Reviewerly/exHarmony huggingface dataset:
Practically Feasible Robust Quantum Money with Classical Verification

We introduce a private quantum money scheme with the note verification procedure based on sampling matching, a problem in a one-way communication complexity model. Our scheme involves a bank who produces and distributes quantum notes, noteholders who are untrusted, and trusted local verifiers of the bank to whom the holders send their notes in order to carry out transactions. The key aspects of our money scheme include: note verification procedure requiring a single round classical interaction between the local verifier and bank; fixed verification circuit that uses only passive linear optical components; re-usability of each note in our scheme which grows linearly with the size of note; and an unconditional security against any adversary trying to forge the banknote while tolerating the noise of up to 21.4%. We further describe a practical implementation technique of our money scheme using weak coherent states of light and the verification circuit involving a single 50/50 beam splitter and two single-photon threshold detectors. Previous best-known matching based money scheme proposal involves a verification circuit where the number of optical components increase proportional to the increase in desired noise tolerance (robustness). In contrast, we achieve any desired noise tolerance (up to a maximal threshold value) with only a fixed number of optical components. This considerable reduction of components in our scheme enables us to reach the robustness values that is not feasible for any existing money scheme with the current technology.
SSCGAN: Facial Attribute Editing via Style Skip Connections

Existing facial attribute editing methods typically employ an encoder-decoder architecture where the attribute information is expressed as a conditional one-hot vector spatially concatenated with the image or intermediate feature maps. However, such operations only learn the local semantic mapping but ignore global facial statistics. In this work, we focus on solving this issue by editing the channel-wise global information denoted as the style feature. We develop a style skip connection based generative adversarial network, referred to as SSCGAN which enables accurate facial attribute manipulation. Specifically, we inject the target attribute information into multiple style skip connection paths between the encoder and decoder. Each connection extracts the style feature of the latent feature maps in the encoder and then performs a residual learning based mapping function in the global information space guided by the target attributes. In the following, the adjusted style feature will be utilized as the conditional information for instance normalization to transform the corresponding latent feature maps in the decoder. In addition, to avoid the vanishing of spatial details (e.g. hairstyle or pupil locations), we further introduce the skip connection based spatial information transfer module. Through the global-wise style and local-wise spatial information manipulation, the proposed method can produce better results in terms of attribute generation accuracy and image quality. Experimental results demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Finding approximate Nash equilibria in zero-sum imperfect-information games is challenging when the number of information states is large. Policy Space Response Oracles (PSRO) is a deep reinforcement learning algorithm grounded in game theory that is guaranteed to converge to an approximate Nash equilibrium. However, PSRO requires training a reinforcement learning policy at each iteration, making it too slow for large games. We show through counterexamples and experiments that DCH and Rectified PSRO, two existing approaches to scaling up PSRO, fail to converge even in small games. We introduce Pipeline PSRO (P2SRO), the first scalable general method for finding approximate Nash equilibria in large zero-sum imperfect-information games. P2SRO is able to parallelize PSRO with convergence guarantees by maintaining a hierarchical pipeline of reinforcement learning workers, each training against the policies generated by lower levels in the hierarchy. We show that unlike existing methods, P2SRO converges to an approximate Nash equilibrium, and does so faster as the number of parallel workers increases, across a variety of imperfect information games. We also introduce an open-source environment for Barrage Stratego, a variant of Stratego with an approximate game tree complexity of $10^{50}$. P2SRO is able to achieve state-of-the-art performance on Barrage Stratego and beats all existing bots. Experiment code is available athttps://github.com/JBLanier/pipeline-psro.
SMOTE–ENN-Based Data Sampling and Improved Dynamic Ensemble Selection for Imbalanced Medical Data Classification

During the last few years, the classification of imbalanced datasets is one of the crucial issues in medical diagnosis since it is related to the distribution of normal and abnormal cases which can potentially affect the performance of the diagnosis system. For solving this problem, various techniques have been designed in order to achieve acceptable quality. Ensemble systems are one of those techniques, and they have proven their ability to be more accurate than single classifier models. Classifier selection is related to the choice of an optimal subset within a pool of classifiers. Selection of classifier can be broadly split into two classes: static and dynamic. This paper proposes a novel set selection scheme for the classification of imbalanced medical datasets. The suggested approach is based on the combination of an improved dynamic ensemble selection called META-DES framework combined with a hybrid sampling method called SMOTE–ENN. The experimental results prove the superiority of the proposed ensemble learning system using three UCI datasets.
Appraisal theories for emotion classification in text

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words “I am afraid.”). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.
The Dynamic Symmetric Four-Key-Generators System for Securing Data Transmission in the Industrial Control System

Most of the communication protocols in the Industrial Control System (ICS) are vulnerable to cyber-attacks. Initially, the network protocol was designed for reliable performance, and thus did not incorporate data transmission security features. Therefore, ICS requires adequate data transmission security. This paper suggests improving the security of data transmission through a dynamic symmetric four-key-generators system, wherein the system anticipates cyberattacks by generating four keys before encryption. It involves four generators: a random initial key generator, a keystream generator, a key scheduling algorithm generator, and a pseudo-random number algorithm generator. In the receiver section, the system generates three keys before decryption to ensure data confidentiality and to avoid cyberattacks. The test results show that the proposed system keyspace is ≈22048 bits, meaning that the key is more secure from brute force attacks. As a result, the cipher data have a correlation value of 0.00007. The entropy value is 7.99, indicating that the cipher data is more secure. Also, speed tests show that the processing time still qualifies as realtime.
Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra

Numerous researchers recently applied empirical spectral analysis to the study of modern deep learning classifiers. We identify and discuss an important formal class/cross-class structure and show how it lies at the origin of the many visually striking features observed in deepnet spectra, some of which were reported in recent articles, others are unveiled here for the first time. These include spectral outliers, spikes, and small but distinct continuous distributions, bumps, often seen beyond the edge of a main bulk. The significance of the cross-class structure is illustrated in three ways: (i) we prove the ratio of outliers to bulk in the spectrum of the Fisher information matrix is predictive of misclassification, in the context of multinomial logistic regression; (ii) we demonstrate how, gradually with depth, a network is able to separate class-distinctive information from class variability, all while orthogonalizing the class-distinctive information; and (iii) we propose a correction to KFAC, a well-known second-order optimization algorithm for training deepnets.
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction

Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, this task is prone to two serious challenges, namely catastrophic forgetting and order-sensitivity. We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction. We combine meta learning and curriculum learning to quickly adapt model parameters to a new task and to reduce interference of previously seen tasks on the current task. We design a novel relation representation learning method through the distribution of domain and range types of relations. Such representations are utilized to quantify the difficulty of tasks for the construction of curricula. Moreover, we also present novel difficulty-based metrics to quantitatively measure the extent of order-sensitivity of a given model, suggesting new ways to evaluate model robustness. Our comprehensive experiments on three benchmark datasets show that our proposed method outperforms the state-of-the-art techniques. The code is available at the anonymous GitHub repository: https://github.com/wutong8023/AAAI_CML.
Focussing Learned Image Compression to Semantic Classes for V2X Applications

Cooperative perception with many sensors involved greatly improves the performance of perceptual systems in autonomous vehicles. However, the increasing amount of sensor data leads to a bottleneck due to limited capacity of vehicle-to-X (V2X) communication channels. We leverage lossy learned image compression by means of an autoencoder with adversarial loss function to reduce the overall bitrate. Our key contribution is to focus image compression on regions of interest (ROIs) governed by a binary mask. A transmitter-sided semantic segmentation network extracts semantically important classes being the basis for the generation of a ROI. A second key contribution is that the mask is not transmitted as side information, only the quantized bottleneck data is transmitted. To train the network, we use a loss function operating only on the pixels in the ROI. We report peak-signal-to-noise ratio (PSNR) both in the entire image and only in the ROI, evaluating various fusion architectures and fusion operations involving input image and mask. Showing the high generalizability of our approach, we achieve consistent improvements in the ROI in all experiments on the Cityscapes dataset.
A Novel Video Steganography-Based Botnet Communication Model in Telegram SNS Messenger

In botnets, a bot master regularly sends command and control messages (C &amp; C messages) to bots for various purposes, such as ordering its commands to bots and collecting critical data from bots. Although such C &amp; C messages can be encrypted by cryptographic methods to hide them, existing botnet detection mechanisms could detect the existence of botnets by capturing suspicious network traffics between the bot master (or the C &amp; C server) and numerous bots. Recently, steganography-based botnets (stego-botnets) have emerged to make C &amp; C communication traffics look normal to botnet detection systems. In stego-botnets, every C &amp; C message is embedded in a multimedia file, such as an image file by using steganography techniques and shared in Social Network Service (SNS) websites (such as Facebook) or online messengers (such as WeChat or KakaoTalk). Consequently, traditional botnet detection systems without steganography detection methods cannot detect them. Meanwhile, according to our survey, we observed that existing studies on the steganography botnet are limited to use only image steganography techniques, although the video steganography method has some obvious advantages over the image steganography method. By this motivation, in this paper, we study a video steganography-based botnet in Social Network Service (SNS) platforms. We first propose a video steganography botnet model based on SNS messengers. In addition, we design a new payload approach-based video steganography method (DECM: Divide-Embed-Component Method) that can embed much more secret data than existing tools by using two open tools VirtualDub and Stegano. We show that our proposed model can be implemented in the Telegram SNS messenger and conduct extensive experiments by comparing our proposed model with DECM with an existing image steganography-based botnet in terms of C &amp; C communication efficiency and undetectability.
Online Community-based Design of Free and Open Source Software for Transgender Voice Training

This paper describes Project Spectra, a collective of open source developers that aims to build free and open source voice training technology for transgender people. We demonstrate how a design prioritizing the agency of trans users was made possible through sustained community collaboration. Using an autoethnographic approach, we discuss our community-based design process, which was documented with memos, online meetings and text conversations, sketches, and other data sources. We illustrate how we articulated our values as a group: deciding our programming framework (including a Statement of Principles), elaborating our "Experience Goals" (the feelings we wanted our design to elicit), and determining the features we wanted to implement in our app. We conclude with a reflection on the benefits and challenges of conducting community-based design research through an open-source organizational model.
Wireless IoT with Blockchain-Enabled Technology amidst Attacks

Blockchain enabled Internet of Things has exhibited high potential in establishing consensus and trust mechanism. To design this type of system, it is necessary to have a better knowledge about blockchain and how it can be used with internet of things. Moreover, it will be easier to gauge the requirements of the system based on the performance constraints that are imposed on the other parts. In the proposed work, we have used spatial domain Poisson distribution to determine the arrival rate at the transaction node and the full function node. The signal to interference and noise is calculated to determine throughput as well as transmission success rate. Based on performance analysis, we have developed an algorithm that is can be used to determine the apt FN deployment, under the condition of maximum transaction throughput. Results indicate the accuracy of the proposed algorithm with theoretical values.
Is Attention Better Than Matrix Decomposition

As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decomposition (MD) model developed 20 years ago regarding the performance and computational cost for encoding the long-distance dependencies. We model the global context issue as a low-rank completion problem and show that its optimization algorithms can help design global information blocks. This paper then proposes a series of Hamburgers, in which we employ the optimization algorithms for solving MDs to factorize the input representations into sub-matrices and reconstruct a low-rank embedding. Hamburgers with different MDs can perform favorably against the popular global context module self-attention when carefully coping with gradients back-propagated through MDs. Comprehensive experiments are conducted in the vision tasks where it is crucial to learn the global context, including semantic segmentation and image generation, demonstrating significant improvements over self-attention and its variants.
MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning

Most recent few-shot learning (FSL) approaches are based on episodic training whereby each episode samples few training instances (shots) per class to imitate the test condition. However, this strict adhering to test condition has a negative side effect, that is, the trained model is susceptible to the poor sampling of few shots. In this work, for the first time, this problem is addressed by exploiting inter-episode relationships. Specifically, a novel meta-learning via modeling episode-level relationships (MELR) framework is proposed. By sampling two episodes containing the same set of classes for meta-training, MELR is designed to ensure that the meta-learned model is robust against the presence of poorly-sampled shots in the meta-test stage. This is achieved through two key components: (1) a Cross-Episode Attention Module (CEAM) to improve the ability of alleviating the effects of poorly-sampled shots, and (2) a Cross-Episode Consistency Regularization (CECR) to enforce that the two classifiers learned from the two episodes are consistent even when there are unrepresentative instances. Extensive experiments for non-transductive standard FSL on two benchmarks show that our MELR achieves 1.0%-5.0% improvements over the baseline (i.e., ProtoNet) used for FSL in our model and outperforms the latest competitors under the same settings.
Generalized Energy Based Models

We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability mass on the learned support. Both the energy function and base jointly constitute the final model, unlike GANs, which retain only the base distribution (the generator). GEBMs are trained by alternating between learning the energy and the base. We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base. Samples from the posterior on the latent space of the trained model can be obtained via MCMC, thus finding regions in this space that produce better quality samples. Empirically, the GEBM samples on image-generation tasks are of much better quality than those from the learned generator alone, indicating that all else being equal, the GEBM will outperform a GAN of the same complexity. When using normalizing flows as base measures, GEBMs succeed on density modelling tasks returning comparable performance to direct maximum likelihood of the same networks.
Light Graph Convolutional Collaborative Filtering With Multi-Aspect Information

The personalized recommendation has become increasingly prevalent in real-world applications, to help users in discovering items of interest. Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering. However, most of the existing GCN based methods can only capture information about the user's purchase (or click) history, reflecting only one aspect of the user preferences and item characteristics. To provide more accurate recommendations, we need to go beyond modeling user-item interactions and take auxiliary information into consideration. In this paper, we propose a Light GCN based Aspect-level Collaborative Filtering model (LGC-ACF) to exploit multi-aspect user-item interaction information. First, we construct aspect-level user-item interaction graphs according to the interaction history and the knowledge information of items, then feed them to a delicately designed Light GCN based model to learn aspect-level representations of users and items. Finally, the representations of all aspects and all propagation layers are fused for recommendation. We apply LGC-ACF to three datasets: Movielens, Amazon, and Taobao. The experiment results show that LGC-ACF achieves average NDCG improvements of 5.31%, 4.06%, 14.9% in Movielens, Amazon, and Taobao datasets, respectively, compared with state-of-the-art baselines for recommendation.