Reviewerly/exHarmony huggingface dataset:
Jurassic Mark: Inattentional Blindness for a Datasaurus Reveals that Visualizations are Explored, not Seen

Graphs effectively communicate data because they capitalize on the visual system’s ability to rapidly extract patterns. Yet, this pattern extraction does not occur in a single glance. Instead, research on visual attention suggests that the visual system iteratively applies a sequence of filtering operations on an image, extracting patterns from subsets of visual information over time, while selectively inhibiting other information at each of these moments. To demonstrate that this powerful series of filtering operations also occurs during the perception of visualized data, we designed a task where participants made judgments from one class of marks on a scatterplot, presumably incentivizing them to relatively ignore other classes of marks. Participants consistently missed a conspicuous dinosaur in the ignored collection of marks (93% for a 1s presentation, and 61% for 2.5s), but not in a control condition where the incentive to ignore that collection was removed (25% for a 1s presentation, and 11% for 2.5s), revealing that data visualizations are not “seen” in a single glance, and instead require an active process of exploration.
opticARe - Augmented Reality Mobile Patient Monitoring in Intensive Care Units

German Intensive Care Units (ICUs) are in crisis, struggling with an increasing shortage of skilled workers, ultimately putting patients' safety at risk. To counteract this process, researchers are increasingly concerned with finding digital solutions which aim to support healthcare professionals by enhancing the efficiency of reoccurring critical caring tasks and thus, improve working conditions. In this regard, this paper evaluates the application of Augmented Reality (AR) for patient monitoring for critical care nursing. Grounded on an observational study, semi-structured interviews, as well as a quantitative analysis, mobile patient monitoring scenarios, present particularly during patient transport, were identified as an innovative context of use of AR in the field. Additionally, user requirements such as high wearability, hands-free operability, and clear data representation could be derived from the obtained study results. For validation of these and identification of further requirements, three prototypes differing in their data illustration format were subsequently developed and quantitatively, as well as qualitatively evaluated by conducting an online survey. Thereby, it became evident that future implementations of a corresponding system for patient monitoring ought to integrate a context-dependent data presentation in particular, as this combines high navigability and availability of required data. Identifying patient monitoring during patient transport as a potential context of use, as well as distinguishing a context-dependent design approach as favorable constitute two key contributions of this work and provide a foundation on which future implementations of AR systems in the nursing domain and other related contexts can be established.
HSGAN: Reducing mode collapse in GANs by the latent code distance of homogeneous samples

In this paper, we propose HSGAN, a novel generative adversarial network (GAN) variant that plays an adversarial game on the distance between two homogeneous samples (HS) in the latent space. HSGAN alleviates the notorious problem of mode collapse by maintaining a certain distance between the latent code of the generated data. Moreover, HSGAN is directly trained on the encoder and the generator, thereby gaining the ability to conduct inference without introducing any other model complexity. We prove theoretically that the objective function is designed to minimize the f-divergence between the distributions of the generated data and the real data. Extensive experiments on a series of synthetic and real image benchmark datasets demonstrate that HSGAN generates diverse images while keeping high quality, and it generally outperforms other GANs that target at the mode collapse problem.
Medical Handwritten Prescription Recognition and Information Retrieval using Neural Network

The automatic interpretation of handwritten documents is one of the popular research areas over the last few decades because of the huge scope of its practical applications such as automatic reading of address, bank cheque processing, and hand written text recognition filled on forms. Moreover, information retrieval in offline doctor’s prescription images was not being focused prior the Covid - 19 pandemic. However, over the last two years such prescription images are extensively being exchanged by the patients’ among offline medical consultants for useful advice. Therefore, in this paper the potential concern has been expressed on using computer technology to assess handwriting. Character recognition from the connected alphabets of a word (cursive writing) is a real time challenge. For this, the usage of Extended MNIST has been explored and the results support the efficiency of proposed model to identify the poor legibility of handwriting and transform it into readable correct text recognition. Further, using proposed model of the system the handwritten medical prescription can be converted digitally using electronic writing pad. Such facility will enables patient to take away prescription in the form of digital media which can further be recognized by running it on our model. The application areas wherein the proposed character recognition system can be utilized are recognizing medicine names from doctor’s prescription, historical document recognition, automatic reading of bank’s cheque, automatic postal code identification, converting handwriting in real time, extracting data from filled-in forms etc.
A novel active shape model-based DeepNeural network for age invariance face recognition

Scientific efforts have expanded in age-invariant face recognition (AIFR). Matching faces of large age difference is, therefore, a problem, mostly because of a substantial disparity in the appearance of both young and old age. Owing to age, both the appearance and shape of the face are impaired, making recognition of the face the most challenging task. In recent years, AIFR has become a very common and demanding task. The set of feature extraction and classification algorithm is of prime importance in this field. As the numbers of features obtained from the datasets are large, there is a need to introduce a dimensionality reduction method to map high dimensionality feature space to low variance filter to form the final integrated face age model to be used in the classification process. In this paper, we introduced a novel concept of an improved Active Shape Model (ASM) in conjunction with a specially designed 7-layered Convolutional Neural Network (CNN) in order to accomplish a combination of feature extraction and classification in a single unit. The study approach involves conducting extensive experiments to evaluate the proposed system's performance using three standard datasets: FG-NET, LAG, and CACD. The results reveal that the proposed method outperforms state-of-the-art approaches and achieves excellent accuracy in face recognition across age. The maximum accuracies achieved by demonstrated ASM-CNN methodology for FG-NET, LAG, and CACD databases are 95.02%, 91.76 % and 99.4 % respectively.
Identifying Malicious DNS Tunnel Tools from DoH Traffic Using Hierarchical Machine Learning Classification

Although the DNS over HTTPS (DoH) protocol has desirable properties for Internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining network security, in this paper, we propose a novel system that identifies malicious DNS tunnel tools through a hierarchical classification method that uses machine-learning technology on DoH traffic. We implemented a prototype of the proposed system and evaluated its performance on the CIRA-CIC-DoHBrw-2020 dataset, obtaining 99.81% accuracy in DoH traffic filtering, 99.99% accuracy in suspicious DoH traffic detection, and 97.22% accuracy in identification of malicious DNS tunnel tools.
Performance Analysis of the Quantum Safe Multivariate Polynomial Public Key Algorithm

The Multivariate Polynomial Public Key (MPPK) algorithm, over a prime Galois field, takes a multiplier multivariate polynomial and two multiplicand univariate solvable polynomials to create two product multivariate polynomials. One of variables is for secret message and all others are for noises. The public key consists of all coefficients of the product multivariate polynomials, except the two constant terms for the message variable. The private key is made of both multiplicands. Encryption takes a list of random numbers, over the prime Galois field. The first number is the secret to exchange. The other random numbers generate noise automatically cancelled by decryption. The secret is easily extracted from the evaluation of a solvable equation. The level of security provided by MPPK is adaptable. The algorithm can be used in several different ways. In this paper, we review the performance achieved by MPPK for several combinations of polynomial configurations and Galois field sizes. For every combination, we calculated key generation time, encryption time and decryption time. We also compare the effectiveness of MPPK with the performance of all four NIST PQC finalists. For MPPK, the data has been collected from the execution of an implementation in Java. In comparison to the NIST PQC finalists, MPPK key generation, encryption and decryption performance is excellent.
Improving Fault Tolerance for Reliable DNN Using Boundary-Aware Activation

In this article, we approach to construct reliable deep neural networks (DNNs) for safety-critical artificial intelligent applications. We propose to modify rectified linear unit (ReLU), a commonly used activation function in DNNs, to tolerate the faults incurred by bit-flip perturbation on weights. Through theoretic analysis of the fault propagation in the layers with ReLU activation, we observe that bounding the output of ReLU activation can help to tolerate the weight faults. Then, we propose a novel ReLU design called boundary-aware ReLU (BReLU) to improve the reliability of DNNs, in which an upper bound of ReLU is determined such that the deviation between the boundary and original outputs cannot affect the final result. We propose a gradient-ascent-based algorithm to find the boundaries for BReLU activations of all DNN layers. Without retraining the network, our approach is cost effective and practical when deployed in safety-critical artificial intelligent systems. Detailed experiments and real-life application benchmarking demonstrate that our approach can improve the accuracy of DNN VGG16 from 16.7% to 82.6% on average assuming the practical weight faults, with only 13% memory and 2.78% time overhead, respectively.
Multimedia Forensics

With the availability of powerful and easy-to-use media editing tools, falsifying images and videos has become widespread in the last few years. Coupled with ubiquitous social networks, this allows for the viral dissemination of fake news. This raises huge concerns on multimedia security. This scenario became even worse with the advent of deep learning. New, sophisticated methods have been proposed to accomplish manipulations that were previously unthinkable (e.g., deepfake). This tutorial will present the most reliable methods for detection of manipulated images and for source identification. These are important tools nowadays to carry out fact checking and authorship verification. Hence, this is a timely and relevant research topic in the multimedia security research community. The tutorial will be focused on digital integrity and source attribution with reference to both images and videos. For media authenticity the main techniques will be presented for forgery detection and localization, starting from methods that rely on camera-based and format-based artifacts. Then the most innovative solutions based on deep learning will be described, considering both supervised and unsupervised approaches. Results will be presented on challenging datasets and realistic scenarios, such as the spreading of manipulated images and videos over social networks. In addition, the robustness of such methods to adversarial attacks will be analyzed. The problem of image and video source attribution to the device used for its acquisition will be analyzed under different viewpoints: detecting the used kind of device (e.g., scanner vs. camera); detecting the used make and model (e.g., one model of camera vs. another model); detecting the used specific device (e.g., one device of a specific model vs. another device of the same model). State-of-the-art solutions exploiting either model-based or data-driven techniques will be presented. Results will be shown considering up-to-date standard datasets used for this topic.
Towards Low-Cost and Efficient Malaria Detection

Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon the availability of costly microscopes and trained experts to analyze blood-smear slides. Deep learning-based methods have the potential to not only decrease the burden of experts but also improve diagnostic accuracy on low-cost microscopes. However, this is hampered by the absence of a reasonable size dataset. One of the most challenging aspects is the reluctance of the experts to annotate the dataset at low magnification on low-cost microscopes. We present a dataset to further the research on malaria microscopy over low-cost microscopes at low magnification. Our large-scale dataset consists of images of blood-smear slides from several malaria-infected patients, collected through micro-scopes at two different cost spectrums and multiple magnifications. Malarial cells are annotated for the localization and life-stage classification task on the images collected through the high-cost microscope at high magnification. We design a mechanism to transfer these annotations from the high-cost microscope at high magnification to the low-cost microscope, at multiple magnifications. Multiple object detectors and domain adaptation methods are presented as the baselines. Furthermore, a partially supervised domain adaptation method is introduced to adapt the object-detector to work on the images collected from the low-cost microscope. The dataset is available here: http://im.itu.edu.pk/m5-malaria-dataset/
Spear

In a payment network, like the Lightning Network, Alice can transfer a payment to Bob by splitting the payment into partial payments and transferring these partial payments through multiple paths. The transfer, however, delays if any of the partial payments fails or delays. To handle this, one can add redundant payment paths. The challenge in doing so is that Bob may now overdraw funds from the redundant paths. To address this, Bagaria, Neu, and Tse introduced Boomerang, a mechanism based on secret sharing and homomorphic one-way functions, which allows Alice to revert the transfer if Bob overdraws.
Indian Currency Denomination Recognition and Fake Currency Identification

Abstract Visually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that relies on specific security features to help distinguish a legitimate currency note from an illegitimate one is much needed. These features are extracted using the boundary box region of interest (ROI) and Canny Edge detection in OpenCV implemented in Python, and the multi scale template matching algorithm is applied to match the security features and differentiate fake notes from legitimate notes.
How to Make an Intrusion Detection SystemAware of Steganographic Transmission

Information hiding techniques are becoming a major threat in network communication. This paper describes how to modify an intrusion detection system (IDS) to detect certain types of steganography. As a sample IDS we use open-source Zeek software. We show how to adapt it for the purpose of steganalysis. Additionally, we propose a set of validation tests that are suitable for detecting steganography and describe how they were applied to different types of covert channels. We also suggest how to build a steganography detection system by integrating Zeek with a security information and event management system with log and alert support. The scripts are freely available for download.
Embedding Hierarchical Structures for Venue Category Representation

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue C ategory E mbedding M odel named Hier-CEM , which generates a latent representation for each venue category by embedding the Hier archical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.
$\ell _1$ Regularization in Two-Layer Neural Networks

A crucial problem of neural networks is to select an architecture that strikes appropriate tradeoffs between underfitting and overfitting. This work shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> regularizations for two-layer neural networks can control the generalization error and sparsify the input dimension. In particular, with an appropriate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> regularization on the output layer, the network can produce a tight statistical risk. Moreover, an appropriate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula> regularization on the input layer leads to a risk bound that does not involve the input data dimension. The results also indicate that training a wide neural network with a suitable regularization provides an alternative bias-variance tradeoff to selecting from a candidate set of neural networks. Our analysis is based on a new integration of dimension-based and norm-based complexity analysis to bound the generalization error.
A Variational Approach to Privacy and Fairness

In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the $\beta$-VAE, the VIB, or the nonlinear IB.
Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.
WAFFLE: Watermarking in Federated Learning

Federated learning is a distributed learning technique where machine learning models are trained on client devices in which the local training data resides. The training is coordinated via a central server which is, typically, controlled by the intended owner of the resulting model. By avoiding the need to transport the training data to the central server, federated learning improves privacy and efficiency. But it raises the risk of model theft by clients because the resulting model is available on every client device. Even if the application software used for local training may attempt to prevent direct access to the model, a malicious client may bypass any such restrictions by reverse engineering the application software. Watermarking is a well-known deterrence method against model theft by providing the means for model owners to demonstrate ownership of their models. Several recent deep neural network (DNN) watermarking techniques use backdooring: training the models with additional mislabeled data. Backdooring requires full access to the training data and control of the training process. This is feasible when a single party trains the model in a centralized manner, but not in a federated learning setting where the training process and training data are distributed among several client devices. In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning. It introduces a retraining step at the server after each aggregation of local models into the global model. We show that WAFFLE efficiently embeds a resilient watermark into models incurring only negligible degradation in test accuracy (-0.17&#x0025;), and does not require access to training data. We also introduce a novel technique to generate the backdoor used as a watermark. It outperforms prior techniques, imposing no communication, and low computational (+3.2&#x0025;) overhead<sup>1</sup><sup>1</sup>The research report version of this paper is also available in https://arxiv.org/abs/2008.07298, and the code for reproducing our work can be found at https://github.com/ssg-research/WAFFLE.