Reviewerly/exHarmony huggingface dataset:
A Game-Based Secure Trading of Big Data and IoT Services: Blockchain as a Two-Sided Market

The blockchain technology has recently proved to be an efficient solution for guaranteeing the security of data transactions in data trading scenarios. The benefits of the blockchain in this domain have been shown to span over several crucial security and privacy aspects such as verifying the identities of data providers, detecting and preventing malicious data consumers, and regulating the trust relationships between the data trading parties. However, the cost and economic aspects of using this solution such as the pricing of mining process have not been addressed yet. In fact, using the blockchain entails high operational costs and puts both the data providers and miners in a continuous dilemma between delivering high-quality security services and adding supplementary costs. In addition, the mining leader requires an efficient mechanism to select the tasks from the mining pool and determine the needed computational resources for each particular task in order to maximize its payoff. Motivated by these two points, we propose in this paper a novel game theoretical model based on the two-sided market approach that exhibits a mix of cooperative and competitive strategies between the (blockchain) miners and data providers. The game helps both the data providers and miners determine the monetary reward and computational resources respectively. Simulations conducted on a real-world dataset show promising potential of the proposed solution in terms of achieving total surpluses for all involved parties, i.e., data providers, data consumers and miners.
RuSemShift: a dataset of historical lexical semantic change in Russian

We present RuSemShift, a large-scale manually annotated test set for the task of semantic change modeling in Russian for two long-term time period pairs: from the pre-Soviet through the Soviet times and from the Soviet through the post-Soviet times.Target words were annotated by multiple crowd-source workers.The annotation process was organized following the DURel framework and was based on sentence contexts extracted from the Russian National Corpus.Additionally, we report the performance of several distributional approaches on RuSemShift, achieving promising results, which at the same time leave room for other researchers to improve.
Synthetic aperture radar image despeckling with a residual learning of convolutional neural network

SAR images despeckling based on deep learning attracted more researchers' attention because of its performance and computational efficiency. In order to embody recent improvements for SAR images despeckling models, we proposed a model base on deep learning, called Synthetic Aperture Radar (SAR) image despeckling with Convolutional Neural Networks (SID-CNN), which is based on residual learning and discriminative training. In this model, the multiplicative noise in the SAR image was removed by residual learning and discriminative training in the form of additive noise. Whilst we illustrated the capability of the proposed SID-CNN model on synthetic images and real SAR images, respectively. Finally, compared with the-state-of-art SAR image despeckling methods, extensive experiments demonstrated that the proposed model had a better capacity to remove SAR images speckle with high restoration quality and computational efficiency.
LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification

This paper presents our system entitled 'LIIR' for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2).We have participated in Subtask A for English, Danish, Greek, Arabic, and Turkish languages.We adapt and fine-tune the BERT and multilingual Bert models made available by Google AI 1 for English and non-English languages respectively.For the English language, we use a combination of two fine-tuned BERT models.For other languages, we propose a cross-lingual augmentation approach in order to enrich training data and we use multilingual BERT to obtain sentence representations.LIIR achieved rank 14/38, 18/47, 24/86, 24/54, and 25/40 in Greek, Turkish, English, Arabic, and Danish languages, respectively.
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations

When machine learning models are deployed on a test distribution different from the training distribution, they can perform poorly, but overestimate their performance. In this work, we aim to better estimate a model's performance under distribution shift, without supervision. To do so, we use a set of domain-invariant predictors as a proxy for the unknown, true target labels. Since the error of the resulting risk estimate depends on the target risk of the proxy model, we study generalization of domain-invariant representations and show that the complexity of the latent representation has a significant influence on the target risk. Empirically, our approach (1) enables self-tuning of domain adaptation models, and (2) accurately estimates the target error of given models under distribution shift. Other applications include model selection, deciding early stopping and error detection.
Mechanical System Control by RGB-D Device

Computer vision for control is a growing domain of research and it is widespread in industry and the autonomous vehicle field. A further step is the employment of low-cost cameras to perform these applications. To apply such an approach, the development of proper algorithms to interpret vision data is mandatory. Here, we firstly propose the development of an algorithm to measure the displacement of a mechanical system in contactless mode. Afterwards, we show two procedures that use a 3D camera as a feedback in control strategies. The first one aims to track a moving object. In the second one, the information gained from vision data acquisition allows the mechanical system control to ensure the equilibrium of a ball placed on a moving slide.
Complementary relation between quantum entanglement and entropic uncertainty

Abstract Quantum entanglement is regarded as one of the core concepts, which is used to describe the non-classical correlation between subsystems, and entropic uncertainty relation plays a vital role in quantum precision measurement. It is well known that entanglement of formation can be expressed by von Neumann entropy of subsystems for arbitrary pure states. An interesting question is naturally raised: is there any intrinsic correlation between the entropic uncertainty relation and quantum entanglement? Or if the relation can be applied to estimate the entanglement. In this work, we focus on exploring the complementary relation between quantum entanglement and the entropic uncertainty relation. The results show that there exists an inequality relation between both of them for an arbitrary two-qubit system, and specifically the larger uncertainty will induce the weaker entanglement of the probed system, and vice versa. Besides, we use randomly generated states as illustrations to verify our results. Therefore, we claim that our observations might offer and support the validity of using the entropy uncertainty relation to estimate quantum entanglement.
Regulating Constraint-Following Bound for Fuzzy Mechanical Systems: Indirect Robust Control and Fuzzy Optimal Design

This article proposes an optimal indirect approach of constraint-following control for fuzzy mechanical systems. The system contains (possibly fast) time-varying uncertainty that lies in a fuzzy set. It aims at an optimal controller for the system to render bounded constraint-following error such that it can stay within a predetermined bound at all time and be sufficiently small eventually. First, for deterministic performance, the original system is transformed into a constructed system. A deterministic (not the usual IF-THEN rules-based) robust control is then designed for the constructed system to render it to be uniformly bounded and uniformly ultimately bounded, regardless of the uncertainty. Second, for optimal performance, a performance index, including the average fuzzy system performance and control effort, is proposed based on the fuzzy information. An optimal design problem associated with the control gain is then formulated and solved by minimizing the performance index. Finally, it is proved when the constructed system renders uniform boundedness and uniform ultimate boundedness, the original system achieves the desired performance of bounded constraint following.
Neural Approaches for Natural Language Interfaces to Databases: A Survey

A natural language interface to databases (NLIDB) enables users without technical expertise to easily access information from relational databases.Interest in NLIDBs has resurged in the past years due to the availability of large datasets and improvements to neural sequence-to-sequence models.In this survey we focus on the key design decisions behind current state of the art neural approaches, which we group into encoder and decoder improvements.We highlight the three most important directions, namely linking question tokens to database schema elements (schema linking), better architectures for encoding the textual query taking into account the schema (schema encoding), and improved generation of structured queries using autoregressive neural models (grammar-based decoders).To foster future research, we also present an overview of the most important NLIDB datasets, together with a comparison of the top performing neural models and a short insight into recent non deep learning solutions.
Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation

The Variational Autoencoder (VAE) is a popular and powerful model applied to text modelling to generate diverse sentences.However, an issue known as posterior collapse (or KL loss vanishing) happens when the VAE is used in text modelling, where the approximate posterior collapses to the prior, and the model will totally ignore the latent variables and be degraded to a plain language model during text generation.Such an issue is particularly prevalent when RNN-based VAE models are employed for text modelling.In this paper, we propose a simple, generic architecture called Timestep-Wise Regularisation VAE (TWR-VAE), which can effectively avoid posterior collapse and can be applied to any RNN-based VAE models.The effectiveness and versatility of our model are demonstrated in different tasks, including language modelling and dialogue response generation.
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples

Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths.In this paper, we propose a geometryinspired attack for generating natural language adversarial examples.Our attack generates adversarial examples by iteratively approximating the decision boundary of Deep Neural Networks (DNNs).Experiments on two datasets with two different models show that our attack fools natural language models with high success rates, while only replacing a few words.Human evaluation shows that adversarial examples generated by our attack are hard for humans to recognize.Further experiments show that adversarial training can improve model robustness against our attack.
Team Rouges at SemEval-2020 Task 12: Cross-lingual Inductive Transfer to Detect Offensive Language

With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains.This phenomenon has given rise to the growing need to detect the offensive language used in social media crosslingually.In OffensEval 2020, the organizers have released the multilingual Offensive Language Identification Dataset (mOLID), which contains tweets in five different languages, to detect offensive language.In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding XLM-RoBERTa (XLM-R).We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of 0.919 and eighth position in the Turkish task with an F1-score of 0.781.Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.
Apparent earthquake rupture predictability

SUMMARY To what extent can the future evolution of an ongoing earthquake rupture be predicted? This question of fundamental scientific and practical importance has recently been addressed by studies of teleseismic source time functions (STFs) but reaching contrasting conclusions. One study concludes that the initial portion of STFs is the same regardless of magnitude. Another study concludes that the rate at which earthquakes grow increases systematically and strongly with final event magnitudes. Here, we show that the latter reported trend is caused by a selection bias towards events with unusually long durations and by estimates of STF growth made when the STF is already decaying. If these invalid estimates are left out, the trend is no longer present, except during the first few seconds of the smallest events in the data set, Mw 5–6.5, for which the reliability of the STF amplitudes is questionable. Simple synthetic tests show that the observations are consistent with statistically indistinguishable growth of smaller and larger earthquakes. A much weaker trend is apparent among events of comparable duration, but we argue that its significance is not resolvable by the current data. Finally, we propose a nomenclature to facilitate further discussions of earthquake rupture predictability and determinism.
Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

Driver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.
Effects of Coronavirus Pandemic Spread on Science, Technology, Engineering and Mathematics Education in Higher Learning Institutions

The traditional pedagogical methods of delivering content for Science, Technology, Engineering and Mathematics (STEM) related subjects have been shaken to their core.Normal delivery under ideal conditions was a face to face approach with the tutor in front of the class.Practical subjects that have laboratory exercises are carried out in the designated laboratory areas with the assistance of technicians or technologists.With the advent of coronavirus pandemic, where isolation and social distancing has been implemented to stop its spread and transmission, these modes of content delivery are not feasible.To fill in the gap and ensure the much needed STEM education continues despite the fear of coronavirus pandemic spread, online delivery mode of various subjects offered has been adopted.This is done using such platforms as Learning Management System, Zoom Meeting and Google Scholar.However, solving the problem of practical laboratory exercises requires much more than online teaching.This paper proposes the use of virtual reality, simulation and modeling platforms as a substitute of face to face laboratory exercises in training STEM related subjects, especially in institutions of higher learning in developing countries.Prerecorded versions of practical experiments will be offered to students and related assignments given for practice purposes.Short webinars with details recorded will guide recipients in carrying out experiments without the need for neither technicians nor technologists.The students will then be able to attend the laboratory session virtually and submit the required reports.Several other types of content deliver exist and a combination of them can also be used depending on the areas to be covered.The exploitation of the suggested content delivery will resolve change of the attitude in learning behavior, thereby ensuring high quality and effective understanding of the subject area.
Image Encryption Algorithm Based on Substitution Principle and Shuffling Scheme

Image Encryption, Number of changing pixel rate (NPCR), S-Substitution, Unified Averaged Changed Intensity (UACI),PermutationThis research presents a method of image encryption that has been designed based on the algorithm of complete shuffling, transformation of substitution box, and predicated image crypto-system.This proposed algorithm presents extra confusion in the first phase because of including an S-box based on using substitution by AES algorithm in encryption and its inverse in Decryption.In the second phase, shifting and rotation were used based on secrete key in each channel depending on the result from the chaotic map, 2D logistic map and the output was processed and used for the encryption algorithm.It is known from earlier studies that simple encryption of images based on the scheme of shuffling is insecure in the face of chosen cipher text attacks.Later, an extended algorithm has been projected.This algorithm performs well against chosen cipher text attacks.In addition, the proposed approach was analyzed for NPCR, UACI (Unified Average Changing Intensity), and Entropy analysis for determining its strength.
A Context Aware Approach for Generating Natural Language Attacks

We study an important task of attacking natural language processing models in a black box setting. We propose an attack strategy that crafts semantically similar adversarial examples on text classification and entailment tasks. Our proposed attack finds candidate words by considering the information of both the original word and its surrounding context. It jointly leverages masked language modelling and next sentence prediction for context understanding. In comparison to attacks proposed in prior literature, we are able to generate high quality adversarial examples that do significantly better both in terms of success rate and word perturbation percentage.
Metrologically resourceful multipartite entanglement under quantum many-body effects

Abstract In traditional quantum metrology protocols, the initial multipartite entangled pure quantum probes are considered to be isolated, i.e., free of quantum many-body effects. Here, we study the impact of inherent many-body effects such as interaction with noisy environment and nonlocal interactions among particles on metrologically resourceful multipartite entanglement of initially mixed quantum probes. In this regard, we employ an information-theoretic multipartite entanglement measure as a figure-of-merit. The inevitable interaction with the noisy environment leads to disentanglement in multipartite quantum probes which restricts its metrological advantage. For this, we use entanglement dissociation to derive bounds on the multipartite entanglement measure that can identify the relevant entanglement structure under global as well as local noisy evolution. Furthermore, we investigate nonlocal interactions in terms of their entangling capability in a multipartite quantum probe. We show that such nonlocal interactions can be exploited as a valuable resource that exhibits better precision scaling in mixed-state quantum metrology. Moreover, we numerically observe these results for GHZ-W class states.
Movie Recommendation Systems using Sentiment Analysis and Cosine Similarity

As Artificial Intelligence and Machine Learning is growing at a rapid rate over the past few years, so is the amount of data increasing exponentially on the internet. Due to this people find it difficult to choose the exact information they are looking for , learners find it difficult to suggest users exactly what they require. Here comes Recommendation Systems into picture to guide users towards the information according to their preferences. In context of Recommendation of Movies and TV shows on Online Streaming platforms ,this paper is aimed to explain making and implementation of Movie Recommendation Systems Using Machine Learning Algorithms, Sentiment Analysis and Cosine Similarity
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) under partial observability has long been considered challenging, primarily due to the requirement for each agent to maintain a belief over all other agents' local histories - a domain that generally grows exponentially over time. In this work, we investigate a partially observable MARL problem in which agents are cooperative. To enable the development of tractable algorithms, we introduce the concept of an information state embedding that serves to compress agents' histories. We quantify how the compression error influences the resulting value functions for decentralized control. Furthermore, we propose an instance of the embedding based on recurrent neural networks (RNNs). The embedding is then used as an approximate information state, and can be fed into any MARL algorithm. The proposed embed-then-learn pipeline opens the black-box of existing (partially observable) MARL algorithms, allowing us to establish some theoretical guarantees (error bounds of value functions) while still achieving competitive performance with many end-to-end approaches.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results

Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by 3, 360 images, including 2, 460 images for training, and 900 images for testing. Specifically, we manually annotate persons with points in each video frame. There are 14 algorithms from 15 institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: http://www.aiskyeye.com/ .