, what they can do and what are their limitations. It discusses the relation of such platforms to human skills.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning  Across Visual Domains

Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-class data in an unseen domain, this becomes an even more challenging task of domain generalized few-shot classification. In this paper, we present a unique learning framework for domain-generalized few-shot classification, where base classes are from homogeneous multiple source domains, while novel classes to be recognized are from target domains which are not seen during training. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features, with FSL ability introduced by metric-learning based mechanisms across support and query data. We conduct extensive experiments to verify the effectiveness of our proposed learning framework and show learning from small yet homogeneous source data is able to perform preferably against learning from large-scale one. Moreover, we provide insights into choices of backbone models for domain-generalized few-shot classification.
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment

We revisit the one- and two-stage detector distillation tasks and present a simple and efficient semantic-aware framework to fill the gap between them. We address the pixel-level imbalance problem by designing the category anchor to produce a representative pattern for each category and regularize the topological distance between pixels and category anchors to further tighten their semantic bonds. We name our method SEA (SEmantic-aware Alignment) distillation given the nature of abstracting dense fine-grained information by semantic reliance to well facilitate distillation efficacy. SEA is well adapted to either detection pipeline and achieves new state-of-the-art results on the challenging COCO object detection task on both one- and two-stage detectors. Its superior performance on instance segmentation further manifests the generalization ability. Both 2x-distilled RetinaNet and FCOS with ResNet50-FPN outperform their corresponding 3x ResNet101-FPN teacher, arriving 40.64 and 43.06 AP, respectively. Code will be made publicly available.
Deep Semantic Manipulation of Facial Videos

Editing and manipulating facial features in videos is an interesting and important field of research with a plethora of applications, ranging from movie post-production and visual effects to realistic avatars for video games and virtual assistants. To the best of our knowledge, this paper proposes the first method to perform photorealistic manipulation of facial expressions in videos. Our method supports semantic video manipulation based on neural rendering and 3D-based facial expression modelling. We focus on interactive manipulation of the videos by altering and controlling the facial expressions, achieving promising photorealistic results. The proposed method is based on a disentangled representation and estimation of the 3D facial shape and activity, providing the user with intuitive and easy-to-use control of the facial expressions in the input video. We also introduce a user-friendly, interactive AI tool that processes human-readable semantic labels about the desired emotion manipulations in specific parts of the input video and synthesizes photorealistic manipulated videos. We achieve that by mapping the emotion labels to valence-arousal (VA) values, which in turn are mapped to disentangled 3D facial expressions through an especially designed and trained expression decoder network. The paper presents detailed qualitative and quantitative experiments, which demonstrate the effectiveness of our system and the promising results it achieves. Additional results and videos can be found at the supplementary material (https://github.com/Girish-03/DeepSemManipulation).
Hypergraph Representation via Axis-Aligned Point-Subspace Cover

We propose a new representation of $k$-partite, $k$-uniform hypergraphs (i.e. a hypergraph with a partition of vertices into $k$ parts such that each hyperedge contains exactly one vertex of each type; we call them $k$-hypergraphs for short) by a finite set $P$ of points in $\mathbb{R}^d$ and a parameter $\ell\leq d-1$. Each point in $P$ is covered by $k={d\choose\ell}$ many axis-aligned affine $\ell$-dimensional subspaces of $\mathbb{R}^d$, which we call $\ell$-subspaces for brevity. We interpret each point in $P$ as a hyperedge that contains each of the covering $\ell$-subspaces as a vertex. The class of $(d,\ell)$-hypergraphs is the class of $k$-hypergraphs that can be represented in this way, where $k={d\choose\ell}$. The resulting classes of hypergraphs are fairly rich: Every $k$-hypergraph is a $(k,k-1)$-hypergraph. On the other hand, $(d,\ell)$-hypergraphs form a proper subclass of the class of all $d\choose\ell$-hypergraphs for $\ell<d-1$. In this paper we give a natural structural characterization of $(d,\ell)$-hypergraphs based on vertex cuts. This characterization leads to a polynomial-time recognition algorithm that decides for a given $d\choose\ell$-hypergraph whether or not it is a $(d,\ell)$-hypergraph and that computes a representation if existing. We assume that the dimension $d$ is constant and that the partitioning of the vertex set is prescribed.
FRIDA -- Generative Feature Replay for Incremental Domain Adaptation

We tackle the novel problem of incremental unsupervised domain adaptation (IDA) in this paper. We assume that a labeled source domain and different unlabeled target domains are incrementally observed with the constraint that data corresponding to the current domain is only available at a time. The goal is to preserve the accuracies for all the past domains while generalizing well for the current domain. The IDA setup suffers due to the abrupt differences among the domains and the unavailability of past data including the source domain. Inspired by the notion of generative feature replay, we propose a novel framework called Feature Replay based Incremental Domain Adaptation (FRIDA) which leverages a new incremental generative adversarial network (GAN) called domain-generic auxiliary classification GAN (DGAC-GAN) for producing domain-specific feature representations seamlessly. For domain alignment, we propose a simple extension of the popular domain adversarial neural network (DANN) called DANN-IB which encourages discriminative domain-invariant and task-relevant feature learning. Experimental results on Office-Home, Office-CalTech, and DomainNet datasets confirm that FRIDA maintains superior stability-plasticity trade-off than the literature.
Learning Spectral Windowing Parameters for Regularization Using Unbiased Predictive Risk and Generalized Cross Validation Techniques for Multiple Data Sets

During the inversion of discrete linear systems noise in data can be amplified and result in meaningless solutions. To combat this effect, characteristics of solutions that are considered desirable are mathematically implemented during inversion, which is a process called regularization. The influence of provided prior information is controlled by non-negative regularization parameter(s). There are a number of methods used to select appropriate regularization parameters, as well as a number of methods used for inversion. New methods of unbiased risk estimation and generalized cross validation are derived for finding spectral windowing regularization parameters. These estimators are extended for finding the regularization parameters when multiple data sets with common system matrices are available. It is demonstrated that spectral windowing regularization parameters can be learned from these new estimators applied for multiple data and with multiple windows. The results demonstrate that these modified methods, which do not require the use of true data for learning regularization parameters, are effective and efficient, and perform comparably to a learning method based on estimating the parameters using true data. The theoretical developments are validated for the case of two dimensional image deblurring. The results verify that the obtained estimates of spectral windowing regularization parameters can be used effectively on validation data sets that are separate from the training data, and do not require known data.
Prospects for Improving Password Selection

User-chosen passwords remain essential to online security, and yet people continue to choose weak, insecure passwords. In this work, we investigate whether prospect theory, a behavioral model of how people evaluate risk, can provide insights into how users choose passwords and whether it can motivate new designs for password selection mechanisms that will nudge users to select stronger passwords. We ran a user study with 762 participants, and we found that an intervention guided by prospect theory -- which leverages the reference-dependence effect by framing selecting weak passwords as a loss relative to choosing a stronger password -- causes approximately 25% of users to improve the strength of their password (significantly more than alternative interventions) and reduced the final number of weak passwords by approximately 25%. We also evaluate the relation between user behavior and users' mental models of hacking and password attacks. These results provide guidance for designing and implementing account registration mechanisms that will significantly improve the strength of user-selected passwords, thereby leveraging insights from prospect theory to improve the security of systems that use password-based authentication.
Improving the Pedestrian Detection Performance in the Absence of Rich Training Datasets: A UK Case Study

The World Health Organization estimates that well in excess of one million of lives are lost each year due to road traffic accidents. Since the human factor is the preeminent cause behind the traffic accidents, the development of reliable Advanced Driver Assistance Systems (ADASs) and Autonomous Vehicles (AVs) is seen by many as a possible solution to improve road safety. ADASs rely on the car perception system input that consists of camera(s), LIDAR and/or radar to detect pedestrians and other objects on the road. Hardware improvements as well as advances done in employing Deep Learning techniques for object detection popularized the Convolutional Neural Networks in the area of autonomous driving research and applications. However, the availability of quality and large datasets continues to be a most important contributor to the Deep Learning based model’s performance. With this in mind, this work analyses how a YOLO-based object detection architecture responded to limited data available for training and containing low-quality images. The work focused on pedestrian detection, since vulnerable road user’s safety is a major concern within AV and ADAS research communities. The proposed model was trained and tested on data gathered from Coventry, United Kingdom, city streets. The results show that the original YOLOv3 implementation reaches a 42.18% average precision (AP) and the main challenge was in detecting small objects. Network modifications were made and our final model, based on the original YOLOv3 implementation, achieved 51.6% AP. It is also demonstrated that the employed data augmentation approach is responsible for doubling the average precision of the final model.
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization

Charts are commonly used for exploring data and communicating insights. Generating natural language summaries from charts can be very helpful for people in inferring key insights that would otherwise require a lot of cognitive and perceptual efforts. We present Chart-to-text, a large-scale benchmark with two datasets and a total of 44,096 charts covering a wide range of topics and chart types. We explain the dataset construction process and analyze the datasets. We also introduce a number of state-of-the-art neural models as baselines that utilize image captioning and data-to-text generation techniques to tackle two problem variations: one assumes the underlying data table of the chart is available while the other needs to extract data from chart images. Our analysis with automatic and human evaluation shows that while our best models usually generate fluent summaries and yield reasonable BLEU scores, they also suffer from hallucinations and factual errors as well as difficulties in correctly explaining complex patterns and trends in charts.