Reviewerly/exHarmony huggingface dataset:
Towards End-to-End In-Image Neural Machine Translation

In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing the same text in another language.We propose an end-to-end neural model for this task inspired by recent approaches to neural machine translation, and demonstrate promising initial results based purely on pixel-level supervision.We then offer a quantitative and qualitative evaluation of our system outputs and discuss some common failure modes.Finally, we conclude with directions for future work.
Online Abuse and Human Rights: WOAH Satellite Session at RightsCon 2020

In 2020 The Workshop on Online Abuse and Harms (WOAH) held a satellite panel at RightsCons 2020, an international human rights conference. Our aim was to bridge the gap between human rights scholarship and Natural Language Processing (NLP) research communities in tackling online abuse. We report on the discussions that took place, and present an analysis of four key issues which emerged: Problems in tackling online abuse, Solutions, Meta concerns and the Ecosystem of content moderation and research. We argue there is a pressing need for NLP research communities to engage with human rights perspectives, and identify four key ways in which NLP research into online abuse could immediately be enhanced to create better and more ethical solutions.
Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

This work proposes dedicated hardware for an intelligent control system on Field Programmable Gate Array (FPGA). The intelligent system is represented as Takagi–Sugeno Fuzzy-PI controller. The implementation uses a fully parallel strategy associated with a hybrid bit format scheme (fixed-point and floating-point). Two hardware designs are proposed; the first one uses a single clock cycle processing architecture, and the other uses a pipeline scheme. The bit accuracy was tested by simulation with a nonlinear control system of a robotic manipulator. The area, throughput, and dynamic power consumption of the implemented hardware are used to validate and compare the results of this proposal. The results achieved allow the use of the proposed hardware in applications with high-throughput, low-power and ultra-low-latency requirements such as teleoperation of robot manipulators, tactile internet, or industry 4.0 automation, among others.
Boundary thickness and robustness in learning models

Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thickness of a classifier, and we describe its connection with and usefulness for model robustness. Thick decision boundaries lead to improved performance, while thin decision boundaries lead to overfitting (e.g., measured by the robust generalization gap between training and testing) and lower robustness. We show that a thicker boundary helps improve robustness against adversarial examples (e.g., improving the robust test accuracy of adversarial training) as well as so-called out-of-distribution (OOD) transforms, and we show that many commonly-used regularization and data augmentation procedures can increase boundary thickness. On the theoretical side, we establish that maximizing boundary thickness during training is akin to the so-called mixup training. Using these observations, we show that noise-augmentation on mixup training further increases boundary thickness, thereby combating vulnerability to various forms of adversarial attacks and OOD transforms. We can also show that the performance improvement in several lines of recent work happens in conjunction with a thicker boundary.
Analyzing Neural Discourse Coherence Models

In this work, we systematically investigate how well current models of coherence can capture aspects of text implicated in discourse organisation.We devise two datasets of various linguistic alterations that undermine coherence and test model sensitivity to changes in syntax and semantics.We furthermore probe discourse embedding space and examine the knowledge that is encoded in representations of coherence.We hope this study shall provide further insight into how to frame the task and improve models of coherence assessment further.Finally, we make our datasets publicly available as a resource for researchers to use to test discourse coherence models.
A Novel Image Dehazing Algorithm via Adaptive Gamma-Correction and Modified AMEF

Captured images are usually influenced by fog or haze.In reality, image dehazing is challenging.This paper proposes a modified artificial multiple-exposure image fusion (AMEF) algorithm to remove the haze from an image.In the algorithm, first, an adaptive gamma-correction transform with the mean and standard deviation values of each component of a hazy image is utilized to verify the intensities.Second, the homomorphic filtering algorithm is introduced into the Gaussian pyramid and Laplacian pyramid to compute the exposed accessible images.Last, a modified Laplacian filter method is presented to calculate the contrast of the exposed accessible images.Further, extensive experimental results demonstrate that the proposed algorithm has superior performance compared with that of some state-of-the-art methods, including higher contrast, richer details and a better visual effect in the dehazed image.
Non-Markovianity and bound states in quantum walks with a phase impurity

We study the discrete-time quantum walk on the line with a single phase impurity. The spread and localisation properties of discrete-time walks initialized at the impurity site are affected by the appearance of bound states and their reflection symmetry. Here, we measure localisation by means of an effective localisation length and an effective participation ratio, which are obtained by averaging over all eigenstates and over all initial states, respectively. We observe that the reduced coin system dynamics undergoes oscillations in the long-time limit with the frequencies determined by the sublattice operator and the bound state quasi-energy differences. The oscillations give rise to non-Markovian evolution, which we quantify using the trace distance and entanglement based measures of non-Markovianity. Indeed, we reveal that the degree of the non-Markovian behaviour is closely related to the emergence of bound states due to the phase impurity. We also show that the considered measures give qualitatively different results depending on the number and symmetries of supported bound states. Finally, comparing localisation and non-Markovianity measures, we demonstrate that the degree of non-Markovianity becomes maximum when the walker is most localised in position space.
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data. They enable the statistical symmetry of underlying physical phenomena to be leveraged, thereby aiding generalization. Prediction in such models can be viewed as a translation equivariant map from observed data sets to predictive SPs, emphasizing the intimate relationship between stationarity and equivariance. Building on this, we propose the Convolutional Neural Process (ConvNP), which endows Neural Processes (NPs) with translation equivariance and extends convolutional conditional NPs to allow for dependencies in the predictive distribution. The latter enables ConvNPs to be deployed in settings which require coherent samples, such as Thompson sampling or conditional image completion. Moreover, we propose a new maximum-likelihood objective to replace the standard ELBO objective in NPs, which conceptually simplifies the framework and empirically improves performance. We demonstrate the strong performance and generalization capabilities of ConvNPs on 1D regression, image completion, and various tasks with real-world spatio-temporal data.
Service-centric Segment Routing Mechanism using Reinforcement Learning for Encrypted Traffic

For the past decade, IP (Internet Protocol) routing approaches utilize TCAM (Ternary Content Addressable Memory) for the rule matching in the switches. These approaches are expensive and require more power consumption. Fortunately, the emerging of segment routing can resolve this drawback by encoding a routing path into the packet header to forward the packets to a destination. However, the standard segment routing algorithm has encountered a main problem. Using the shortest path to forward the packets can lead to a high traffic load on these paths and a performance reduction. It results in a decrease in user's perception and some negative economic impacts for ISPs (Internet Service Providers). Therefore, in this paper, we propose a novel service-centric segment routing mechanism using reinforcement learning in the context of encrypted traffic. Our proposal aims to help ISPs to decrease the influence of the network problems and meet the strict user's requirement related to QoE (Quality of Experience). The obtained results under the considered conditions demonstrate that our approach out-performs the standard segment routing algorithm and requires reasonable computational cost.
HOLNET: A Holistic Traffic Control Framework for Datacenter Networks

In this paper, we put forward a HOListic traffic control framework for datacenter NETworks (HOLNET). HOLNET reformulates the network utility maximization (NUM) framework into a HOLNET NUM framework that fully harnesses the potential of the existing NUM-based solutions to allow large families of traffic control protocols of various degrees of sophistication to be developed, i.e., host-based, single or multiple Class-of-Service (CoS) enabled, single or multi-path congestion control, with or without in-network load balancing. Unlike the existing solutions that are largely empirical and point by design, HOLNET is a principled, systematic framework. All the protocols in a family developed under HOLNET share a common, user-defined global optimization objective and fairness criterion. As a result, the protocols in a family can be fairly compared and carefully selected to fully explore the performance, scalability and design complexity tradeoffs. Case studies, based on both a single and a multi-path host-based solutions, demonstrate the viability and flexibility in HOLNET design space exploration. To further test the backward compatibility and performance with respect to some existing lightweight solutions, we develop HOLNET-UTA, an integrated congestion control and load balancing protocol, achieving TCP-fair resource allocation. HOLNET-UTA is found by simulation to improve the average flow completion time (FCT) by more than 20%, compared to DRILL with DCTCP.
ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation

Plug-and-play language models (PPLMs) enable topic-conditioned natural language generation by combining large pre-trained generators with attribute models to steer the predicted token distribution towards selected topics.Despite their efficiency, the large amounts of labeled texts required by PPLMs to effectively balance generation fluency and proper conditioning make them unsuitable to low-resource scenarios.We present ETC-NLG, an approach leveraging topic modeling annotations to produce End-to-end Topic-Conditioned Natural Language Generations over emergent topics in unlabeled document collections.We test our method's effectiveness in a low-resource setting for Italian and perform a comparative evaluation of ETC-NLG for Italian and English using a parallel corpus.Finally, we propose an evaluation method to automatically estimate the conditioning effectiveness from generated utterances.
Personal Digital Assistants: The Need for Governance

New technologies such as voice-based personal digital assistants (PDA) provide many benefits and new efficiencies, but they also may have negative effects on socially vulnerable groups. There are a number of ways in which PDAs could be used that can create new ethical and legal issues. In this article, we discuss the conceptual and practical challenges around PDAs and their impact on digital governance.
Blockchain-driven privacy-preserving machine learning

With the integration of blockchain with current leading privacy-preserving machine learning mechanism, the performances of FL and GAN-DP can be further improved, especially the robustness against poisoning attacks. In addition, the deployment of blockchain as the underlying architecture enables decentralization while providing incentive mechanisms. Furthermore, the efficiency can be guaranteed, and the storage resources can be saved with an off-chain structure. Future directions in this field may include the optimization using game theory and reversible blockchain using chameleon hash.
Continuous micro finger writing recognition with a commodity smartwatch

Input is a significant problem for wearable devices, particularly for head-mounted virtual and augmented reality systems. Contemporary AR/VR systems use in-air gestures or handheld controllers for interactivity. However, mid-air handwriting provides a natural, subtle, and easy-to-use way to input commands and text. In this demo, we propose and investigate ViFin, a new technique for input commands and text entry which tracks continuous micro finger-level writing with a commodity smartwatch through vibrations. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing and works across different users and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. Finally, a real-time writing system with two specific applications using AR smartglasses are implemented.
Hardware Architecture and Implementation of an AI Pet Robot

The concept of AI companionship is gaining popularity in recent times. Our project is an attempt to create a robotic companion that can act just like a pet. We created a Pet Robot that can perform various functions like recognise people, recognise objects, recognise emotions, play games, follow a person around, listen to voice commands, dance and so on. This paper focuses on the hardware aspect of the robot and the working of the Core program (Kernel). It further discusses the algorithm that was implemented in this architecture and compares the final version with other variations. Our work was able to create a fully functioning AI pet robot by using just cheap commercially available development boards, motors and a Desktop PC.
Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method. The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.
LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction

Software defect prediction (SDP) is an effective technique to lower software module testing costs. However, the imbalanced distribution almost exists in all SDP datasets and restricts the accuracy of defect prediction. In order to balance the data distribution reasonably, we propose a novel resampling method LIMCR on the basis of Naïve Bayes to optimize and improve the SDP performance. The main idea of LIMCR is to remove less-informative majorities for rebalancing the data distribution after evaluating the degree of being informative for every sample from the majority class. We employ 29 SDP datasets from the PROMISE and NASA dataset and divide them into two parts, the small sample size (the amount of data is smaller than 1100) and the large sample size (larger than 1100). Then we conduct experiments by comparing the matching of classifiers and imbalance learning methods on small datasets and large datasets, respectively. The results show the effectiveness of LIMCR, and LIMCR+GNB performs better than other methods on small datasets while not brilliant on large datasets.
DAPA: Differential Analysis aided Power Attack on (Non-) Linear Feedback Shift Registers

Differential power analysis (DPA) is a form of side-channel analysis (SCA) that performs statistical analysis on the power traces of cryptographic computations. DPA is applicable to many cryptographic primitives, including block ciphers, stream ciphers and even hash-based message authentication code (HMAC). At COSADE 2017, Dobraunig et al. presented a DPA on the fresh re-keying scheme Keymill to extract the bit relations of neighbouring bits in its shift registers, reducing the internal state guessing space from 128 to 4 bits. In this work, we generalise their methodology and combine with differential analysis, we called it differential analysis aided power attack (DAPA), to uncover more bit relations and take into account the linear or non-linear functions that feedback to the shift registers (i.e. LFSRs or NLFSRs). Next, we apply our DAPA on LR-Keymill, the improved version of Keymill designed to resist the aforementioned DPA, and breaks its 67.9-bit security claim with a 4-bit internal state guessing. We experimentally verified our analysis. In addition, we improve the previous DPA on Keymill by halving the amount of data resources needed for the attack. We also applied our DAPA to Trivium, a hardware-oriented stream cipher from the eSTREAM portfolio and reduces the key guessing space from 80 to 14 bits.
Assessing Neural Network Approaches for Solar Radiation Estimates Using Limited Climatic Data in the Mediterranean Sea

One of the most crucial variables in agricultural meteorology is solar radiation (Rs), although it is measured in a very limited number of weather stations due to its high cost in both installation and maintenance. Moreover, the quality of the data is usually low because of sensor failure and/or lack of calibration, which made scientists search for new approaches such as neural network models. Thus, the improvement of traditional solar radiation estimation models with minimum data availability is still needed for different purposes. In this work, several neural network models were developed and assessed (Multilayer Perceptron—MLP, Support Vector Machines—SVM, Extreme Learning Machine, Convolutional Neural Networks—CNN, and Long Short-Term Memory—LSTM) with different temperature-based input variables configurations in Southern Spain (weather station located in the Mediterranean Sea coast). The performances were analyzed using different statistical indices (Root Mean Square Error—RMSE, Mean Bias Error—MBE, and Nash-Sutcliffe model efficiency coefficient—NSE).
Research on statistical detection method of micro bubbles in transparent layer of quartz crucible based on image processing

Quartz crucible is a widely used manufacturing equipment in silicon crystal manufacturing industry, and the detection of its quality has a great influence on the purity of subsequent silicon crystals. In this paper, a machine vision-based bubble detection statistical system for transparent layer of quartz crucible is designed, and an intelligent algorithm for measuring the diameter, area and number of bubbles is proposed. Firstly, the bubble image is filtered and enhanced pre-processing operation to enhance the difference between the bubble and the background, and then the binary contour of the bubble is extracted by using a bubble contour extraction algorithm that fuses the morphological algorithm. In order to improve the accuracy of bubble detection, a curvature-based corner detection algorithm is proposed for overlapping bubbles.The experimental results show that the algorithm can accurately and real-time detect the number and diameter of bubbles. Compared with the connected component algorithm(CCA) and edge chain code information (EDCI) algorithm, the proposed method has lower cost and higher detection efficiency.
News Article Text Classification and Summary for Authors and Topics

News articles are important for providing timely, historic information. However, the Internet is replete with text that may contain irrelevant or unhelpful information, therefore means of processing it and distilling content is important and useful to human readers as well as information extracting tools. Some common questions we may want to answer are “what is this article about?” and “who wrote it?”. In this work we compare machine learning models for evaluating two common NLP tasks, topic and authorship attribution, on the 2017 Vox Media dataset. Additionally, we use the models to classify on a subsection, about ~20%, of the original text which show to be better for classification than the provided blurbs. Because of the large number of topics, we take into account topic overlap and address it via top-n accuracy and hierarchical groupings of topics. We also consider edge cases in authorship by classifying on inter-topic and intra-topic author distributions. Our results show that both topics and authors readily identifiable consistently perform best when using neural networks rather than support vector, random forests, or naive Bayes classifiers, although the latter methods perform acceptably.
Is This My Phone? Operating a Physical Smartphone in Virtual Reality

Using a Virtual Reality (VR) headset significantly improves the immersive experience of a virtual environment. However, it separates the user from the physical world for interacting with daily objects like smartphones. This demo shows a prototype system that enables VR users to operate a full-featured smartphone in VR, in the same way they do in reality without taking the VR headset off. We explain the system design and strategies adopted to run the system at high resolution and with low latency. We also present a virtual hand rendering method to improve the phone interaction experience.
Markerless 3D Human Pose Tracking in the Wild with Fusion of Multiple Depth Cameras: Comparative Experimental Study with Kinect 2 and 3

Human–robot interaction requires a robust estimate of human motion in real time. This work presents a fusion algorithm for joint center positions tracking from multiple depth cameras to improve human motion analysis accuracy. The main contribution is the proposed algorithm based on body tracking measurement fusion with an extended Kalman filter and anthropomorphic constraints, independent of sensors. As an illustration of the use of this algorithm, this paper presents the direct comparison of joint center positions estimated with a reference stereophotogrammetric system and the ones estimated with the new Kinect 3 (Azure Kinect) sensor and its older version the Kinect 2 (Kinect for Windows). The experiment was made in two parts, one for each model of Kinect, by comparing raw and merging body tracking data of a two-sided Kinect with the proposed algorithm. The proposed approach improves body tracker data for Kinect 3 which does not have the same characteristics as Kinect 2. This study shows also the importance of defining good heuristics to merge data depending on how the body tracking works. Thus, with proper heuristics, the joint center position estimates are improved by at least 14.6 %. Finally, we propose an additional comparison between Kinect 2 and Kinect 3 exhibiting the pros and cons of the two sensors.