Reviewerly/exHarmony huggingface dataset:
Reliability Modeling of NISQ- Era Quantum Computers

Recent developments in quantum computers have been pushing up the number of qubits. However, the state-of-the-art Noisy Intermediate Scale Quantum (NISQ) computers still do not have enough qubits to accommodate the error correction circuit. Noise in quantum gates limits the reliability of quantum circuits. To characterize the noise effects, prior methods such as process tomography, gateset tomography and randomized benchmarking have been proposed. However, the challenge is that these methods do not scale well with the number of qubits. Noise models based on the understanding of underneath physics have also been proposed to study different kinds of noise in quantum computers. The difficulty is that there is no widely accepted noise model that incorporates all different kinds of errors. The realworld errors can be very complicated and it remains an active area of research to produce accurate noise models. In this paper, instead of using noise models to estimate the reliability, which is measured with success rates or inference strength, we treat the NISQ quantum computer as a black box. We use several quantum circuit characteristics such as the number of qubits, circuit depth, the number of CNOT gates, and the connection topology of the quantum computer as inputs to the black box and derive a reliability estimation model using (1) polynomial fitting and (2) a shallow neural network. We propose randomized benchmarks with random numbers of qubits and basic gates to generate a large data set for neural network training. We show that the estimated reliability from our black-box model outperforms the noise models from Qiskit. We also showcase that our black-box model can be used to guide quantum circuit optimization at compile time.
Interactive Multi-dimension Modulation with Dynamic Controllable Residual Learning for Image Restoration

Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient. However, real images always contain multiple types of degradation, which cannot be well determined by one coefficient. To make a step forward, this paper presents a new problem setup, called multi-dimension (MD) modulation, which aims at modulating output effects across multiple degradation types and levels. Compared with the previous single-dimension (SD) modulation, the MD is setup to handle multiple degradations adaptively and relief unbalanced learning problem in different degradations. We also propose a deep architecture - CResMD with newly introduced controllable residual connections for multi-dimension modulation. Specifically, we add a controlling variable on the conventional residual connection to allow a weighted summation of input and residual. The values of these weights are generated by another condition network. We further propose a new data sampling strategy based on beta distribution to balance different degradation types and levels. With corrupted image and degradation information as inputs, the network can output the corresponding restored image. By tweaking the condition vector, users can control the output effects in MD space at test time. Extensive experiments demonstrate that the proposed CResMD achieve excellent performance on both SD and MD modulation tasks. Code is available at https://github.com/hejingwenhejingwen/CResMD .
Securely and Efficiently Outsourcing Decision Tree Inference

Outsourcing machine learning inference services to the cloud is getting increasingly popular. However, this also entails privacy risks to the provider's proprietary model and the client's sensitive data. Focusing on inference with decision trees, this article proposes a framework for securely and efficiently outsourcing decision tree inference. Targeting both privacy and efficiency, we propose a customized protocol using only lightweight cryptography in the online execution of secure inference. We resort to additive secret sharing and tackle the problems in various components including secure input feature selection, decision node evaluation, and inference result generation. Our protocol requires no interaction from the provider and client during online secure inference, a distinct advantage over prior works for practical deployment as they all operate under the client-provider setting where synchronous and continuous interaction is required. Performance evaluation demonstrates our security design's efficiency, as well as substantial performance benefits for the client (up to four orders of magnitude in computation and 163 times in communication), as opposed to prior art in the non-outsourcing setting. To facilitate the practical usage for meeting more service demands, we also investigate the extensions for secure outsourced inference of random forests and categorical feature-based decision trees.
Residual error based knowledge distillation

Knowledge distillation (KD) is one of the most popular ways for model compression. The key idea is to transfer the knowledge from a deep teacher model (T) to a shallower student (S). However, existing methods suffer from performance degradation due to the substantial gap between the learning capacities of S and T. To remedy this problem, this paper proposes Residual error based Knowledge Distillation (RKD), which further distills the knowledge by introducing an assistant model(A). Specifically, S is trained to mimic the feature maps of T, and A aids this process by learning the residual error between them. In this way, S and A complement with each other to get better knowledge from T. Furthermore, we devise an effective method to derive S and A from a given model without increasing the total computational cost. Extensive experiments show that our approach achieves appealing results on popular classification datasets, CIFAR-100 and ImageNet, surpassing state-of-the-art methods and keep strong robustness to adversarial samples.
Data-driven symbol detection via model-based machine learning

We present a data-driven framework to symbol detection design that combines machine learning (ML) and model-based algorithms. The resulting data-driven receivers are most suitable for systems where the underlying channel models are poorly understood, highly complex, or do not well-capture the underlying physics. Our approach is unique in that it only replaces the channel-model-based computations with dedicated neural networks that can be trained from a small amount of data, while keeping the general algorithm intact. Our results demonstrate that these techniques can yield performance close to that of model-based algorithms with perfect model knowledge without knowing the exact channel model or state.
Visually Induced Motion Sickness on the Horizon

Visually induced motion sickness is an unpleasant but common side-effect of many simulations and VR-applications. We investigated whether an earth-fixed reference frame provided in the simulation is able to reduce motion sickness. To do so, we created a moving starfield that did not contain any indicators of the spatial orientation of the observer. As the observer was simulated to move through the randomly oscillating starfield, a time-to-contact task had to be carried out. Two colored stars on collision course with each other had to be spotted, then they disappeared and the time of their collision had to be judged. Eye-movements, task performance, and motion sickness were recorded. This condition without visual reference to the observer's upright was supplemented with three conditions containing either an earth-fixed fixation cross, an earth-fixed horizon line, or a line that was yoked to the head. Results show that only the earth-fixed horizon was able to significantly reduce visually induced motion sickness. Thus, a mere earth-stationary anchor does not suffice, a clear indication of earth horizontal seems necessary to reap a modest benefit.
Learning to Predict Context-Adaptive Convolution for Semantic Segmentation

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K.
Visual Object Tracking and Servoing Control of a Nano-Scale Quadrotor: System, Algorithms, and Experiments

There are two main trends in the development of unmanned aerial vehicle (UAV) technologies: miniaturization and intellectualization, in which realizing object tracking capabilities for a nano-scale UAV is one of the most challenging problems. In this paper, we present a visual object tracking and servoing control system utilizing a tailor-made 38 g nano-scale quadrotor. A lightweight visual module is integrated to enable object tracking capabilities, and a micro positioning deck is mounted to provide accurate pose estimation. In order to be robust against object appearance variations, a novel object tracking algorithm, denoted by RMCTer, is proposed, which integrates a powerful short-term tracking module and an efficient long-term processing module. In particular, the long-term processing module can provide additional object information and modify the short-term tracking model in a timely manner. Furthermore, a position-based visual servoing control method is proposed for the quadrotor, where an adaptive tracking controller is designed by leveraging backstepping and adaptive techniques. Stable and accurate object tracking is achieved even under disturbances. Experimental results are presented to demonstrate the high accuracy and stability of the whole tracking system.
A Low-Cost Multi-Failure Resilient Replication Scheme for High-Data Availability in Cloud Storage

Data availability is one of the most important performance factors in cloud storage systems. To enhance data availability, replication is a common approach to handle the machine failures. However, previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures, especially while increasing the data availability with limited resources. The schemes for correlated machine failures must create a constant number of replicas for each data object, which often neglects diverse data popularities and does not utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability (hence minimize SLA violations) while minimizing costs caused by replication in order to maximize the revenue. In this paper, we build a nonlinear integer programming model to maximize data availability in both types of failures, and therefore minimize the cost caused by replication. Based on the model's solution for the replication degree of each data object, we propose a low-cost multi-failure (correlated and non-correlated machine failures) resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. Extensive numerical results from trace parameters and experiments from real-world Amazon S3 demonstrate that MRR achieves high data availability, low data loss probability and low consistency maintenance and storage costs when compared to previous replication schemes.
Radio frequency fingerprinting identification for Zigbee via lightweight CNN

Zigbee is a popular communication protocol in the Internet of things (IoT) which shows great potential in smart home. However, the smart device has the risk of being hijacked by unauthorized users and may result in privacy disclosure. Traditional device identification is based on cryptography which is easy to be cracked. Recently, radio frequency fingerprinting identification (RFFID) is popular in device identification. Traditional RFFID's power consumption and cost is unacceptable to Zigbee. In order to reduce the cost, more effective model can be used to reduce the number of neurons. This paper proposes a RFFID method based on lightweight convolution neural network (CNN) which can adopt low power consumption and cost. The simulation result shows that this method can identification Zigbee device, and the accuracy reached 100%. Also, the parameter has reduced to about 93%.
Examining augmented reality in journalism: Presence, knowledge gain, and perceived visual authenticity

This article examines the impact of augmented reality (AR) visualizations on users’ sense of physical presence, knowledge gain, and perceptions of the authenticity of journalistic visuals. In a mixed experimental design, 79 participants were randomly assigned to view three The New York Times articles on a mobile phone featuring one of three viewing modalities: (1) AR visualizations, (2) interactive (non-AR) visualizations, or (3) non-interactive, static visualizations. AR induced a greater sense of physical presence compared to the other modalities. The findings suggest that immersive properties of AR can contribute to journalism’s goal of engaging the audience. However, AR was not a superior medium for informing the participants, and the viewing modality did not have an effect on the perceived authenticity of the visuals. The findings indicate a need for more efficient ways to relay information through journalistic AR visualizations while keeping the user engaged in an immersive experience.
Adversarial Ranking Attack and Defense

Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations. Specifically, the expected ranking order is first represented as a set of inequalities, and then a triplet-like objective function is designed to obtain the optimal perturbation. Conversely, a defense method is also proposed to improve the ranking system robustness, which can mitigate all the proposed attacks simultaneously. Our adversarial ranking attacks and defense are evaluated on datasets including MNIST, Fashion-MNIST, and Stanford-Online-Products. Experimental results demonstrate that a typical deep ranking system can be effectively compromised by our attacks. Meanwhile, the system robustness can be moderately improved with our defense. Furthermore, the transferable and universal properties of our adversary illustrate the possibility of realistic black-box attack.
Deep Image Clustering with Category-Style Representation

Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment. To achieve this goal, mutual information maximization is applied to embed relevant information in the latent representation. Moreover, augmentation-invariant loss is employed to disentangle the representation into category part and style part. Last but not least, a prior distribution is imposed on the latent representation to ensure the elements of the category vector can be used as the probabilities over clusters. Comprehensive experiments demonstrate that the proposed approach outperforms state-of-the-art methods significantly on five public datasets (Project address: https://github.com/sKamiJ/DCCS ).
Object detection system based on SSD algorithm

SSD (Single Shot Multi Box Detector) is an object detection algorithm based on deep learning. As one of the most mainstream detection algorithms, it can greatly improve the detection speed and ensure the detection accuracy. In this paper, the Batch Norm operation is added to the network in order to improve the generalization of the network and speed up network training. The object counting function is added to the image recognition. This paper uses SSD algorithm that incorporates Batch Norm algorithm. The object detection system was built by the Flask framework and the Layui framework. The system can select the data to be detected on the front-end page, the detection results and the number of each type of object were displayed on the front-end page in real time.
Color Facial Expression Recognition by Quaternion Convolutional Neural Network With Gabor Attention

Facial expression recognition (FER) plays an important role in cognitive psychology research. In FER studies, deep convolutional neural networks (CNNs) and attention mechanisms have shown great advantages over traditional techniques. Although many attention-based CNN models have been developed, most of them are proposed to deal with grayscale images. For color images, these network models usually take their grayscale versions as the input or convert them to grayscale data by the first hidden layer. Furthermore, their attention modules focus on only grayscale information. This mechanism does not fully consider the correlation between color channels, which will degrade the performance of color FER. To alleviate these shortcomings, we introduce quaternion techniques to CNNs and propose a quaternion CNN integrated with an attention mechanism (QA-CNN) for color FER. The proposed QA-CNN takes into account not only the correlation between color channels but also expressional attention. The new attention mechanism is based on a multidirectional quaternion Gabor filter. Besides, the proposed QA-CNN reduces the number of network parameters by 75% compared with the real-valued CNN with the same structure. Experimental results on three widely used data sets demonstrate the effectiveness of the proposed QA-CNN by showing clear performance improvements over other state-of-the-art FER methods.
Natural language processing for similar languages, varieties, and dialects: A survey

Abstract There has been a lot of recent interest in the natural language processing (NLP) community in the computational processing of language varieties and dialects, with the aim to improve the performance of applications such as machine translation, speech recognition, and dialogue systems. Here, we attempt to survey this growing field of research, with focus on computational methods for processing similar languages, varieties, and dialects. In particular, we discuss the most important challenges when dealing with diatopic language variation, and we present some of the available datasets, the process of data collection, and the most common data collection strategies used to compile datasets for similar languages, varieties, and dialects. We further present a number of studies on computational methods developed and/or adapted for preprocessing, normalization, part-of-speech tagging, and parsing similar languages, language varieties, and dialects. Finally, we discuss relevant applications such as language and dialect identification and machine translation for closely related languages, language varieties, and dialects.
Moth Swarm Algorithm for Image Contrast Enhancement

Image Contrast Enhancement (ICE) is a crucial step in several image processing and computer vision applications. Its main objective is to improve the quality of the visual information contained in the processed images. The presence of noise and small sets of pixels in images are not only irrelevant for their visualization. It also negatively affects the improvement process of ICE schemes since the inclusion of irrelevant information avoids the appropriate distribution of significant pixel intensities in the enhanced image. As a consequence of this effect, most of the proposed ICE methods present different associated problems such as the production of undesirable artifacts, noise amplification, over saturation and bad human visual perception. In this paper, an Image Contrast Enhancement (ICE) method for grayscale and color images is presented. The proposed approach has the propriety of eliminating noisy and irrelevant information in order to improve the distribution capacity of significant pixel intensities in the enhanced image. Our method eliminates multiple groups of a very small number of pixels that, according to their characteristics, do not represents any object or important detail of the image. This process is done by the Mean-shift algorithm, which is used to replace such sets of irrelevant pixels in the original histogram by significant pixel densities represented by local maxima. Then, the Moth Swarm Algorithm (MSA) is used to redistribute the pixel intensities of the reduced histogram so that the value from Kullback–Leibler entropy (KL-entropy) has been maximized. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image also improving its human visual appearance.
Modeling spread, interlace and interchange of information processes with variable domains

In this paper a semantic metalanguage is developed and designed to study the occurrence, spread and safe interaction of semantic processes in information modeling systems, including cognitive interference. An approach to construe a semantic network is proposed and based on a computational model in which both nodes and arcs are information processes. Concepts are represented by intensional objects within the framework of theories without types, and they, in turn, are considered as special counterparts of typed theories. Similar mixing was used in model studies for lambda calculus. To a contrast with them, in this paper, information processes correspond to parameterized metadata objects, which are variable domain constructs. Transformations of variable domains correspond to the spread of the process. Directional transformation provides the generation of metadata targets in the form of parameterized concepts. This simulates the development of the process, which corresponds to the spread of cognitive interference and allows the interpretation of a hidden time factor. The emerging model is purely process based and provides such a conceptual framework. The possibility of coding this framework with a system of interdependent lambda terms is reflected.