Reviewerly/exHarmony huggingface dataset:
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime.Why can we use relatively vanilla gradient descent algorithms (e.g., without strong regularization) to tune a model with hundreds of millions of parameters on datasets with only hundreds or thousands of labeled examples?In this paper, we argue that analyzing finetuning through the lens of intrinsic dimension provides us with empirical and theoretical intuitions to explain this remarkable phenomenon.We empirically show that common pre-trained models have a very low intrinsic dimension; there exists a low dimension reparameterization that is as effective for fine-tuning as the full parameter space.For example, by optimizing only 200 trainable parameters randomly projected back into the full space, we can tune a RoBERTa model to achieve 90% of the full parameter performance levels on MRPC.Furthermore, we empirically show that pretraining implicitly minimizes intrinsic dimension and, perhaps surprisingly, larger models tend to have lower intrinsic dimension after a fixed number of pre-training updates, at least in part explaining their extreme effectiveness.Lastly, we connect intrinsic dimensionality with low dimensional task representations and compression based generalization bounds to provide generalization bounds that are independent of the full parameter count.
A Secure and Multiobjective Virtual Machine Placement Framework for Cloud Data Center

To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data center. The inefficient VM placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased intercommunication cost, and security breaches. To address the aforementioned challenges, a novel secure and multiobjective VMP (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs, which emphasizes secure and timely execution of user application by reducing intercommunication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state of the arts observed a notable reduction in shared servers, intercommunication cost, power consumption, and execution time up to 28.81%, 25.7%, 35.9%, and 82.21%, respectively with increased resource utilization up to 30.21%.
Proposal for space-borne quantum memories for global quantum networking

Abstract Global-scale quantum communication links will form the backbone of the quantum internet. However, exponential loss in optical fibres precludes any realistic application beyond few hundred kilometres. Quantum repeaters and space-based systems offer solutions to overcome this limitation. Here, we analyse the use of quantum memory (QM)-equipped satellites for quantum communication focussing on global range repeaters and memory-assisted (MA-) QKD, where QMs help increase the key rate by synchronising otherwise probabilistic detection events. We demonstrate that satellites equipped with QMs provide three orders of magnitude faster entanglement distribution rates than existing protocols based on fibre-based repeaters or space systems without QMs. We analyse how entanglement distribution performance depends on memory characteristics, determine benchmarks to assess the performance of different tasks and propose various architectures for light-matter interfaces. Our work provides a roadmap to realise unconditionally secure quantum communications over global distances with near-term technologies.
Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.
Serverless Computing: State-of-the-Art, Challenges and Opportunities

Serverless computing is growing in popularity by virtue of its lightweight and simplicity of management. It achieves these merits by reducing the granularity of the computing unit to the function level. Specifically, serverless allows users to focus squarely on the function itself while leaving other cumbersome management and scheduling issues to the platform provider, who is responsible for striking a balance between high-performance scheduling and low resource cost. In this article, we conduct a comprehensive survey of serverless computing with a particular focus on its infrastructure characteristics. Whereby some existing challenges are identified, and the associated cutting-edge solutions are analyzed. With these results, we further investigate some typical open-source frameworks and study how they address the identified challenges. Given the great advantages of serverless computing, it is expected that its deployment would dominate future cloud platforms. As such, we also envision some promising research opportunities that need to be further explored in the future. We hope that our work in this article can inspire those researchers and practitioners who are engaged in related fields to appreciate serverless computing, thereby setting foot in this promising area and making great contributions to its development.
Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network

The continuous development of deep learning improves target detection technology day by day. The current research focuses on improving the accuracy of target detection technology, resulting in the target detection model being too large. The number of parameters and detection speed of the target detection model are very important for the practical application of target detection technology in embedded systems. This article proposed a real-time target detection method based on a lightweight convolutional neural network to reduce the number of model parameters and improve the detection speed. In this article, the depthwise separable residual module is constructed by combining depthwise separable convolution and non-bottleneck-free residual module, and the depthwise separable residual module and depthwise separable convolution structure are used to replace the VGG backbone network in the SSD network for feature extraction of the target detection model to reduce parameter quantity and improve detection speed. At the same time, the convolution kernels of 1 × 3 and 3 × 1 are used to replace the standard convolution of 3 × 3 by adding the convolution kernels of 1 × 3 and 3 × 1, respectively, to obtain multiple detection feature graphs corresponding to SSD, and the real-time target detection model based on a lightweight convolutional neural network is established by integrating the information of multiple detection feature graphs. This article used the self-built target detection dataset in complex scenes for comparative experiments; the experimental results verify the effectiveness and superiority of the proposed method. The model is tested on video to verify the real-time performance of the model, and the model is deployed on the Android platform to verify the scalability of the model.
Automatic Summarization of Events from Social Media

Social media services such as Twitter generate phenomenal volume of content for most real-world events on a daily basis. Digging through the noise and redundancy to understand the important aspects of the content is a very challenging task. We propose a search and summarization framework to extract relevant representative tweets from a time-ordered sample of tweets to generate a coherent and concise summary of an event. We introduce two topic models that take advantage of temporal correlation in the data to extract relevant tweets for summarization. The summarization framework has been evaluated using Twitter data on four real-world events. Evaluations are performed using Wikipedia articles on the events as well as using Amazon Mechanical Turk (MTurk) with human readers (MTurkers). Both experiments show that the proposed models outperform traditional LDA and lead to informative summaries.
Modified integral imaging reconstruction and encryption using an improved SR reconstruction algorithm

We propose a monospectral image encryption method in which the multispectral color image acquisition by using heterogeneous monospectral cameras. Because the captured monospectral elemental images (EIs) belongs to grayscale image, it is means that the captured EIs can be directly encrypted by the proposed encoding method. Subsequently, the linear cellular automata (CA) and hyperchaotic encoding algorithm are employed to encrypt the captured EIs. Different from previous methods, the proposed method can directly encrypt the multispectral color information rather than having to divide into three color channels (R, G and B), thereby, the proposed method can greatly reduce the encryption calculation.
Evidential query-by-committee active learning for pedestrian detection in high-density crowds

The automatic detection of pedestrians in dense crowds has become recently a very active topic of research due to the implications for public safety, and also due to the increased frequency of large scale social events. The detection task is complicated by multiple factors such as strong occlusions, high homogeneity, small target size, etc., and different types of detectors are able to provide complementary interpretations of the input data, with varying individual levels of performance. Our first contribution consists in outlining a fusion strategy under the form of an ensemble method, which models the imprecision arising from each of the detectors, both in the calibration and in the spatial domains in an evidential framework. Then, we propose a sample selection for augmenting the training set used jointly by the committee of classifiers, based on evidential disagreement measures among the base members in a Query-by-Committee context. The results show that the proposed fusion algorithm is effective in exploiting the strengths of the individual classifiers, as well as in augmenting the training set with informative samples which allow the resulting detector to enhance its performance.
Local-Diffusion-Based Distributed SMC-PHD Filtering Using Sensors With Limited Sensing Range

We investigate the problem of distributed multitarget tracking by using a set of netted, collaborative sensors with limited sensing range (LSR), where each sensor runs a sequential Monte Carlo probability hypothesis density filter and exchanges relevant posterior information with its neighbors. The key challenge stems from the LSR of neighbor sensors whose fields of view (FoVs) are partially/non-overlapped, and therefore, they may observe different targets at the same time. With regard to the local common FoVs among neighbor sensors, the proposed distributed fusion approach, called local diffusion, performs one iteration of neighbor communication per filtering step in either of two means. One is given by immediate particle exchange, in which a reject-control operation is devised to reduce the number of communicating particles. The other is done by converting the particle distribution to Gaussian functions for parametric information exchange and fusion. The performance of both approaches has been experimentally investigated via simulation for different LSR situations and compared with cutting-edge approaches.
Ranking-Based Deep Cross-Modal Hashing

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications.
TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding

Continuous-valued deep convolutional networks (DNNs) can be converted into accurate rate-coding based spike neural networks (SNNs). However, the substantial computational and energy costs, which is caused by multiple spikes, limit their use in mobile and embedded applications. And recent works have shown that the newly emerged temporal-coding based SNNs converted from DNNs can reduce the computational load effectively. In this paper, we propose a novel method to convert DNNs to temporal-coding SNNs, called TDSNN. Combined with the characteristic of the leaky integrate-andfire (LIF) neural model, we put forward a new coding principle Reverse Coding and design a novel Ticking Neuron mechanism. According to our evaluation, our proposed method achieves 42% total operations reduction on average in large networks comparing with DNNs with no more than 0.5% accuracy loss. The evaluation shows that TDSNN may prove to be one of the key enablers to make the adoption of SNNs widespread.
Connecting the Digital and Physical World: Improving the Robustness of Adversarial Attacks

While deep learning models have achieved unprecedented success in various domains, there is also a growing concern of adversarial attacks against related applications. Recent results show that by adding a small amount of perturbations to an image (imperceptible to humans), the resulting adversarial examples can force a classifier to make targeted mistakes. So far, most existing works focus on crafting adversarial examples in the digital domain, while limited efforts have been devoted to understanding the physical domain attacks. In this work, we explore the feasibility of generating robust adversarial examples that remain effective in the physical domain. Our core idea is to use an image-to-image translation network to simulate the digital-to-physical transformation process for generating robust adversarial examples. To validate our method, we conduct a large-scale physical-domain experiment, which involves manually taking more than 3000 physical domain photos. The results show that our method outperforms existing ones by a large margin and demonstrates a high level of robustness and transferability.
Optical fringe patterns filtering based on multi-stage convolution neural network

Optical fringe patterns are often contaminated by speckle noise, making it difficult to accurately and robustly extract their phase fields. To deal with this problem, we propose a filtering method based on deep learning, called optical fringe patterns denoising convolutional neural network (FPD-CNN), for directly removing speckle from the input noisy fringe patterns. Regularization technology is integrated into the design of deep architecture. Specifically, the FPD-CNN method is divided into multiple stages, each stage consists of a set of convolutional layers along with batch normalization and leaky rectified linear unit (Leaky ReLU) activation function. The end-to-end joint training is carried out using the Euclidean loss. Extensive experiments on simulated and experimental optical fringe patterns, especially finer ones with high-density regions, show that the proposed method is competitive with some state-of-the-art denoising techniques in spatial or transform domains, efficiently preserving main features of fringe at a fairly fast speed.
MARVisT: Authoring Glyph-Based Visualization in Mobile Augmented Reality

Recent advances in mobile augmented reality (AR) techniques have shed new light on personal visualization for their advantages of fitting visualization within personal routines, situating visualization in a real-world context, and arousing users' interests. However, enabling non-experts to create data visualization in mobile AR environments is challenging given the lack of tools that allow in-situ design while supporting the binding of data to AR content. Most existing AR authoring tools require working on personal computers or manually creating each virtual object and modifying its visual attributes. We systematically study this issue by identifying the specificity of AR glyph-based visualization authoring tool and distill four design considerations. Following these design considerations, we design and implement MARVisT, a mobile authoring tool that leverages information from reality to assist non-experts in addressing relationships between data and virtual glyphs, real objects and virtual glyphs, and real objects and data. With MARVisT, users without visualization expertise can bind data to real-world objects to create expressive AR glyph-based visualizations rapidly and effortlessly, reshaping the representation of the real world with data. We use several examples to demonstrate the expressiveness of MARVisT. A user study with non-experts is also conducted to evaluate the authoring experience of MARVisT.
Toward unrestricted use of public genomic data

Publication interests should not limit access to public data
Dynamic Cooperative Coevolution for Large Scale Optimization

The cooperative coevolution (CC) framework achieves a promising performance in solving large scale global optimization problems. The framework encounters difficulties on nonseparable problems, where variables interact with each other. Using the static grouping methods, variables will be theoretically grouped into one big subcomponent, whereas the random grouping strategy endures low efficiency. In this paper, a dynamic CC framework is proposed to tackle the challenge. The proposed framework works in a computationally efficient manner, in which the computational resources are allocated to a series of elitist subcomponents consisting of superior variables. First, a novel estimation method is proposed to evaluate the contribution of variables using the historical information of the best overall fitness. Based on the contribution and the interaction information, a dynamic grouping strategy is conducted to construct the dynamic subcomponent that evolves in the next evolutionary period. The constructed subcomponents are different from each other, and therefore the required parameters to control the optimization of each subcomponent vary a lot in each evolutionary period. A stage-by-stage parameter adaptation strategy is proposed to adapt the optimizer to the dynamic optimization environment. Experimental results indicate that the proposed framework achieves competitive results compared with the state-of-the-art CC frameworks.
CapSurv: Capsule Network for Survival Analysis With Whole Slide Pathological Images

Survival analysis is a branch of statistics to analyze the time duration that is expected until some events of interest happen, like the death in the organisms of biology.Currently, survival analysis based on pathological images has turned out to be a truly energetic area in the research of healthcare for making primary decisions on therapy and improving patients' quality of treatment.In this regard, the interest to design convolutional neural networks for survival analysis with pathological images is increasing greatly at present.Furthermore, to consider the important spatial hierarchies between features and improve the robustness to affine transformation, capsule network (referred to as CapsNet) has been put forward in recent years.A novel capsule network named CapSurv is introduced in this paper, with a new loss function named survival loss to make survival analysis with whole slide pathological images.In addition, to train CapSurv preferably, semantic-level features extracted by VGG16, are used to distinguish discriminative patches from whole slide pathological images.Our method is applied to the predictions of the survival of glioblastoma and lung squamous cell carcinoma with a public cancer dataset.The results illustrate the proposed CapSurv model has the ability to improve the performance of the prediction by comparing with state-of-the-art survival models.
Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Recommender systems that possess adequate information about users and analyze their information, are capable of offering appropriate items to customers.Collaborative filtering method is one of the popular recommender system approaches that produces the best suggestions by identifying similar users or items based on their previous transactions.The low accuracy of suggestions is one of the major concerns in the collaborative filtering method.Several methods have been introduced to enhance the accuracy of this method through the discovering association rules and using evolutionary algorithms such as particle swarm optimization.However, their runtime performance does not satisfy this need, thus this article proposes an efficient method of producing cred associations rules with higher performances based on a genetic algorithm.Evaluations were performed on the data set of MovieLens.The parameters of the assessment are: run time, the average of quality rules, recall, precision, accuracy and F1-measurement.The experimental evaluation of a system based on our algorithm outperforms show than the performance of the multiobjective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.
Proactive Deployment of Aerial Drones for Coverage over Very Uneven Terrains: A Version of the 3D Art Gallery Problem

The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method.