Reviewerly/exHarmony huggingface dataset:
Lifelong Metric Learning

The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a lifelong learning problem to mimic “human learning,” i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating the previous experiences. Therefore, we propose a new metric learning framework: lifelong metric learning (LML), which only utilizes the data of the new task to train the metric model while preserving the original capabilities. More specifically, the proposed LML maintains a common subspace for all learned metrics, named lifelong dictionary, transfers knowledge from the common subspace to learn each new metric learning task with task-specific idiosyncrasy, and redefines the common subspace over time to maximize performance across all metric tasks. For model optimization, we apply online passive aggressive optimization algorithm to achieve lifelong metric task learning, where the lifelong dictionary and task-specific partition are optimized alternatively and consecutively. Finally, we evaluate our approach by analyzing several multitask metric learning datasets. Extensive experimental results demonstrate effectiveness and efficiency of the proposed framework.
Bitcoin: A Natural Oligopoly

Although Bitcoin was intended to be a decentralized digital currency, in practice, mining power is quite concentrated. This fact is a persistent source of concern for the Bitcoin community.We provide an explanation using a simple model to capture miners' incentives to invest in equipment. In our model, n miners compete for a prize of fixed size. Each miner chooses an investment q_i, incurring cost c_i q_i, and then receives reward (q_i^alpha)/(sum_j q_j^alpha), for some alpha >= 1. When c_i = c_j for all i,j, and alpha = 1, there is a unique equilibrium where all miners invest equally. However, we prove that under seemingly mild deviations from this model, equilibrium outcomes become drastically more centralized. In particular,- When costs are asymmetric, if miner i chooses to invest, then miner j has market share at least 1-c_j/c_i. That is, if miner j has costs that are (e.g.) 20% lower than those of miner i, then miner j must control at least 20% of the total mining power.- In the presence of economies of scale (alpha > 1), every market participant has a market share of at least 1-1/(alpha), implying that the market features at most alpha/(alpha - 1) miners in total.We discuss the implications of our results for the future design of cryptocurrencies. In particular, our work further motivates the study of protocols that minimize orphaned blocks, proof-of-stake protocols, and incentive compatible protocols.
Solving Empirical Risk Minimization in the Current Matrix Multiplication Time

Many convex problems in machine learning and computer science share the same form: \begin{align*} \min_{x} \sum_{i} f_i( A_i x + b_i), \end{align*} where $f_i$ are convex functions on $\mathbb{R}^{n_i}$ with constant $n_i$, $A_i \in \mathbb{R}^{n_i \times d}$, $b_i \in \mathbb{R}^{n_i}$ and $\sum_i n_i = n$. This problem generalizes linear programming and includes many problems in empirical risk minimization. In this paper, we give an algorithm that runs in time \begin{align*} O^* ( ( n^{\omega} + n^{2.5 - \alpha/2} + n^{2+ 1/6} ) \log (n / \delta) ) \end{align*} where $\omega$ is the exponent of matrix multiplication, $\alpha$ is the dual exponent of matrix multiplication, and $\delta$ is the relative accuracy. Note that the runtime has only a log dependence on the condition numbers or other data dependent parameters and these are captured in $\delta$. For the current bound $\omega \sim 2.38$ [Vassilevska Williams'12, Le Gall'14] and $\alpha \sim 0.31$ [Le Gall, Urrutia'18], our runtime $O^* ( n^{\omega} \log (n / \delta))$ matches the current best for solving a dense least squares regression problem, a special case of the problem we consider. Very recently, [Alman'18] proved that all the current known techniques can not give a better $\omega$ below $2.168$ which is larger than our $2+1/6$. Our result generalizes the very recent result of solving linear programs in the current matrix multiplication time [Cohen, Lee, Song'19] to a more broad class of problems. Our algorithm proposes two concepts which are different from [Cohen, Lee, Song'19] : $\bullet$ We give a robust deterministic central path method, whereas the previous one is a stochastic central path which updates weights by a random sparse vector. $\bullet$ We propose an efficient data-structure to maintain the central path of interior point methods even when the weights update vector is dense.
Efficient traceable ring signature scheme without pairings

<p style='text-indent:20px;'>Although currently several traceable (or linkable) ring signature schemes have been proposed, most of them are constructed on pairings. In this paper, we present an efficient traceable ring signature (TRS) scheme without pairings, which is based on the modified EDL signature (first proposed by D.Chaum et al. in Crypto 92). Compared with other ring signature schemes, the proposed scheme does not employ pairing computation and has some computational advantages, whose security can be reduced to the computational Diffie-Hellman (CDH) and decisional Diffie-Hellman (DDH) assumptions in the random oracle model. Also, the proposed scheme is similar to certificateless signature scheme, where user and key generating center make interaction to generate ring key. We give a formal security model for ring signature and prove that the proposed scheme has the properties of traceability and anonymity.
The Reality of Reality-Based Interaction

Frameworks such as Direct Manipulation or Instrumental Interaction have been an important force in HCI research. Evaluating the impact of frameworks can identify whether and how a framework was used, how it has evolved, and what trends have developed over time. However, studying the impact of such theoretical contributions requires consideration of various perspectives and level of impact. As a case study for investigating the impact of theoretical work in HCI, we present our evaluation of the impact of the Reality Based Interaction (RBI) framework, introduced by the authors in 2008. We provide our findings about the impact of the framework both on contemporary research, through content-based citation analysis, and in HCI education, through a survey we conducted on emerging interaction frameworks. The article contributes a comprehensive methodology for evaluating the impact of frameworks through our twofold approach: content-based citation analysis, including the design of a new citation typology; and a survey on the use of frameworks in education using a taxonomy of learning goals. We also consider the role of frameworks in HCI as well as the future of the RBI framework.
Learning the Dyck Language with Attention-based Seq2Seq Models

The generalized Dyck language has been used to analyze the ability of Recurrent Neural Networks (RNNs) to learn context-free grammars (CFGs).Recent studies draw conflicting conclusions on their performance, especially regarding the generalizability of the models with respect to the depth of recursion.In this paper, we revisit several common models and experimental settings, discuss the potential problems of the tasks and analyses.Furthermore, we explore the use of attention mechanisms within the seq2seq framework to learn the Dyck language, which could compensate for the limited encoding ability of RNNs.Our findings reveal that attention mechanisms still cannot truly generalize over the recursion depth, although they perform much better than other models on the closing bracket tagging task.Moreover, this also suggests that this commonly used task is not sufficient to test a model's understanding of CFGs.
Do Neural Language Representations Learn Physical Commonsense

Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language. In addition to the properties of objects (e.g., boats require fuel) and their affordances, i.e., the actions that are applicable to them (e.g., boats can be driven), we can also reason about if-then inferences between what properties of objects imply the kind of actions that are applicable to them (e.g., that if we can drive something then it likely requires fuel). In this paper, we investigate the extent to which state-of-the-art neural language representations, trained on a vast amount of natural language text, demonstrate physical commonsense reasoning. While recent advancements of neural language models have demonstrated strong performance on various types of natural language inference tasks, our study based on a dataset of over 200k newly collected annotations suggests that neural language representations still only learn associations that are explicitly written down.
Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based Approach

Spiking neural networks have been widely used for supervised pattern recognition exploring the underlying spatio-temporal correlation. Meanwhile, spatio-temporal correlation manifests significantly between different components in a smart grid making the spiking neural network a desirable candidate for false data injection attack detection. In this paper, we develop a spiking-neural-network-based technique for dynamic cyber-attack detection in a smart grid. This is achieved through judiciously integrating spiking neurons with a special recurrent neural network called the delayed feedback reservoir computing. The inter-spike interval encoding is also explored in the precise-spike-driven synaptic plasticity based training process. The simulation results suggest that the introduced method outperforms multi-layer perceptrons and can achieve a significantly better performance compared to the state-of-the-art techniques. Furthermore, our analysis indicates that the delay value in the delayed feedback reservoir will have a substantial impact on the overall system performance.
Digital and media literacy in pre-service teachereducation

In the age of digitalization, Digital and Media Literacy (DML) has gained increasing attention in European compulsory education, blending insights and experiences from the media education and digital literacy domains. Teacher education, starting from pre-service education, is central for the actual integration of DML education in classroom practice. This article discusses the case study of a two-credit introductory course to DML education for pre-service pre-primary and primary school teachers in Switzerland. The course, partially co-designed with its participants, intentionally explored many topics (as opposed to the in-depth analysis of a few) and focused on hands-on experimentation and reflection. The data collected with a pre/post survey and follow-up interviews offer insights on the evolution of pre-service teachers’ approach to DML, on their perceived role as teachers in this domain, on self-efficacy, and on potential enablers and obstacles to implementing DML activities in class. The case study suggests that, despite the limited space in the curriculum and resources available, even a short course can make a difference and enable teachers to integrate DML in their profession.
Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification

Logo classification has gained increasing attention for its various applications, such as copyright infringement detection, product recommendation and contextual advertising. Compared with other types of object images, the real-world logo images have larger variety in logo appearance and more complexity in their background. Therefore, recognizing the logo from images is challenging. To support efforts towards scalable logo classification task, we have curated a dataset, Logo-2K+, a new large-scale publicly available real-world logo dataset with 2,341 categories and 167,140 images. Compared with existing popular logo datasets, such as FlickrLogos-32 and LOGO-Net, Logo-2K+ has more comprehensive coverage of logo categories and larger quantity of logo images. Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification. DRNA-Net consists of four sub-networks: the navigator sub-network first selected informative logo-relevant regions guided by the teacher sub-network, which can evaluate its confidence belonging to the ground-truth logo class. The data augmentation sub-network then augments the selected regions via both region cropping and region dropping. Finally, the scrutinizer sub-network fuses features from augmented regions and the whole image for logo classification. Comprehensive experiments on Logo-2K+ and other three existing benchmark datasets demonstrate the effectiveness of proposed method. Logo-2K+ and the proposed strong baseline DRNA-Net are expected to further the development of scalable logo image recognition, and the Logo-2K+ dataset can be found at https://github.com/msn199959/Logo-2k-plus-Dataset.
Analysis of content topics, user engagement and library factors in public library social media based on text mining

Purpose The purpose of this paper is to explore topics of Facebook posts created by public libraries using the bi-term topic model, and examine the relationships between types of topics and user engagement. The authors further investigated the effects of three library factors, namely, staff size, budget and urbanization degrees, on Facebook content and user engagement based on multilevel generalized linear modeling. Design/methodology/approach This study suggested a novel method, a combination of the bi-term topic modeling and MGLM, to enhance the understanding of social media in the context of public libraries. Findings The findings revealed that posts related to community events, awards and photos were likely to receive more likes and shares, whereas posts about summer reading programs received relatively more comments. In addition, the authors found that a larger staff size and the inclusion of multimedia had positive impacts on user engagement. Originality/value This study analyzed the content of public library-generated social media based on text mining. Then, the authors examined the effects of contextual library-level factors on social media practice in public libraries. Based on empirical findings, the study suggested a range of practical implications for effective use of social media in public libraries.
The Relationship between Knowledge Management and Organizational Learning with Academic Staff Readiness for Education 4.0

Purpose: Education 4.0 paves the way for a new form of the university where academicians teach, research and provide services in different ways, which raises the question of the readiness of academicians to face the challenges. This study attempts to explain to which extent the university and its academic staff are ready to face Education 4.0 challenges through the initiatives of knowledge management and organizational learning. Research Methods: Questionnaires were administered to all academic staff in one education university in Malaysia and 218 completed questionnaires were received and analyzed in this study. Data were collected using modified items from two sources to measure knowledge management and organizational learning practices at the university. Items for measuring staff readiness for Education 4.0 were developed based on criteria found in the recent literature. Descriptive and inferential statistics were used to analyze the data. Findings: Results indicated that the academic staff and the university were ready to face the Education 4.0 challenges. The level of organizational learning practiced by the academic staff is high, the same as the knowledge management practiced by the university. Pearson’s correlation analysis showed that both organizational learning and knowledge management practices had significant positive relationships with the readiness of academic staff for Education 4.0. However, multiple regression analysis with stepwise procedures found that only one knowledge management practice and one organizational learning practice were the predictors of the readiness of the academic staff in facing the Education 4.0 challenges. Implications for Research and Practices: This study provides new insight into Education 4.0 through the perspective of knowledge management and organizational learning. Universities need to put more initiatives for knowledge creation, knowledge organization, knowledge storing, knowledge dissemination and knowledge application. Furthermore, academicians also need to put more effort into supporting new learning and ideas, formal learning and external/interface learning.
An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see text]-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.
Hierarchical clustering with optimal transport

Optimal Transport (OT) distances result in a powerful technique to compare the probability distributions. Defining a similarity measure between clusters has been an open problem in Statistics. This paper introduces a hierarchical clustering algorithm using the OT based distance measures and analyzes the performance of the proposed algorithm on standard datasets with respect to the existing and popular hierarchical clustering methods.
Effects and solutions of Cover-Source Mismatch in image steganalysis

The Cover-Source Mismatch (CSM) has been long recognized as a major problem in modern steganography and steganalysis. Indeed, while a vast majority of works in steganography and steganalysis had been tailored to a specific reference database, namely BOSSbase, recent works show that, because of CSM, the results may greatly differ when changing this dataset. Although the CSM has already been the subject of several publications, these prior works investigated only a few elements in a limited setup. The goal of the current paper is to study the effects of the CSM in a more comprehensive manner and then to examine and compare different strategies for mitigating it. It first defines two different parameters, the source difficulty and the source inconsistency, which are involved in the CSM. Then, using different steganographic schemes and feature sets, it aims at providing a systematic study regarding the various factors that can give birth to CSM for image steganalysis. Finally, two practical ways to mitigate the CSM, using training techniques promoting either diversity of different sources or the specificity of one targeted source which is beforehand identified by training a multi-class classifier, are presented and their performances are compared for different training set sizes.
An Intent-guided Collaborative Machine for Session-based Recommendation

Session-based recommendation produces item predictions mainly based on anonymous sessions. Previous studies have leveraged collaborative information from neighbor sessions to boost the recommendation accuracy for a given ongoing session. Previous work often selects the most recent sessions as candidate neighbors, thereby failing to identify the most related neighbors to obtain an effective neighbor representation. In addition, few existing methods simultaneously consider the sequential signal and the most recent interest in an ongoing session. In this paper, we introduce an Intent-guided Collaborative Machine for Session-based Recommendation (ICM-SR). ICM-SR encodes an ongoing session by leveraging the prior sequential items and the last item to generate an accurate session representation, which is then used to produce initial item predictions as intent. After that, we design an intent-guided neighbor detector to locate the correct neighbor sessions. Finally, the representations of the current session and the neighbor sessions are adaptively combined by a gated fusion layer to produce the final item recommendations. Experiments conducted on two public benchmark datasets show that ICM-SR achieves a significant improvement in terms of Recall and MRR over the state-of-the-art baselines.
Some recent advances in digital volume correlation

Over the past years, we have been developing new algorithms and approaches for robust, efficient and accurate internal displacement and strain field measurements using digital volume correlation (DVC). This paper will summarize our recent work on the following four major aspects: (a) advanced three-dimensional inverse-compositional Gauss-Newton (3D IC-GN) algorithm for subvoxel registration with enhanced accuracy and efficiency; (b) self-adaptive DVC algorithm with optimal calculation parameters; (c) practical and effective strategies for DVC analyses of high-resolution volumetric images and deformed volumetric images encoded with large deformation; (d) quantitative analysis and correction of thermal errors in DVC measurements due to self-heating effect of X-ray CT scanners. We hope that this paper can guide the readers to know the state-of-art DVC method.
Embodiment, Presence, and Their Intersections

Subjective experience of human control over remote, artificial, or virtual limbs has traditionally been investigated from two separate angles: presence research originates from teleoperation, aiming to capture to what extent the user feels like actually being in the remote or virtual environment. Embodiment captures to what extent a virtual or artificial limb is perceived as one’s own limb. Unfortunately, the two research fields have not interacted much. This survey intends to provide a coherent overview of the literature at the intersection of these two fields to further that interaction. Two rounds of systematic research in topic-related data bases resulted in 414 related articles, 14 of which satisfy the deliberately strict inclusion criteria: 2 theoretical frameworks that highlighted intersections and 12 experimental studies that evaluated subjective measures for both concepts. Considering the surrounding literature as well, theoretical and experimental potential of embodiment and presence are discussed and suggestions to apply them in teleoperation research are derived. While increased publication activity is observed between 2016 and 2018, potentially caused by affordable virtual reality technologies, various open questions remain. To tackle them, human-in-the-loop experiments and three guiding principles for teleoperation system design (mechanical fidelity, spatial bodily awareness, and self-identification) are suggested.