Reviewerly/exHarmony huggingface dataset:
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification

The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks. However, the update of parameters in GCNs is only from labeled nodes, lacking the utilization of unlabeled data. In this paper, we apply Virtual Adversarial Training (VAT), an adversarial regularization method based on both labeled and unlabeled data, on the supervised loss of GCN to enhance its generalization performance. By imposing virtually adversarial smoothness on the posterior distribution in semi-supervised learning, VAT yields an improvement on the performance of GCNs. In addition, due to the difference of property in features, we perturb virtual adversarial perturbations on sparse and dense features, resulting in GCN Sparse VAT (GCNSVAT) and GCN Dense VAT (GCNDVAT) algorithms, respectively. Extensive experiments verify the effectiveness of our two methods across different training sizes. Our work paves the way towards better understanding the direction of improvement on GCNs in the future.
Knowledge Base Reasoning with Convolutional-based Recurrent Neural Networks

Recurrent neural network(RNN) has achieved remarkable performances in complex reasoning on knowledge bases, which usually takes as inputs vector embeddings of relations along a path between an entity pair. However, it is insufficient to extract local correlations of a path due to RNN is better at capturing global sequential information of a path. In this paper, we take full advantages of convolutional neural network that can effectively extract local features, and propose a convolutional-based RNN architecture denoted as C-RNN to perform reasoning. C-RNN first utilizes CNN to extract local high-level correlation features of a path, and then feeds the correlation features into recurrent neural network to model the path representation. Our C-RNN architecture is adaptable to obtain not only local features but also global sequential features of a path. Based on C-RNN architecture, we devise two models, the unidirectional C-RNN and bidirectional C-RNN. We empirically evaluate them on a large-scale FreeBase+ClueWeb prediction task. Experimental results show that C-RNN models achieve state-of-the-art predictive performance.
Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

With the explosive growth of information data in today's society, the continuous accumulation and increase of data in recent years make it difficult to extract useful information from it, so data mining comes into being.Association rule mining is an important part of data mining technology.Association rule mining is the discovery of frequent item sets in a large amount of data and the mining of strong association relations between them.Traditional association rule algorithms need to set minimum support and minimum confidence in advance.However, these two values are largely influenced by human subjectivity.Many scholars use average and weight to set these two values, but the effect is still not very good.In order to solve this problem, this paper proposed an improved algorithm of association rules -PSOFP growth algorithm, this algorithm is introduced into intelligent algorithm, particle swarm optimization algorithm, it can find the global optimal solution, we use this fact to find the optimal support, then using FP -growth algorithm for mining association rules, and finally put forward by information entropy to measure effectiveness in association rules mining, and the improved algorithm was applied to the social security event correlation analysis, the improved algorithm proved to our expectations.
Floating-Point Multiplication Timing Attack on Deep Neural Network

Deep neural network (DNN) is being adopted in many security-critical and privacy-sensitive smart Internet of Things areas, such as smart city, smart home and intelligent transportation. Therefore, the data privacy of these DNN systems is of great concern. In this paper, we present a new timing side-channel attack, called FPMT attack, to recover the input images of a DNN implemented on microcontrollers. The proposed approach is the first to attack a DNN by exploiting the running time of floating-point multiplications. A passive attacker can obtain the running time from the power consumption trace and then use the time to infer the pixel values without knowing the detailed parameters of the network. Our results show that the FPMT attack can achieve 96.20% recognition accuracy for the MNIST dataset. When the inputs are handwritten digits or letters, attackers can even know what you write with the recovered images. This work puts forward a new direction of attacks on DNN and can be extended to more scenarios.
NEC-TT System for Mixed-Bandwidth and Multi-Domain Speaker Recognition

This paper describes the NEC-TT speaker recognition system designed for the 2018 Speaker Recognition Evaluation (SRE’18) benchmarking. The NEC-TT submission was among the best-performing systems in this latest edition of SRE organized by the National Institute of Standards and Technology (NIST). It comprises multiple sub-systems based on a deep speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. Speaker embeddings are continuous-valued vector representations that allow easy comparison between speaker voices with simple geometric operations. The effectiveness of deep speaker embeddings relies on the quantity and diversity of the training data. To this end, we hinge on data augmentation and mixed-bandwidth training strategies to increase the number of training examples and speakers. By doing so, we not only increase the quantity of the training data but also expand the output softmax layer with a larger number of speaker classes. From a system design perspective, we adopted a two-stage pipeline consisting of a general multi-domain speaker embedding front-end followed by a domain-specific PLDA back-end. This has a significant benefit in commercial deployment since the same speaker embedding front-end could be used with multiple domain-adapted PLDA back-ends to cater to every specific deployment. This paper provides a detailed description and analysis of the design methodology, data augmentation, bandwidth extension, multi-head attention, PLDA adaptation, and other components that have contributed to good performance in NEC-TT’s SRE’18 results.
Neural Networks with Multidimensional Cross-Entropy Loss Functions

Deep neural networks have emerged as an effective machine learning tool successfully applied for many tasks, such as misinformation detection, natural language processing, image recognition, machine translation, etc. Neural networks are often applied to binary or multi-class classification problems. In these settings, cross-entropy is used as a loss function for neural network training. In this short note, we propose an extension of the concept of cross-entropy, referred to as multidimensional cross-entropy, and its application as a loss function for classification using neural networks. The presented computational experiments on a benchmark dataset suggest that the proposed approaches may have a potential for increasing the classification accuracy of neural network based algorithms.
On multi-resident activity recognition in ambient smart-homes

Increasing attention to the research on activity monitoring in smart homes has motivated the employment of ambient intelligence to reduce the deployment cost and solve the privacy issue. Several approaches have been proposed for multi-resident activity recognition, however, there still lacks a comprehensive benchmark for future research and practical selection of models. In this paper, we study different methods for multi-resident activity recognition and evaluate them on the same sets of data. In particular, we explore the effectiveness and efficiency of temporal learning algorithms using sequential data and non-temporal learning algorithms using temporally-manipulated features. In the experiments we compare and analyse the results of the studied methods using datasets from three smart homes.
Spatiotemporal Modeling and Label Distribution Learning for Video Summarization

For a video which content does not follow specific production rules, or without professional editing, at least two problems should be solved to generate a good video summary. First, the summarization system should jointly model visual content in the spatial domain and visual dynamics in the temporal domain. Second, the system should consider the inconsistency between users, i.e., different users may annotate the same video segment with different importance scores. In this paper, we present a video summarization system that models spatiotemporal information of video segments, and predicts the distribution of importance scores for each segment. Based on the estimated importance scores, video summaries are generated by picking the ones with higher scores. We especially demonstrate the effectiveness of label distribution learning based on two video benchmarks.
Robust Embedded Deep K-means Clustering

Deep neural network clustering is superior to the conventional clustering methods due to deep feature extraction and nonlinear dimensionality reduction. Nevertheless, deep neural network leads to a rough representation regarding the inherent relationship of the data points. Therefore, it is still difficult for deep neural network to exploit the effective structure for direct clustering. To address this issue, we propose a robust embedded deep K-means clustering (RED-KC) method. The proposed RED-KC approach utilizes the δ-norm metric to constrain the feature mapping process of the auto-encoder network, so that data are mapped to a latent feature space, which is more conducive to the robust clustering. Compared to the existing auto-encoder networks with the fixed prior, the proposed RED-KC is adaptive during the process of feature mapping. More importantly, the proposed RED-KC embeds the clustering process with the auto-encoder network, such that deep feature extraction and clustering can be performed simultaneously. Accordingly, a direct and efficient clustering could be obtained within only one step to avoid the inconvenience of multiple separate stages, namely, losing pivotal information and correlation. Consequently, extensive experiments are provided to validate the effectiveness of the proposed approach.
Blockchain Based Identity Verification Model

Blockchain is peer-to-peer decentralized system that provides security, reliability, authenticity, immutability and transparency. It is an expandable list of records called blocks which are linked using cryptography. Blockchain by design, is resilient to modifications, it consists of an open distributed ledger that records all the transactions made between two parties which cannot be forged. This paper provides a unique model for document verification with use case of the government. Currently all issuing authorities are separate cells providing identity documents to a citizen. Due to this cellular nature document verification is a tedious and long process. The problem of falsifying documents is another issue within government bodies. We discuss the use of Blockchain to improve efficiency and security. The purpose of this project is to develop a private permissioned blockchain network where an individuals official documents can be shared by government bodies, organizations and educational institutions. Using the Blockchain technology, our platform achieves a decentralized system to share authenticated government documents between government bodies and private organizations without the need of a certain level of human intervention. With the concluding facts of this paper, it is observed that the process of document verification and issuing process's efficiency is drastically improved with our blockchain model. Along with this we reap benefits of security, reliability and transparency.
Effective Heuristics for Multi-Robot Path Planning in Warehouse Environments

In this preliminary study, we propose a new centralized decoupled algorithm for solving one-shot and dynamic optimal multi-robot path planning problems in a grid-based setting mainly targeting warehouse like environments. In particular, we exploit two novel and effective heuristics: path diversification and optimal sub-problem solution databases. Preliminary evaluation efforts demonstrate that our method achieves promising scalability and good solution optimality.
Visual abstraction and exploration of large-scale geographical social media data

A great deal of text and geographical information is provided in the geo-tagged social media data, which offers unprecedented opportunities to get insights into the social behaviors across different local areas. With the increasing size of geo-tagged social media data, a large number of visual mapping elements overlap with each other, which makes it difficult to visually capture topics of interest as well as their spatial distribution. In this paper, we propose a visual abstraction framework for the exploration of large scale geo-tagged social media data. Probabilistic topic modeling is firstly utilized to summarize the semantics of texts and extract a set of topic features of interest. Then, a multi-objective sampling model is designed to generate a subset of original dataset, which will not only reduce the visual clutter of large-scale social media data visualization, but also retain the ordering of topic features of interest as well as the geographical distribution of original social media datasets. A rich set of visual designs such as word cloud, text stream and heat map are integrated into the visual abstraction framework, enabling users to evaluate the sampled results from different perspectives including semantic topics, temporal changes and spatial distribution. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in simplifying the geographical visualization of large scale geo-tagged social media data and exploring the social behaviors across different local areas.
Static Power Side-Channel Analysis—An Investigation of Measurement Factors

The static power consumption of modern CMOS devices has become a substantial concern in the context of the side-channel security of cryptographic hardware. Its continuous growth in nanometer-scaled technologies is not only inconvenient for effective low-power designs but does also create a new target for power analysis adversaries. Additionally, it has to be noted that several of the numerous sources of static power dissipation in CMOS circuits exhibit an exponential dependence on environmental factors which a classical power analysis adversary is in control of. These factors include the operating conditions' temperature and supply voltage. Furthermore, in the case of clock control, the measurement interval can be adjusted arbitrarily. Our experiments on a 150-nm CMOS ASIC reveal that with respect to the signal-to-noise ratio in static power side-channel analyses, stretching the measurement interval decreases the noise exponentially and even more importantly that raising the working temperature increases the signal exponentially. Control over the supply voltage has a far smaller, but still noticeable, positive impact as well. In summary, a static power analysis adversary can physically force a device to leak more information by controlling its operating environment and furthermore measure these leakages with arbitrary precision by modifying the interval length.
Missing multi-label learning with non-equilibrium based on classification margin

Multi-labels are more suitable for the ambiguity of the real world. However, missing labels are common in multi-label learning datasets; this results in unbalanced labeling and label diversity, which directly affect the performance of multi-label learning. Therefore, the classification and modeling of imbalanced data in missing multi-label learning are problems that need to be urgently solved Current methods mostly focus on combining sampling techniques with cost-sensitive learning and incorporating label correlation to improve the performance of the classifier, but generally they do not consider label loss caused by label cost. In fact, labeling unknown instances is often affected by the threshold of the discriminant function, especially for the label types near the threshold. Based on our previous research, we believe that information such as data distribution density and label density can be integrated into the label correlation, and that the classification margin can be expanded to effectively solve the labeling quality of labels near the threshold. Therefore, in this paper we propose a non-equilibrium multi-label learning algorithm based on the classification margin and aimed at completing the missing labels. First, the classification margin is proposed, and the label space is expanded by the label density. Then, the information entropy is used to measure the correlation between labels, and the label confidence matrix is constructed. The label confidence matrix is then unbalanced using the positive and negative label density, and the non-equilibrium label confidence matrix is used for label completion to obtain an informative label completion matrix. Finally, the kernel extreme learning machine and the label completion matrix are used for linear prediction. The experimental results show that the proposed algorithm has some advantages over other multi-label learning algorithms.
Multi-objective Particle Swarm Optimization: Theory, Literature Review, and Application in Feature Selection for Medical Diagnosis

Disease prediction has a vital role in health informatics. The early detection of diseases assists in taking preventive steps and more functional treatment. Incorporating intelligent classification models and data analysis methods has intrinsic impact on converting such trivial, row data into worthy useful knowledge. Due to the explosion in computational and medical technologies, we observe an explosion in the volume of health- and medical-related data. Medical datasets are high-dimensional datasets, which make the process of building a classification model that searches for optimal set of features a hard, yet more challenging task. Hence, this chapter introduces a fundamental class of optimization known as the multi-objective evolutionary algorithms (MOEA) for optimization, which handles the feature selection for classification in medical applications. The chapter presents an introduction to multi-objective optimization and their related mathematical models. Furthermore, this chapter investigates the utilization of a well-regarded multi-objective particle swarm optimization (MOPSO) as wrapper-based feature selection method, in order to detect the presence or absence of different types of diseases. Therefore, the performance of MOPSO and its behavior are examined by comparing it with other well-regarded MOEAs on several medical datasets. The experimental results on most of the medical datasets show that the MOPSO algorithm outperforms other algorithms such as non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition (MOEA/D) in terms of classification accuracy and minimum number of features.
Investigation of the Moderating Effect of Culture on Users’ Susceptibility to Persuasive Features in Fitness Applications

Persuasive technologies have been identified as a potential motivational tool to tackle the rising problem of physical inactivity worldwide, with research showing they are more likely to be successful if tailored to the target audience. However, in the physical activity domain, there is limited research on how culture moderates users’ susceptibility to the various persuasive features employed in mobile health applications aimed to motivate behavior change. To bridge this gap, we conducted an empirical study among 256 participants from collectivist (n = 67) and individualist (n = 189) cultures to determine their culture-specific persuasion profiles with respect to six persuasive features commonly employed in fitness applications on the market. The persuasive features include two personal features (goal-setting/self-monitoring and reward) and four social features (competition, cooperation, social learning and social comparison). We based our study on the rating of storyboards (on which each of the six persuasive features is illustrated) and the ranking of the six persuasive features in terms of perceived persuasiveness. The results of our analysis showed that users from individualist and collectivist cultures significantly differ in their persuasion profiles. Based on our rating measure, collectivist users are more likely to be susceptible to all six persuasive features (personal and social) than individualist users, who are only likely to be susceptible to personal features. However, based on our ranking measure, individualist users are more likely to be susceptible to personal features (goal-setting/self-monitoring and reward) than collectivist users. In contrast, collectivist users are more likely to be susceptible to social features (cooperation and social learning) than individualist users. Based on these findings, we provide culture-specific persuasive technology design guidelines. Our study is the first to uncover the moderating effect of culture on users’ susceptibility to commonly employed persuasive features in fitness applications.
Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution

We explore the question of how the resolution of the input image ("input resolution") affects the performance of a neural network when compared to the resolution of the hidden layers ("internal resolution"). Adjusting these characteristics is frequently used as a hyperparameter providing a trade-off between model performance and accuracy. An intuitive interpretation is that the reduced information content in the low-resolution input causes decay in the accuracy. In this paper, we show that up to a point, the input resolution alone plays little role in the network performance, and it is the internal resolution that is the critical driver of model quality. We then build on these insights to develop novel neural network architectures that we call Isometric Neural Networks. These models maintain a fixed internal resolution throughout their entire depth. We demonstrate that they lead to high accuracy models with low activation footprint and parameter count.