Reviewerly/exHarmony huggingface dataset:
UV-C Mobile Robots with Optimized Path Planning: Algorithm Design and On-Field Measurements to Improve Surface Disinfection Against SARS-CoV-2

Ultraviolet type-C irradiation (UV-C) is an effective no-contact disinfection procedure for surfaces and environments to reduce the spread of severe acute respiratory syndrome coron avirus 2 (SARS-CoV-2), the virus that causes COVID-19. This work evaluates the effect of the adoption of mobile robots for UV-C irradiation, compared to conventional disinfection methods based on static UV-C lamps. On-field evaluation was conducted to measure the energy dose delivered by a robot-based moving source of UV-C radiation at different locations in an indoor environment. The effectively released radiation dose was experimentally measured using distributed UV-C-sensitive detectors, considering all of the environmental factors involved. Moreover, this article proposes a novel trajectory planner consisting of a genetic algorithm (GA) that explores the possible trajectories and disinfection outcomes of a robot moving in a tunable artificial potential field (APF) and is capable of maximizing the delivered UV dose based on ambient geometry. The experimental results show that, compared to a conventional trajectory, an optimized one has better performance in terms of both the coverage of the radiated energy in the environment and the time required to complete the disinfection task.
Sentiment Analysis of Filipinos and Effects of Extreme Community Quarantine Due to Coronavirus (COVID-19) Pandemic

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Deep Learning Meets Game Theory: Bregman-Based Algorithms for Interactive Deep Generative Adversarial Networks

This paper presents an interplay between deep learning and game theory. It models basic deep learning tasks as strategic games. Then, distributionally robust games and their relationship with deep generative adversarial networks (GANs) are presented. To achieve a higher order convergence rate without using a second derivative of the objective function, a Bregman discrepancy is used to construct a speed-up deep learning. Each player has a continuous action space which corresponds to weight space and aims to learn his/her optimal strategy. The convergence rate of the proposed deep learning algorithm is derived using a mean estimate. Experiments are carried out on a real dataset in both shallow and deep GANs. Both qualitative and quantitative evaluation results show that the generative model trained by the Bregman deep learning algorithm can speed up the state-of-the-art performance.
Data-Driven Anomaly Detection for UAV Sensor Data Based on Deep Learning Prediction Model

Unmanned Aerial Vehicle (UAV) can accomplish various specific tasks and play an increasingly essential role in military, industrial and civil fields. However, the safety of the UAV is lower than that of manned aircraft, and great economic loss is caused due to its relatively high failure rate. Therefore, it is of great significance to study the anomaly detection method for the UAV system. In recent years, the deep learning method has been widely applied in various fields due to its outstanding advantages such as strong ability to approximate complex functions and automatic feature extraction. In this paper, a Long Short Term Memory (LSTM) Recurrent Neural Network method is proposed for the UAV anomaly detection. Firstly, a prediction model is formulated based on the training data set which contains only normal data, then the data at next time can be predicted. Secondly, according to the prediction results in train phase, we give an estimation of the prediction uncertainty. Finally, anomaly detection is achieved by comparing the prediction value with the uncertain interval. Real UAV sensor data with point anomalies in north velocity and pneumatic lifting velocity are used to verify the proposed method, and experimental results show that the proposed method can effectively detect point anomalies.
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

Model-agnostic meta-learners aim to acquire meta-learned parameters from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. With the flexibility in the choice of models, those frameworks demonstrate appealing performance on a variety of domains such as few-shot image classification and reinforcement learning. However, one important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify the mode of tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML (MMAML) framework, which is able to modulate its meta-learned prior parameters according to the identified mode, allowing more efficient fast adaptation. We evaluate the proposed model on a diverse set of few-shot learning tasks, including regression, image classification, and reinforcement learning. The results not only demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks but also show that training on a multimodal distribution can produce an improvement over unimodal training. The code for this project is publicly available at https://vuoristo.github.io/MMAML.
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

Learning generative models that span multiple data modalities, such as vision and language, is often motivated by the desire to learn more useful, generalisable representations that faithfully capture common underlying factors between the modalities. In this work, we characterise successful learning of such models as the fulfilment of four criteria: i) implicit latent decomposition into shared and private subspaces, ii) coherent joint generation over all modalities, iii) coherent cross-generation across individual modalities, and iv) improved model learning for individual modalities through multi-modal integration. Here, we propose a mixture-of-experts multi-modal variational autoencoder (MMVAE) for learning of generative models on different sets of modalities, including a challenging image language dataset, and demonstrate its ability to satisfy all four criteria, both qualitatively and quantitatively.
Coq Coq correct! verification of type checking and erasure for Coq, in Coq

Coq is built around a well-delimited kernel that perfoms typechecking for definitions in a variant of the Calculus of Inductive Constructions (CIC). Although the metatheory of CIC is very stable and reliable, the correctness of its implementation in Coq is less clear. Indeed, implementing an efficient type checker for CIC is a rather complex task, and many parts of the code rely on implicit invariants which can easily be broken by further evolution of the code. Therefore, on average, one critical bug has been found every year in Coq. This paper presents the first implementation of a type checker for the kernel of Coq (without the module system and template polymorphism), which is proven correct in Coq with respect to its formal specification and axiomatisation of part of its metatheory. Note that because of Gödel's incompleteness theorem, there is no hope to prove completely the correctness of the specification of Coq inside Coq (in particular strong normalisation or canonicity), but it is possible to prove the correctness of the implementation assuming the correctness of the specification, thus moving from a trusted code base (TCB) to a trusted theory base (TTB) paradigm. Our work is based on the MetaCoq project which provides metaprogramming facilities to work with terms and declarations at the level of this kernel. Our type checker is based on the specification of the typing relation of the Polymorphic, Cumulative Calculus of Inductive Constructions (PCUIC) at the basis of Coq and the verification of a relatively efficient and sound type-checker for it. In addition to the kernel implementation, an essential feature of Coq is the so-called extraction: the production of executable code in functional languages from Coq definitions. We present a verified version of this subtle type-and-proof erasure step, therefore enabling the verified extraction of a safe type-checker for Coq.
Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.
Quantum image processing algorithm using edge extraction based on Kirsch operator

In this work, a quantum image processing algorithm is developed using the edge extraction method together with the Kirsch operator. In our approach, novel enhanced quantum representation (NEQR) is employed as the image representation model for processing quantum image, which generates results of edge extraction using the Kirsch operator. The proposed algorithm can perform real-time image processing with high accuracy. We carry out the design, analyses, and simulations of quantum circuits based on our approach, which shows that the image processing speed and acuracy are much better than the classical edge extraction algorithms.
Digital Literacy and Digital Didactics as the Basis for New Learning Models Development

An efficient organization of the educational process within rapid acceleration of the society digitization is impossible without increasing its digital literacy, if the latter is really estimated and new models of digital learning didactics are devel-oped respectively. So, it is necessary to study an actual state of digital literacy, as well as to search for and introduce new learning models in the educational system based on the use of modern innovative technologies and digital learning methods. The objective of the research is to examine the level of the digital literacy of teachers, and also opportunities and promising trends within the digital didactics for the formation of competencies as the basis of a new model for the develop-ment of a new learning model in the context of the formation of the digital knowledge society. Two main methods have been used – elaboration of the ab-stract and logical model and a survey. In the research the data obtained as a result of the survey conducted in 2018 by NAFI Analytical Centre related to the meas-urement of the digital literacy level of teachers and lecturers in Russia were used. The number of respondents made up 634 lecturers (higher education) as well as 555 teachers (general education). The digital literacy formation model was devel-oped based on the didactic learning triangle taking into account the synthesis of three learning environments – real, virtual and neurocognitive. The analysis of the survey data demonstrated that in the aggregate of the compared indices for differ-ent social groups of the population, school teachers and lecturers of higher educa-tional institutions have reached the higher level of the digital literacy, which sig-nificantly exceeds the average Russian level. So, in Russia as a whole the index of the digital literacy of the adult population by a similar measurement method makes up 52% out of 100% possible ones, school teachers – 87%, lecturers of higher educational institutions – 88%. The use of research results in practice is possible with the improvement of training programs, the exchange of internation-al experience and further researches.
Adversarial Semantic Data Augmentation for Human Pose Estimation

Human pose estimation is the task of localizing body keypoints from still images. The state-of-the-art methods suffer from insufficient examples of challenging cases such as symmetric appearance, heavy occlusion and nearby person. To enlarge the amounts of challenging cases, previous methods augmented images by cropping and pasting image patches with weak semantics, which leads to unrealistic appearance and limited diversity. We instead propose Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity. Furthermore, we propose Adversarial Semantic Data Augmentation (ASDA), which exploits a generative network to dynamically predict tailored pasting configuration. Given off-the-shelf pose estimation network as discriminator, the generator seeks the most confusing transformation to increase the loss of the discriminator while the discriminator takes the generated sample as input and learns from it. The whole pipeline is optimized in an adversarial manner. State-of-the-art results are achieved on challenging benchmarks. The code has been publicly available at https://github.com/Binyr/ASDA .
Emerging devices and packaging strategies for electronic-photonic AI accelerators: opinion

The field of mimicking the structure of the brain on a chip is experiencing interest driven by the demand for machine intelligent applications. However, the power consumption and available performance of machine-learning (ML) accelerating hardware still leave much desire for improvement. In this letter, we share viewpoints, challenges, and prospects of electronic-photonic neural network (NN) accelerators. Combining electronics with photonics offers synergistic co-design strategies for high-performance AI Application-specific integrated circuits (ASICs) and systems. Taking advantages of photonic signal processing capabilities and combining them with electronic logic control and data storage is an emerging prospect. However, the optical component library leaves much to be desired and is challenged by the enormous size of photonic devices. Within this context, we will review the emerging electro-optic materials, functional devices, and systems packaging strategies that, when realized, provide significant performance gains and fuel the ongoing AI revolution, leading to a stand-alone photonics-inside AI ASIC ‘black-box’ for streamlined plug-and-play board integration in future AI processors.
A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks

Graph representation learning has re-emerged as a fascinating research topic due to the successful application of graph convolutional networks (GCNs) for graphs and inspires various downstream tasks, such as node classification and link prediction. Nevertheless, existing GCN-based methods for graph representation learning mainly focus on static graphs. Although some methods consider the dynamic characteristics of networks, the global structure information, which helps a node to gain worthy features from distant but valuable nodes, has not received enough attention. Moreover, these methods generally update the features of the nodes by averaging the features of neighboring nodes, which may not effectively consider the importance of different neighboring nodes during the aggregation. In this article, we propose a novel representation learning for dynamic graphs based on the GCNs, called DGCN. More specifically, the long short-term memory (LSTM) is utilized to update the weight parameters of GCN for capturing the global structure information across all time steps of dynamic graphs. Besides, a new Dice similarity is proposed to overcome the problem that the influence of directed neighbors is unnoticeable, which is further used to guide the aggregation. We evaluate the performance of the proposed method in the field of node clustering and link prediction, and the experimental results show a generally better performance of our proposed DGCN than baseline methods.
An Overview of Blockchain Integration with Robotics and Artificial Intelligence

Blockchain technology is growing everyday at a fast-passed rhythm and it is possible to integrate it with many systems, namely Robotics with AI services. However, this is still a recent field and there is not yet a clear understanding of what it could potentially become. In this paper, we conduct an overview of many different methods and platforms that try to leverage the power of blockchains into robotic systems, to improve AI services, or to solve problems that are present in the major blockchains, which can lead to the ability of creating robotic systems with increased capabilities and security. We present an overview, discuss the methods, and conclude the paper with our view on the future of the integration of these technologies.
Generating Textual Adversarial Examples for Deep Learning Models: A Survey.

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs were vulnerable to strategically modified samples, named adversarial examples. These samples are generated with some imperceptible perturbations but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples for image DNNs, research efforts on attacking DNNs for textual applications emerges in recent years. However, existing perturbation methods for images cannotbe directly applied to texts as text data is discrete. In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs. We collect, select, summarize, discuss and analyze these works in a comprehensive way andcover all the related information to make the article self-contained. Finally, drawing on the reviewed literature, we provide further discussions and suggestions on this topic.
Mining Users Trust From E-Commerce Reviews Based on Sentiment Similarity Analysis

Consumers' reviews in E-commerce systems are usually treated as the important resources that reflect user's experience, feelings, and willingness to purchase items.All this information may involve consumers' views on things that can express interest, sentiments, and opinions.Many kinds of research have shown that people are more likely to trust each other with the same attitude toward similar things.In this paper, we consider seeking and accepting sentiments and suggestions in E-commerce systems somewhat implies a form of trust between consumers during shopping.Following this view of point, an E-commerce system reviews mining oriented sentiment similarity analysis approach is put forward to exploring users' similarity and their trust.We divide the trust into two categories, namely direct trust, and propagation of trust, which represents a trust relationship between two individuals.The direct trust degree is obtained from sentiment similarity, and we present an entity-sentiment word pair mining method for similarity feature extraction.The propagation of trust is calculated according to the transitivity feature.Using the proposed trust representation model, we use the shortest path to describe the tightness of trust and put forward an improved shortest path algorithm to figure out the propagation trust relationship between users.A large-scale E-commerce website reviews dataset is collected to examine the accuracy of the algorithms and feasibility of the models.The experimental results indicate that the sentiment similarity analysis can be an efficient method to find trust between users in E-commerce systems.
Quantum Chosen-Ciphertext Attacks Against Feistel Ciphers

Seminal results by Luby and Rackoff show that the 3-round Feistel cipher is secure against chosen-plaintext attacks (CPAs), and the 4-round version is secure against chosen-ciphertext attacks (CCAs). However, the security significantly changes when we consider attacks in the quantum setting, where the adversary can make superposition queries. By using Simon’s algorithm that detects a secret cycle-period in polynomial-time, Kuwakado and Morii showed that the 3-round version is insecure against quantum CPA by presenting a polynomial-time distinguisher. Since then, Simon’s algorithm has been heavily used against various symmetric-key constructions. However, its applications are still not fully explored. In this paper, based on Simon’s algorithm, we first formalize a sufficient condition of a quantum distinguisher against block ciphers so that it works even if there are multiple collisions other than the real period. This distinguisher is similar to the one proposed by Santoli and Schaffner, and it does not recover the period. Instead, we focus on the dimension of the space obtained from Simon’s quantum circuit. This eliminates the need to evaluate the probability of collisions, which was needed in the work by Kaplan et al. at CRYPTO 2016. Based on this, we continue the investigation of the security of Feistel ciphers in the quantum setting. We show a quantum CCA distinguisher against the 4-round Feistel cipher. This extends the result of Kuwakado and Morii by one round, and follows the intuition of the result by Luby and Rackoff where the CCA setting can extend the number of rounds by one. We also consider more practical cases where the round functions are composed of a public function and XORing the subkeys. We show the results of both distinguishing and key recovery attacks against these constructions.
Variational Autoencoder with Arbitrary Conditioning

We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in one shot. The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bayes. The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.
DeepZip: Lossless Data Compression Using Recurrent Neural Networks

Sequential data is being generated at an unprecedented pace in various forms, including text and genomic data. This creates the need for efficient compression mechanisms to enable better storage, transmission and processing of such data. To solve this problem, many of the existing compressors attempt to learn models for the data and perform prediction-based compression. Since neural networks are known as universal function approximators with the capability to learn arbitrarily complex mappings, and in practice show excellent performance in prediction tasks, we explore and devise methods to compress sequential data using neural network predictors. We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets. The proposed compressor outperforms Gzip on the real datasets and achieves near-optimal compression for the synthetic datasets. The results also help understand why and where neural networks are good alternatives for traditional finite context models.