Reviewerly/exHarmony huggingface dataset:
Differentiable Subset Sampling.

Many machine learning tasks require sampling a subset of items from a collection based on a parameterized distribution. The Gumbel-softmax trick can be used to sample a single item, and allows for low-variance reparameterized gradients with respect to the parameters of the underlying distribution. However, stochastic optimization involving subset sampling is typically not reparameterizable. To overcome this limitation, we define a continuous relaxation of subset sampling that provides reparameterization gradients by generalizing the Gumbel-max trick. We use this approach to sample subsets of features in an instance-wise feature selection task for model interpretability, subsets of neighbors to implement a deep stochastic k-nearest neighbors model, and sub-sequences of neighbors to implement parametric t-SNE by directly comparing the identities of local neighbors. We improve performance in all these tasks by incorporating subset sampling in end-to-end training.
Build your Own!

Hand-held controllers enable all kinds of interaction in Virtual Reality (VR), such as object manipulation as well as for locomotion. VR shoes allow using the hand exclusively for naturally manual tasks, such as object manipulation, while locomotion could be realized through feet input -- just like in the physical world. While hand-held VR controllers became standard input devices for consumer VR products, VR shoes are only barely available, and also research on that input modality remains open questions. We contribute here with open-source VR shoes and describe how to build and implement them as Unity3D input device. We hope to support researchers in VR research and practitioners in VR product design to increase usability and natural interaction in VR.
Vertical and Horizontal Distances to Approximate Edit Distance for Rooted Labeled Caterpillars

A rooted labeled caterpillar (caterpillar, for short) is a rooted labeled tree transformed to a rooted path (called a backbone) after removing all the leaves in it and we can compute the edit distance between caterpillars in quartic time. In this paper, we introduce two vertical distances and two horizontal distances for caterpillars. The former are based on a string edit distance between the string representations of the backbones and the latter on a multiset edit distance between the multisets of labels occurring in all the leaves. Then, we show that these distances give both lower bound and upper bound of the edit distance and we can compute the vertical distances in quadratic time and the horizontal distances in linear time under the unit cost function.
Vine copula structure learning via Monte Carlo tree search

Monte Carlo tree search (MCTS) has been widely adopted in various game and planning problems. It can efficiently explore a search space with guided random sampling. In statistics, vine copulas are flexible multivariate dependence models that adopt vine structures, which are based on a hierarchy of trees to express conditional dependence, and bivariate copulas on the edges of the trees. The vine structure learning problem has been challenging due to the large search space. To tackle this problem, we propose a novel approach to learning vine structures using MCTS. The proposed method has significantly better performance over the existing methods under various experimental setups.
Theory III: Dynamics and Generalization in Deep Networks.

The key to generalization is controlling the complexity of the network. However, there is no obvious control of complexity -- such as an explicit regularization term -- in the training of deep networks for classification. We will show that a classical form of norm control -- but kind of hidden -- is present in deep networks trained with gradient descent techniques on exponential-type losses. In particular, gradient descent induces a dynamics of the normalized weights which converge for $t \to \infty$ to an equilibrium which corresponds to a minimum norm (or maximum margin) solution. For sufficiently large but finite $\rho$ -- and thus finite $t$ -- the dynamics converges to one of several margin maximizers, with the margin monotonically increasing towards a limit stationary point of the flow. In the usual case of stochastic gradient descent, most of the stationary points are likely to be convex minima corresponding to a constrained minimizer -- the network with normalized weights-- which corresponds to vanishing regularization. The solution has zero generalization gap, for fixed architecture, asymptotically for $N \to \infty$, where $N$ is the number of training examples. Our approach extends some of the original results of Srebro from linear networks to deep networks and provides a new perspective on the implicit bias of gradient descent. We believe that the elusive complexity control we describe is responsible for the puzzling empirical finding of good predictive performance by deep networks, despite overparametrization.
Towards a Catalogue of Mobile Elicitation Techniques

[Context and Motivation] Mobile apps are crucial for many businesses. Their reach and impact on the end users and on the business in return demands that requirements are elicited carefully and properly. Traditional requirements elicitation techniques may not be adequate in the mobile apps domain. [Question/problem] Researchers have proposed numerous requirements elicitation techniques for the mobile app domain, but unfortunately, the community still lacks a comprehensive overview of available techniques. [Principle ideas/results] This paper presents a literature survey of about 60 relevant publications, in which we identify 24 techniques that target mobile apps. We found that only every second strategy was evaluated empirically, and even worse, non-functional requirements were rarely considered. We provide an evaluation scheme that is intended to support readers in efficiently finding opportune elicitation techniques for mobile apps. [Contribution] The found literature characteristics may guide future research and help the community to create more efficient, yet better, apps.
Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies

We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, and identifies a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture. Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, forgetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using classification and clustering tasks. Even though there are no prior approaches that are directly applicable to the UPL problem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context.
Medical image super-resolution for remote medical diagnosis in smart city: A case study based on the new healthcare reform of China

China is confronting with difficulty to tackle increased inequality in medical service between urban and rural areas. With the development of Internet of Things, telemedicine could provide remote clinic diagnosis in smart city via embedded systems for rural area. However, it is hard to acquire deserved resolution medical image since the limitation of hardware in rural area. Enhancing the image resolution is necessary for the medical images obtained in rural area. Anchor neighborhood regression based super-resolution is promising in enhancing the resolution of medical images. However, anchor neighborhood projections of ANR method are instability to cover variety styles of mapping relationships. In this respect, this paper propose a guided anchored neighborhood regression (GANR) based super-resolution method. The experiment results validates that our method has better reconstruction results in terms of both objective evaluation and subjective evaluation compare with previous methods.
Cybersecurity Curricula 2017 and Boeing: Linking Curricular Guidance to Professional Practice

The global cybersecurity workforce shortage is one of the world's most pressing challenges. With an estimated shortfall of 1.8 million workers by the year 2022, global cybersecurity workforce needs are acute, broad, and growing. Employers are collaborating with academic institutions to develop robust cybersecurity programs.
User interest dynamics on personalized recommendation

Four real recommender system datasets, the Netflix, SMovieLens, LMovieLens and RYM datasets, are analyzed to gain an insight into their user interest characteristics. A preference of active users to cold objects and a diverse interest of inactive users are revealed, which characteristics are introduced to improve the personalized recommendation algorithms. Based on seven different algorithms, we propose a general improvement formula for them, and finally four new algorithms are obtained. Tested on the four datasets, all the new algorithms are found to outperform the original ones in recommendation accuracy, diversity and novelty, except for the diversity and novelty compared with a heat conduction algorithm. And the recommendation accuracy for the cold objects, referring to the objects with small degrees, is also improved. Moreover, one of the new algorithms shows better performance than two other excellent algorithms in many aspects, i.e., the hybrid algorithm of heat conduction and mass diffusion, and the biased heat conduction algorithm. Our work may shed a new light on personalized recommendation from the perspective of connecting empirical study with algorithm design.
Fine-tuning Approach to NIR Face Recognition

Despite extensive researches for face recognition (FR), it is still difficult to apply deep CNN models to NIR FR due to a lack of training data. In this study, we propose a fine-tuning approach to allow deep CNN models to be applied to NIR FR with small training datasets. In the proposed approach, parameters of deep CNN models for RGB FR are utilized as initial parameters to train deep CNN models for NIR FR. The proposed approach has two main advantages: 1) High NIR FR performances can be achieved with very small public training datasets. 2) We can easily secure good generalization for NIR FR in various environments. Our fine-tuning approach achieved a validation rate of 99.70% with the PolyU-NIRFD database. In addition, we constructed private face databases with Intel® RealSense™ SR300. On the VF_NIR database, which is one of the private databases, we achieved a validation rate of 94.47%.
Pre-training of Speaker Embeddings for Low-latency Speaker Change Detection in Broadcast News

In this work, we investigate pre-training of neural network based speaker embeddings for low-latency speaker change detection. Our proposed system takes two speech segments, generates embeddings using shared Siamese layers and then classifies the concatenated embeddings depending on whether they are spoken by the same speaker. We investigate gender classification, contrastive loss and triplet loss based pre-training of the embedding layers and also joint training of the embedding layers along with a same/different classifier. Training is performed on 2-second single speaker segments based on ground truth speaker segmentation of broadcast news data. However, during test, we use the detection system in a practical low-latency setting for annotating automatic closed captions. In contrast to training, test pairs are now created around automatic speech recognition (ASR) based segmentation boundaries. The ASR segments are often shorter than 2 seconds causing duration mismatch during testing. In our experiments, although the baseline i-vector based classifier performs well, the proposed triplet loss based pre-training followed by joint training provides 7-50% relative F-measure improvement in matched and mismatched conditions. In addition, the degradation in performance is less severe for network based embeddings as compared to using i-vectors in the variable duration test conditions.
Real-time Object Detection via Pruning and a Concatenated Multi-feature Assisted Region Proposal Network

Object detection is an important research area in the field of computer vision. Its purpose is to find all objects in an image and recognize the class of each object. Since the development of deep learning, an increasing number of studies have applied deep learning in object detection and have achieved successful results. For object detection, there are two types of network architectures: one-stage and two-stage. This study is based on the widely-used two-stage architecture, called Faster R-CNN, and our goal is to improve the inference time to achieve real-time speed without losing accuracy.First, we use pruning to reduce the number of parameters and the amount of computation, which is expected to reduce accuracy as a result. Therefore, we propose a multi-feature assisted region proposal network composed of assisted multi-feature concatenation and a reduced region proposal network to improve accuracy. Assisted multi-feature concatenation combines feature maps from different convolutional layers as inputs for a reduced region proposal network. With our proposed method, the network can find regions of interest (ROIs) more accurately. Thus, it compensates for loss of accuracy due to pruning. Finally, we use ZF-Net and VGG16 as backbones, and test the network on the PASCAL VOC 2007 dataset.
Tracking a Cluster of Space Debris in Low Orbit by Filtering on Lie Groups

This paper addresses the problem of tracking a cluster of space debris sufficiently close to each other to be considered as a single ex-tended object. State-of-the-art random-matrix methods estimate the kinematics of the object centroid by assuming that its shape is elliptic and that the observations are randomly distributed within this ellipsoid. However, space debris, whose motion is driven by the gravitational force, spread out into a "banana"-like-shaped cluster. In this paper, we propose a novel Lie-group based parameterization to intrinsically capture the "banana"-like shape. More precisely, we first formulate the centroid tracking problem as filtering on Lie groups. Then, we derive an iterated extended Kalman filter on Lie groups to perform the estimation.
Flexible non-greedy discriminant subspace feature extraction

Recently, L1-norm-based non-greedy linear discriminant analysis (NLDA-L1) for feature extraction has been shown to be effective for dimensionality reduction, which obtains projection vectors by a non-greedy algorithm. However, it usually acquires unsatisfactory performances due to the utilization of L1-norm distance measurement. Therefore, in this brief paper, we propose a flexible non-greedy discriminant subspace feature extraction method, which is an extension of NLDA-L1 by maximizing the ratio of Lp-norm inter-class dispersion to intra-class dispersion. Besides, we put forward a powerful iterative algorithm to solve the resulted objective function and also conduct theoretical analysis on the algorithm. Finally, experimental results on image databases show the effectiveness of our method.
A Cultural Algorithm for Determining Similarity Values Between Users in Recommender Systems

Recommendation systems for online marketing often rely on users’ ratings to evaluate the similarity between users in Collaborative Filtering (CF) recommender systems. This paper applies knowledge-based evolutionary optimization algorithms called Cultural Algorithms (CA) to evaluate the similarity between users. To deal with the sparsity of data, we combine CF with a trust network between users. The trust network is then clustered using Singular Value Decomposition (SVD) which helps to discover the top neighbors’ trust value. By incorporating trust relationships with CF, we predict the rating by each user on a given item. This study uses the Epinions dataset in order to train and test the accuracy of the results of the approach. The results are then compared against those produced by Genetic Algorithms (GA), Cosine, and Pearson Correlation Coefficient (PCC) methods. The comparison of the results suggests that the proposed algorithm outperforms the other similarity functions.
Particle swarm optimization–Markov Chain Monte Carlo for accurate visual tracking with adaptive template update

A novel tracking method is proposed, which infers a target state and appearance template simultaneously. With this simultaneous inference, the method accurately estimates the target state and robustly updates the target template. The joint inference is performed by using the proposed particle swarm optimization–Markov chain Monte Carlo (PSO–MCMC) sampling method. PSO–MCMC is a combination of the particle swarm optimization (PSO) and Markov chain Monte Carlo sampling (MCMC), in which the PSO evolutionary algorithm and MCMC aim to find the target state and appearance template, respectively. The PSO can handle multi-modality in the target state and is therefore superior to a standard particle filter. Thus, PSO–MCMC achieves better performance in terms of accuracy when compared to the recently proposed particle MCMC. Experimental results demonstrate that the proposed tracker adaptively updates the target template and outperforms state-of-the-art tracking methods on a benchmark dataset.
Merging Intelligent API Responses Using a Proportional Representation Approach

Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs varies over time, with low reliability in some cases when compared against each other. Merging multiple unreliable API responses from multiple vendors may increase the reliability of the overall response, and thus the reliability of the intelligent end-product. We introduce a novel methodology – inspired by the proportional representation used in electoral systems – to merge outputs of different intelligent computer vision APIs provided by multiple vendors. Experiments show that our method outperforms both naive merge methods and traditional proportional representation methods by 0.015 F-measure.
HCI for Participatory Futuring in Sustainable Communities

Current food consumption patterns are unsustainable. The food system is globalized and dominated by a few large organisations, which dis-empowers people to make changes to it. However, grassroots communities are important in engendering positive change from the bottom up. Long-term thinking is a key to empowering these communities in transitioning towards sustainable food systems. This research is concerned with practices of "futuring" in grassroots communities and how HCI can facilitate openness, participation, and coordination in constructing visions of the future, and in reconciling these with the everyday practices of the communities.
Efficient fuzzy identity-based signature from lattices for identities in a small (or large) universe

Fuzzy identity-based signature (FIBS) is exactly like a traditional identity-based signature (IBS) except that a signature issued under an identity id can be verified under any identity id′ that is “close enough” to id. This property allows FIBS having an efficient application in biometric authentication and only four schemes on lattices exist, among which three constructions are existentially unforgetable against adaptively chosen identity and chosen message attacks (EU-aID-CMA) in the random oracle model, the only exception proved to be strongly unforgetable against selectively chosen identity and chosen message attacks (SU-sID-CMA) is constructed in the standard model. In this work, we first propose a new FIBS from the hardness of lattice problems for identities living in a small universe, i.e., {0, 1}ℓ, this new construction is proved to be SU-sID-CMA in the standard model. Furthermore, a new FIBS scheme for identities living in a large universe, i.e., (Zqn)ℓ, is proposed, so that it can capture more expressive attributes and this construction is proved to be EU-aID-CMA in the random oracle model. In particular, compared with the existing lattice FIBS schemes, both new constructions enjoy a smaller communication cost, and the faster signing and verifying operations, thus, they are more practical.
Quality Education and Digitalization of the Economy

Education significantly affects the socio - economic sector of the national economy development. Currently, transformations and changes occurring in numerous spheres of human activity also imply the corresponding requirements for education quality and knowledge obtained while training specialists in various spheres of human activity. The digitalization of the economy makes both great demands and can provide great opportunities for the education system and educational organizations in respect of implementation of research competencies, intensive improvement of education quality, active strengthening of their positions and authority in the educational services markets. For effective implementation of digitalization of the economy and improving the quality of educational organizations activities in the innovation process, it is proposed to use a tool such as a three-level role-based management model - the “digital platform of an educational organization”; “digital competence centers”; “project working groups on improvement of education quality”, built on the principles of ISO 9001: 2015 [1], and allowing constructively consolidate special competences while comprehensively solving the tasks of managing an educational organization and improving quality of education.
Cybersecurity Behaviour: A Conceptual Taxonomy

User cybersecurity behaviour is a concern for organisations as well as home users. This is because cyber-criminals have made a shift from targeting security systems to targeting the users of the systems. As a result, an increasing number of studies have been conducted in efforts to understand user cybersecurity behaviour. The advantage in understanding user behaviour is that researchers and security practitioners can apply this knowledge and begin to change behaviour to benefit cybersecurity. Different studies have categorised similar cybersecurity behaviours, however the naming conventions differ across studies. This brings out the first contribution of the paper, unified terminology for the cybersecurity behaviour. Secondly, most studies were conducted in an organisational setting. User behaviour in other environments is yet to be identified and categorised. The second contribution of this study is the identification and categorisation of home user cybersecurity behaviour. The identification and classification of more cybersecurity behaviour is aimed to have a positive impact in the creation of strategic interventions to change and maintain good cybersecurity behaviour.
Common language effect size for correlations

The Pearson correlation coefficient can be translated to a common language effect size, which shows the probability of obtaining a certain value on one variable, given the value on the other variable. This common language effect size makes the size of a correlation coefficient understandable to laypeople. Three examples are provided to demonstrate the application of the common language effect size in interpreting Pearson correlation coefficients and multiple correlation coefficients.