Reviewerly/exHarmony huggingface dataset:
Gaze-angle dependency of pupil-size measurements in head-mounted eye tracking

Pupillometry - the study of temporal changes in pupil diameter as a function of external light stimuli or cognitive processing - requires the accurate and gaze-angle independent measurement of pupil dilation. Expected response amplitudes often are only a few percent relative to a pre-stimulus baseline, thus demanding for sub-millimeter accuracy. Video-based approaches to pupil-size measurement aim at inferring pupil dilation from eye images alone. Eyeball rotation in relation to the recording camera as well as optical effects due to refraction at corneal interfaces can, however, induce so-called pupil foreshortening errors (PFE), i.e. systematic gaze-angle dependent changes of apparent pupil size that are on a par with typical response amplitudes. While PFE and options for its correction have been discussed for remote eye trackers, for head-mounted eye trackers such an assessment is still lacking. In this work, we therefore gauge the extent of PFE in three measurement techniques, all based on eye images recorded with a single near-eye camera. We present both real world experimental data as well as results obtained on synthetically generated eye images. We discuss PFE effects at three different levels of data aggregation: the sample, subject, and population level. In particular, we show that a recently proposed refraction-aware approach employing a mathematical 3D eye model is successful in providing pupil-size measurements which are gaze-angle independent at the population level.
Polyblur: Removing Mild Blur by Polynomial Reblurring

We present a highly efficient blind restoration method to remove mild blur in natural images. Contrary to the mainstream, we focus on removing slight blur that is often present, damaging image quality and commonly generated by small out-of-focus, lens blur, or slight camera motion. The proposed algorithm first estimates image blur and then compensates for it by combining multiple applications of the estimated blur in a principled way. To estimate blur we introduce a simple yet robust algorithm based on empirical observations about the distribution of the gradient in sharp natural images. Our experiments show that, in the context of mild blur, the proposed method outperforms traditional and modern blind deblurring methods and runs in a fraction of the time. Our method can be used to blindly correct blur before applying off-the-shelf deep super-resolution methods leading to superior results than other highly complex and computationally demanding techniques. The proposed method estimates and removes mild blur from a 12MP image on a modern mobile phone in a fraction of a second.
FlexMiner: A Pattern-Aware Accelerator for Graph Pattern Mining

Graph pattern mining (GPM) is a class of algorithms widely used in many real-world applications in bio-medicine, e-commerce, security, social sciences, etc. GPM is a computationally intensive problem with an enormous amount of coarse-grain parallelism and therefore, attractive for hardware acceleration. Unfortunately, existing GPM accelerators have not used the best known algorithms and optimizations, and thus offer questionable benefits over software implementations.We present FlexMiner, a software/hardware co-designed GPM accelerator that improves the efficiency without compromising the generality or productivity of state-of-the-art software GPM frameworks. FlexMiner exploits massive amount of coarse-grain parallelism in GPM by deploying a large number of specialized processing elements. For efficient searches, the FlexMiner hardware accepts pattern-specific execution plans, which are generated automatically by the FlexMiner compiler from the given pattern(s). To avoid repetitive computation on neighborhood connectivity, we provide dedicated on-chip storage to memoize reusable connectivity information in a connectivity map (c-map ) which is implemented with low-cost yet high-throughput hardware. The on-chip memories in FlexMiner are managed dynamically using heuristics derived by the compiler, and thus are fully utilized. We have evaluated FlexMiner with 4 GPM applications on a wide range of real-world graphs. Our cycle-accurate simulation shows that FlexMiner with 64 PEs achieves 10.6× speedup on average over the state-of-the-art software system executing 20 threads on a 10-core Intel CPU.
Deep Federated Learning for IoT-based Decentralized Healthcare Systems

Recent trends in the healthcare industry, such as the use of wearable IoT for continuous health monitoring, are setting new requirements for healthcare systems that boost data analysis. These systems should support decentralization and maintain the privacy and ownership of users' data due to the sensitivity of healthcare data. Therefore, the use of federated learning techniques is recommended for systems that need such requirements. This paper proposes a Deep Federated Learning framework for decentralized healthcare systems that maintain user privacy in a distributed architecture. It also proposes an algorithm for an automated training data acquiring process. Furthermore, it presents an experiment for using deep federated learning in detecting skin diseases and using Transfer Learning to address the problem of limited availability of healthcare data in building deep learning models. The evaluated results show how the federated learning increased the Area Under the Curve of the centralized learning model up to 0.97, as it also shows good model performance during federated rounds in terms of accuracy, precision, recall, and F1-score. Moreover, although the FL system has affected the quality of service to the user in terms of model conversion time, the Federated Learning system meets the requirements of building models in a decentralized manner with no sharing of users' private data.
FROM THE DEFINITION OF USER EXPERIENCE TO A FRAMEWORK TO CLASSIFY ITS APPLICATIONS IN DESIGN

Abstract The concept of User Experience (UX) dates back to the 1990s, but a shared definition of UX is not available. As design integrates UX, different interpretations thereof can complicate the possibility to build upon previous literature and develop the field autonomously. Indeed, by analysing the literature, UX emerges as a cauldron of related and closely linked concepts. However, it is possible to find recurring attributes that emerge from those definitions, which are ascribable to two foci: the fundamental elements of the interaction (user, system, context) and typologies of experience (ergonomic, cognitive, and emotional). Those are used to build a framework. We have preliminarily investigated how UX is dealt with in design by mapping a sample of UX-related experimental articles published in design journals. We classified UX case studies based on the framework to individuate the UXs that emerge most frequently and the most studied ones in the design field. The two-focus framework allows the mapping of experiments involving UX in design, without highlighting specific favorable combinations. However, comprehensive studies dealing with all elements and UX typologies have not been found.
Managing non‐functional requirements in agile software development

Non-functional requirements define qualities of the software system that ensure effectiveness while embedding any constraints and restrictions on the design. A challenge rises with agile implementation in handling non-function requirements in regulated environments. Thus, a practitioner's perceptions of agile method tailoring are described in relation to inter-team boundaries and non-functional requirements. The research comprises 18 practitioner interviews from two multinational agile software development companies. Interviews were recorded, transcribed, and analysed using an approach informed by grounded theory and information flow models were used to compare and contrast interactions of processes. It was discovered that one of the case study companies managed non-functional requirements as artefacts in their agile methodology, while the other company reverts to conventional plan-based software development practices of documentation, timeline estimations, and safety critical requirements. This research creates a detailed comparison of these contrasting approaches. The main contribution of this study is a set of proposed recommendations to deal with non-functional requirements in a regulated environment using agile techniques. The introduction of two new artefacts, Documentation Work Item and Safety Critical Work Item, is recommended and it is accompanied with an illustrative example, to transform the handling of documentation and safety critical requirements in a more agile way.
Anomalous jet identification via sequence modeling

This paper presents a novel method of searching for boosted hadronically decaying objects by treating them as anomalous elements of a contaminated dataset. A Variational Recurrent Neural Network (VRNN) is used to model jets as sequences of constituent four-vectors. After applying a pre-processing method which boosts each jet to the same reference mass and energy, the VRNN provides each jet an Anomaly Score that distinguishes between the structure of signal and background jets. The model is trained in an entirely unsupervised setting and without high level variables, making the score more robust against mass and $p_{T}$ correlations when compared to methods based primarily on jet substructure. Performance is evaluated on the jet level, as well as in an analysis context by searching for a heavy resonance with a final state of two boosted jets. The Anomaly Score shows consistent performance along a wide range of signal contamination amounts, for both two and three-pronged jet substructure hypotheses. Analysis results demonstrate that the use of Anomaly Score as a classifier enhances signal sensitivity while retaining a smoothly falling background jet mass distribution. The model's discriminatory performance resulting from an unsupervised training scenario opens up the possibility to train directly on data without a pre-defined signal hypothesis.
Measurement accuracy enhancement with multi-event detection using the deep learning approach in Raman distributed temperature sensors

In this work, we present a novel deep learning framework for multi-event detection with enhanced measurement accuracy from the measured data of a Raman Optical Time Domain Reflectometer (Raman-OTDR). We demonstrate the utility of a deep learning-based approach by comparing the results from three popular neural networks, i.e. vanilla recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Before feeding the experimentally obtained data to the neural network, we sanitize our data through a correlation filtering operation to suppress outlier noise spikes. Based on experiments with Raman-OTDR traces consisting of single temperature event, we show that the GRU is able to provide better performance compared to RNN and LSTM models. Specifically, a bidirectional-GRU (bi-GRU) architecture is found to outperform other architectures owing to its use of data from both previous as well as later time steps. Although this feature is similar to that used recently in one dimension convolutional neural network (1D-CNN), the bi-GRU is found to be more effective in providing enhanced measurement accuracy while maintaining good spatial resolution. We also propose and demonstrate a threshold-based algorithm for accurate and fast estimation of multiple events. We demonstrate a 4x improvement in the spatial resolution compared to post-processing using conventional total variational denoising (TVD) filters, while the temperature accuracy is maintained within ± 0.5 o C of the set temperature.
Automatic Object Tracking and Segmentation Using Unsupervised SiamMask

In this paper we address the basic limitation of SiamMask - the state of the art single object tracking and segmentation algorithm. SiamMask requires semi-supervision in that it needs a bounding box to be drawn manually around the object that has to be tracked. This is however not always possible or feasible, and slows down the pipeline even in the best case. We overcome this limitation by using state-of-the-art object detection algorithms: Detectron2 and YOLO to automatically detect the object and then track using SiamMask. The main purpose of this study is to devise an efficient technique for an end-to-end object detection and tracking, which can then be used in other applications like self-driving cars, etc. We compared different approaches using current state-of-the-art tools for time and detection efficiency. One of the secondary aim was to test how the two approaches perform on different types of datasets. We note that YOLO gives better and more meaningful detection of objects in the scene. However, Detectron2 gives a higher detection speed than YOLO, making the overall detection and tracking process faster.
Secure and Privacy-Preserving Federated Learning via Co-Utility

The decentralized nature of federated learning, that often leverages the power of edge devices, makes it vulnerable to attacks against privacy and security. The privacy risk for a peer is that the model update she computes on her private data may, when sent to the model manager, leak information on those private data. Even more obvious are security attacks, whereby one or several malicious peers return wrong model updates in order to disrupt the learning process and lead to a wrong model being learned. In this article, we build a federated learning framework that offers privacy to the participating peers as well as security against the Byzantine and poisoning attacks. Our framework consists of several protocols that provide strong privacy to the participating peers via unlinkable anonymity and that are rationally sustainable based on the co-utility property. In other words, no rational party is interested in deviating from the proposed protocols. We leverage the notion of co-utility to build a decentralized co-utile reputation management system that provides incentives for parties to adhere to the protocols. Unlike privacy protection via differential privacy, our approach preserves the values of model updates and, hence, the accuracy of plain federated learning; unlike privacy protection via update aggregation, our approach preserves the ability to detect bad model updates while substantially reducing the computational overhead compared to methods based on homomorphic encryption.
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding

Single-image super-resolution (SR) and multi-frame SR are two ways to super resolve low-resolution images. Single-Image SR generally handles each image independently, but ignores the temporal information implied in continuing frames. Multi-frame SR is able to model the temporal dependency via capturing motion information. However, it relies on neighbouring frames which are not always available in the real world. Meanwhile, slight camera shake easily causes heavy motion blur on long-distance-shot low-resolution images. To address these problems, a Blind Motion Deblurring Super-Reslution Networks, BMDSRNet, is proposed to learn dynamic spatio-temporal information from single static motion-blurred images. Motion-blurred images are the accumulation over time during the exposure of cameras, while the proposed BMDSRNet learns the reverse process and uses three-streams to learn Bidirectional spatio-temporal information based on well designed reconstruction loss functions to recover clean high-resolution images. Extensive experiments demonstrate that the proposed BMDSRNet outperforms recent state-of-the-art methods, and has the ability to simultaneously deal with image deblurring and SR.
From Online Handwriting to Synthetic Images for Alzheimer's Disease Detection Using a Deep Transfer Learning Approach

Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of AD. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. To verify the role played by dynamic information, we also generated simple binary images, containing only shape information. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. The experimental results have confirmed that dynamic information allows a performance improvement with respect to the binary images.
Content-based image retrieval using feature-fusion of GroupNormalized-Inception-Darknet-53 features and handcraft features

This paper proposed an efficient image retrieval framework by feature-fusion of high-level features from the improved version of DarkNet-53, named GroupNormalized-Inception-Darknet-53 (GN-Inception-Darknet-53), and handcraft features extracted from both RGB and HSI color models. To extract the more detailed features of an image, we augmented one inception layer, which includes 1 × 1, 3 × 3, and 5 × 5 kernels in place of an existing 3 × 3 kernel. To make the normalization process of the proposed model less dependent on batch size, Group Normalization (GN) layer is used instead of Batch Normalization (BN). A modified version of dot-diffused block truncation coding (DDBTC) is used to extract handcraft features in RGB color space. For HSI color space, interchannel voting between hue, saturation, and intensity components is used as color feature. To extract shape features histogram of orientated gradient (HOG) is applied on RGB color space. To evaluate the efficiency of our proposed method, Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are calculated for Corel-1 K, Corel-5 K, Corel-10 K, VisTex, Stex & Color Brodatz datasets. In all datasets, the proposed method shows the best results for all the instances with a minimum average improvement of 7.02%.
Signature-Based Traffic Classification and Mitigation for DDoS Attacks Using Programmable Network Data Planes

Distributed Denial of Service (DDoS) mitigation typically relies on source IP-based filtering rules; these may present scaling issues due to the vast amount of involved sources.By contrast, we propose a source IP-agnostic DDoS traffic classification and filtering schema that identifies malicious packet signatures via supervised Machine Learning methods and subsequently generates signature-based filtering rules.To accelerate packet processing, our schema utilizes XDP middleboxes operating as programmable Deep Packet Inspectors.Signatures are extracted from network traffic as unique combinations of the most significant packet features; these are subsequently fed to supervised Machine Learning algorithms that classify them as malicious or benign.Malicious signatures undergo a reduction process tailored to the attack vector in order to generate a concise set of filtering rules, thus expediting mitigation performance.Our schema was implemented as a proof-of-concept and evaluated for DNS volumetric attacks in terms of signature classification accuracy and packet filtering throughput.Experiments were based on benign and malicious traffic datasets recorded in production network environments.Our approach was compared to source-based mechanisms in terms of (i) malicious traffic identification, (ii) filtering rules cardinality, and (iii) packet processing throughput required in modern high speed networks.The experimental results demonstrate that our signature-based approach outperforms IP-based alternatives, achieving high detection accuracy and significant generalization capabilities.
Lightweight global and local contexts guided method name recommendation with prior knowledge

The quality of method names is critical for the readability and maintainability of source code. However, it is often challenging to construct concise method names. To alleviate this problem, a number of approaches have been proposed to automatically recommend high-quality names for methods. Despite being effective, existing approaches meet their bottlenecks mainly in two aspects: (1) the leveraged information is restricted to the target method itself; and (2) lack of distinctions towards the contributions of tokens extracted from different program contexts. Through a large-scale empirical analysis on +12M methods from +14K real-world projects, we found that (1) the tokens composing a method's name can be frequently observed in its callers/callees; and (2) tokens extracted from different specific contexts have diverse probabilities to compose the target method's name. Motivated by our findings, we propose, in this paper, a context-guided method name recommender, which mainly embodies two key ideas: (1) apart from the local context, which is extracted from the target method itself, we also consider the global context, which is extracted from other methods in the project that have call relations with the target method, to include more useful information; and (2) we utilize our empirical results as the prior knowledge to guide the generation of method names and also to restrict the number of tokens extracted from the global contexts. We implemented the idea as Cognac and performed extensive experiments to assess its effectiveness. Results reveal that can (1) perform better than existing approaches on the method name recommendation task (e.g., it achieves an F-score of 63.2%, 60.8%, 66.3%, and 68.5%, respectively, on four widely-used datasets, which all outperform existing techniques); and (2) achieve higher performance than existing techniques on the method name consistency checking task (e.g., its overall accuracy reaches 76.6%, outperforming the state-of-the-art MNire by 11.2%). Further results reveal that the caller/callee information and the prior knowledge all contribute significantly to the overall performance of Cognac.
Exploring Adversarial Fake Images on Face Manifold

Images synthesized by powerful generative adversarial network (GAN) based methods have drawn moral and privacy concerns. Although image forensic models have reached great performance in detecting fake images from real ones, these models can be easily fooled with a simple adversarial attack. But, the noise adding adversarial samples are also arousing suspicion. In this paper, instead of adding adversarial noise, we optimally search adversarial points on face manifold to generate anti-forensic fake face images. We iteratively do a gradient-descent with each small step in the latent space of a generative model, e.g. Style-GAN, to find an adversarial latent vector, which is similar to norm-based adversarial attack but in latent space. Then, the generated fake images driven by the adversarial latent vectors with the help of GANs can defeat main-stream forensic models. For examples, they make the accuracy of deepfake detection models based on Xception or EfficientNet drop from over 90% to nearly 0%, mean-while maintaining high visual quality. In addition, we find manipulating noise vectors n at different levels have different impacts on attack success rate, and the generated adversarial images mainly have changes on facial texture or face attributes.