Reviewerly/exHarmony huggingface dataset:
Polynomial-time trace reconstruction in the smoothed complexity model

In the trace reconstruction problem, an unknown source string x ∊ {0, 1}n is sent through a probabilistic deletion channel which independently deletes each bit with probability δ and concatenates the surviving bits, yielding a trace of x. The problem is to reconstruct x given independent traces. This problem has received much attention in recent years both in the worst-case setting where x may be an arbitrary string in {0, 1}n [6, 19, 7, 8, 4] and in the average-case setting where x is drawn uniformly at random from {0, 1}n [21, 9, 8, 4].This paper studies trace reconstruction in the smoothed analysis setting, in which a “worst-case” string xworst is chosen arbitrarily from {0, 1}n, and then a perturbed version x of xworst is formed by independently replacing each coordinate by a uniform random bit with probability σ. The problem is to reconstruct x given independent traces from it.Our main result is an algorithm which, for any constant perturbation rate 0 < σ < 1 and any constant deletion rate 0 < δ < 1, uses poly(n) running time and traces and succeeds with high probability in reconstructing the string x. This stands in contrast with the worst-case version of the problem, for which the best known sample complexity is exp(Õ(n1/5)) [5], a recent improvement on exp(O(n1/3)) [6, 19].Our approach is based on reconstructing x from the multiset of its short subwords and is quite different from previous algorithms for either the worst-case or average-case versions of the problem. The heart of our work is a new poly(n)-time procedure for reconstructing the multiset of all O(log n)-length subwords of any source string x ∊ {0, 1}n given access to traces of x.
A Diffusion Theory For Deep Learning Dynamics: Stochastic Gradient Descent Exponentially Favors Flat Minima

Stochastic Gradient Descent (SGD) and its variants are mainstream methods for training deep networks in practice. SGD is known to find a flat minimum that often generalizes well. However, it is mathematically unclear how deep learning can select a flat minimum among so many minima. To answer the question quantitatively, we develop a density diffusion theory (DDT) to reveal how minima selection quantitatively depends on the minima sharpness and the hyperparameters. To the best of our knowledge, we are the first to theoretically and empirically prove that, benefited from the Hessian-dependent covariance of stochastic gradient noise, SGD favors flat minima exponentially more than sharp minima, while Gradient Descent (GD) with injected white noise favors flat minima only polynomially more than sharp minima. We also reveal that either a small learning rate or large-batch training requires exponentially many iterations to escape from minima in terms of the ratio of the batch size and learning rate. Thus, large-batch training cannot search flat minima efficiently in a realistic computational time.
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. Our proposed pseudo-labeling strategy is network structure agnostic to apply in a one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for segmentation. The source code will be released.
An End-to-End Deep Model With Discriminative Facial Features for Facial Expression Recognition

Due to the complex challenges of the environment and emotion expressions, most facial expression recognition systems cannot achieve a high recognition rate. More discriminative features can describe facial expressions more accurately, so facial feature extraction is the key technology for facial expression recognition. In this article, an effective end-to-end deep model is proposed to improve the accuracy of face recognition. Considering the importance of data pre-processing (very few studies have focused on this process), first, a data enhancement method is proposed to locate the range of the face target and enhance the image contrast. Next, to obtain further discriminative features, a hybrid feature representation method is proposed, in which four typical feature extraction method are combined. After that, an effective deep model is designed to train and test the samples which can obtain the optimal parameters with less computation cost. Ablation study results show that the proposed hybrid feature representation method can help improve recognition accuracy. Finally, to comprehensively evaluate the performance of the proposed model, a series of experiments are conducted on three benchmark datasets. The recognition rate is achieved 94.5%, 98.6%, and 97.2% for FER2013, AR dataset, and CK+ dataset, respectively.
Chiral single-photon switch-assisted quantum logic gate with a nitrogen-vacancy center in a hybrid system

We propose what we believe is a novel proposal for realizing a quantum C-NOT logic gate, through fabricating an interesting hybrid device with a chiral photon-pulse switch, a single nitrogen-vacancy (NV) center, and an optical microcavity. Three major different practical routes on realizing a chiral photon emitter are discussed, which can implement a chiral control unit via the nonreciprocal emitter–photon interactions, so-called “propagation-direction-dependent” emission. With the assistance of dichromatic microwave driving fields, we carry out the relevant C-NOT operations by engineering the interactions on a single NV spin in a cavity. We note that this logic gate is robust against practical noise and experimental imperfection, and this attempt may evoke wide and fruitful applications in quantum information processing.
Spatial and Frequency Approaches for Audio File Protection

In order to protect audio files, we propose in this paper a new integration scheme for blind audio file watermarking. The goal is to find a compromise between capacity and imperceptibility in order to hide as much data as possible while minimizing file degradation. This integration scheme is implemented in the three insertion domains: spatial, frequency and multi-resolution domains. For the spatial-domain integration, the mark is inserted directly into the data samples. For the frequency-domain integration, a Discrete Cosine Transform is applied to the audio frames; after the thresholding and quantification step, the watermark is inserted into the Discrete Cosine Transform coefficients to obtain the watermarked file. For the multi-resolution-domain insertion, a single-level Discrete Wavelet Transform is applied using the scaling low-pass filter and wavelet high-pass filter. The watermark integration is then performed using the obtained AC coefficients. The proposed concealment process combines three values to integrate two bits and only one may be modified, which reduces the probability of change unlike other approaches. This implies less modification and therefore less distortion of the host file; this explains the good Signal-to-Noise Ratio obtained of more than 59[Formula: see text]dB for the spatial-domain integration and therefore a reasonable imperceptibility. An evaluation of the watermark’s robustness demonstrates that the proposed schemes generate reasonably robust watermarked samples against various attacks with a high-quality watermark with normalized cross-correlation greater than 0.9 for the three insertion domains.
Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading

This paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and difficulty of resources) in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills.
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models

In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator.We focus on the transformer encoderdecoder model for the open-domain dialogue response generation task.Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining.We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection.As a preliminary attempt to alleviate the forgetting problem, we propose an intuitive finetuning strategy named "mix-review".We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent.Finally, we discuss interesting behavior of the resulting dialogue model and its implications.
Search Algorithms and Loss Functions for Bayesian Clustering

We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. Given the large size and awkward discrete nature of the search space, the minimization of the posterior expected loss is challenging. Our approach is a stochastic search based on a series of greedy optimizations performed in a random order and is embarrassingly parallel. We consider several loss functions, including Binder loss and variation of information. We note that criticisms of Binder loss are the result of using equal penalties of misclassification and we show an efficient means to compute Binder loss with potentially unequal penalties. Furthermore, we extend the original variation of information to allow for unequal penalties and show no increased computational costs. We provide a reference implementation of our algorithm. Using a variety of examples, we show that our method produces clustering estimates that better minimize the expected loss and are obtained faster than existing methods. Supplementary materials for this article are available online.
A low-cost assistive wheelchair for handicapped &amp; elderly people

In this paper, a mixed control wheelchair (MCW) is presented to overcome problem related to movement and user command for elderly and paralyzed people. The MCW comprises of five individual control blocks, which are fed at the main control unit to provide appropriate control actions. The main feature of this assistive device is that it can navigate by following the eyesight of the user. The proposed MCW can also be controlled using either joystick or voice command or finger movement or through mobile app or a combination of all. This system uses multiple sensor networks to measure the terrain condition, users command and translate it into control action. The proposed control system runs on a Raspberry Pi, which can able to take a proper turn as well as capable to control forward or backward motion. Raspberry Pi, camera module and sensors networks, etc. were all connected into the android mobile system, shortening the physical distance between the disabled person end and the supervisor end and serial communication was used. Multiple-Layer system architecture with outstanding control functions was constructed that uses android mobile interfacing to realize the automatic control and capture videos of the room remotely. It is shown that the users can carry out all essential tasks with the proposed MCW, almost as fast as with the traditional controller. The smart wheelchair can assist elderly people and also improve their quality of life.
Steganography based on quotient value differencing and pixel value correlation

Pixel value differencing (PVD) steganography techniques produce imperceptible stego-images. Most PVD-based techniques have a fall-off boundary problem (FOBP). To avoid FOBP, a steganographic scheme is presented that combines the quotient value difference (QVD) with pixel value correlation (PVC). It performs data embedding in two stages on 3 × 3 size pixel blocks. The first stage of the embedding procedure performs QVD and remainder substitution on five pixels. These five pixels are central pixel and it's neighbours on the left, right, upper, and lower sides. Based on the new values of these five pixels, the PVC embedding procedure is applied on the remaining four corner pixels of the block. The experimental results are found to be improved. The hiding capacity is high that is 3.92 bits per byte with an acceptable peak signal-to-noise ratio value. A security check was performed using two tests: (i) a regular-singular (RS) test, (ii) a pixel difference histogram (PDH) test. The experimental outcomes demonstrate that PDH curves of the stego-images do not have a zigzag shape, that means the PDH test could not detect this steganography technique. Furthermore, the RS curves show that the RS test could not detect this steganography technique.
The Case for Adaptive Security Interventions

Despite the availability of various methods and tools to facilitate secure coding, developers continue to write code that contains common vulnerabilities. It is important to understand why technological advances do not sufficiently facilitate developers in writing secure code. To widen our understanding of developers' behaviour, we considered the complexity of the security decision space of developers using theory from cognitive and social psychology. Our interdisciplinary study reported in this article (1) draws on the psychology literature to provide conceptual underpinnings for three categories of impediments to achieving security goals, (2) reports on an in-depth meta-analysis of existing software security literature that identified a catalogue of factors that influence developers' security decisions, and (3) characterises the landscape of existing security interventions that are available to the developer during coding and identifies gaps. Collectively, these show that different forms of impediments to achieving security goals arise from different contributing factors. Interventions will be more effective where they reflect psychological factors more sensitively and marry technical sophistication, psychological frameworks, and usability. Our analysis suggests “adaptive security interventions” as a solution that responds to the changing security needs of individual developers and a present a proof-of-concept tool to substantiate our suggestion.
Using eye tracking to investigate failure to notice word transpositions in reading

Previous research (Mirault, Snell, & Grainger, 2018) has demonstrated that subjects sometimes incorrectly judge an ungrammatical sentence as grammatical when it is created by the transposition of two words in a grammatical sentence (e.g., The white was cat big). Here we present two eye-tracking experiments designed to assess the prevalence of this phenomenon in a more natural reading task, and to explore theoretical explanations. Readers failed to notice transpositions at about the same rate as in Mirault et al. (2018). Failure to notice the transposition was more common when both words were short, and when readers' eyes skipped, rather than directly fixated, one of the two words. The status of the transposed words as open- or closed-class did not have a reliable effect. The transposed words caused disruption in the eye movement record only on trials when participants ultimately judged the sentence to be ungrammatical, not when they judged the sentence to be grammatical. We argue that the results are not entirely consistent with the account offered by Mirault et al. (2018), which attributes failure to notice transpositions to parallel processing of adjacent words, or with a late, post-perceptual rational inference account (Gibson, Bergen, & Piantadosi, 2013). We propose that word recognition is serial, but post-lexical integration of each word into its context may not be perfectly incremental.
An Authentication Model with High Security for Cloud Database

The cloud computing standards are gaining an increased research interest due to various benefits they offer. Though there are so many influences with cloud computing, security and privacy problems are various issues handling with the extensive adaption by the model. Malicious problem of service provider is one more issue which cannot be traceable by data proprietors. Hence, finding the appropriate solutions to these security issues at both administrator level and customer level is very attractive in various directions. Cryptographically enforced access control for securing electronic pathological records (CEASE) is formulated by extending the proposed ciphertext-based attribute-based encryption (CP-ABE) with advanced encryption standard (AES) through limited-shuffle techniques. The main objective of CEASE is to provide data confidentiality, and access control limited-shuffle protects the data from inference attacks and protects the data confidentiality for hot data. In the next step, this research works design a multistage encrypt-or model by differentiating the users as public and personal. Two separate algorithms such as Vigenere encryption algorithm and two-fish encryption are applied in personal and public domain, respectively. Further, where, hierarchical agglomerative clustering (HAC) algorithm is also processed for clustering of users in the public domain by which the overhead decreases effectively. As a final system, this work develops an integrated framework by combining the CP-ABE with AES, multistage encryptor and limited-shuffle. As it is combined with individual methods, this method achieves an efficient performance in the provision of security and data confidentiality.
Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

With the advancement of science and technology, the combination of the unmanned aerial vehicle (UAV) and camera surveillance systems (CSS) is currently a promising solution for practical applications related to security and surveillance operations. However, one of the biggest risks and challenges for the UAV-CSS is analysis, process, and transmission data, especially, the limitations of computational capacity, storage and overloading the transmission bandwidth. Regard to conventional methods, almost the data collected from UAVs is processed and transmitted that cost huge energy. A certain amount of data is redundant and not necessary to be processed or transmitted. This paper proposes an efficient algorithm to optimize the transmission and reception of data in UAV-CSS systems, based on the platforms of artificial intelligence (AI) for data processing. The algorithm creates an initial background frame and update to the complete background which is sent to server. It splits the region of interest (moving objects) in the scene and then sends only the changes. This supports the CSS to reduce significantly either data storage or data transmission. In addition, the complexity of the systems could be significantly reduced. The main contributions of the algorithm can be listed as follows; The developed solution can reduce data transmission significantly. The solution can empower smart manufacturing via camera surveillance. Simulation results have validated practical viability of this approach.
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models

Backdoor attacks, which maliciously control a well-trained model's outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusing deep neural networks (DNNs).In this work, we propose an efficient online defense mechanism based on robustness-aware perturbations.Specifically, by analyzing the backdoor training process, we point out that there exists a big gap of robustness between poisoned and clean samples.Motivated by this observation, we construct a word-based robustness-aware perturbation to distinguish poisoned samples from clean samples to defend against the backdoor attacks on natural language processing (NLP) models.Moreover, we give a theoretical analysis about the feasibility of our robustness-aware perturbation-based defense method.Experimental results on sentiment analysis and toxic detection tasks show that our method achieves better defending performance and much lower computational costs than existing online defense methods.Our code is available at https://github.com/lancopku/RAP.
Novel nondelay-based reservoir computing with a single micromechanical nonlinear resonator for high-efficiency information processing

Reservoir computing is a potential neuromorphic paradigm for promoting future disruptive applications in the era of the Internet of Things, owing to its well-known low training cost and compatibility with hardware. It has been successfully implemented by injecting an input signal into a spatially extended reservoir of nonlinear nodes or a temporally extended reservoir of a delayed feedback system to perform temporal information processing. Here we propose a novel nondelay-based reservoir computer using only a single micromechanical resonator with hybrid nonlinear dynamics that removes the usually required delayed feedback loop. The hybrid nonlinear dynamics of the resonator comprise a transient nonlinear response, and a Duffing nonlinear response is first used for reservoir computing. Due to the richness of this nonlinearity, the usually required delayed feedback loop can be omitted. To further simplify and improve the efficiency of reservoir computing, a self-masking process is utilized in our novel reservoir computer. Specifically, we numerically and experimentally demonstrate its excellent performance, and our system achieves a high recognition accuracy of 93% on a handwritten digit recognition benchmark and a normalized mean square error of 0.051 in a nonlinear autoregressive moving average task, which reveals its memory capacity. Furthermore, it also achieves 97.17 ± 1% accuracy on an actual human motion gesture classification task constructed from a six-axis IMU sensor. These remarkable results verify the feasibility of our system and open up a new pathway for the hardware implementation of reservoir computing.