Reviewerly/exHarmony huggingface dataset:
A wearable magnetic field based proximity sensing system for monitoring COVID-19 social distancing

We present a wearable, oscillating magnetic field-based proximity sensing system to monitor social distancing as suggested to prevent COVID 19 spread (being between 1.5 and 2.0m) apart. We evaluate the system both in controlled lab experiments and in a real life large hardware store setting. We demonstrate that, due physical properties of the magnetic field, the system is much more robust than current BT based sensing, in particular being nearly 100% correct when it comes to distinguishing between distances above and below the 2.0m threshold.
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.
Analyzing Malaria Disease Using Effective Deep Learning Approach

Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.
Multi-robot path planning using an improved self-adaptive particle swarm optimization

Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.
An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown.
No-reference stereoscopic image quality assessment based on global and local content characteristics

No-reference stereoscopic images quality assessment (NR-SIQA) via deep learning has gained increasing attention. In this paper, we propose a no-reference stereoscopic image quality assessment method based on global and local content characteristics. The proposed method simulates the perception route of human visual system, and derives features from the fused view and single view through the global feature fusion sub-network and local feature enhancement sub-network. As for the fused view, a cross-fusion strategy is applied to model the process in the V1 visual cortex, and the multi-scales pooling (MSP) is utilized to integrate context information under different sub-regions for effective global feature extraction. As for the single view, the asymmetric convolution block (ACB) is introduced to strengthen the local information description. By jointly considering the fused view and single view, the proposed network can efficiently extract the features for quality assessment. Finally, a weighted average strategy is applied to estimate the visual quality of stereoscopic image. Experimental results on 3D quality databases demonstrate that the proposed network is superior to the state-of-the-art metrics, and achieves an excellent performance.
Alignment for Advanced Machine Learning Systems

This chapter surveys eight research areas organized around one question: As learning systems become increasingly intelligent and autonomous, what design principles can best ensure that their behavior is aligned with the interests of the operators? The chapter focuses on two major technical obstacles to AI alignment: the challenge of specifying the right kind of objective functions and the challenge of designing AI systems that avoid unintended consequences and undesirable behavior even in cases where the objective function does not line up perfectly with the intentions of the designers. The questions surveyed include the following: How can we train reinforcement learners to take actions that are more amenable to meaningful assessment by intelligent overseers? What kinds of objective functions incentivize a system to “not have an overly large impact” or “not have many side effects”? The chapter discusses these questions, related work, and potential directions for future research, with the goal of highlighting relevant research topics in machine learning that appear tractable today.
Blockchain Technology: Redefining Trust for Digital Certificates

University certificates can have a significant impact on people’s lives: they can help people get the job they want or allow companies to decide whether a candidate for a job has the appropriate skills. Despite their important social role, current systems for certifying academic achievements are slow, complicated, expensive, and vulnerable to forgery. In the education field, the Blockcerts project, an open source project launched by MIT and Learning Machine in 2016, seems to have the potential to become a new standard for issuing certificates using the Blockchain technology as a platform. It enables students to quickly and easily get a verifiable, tamper-proof version of their diploma. Additionally, the infrastructure provides permanence, convenience, and a level of security appropriate to the importance of the record, guaranteeing the legitimacy of the diploma. The University of Rome “Tor Vergata” started a pilot program in 2018 adopting the Blockcerts framework to issue digital diplomas. In this paper, we describe the whole process from a technical perspective and analyze the impact that a broad adoption of the Blockcerts standard might have, as compared to the current way of issuing diplomas at the University of “Tor Vergata”. Our aim is that our work might contribute to building momentum for the application of the Blockchain technology to digital certificates and stimulate further discussion with other institutions to fully exploit the potential of the technology.
TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter

In recent years, the Natural Language Processing community have been moving from uncontextualized word embeddings towards contextualized word embeddings. Among these contextualized architectures, BERT stands out due to its capacity to compute bidirectional contextualized word representations. However, its competitive performance in English downstream tasks is not obtained by its multilingual version when it is applied to other languages and domains. This is especially true in the case of the Spanish language used in Twitter. In this work, we propose TWiLBERT, a specialization of BERT architecture both for the Spanish language and the Twitter domain. Furthermore, we propose a Reply Order Prediction signal to learn inter-sentence coherence in Twitter conversations, which improves the performance of TWilBERT in text classification tasks that require reasoning on sequences of tweets. We perform an extensive evaluation of TWilBERT models on 14 different text classification tasks, such as irony detection, sentiment analysis, or emotion detection. The results obtained by TWilBERT outperform the state-of-the-art systems and Multilingual BERT. In addition, we carry out a thorough analysis of the TWilBERT models to study the reasons of their competitive behavior. We release the pre-trained TWilBERT models used in this paper, along with a framework for training, evaluating, and fine-tuning TWilBERT models.
Interpretable Multi-dataset Evaluation for Named Entity Recognition

With the proliferation of models for natural language processing tasks, it is even harder to understand the differences between models and their relative merits.Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 does not tell us why or how particular methods perform differently and how diverse datasets influence the model design choices.In this paper, we present a general methodology for interpretable evaluation for the named entity recognition (NER) task.The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them, identifying the strengths and weaknesses of current systems.By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area: https: //github.com/neulab/InterpretEval.
Analog errors in Ising machines

Recent technological breakthroughs have precipitated the availability of specialized devices that promise to solve NP-Hard problems faster than standard computers. These 'Ising Machines' are however analog in nature and as such inevitably have implementation errors. We find that their success probability decays exponentially with problem size for a fixed error level, and we derive a sufficient scaling law for the error in order to maintain a fixed success probability. We corroborate our results with experiment and numerical simulations and discuss the practical implications of our findings.
Distorted pinhole camera modeling and calibration

Camera modeling and calibration are essential tasks in modern optics. Conventionally, the pinhole model is adopted with a further extension for lens distortion. However, pinhole and distortion models are mutually dependent; thus, the standard approach induces systematic camera calibration errors. This research presents a unifying distorted pinhole camera model that includes a telecentric, distortion-free pinhole, and radial lens distortion as particular cases. An iterative calibration method based on the derived distorted pinhole model is proposed, and experimental evaluation by calibrating a camera with high radial distortion is performed. The calibration results are compared with the standard and fisheye models using a well-known commercial camera calibrator software. The proposed method outperforms the standard model and achieves accuracy comparable to the fisheye model, and the proposed approach is a versatile and accurate tool for diverse optical metrology applications.
Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup*

Abstract Deep neural networks achieve stellar generalisation even when they have enough parameters to easily fit all their training data. We study this phenomenon by analysing the dynamics and the performance of over-parameterised two-layer neural networks in the teacher–student setup, where one network, the student, is trained on data generated by another network, called the teacher. We show how the dynamics of stochastic gradient descent (SGD) is captured by a set of differential equations and prove that this description is asymptotically exact in the limit of large inputs. Using this framework, we calculate the final generalisation error of student networks that have more parameters than their teachers. We find that the final generalisation error of the student increases with network size when training only the first layer, but stays constant or even decreases with size when training both layers. We show that these different behaviours have their root in the different solutions SGD finds for different activation functions. Our results indicate that achieving good generalisation in neural networks goes beyond the properties of SGD alone and depends on the interplay of at least the algorithm, the model architecture, and the data set.
Rethinking Skip Connection with Layer Normalization

Skip connection is a widely-used technique to improve the performance and the convergence of deep neural networks, which is believed to relieve the difficulty in optimization due to non-linearity by propagating a linear component through the neural network layers. However, from another point of view, it can also be seen as a modulating mechanism between the input and the output, with the input scaled by a pre-defined value one. In this work, we investigate how the scale factors in the effectiveness of the skip connection and reveal that a trivial adjustment of the scale will lead to spurious gradient exploding or vanishing in line with the deepness of the models, which could by addressed by normalization, in particular, layer normalization, which induces consistent improvements over the plain skip connection. Inspired by the findings, we further propose to adaptively adjust the scale of the input by recursively applying skip connection with layer normalization, which promotes the performance substantially and generalizes well across diverse tasks including both machine translation and image classification datasets.
Where Have All the Data Gone? A Critical Reflection on Academic Digital Research in the Post-API Age

In the wake of the 2018 Facebook–Cambridge Analytica scandal, social media companies began restricting academic researchers’ access to the easiest, most reliable means of systematic data collection via their application programming interfaces (APIs). Although these restrictions have been decried widely by digital researchers, in this essay, I argue that relatively little has changed. The underlying relationship between researchers, the platforms, and digital data remains largely the same. The platforms and their APIs have always been proprietary black boxes, never intended for scholarly use. Even when researchers could mine data seemingly endlessly, we rarely knew what type or quality of data were at hand. Moreover, the largesse of the API era allowed many researchers to conduct their work with little regard for the rigor, ethics, or focus on societal value, we should expect from scholarly inquiry. In other words, our digital research processes and output have not always occupied the high ground. Rather than viewing 2018 and Cambridge Analytica as a profound disjuncture and loss, I suggest that digital researchers need to take a more critical look at how our community collected and analyzed data when it still seemed so plentiful, and use these reflections to inform our approaches going forward.
A blockchain-based certificate revocation management and status verification system

Revocation management is one of the main tasks of the Public Key Infrastructure (PKI). It is also critical to the security of any PKI. As a result of the increase in the number and sizes of networks as well as the adoption of novel paradigms such as the Internet of Things and their usage of the web, current revocation mechanisms are vulnerable to single point of failures as the network loads increase. To address this challenge, we take advantage of blockchains power and resiliency in order to propose an efficient decentralized certificates revocation management and status verification system. We use the extension field of the X509 certificate’s structure to introduce a field that describes to which distribution point the certificate will belong to if revoked. Each distribution point is represented by a Bloom filter filled with revoked certificates. Bloom filters and revocation information are stored in a public blockchain. We developed a real implementation of our proposed mechanism in Python and the Namecoin blockchain. Then, we conducted an extensive evaluation of our scheme using performance metrics such as execution time and data consumption to demonstrate that it can meet the needed requirements with high efficiency and low cost. Moreover, we compare the performance of our approach with two of the most well-known/used revocation techniques which are Online Certificate Status Protocol (OCSP) and Certificate Revocation List (CRL). The results obtained show that our proposed approach outperforms these current schemes.
An End-to-End Foreground-Aware Network for Person Re-Identification

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach.