Reviewerly/exHarmony huggingface dataset:
A Physically Secure, Lightweight Three-Factor and Anonymous User Authentication Protocol for IoT

Internet of Things has remarkable effects in human's daily life. It is important for users and sensors to securely access data collected by low-cost sensors via Internet in real-time IoT applications. There exist many authentication protocols for guaranteeing secure communication between users and sensors. However, in some protocols, the privacy of unattended sensors subjected to capture node attacks cannot be guaranteed. Moreover, the sensors subjected to physical tampering attacks can still execute normally the authentication process. Besides, an authentication protocol should be lightweight due to the restricted computing power and storage of the sensors. The idea of designing a more secure and lightweight authentication protocol engender this article. The proposed protocol can provide the physical security through physically unclonable function (PUF), require no additional phase to update challenge-response pairs (CRPs), and store a single CRP for each sensor. At the same time, the proposed protocol utilizes three factors, such as personal biometrics, smartcard and password, to strengthen the security contrasting with two factors, and manipulates some basic cryptographic operations, including bitwise-exclusive-OR (XOR) and hash function, to achieve the lightweight performance. Moreover, both formal security analysis based on Real-Or-Random (ROR) and informal security analysis demonstrate the security of the proposed protocol. Compared with the existing related protocols, the proposed protocol has the advantage in terms of security, functionality and computation costs. Finally, a NS3 simulation on measuring various network performance parameters indicates that the proposed protocol is practical in IoT environment.
High-Performance Parallel Implementation of Genetic Algorithm on FPGA

Genetic Algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposes in this paper is able to work with more variable from some adjustments on hardware architecture.
A Taxonomy of Blockchain Consensus Methods

For a blockchain, consensus is the foundation protocol that enables cryptocurrencies such as Bitcoin to maintain state. Additionally, to ensure safety and liveness for a publicly accessible and verifiable ledger, fault tolerance must be robust. However, there appears to be a degree of misunderstanding about how consensus is applied across blockchains. To assist researchers considering variations between them, this study presents a rational classification of consensus methods applied to current blockchains. The study provides a survey of 19 methods classified by the scarce resource they employ: clock-cycles, bits, tokens, votes, time, and biometrics. Blockchain implementations are split between consensus algorithms requiring proof of resource and those that use majority voting to update the ledger.
Self-Distillation Amplifies Regularization in Hilbert Space

Knowledge distillation introduced in the deep learning context is a method to transfer knowledge from one architecture to another. In particular, when the architectures are identical, this is called self-distillation. The idea is to feed in predictions of the trained model as new target values for retraining (and iterate this loop possibly a few times). It has been empirically observed that the self-distilled model often achieves higher accuracy on held out data. Why this happens, however, has been a mystery: the self-distillation dynamics does not receive any new information about the task and solely evolves by looping over training. To the best of our knowledge, there is no rigorous understanding of this phenomenon. This work provides the first theoretical analysis of self-distillation. We focus on fitting a nonlinear function to training data, where the model space is Hilbert space and fitting is subject to $\ell_2$ regularization in this function space. We show that self-distillation iterations modify regularization by progressively limiting the number of basis functions that can be used to represent the solution. This implies (as we also verify empirically) that while a few rounds of self-distillation may reduce over-fitting, further rounds may lead to under-fitting and thus worse performance.
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

We show that the sum of the implicit generator log-density $\log p_g$ of a GAN with the logit score of the discriminator defines an energy function which yields the true data density when the generator is imperfect but the discriminator is optimal, thus making it possible to improve on the typical generator (with implicit density $p_g$). To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score. This can be achieved by running a Langevin MCMC in latent space and then applying the generator function, which we call Discriminator Driven Latent Sampling~(DDLS). We show that DDLS is highly efficient compared to previous methods which work in the high-dimensional pixel space and can be applied to improve on previously trained GANs of many types. We evaluate DDLS on both synthetic and real-world datasets qualitatively and quantitatively. On CIFAR-10, DDLS substantially improves the Inception Score of an off-the-shelf pre-trained SN-GAN~\citep{sngan} from $8.22$ to $9.09$ which is even comparable to the class-conditional BigGAN~\citep{biggan} model. This achieves a new state-of-the-art in unconditional image synthesis setting without introducing extra parameters or additional training.
Reversing the Cycle: Self-supervised Deep Stereo Through Enhanced Monocular Distillation

In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with supervised approaches. This fact occurs for depth estimation based on either monocular or stereo, with the latter often providing a valid source of self-supervision for the former. In contrast, to soften typical stereo artefacts, we propose a novel self-supervised paradigm reversing the link between the two. Purposely, in order to train deep stereo networks, we distill knowledge through a monocular completion network. This architecture exploits single-image clues and few sparse points, sourced by traditional stereo algorithms, to estimate dense yet accurate disparity maps by means of a consensus mechanism over multiple estimations. We thoroughly evaluate with popular stereo datasets the impact of different supervisory signals showing how stereo networks trained with our paradigm outperform existing self-supervised frameworks. Finally, our proposal achieves notable generalization capabilities dealing with domain shift issues. Code available at https://github.com/FilippoAleotti/Reversing .
Improving Attention-Based Handwritten Mathematical Expression Recognition with Scale Augmentation and Drop Attention

Handwritten mathematical expression recognition (HMER) is an important research direction in handwriting recognition. The performance of HMER suffers from the two-dimensional structure of mathematical expressions (MEs). To address this issue, in this paper, we propose a high-performance HMER model with scale augmentation and drop attention. Specifically, tackling ME with unstable scale in both horizontal and vertical directions, scale augmentation improves the performance of the model on MEs of various scales. An attention-based encoder-decoder network is used for extracting features and generating predictions. In addition, drop attention is proposed to further improve performance when the attention distribution of the decoder is not precise. Compared with previous methods, our method achieves state-of-the-art performance on two public datasets of CROHME 2014 and CROHME 2016.
Data-free Knowledge Distillation for Object Detection

We present DeepInversion for Object Detection (DIODE) to enable data-free knowledge distillation for neural networks trained on the object detection task. From a data-free perspective, DIODE synthesizes images given only an off-the-shelf pre-trained detection network and without any prior domain knowledge, generator network, or pre-computed activations. DIODE relies on two key components-first, an extensive set of differentiable augmentations to improve image fidelity and distillation effectiveness. Second, a novel automated bounding box and category sampling scheme for image synthesis enabling generating a large number of images with a diverse set of spatial and category objects. The resulting images enable data-free knowledge distillation from a teacher to a student detector, initialized from scratch. In an extensive set of experiments, we demonstrate that DIODE's ability to match the original training distribution consistently enables more effective knowledge distillation than out-of-distribution proxy datasets, which unavoidably occur in a data-free setup given the absence of the original domain knowledge.
Generative adversarial dimensionality reduction for diagnosing faults and attacks in cyber-physical systems

In cyber-physical systems, transforming a large amount of data collected from various sensors onto informative low-dimension data is of paramount importance for efficient monitoring and safe and secure operation of the system. To this aim, this paper proposes two novel dimensionality reduction techniques, where each makes use of two duelling neural networks along with two newly defined constraints of class separability and affinity correlation. Using the original distribution of the high-dimensional data, the goal is to achieve an ideal distribution in a lower-dimensional feature space, while preserving the correlation of features and discriminating samples of distinct classes. These classes are the system states including faults and cyber-attacks. The proposed novel techniques are compared with state-of-the-art dimensionality reduction techniques over several datasets collected from a cyber-physical system. The attained results show that the proposed techniques significantly outperform other techniques.
A Study on the Causal Process of Virtual Reality Tourism and Its Attributes in Terms of Their Effects on Subjective Well-Being during COVID-19

In the landscape of Covid-19 pandemic, people’s well-being, to some extent, can be affected through virtual reality tourism because it has the opportunity to enhance their level of well-being and destination recovery. To verify this empirically an investigation was conducted among people who used Quanjingke, the largest tourism-related virtual reality website in China, during the pandemic. The specific the aim of this paper is to prove the effectiveness of virtual reality tourism in promoting people’s subjective well-being. Hence, an integrated model with the constructs of peripheral attribute, core attribute and pivotal attribute, presence, perceived value, satisfaction, and subjective well-being was proposed and tested. The results indicate that attributes of virtual reality tourism have a positive effect on presence during virtual reality experiences, which positively influences perceived value. The values of virtual reality tourism perceived by tourists result in their satisfaction. Eventually, it was found that tourists’ subjective well-being is improved due to their satisfaction with virtual reality tourism. Practical suggestions are also provided based on the findings.
Freely scalable and reconfigurable optical hardware for deep learning

Abstract As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$&gt;10\,\upmu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>&gt;</mml:mo> <mml:mn>10</mml:mn> <mml:mspace /> <mml:mi>μ</mml:mi> </mml:mrow> </mml:math> m.
Hands-free interaction in immersive virtual reality: A systematic review

Hands are the most important tool to interact with virtual environments, and they should be available to perform the most critical tasks. For example, a surgeon in VR should keep his/her hands on the instruments and be able to do secondary tasks without performing a disruptive event to the operative task. In this common scenario, one can observe that hands are not available for interaction. The goal of this systematic review is to survey the literature and identify which hands-free interfaces are used, the performed interaction tasks, what metrics are used for interface evaluation, and the results of such evaluations. From 79 studies that met the eligibility criteria, the voice is the most studied interface, followed by the eye and head gaze. Some novel interfaces were brain interfaces and face expressions. System control and selection represent most of the interaction tasks studied and most studies evaluate interfaces for usability. Despite the best interface depending on the task and study, the voice was found to be versatile and showed good results amongst the studies. More research is recommended to improve the practical use of the interfaces and to evaluate the interfaces more formally.
Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest

Abstract Feature selection is a pre-processing technique used to remove unnecessary characteristics, and speed up the algorithm's work process. A part of the technique is carried out by calculating the information gain value of each dataset characteristic. Also, the determined threshold rate from the information gain value is used in feature selection. However, the threshold value is used freely or through a rate of 0.05. Therefore this study proposed the threshold rate determination using the information gain value’s standard deviation generated by each feature in the dataset. The threshold value determination was tested on 10 original datasets transformed by FFT and IFFT and classified using Random Forest. On processing the transformed dataset with the proposed threshold this study resulted in lower accuracy and longer execution time compared to the same process with Correlation-Base Feature Selection (CBF) and a standard 0.05 threshold method. Similarly, the required accuracy value is lower when using transformed features. The study showed that by processing the original dataset with a standard deviation threshold resulted in better feature selection accuracy of Random Forest classification. Furthermore, by using the transformed feature with the proposed threshold excluding the imaginary numbers leads to a faster average time than the three methods compared.
A Blockchain-Based Medical Data Sharing Mechanism with Attribute-Based Access Control and Privacy Protection

The rapid development of wearable sensors and the 5G network empowers traditional medical treatment with the ability to collect patients’ information remotely for monitoring and diagnosing purposes. Meanwhile, the health-related mobile apps and devices also generate a large amount of medical data, which is critical for promoting disease research and diagnosis. However, medical data is too sensitive to share, which is also a common issue for IoT (Internet of Things) data. The traditional centralized cloud-based medical data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single-point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the blockchain-based smart contract technique enables the programmability for participants to access the data. All the interactions are authenticated and recorded by the other participants of the blockchain network, which is tamper resistant. In this paper, we leverage the K-anonymity and searchable encryption techniques and propose a blockchain-based privacy-preserving scheme for medical data sharing among medical institutions and data users. To be specific, the consortium blockchain, Hyperledger Fabric, is adopted to allow data users to search for encrypted medical data records. The smart contract, i.e., the chaincode, implements the attribute-based access control mechanisms to guarantee that the data can only be accessed by the user with proper attributes. The K-anonymity and searchable encryption ensure that the medical data is shared without privacy leaking, i.e., figuring out an individual patient from queries. We implement a prototype system using the chaincode of Hyperledger Fabric. From the functional perspective, security analysis shows that the proposed scheme satisfies security goals and precedes others. From the performance perspective, we conduct experiments by simulating different numbers of medical institutions. The experimental results demonstrate that the scalability and performance of our scheme are practical.
GO FIGURE: A Meta Evaluation of Factuality in Summarization

While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question.This major discrepancy between the surface-level fluency and the content-level correctness of neural generation has motivated a new line of research that seeks automatic metrics for evaluating the factuality of machine text.In this paper, we introduce GO FIGURE, a metaevaluation framework for evaluating factuality evaluation metrics.We propose five necessary conditions to evaluate factuality metrics on diagnostic factuality data across three different summarization tasks.Our benchmark analysis on ten factuality metrics reveals that our metaevaluation framework provides a robust and efficient evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics.It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.
PolarDB Serverless

\beginabstract The trend in the DBMS market is to migrate to the cloud for elasticity, high availability, and lower costs. The traditional, monolithic database architecture is difficult to meet these requirements. With the development of high-speed network and new memory technologies, disaggregated data center has become a reality: it decouples various components from monolithic servers into separated resource pools (e.g., compute, memory, and storage) and connects them through a high-speed network. The next generation cloud native databases should be designed for disaggregated data centers. In this paper, we describe the novel architecture of \name, which follows thedisaggregation design paradigm: the CPU resource on compute nodes is decoupled from remote memory pool and storage pool. Each resource pool grows or shrinks independently, providing \revon-demand provisoning at multiple dimensions while improving reliability. We also design our system to mitigate the inherent penalty brought by resource disaggregation, and introduce optimizations such as optimistic locking and index awared prefetching. Compared to the architecture that uses local resources, \name achieves better dynamic resource provisioning capabilities and 5.3 times faster failure recovery speed, while achieving comparable performance. \endabstract
Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.
Disturbed-entropy: A simple data quality assessment approach

From the perspective of information value, we proposed a simple and effective approach to assess data quality, called disturbed-entropy. In specific, considering image classification task, the existing samples per category are statistically represented as a pixel prototype, which is used to disturb the unseen samples. Then, the entropy of disturbed image is calculated based on predicted probability. Both the numerical and visual experiments are conducted to show the effect. In case of same data budget, the performance comparison based on selected good and bad data is significant and consistent. This work attempts to gain insight into data quality and redundancy.