Reviewerly/exHarmony huggingface dataset:
Swarm Relays: Distributed Self-Healing Ground-and-Air Connectivity Chains

The coordination of robot swarms - large decentralized teams of robots - generally relies on robust and efficient inter-robot communication. Maintaining communication between robots is particularly challenging in field deployments. Unstructured environments, limited computational resources, low bandwidth, and robot failures all contribute to the complexity of connectivity maintenance. In this paper, we propose a novel lightweight algorithm to navigate a group of robots in complex environments while maintaining connectivity by building a chain of robots. The algorithm is robust to single robot failures and can heal broken communication links. The algorithm works in 3D environments: when a region is unreachable by wheeled robots, the chain is extended with flying robots. We test the performance of the algorithm using up to 100 robots in a physics-based simulator with three mazes and different robot failure scenarios. We then validate the algorithm with physical platforms: 7 wheeled robots and 6 flying ones, in homogeneous and heterogeneous scenarios.
Aggregation Signature for Small Object Tracking

Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision.The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens.In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift.We make three-fold contributions in this work.First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects.Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability.Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins.Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking.
Controlling the entropic uncertainty lower bound in two-qubit systems under decoherence

The uncertainty principle is an inherent characteristic of quantum mechanics. This principle can be formulated in various forms. Fundamentally, this principle can be expressed in terms of the standard deviation of the measured observables. In quantum information theory, the preferred mathematical quantity to express the entropic uncertainty relation is Shannon's entropy. In this work, we consider the generalized entropic uncertainty relation in which there is an additional particle as a quantum memory. Alice measures on her particle A and Bob, with memory particle B, predicts Alice's measurement outcomes. It has a wide range of applications, for example, entanglement witnessing and quantum key distribution. We study the effects of the environment on the entropic uncertainty lower bound in the presence of weak measurement and measurement reversal. The dynamical model that is intended in this work is as follows: first the weak measurement is performed, second, the decoherence affects the system and finally the measurement reversal is performed on the quantum system. Here, we consider the generalized amplitude damping channel and depolarizing channel as environmental noises. We will show that in the presence of weak measurement and measurement reversal, despite the presence of environmental factors, the entropic uncertainty lower bound drops to an optimal minimum value. In fact, weak measurement and measurement reversal enhance the quantum correlation between the subsystems A and B, and thus the uncertainty of Bob regarding Alice's measurement outcomes reduces. Based on the entropic uncertainty relation, one can also show that the weak measurement and measurement reversal can preserve the quantum secret key rate lower bound from decoherence.
Deep Heterogeneous Hashing for Face Video Retrieval

Retrieving videos of a particular person with face image as a query via hashing technique has many important applications. While face images are typically represented as vectors in Euclidean space, characterizing face videos with some robust set modeling techniques (e.g. covariance matrices as exploited in this study, which reside on Riemannian manifold), has recently shown appealing advantages. This hence results in a thorny heterogeneous spaces matching problem. Moreover, hashing with handcrafted features as done in many existing works is clearly inadequate to achieve desirable performance for this task. To address such problems, we present an end-to-end Deep Heterogeneous Hashing (DHH) method that integrates three stages including image feature learning, video modeling, and heterogeneous hashing in a single framework, to learn unified binary codes for both face images and videos. To tackle the key challenge of hashing on the manifold, a well-studied Riemannian kernel mapping is employed to project data (i.e. covariance matrices) into Euclidean space and thus enables to embed the two heterogeneous representations into a common Hamming space, where both intra-space discriminability and inter-space compatibility are considered. To perform network optimization, the gradient of the kernel mapping is innovatively derived via structured matrix backpropagation in a theoretically principled way. Experiments on three challenging datasets show that our method achieves quite competitive performance compared with existing hashing methods.
Reduced Dilation-Erosion Perceptron for Binary Classification

Dilation and erosion are two elementary operations from mathematical morphology, a non-linear lattice computing methodology widely used for image processing and analysis. The dilation-erosion perceptron (DEP) is a morphological neural network obtained by a convex combination of a dilation and an erosion followed by the application of a hard-limiter function for binary classification tasks. A DEP classifier can be trained using a convex-concave procedure along with the minimization of the hinge loss function. As a lattice computing model, the DEP classifier assumes the feature and class spaces are partially ordered sets. In many practical situations, however, there is no natural ordering for the feature patterns. Using concepts from multi-valued mathematical morphology, this paper introduces the reduced dilation-erosion (r-DEP) classifier. An r-DEP classifier is obtained by endowing the feature space with an appropriate reduced ordering. Such reduced ordering can be determined using two approaches: one based on an ensemble of support vector classifiers (SVCs) with different kernels and the other based on a bagging of similar SVCs trained using different samples of the training set. Using several binary classification datasets from the OpenML repository, the ensemble and bagging r-DEP classifiers yielded mean higher balanced accuracy scores than the linear, polynomial, and radial basis function (RBF) SVCs as well as their ensemble and a bagging of RBF SVCs.
Sesqui-Pushout Rewriting: Concurrency, Associativity and Rule Algebra Framework

Sesqui-pushout (SqPO) rewriting is a variant of transformations of graph-like and other types of structures that fit into the framework of adhesive categories where deletion in unknown context may be implemented. We provide the first account of a concurrency theorem for this important type of rewriting, and we demonstrate the additional mathematical property of a form of associativity for these theories. Associativity may then be exploited to construct so-called rule algebras (of SqPO type), based upon which in particular a universal framework of continuous-time Markov chains for stochastic SqPO rewriting systems may be realized.
A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.

In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
The MoCA dataset, kinematic and multi-view visual streams of fine-grained cooking actions

MoCA is a bi-modal dataset in which we collect Motion Capture data and video sequences acquired from multiple views, including an ego-like viewpoint, of upper body actions in a cooking scenario. It has been collected with the specific purpose of investigating view-invariant action properties in both biological and artificial systems. Besides that, it represents an ideal test bed for research in a number of fields - including cognitive science and artificial vision - and application domains - as motor control and robotics. Compared to other benchmarks available, MoCA provides a unique compromise for research communities leveraging very different approaches to data gathering: from one extreme of action recognition in the wild - the standard practice nowadays in the fields of Computer Vision and Machine Learning - to motion analysis in very controlled scenarios - as for motor control in biomedical applications. In this work we introduce the dataset and its peculiarities, and discuss a baseline analysis as well as examples of applications for which the dataset is well suited.
Without a trace: Why did corona apps fail?

At the beginning of the COVID-19 pandemic, high hopes were put on digital contact tracing, using mobile phone apps to record and immediately notify contacts when a user reports as infected. Such apps can now be downloaded in many countries, but as second waves of COVID-19 are raging, these apps are playing a less important role than anticipated. We argue that this is because most countries have opted for app configurations that cannot provide a means of rapidly informing users of likely infections while avoiding too many false positive reports. Mathematical modelling suggests that differently configured apps have the potential to do this. These require, however, that some pseudonymised data be stored on a central server, which privacy advocates have cautioned against. We contend that their influential arguments are subject to two fallacies. First, they have tended to one-sidedly focus on the risks that centralised data storage entails for privacy, while paying insufficient attention to the fact that inefficient contact tracing involves ethical risks too. Second, while the envisioned system does entail risks of breaches, such risks are also present in decentralised systems, which have been falsely presented as 'privacy preserving by design'. When these points are understood, it becomes clear that we must rethink our approach to digital contact tracing in our fight against COVID-19.
AutoFCL: automatically tuning fully connected layers for handling small dataset

Deep convolutional neural networks (CNN) have evolved as popular machine learning models for image classification during the past few years, due to their ability to learn the problem-specific features directly from the input images. The success of deep learning models solicits architecture engineering rather than hand-engineering the features. However, designing state-of-the-art CNN for a given task remains a non-trivial and challenging task, especially when training data size is less. To address this phenomena, transfer learning has been used as a popularly adopted technique. While transferring the learned knowledge from one task to another, fine-tuning with the target-dependent fully connected (FC) layers generally produces better results over the target task. In this paper, the proposed AutoFCL model attempts to learn the structure of FC layers of a CNN automatically using Bayesian Optimization. To evaluate the performance of the proposed AutoFCL, we utilize five pre-trained CNN models such as VGG-16, ResNet, DenseNet, MobileNet, and NASNetMobile. The experiments are conducted on three benchmark datasets, namely CalTech-101, Oxford-102 Flowers, and UC Merced Land Use datasets. Fine-tuning the newly learned (target-dependent) FC layers leads to state-of-the-art performance, according to the experiments carried out in this research. The proposed AutoFCL method outperforms the existing methods over CalTech-101 and Oxford-102 Flowers datasets by achieving the accuracy of $$94.38\%$$ and $$98.89\%$$ , respectively. However, our method achieves comparable performance on the UC Merced Land Use dataset with $$96.83\%$$ accuracy. The source code of this research is available at https://github.com/shabbeersh/AutoFCL .
Sentiment Infomation based Model For Chinese text Sentiment Analysis

As an important task of natural language processing, chinese text sentiment analysis aims to analyze the comprehensive sentiment polarity of chinese text. With the emergence of various deep neural network models, sentiment analysis tasks have once again made significant progress. However, these neural network models could not accurately capture sentiment information on sentiment analysis tasks, which leads to their instability. In order to enable the model to explicitly learn the sentiment knowledge in chinese text, this paper proposes a sentiment information based network model(SINM). We use Transfomer encoder and LSTM as model components. With the help of Chinese emotional dictionary, we can automatically find sentiment knowledge in chinese text. In SINM, we designed a hybrid task learning method to learn valuable emotional expressions and predict sentiment tendencies. First of all, SINM needs to learn the sentiment knowledge in the text. Under the auxiliary influence of emotional information, SINM will pay more attention to sentiment information rather than useless information. Experiments on the dataset of ChnSentiCorp and ChnFoodReviews have found that SINM can achieve better performance and generalization ability than most existing methods.
The state-of-practice in requirements elicitation: an extended interview study at 12 companies

Context. Requirements engineering remains a discipline that is faced with a large number of challenges, including the implementation of a requirements elicitation process in industry. Although several proposals have been suggested by researchers and academics, little is known of the practices that are actually followed in industry. Objective. We investigate the SoTA with respect to requirements elicitation, examining practitioners' practices. We focus on the techniques, the roles involved, and the challenges associated to the process. Method. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. Results. We found that group interaction techniques, including meetings and workshops, are the most popular type of elicitation techniques that are employed by the practitioners, except in the case of small projects. We noted that customers are frequently involved in the elicitation process, except in the case of market-driven organizations. Technical staff (for example, developers and architects) are more frequently involved in the elicitation process compared to the involvement of business- or strategic staff. Finally, we identified a number of challenges with respect to stakeholders. These challenges include difficulties in understanding and prioritizing their needs. Further, it was noted that requirements instability (i.e., caused by changing needs or priorities) was a predominant challenge. These observations need to be interpreted in the context of the study. Conclusion. The relevant observations regarding the survey participants' experiences should be of interest to the industry; experiences that should be analyzed in the practitioners' context. Researchers may find evidence for the use of academic results in practice, thereby inspiring future theoretical work, as well as further empirical studies in the same area.
An auto-detection network to provide an automated real-time early warning of rock engineering hazards using microseismic monitoring

The hazards associated with the extraction of ores and the construction of underground geoengineering structures should be mitigated to prevent the loss of human life. Microseismic monitoring (MM) has been used extensively in underground engineering around the world to assess the condition of underground structures and to improve personnel safety. However, MM cannot be currently used as a real-time early warning system because arrival detection still requires manual processing. Although studies have forwarded automated algorithms to perform this task, they are far less accurate than a human expert and therefore provide inaccurate source locations. In this study, we propose a workflow to specifically obtain the state-of-the-art performance of arrival detection. First, to avoid severe class-imbalance problems and speed up convergence in training, a new label form is proposed. We then propose an innovative self-attention mechanism in deep learning to efficiently model distant correlation (“segmentation-integration self-attention”). This new self-attention mechanism can effectively encode the distant inner dependencies by processing signals in several segments and by building correlations between the corresponding descriptors for the segments. Additionally, a new neural network architecture (“extractor-encoder-generator”) for automated arrival picking is proposed based on the above self-attention. This architecture extracts functional features of the signals, models correlations by an attention-related sublayer and generates a score map defining the confidence of the first break. The combination of the above technology is referred to as the “auto-detection” network. The new architecture outperforms all other algorithms by a considerable margin, provides a similar accuracy to that of a human expert, and is well balanced in terms of inference speed and accuracy. The auto-detection network allows for a practical and automated MM technique that can be used to provide an accurate real-time early warning system.
Double-Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits

Computers built of naturally probabilistic bits, as envisioned by Richard Feynman, share a large class of the applications anticipated for noisy intermediate-scale quantum (NISQ) computers. The authors show how the basic building block of a highly scaled memory technology, namely the familiar magnetic tunnel junction, can be straightforwardly modified by making use of two free layers that are each in the superparamagnetic regime, such that their natural fluctuations create highly efficient probabilistic bits at room temperature. The key insights obtained here may help to repurpose existing memory chips to build massively parallel probabilistic computers, with up to billions of p-bits.
News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston

Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level is needed as opposed to the state or country level. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the number of tests normalizes the latter. Positivity gives a better picture of the spread of this pandemic as, with time, more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD (Susceptible, Exposed, Infectious, Recovered, and Deceased) lacks information about the sentiment of people concerning coronavirus. Thirdly, models that make use of social media posts might have too much noise and misinformation. On the other hand, news sentiment can capture long-term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new artificial intelligence (i.e., AI) model, viz., Sentiment Informed Time-series Analyzing AI (SITALA), trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76, and that for the test dataset of 22 consecutive days is 9.6. A cone of uncertainty is provided within which future COVID-19 test positivity has been shown to fall with high accuracy. The model predictions fare better than a published Bayesian-based SEIRD model. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment (e.g., death count for COVID-19 will grow at an alarming rate in Houston if mask orders are not followed) will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.
Rethinking Teacher Education Policy in ICT: Lessons from Emergency Remote Teaching (ERT) during the COVID-19 Pandemic Period in Korea

This paper examines ICT policy in education with a particular focus on teachers’ engagement in emergent remote teaching (ERT) during the initial COVID-19 school closure in South Korea. It involves a documentary analysis of newspaper articles on “starting school online” from the highest read daily newspapers published in South Korea, through which three issues regarding teachers and teaching are identified: teachers’ digital competency, teachers’ sense of professional identity, and the revalorisation of the teacher role. Discussion of the three issues points to the need to reflect on and rethink the government’s policies for ICT in education. This paper provides an overview of the ICT policies to show their overall inadequacy with respect to providing teachers with the necessary training and framework for technology-related professional development. It highlights the need to understand the changing nature of teaching and learning in a digital education environment and it suggests a possible redesign of the education and training provisions to teachers to support their professional competency in the digital age.