Reviewerly/exHarmony huggingface dataset:
Pelican Gorilla Troop Optimization Based on Deep Feed Forward Neural Network for Human Activity Abnormality Detection in Smart Spaces

Healthcare management can be improved using artificial intelligence-powered Internet of Things (IoT) for remotely collecting and analyzing medical data. Home-based IoT healthcare proved its effectiveness in helping the elderly and people with special care needs enjoy safer and more independent living. Deep learning techniques improve the management of healthcare systems through intelligent analysis and real-time tracking of health indicators and auto-administering medication. This article proposes a novel pelican gorilla troop optimization-assisted deep feed-forward neural network for health indicators abnormality detection. As deep learning requires a significant data dimension to provide reliable results, the data augmentation process is carried out through the bootstrapping approach to improve abnormality detection. Furthermore, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula> -score normalization is used to complete data preprocessing and achieve better detection outcomes. The experimental evaluation results show that the proposed solution realizes a detection performance in terms of accuracy, precision, and recall achieving of 0.879, 0.902, and 0.929, respectively.
Residual in Residual Scaling Networks for Polarimetric SAR Image Despeckling

Speckle reduction is a longstanding topic for polarimetric synthetic aperture radar (PolSAR) images. In this paper, we propose a novel end-to-end PolSAR image despeckling framework for the first time, which predicts the weight matrices of neighboring pixels instead of the target pixel itself nor the nor the noise, to achieve image despeckling. It hardly relies on any assumptions on the speckle noise distribution. Within this framework, residual in residual scaling network (RIRSN) is developed by combining the advantages of residual connections and residual scaling. To reduce network redundancy further, a dynamic version of RIRSN (DRIRSN) is also proposed by adjusting the network structure dynamically based on noise level and image content. Specifically, in DRIRSN, we introduce a lightweight network called picture2vector to estimate noise level, and a well-designed loss function to estimate image information level and measure image denoising quality simultaneously. The proposed picture2vector and loss function guide DRIRSN to focus on image areas with rich content and information, enhancing the adaptability of the network. DRIRSN inherits the properties of RIRSN for adaptively selecting and weighting the pixels of the neighborhood, and dynamically adjusts the network structure according to the estimated noise level and image content. We compare the proposed networks with reference methods on both simulated images and real images. Experimental results demonstrate that the proposed networks can effectively reduce speckle noise with low time consumption and, meanwhile, better preserve the details and the repetitive structures such as textures and edges, and the polarimetric scattering characteristics, compared with the other methods.
Efficient and Robust KPI Outlier Detection for Large-Scale Datacenters

To ensure the performance of large-scale datacenters, operators need to monitor up to tens of millions of various-type KPIs, e.g., CPU utilization, memory utilization. For each KPI, it is crucial but challenging to detect outliers that deviate from its historical patterns or the patterns of other KPIs in the same period. In this work, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OutSpot</i> , an unsupervised outlier detection framework that integrates hierarchical agglomerative clustering (HAC) with conditional variational autoencoder (CVAE), which significantly improves computational efficiency and comprehensively learns the above two patterns. Additionally, two simple yet effective techniques, soft threshold and median filter, are applied to precisely determine outlier KPIs. Using two real-world datasets collected from the datacenters owned by a top-tier global short video service provider and a top-tier domestic operator,respectively. It demonstrates that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OutSpot</i> achieves the best F1 score of 0.95 and 0.91, AUC of 0.99 and 0.99 on the two datasets, significantly outperforming seven baseline outlier detection methods.
CANE: A Cascade Control Approach for Network-Assisted Video QoE Management

Prior efforts have shown that network-assisted schemes can improve the quality of experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: 1) the network has limited visibility into the client players’ internal state and actions; 2) players’ actions may nullify or negate the network’s actions; and 3) the players’ objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CAscade control-based NEtwork-assisted framework (CANE), a practical network-assisted QoE framework. CANE uses machine learning (ML) techniques to approximate each player’s behavior as a black-box model and model predictive control (MPC) to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 50% compared with pure client-side adaptive bitrate (ABR) algorithms and by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 20% compared with uniform traffic shaping.
eHotSnap: An Efficient and Hot Distributed Snapshots System for Virtual Machine Cluster

With the popularity of IaaS clouds, many distributed and networked applications are running in virtual machine cluster (VMC). The distributed snapshots of VMC are a practical approach to guarantee system reliability. It rewinds the system to an intermediate state from failures so that the applications can continue execution from a point near the failure. However, the applications running in the VMC suffer from long disruption and significant performance degradation due to the heavy cost distributed snapshots, especially when designed to guarantee global consistency of VMC snapshots. This article presents eHotSnap, which takes distributed snapshots of a VMC efficiently. eHotSnap divides the native snapshot into light cost transient snapshot and heavy cost memory snapshot and then coordinates the VM snapshots immediately after transient snapshots. In this way, it decouples coordination from heavy cost snapshots so that the distributed snapshots are taken (completed in logic) within a second. Then, it performs memory snapshot and optimizes it with a two-layer optimization, which first employs de-duplication to reduce the amount of snapshot data and then leverages priority queue to serve guest write operations preferentially. In addition to presenting eHotSnap, we have implemented a prototype on QEMU/KVM. The experimental results demonstrate the effectiveness and efficiency of the proposed approach.
Bridging the Gap: Examples of Making Social Science Research Accessible to the Public

This work-in-progress article aims to make academic research in social sciences more accessible by presenting three studies in digital health, data privacy, and information technology artifact in a clear and understandable way. The first study investigates the importance of consistency in electronic medical record (EMR) use in a large Australian hospital and the implications for patient care and safety. The study found that inconsistency in EMR use is complex and has multiple dimensions, and that consistent use is important for patient care and safety. The second study explores the role of technology in organizational change and highlights the need for a clear definition of the IT artifact concept in information systems research. The authors suggest that a clearer definition of IT artifacts is essential to advance the field and prevent ambiguity that could stifle innovation. The third study examines the ethical implications of data collection and usage in digital health and the need for a balance between privacy and utility. The article concludes that social science research has an important impact on our daily lives and should be made accessible to the general public.
Design and Valuation of Cryptocurrencies

We analyze whether the design of cryptocurrencies helps to explain the huge cross-sectional variation in the market values of cryptocurrencies. We propose a taxonomy of design features and hand-collect data on these features for a sample of 79 cryptocurrencies. Using a two-stage regression approach and LASSO regressions, we find, inter alia, that forks and deviations from the design of Bitcoin are associated with lower valuation. In contrast, non-anonymous cryptocurrencies and cryptocurrencies that do not pass on any transaction fees and/or tips to agents who maintain the integrity of the network have, on average, higher market values. These results are robust to variations in the way we measure market valuation.
UAED: Unsupervised Abnormal Emotion Detection Network Based on Wearable Mobile Device

With the development of the internet-of-medical-things, health monitoring through physiological signals has become a critical task. Given this opportunity, research on personal healthcare systems for abnormal emotion detection using physiological signals brings significant benefits to the field of digital healthcare and human-computer interaction. However, it is a challenging task because of the diverse patterns of time series and the lack of labels. In this work, we present a novel model for Unsupervised Abnormal Emotion Detection (UAED) combining Gaussian mixture variational autoencoder (VAE) and convolutional neural networks (CNNs), whose core idea is to reconstruct the input by learning its latent representation thus capturing the normal patterns and applying whitening distance as the anomaly score to detect outliers. In addition, UAED uses stacking operation to transform one-dimensional time series into high-dimension to help the model capture the periodic features and reflect diverse normal patterns. We conduct extensive experiments on four public datasets to demonstrate that our UAED obtains the best performance in various metrics. Furthermore, we deploy UAED in a real environment using a low-cost wearable sensor developed by us to collect electrocardiogram signals and run UAED on mobile terminals with an accuracy of 85%, validating the feasibility of our healthcare system for detecting abnormal emotions.
Learning Weak Semantics by Feature Graph for Attribute-Based Person Search

Attribute-based person search aims to find the target person from the gallery images based on the given query text. It often plays an important role in surveillance systems when visual information is not reliable, such as identifying a criminal from a few witnesses. Although recent works have made great progress, most of them neglect the attribute labeling problems that exist in the current datasets. Moreover, these problems also increase the risk of non-alignment between attribute texts and visual images, leading to large semantic gaps. To address these issues, in this paper, we propose Weak Semantic Embeddings (WSEs), which can modify the data distribution of the original attribute texts and thus improve the representability of attribute features. We also introduce feature graphs to learn more collaborative and calibrated information. Furthermore, the relationship modeled by our feature graphs between all semantic embeddings can reduce the semantic gap in text-to-image retrieval. Extensive evaluations on three challenging benchmarks - PETA, Market-1501 Attribute, and PA100K, demonstrate the effectiveness of the proposed WSEs, and our method outperforms existing state-of-the-art methods.
Variational Data-Free Knowledge Distillation for Continual Learning

Deep neural networks suffer from catastrophic forgetting when trained on sequential tasks in continual learning. Various methods rely on storing data of previous tasks to mitigate catastrophic forgetting, which is prohibited in real-world applications considering privacy and security issues. In this paper, we consider a realistic setting of continual learning, where training data of previous tasks are unavailable and memory resources are limited. We contribute a novel knowledge distillation-based method in an information-theoretic framework by maximizing mutual information between outputs of previously learned and current networks. Due to the intractability of computation of mutual information, we instead maximize its variational lower bound, where the covariance of variational distribution is modeled by a graph convolutional network. The inaccessibility of data of previous tasks is tackled by Taylor expansion, yielding a novel regularizer in network training loss for continual learning. The regularizer relies on compressed gradients of network parameters. It avoids storing previous task data and previously learned networks. Additionally, we employ self-supervised learning technique for learning effective features, which improves the performance of continual learning. We conduct extensive experiments including image classification and semantic segmentation, and the results show that our method achieves state-of-the-art performance on continual learning benchmarks.
An Efficient and Robust Underwater Image Compression Scheme Based on Autoencoder

Images occupy a prominent place in data because they are more visually appealing than sounds or texts. Acoustic waves are the only feasible alternative for long-distance underwater transmission since seawater has a high absorption impact on lighting and electromagnetic signals. However, underwater acoustic (UWA) communication technology can only provide relatively limited bandwidth (low effectiveness) and insufficiently stable links (low reliability). As a result, it is challenging to send high-resolution underwater images via the UWA channel. This article proposes an effective and robust underwater image compression scheme. First, an autoencoder is used for underwater image extreme bit rate compression. Then, a multistep training strategy is proposed to improve the robustness of the decoder by gradually learning channel degradation features. Finally, the autoencoder encodes images in two paths to achieve efficient compression and higher image quality when reconstructing images. The main path completes the compression task with a low bit rate and high robustness, while the branching path implements the image block retransmission compensation through the feedback signal. The experimental results demonstrate that the content of the reconstructed image can still be recognized under the conditions of a compression ratio of up to 1/768 and an average bit error rate of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{10}^{-1}$</tex-math></inline-formula> . The joint multistep training strategy and multidescription coding achieve a low bit rate and high robustness for underwater image communication, which has good application prospects.
A Unified Acceleration Solution Based on Deformable Network for Image Pixel Processing

Recently, deep neural networks, including convolutional neural networks, deformable convolutional networks, and generative adversarial networks, have shown promising potential for image pixel processing tasks, such as image super-resolution and denoising. These various networks bring different memory accesses and complex computation patterns, challenging their hardware deployments seriously. Therefore, this brief proposes a field-unified method to transform various convolutions into a specific deformable convolution with an adaptive receptive field. Based on the transformation, a tile engine, together with a novel position-decoupling computing flow, is developed to unify the multiple memory access patterns, which maintains the consistency of input/output layout formats. Then, a high-level hardware architecture is presented to flexibly support multiple types of convolutional layers. The proposed design is implemented on a Zynq UltraScale+ FPGA board. Experimental results show that our design obtains around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula> computation efficiency improvement, surpassing existing works in terms of hardware flexibility and computation efficiency significantly.
Twisted Goppa Codes With an Efficient Decoding Algorithm and Quasi-Cyclic Properties

In this paper, we introduce twisted Goppa codes, which generalize classical Goppa codes by adding a twisted term. Then we provide an efficient decoding algorithm for twisted Goppa codes. The Niederreiter cryptosystem is bassed on linear error-correcting codes in which the public key is a parity check matrix. When twisted Goppa codes are applied to the Niederreiter cryptosystem, the public key size is overlarge. To reduce the public key size, we construct quasi-cyclic twisted Goppa codes via a non-trivial automorphism group carefully selecting the defining set and the matched polynomial. Moreover, we obtain a family of cyclic twisted Goppa codes.
Certifying Giant Nonprimes

GIMPS and PrimeGrid are large-scale distributed projects dedicated to searching giant prime numbers, usually of special forms like Mersenne and Proth primes. The numbers in the current search-space are millions of digits large and the participating volunteers need to run resource-consuming primality tests. Once a candidate prime N has been found, the only way for another party to independently verify the primality of N used to be by repeating the expensive primality test. To avoid the need for second recomputation of each primality test, these projects have recently adopted certifying mechanisms that enable efficient verification of performed tests. However, the mechanisms presently in place only detect benign errors and there is no guarantee against adversarial behavior: a malicious volunteer can mislead the project to reject a giant prime as being non-prime. In this paper, we propose a practical, cryptographically-sound mechanism for certifying the non-primality of Proth numbers. That is, a volunteer can – parallel to running the primality test for N – generate an efficiently verifiable proof at a little extra cost certifying that N is not prime. The interactive protocol has statistical soundness and can be made non-interactive using the Fiat-Shamir heuristic. Our approach is based on a cryptographic primitive called Proof of Exponentiation (PoE) which, for a group $${\mathbb {G}}$$ , certifies that a tuple $$(x,y,T)\in {\mathbb {G}}^2\times \mathbb {N}$$ satisfies $$x^{2^T}=y$$ (Pietrzak, ITCS 2019 and Wesolowski, J. Cryptol. 2020). In particular, we show how to adapt Pietrzak’s PoE at a moderate additional cost to make it a cryptographically-sound certificate of non-primality.
The Hidden Number Problem with Small Unknown Multipliers: Cryptanalyzing MEGA in Six Queries and Other Applications

In recent work, Backendal, Haller, and Paterson identified several exploitable vulnerabilities in the cloud storage provider MEGA. They demonstrated an RSA key recovery attack in which a malicious server could recover a client's private RSA key after 512 client login attempts. We show how to exploit additional information revealed by MEGA's protocol vulnerabilities to give an attack that requires only six client logins to recover the secret key. Our optimized attack combines several cryptanalytic techniques. In particular, we formulate and give a solution to a variant of the hidden number problem with small unknown multipliers, which may be of independent interest. We show that our lattice construction for this problem can be used to give improved results for the implicit factorization problem of May and Ritzenhofen.
Simple, Fast, Efficient, and Tightly-Secure Non-malleable Non-interactive Timed Commitments

Timed commitment schemes, introduced by Boneh and Naor (CRYPTO 2000), can be used to achieve fairness in secure computation protocols in a simple and elegant way. The only known non-malleable construction in the standard model is due to Katz, Loss, and Xu (TCC 2020). This construction requires general-purpose zero knowledge proofs with specific properties, and it suffers from an inefficient commitment protocol, which requires the committing party to solve a computationally expensive puzzle. We propose new constructions of non-malleable non-interactive timed commitments, which combine (an extension of) the Naor-Yung paradigm used to construct IND-CCA secure encryption with a non-interactive ZK proof for a simple algebraic language. This yields much simpler and more efficient non-malleable timed commitments in the standard model. Furthermore, our constructions also compare favourably to known constructions of timed commitments in the random oracle model, as they achieve several further interesting properties that make the schemes very practical. This includes the possibility of using a homomorphism for the forced opening of multiple commitments in the sense of Malavolta and Thyagarajan (CRYPTO 2019), and they are the first constructions to achieve public verifiability, which seems particularly useful to apply the homomorphism in practical applications.
Development of UAV Deployment System using Split Ring Compound Planetary Gear System

Drones are consequently gaining popularity in autonomous sampling operations and load transporting systems due to their high efficiency and low risk. They are typically equipped with a winch or deployment system that transports certain cargoes. In this paper, the team aimed to design a UAV deployment method utilizing compound split-ring planetary gear capable of supporting a minimum load of 5 kg and a lifting rate of 5cm/s. High torque gearbox, motor selection, rigidity, capacity for rope, precision control, and payload were all considered to achieve this.