Reviewerly/exHarmony huggingface dataset:
Multi-Modal Subspace Fusion via Cauchy Multi-Set Canonical Correlations

Multi-set canonical correlation analysis (MCCA) is a famous multi-modal coherent subspace learning method. However, sample-based between-modal and within-modal covariance matrices of MCCA usually deviate from real covariance matrices due to noise information and limited sample size. The deviation will weaken the performance of MCCA, especially in image recognition. Aiming at this challenging issue, we correct singular values of sample covariance matrices with the employment of Cauchy estimate theory and further obtain Cauchy covariance matrices that are closer to real covariance matrices. On the basis of Cauchy covariance matrices, we develop a novel multi-modal subspace fusion method, i.e. Cauchy multi-set canonical correlations. By maximizing Cauchy correlations between different modalities and constraining Cauchy scatters of within-modal data, the method can learn a Cauchy coherent fusion subspace with well discriminative power from a few images. Experiment results have shown the effectiveness of the proposed method, promising to the aims of this research.
A novel soft-aided bit-marking decoder for product codes

We introduce a novel soft-aided hard-decision decoder for product codes adopting bit marking via updated reliabilities at each decoding iteration. Gains up to 0.8 dB vs. standard iterative bounded distance decoding and up to 0.3 dB vs. our previously proposed bit-marking decoder are demonstrated.
Regularization via Structural Label Smoothing

Regularization is an effective way to promote the generalization performance of machine learning models. In this paper, we focus on label smoothing, a form of output distribution regularization that prevents overfitting of a neural network by softening the ground-truth labels in the training data in an attempt to penalize overconfident outputs. Existing approaches typically use cross-validation to impose this smoothing, which is uniform across all training data. In this paper, we show that such label smoothing imposes a quantifiable bias in the Bayes error rate of the training data, with regions of the feature space with high overlap and low marginal likelihood having a lower bias and regions of low overlap and high marginal likelihood having a higher bias. These theoretical results motivate a simple objective function for data-dependent smoothing to mitigate the potential negative consequences of the operation while maintaining its desirable properties as a regularizer. We call this approach Structural Label Smoothing (SLS). We implement SLS and empirically validate on synthetic, Higgs, SVHN, CIFAR-10, and CIFAR-100 datasets. The results confirm our theoretical insights and demonstrate the effectiveness of the proposed method in comparison to traditional label smoothing.
Privately Answering Classification Queries in the Agnostic PAC Model.

We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC model and derive a new upper bound on the private sample complexity. Our results improve over those obtained in a recent work [BTT18] for the agnostic PAC setting. In particular, we give an improved construction that yields a tighter upper bound on the sample complexity. Moreover, unlike [BTT18], our accuracy guarantee does not involve any blow-up in the approximation error associated with the given hypothesis class. Given any hypothesis class with VC-dimension $d$, we show that our construction can privately answer up to $m$ classification queries with average excess error $\alpha$ using a private sample of size $\approx \frac{d}{\alpha^2}\,\max\left(1, \sqrt{m}\,\alpha^{3/2}\right)$. Using recent results on private learning with auxiliary public data, we extend our construction to show that one can privately answer any number of classification queries with average excess error $\alpha$ using a private sample of size $\approx \frac{d}{\alpha^2}\,\max\left(1, \sqrt{d}\,\alpha\right)$. When $\alpha=O\left(\frac{1}{\sqrt{d}}\right)$, our private sample complexity bound is essentially optimal.
Meta self-paced learning

Self-paced learning (SPL) is a learning regime, inspired by human and animal learning processes, that gradually incorporates simple to more complex samples into a training dataset. Recently, SPL has seen significant research progress. However, current SPL algorithms still have critical limitations, such as how to determine the age hyper-parameters (especially the age parameters). Some heuristic strategies based on cross-validation have been designed. In addition, setting these parameters manually has been proposed. However, such strategies are very inefficient, not supported by theoretical evidence, and are very difficult to apply generally in practice. To address these issues, we propose a meta-learning regime for adaptively learning age parameters involved in SPL. Three types of typical SPL algorithms are integrated into the proposed regime, and their accuracy and generalization capability are substantiated through regression and classification experiments, and compared to conventional SPL paradigms that do not include adaptive age parameter tuning.
From QoS Distributions to QoE Distributions: a System's Perspective

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating “good or better”, etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates.
“Real” Insider Threat: Toxic Workplace Behavior in the Intelligence Community

The court case concluding in March 2020, involving former Intelligence Community (IC) software engineer Joshua Schulte, was a blunt revelation of the toxic dynamic inside one Central Intelligence A...
Terminology-Constrained Neural Machine Translation at SAP.

This paper examines approaches to bias a neural machine translation model to adhere to terminology constraints in an industrial setup. In particular, we investigate variations of the approach by Dinu et al. (2019), which uses inline annotation of the target terms in the source segment plus source factor embeddings during training and inference, and compare them to constrained decoding. We describe the challenges with respect to terminology in our usage scenario at SAP and show how far the investigated methods can help to overcome them. We extend the original study to a new language pair and provide an in-depth evaluation including an error classification and a human evaluation.
Ontologies for observations and actuations in buildings: A survey

Spaces and elements in the built environment have emerged as platforms where materializations of observations and actuations promise to be very profitable. The advent of the Internet of Things (IoT) paves the way to address this challenge but the heterogeneity of the represented knowledge about the se artifact systems poses a real problem. Ontologies can be considered as part of the solution to overcome the IoT’s inherent hurdles. A wise option promoted by recent approaches is to design networks of complementary ontologies. However, different points of view are possible and such diversity could lead to interoperability problems. This article advocates for a networked ontology infrastructure conceived on a principled basis guided by documented judicious conceptualizations. In this regard, this survey points towards ontologies involved in conceptualizations of observations and actuations, where the utility of that conceptualization arises when some features of interest need to be observed or acted upon. For each of the reviewed ontologies, their fundamentals are described, their potential advantages and shortcomings are highlighted, and the use cases where these ontologies have been used are indicated. Additionally, use case examples are annotated with different ontologies in order to illustrate their capabilities and showcase the differences between reviewed ontologies. Finally, this article tries to answer two research questions: Is there a firm basis, broadly admitted by the community, for the development of such a networked ontology infrastructure for the observations and actuations in buildings? What ontologies may be considered helpful towards that goal?
Why do large-scale agricultural investments induce different socio-economic, food security, and environmental impacts? Evidence from Kenya, Madagascar, and Mozambique

Large-scale agricultural investments (LAIs) transform land use systems worldwide.There is, however, limited understanding about how the common global drivers of land use change induce different forms of agricultural investment and produce different impacts on the ground.This article provides a cross-country comparative analysis of how differences in business models, land use changes, and governance systems explain differences in socio-economic, food security, and environmental impacts of LAIs in Kenya, Madagascar, and Mozambique.It brings together results on these aspects generated in the AFGROLAND project that collected data in a multi-method approach via household surveys, business model surveys, semi-structured household interviews, life-cycle assessments of farm production, analysis of remote-sensing data, key informant interviews, and document analysis.For the present project synthesis, we combined a collaborative expert workshop with a comparative analysis of 16 LAIs.The results show that the LAIs follow four distinctive impact patterns, ranging from widespread adverse impacts to moderate impacts.Results demonstrate how the following conditions influence how the global drivers of land use change translate into different LAIs and different impacts on the ground: labor intensity, prior land use, utilization of land, farm size, type of production, experience in local agriculture, land tenure security, accountability of state and local elites, the mobilization capacity of civil society, expansion of resource frontiers, agricultural intensification, and indirect land use change.The results indicate that commercial agriculture can be a component in sustainable development strategies under certain conditions, but that these strategies will fail without substantial, sustained increases in the economic viability and inclusiveness of smallholder agriculture, land tenure security, agro-ecological land management, and support for broader patterns of endogenous agrarian transformation.
Improved potential field method path planning based on genetic algorithm

In order to overcome the problem of path planning failure in traditional potential field method, an improved potential field path planning method based on genetic algorithm is proposed in this paper. Firstly, for the problem of inaccessibility of the target, a factor method is added to the traditional gravity function to solve it; then the local minimum point problem of the traditional artificial potential field method is solved to ensure that the robot can reach the target point. At the same time, in order to get the optimal path, genetic algorithm is used to optimize the combined potential field function of gravity and repulsion, to find the lowest point of potential energy directly, so as to determine the step size and moving direction of the robot. Simulation is carried out to verify the effectiveness of the method.
Performance Analysis Among Predictive Models of Lightning Occurrence Using Artificial Neural Networks and SMOTE

Lightning represent a potential threat to various society activities, such as damage to telecommunication systems and the distribution of electric power, as well as injury or death of humans beings. Predicting the occurrence of lightning can help in making decisions about the actions that must be taken to minimize the risks of this natural phenomenon. In this study, data from air temperature profiles, dew point temperature and historical lightning data were used to obtain two predictive models of lightning occurrence. The models were obtained by using an artificial neural network. The first model was obtained through unbalanced data and the second one with data balanced with Synthetic Minority Over-sampling Technique (SMOTE). The model performance was tested in five different classes of lightning predictions: ABSENCE, LOW, MODERATE, VERY and SEVERE, considering five prediction periods: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). It was observed that the use of the Synthetic Minority Over-sampling Technique improved accuracy in the recognition of atmospheric patterns that lead to the incidence of lightning in the five classes used in the five prediction cases.
The Dilemma of contact-tracing apps: Can this crucial technology be both effective and private?

IN JUNE, as the coronavirus swept across the United States, Paloma Beamer spent hours each day helping her university plan for a September reopening. Beamer, an associate professor of public health at the University of Arizona, was helping to test a mobile app that would notify users if they crossed paths with confirmed COVID-19 patients. · A number of such “contact tracing” apps have recently had their trials by fire, and many of the developers readily admit that the technology has not yet proven that it can slow the spread of the virus. But that caveat has not stopped national governments and local communities from using the apps. · “Right now, in Arizona, we're in the full-blown pandemic phase,” Beamer said, speaking in June, well before the new-case count had peaked. “And even manual contact tracing is very limited here-we need whatever tool we can get right now to curb our epidemic.” · Traditionally, tracers would ask newly diagnosed patients to list the people they'd spent time with recently, then ask those people to provide contacts of their own. Such legwork has helped to control other infectious-disease outbreaks, such as syphilis in the United States and Ebola in West Africa. However, while these methods can extinguish the first spark or the last embers of an epidemic, they're no good in the wildfire stage, when the caseload expands exponentially.
Multi-user Boolean searchable encryption supporting fast ranking in mobile clouds

Mobile cloud computing has become an important technology for mobile services. It overcomes physical limitations of mobile devices towards flexible and scalable mobile services. When using mobile clouds, people usually upload and store their data in cloud servers, and access the data through mobile devices from anywhere. As the privacy of the sensitive data is the major concern of users, the data is often encrypted before storing in cloud servers. However, the flexibility using the data is thereby affected, such as a Boolean search over the encrypted data, or ranking search results. To address these problems, in this paper we first propose a novel multi-user Boolean keyword search scheme (MBKSS) to achieve a rapid Boolean query outcome, while eliminating query interactions between users and the data owner. To rank search results and protect the privacy of relevance scores between files and keywords, we design a new homomorphic cryptosystem with partial decryption, which could be used as a basis for constructing a fast ranking search protocol (FRSP). A comprehensive security analysis shows that the proposed MBKSS and FRSP can achieve secure Boolean search and ranking. The experimental results demonstrate that the proposed MBKSS and FRSP are efficient and suitable for mobile devices.
On the Banach Spaces Associated with Multi-Layer ReLU Networks: Function Representation, Approximation Theory and Gradient Descent Dynamics

We develop Banach spaces for ReLU neural networks of finite depth $L$ and infinite width. The spaces contain all finite fully connected $L$-layer networks and their $L^2$-limiting objects under bounds on the natural path-norm. Under this norm, the unit ball in the space for $L$-layer networks has low Rademacher complexity and thus favorable generalization properties. Functions in these spaces can be approximated by multi-layer neural networks with dimension-independent convergence rates. The key to this work is a new way of representing functions in some form of expectations, motivated by multi-layer neural networks. This representation allows us to define a new class of continuous models for machine learning. We show that the gradient flow defined this way is the natural continuous analog of the gradient descent dynamics for the associated multi-layer neural networks. We show that the path-norm increases at most polynomially under this continuous gradient flow dynamics.
From Homogeneous to Heterogeneous: Leveraging Deep Learning based Power Analysis across Devices

In this paper, we raise practical situations in profiling based power analysis when profiling and target devices are quite different at several levels. "Crossed devices" are newly termed, including homogeneous and heterogeneous devices, which have not been carefully investigated. We identify such device variations and take a further step towards leveraging the deep learning based power analysis. Traditional template attacks and straight-forward deep learning based power analysis will fail, when the gap across devices is significantly enlarged. In this paper, we propose a noval frequency and learning based power analysis machanism, which is able to explore new attacking power of deep learning and address challenges caused by device variations. For the first time, power traces collected from our own PIC devices can be utilized to successfully attack the public dataset in DPAContest v4 which is based on a totally different AVR microcontroller.
Experimental dataset supporting the physical and mechanical characterization of industrial base tissue papers

Tissue paper is defined by its physical and mechanical properties, namely: high softness, low grammage, high bulk and high liquid absorption capacity. It is expected that the production of tissue paper will continue to grow, which increases the importance of better understanding the processes involved in its production as well as its optimization [1]. The experimental data presented in this article, are the physical-mechanical characterization of a group of 13 industrial base tissue papers, which were collected at the end of the tissue paper machine on Portuguese factories. These samples vary in grammage, composition and creping [2], enabling a later evaluation of the crepe type [3] and its relationship with the final properties of the tissue paper.
Conceptual approaches to the identification of teachers’ digital competence: cognitive modelling

The need for developing a teacher’s digital competency requires the creation of a fundamental scientific base for the process of training teachers and their professional development to enable them to work in the context of digitization of general education. In the Covid-19 pandemic, global transformations are taking place in the field of education. All regions of Russia have switched completely into remote mode. According to Interfax, based on the research of the Higher School of Economics NRU, the Republic of Sakha (Yakutia) is the best in Russia in terms of technical equipment for teachers and schoolchildren today. This study covered 22,600 teachers in 73 regions of the country. In Yakutia 61% of teachers conduct online learning, while the average in Russia is 25%. However, the common problem across all regions is that teachers, schoolchildren and their parents lack psychological readiness for a complete transition to digital education. This explains the emerging situation of conflict, background stress, as well as frequent cases of imitative distance education and e-learning. In this regard, there is a need to discuss the problems facing education in the digitization process and the need to overcome them. This paper analyses the current situation in school education in the context of digitization and a universal transition to online learning. Graduate students of the NEFU Institute for Continuing Professional Education conducted a survey of teachers and school leaders. This was aimed at identifying attitudes towards the process of digitization of school education and ownership of ICT, and some digital competencies of teachers and educational leaders. The results confirmed the relevance of the research problem and revealed the most acute contradictions associated with the lack of digital competencies among teachers in Yakutia. The study examined modern conceptual approaches to the digitization of education, the identification of digital competencies of teachers, and the development of objective measurement tools. A digital teacher competency map is being developed, with a toolkit that integrates key competencies of the digital economy and digital competencies, together with tester program (a digital simulator) to identify the formation of: (a) a set of the teacher’s core competencies in the digital economy and (b) a set of teachers’ digital competencies. The following factors were taken into account in developing the tools: conceptual approaches in modern scientific and pedagogical research to identify, identify and assess the level of formation of digital competencies of a modern teacher; methodological substantiation of cognitive modeling of the competencies studied; assumptions of the most optimal combinations of key and digital competencies for teachers of different subject areas and levels of education; interconnection and interdependence of some professional deficits associated with a deficit of key and digital competencies.