Reviewerly/exHarmony huggingface dataset:
Deep Variational Metric Learning for Transfer of Expressivity in Multispeaker Text to Speech

In this paper, we propose to use the deep metric learning based multi-class N-pair loss, for text-to-speech (TTS) synthesis. We use the proposed loss function in a recurrent conditional variational autoencoder (RCVAE) for transferring expressivity in a French multispeaker TTS system. We extracted the speaker embeddings from the x-vector based speaker recognition model trained on speech data from many speakers to represent the speaker identity. We use mean of the latent variables to transfer expressivity for each emotion to generate expressive speech in the desired speaker’s voice. In contrast to the commonly used loss functions such as triplet loss or contrastive loss, multi-class N-pair loss considers all the negative examples which make each class of emotion distinguished from one another. Furthermore, the presented approach assists in creating a robust representation of expressivity irrespective of speaker identities. Our proposed approach demonstrates the improved performance for transfer of expressivity in the target speaker’s voice in a synthesized speech. To our knowledge, it is for the first time multi-class N-pair loss and x-vector based speaker embeddings are used in a TTS system.
Generation of scalable wavefront for testing optical systems

In this paper, we study the possibility of forming beams with a scalable wavefront. To multiply the wavefront with a scale factor, it is proposed to use a combination of the wavefront distribution generator and a diffractive grating with binarization operation. It is shown numerically and experimentally using spatial light modulator that such beams as the Zernike basis functions and Airy beams are suitable for testing optical systems.
Matrix Decomposition

The Matrix message-oriented middleware (see https://matrix.org) is gaining momentum as a basis for a decentralized, secure messaging system as shown, for example, by its deployment within the French government and by the Mozilla foundation. Thus, understanding the corresponding access control approach is important. This paper provides an ab- straction and an analysis of the access control approach followed by Matrix. We show that Matrix can be seen as a form of Distributed Ledger Technology (DLT) based on Transaction-based Directed Acyclic Graphs (TDAGs). TDAGs connect individual transactions to form a DAG, instead of collecting transactions in blocks as in blockchains. These TDAGs only provide causal order, eventual consistency, and no finality. However, unlike conventional DLTs, Matrix does not aim for a strict system-wide consensus. Thus, there is also no guarantee for a strict consensus on access rights. By de- composition of the Matrix approach, we show that a sound decen- tralized access control can be implemented for TDAGs in general, and for Matrix in particular, despite those weak guarantees. In ad- dition, we discovered security issues in popular implementations and emphasize the need for a formal verification of the employed conflict resolution mechanism.
Banknote serial number recognition using deep learning

In this paper, we recognize serial numbers on banknotes using deep learning. The samples used in this paper are digital images which have been preprocessed with data labelling and data augmentation, e.g., scaling transformation, etc. The algorithms based on deep learning are proposed and have the stability for serial number recognition with complex backgrounds. In this paper, a pipeline of deep neural networks is established for the recognition of banknote serial numbers. Because high reliability is more important than accuracy in financial applications, DenseNet is set forth as the primary classifier, the scaling transformation of SegLink is put forward to locate the characters, the detection rate is up to 95.80%. A convolutional neural network with residual attention model is proposed for serial number recognition, the precision is up to 97.09%.
Effect of Activation Functions on the Training of Overparametrized Neural Nets

It is well-known that overparametrized neural networks trained using gradient based methods quickly achieve small training error with appropriate hyperparameter settings. Recent papers have proved this statement theoretically for highly overparametrized networks under reasonable assumptions. These results either assume that the activation function is ReLU or they depend on the minimum eigenvalue of a certain Gram matrix. In the latter case, existing works only prove that this minimum eigenvalue is non-zero and do not provide quantitative bounds which require that this eigenvalue be large. Empirically, a number of alternative activation functions have been proposed which tend to perform better than ReLU at least in some settings but no clear understanding has emerged. This state of affairs underscores the importance of theoretically understanding the impact of activation functions on training. In the present paper, we provide theoretical results about the effect of activation function on the training of highly overparametrized 2-layer neural networks. A crucial property that governs the performance of an activation is whether or not it is smooth: • For non-smooth activations such as ReLU, SELU, ELU, which are not smooth because there is a point where either the ﬁrst order or second order derivative is discontinuous, all eigenvalues of the associated Gram matrix are large under minimal assumptions on the data. • For smooth activations such as tanh, swish, polynomial, which have derivatives of all orders at all points, the situation is more complex: if the subspace spanned by the data has small dimension then the minimum eigenvalue of the Gram matrix can be small leading to slow training. But if the dimension is large and the data satisﬁes another mild condition, then the eigenvalues are large. If we allow deep networks, then the small data dimension is not a limitation provided that the depth is sufﬁcient. We discuss a number of extensions and applications of these results.
Multimodal Attention Branch Network for Perspective-Free Sentence Generation

In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as bring me the yellow toy the upper part of the white shelf includes referring expressions, i.e., from the white upper part of the white shelf. To solve this task, we propose a multimodal attention branch network (Multi-ABN) which generates natural sentences in an end-to-end manner. Multi-ABN uses multiple images of the same fixed scene to generate sentences that are not tied to a particular viewpoint. This approach combines a linguistic attention branch mechanism with several attention branch mechanisms. We evaluated our approach, which outperforms the state-of-the-art method on a standard metrics. Our method also allows us to visualize the alignment between the linguistic input and the visual features.
Recognition and translation of Ancient Brahmi Letters using deep learning and NLP

Inscriptions are major resources for studying the ancient history and culture of civilization in any country. Analyzing, recognizing and translating the ancient letters (Brahmi letters) from the inscription is a very difficult work for present generation. There is no any automatic system for translating Brahmi letters to Sinhala language. However, they are using manual method for translating inscriptions. The method that used in epigraphy is being taken a long period to decipher, analyze and translate the inscribed text in inscriptions. This research mainly focuses on recognition of ancient Brahmi characters written the time period between 3rd B.C and 1st A. D. First, we remove the noise, segment the letters from the inscription image and convert it into the binary image using image processing techniques. Secondly, we recognize the correct Brahmi letters, broken letters and then identify the time period of the inscriptions using Convolution Neural Networks in deep learning. Finally, the Brahmi letters are translated into modern Sinhala letters and provide the meaning of the inscription using Natural Language Processing. This proposed system builds up solution to overcome the existing problems in epigraphy.
Encoding primitives generation policy learning for robotic arm to overcome catastrophic forgetting in sequential multi-tasks learning

Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially without storing or accessing previous task information. Unfortunately, current learning systems, e.g., neural networks, are prone to deviate the weights learned in previous tasks after training new tasks, leading to catastrophic forgetting, especially in a sequential multi-tasks scenario. To address this problem, in this paper, we propose to overcome catastrophic forgetting with the focus on learning a series of robotic tasks sequentially. Particularly, a novel hierarchical neural network’s framework called Encoding Primitives Generation Policy Learning (E-PGPL) is developed to enable continual learning with two components. By employing a variational autoencoder to project the original state space into a meaningful low-dimensional feature space, representative state primitives could be sampled to help learn corresponding policies for different tasks. In learning a new task, the feature space is required to be close to the previous ones so that previously learned tasks can be protected. Extensive experiments on several simulated robotic tasks demonstrate our method’s efficacy to learn control policies for handling sequentially arriving multi-tasks, delivering improvement substantially over some other continual learning methods, especially for the tasks with more diversity.
Hacking Machine Learning

The research on machine learning systems in adversarial environments is a relatively new discipline at the intersection between machine learning and cybersecurity. Still, machine learning algorithms that beat human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to influence training and/or usage of machine learning system. Machine learning is already used for many extremely significant applications and will be used on a much greater scale and will have even greater significance in the approaching future. The aim of this article is to provide a comprehensive review of scientific works in the field of cybersecurity of machine learning and to present an original taxonomy of adversarial attacks against machine learning systems in this context. A pertinent taxonomy enables good understanding of full spectrum of threats and development of systems resistant to intentional hackers' attacks.
The Perception Engineer’s Toolkit for Eye-Tracking data analysis

Tools for eye-tracking data analysis are as of now either provided as proprietary software by the eye-tracker manufacturer or published by researchers under licenses that are problematic for some use-cases (e.g., GPL3). This lead to repeated re-implementation of the most basic building blocks, such as event filters, often resulting in incomplete, incomparable and even erroneous implementations.
Progress on Open Science: Towards a Shared Research Knowledge System. Final Report of the Open Science Policy Platform

This final report of the EU Open Science Policy Platform (OSPP) provides a brief overview of its four-year mandate from 2016 to 2020, followed by an update on progress by each stakeholder group over the past two years since the publication of the OSPP’s recommendations across the European Commission’s eight ambitions on Open Science, (OSPP-REC1). This summary of Practical Commitments for Implementation with specific examples of progress by each stakeholder community across Europe (see Annex A) is followed by a perspective from each group on the major outstanding blockers to progress and possible next steps. The group of 25 key stakeholder representatives have then come together to propose a vision for moving beyond Open Science to create a shared research knowledge system by 2030.
Flower pollination algorithm for data generation and analytics - a diagnostic analysis

The effectiveness of optimization in scientific and engineering applications has made optimization a popular area of scientific investigation in data generation and analytics leading to the design of several optimization algorithms. In view of the huge number of optimization algorithms in literature, there is the need for a thorough diagnostic evaluation so as to bring out the strengths and weaknesses of each technique: that way, assist researchers in making informed choices whenever they are confronted with an optimization problem. This paper aims to fill the gap in literature of the Flower Pollination Algorithm in terms of diagnostic assessment of the impact of the number of iteration and search agents in solving the popular benchmark Sphere function and the unpopular but complex multimodal Dejong 5 function, otherwise called Shekel Foxhole function. After a number of empirical evaluations, the study finds out that the Flower Pollination Algorithm is not only a fast technique but also obtained good results when the appropriate iteration and flower population is used.
Fast Arithmetic Hardware Library For RLWE-Based Homomorphic Encryption

With billions of devices connected over the internet, the rise of sensor-based electronic devices have led to cloud computing being used as a commodity technology service. These sensor-based devices are often small and limited by power, storage, or compute capabilities, and hence, they achieve these capabilities via cloud services. However, this gives rise to data privacy issues as sensitive data is stored and computed over the cloud, which at most times, is a shared resource. Homomorphic encryption can be used along with cloud services to perform computations on encrypted data, guaranteeing data privacy. While about a decade’s work on improving homomorphic encryption has ensured its practicality, it is still several magnitudes slower than expected, making it expensive and infeasible to use. In this work, we propose a first-of-its-kind FPGA-based arithmetic hardware library that focuses on accelerating the key arithmetic operations involved in Ring Learning with Error (RLWE) based homomorphic encryption. We design and implement the FPGAbased Residue Number System (RNS), Chinese Remainder Theorem (CRT), modulo inverse and modulo reduction operations as a first step. For all of these operations, we include a hardware cost efficient serial, and a fast parallel implementation in the library. A modular and parameterized design approach helps in easy customization, provides flexibility to extend these operations for use in most homomorphic encryption applications, and fits well into emerging FPGA-equipped cloud architectures.
Regularized Optimal Transport is Ground Cost Adversarial

Regularizing the optimal transport (OT) problem has proven crucial for OT theory to impact the field of machine learning. For instance, it is known that regularizing OT problems with entropy leads to faster computations and better differentiation using the Sinkhorn algorithm, as well as better sample complexity bounds than classic OT. In this work we depart from this practical perspective and propose a new interpretation of regularization as a robust mechanism, and show using Fenchel duality that any convex regularization of OT can be interpreted as ground cost adversarial. This incidentally gives access to a robust dissimilarity measure on the ground space, which can in turn be used in other applications. We propose algorithms to compute this robust cost, and illustrate the interest of this approach empirically.
Learning to Generate Noise for Robustness against Multiple Perturbations.

Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations. However, the majority of existing defense methods are tailored to defend against a single category of adversarial perturbation (e.g. $\ell_\infty$-attack). In safety-critical applications, this makes these methods extraneous as the attacker can adopt diverse adversaries to deceive the system. To tackle this challenge of robustness against multiple perturbations, we propose a novel meta-learning framework that explicitly learns to generate noise to improve the model's robustness against multiple types of attacks. Specifically, we propose Meta Noise Generator (MNG) that outputs optimal noise to stochastically perturb a given sample, such that it helps lower the error on diverse adversarial perturbations. However, training on multiple perturbations simultaneously significantly increases the computational overhead during training. To address this issue, we train our MNG while randomly sampling an attack at each epoch, which incurs negligible overhead over standard adversarial training. We validate the robustness of our framework on various datasets and against a wide variety of unseen perturbations, demonstrating that it significantly outperforms the relevant baselines across multiple perturbations with marginal computational cost compared to the multiple perturbations training.
Faceless: The visual representation of overweight people in Flemish and Dutch online newspapers

We conducted a quantitative visual content analysis to examine how overweight and obese people are portrayed in online newspaper photos in the Netherlands and Flanders compared to non-overweight individuals.Overall, we found that 68% of the obese/overweight people were depicted in a stigmatizing way.Their heads were more likely to be cut out of the image, they were more often dressed sloppily and more frequently portrayed with only their lower body in comparison with non-overweight people.Furthermore, overweight people were more often associated with passive behavior and portrayed as patients in a medical environment.Tabloids were found less likely to publish stigmatizing images than broadsheets as they used more often photos supplied by the subjects themselves.
Sales Prediction using Online Sentiment with Regression Model

The main aim of this paper is to predict the sales of a vehicle using sentiment analysis from various places on the internet. The online presence of a vehicle, as well as its brand, plays a key role in the sales of the vehicle. However, many other parameters are required and will be discussed in this paper. Sales prediction in today's market is not only beneficial for the manufacturer but also for the various other companies that manufacture parts or accessories for vehicles. It can also be considered as a boon for retailers, showroom owners, and service mechanics. In this application, we have made use of linear regression for sentiment analysis and polynomial regression for sales prediction.
Capacity to Address Natural and Man-Made Vulnerabilities: The Administrative Structure of U.S. Election System Security

This article examines the efforts of American state election offices to address election emergency preparedness including contingency planning and cybersecurity planning.The election infrastructure that supports election security is cataloged to reflect the networked nature of American public service and the characteristics of American federalism.Public data are collected to identify functions of state offices in providing training, resources, and direct assistance to local election administrators for contingency planning and cybersecurity preparedness.Relationships between state efforts regarding election contingency planning generally and cybersecurity planning are compared.We find significant relationships that suggest a strong institutional foundation predictive of enhanced capacity to address election security issues and degraded environments more broadly.
Seeing is Believing: The Effect of Video Quality on Quality of Experience in Virtual Reality

The omnidirectional nature of Virtual Reality (VR) content provides an immersive experience for the viewer. At the same time, VR content relies heavily on the quality of the video to deliver an immersive experience. This study investigates the effect of video quality degradation on aspects of the viewer's quality of experience (QoE) via subjective (i.e., a questionnaire) and objective (i.e., electroencephalogram) methods. We measured the viewer's experience of watching a five-minute-long 6DoF VR movie in four video quality versions. Analysis of the questionnaire data showed that subjective ratings of the video quality decreased in parallel with the degradation of the quality. Also, the lower video quality versions yielded lower sensory immersion and simulator sickness scores. Finally, analysis of the EEG data revealed significantly lower parietal and occipital alpha values for the low video quality versions of the content.
Multi-Resolution A.

Heuristic search-based planning techniques are commonly used for motion planning on discretized spaces. The performance of these algorithms is heavily affected by the resolution at which the search space is discretized. Typically a fixed resolution is chosen for a given domain. While a finer resolution allows for better maneuverability, it significantly increases the size of the state space, and hence demands more search efforts. On the contrary, a coarser resolution gives a fast exploratory behavior but compromises on maneuverability and the completeness of the search. To effectively leverage the advantages of both high and low resolution discretizations, we propose Multi-Resolution A* (MRA*) algorithm, that runs multiple weighted-A*(WA*) searches having different resolution levels simultaneously and combines the strengths of all of them. In addition to these searches, MRA* uses one anchor search to control expansions from these searches. We show that MRA* is bounded suboptimal with respect to the anchor resolution search space and resolution complete. We performed experiments on several motion planning domains including 2D, 3D grid planning and 7 DOF manipulation planning and compared our approach with several search-based and sampling-based baselines.
Explainable Multi Agent Path Finding

Multi Agent Path Finding (MAPF) is the problem of planning paths for agents to reach their targets from their start locations, such that the agents do not collide while executing the plan. In safety-critical systems, the plan is typically checked by a human supervisor, who decides on whether to allow its execution. In such cases, we wish to convince the human that the plan is indeed collision free. To this end, we propose an explanation scheme for MAPF, which bases explanations on simplicity of visual verification by human's cognitive process. The scheme decomposes a plan into segments such that within each segment, the paths of the agents are disjoint. Then, we can convince the supervisor that the plan is collision free using a small number of images (dubbed an explanation). In addition, we can measure the simplicity of a plan by the number of segments required for the decomposition. We study the complexity of algorithmic problems that arise by the explanation scheme, as well as the tradeoff between the length (makespan) of a plan and its minimal decomposition. We also provide experimental results of our scheme both in a continuous and in a discrete setting.