Reviewerly/exHarmony huggingface dataset:
STDP-Based Unsupervised Spike Pattern Learning in a Photonic Spiking Neural Network With VCSELs and VCSOAs

We propose a photonic spiking neural network (SNN) consisting of photonic spiking neurons based on vertical-cavity surface-emitting lasers (VCSELs). The photonic spike timing dependent plasticity (STDP) is implemented in a vertical-cavity semiconductor optical amplifier (VCSOA). A versatile computational model of the photonic SNN is presented based on the rate equation models. Through numerical simulation, a spike pattern learning and recognition task is performed based on the photonic STDP. The results show that the post-synaptic spike timing (PST) is eventually converged iteratively to the first spike timing of the input spike pattern via unsupervised learning. Additionally, the convergence rate of the PST can be accelerated for a photonic SNN with more pre-synaptic neurons. The effects of VCSOA parameters on the convergence performance of the unsupervised spike learning are also considered. To the best of our knowledge, such a versatile computational model of photonic SNN for unsupervised learning and recognition of arbitrary spike pattern has not yet been reported, which would contribute one step forward toward numerical implementation of a large-scale energy-efficient photonic SNN, and hence is interesting for neuromorphic photonic systems and spiking information processing.
Mixed Reality Remote Collaboration Combining 360 Video and 3D Reconstruction

Remote Collaboration using Virtual Reality (VR) and Augmented Reality (AR) has recently become a popular way for people from different places to work together. Local workers can collaborate with remote helpers by sharing 360-degree live video or 3D virtual reconstruction of their surroundings. However, each of these techniques has benefits and drawbacks. In this paper we explore mixing 360 video and 3D reconstruction together for remote collaboration, by preserving benefits of both systems while reducing drawbacks of each. We developed a hybrid prototype and conducted user study to compare benefits and problems of using 360 or 3D alone to clarify the needs for mixing the two, and also to evaluate the prototype system. We found participants performed significantly better on collaborative search tasks in 360 and felt higher social presence, yet 3D also showed potential to complement. Participant feedback collected after trying our hybrid system provided directions for improvement.
SentiDiff: Combining Textual Information and Sentiment Diffusion Patterns for Twitter Sentiment Analysis

Twitter sentiment analysis has become a hot research topic in recent years. Most of existing solutions to Twitter sentiment analysis basically only consider textual information of Twitter messages, and struggle to perform well when facing short and ambiguous Twitter messages. Recent studies show that sentiment diffusion patterns on Twitter have close relationships with sentiment polarities of Twitter messages. Therefore, in this paper, we focus on how to fuse textual information of Twitter messages and sentiment diffusion patterns to obtain better performance of sentiment analysis on Twitter data. To this end, we first analyze sentiment diffusion by investigating a phenomenon called sentiment reversal, and find some interesting properties of sentiment reversals. Then, we consider the inter-relationships between textual information of Twitter messages and sentiment diffusion patterns, and propose an iterative algorithm called SentiDiff to predict sentiment polarities expressed in Twitter messages. To the best of our knowledge, this work is the first to utilize sentiment diffusion patterns to help improve Twitter sentiment analysis. Extensive experiments on real-world dataset demonstrate that compared with state-of-the-art textual information based sentiment analysis algorithms, our proposed algorithm yields PR-AUC improvements between 5.09 and 8.38 percent on Twitter sentiment classification tasks.
Nonlocal Low-Rank Tensor Completion for Visual Data

In this paper, we propose a novel nonlocal patch tensor-based visual data completion algorithm and analyze its potential problems. Our algorithm consists of two steps: the first step is initializing the image with triangulation-based linear interpolation and the second step is grouping similar nonlocal patches as a tensor then applying the proposed tensor completion technique. Specifically, with treating a group of patch matrices as a tensor, we impose the low-rank constraint on the tensor through the recently proposed tensor nuclear norm. Moreover, we observe that after the first interpolation step, the image gets blurred and, thus, the similar patches we have found may not exactly match the reference. We name the problem “Patch Mismatch,” and then in order to avoid the error caused by it, we further decompose the patch tensor into a low-rank tensor and a sparse tensor, which means the accepted horizontal strips in mismatched patches. Furthermore, our theoretical analysis shows that the error caused by Patch Mismatch can be decomposed into two components, one of which can be bounded by a reasonable assumption named local patch similarity, and the other part is lower than that using matrix completion. Extensive experimental results on real-world datasets verify our method's superiority to the state-of-the-art tensor-based image inpainting methods.
Effect of Mobile Augmented Reality on Learning Performance, Motivation, and Math Anxiety in a Math Course

Motivation and math anxiety are crucial in performance and satisfaction, and augmented reality (AR) may be a useful tool in enhancing these factors because it provides users with interesting visual experiences. Since related empirical research is limited in investigating the effects of using free mobile AR apps integrating Keller’s ARCS (attention-relevance-confidence-satisfaction) motivation model on learning motivation, anxiety, and outcomes between students with different levels of anxiety in primary math education, this study investigated whether mobile AR differently affected learning, motivation, and math anxiety between students with high and low anxiety. The results showed that the AR group performed better than the non-AR group, and high-anxiety learners in the AR group outperformed in algebra and geometry. The AR group had higher motivation based on Keller’s ARCS model. The high-anxiety learners had higher confidence and satisfaction and lower anxiety when learning using mobile AR. The AR users were satisfied with ease of use, usefulness, playfulness, and benefit from exploration and hands-on experiences. Moreover, high-anxiety users in the AR group had higher perceptions of exploration, hands-on experiences, and playfulness. This study includes the participants’ experience in adopting mobile AR for their learning and discusses its constraints.
A Secure and Efficient Blockchain-Based Data Trading Approach for Internet of Vehicles

In this paper, we propose a secure and efficient blockchain-based data trading approach for the Internet of Vehicles (IoV). First, we apply consortium blockchain technologies to ensure secure and truthful data trading, and propose a general blockchain-based data trading framework for IoV. Second, to improve the efficiency of data trading and encourage more participants to trade data, we propose an iterative double auction mechanism with the purpose of achieving social welfare maximization, in which pricing rules of buyers and sellers are designed to induce participants to submit bids and to decide the amount of traded data and its price among buyers and sellers. In particular, in our algorithm, the hidden information of individuals can be extracted gradually so that the privacy of participants in data trading can be protected well. Finally, the experimental results show the efficiency of our proposed algorithm. Moreover, the correctness of social welfare maximization, incentive compatibility, individually rationality, and weakly budget balance of our auction mechanism are verified in the experiments.
Performance Analysis of Various Activation Functions in Artificial Neural Networks

The development of Artificial Neural Networks (ANNs) has achieved a lot of fruitful results so far, and we know that activation function is one of the principal factors which will affect the performance of the networks. In this work, the role of many different types of activation functions, as well as their respective advantages and disadvantages and applicable fields are discussed, so people can choose the appropriate activation functions to get the superior performance of ANNs.
Leveraging Shape Completion for 3D Siamese Tracking

Point clouds are challenging to process due to their sparsity, therefore autonomous vehicles rely more on appearance attributes than pure geometric features. However, 3D LIDAR perception can provide crucial information for urban navigation in challenging light or weather conditions. In this paper, we investigate the versatility of Shape Completion for 3D Object Tracking in LIDAR point clouds. We design a Siamese tracker that encodes model and candidate shapes into a compact latent representation. We regularize the encoding by enforcing the latent representation to decode into an object model shape. We observe that 3D object tracking and 3D shape completion complement each other. Learning a more meaningful latent representation shows better discriminatory capabilities, leading to improved tracking performance. We test our method on the KITTI Tracking set using car 3D bounding boxes. Our model reaches a 76.94% Success rate and 81.38% Precision for 3D Object Tracking, with the shape completion regularization leading to an improvement of 3% in both metrics.
Transformative agency in teacher education: Fostering professional digital competence

• Student teachers face complex challenges in their future profession, incl. developing professional digital competence (PDC). • In response, teacher education needs to address the reciprocity of PDC and students' transformative agency. • We demonstrate how a small private online course (SPOC) integrates PDC, university seminars and practice. • Student teachers respond to challenges by transforming them into opportunities for their professional development. • The SPOC fosters PDC development throught transformative digital agency, conceptualised as a new PDC pillar.
A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning

Backdoor attacks against CNNs represent a new threat against deep learning systems, due to the possibility of corrupting the training set so to induce an incorrect behaviour at test time. To avoid that the trainer recognises the presence of the corrupted samples, the corruption of the training set must be as stealthy as possible. Previous works have focused on the stealthiness of the perturbation injected into the training samples, however they all assume that the labels of the corrupted samples are also poisoned. This greatly reduces the stealthiness of the attack, since samples whose content does not agree with the label can be identified by visual inspection of the training set or by running a pre-classification step. In this paper we present a new backdoor attack without label poisoning Since the attack works by corrupting only samples of the target class, it has the additional advantage that it does not need to identify beforehand the class of the samples to be attacked at test time. Results obtained on the MNIST digits recognition task and the traffic signs classification task show that backdoor attacks without label poisoning are indeed possible, thus raising a new alarm regarding the use of deep learning in security-critical applications.
XCLAIM: Trustless, Interoperable, Cryptocurrency-Backed Assets

Building trustless cross-blockchain trading protocols is challenging.Centralized exchanges thus remain the preferred route to execute transfers across blockchains.However, these services require trust and therefore undermine the very nature of the blockchains on which they operate.To overcome this, several decentralized exchanges have recently emerged which offer support for atomic cross-chain swaps (ACCS).ACCS enable the trustless exchange of cryptocurrencies across blockchains, and are the only known mechanism to do so.However, ACCS suffer significant limitations; they are slow, inefficient and costly, meaning that they are rarely used in practice.We present XCLAIM: the first generic framework for achieving trustless and efficient cross-chain exchanges using cryptocurrencybacked assets (CBAs).XCLAIM offers protocols for issuing, transferring, swapping and redeeming CBAs securely in a non-interactive manner on existing blockchains.We instantiate XCLAIM between Bitcoin and Ethereum and evaluate our implementation; it costs less than USD 0.50 to issue an arbitrary amount of Bitcoin-backed tokens on Ethereum.We show XCLAIM is not only faster, but also significantly cheaper than atomic cross-chain swaps.Finally, XCLAIM is compatible with the majority of existing blockchains without modification, and enables several novel cryptocurrency applications, such as crosschain payment channels and efficient multi-party swaps.
Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification

In data mining, common classification algorithms cannot effectively learn from imbalanced data. Oversampling addresses this problem by creating data for the minority class in order to balance the class distribution before the model is trained. The Traditional oversampling approaches are based on Synthetic Minority Oversampling TEchnique (SMOTE), which focus on local information but generates insufficiently realistic data. In contrast, the Generative Adversarial Network (GAN) captures the true data distribution in order to generate data for the minority class. However, both approaches are problematic owing to mode collapse and unstable training. To overcome these problems, we propose Conditional Wasserstein GAN- Gradient Penalty (CWGAN-GP), a novel and efficient synthetic oversampling approach for imbalanced datasets, which can be constructed by adding auxiliary conditional information to the WGAN-GP. CWGAN-GP generates more realistic data and overcomes the aforementioned problems. Experiments on 15 different benchmarked datasets and two real imbalanced datasets empirically demonstrate that CWGAN-GP increases the quality of synthetic data; furthermore, our approach outperforms the other oversampling approaches based on three evaluation metrics (F-measure, G-mean, and the area under the receiver operating characteristic curve) for five classifiers. Friedman and Nemenyi post hoc statistical tests also confirm that CWGAN-GP is superior to the other oversampling approaches.
ReachNN

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for a small subset of inputs. To quantify the error introduced by abstraction, we provide both theoretical error bound estimation based on the theory of Bernstein polynomials and more practical sampling based error bound estimation, following a tight Lipschitz constant estimation approach based on forward reachability analysis. Compared with previous methods, our approach addresses a much broader set of neural networks, including heterogeneous neural networks that contain multiple types of activation functions. Experiment results on a variety of benchmarks show the effectiveness of our approach.
<i>No Radio Left Behind:</i> Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments

Due to the unprecedented scale of the Internet of Things, designing scalable, accurate, energy-efficient and tamper-proof authentication mechanisms has now become more important than ever. To this end, in this paper we present ORACLE, a novel system based on convolutional neural networks (CNNs) to “fingerprint” (i.e., identify) a unique radio from a large pool of devices by deep-learning the fine-grained hardware impairments imposed by radio circuitry on physical-layer I/Q samples. First, we show how hardware-specific imperfections are learned by the CNN framework. Then, we extensively evaluate the performance of ORACLE on several first-of-its-kind large-scale datasets of WiFi-transmissions collected “in the wild”, as well as a dataset of nominally-identical (i.e., equal baseband signals) WiFi devices, reaching 80-90% accuracy is many cases with the error gap arising due to channel-induced effects. Finally, we show through an experimental testbed, how this accuracy can reach over 99% by intentionally inserting and learning the effect of controlled impairments at the transmitter side, to completely remove the impact of the wireless channel. Furthermore, to scale this approach for classifying potential thousands of radios, we propose an impairment hopping spread spectrum (IHOP) technique that is resilient to spoofing attacks.
Unsupervised Domain Adaptation via Regularized Conditional Alignment

We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignment ensures that not only the marginal distributions of the domains are aligned, but the labels as well. We propose a novel objective function that encourages the class-conditional distributions to have disjoint support in feature space. We further exploit adversarial regularization to improve the performance of the classifier on the domain for which no annotated data is available.
Engaging Image Captioning via Personality

Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, PERSONALITY-CAPTIONS, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 241,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations [36] with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations [32] with ResNets trained on 3.5 billion social media images. We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.
360-Degree Textures of People in Clothing from a Single Image

In this paper we predict a full 3D avatar of a person from a single image. We infer texture and geometry in the UV-space of the SMPL model using an image-to-image translation method. Given partial texture and segmentation layout maps derived from the input view, our model predicts the complete segmentation map, the complete texture map, and a displacement map. The predicted maps can be applied to the SMPL model in order to naturally generalize to novel poses, shapes, and even new clothing. In order to learn our model in a common UV-space, we non-rigidly register the SMPL model to thousands of 3D scans, effectively encoding textures and geometries as images in correspondence. This turns a difficult 3D inference task into a simpler image-to-image translation one. Results on rendered scans of people and images from the DeepFashion dataset demonstrate that our method can reconstruct plausible 3D avatars from a single image. We further use our model to digitally change pose, shape, swap garments between people and edit clothing. To encourage research in this direction we will make the source code available for research purpose [5].
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. Most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics. However, how to coherently take them into account is still far from studied. In addition, most existing methods treat output labels as independent medoids, ignoring the hierarchical relationships among them, which leads to a substantial loss of useful semantic information. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding word matrix representation preserving both the non-consecutive, long-distance and local sequential semantics. Then the word matrix is input to the proposed attentional graph capsule recurrent CNNs for effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.
Radial-Based Undersampling for imbalanced data classification

Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient number of training observations. Aforementioned difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE. Radial-Based Oversampling (RBO) was previously proposed to mitigate some of the limitations of the neighborhood-based methods. In this paper we examine the possibility of utilizing the concept of mutual class potential, used to guide the oversampling process in RBO, in the undersampling procedure. Conducted computational complexity analysis indicates a significantly reduced time complexity of the proposed Radial-Based Undersampling algorithm, and the results of the performed experimental study indicate its usefulness, especially on difficult datasets.