Reviewerly/exHarmony huggingface dataset:
FL-MAB

Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that the integration of blockchain and smart contract technologies to FL can solve the two aforementioned issues. In this paper, we present FL-MAB (FL- Multi-Auction using Blockchain), a client selection mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. FL-MAB employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows FL-MAB to measure the relative contribution of each client by calculating a Shapley value, and allocating rewards accordingly. We have implemented FL-MAB using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that FL-MAB outperforms existing baseline schemes by improving accuracy and reducing the no. of FL rounds.
Evolutionary computation

tutorial Evolutionary computation: a unified approach Share on Author: Kenneth De Jong George Mason University George Mason UniversityView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 327–342https://doi.org/10.1145/3377929.3389871Online:08 July 2020Publication History 2citation281DownloadsMetricsTotal Citations2Total Downloads281Last 12 Months19Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Predictive Model for Dyslexia from Eye Fixation Events

Dyslexia is a specific learning disorder where the individual often find difficulty in spelling and reading words fluently. Dyslexia is non-curable but with right remedial support, dyslexics can become highly successful in academics and life. Eye movement patterns during reading process can provide an in-depth understanding about reading disorders caused by dyslexia. Eye movements can be captured using eye-tracker, from which the relationship between how eyes move with respect to the words they read can be understood. In this work, a set of binocular fixation and saccade features were extracted from raw eye tracking data based on statistical measures. Machine learning algorithms such as Random Forest Classifier (RF), Support Vector Machine (SVM) for classification and K-Nearest Neighbor (KNN) were analyzed to output classification models for prediction of dyslexia. KNN gave higher levels of accuracy of 95% compared to SVM and RF over a small feature set of features related to fixations and saccades. These eye features can be used as a basis for developing screening means for prediction of dyslexia. Prediction of dyslexia at an early stage can help children to go for remediation which helps them for academic excellence.
JPEG Steganography With Content Similarity Evaluation

Content similarity is a representative property of natural images, for example, similar regions, which is utilized by modern steganalysis. Existing JPEG steganographic methods mainly focus on the complexity of content but ignore content similarity. This article investigates content similarity to improve the undetectability of JPEG steganography. Specifically, the content similarity of DCT blocks and the 64 parallel channels is used to design the distortion function. Given a JPEG image, initial embedding costs are assigned for quantized DCT coefficients using an appropriate algorithm among the existing distortion functions. Then, the similarities of blocks and channels are used to update the initial embedding costs, respectively. After combination, the final distortion function can be obtained. Using syndrome trellis coding (STC), which achieves minimal embedding distortion with respect to a given distortion function, secret data are embedded into the cover image with a final distortion function. Experimental results show that our scheme achieves better undetectability than current state-of-the-art JPEG steganographic methods.
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children’s ASR

Self-supervised learning (SSL) in the pretraining stage using un-annotated speech data has been successful in low-resource automatic speech recognition (ASR) tasks. However, models trained through SSL are biased to the pretraining data which is usually different from the data used in finetuning tasks, causing a domain shifting problem, and thus resulting in limited knowledge transfer. We propose a novel framework, domain responsible adaptation and finetuning (DRAFT), to reduce domain shifting in pretrained speech models through an additional adaptation stage. In DRAFT, residual adapters (RAs) are inserted in the pretrained model to learn domain-related information with the same SSL loss as the pretraining stage. Only RA parameters are updated during the adaptation stage. DRAFT is agnostic to the type of SSL method used and is evaluated with three widely used approaches: APC, Wav2vec2.0, and HuBERT. On two child ASR tasks (OGI and MyST databases), using SSL models trained with un-annotated adult speech data (Librispeech), relative WER improvements of up to 19.7% are observed when compared to the pretrained models without adaptation. Additional experiments examined the potential of cross knowledge transfer between the two datasets and the results are promising, showing a broader usage of the proposed DRAFT framework.
Bandwidth-Efficient Multi-video Prefetching for Short Video Streaming

Applications that allow sharing of user-created short videos exploded in popularity in recent years. A typical short video application allows a user to swipe away the current video being watched and start watching the next video in a video queue. Such user interface causes significant bandwidth waste if users frequently swipe a video away before finishing watching. Solutions to reduce bandwidth waste without impairing the Quality of Experience (QoE) are needed. Solving the problem requires adaptively prefetching of short video chunks, which is challenging as the download strategy needs to match unknown user viewing behavior and network conditions. In our work, we first formulate the problem of adaptive multi-video prefetching in short video streaming. Then, to facilitate the integration and comparison of researchers' algorithms towards solving the problem, we design and implement a discrete-event simulator, which we release as open source. Finally, based on the organization of the Short Video Streaming Grand Challenge at ACM Multimedia 2022, we analyze and summarize the algorithms of the contestants, with the hope of promoting the research community towards addressing this problem.
Handling tactful data in cloud using PKG encryption technique

With the availability of the cloud storage services, users can store data to the cloud and have the access to share the data to others. The cloud file may contain some sensitive information, this information shouldn&apos;t be exposed to others. The possible solution could be to encrypt the whole file which is shared before sending it to the cloud and to generate the signature to verify the encrypted file. Then, upload the encrypted file and signatures to the cloud. This enables the sensitive information to be private and only the owner can decrypt the file. Hence, it is not feasible to use by others. Distributing the decryption is the possible way to access it by others. However, it is not the efficient solution to implement this method in real scenarios. In order to overcome this approach, an efficient solution is implemented. In this new approach, sensitive information is kept private and can easily execute without any complications. Here, a sanitizer is used to sanitize the block which contains the sensitive information of the file. Firstly, the user binds the data blocks with respect to the sensitive information of the original data and generates a digital signature to that block and then sends it to the sanitizer. The sanitizer filters these data blocks and also sanitizes the data corresponding to the sensitive information. It also evaluates the corresponding signatures into valid ones. The signature is used to prove that the cloud truly possesses the data blocks. The third-party auditor is a public verifier, it verifies the data stored in the cloud on behalf of the user. This method supports the data sharing so that the sensitive information is protected in the cloud.
Meta Self-Supervised Learning for Distribution Shifted Few-Shot Scene Classification

Few-shot classification tries to recognize novel remote sensing image categories with a few shot samples. However, current methods assume that the test dataset shares the same domain with the labeled training dataset where prior knowledge is learned. It is infeasible to collect a training dataset for each domain, since remote sensing images may come from various domains. Exploiting the existing labeled dataset from another domain (source domain) to help the target dataset (target domain) classification would be efficient. In this paper, both meta-learning and self-supervised learning are jointly conducted for few-shot classification. Specifically, meta-learning is executed over a pre-trained network for few-shot classification. Furthermore, self-supervised learning is exploited to fit the target domain distribution by training on unlabeled target domain images. Experiments are conducted on NWPU, EuroSAT and Merced datasets to validate the effectiveness.
Contrastive Representation Learning for Hand Shape Estimation

This work presents improvements in monocular hand shape estimation by building on top of recent advances in unsupervised learning. We extend momentum contrastive learning and contribute a structured collection of hand images, well suited for visual representation learning, which we call HanCo. We find that the representation learned by established contrastive learning methods can be improved significantly by exploiting advanced background removal techniques and multi-view information. These allow us to generate more diverse instance pairs than those obtained by augmentations commonly used in exemplar based approaches. Our method leads to a more suitable representation for the hand shape estimation task and shows a 4.7% reduction in mesh error and a 3.6% improvement in F-score compared to an ImageNet pretrained baseline. We make our benchmark dataset publicly available, to encourage further research into this direction.
Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations

DeepFake is becoming a real risk to society and brings potential threats to both individual privacy and political security due to the DeepFaked multimedia are realistic and convincing. However, the popular DeepFake passive detection is an ex-post forensics countermeasure and failed in blocking the disinformation spreading in advance. To address this limitation, researchers study the proactive defense techniques by adding adversarial noises into the source data to disrupt the DeepFake manipulation. However, the existing studies on proactive DeepFake defense via injecting adversarial noises are not robust, which could be easily bypassed by employing simple image reconstruction revealed in a recent study MagDR. In this paper, we investigate the vulnerability of the existing forgery techniques and propose a novel anti-forgery technique that helps users protect the shared facial images from attackers who are capable of applying the popular forgery techniques. Our proposed method generates perceptual-aware perturbations in an incessant manner which is vastly different from the prior studies by adding adversarial noises that is sparse. Experimental results reveal that our perceptual-aware perturbations are robust to diverse image transformations, especially the competitive evasion technique, MagDR via image reconstruction. Our findings potentially open up a new research direction towards thorough understanding and investigation of perceptual-aware adversarial attack for protecting facial images against DeepFakes in a proactive and robust manner. Code is available at https://github.com/AbstractTeen/AntiForgery.
A Comprehensive Survey on Federated Learning: Concept and Applications

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on components, challenges, applications and FL environment. FL can be applicable in multiple fields and domains in real-life models. in the medical system, the privacy of patients records and their medical condition is critical data, therefore collaborative learning or federated learning comes into the picture. On other hand build an intelligent system assist the medical staff without sharing the data lead into the FL concept and one of the applications that are used is a brain tumor diagnosis intelligent system based on AI methods that can efficiently work in a collaborative environment. This paper will introduce some of the applications and related work in the medical field and work under the FL concept then summarize them to introduce the main limitations of their work.
Privacy-Preserving Searchable Encryption Scheme Based on Public and Private Blockchains

While users enjoy the convenience of data outsourcing in the cloud, they also face the risks of data modification and private information leakage. Searchable encryption technology can perform keyword searches over encrypted data while protecting their privacy and guaranteeing the integrity of the data by verifying the search results. However, some associated problems are still encountered, such as the low efficiency of verification and uncontrollable query results. Accordingly, this paper proposes a Privacy-Preserving Searchable Encryption (PPSE) scheme based on public and private blockchains. First, we store an encrypted index in a private blockchain while outsourcing corresponding encrypted documents to a public blockchain. The encrypted documents are located through the encrypted index. This method can reduce the storage overhead on the blockchains, and improve the efficiency of transaction execution and the security of stored data. Moreover, we adopt a smart contract to introduce a secondary verification access control mechanism and restrict data users' access to the private blockchain through authorization for the purpose of guaranteeing data privacy and the correctness of access control verification. Finally, the security analysis and experimental results indicate that compared with existing schemes, the proposed scheme can not only improve the security of encrypted data but also guarantee the efficiency of the query.
Category Accessibility

The impression one forms of a person is often thought to consist of a set of personality trait labels that, separately or in combination, provide the basis for affective reactions to the person, predictions about the person’s behavior in different situations, inferences about other general attributes of the person, and one’s own future behavior toward the person. The storage units include a Sensory Store, a Goal Specification Box, a Work Space, and a Permanent Storage Unit. Permanent Storage bins contain only the output of the processing units that has been transmitted to them after processing is completed. This chapter describes five processing units comprising the system: the Preencoder, the Encoder/Organizer, the Integrator, the Response Selector, and the Executive Unit. The interpretation of information in terms of a schema may often assume the existence of features that were not stated explicitly in the information, but are required for the schema to apply.
Sharing Multiple Secrets in XOR-Based Visual Cryptography by Non-Monotonic Threshold Property

Multiple-secret visual cryptography scheme (MVCS) and fully incrementing visual cryptography scheme (FIVCS) have the same functionality that different secrets are gradually revealed by stacking different numbers of shadows. In essence, MVCS and FIVCS are the same. However, both of the two schemes suffer from large pixel expansion and deteriorated reconstructed image quality. In addition, MVCS and FIVCS require intensive computations to create base matrices. In this research, we exploit the capacity of sharing multiple secrets in XOR-based VCS (XVCS). First of all, three efficient base matrix constructions are proposed for realizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k, n)$ </tex-math></inline-formula> non-monotonic XVCS (NXVCS), where the secret image is only revealed by XOR-ing exact <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> shadows. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k, n)$ </tex-math></inline-formula> -NXVCS is adopted to constitute the multiple-secret XVCS (MXVCS). Theoretical analysis on the proposed constructions is provided. Extensive experiments and comparisons are conducted to illustrate that the pixel expansion, the visual quality of recovered image and the efficiency of generating base matrices are significantly improved by the proposed MXVCS, while comparing to MVCS and FIVCS.
Compression of deep neural networks: bridging the gap between conventional-based pruning and evolutionary approach

Abstract Recently, many studies have been carried out on model compression to handle the high computational cost and high memory footprint brought by the implementation of deep neural networks. In this paper, model compression of convolutional neural networks is constructed as a multiobjective optimization problem with two conflicting objectives, reducing the model size and improving the performance. A novel structured pruning method called Conventional-based and Evolutionary Approaches Guided Multiobjective Pruning (CEA-MOP) is proposed to address this problem, where the power of conventional pruning methods is effectively exploited for the evolutionary process. A delicate balance in pruning rate and model accuracy has been automated achieved by a multiobjective optimization evolutionary model. First, an ensemble framework integrates pruning metrics to establish a codebook for further evolutionary operations. Then, an efficient coding method is developed to shorten the length of chromosome, thus ensuring its superior scalability. Finally, sensitivity analysis is automatically carried out to determine the upper bound of pruning rate for each layer. Notably, on CIFAR-10, CEA-MOP reduces more than 50% FLOPs on ResNet-110 and improves the relative accuracy. Moreover, on ImageNet, CEA-MOP reduces more than 50% FLOPs on ResNet-101 with negligible top-1 accuracy drop.
A Real-Time FPGA Implementation of the CCSDS 123.0-B-2 Standard

Hyperspectral images are a useful remote sensing tool that often reaches hundreds of megabytes in size. The CCSDS 123.0-B-2 is a recent algorithm that achieves lossless and near-lossless compression of hyperspectral images by introducing a configurable maximum error over its predecessor CCSDS 123.0-B-1. In this article, a field-programmable gate array (FPGA) implementation of the revised standard that works in real-time is presented. We have developed an extremely pipelined and fast core in VHDL, that is able to process a sample per cycle at over 250 MHz, working eight times faster than in real time for the Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor. New dependencies in the revised standard are avoided by using a novel sample ordering called frame interleaved by diagonal. The predictor stage has been designed to work in this order, and two reorder buffers encapsulate it to be band interleaved by pixel compliant. Predictor data are encoded using a novel FPGA implementation of the CCSDS 123.0-B-2 hybrid coder. The modules are tested and verified on a Virtex-7 VC709 board. For medium (256 bands <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times4096$ </tex-math></inline-formula> frames <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times512$ </tex-math></inline-formula> samples) and large ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512\times 4096\times 1024$ </tex-math></inline-formula> ) images, the core occupies, respectively, 14% and 50% of an XQRKU060 FPGA.
Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform

Abstract In the era of information explosion, the energy consumption of cloud data centers is significant. It’s critical to reduce the energy consumption of large-scale data centers while guaranteeing quality of service (QoS), especially the energy consumption of video cloud computing platforms. The application of virtual machine (VM) consolidation has been regarded as a promising approach to improve resource utilization and save energy of the data centers. In this paper, an energy efficient and QoS-aware VM consolidation method is proposed to address the issues. A combined prediction model based on grey model and ARIMA is applied to host status detection, and we provide a new scheme that VM placement policy based on resource utilization and varying energy consumption to search most suitable host and VM selection policy called AUMT selecting VM with low average CPU utilization and migration time. Extensive experimental results based on the cloudsim simulator demonstrate that proposed approach enables to achieve the objectives reducing energy consumption, number of migrations, SLAV and ESV by an average of 56.07%, 79.21%, 91.01% and 84.34% compared with the benchmark methods and the AUMT can reduce energy consumption, the number of migrations and ESV by an average of 15.46%, 28.11% and 3.96% compared with the state-of-the-art method.