Reviewerly/exHarmony huggingface dataset:
Achievable Second-Order Asymptotics for Successive Refinement Using Gaussian Codebooks

We study the mismatched successive refinement problem where one uses a fixed code to compress an arbitrary source with random Gaussian codebooks and minimum Euclidean distance encoding in a successive manner. Specifically, we generalize the mismatched rate-distortion framework by Lapidoth (T-IT, 1997) to the successive refinement setting and derive the achievable second-order asymptotics. Our result implies that any source that satisfies a mild moment constraint is successive refinable under our code. Furthermore, our proof, when specialized to a Gaussian memoryless source, provides an alternative achievability proof with structured codebooks for the successive refinement problem, which was studied by Zhou, Tan, Motani (T-IT, 2018) where a covering lemma without specifying the locations of codewords was used.
Semantic Human Face Analysis for Multi-level Age Estimation

Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity.Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression.Thus, it becomes a prominent challenge for many researchers.One of the most influential factors on age estimation is the type of features used in the model training process.Computer-vision is characterized by its superior ability to extract traditional facial features such as shape, size, texture, and deep features.However, it is still difficult for computers to extract and deal with semantic features inferred by human-vision.Therefore, we need somehow to bridge the semantic gap between machines and humans to enable utilization of the human brain capabilities of perceiving and processing visual information in semantic space.Our research aims to exploit human-vision in semantic facial feature extraction and fusion with traditional computer-vision features to obtain integrated and more informative features as an initial study paving the way to further augment the outperforming state-of-the-art age estimation models.A hierarchical automatic age estimation is achieved upon two consecutive stages: classification to predict (high-level) age group, followed by regression to estimate (low-level) exact age.The results showed noticeable performance improvements, when fusing semantic-based features with traditional vision-based features, surpassing the performance of traditional features alone.
Developing a Methodology Towards Designing Aesthetical BIPV Modules

As building integrated photovoltaics (BIPV) adoption increases, demand for new aesthetic designs that differ from conventional modules increases. Hence, this paper aims to incorporate Marble and Wood textures via digital ceramic printing on PV modules. The features in Marble and Wood (e.g. line density and wood panelling) were tuned via an imaging software before the modules were fabricated and characterized. Results indicate that the drop in I <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sc</inf> , V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">oc</inf> , and P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mpp</inf> against the conventional PV module was less in the Marble than in the Wood texture. The findings of this study could untap potential in designing novel aesthetical patterns for BIPV.
How to Transform Kernels for Scale-Convolutions

Scale is often seen as a given, disturbing factor in many vision tasks. When doing so it is one of the factors why we need more data during learning. In recent work scale equivariance was added to convolutional neural networks. It was shown to be effective for a range of tasks. We aim for accurate scale-equivariant convolutional neural networks (SE-CNNs) applicable for problems where high granularity of scale and small kernel sizes are required. Current SE-CNNs rely on weight sharing and kernel rescaling, the latter of which is accurate for integer scales only. To reach accurate scale equivariance, we derive general constraints under which scale-convolution remains equivariant to discrete rescaling. We find the exact solution for all cases where it exists, and compute the approximation for the rest. The discrete scale-convolution pays off, as demonstrated in a new state-of-the-art classification on MNIST-scale and on STL-10 in the supervised learning setting.
Virtual Reality Applications for Experiential Tourism - Curator Application for Museum Visitors

The possibility to create an additional or alternative experience for tourists using Virtual Reality (VR) has considerable potential in marketing, as an alternative to physical visits, as well as an enriching factor of a real-world experience. However, the positive experience in VR can be quickly marred by lack of usability, unmerited task load, or an onset of cybersickness. This paper presents the findings from a study of an application for museums allowing visitors to curate their own virtual rooms. The findings show that even such a simple VR application can still pose usability challenges to the users. Moreover, a considerable proportion of the study participants developed cybersickness symptoms despite a comparatively short use. We conclude that the design of experiential VR applications for tourism must pay attention to user-centred development in order to realise the promise of the content.
CryptoVault - A Secure Hardware Wallet for Decentralized Key Management

In traditional centralized internet services, third parties authenticate the transactions of the users. An important property of decentralized blockchain networks is the unrestricted and secured access to the private keys of users, which may often be threatened for several reasons. One considerable problem in systems based on blockchain technology is when users lose access to their keys due to, e.g., a broken or lost device. This paper, firstly, introduces an implementation that generates and maintains the private key in an Intel Software Guard Extension (SGX) enclave. The implementation allows using the private key in a process isolated from all other processes running on the same system. Secondly, the paper provides a method that enables the secure storage and recovery of a back-up key to and from an external repository, using an end-to-end secure connection. One proposed application, with which this technology could be exploited, is the social wallet.
Strings from Linear Recurrences: A Gray Code

Each strictly increasing sequence of positive integers can be used to define a numeration system so that any non-negative integer can be represented by a suitable and unique string of digits. We consider sequences defined by a two termed linear recurrence with constant coefficients having some particular properties and investigate on the possibility to define a Gray code for the set of the strings arising from them.
Immersive Video Sketching: Low-Fidelity Extended Reality Prototyping for Everyone

As the extended reality (XR) field advance, the need for tools assisting designers in the early-design phases for these immersive environments also increases. Although several tools exist, we still need a method that allows non-experts to engage in designing for XR, especially for collaborative contexts such as participatory design (PD) workshops. In this paper, we introduce Immersive Video Sketching (IVS), a low-cost prototyping method for early-phase XR design that can be easily employed by novice and non-expert designers. IVS combines body storming, paper prototyping and video sketching for XR environments. We tested IVS with 23 participants in a PD session focusing on XR game wearables. Our results showed that IVS can help non-experts to grasp the immersive nature of XR environments easily. On the other hand, a 4-hour design session might not be enough for iterations on design ideas and different design skills might create discrepancies in the outcomes.
Empirical Study of Standard Elliptic Curve Domain Parameters for IoT Devices

In recent times, security and privacy concerns associated with IoT devices have caught the attention of research community. The problem of securing IoT devices is immensely aggravating due to advancement in technology. These IoT devices are resource-constraint i.e. in terms of power, memory, computation, etc., so they are less capable to secure themselves. So we need a better approach to secure IoT devices within the limited resources. Several studies state that for these lightweight IoT devices Elliptic Curve Cryptography (ECC) suits perfectly. But there are several elliptic curve domain parameter standards, which may be used for different security levels. When any ECC based product is deployed then the selection of a suitable elliptic curve standard according to usability is become very important. So we have to choose one suitable standard domain parameter for the required security level. In this paper, two different elliptic curve standard domain parameters named secp256k1 and secp192k1 proposed by an industry consortium named Standards for Efficient Cryptography Group (SECG) [1] are implemented and then analyzed their performances metrics. The performance of each domain parameter is measured in computation time.
Transformer-based end-to-end scene text recognition

In recent years, regular scene text recognition has made great progress, but irregular text recognition still has certain difficulties. Most current text recognition methods treat text detection and text recognition as two separate tasks. In order to better recognize irregular text, this paper proposes an end-to-end scene text recognition based on a Transformer model, which not only uses the attention mechanism to perform Decode, but also introduce a network for correcting pictures and a network structure that expands its model through a bidirectional decoder. In order to better evaluate the performance of this model, experiments are carried out on data sets such as SVT and ICDAR 2013. The experiments prove that the method in this paper relatively balances complexity and accuracy, and has obvious performance advantages.
Researching Pandemics Through Time: A Covid-19 Inspired Data-Driven Approach to Explore Historical Newspapers

Heritage institutions are exploring new ways to open up their digital collections. In this context, the KB, national library of the Netherlands, has built a data-driven demonstration website based on historical newspapers. This website centers around a currently relevant topic due to the Covid-19 crisis: pandemics. A Toolbox with Notebooks and a sample data set is provided to support students and starting researchers. This paper describes the data selection process, the functionality of the website and corresponding Toolbox, as well as the initial reception.
Gaming at Warp Speed: Improving Aiming with Late Warp

Latency can make all the difference in competitive online games. Late warp is a class of techniques used in VR that can reduce latency in FPS games as well. Prior work has demonstrated these techniques can recover most of the player performance lost to computer or network latency. Inspired by work demonstrating the usefulness of late warp as a potential solution to FPS latency, we provide an interactive demonstration, playable in a web browser, that shows how much latency limits aiming performance, and how late warp can help.
Traffic Sign Recognition with Convolutional Neural Network

Traffic sign recognition is a computer vision technique to recognize the traffic signs put on the road. In this paper, a traffic sign dataset with approximately 5000 images is collected. This paper presents an ablation analysis of Multilayer Perceptron and Convolutional Neural Networks in traffic sign recognition. The ablation analysis studies the effects of different architectures of Multilayer Perceptron and Convolutional Neural Networks, batch normalization, and dropout. A total of 8 different models are reviewed and their performance is studied. The experimental results demonstrate that Convolutional Neural Networks outperform Multilayer Perceptron in general. Leveraging dropout layer and batch normalization is effective in improving the stability of the model and achieved 98.62% accuracy in traffic sign recognition.
FIP-SHA - Finding Individual Profiles Through SHared Accounts

Many recommendation systems rely on users’ account history (such as visited/purchased/classified items) to predict which other items they may be interested in. In practice, family members or friends can share a single account. For this reason, deriving a single user profile from an account’s history can lead to imprecise item suggestions. In this work, we propose to identify individual profiles behind shared accounts to better customize the suggestions of items for the person who is currently logged in. In short, the problem is solved by identifying online sessions on a platform and afterward, clustering these sessions to identify the profiles of the users behind the (potentially) shared account.
Towards a Modular On-Premise Approach for Data Sharing

The growing demand for everyday data insights drives the pursuit of more sophisticated infrastructures and artificial intelligence algorithms. When combined with the growing number of interconnected devices, this originates concerns about scalability and privacy. The main problem is that devices can detect the environment and generate large volumes of possibly identifiable data. Public cloud-based technologies have been proposed as a solution, due to their high availability and low entry costs. However, there are growing concerns regarding data privacy, especially with the introduction of the new General Data Protection Regulation, due to the inherent lack of control caused by using off-premise computational resources on which public cloud belongs. Users have no control over the data uploaded to such services as the cloud, which increases the uncontrolled distribution of information to third parties. This work aims to provide a modular approach that uses cloud-of-clouds to store persistent data and reduce upfront costs while allowing information to remain private and under users’ control. In addition to storage, this work also extends focus on usability modules that enable data sharing. Any user can securely share and analyze/compute the uploaded data using private computing without revealing private data. This private computation can be training machine learning (ML) models. To achieve this, we use a combination of state-of-the-art technologies, such as MultiParty Computation (MPC) and K-anonymization to produce a complete system with intrinsic privacy properties.
Coset-wise affine functions and cycle types of complete mappings

Let K be a finite field of characteristic p. We study a certain class of functions K→K that agree with an Fp-affine function K→K on each coset of a given additive subgroup W of K – we call them W-coset-wise Fp-affine functions of K. We show that these functions form a permutation group on K with the structure of an imprimitive wreath product and characterize which of them are complete mappings of K. As a consequence, we are able to provide various new examples of cycle types of complete mappings of K – for instance, if p>2, then all cycle types where each cycle has length a power of p are achieved by complete mappings of K.
A Six-degree-of-freedom Face Tracking Method for Non-contact Physiological Detection Robot

Due to the COVID-19 epidemic, there has been a high demand for non-contact diagnostic equipment that can reduce exposure and cross-infection. A non-contact medical detection robot is a type of diagnostic equipment and medical service robot with a wide application prospect. However, few non-contact medical detection robots have been designed to collect patients&#x0027; physiological parameters when they are in inconvenient situations, such as bedridden, during clinical usage. A six-degree-of-freedom (six-DOF) face tracking method based on a six-DOF robot is proposed in this paper. In the proposed system, a face detector equipped with a camera attached to the robot&#x0027;s wrist is used to obtain the real-time face depth and attitude information. The optimal target attitude of the camera is calculated according to the constraints in the base coordinate system. A closed-loop controller is designed to control the robot to reach the target position and posture smoothly. The experiment with a six-DOF robot has verified that the proposed system can achieve the real-time tracking of human faces by a camera. The proposed method can also be used in many other scenarios where six-DOF face tracking is required by robots.
A Hierarchy of Efficient Bounds on Quantum Capacities Exploiting Symmetry

Optimal rates for achieving an information processing task are often characterized in terms of regularized information measures. In many cases of quantum tasks, we do not know how to compute such quantities. Here, we exploit the symmetries in the recently introduced $D^{\#}$ in order to obtain a hierarchy of semidefinite programming bounds on various regularized quantities. As applications, we give a general procedure to give efficient bounds on the regularized Umegaki channel divergence as well as the classical capacity and two-way assisted quantum capacity of quantum channels. In particular, we obtain slight improvements for the capacity of the amplitude damping channel. We also prove that for fixed input and output dimensions, the regularized sandwiched Rényi divergence between any two quantum channels can be approximated up to an $ε$ accuracy in time that is polynomial in $1/ε$.
Robust Local Differential Privacy

We consider data release protocols for data X = (S, U ), where S is sensitive; the released data Y contains as much information about X as possible, measured as I(X; Y ), without leaking too much about S. We introduce the Robust Local Differential Privacy (RLDP) framework to measure privacy.This framework relies on the underlying distribution of the data, which needs to be estimated from available data.Robust privacy guarantees ensure privacy for all distributions in a confidence set based on this estimate.We also present three algorithms that construct RLDP protocols from a given dataset.One of these approximates the confidence set by a polytope and uses results from robust optimisation to yield high utility release protocols.However, it relies on vertex enumeration and becomes computationally infeasible for large input alphabets.The other two algorithms are low-complexity and build on randomised response.Experiments verify that all three algorithms offer significantly improved utility over regular LDP.
On Deep Speaker Embeddings for Speaker Verification

In recent years, there has been a tremendous application spike in the field of deep neural networks (DNN), including increasing interest in automatic speaker recognition systems development. Currently, the utilization of DNN-based speaker embeddings, such as x-vectors or d-vectors, is a common way of creating speaker-specific acoustic models. In recent years, these DNN embedings have begun to replace standard i-vectors extracted by factor analysis. We evaluated the performance and training time of the developed systems utilising these three state-of-the-art approaches. The results obtained on the VoxCeleb1 evaluation set show that x-vectors outperformed both conventional i-vectors and DNN-based d-vectors solutions, however at the cost of a higher computational load. We also show that the x-vector system with attentive pooling, AM-Softmax activation and PLDA back-end gives the lowest error rate over other architectures.
Computation of Kullback–Leibler Divergence in Bayesian Networks

Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.
An Exploratory Study on Architectural Knowledge in Issue Tracking Systems

Software developers use issue trackers (e.g. Jira) to manage defects, bugs, tasks, change requests, etc. In this paper we explore (a) how architectural knowledge concepts (e.g. architectural component behavior, contextual constraints) are textually represented in issues (e.g. as adjectives), (b) which architectural knowledge concepts commonly occur in issues, and (c) which architectural knowledge concepts appear together. We analyzed issues in the Jira issue trackers of three large Apache projects. To identify “architecturally relevant” issues, we linked issues to architecturally relevant source code changes in the studied systems. We then developed a code book by manually labeling a subset of issues. After reaching conceptual saturation, we coded remaining issues. Our findings support empirically-grounded search tools to identify architectural knowledge concepts in issues for future reuse.
How Much End-to-End is Tacotron 2 End-to-End TTS System

In recent years, the concept of end-to-end text-to-speech synthesis has begun to attract the attention of researchers. The motivation is simple – replacing the individual modules that TTS traditionally built on with a powerful deep neural network simplifies the architecture of the entire system. However, how capable are such end-to-end systems of dealing with classic tasks such as G2P, text normalisation, homograph disambiguation and other issues inseparably linked to text-to-speech systems?In the present paper, we explore three free implementations of the Tacotron 2-based speech synthesizers, focusing on their abilities to transform the input text into correct pronunciation, not only in terms of G2P conversion but also in handling issues related to text analysis and the prosody patterns used.