Reviewerly/exHarmony huggingface dataset:
Relation between spectra of Narain CFTs and properties of associated boolean functions

Recently, the construction of Narain CFT from a certain class of quantum error correcting codes has been discovered. In particular, the spectral gap of Narain CFT corresponds to the binary distance of the code, not the genuine Hamming distance. In this paper, we show that the binary distance is identical to the so-called EPC distance of the boolean function uniquely associated with the quantum code. Therefore, seeking Narain CFT with high spectral gap is equivalent to getting a boolean function with high EPC distance. Furthermore, this problem can be addressed by finding lower Peak-to-Average Power ratio (PAR) with respect to the binary truth table of the boolean function. Though this is neither sufficient nor necessary condition for high EPC distance, we construct some examples of relatively high EPC distances referring to the constructions for lower PAR. We also see that codes with high distance are related to induced graphs with low independence numbers.
SADN: Learned Light Field Image Compression with Spatial-Angular Decorrelation

Light field image becomes one of the most promising media types for immersive video applications. In this paper, we propose a novel end-to-end spatial-angular-decorrelated network (SADN) for high-efficiency light field image compression. Different from the existing methods that exploit either spatial or angular consistency in the light field image, SADN decouples the angular and spatial information by dilation convolution and stride convolution in spatial-angular interaction, and performs feature fusion to compress spatial and angular information jointly. To train a stable and robust algorithm, a large-scale dataset consisting of 7549 light field images is proposed and built. The proposed method provides 2.137 times and 2.849 times higher compression efficiency relative to H.266/VVC and H.265/HEVC inter coding, respectively. It also outperforms the end-to-end image compression networks by an average of 79.6% bitrate saving with much higher subjective quality and light field consistency.
ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections

With the continuous development of neural networks for computer vision tasks, more and more network architectures have achieved outstanding success. As one of the most advanced neural network architectures, DenseNet shortcuts all feature maps to solve the model depth problem. Although this network architecture has excellent accuracy with low parameters, it requires an excessive inference time. To solve this problem, HarDNet reduces the connections between the feature maps, making the remaining connections resemble harmonic waves. However, this compression method may result in a decrease in the model accuracy and an increase in the parameters and model size. This network architecture may reduce the memory access time, but its overall performance can still be improved. Therefore, we propose a new network architecture, ThreshNet, using a threshold mechanism to further optimize the connection method. Different numbers of connections for different convolution layers are discarded to accelerate the inference of the network. The proposed network has been evaluated with image classification using CIFAR 10 and SVHN datasets under platforms of NVIDIA RTX 3050 and Raspberry Pi 4. The experimental results show that, compared with HarDNet68, GhostNet, MobileNetV2, ShuffleNet, and EfficientNet, the inference time of the proposed ThreshNet79 is 5%, 9%, 10%, 18%, and 20% faster, respectively. The number of parameters of ThreshNet95 is 55% less than that of HarDNet85. The new model compression and model acceleration methods can speed up the inference time, enabling network models to operate on mobile devices.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instance-instance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that Uni-VIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space

Local differential privacy (LDP), which perturbs each user's data locally and only sends the noisy version of her information to the aggregator, is a popular privacy-preserving data collection mechanism. In LDP, the data collector could obtain accurate statistics without access to original data, thus guaranteeing users' privacy. However, a primary drawback of LDP is its disappointing utility in high-dimensional space. Although various LDP schemes have been proposed to reduce perturbation, they share the same and naive aggregation mechanism at the collector's side. In this paper, we first bring forward an analytical framework to generally measure the utilities of LDP mechanisms in high-dimensional space, which can benchmark existing and future LDP mechanisms without conducting any experiment. Based on this, the framework further reveals that the naive aggregation is sub-optimal in high-dimensional space, and there is much room for improvement. Motivated by this, we present a re-calibration protocol HDR4ME for high-dimensional mean estimation, which improves the utilities of existing LDP mechanisms without making any change to them. Both theoretical analysis and extensive experiments confirm the generality and effectiveness of our framework and protocol.
Emoji Identification and Emoji Effects on Sentence Emotionality in ASD-Diagnosed Adults and Neurotypical Controls

We investigated ASD-diagnosed adults' and neurotypical (NT) controls' processing of emoji and emoji influence on the emotionality of otherwise-neutral sentences. Study 1 participants categorised emoji representing the six basic emotions using a fixed-set of emotional adjectives. Results showed that ASD-diagnosed participants' classifications of fearful, sad, and surprised emoji were more diverse and less 'typical' than NT controls' responses. Study 2 participants read emotionally-neutral sentences; half paired with sentence-final happy emoji, half with sad emoji. Participants rated sentence + emoji stimuli for emotional valence. ASD-diagnosed and NT participants rated sentences + happy emoji as equally-positive, however, ASD-diagnosed participants rated sentences + sad emoji as more-negative than NT participants. We must acknowledge differential perceptions and effects of emoji, and emoji-text inter-relationships, when working with neurodiverse stakeholders.
Effective memetic algorithm for multilabel feature selection using hybridization-based communication

Multilabel feature selection, which is used to select features relevant to multiple labels, has demonstrated its effectiveness in prediction time and learning accuracy. A memetic algorithm that uses a feature subset refining process has been verified to outperform existing genetic algorithms in identifying an optimal feature subset. However, as the refinement process is consistently applied to all solutions of feature subsets, similar feature subsets can be over-produced, thereby limiting the synergy of hybridization. Here, we propose an evolutionary multilabel feature selection algorithm that searches the final feature subset using multiple populations to prevent limiting the synergy of hybridization. A new hybridization-based communication process refines solutions originated from each best solution of multiple populations, then, randomly distributes the produced solutions. With this approach, the proposed method circumvents the degradation of search capability and keeps the synergy of hybridization. Our experimental results indicate that the proposed method could identify better feature subsets than conventional methods.
Localization of stroke lesion in MRI images using object detection techniques: A comprehensive review

Stroke is one of the lethal diseases that has significant negative impact on an individual's life. To diagnose stroke, MRI images play an important role. A large number of images are being produced day by day such as MRI (Medical Resonance Imaging), CT (Computed Tomography) X-Ray images and many more. Machine Learning algorithms are less efficient and time-consuming in localization of such medical images. Object detection using deep learning can reduce the efforts and time required in screening and evaluation of these images. In the proposed paper, several approaches such as RCNN (Region-based Convolutional Neural-Network), Fast R-CNN (Fast Region-based Convolutional Neural Network), Faster R-CNN (Faster Region-based Convolutional Neural Network with Region proposal Network), YOLO (You Only Look Once), SSD (Single-Shot Multibox Detector) and Efficient-Det are listed which can be used for stroke localization and classification. Comparison of RCNN, Fast R-CNN, Faster R-CNN, YOLO, SSD and Efficient-Det with accuracy are also present in this paper. A Chart of the Data Set available for object detection is also considered in this paper. By The maP (Mean-Average Precision) and the accuracy of every single method, it is identified that the speed and accuracy need to poise.
Complete quadruple extraction using a two-stage neural model for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task which aims to identify the emotional polarity of a specific aspect in a text or sentence. Aspect term extraction (ATE), opinion term extraction (OTE) and aspect polarity classification (APC) are three main subtasks of the ABSA task. Nowadays, researchers mainly focus on a single task or a joint task composed of these three subtasks, and such investigation on the sentiment analysis is not sufficient. In this paper, we firstly introduce a complete aspect sentiment analysis task, called Aspect Sentiment Quadruple Extraction, which also includes the category detection beside ATE, OTE and APC. Then we propose a two-stage neural network model composed of several modules, including BiLSTM, simple gated self-attention and position encoding for this joint task. In the first stage, the proposed model extracts aspect and opinion terms as well as their categories and polarities. Moreover, the second stage mainly includes a relation classifier to validate the aspect-opinion pairs and then finalizes the complete quadruple extraction. The experimental results, evaluated on a benchmark dataset of Chinese product reviews, show that our proposed model outperforms other baseline methods and achieves the start-of-art performance.
Long-distance distribution of qubit-qubit entanglement using Gaussian-correlated photonic beams

We investigate the deterministic generation and distribution of entanglement in large quantum networks by driving distant qubits with the output fields of a non-degenerate parametric amplifier. In this setting, the amplifier produces a continuous Gaussian two-mode squeezed state, which acts as a quantum-correlated reservoir for the qubits and relaxes them into a highly entangled steady state. Here we are interested in the maximal amount of entanglement and the optimal entanglement generation rates that can be achieved with this scheme under realistic conditions taking, in particular, the finite amplifier bandwidth, waveguide losses and propagation delays into account. By combining exact numerical simulations of the full network with approximate analytic results, we predict the optimal working point for the amplifier and the corresponding qubit-qubit entanglement under various conditions. Our findings show that this passive conversion of Gaussian- into discrete- variable entanglement offers a robust and experimentally very attractive approach for operating large optical, microwave or hybrid quantum networks, for which efficient parametric amplifiers are currently developed.
CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection

Lifelong learning addresses the challenge of acquiring new knowledge and tackling new tasks in a continually evolving environment. Although this thread of research has recently received increased interest, most lifelong machine learning approaches proposed thus far focus on object recognition or classification tasks. In contrast, lifelong approaches for anomaly detection are still unexplored. This paper presents a method for lifelong anomaly detection loosely based on biological principles, which can adapt to the environment and efficiently recall old information from its memory bank. Inspired by the interaction between the cortex and the hippocampus in biology, we combine deep learning with statistical change point detection. Our method induces concepts from its environment and organizes them in a semantically coherent forest structure in an unsupervised manner. At runtime, we analyze new objects, one by one, with respect to the current forest of concepts. If a new object fits an existing concept, it is added to the pool of objects representing that concept. Otherwise, it is further analyzed to determine whether it represents a new concept, a new sub-concept, or it is an anomaly. Experiments conducted over different applied settings show that the synergic interaction of change point detection with an evolving forest of concepts yields a higher anomaly detection performance than state-of-the-art methods.
A dual attention model based on probabilistically mask for 3D human motion prediction

Previous work based on attention and Graph Convolutional Network (GCN) have been shown impressive performance in 3D human motion prediction. We believe that 3D human motion prediction problem can be explained as a modeling task of understanding and synthesizing human poses based on learning sub-sequences of historical moments. However, existing methods fail to model the observation that human motion can be learned from historical state. In this paper, we propose a dual attention architecture, optimizing the utilization of historical sequences in two aspects. First, we introduce the local pre-attention based on temporal autocorrelation inside each sub-sequence for the current and historical sub-sequences. Specifically, our research uses the dependence of each moment and its several historical moments to access and capture past information selectively, generating reasonable historical time sub-sequence representation, which is a kind of generative sampling model matching with scores. Then, the current and historical sub-sequences are embedded into global attention to obtain the historical information that contributes the most to the observed sub-sequence. On the other hand, instead of using simple GCN with over-smoothing problem, We extend GCN to a multi-layer residual graph structure. The 3D mean per joint position error (MPJPE) and the mean angle error (MAE) experiments on the datasets of Human3.6 M and 3DPW prove that our model can obtain more accurate prediction results than previous methods. Our code is available at https://github.com/lishuangshuang2022/DANet.
Boosting adversarial attacks with transformed gradient

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to benign examples. Increasing the attack success rate usually requires a larger noise magnitude, which leads to noticeable noise. To this end, we propose a Transformed Gradient method (TG), which achieves a higher attack success rate with lower perturbations against the target model, i.e. an ensemble of black-box defense models. It consists of three steps: original gradient accumulation, gradient amplification, and gradient truncation. Besides, we introduce the Fr e ´ chet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS) respectively to evaluate fidelity and perceived distance from the original example, which is more comprehensive than only using L ∞ norm as evaluation metrics. Furthermore, we propose optimizing coefficients of the source-model ensemble to improve adversarial attacks. Extensive experimental results demonstrate that the perturbations of adversarial examples generated by our proposed method are less than the state-of-the-art baselines, namely MI, DI, TI, RF-DE based on vanilla iterative FGSM and their combinations. Compared with the baseline method, the average black-box attack success rate and total score are improved by 6.6% and 13.8, respectively. We make our codes public at Github https://github.com/Hezhengyun/Transformed-Gradient .
Data-based model maintenance in the era of industry 4.0: A methodology

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A concept drift is a change in data distribution which will, at some point, decrease the accuracy of the model. To address this problem, various frameworks are presented in the literature, but there is no optimal methodology for implementing them. This paper presents a methodology to implement a problem-oriented complete solution to ensure the maintenance of an industrial data-based model. The final drift-handling solution is composed of a sampling decision system and an update system. The methodology begins with a concept-drift identification phase. Solutions are then pre-selected based on the identified concept drifts. Next, an optimization problem is designed to select the solution that optimizes the costs and respects the constraints. To better link the concept drift characteristics and the drift-handling solutions, a causal concept-drift classification system is proposed. The industrial implementation of such a solution is discussed and several questions are raised. This paper presents an original and detailed methodology that shows encouraging results to address the model-maintenance challenge; however, concept drift identification, and links between concept-drift characteristics and drift detection, require further research.
ProvNet: Networked bi-directional blockchain for data sharing with verifiable provenance

Data sharing is increasingly popular especially for scientific research and business fields where large volume of datasets need to be used, but it involves data security and privacy concerns. This paper mitigates such concerns by tracking and logging the history of shared data (i.e., provenance records) while preserving data privacy. This is a challenging problem in the data sharing scenario in this paper because the environment is decentralized and internal logs are not accessible publicly due to privacy concerns. We present ProvNet, a decentralized data sharing platform that can detect malicious users and provide secure provenance records using the newly proposed networked blockchain without disclosing raw data contents. Valid sharing records are collected and stored in the blocknet and misbehavior is detected with the stored provenance records according to our accountable protocols. We give a proof-of-concept implementation, and evaluation results show that the overhead is acceptable.
Implementation of quantum compression on IBM quantum computers

Advances in development of quantum computing processors brought ample opportunities to test the performance of various quantum algorithms with practical implementations. In this paper we report on implementations of quantum compression algorithm that can efficiently compress unknown quantum information. We restricted ourselves to compression of three pure qubits into two qubits, as the complexity of even such a simple implementation is barely within the reach of today's quantum processors. We implemented the algorithm on IBM quantum processors with two different topological layouts-a fully connected triangle processor and a partially connected line processor. It turns out that the incomplete connectivity of the line processor affects the performance only minimally. On the other hand, it turns out that the transpilation, i.e. compilation of the circuit into gates physically available to the quantum processor, crucially influences the result. We also have seen that the compression followed by immediate decompression is, even for such a simple case, on the edge or even beyond the capabilities of currently available quantum processors.
Enhancing dynamic energy network management using a multiagent cloud-fog structure

Smart Grid benefits from information and communications technology (ICT) to integrate data from different sources across the network. The growing market for electric vehicles (EV) and electric devices is increasing energy demand. To manage a large amount of complex intelligent equipment, EV charging and discharging, and data-intensive devices, a reliable modern smart grid with a service-oriented, secure, efficient, and cost-effective system is compelling. Cloud computing is a promising approach to achieve that objective as monitoring power grid data flow and data center management are the key. This paper proposes an optimization model for energy management within the power cloud. Our energy system model enables accessible services to computing resources and real-time data stream processing within an integrated environment. Using digital technology, the proper implementation of our model reduces costs and energy consumption and improves the smart grid reliability for customers and energy providers in a distributed manner. This paper presents the implementation of a three-procedure optimization algorithm within a novel Multi-Agent Cloud-fog Structure (MACS) to meet the requirements raised by smart grid communication and distribution. Our model promotes reliable energy consumption adjustments by end-users who can choose power supplied by solar, wind, geothermal, biomass, or other renewable sources or from non-renewable sources in ways that reveal opportunities for demand-supply balance and energy saving.