Reviewerly/exHarmony huggingface dataset:
Improved Rectangle Attacks on SKINNY and CRAFT

The boomerang and rectangle attacks are adaptions of differential cryptanalysis that regard the target cipher E as a composition of two sub-ciphers, i.e., E = E1 ∘ E0, to construct a distinguisher for E with probability p2q2 by concatenating two short differential trails for E0 and E1 with probability p and q respectively. According to the previous research, the dependency between these two differential characteristics has a great impact on the probability of boomerang and rectangle distinguishers. Dunkelman et al. proposed the sandwich attack to formalise such dependency that regards E as three parts, i.e., E = E1 ∘ Em ∘ E0, where Em contains the dependency between two differential trails, satisfying some differential propagation with probability r. Accordingly, the entire probability is p2q2r. Recently, Song et al. have proposed a general framework to identify the actual boundaries of Em and systematically evaluate the probability of Em with any number of rounds, and applied their method to accurately evaluate the probabilities of the best SKINNY’s boomerang distinguishers. In this paper, using a more advanced method to search for boomerang distinguishers, we show that the best previous boomerang distinguishers for SKINNY can be significantly improved in terms of probability and number of rounds. More precisely, we propose related-tweakey boomerang distinguishers for up to 19, 21, 23, and 25 rounds of SKINNY-64-128, SKINNY-128-256, SKINNY-64-192 and SKINNY-128-384 respectively, which improve the previous boomerang distinguishers of these variants of SKINNY by 1, 2, 1, and 1 round respectively. Based on the improved boomerang distinguishers for SKINNY, we provide related-tweakey rectangle attacks on 23 rounds of SKINNY-64-128, 24 rounds of SKINNY-128-256, 29 rounds of SKINNY-64-192, and 30 rounds of SKINNY-128-384. It is worth noting that our improved related-tweakey rectangle attacks on SKINNY-64-192, SKINNY-128-256 and SKINNY-128-384 can be directly applied for the same number of rounds of ForkSkinny-64-192, ForkSkinny-128-256 and ForkSkinny-128-384 respectively. CRAFT is another SKINNY-like tweakable block cipher for which we provide the security analysis against rectangle attack for the first time. As a result, we provide a 14-round boomerang distinguisher for CRAFT in the single-tweak model based on which we propose a single-tweak rectangle attack on 18 rounds of this cipher. Moreover, following the previous research regarding the evaluation of switching in multiple rounds of boomerang distinguishers, we also introduce new tools called Double Boomerang Connectivity Table (DBCT), LBCT⫤, and UBCT⊨ to evaluate the boomerang switch through the multiple rounds more accurately.
Adaptive Margin Diversity Regularizer for Handling Data Imbalance in Zero-Shot SBIR

Data from new categories are continuously being discovered, which has sparked significant amount of research in developing approaches which generalize to previously unseen categories, i.e. zero-shot setting. Zero-shot sketch-based image retrieval (ZS-SBIR) is one such problem in the context of cross-domain retrieval, which has received lot of attention due to its various real-life applications. Since most real-world training data have a fair amount of imbalance; in this work, for the first time in literature, we extensively study the effect of training data imbalance on the generalization to unseen categories, with ZS-SBIR as the application area. We evaluate several state-of-the-art data imbalance mitigating techniques and analyze their results. Furthermore, we propose a novel framework AMDReg (Adaptive Margin Diversity Regularizer), which ensures that the embeddings of the sketches and images in the latent space are not only semantically meaningful, but they are also separated according to their class-representations in the training set. The proposed approach is model-independent, and it can be incorporated seamlessly with several state-of-the-art ZS-SBIR methods to improve their performance under imbalanced condition. Extensive experiments and analysis justify the effectiveness of the proposed AMDReg for mitigating the effect of data imbalance for generalization to unseen classes in ZS-SBIR.
Two-Stage Cost-Sensitive Learning for Data Streams With Concept Drift and Class Imbalance

Most methods for classifying data streams operate under the hypothesis that the distribution of classes is balanced. Unfortunately, the phenomenon of class imbalance widely exists in many real-world applications. In addition, the underlying concept of data stream may change in a certain way over time, and attacks increase the difficulty of data stream mining. Motivated by this challenge, a Two-Stage Cost-Sensitive (TSCS) classification is proposed for addressing the class imbalance issue in non-stationary data streams. We propose a novel two-stage cost-sensitive framework for data stream classification by utilizing cost information in both feature selection stage and classification stage. Moreover, a window adaptation and drift detection mechanism, which guarantees that an ensemble can adapt promptly to concept drift, is embedded in our method. Our algorithm is compared with competitive algorithms on different kinds of datasets. The result demonstrates that TSCS obtains significant improvement in terms of class imbalance data stream metrics.
Interpretation of the category of “complex” in terms of dialectical positivism

Abstract The interpretation of the category of complex is given on the basis of dialectical positivism, in the framework of which the principle of dialectic symmetry is formulated. It, in turn, is based on the consideration of matter and information as paired dialectic categories. In accordance with this principle, information objects form a hierarchy similar to that which corresponds to different levels of matter organization (mechanical, chemical, biological, social). As “complex”, one should interpret the system that is the medium for the information object at least one level higher than the level related to trivial information (that is, one that is inextricably linked with the object itself). This level is the level corresponding to estranged information (simplifying information about a certain object recorded on another medium).
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When training deep learning models under an FL setting, people employ the predefined model architecture discovered in the centralized environment. However, this predefined architecture may not be the optimal choice because it may not fit data with non-identical and independent distribution (non-IID). Thus, we advocate automating federated learning (AutoFL) to improve model accuracy and reduce the manual design effort. We specifically study AutoFL via Neural Architecture Search (NAS), which can automate the design process. We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy. We also build a system based on FedNAS. Our experiments on non-IID dataset show that the architecture searched by FedNAS can outperform the manually predefined architecture.
Variational model for simultaneously image denoising and contrast enhancement

The performance of contrast enhancement is degraded when input images are noisy. In this paper, we propose and develop a variational model for simultaneously image denoising and contrast enhancement. The idea is to propose a variational approach containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform and the noise of the image can be removed. In the proposed model, a histogram equalization term is considered for image contrast enhancement, a total variational term is incorporate to remove the noise of the input image, and a fidelity term is added to keep the structure and the texture of the input image. The existence of the minimizer and the convergence of the proposed algorithm are studied and analyzed. Experimental results are presented to show the effectiveness of the proposed model compared with existing methods in terms of several measures: average local contrast, discrete entropy, structural similarity index, measure of enhancement, absolute measure of enhancement, and second derivative like measure of enhancement.
Multimodal Deep Learning for Social Media Popularity Prediction With Attention Mechanism

Social media popularity estimation refers to predict the post's popularity using multimodal contents. The prediction performance heavily relies on the feature extraction part and fully leveraging multimodal heterogeneous data is of a great challenge in the practical settings. Despite remarkable progress have been made, most of the previous attempts are restrained from the essentially limited property of the employed single modality. Inspired by the recent success of multimodal learning, we propose a novel multimodal deep learning framework for the popularity prediction task, which aims to leverage the complementary knowledge from different modalities. Moreover, an attention mechanism is introduced in our framework, with the goal to assign large weights to specified modalities during the training and inference phases. To empirically investigate the effectiveness and robustness of the proposed approach, we conduct extensive experiments on the 2020 SMP challenge. The obtained results show that the proposed framework outperforms related approaches.
DurIAN-SC: Duration Informed Attention Network Based Singing Voice Conversion System

Singing voice conversion is converting the timbre in the source singing to the target speaker's voice while keeping singing content the same. However, singing data for target speaker is much more difficult to collect compared with normal speech this http URL this paper, we introduce a singing voice conversion algorithm that is capable of generating high quality target speaker's singing using only his/her normal speech data. First, we manage to integrate the training and conversion process of speech and singing into one framework by unifying the features used in standard speech synthesis system and singing synthesis system. In this way, normal speech data can also contribute to singing voice conversion training, making the singing voice conversion system more robust especially when the singing database is small.Moreover, in order to achieve one-shot singing voice conversion, a speaker embedding module is developed using both speech and singing data, which provides target speaker identify information during conversion. Experiments indicate proposed sing conversion system can convert source singing to target speaker's high-quality singing with only 20 seconds of target speaker's enrollment speech data.
HALO: Hardware-Aware Learning to Optimize

There has been an explosive demand for bringing machine learning (ML) powered intelligence into numerous Internet-of-Things (IoT) devices. However, the effectiveness of such intelligent functionality requires in-situ continuous model adaptation for adapting to new data and environments, while the on-device computing and energy resources are usually extremely constrained. Neither traditional hand-crafted (e.g., SGD, Adagrad, and Adam) nor existing meta optimizers are specifically designed to meet those challenges, as the former requires tedious hyper-parameter tuning while the latter are often costly due to the meta algorithms' own overhead. To this end, we propose hardware-aware learning to optimize (HALO), a practical meta optimizer dedicated to resource-efficient on-device adaptation. Our HALO optimizer features the following highlights: (1) faster adaptation speed (i.e., taking fewer data or iterations to reach a specified accuracy) by introducing a new regularizer to promote empirical generalization; and (2) lower per-iteration complexity, thanks to a stochastic structural sparsity regularizer being enforced. Furthermore, the optimizer itself is designed as a very light-weight RNN and thus incurs negligible overhead. Ablation studies and experiments on five datasets, six optimizees, and two state-of-the-art (SOTA) edge AI devices validate that, while always achieving a better accuracy ( $$\uparrow $$ 0.46% - $$\uparrow $$ 20.28%), HALO can greatly trim down the energy cost (up to $$\downarrow $$ 60%) in adaptation, quantified using an IoT device or SOTA simulator. Codes and pre-trained models are at https://github.com/RICE-EIC/HALO .
An Integrated Approach to Goal Selection in Mobile Robot Exploration

This paper deals with the problem of autonomous navigation of a mobile robot in an unknown2D environment to fully explore the environment as efficiently as possible. We assume a terrestrial mobilerobot equipped with a ranging sensor with a limited range and 360º field of view. The key part of theexploration process is formulated as the d-Watchman Route Problem which consists of two coupledtasks—candidate goals generation and finding an optimal path through a subset of goals—which aresolved in each exploration step. The latter has been defined as a constrained variant of the GeneralizedTraveling Salesman Problem and solved using an evolutionary algorithm. An evolutionary algorithmthat uses an indirect representation and the nearest neighbor based constructive procedure was proposedto solve this problem. Individuals evolved in this evolutionary algorithm do not directly code thesolutions to the problem. Instead, they represent sequences of instructions to construct a feasible solution.The problems with efficiently generating feasible solutions typically arising when applying traditionalevolutionary algorithms to constrained optimization problems are eliminated this way. The proposedexploration framework was evaluated in a simulated environment on three maps and the time needed toexplore the whole environment was compared to state-of-the-art exploration methods. Experimentalresults show that our method outperforms the compared ones in environments with a low density ofobstacles by up to 12.5%, while it is slightly worse in office-like environments by 4.5% at maximum.The framework has also been deployed on a real robot to demonstrate the applicability of the proposedsolution with real hardware.
PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices

This article presents PreVIous, a methodology to predict the performance of convolutional neural networks (CNNs) in terms of throughput and energy consumption on vision-enabled devices for the Internet of Things. CNNs typically constitute a massive computational load for such devices, which are characterized by scarce hardware resources to be shared among multiple concurrent tasks. Therefore, it is critical to select the optimal CNN architecture for a particular hardware platform according to the prescribed application requirements. However, the zoo of CNN models is already vast and rapidly growing. To facilitate a suitable selection, we introduce a prediction framework that allows to evaluate the performance of CNNs prior to their actual implementation. The proposed methodology is based on PreVIousNet, a neural network specifically designed to build accurate per-layer performance predictive models. PreVIousNet incorporates the most usual parameters found in state-of-the-art network architectures. The resulting predictive models for inference time and energy have been tested against comprehensive characterizations of seven well-known CNN models running on two different software frameworks and two different embedded platforms. To the best of our knowledge, this is the most extensive study in the literature concerning CNN performance prediction on low-power low-cost devices. The average deviation between predictions and real measurements is remarkably low, ranging from 3% to 10%. This means state-of-the-art modeling accuracy. As an additional asset, the fine-grained a priori analysis provided by PreVIous could also be exploited by neural architecture search (NAS) engines.
Robust Alignment for Panoramic Stitching Via an Exact Rank Constraint

We study the problem of image alignment for panoramic stitching. Unlike most existing approaches that are feature-based, our algorithm works on pixels directly, and accounts for errors across the whole images globally. Technically, we formulate the alignment problem as rank-1 and sparse matrix decomposition over transformed images, and develop an efficient algorithm for solving this challenging non-convex optimization problem. The algorithm reduces to solving a sequence of subproblems, where we analytically establish exact recovery conditions, convergence and optimality, together with convergence rate and complexity. We generalize it to simultaneously align multiple images and recover multiple homographies, extending its application scope towards vast majority of practical scenarios. Experimental results demonstrate that the proposed algorithm is capable of more accurately aligning the images and generating higher quality stitched images than state-of-the-art methods.
When Do You Need Billions of Words of Pretraining Data?

NLP is currently dominated by general-purpose pretrained language models like RoBERTa, which achieve strong performance on NLU tasks through pretraining on billions of words. But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that they cannot learn from less data? We adopt four probing methods---classifier probing, information-theoretic probing, unsupervised relative acceptability judgment, and fine-tuning on NLU tasks---and draw learning curves that track the growth of these different measures of linguistic ability with respect to pretraining data volume using the MiniBERTas, a group of RoBERTa models pretrained on 1M, 10M, 100M and 1B words. We find that LMs require only about 10M or 100M words to learn representations that reliably encode most syntactic and semantic features we test. A much larger quantity of data is needed in order to acquire enough commonsense knowledge and other skills required to master typical downstream NLU tasks. The results suggest that, while the ability to encode linguistic features is almost certainly necessary for language understanding, it is likely that other forms of knowledge are the major drivers of recent improvements in language understanding among large pretrained models.
Probabilistic Jacobian-Based Saliency Maps Attacks

Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or L0 attacks. Effective and fast L0 attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical to fool NNCs but also to improve their robustness. In this paper, we show that penalising saliency maps of JSMA by the output probabilities and the input features of the NNC leads to more powerful attack algorithms that better take into account each input’s characteristics. This leads us to introduce improved versions of JSMA, named Weighted JSMA (WJSMA) and Taylor JSMA (TJSMA), and demonstrate through a variety of white-box and black-box experiments on three different datasets (MNIST, CIFAR-10 and GTSRB), that they are both significantly faster and more efficient than the original targeted and non-targeted versions of JSMA. Experiments also demonstrate, in some cases, very competitive results of our attacks in comparison with the Carlini-Wagner (CW) L0 attack, while remaining, like JSMA, significantly faster (WJSMA and TJSMA are more than 50 times faster than CW L0 on CIFAR-10). Therefore, our new attacks provide good trade-offs between JSMA and CW for L0 real-time adversarial testing on datasets such as the ones previously cited.
A Comparison of Deep Learning Object Detection Models for Satellite Imagery

In this work, we compare the detection accuracy and speed of several state-of-the-art models for the task of detecting oil and gas fracking wells and small cars in commercial electro-optical satellite imagery. Several models are studied from the single-stage, two-stage, and multi-stage object detection families of techniques. For the detection of fracking well pads (50m - 250m), we find single-stage detectors provide superior prediction speed while also matching detection performance of their two and multi-stage counterparts. However, for detecting small cars, two-stage and multi-stage models provide substantially higher accuracies at the cost of some speed. We also measure timing results of the sliding window object detection algorithm to provide a baseline for comparison. Some of these models have been incorporated into the Lockheed Martin Globally-Scalable Automated Target Recognition (GATR) framework.
Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation

Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing, we introduce a Latent Disentanglement method with a siamese auto-encoder structure with an adapted gradient reversal layer. Our structure enables the separation of the semantic latent information and gender latent information of given word into the disjoint latent dimensions. Afterwards, we introduce a Counterfactual Generation to convert the gender information of words, so the original and the modified embeddings can produce a gender-neutralized word embedding after geometric alignment regularization, without loss of semantic information. From the various quantitative and qualitative debiasing experiments, our method shows to be better than existing debiasing methods in debiasing word embeddings. In addition, Our method shows the ability to preserve semantic information during debiasing by minimizing the semantic information losses for extrinsic NLP downstream tasks.
Secure and efficient synchronization scheme for quantum key distribution

To establish a time reference frame between two users in quantum key distribution, a synchronization calibration process is usually applied for the case of using gated mode single-photon detectors (SPDs). Traditionally, the synchronization calibration is independently implemented by the line length measurement for each SPD. However, this will leave a loophole which has been experimentally demonstrated by a special attack. Here, we propose an alternative synchronization scheme by fixing the relative delay of the signal time window among all SPDs and jointly performing the line length measurement with multiple SPDs under combining low-precision with high-precision synchronization. The new scheme is not only immune to the vulnerability but also improves the synchronization time from usually a few seconds to tens of milliseconds.