Reviewerly/exHarmony huggingface dataset:
Resource Allocation Model Against Multiple Failures With Workload-Dependent Failure Probability

Fault tolerance and load balancing are two key roles in resource allocation against failures. This paper proposes a primary and backup resource allocation model with preventive recovery priority setting to minimize a weighted value of unavailable probability (W-UP) against multiple failures. W-UP considers the probability of unsuccessful recovery and the maximum unavailable probability after recovery among physical nodes. We consider that each node fails with a workload-dependent failure probability; each failure pattern occurs with a probability. The workload-dependent failure probability is a non-decreasing function revealing an empirical relationship between the workload and the failure probability for each physical node. We introduce a recovery strategy to handle the workload variation which is determined at the operation start time and can be applied for each failure pattern. Once a failure pattern occurs, the recoveries are operated according to the priority setting to promptly recover the functions hosted by failed nodes. We also discuss an approach to obtain unsuccessful recovery probability with considering the maximum number of arbitrary recoverable functions by a set of available nodes without the priority setting. We formulate the optimization problem as a mixed integer linear programming (MILP) problem. We develop a heuristic algorithm to solve larger size problems in a practical time. The developed heuristic algorithm is approximately 729 times faster than the MILP approach with 1.6% performance penalty on W-UP. The numerical results observe that the proposed model reduces W-UP compared with baselines.
Tumbleweed Algorithm and Its Application for Solving Location Problem of Logistics Distribution Center

In this paper, a new swarm intelligence optimization algorithm named Tumbleweed Algorithm (TA) is proposed. The TA algorithm simulates the two processes of tumbleweed from seedling to adulthood and the propagation of tumbleweed seeds after adulthood. And by introducing the concept of growth cycle, the two stages are combined. In order to verify the effectiveness of the new algorithm proposed to solve the problems, this paper uses the CEC2013 function set to test, and compares the 10D, 30D and 50D dimensions with six swarm intelligence optimization algorithms. By comparing the experimental results under different dimensions, the TA algorithm proposed in this paper is generally superior to other intelligent optimization algorithms compared, and has strong optimization ability and competitiveness. Finally, the TA algorithm is applied to the location problem of logistics distribution center to verify the practicability of the algorithm. In solving this problem, the TA algorithm can also obtain better optimization results.
A Survey on Post-Quantum Public-Key Signature Schemes for Secure Vehicular Communications

Basic security requirements such as confidentiality, user authentication and data integrity, are assured by using public-key cryptography (PKC). In particular, public-key signature schemes provide non-repudiation, integrity of transmitted messages and authentication. The presence of a large scale quantum computer would be a real threat to break the most widely used public-key cryptographic algorithms in practice, RSA, DSA, ECDSA signature schemes and Diffie-Hellman key exchange. Thus, all security protocols and applications where these public-key cryptographic algorithms are used are vulnerable to quantum-computer attacks. There are five directions of cryptographic primitives secure against a quantum computer: multivariate quadratic equation-based, hash-based, lattice-based, code-based and supersingular isogeny-based cryptography. These primitives could serve as replacements for current public-key cryptographic algorithms to prepare for post-quantum era. It is important to prioritize the fields to be replaced by post-quantum cryptography (PQC) since it is hard to replace the currently deployed PKC with PQC at the same time. The fields directly connected to human life such as vehicular communications should be the primary targets of PQC applications. This survey is dedicated to providing guidelines for adapting the most suitable post-quantum candidates to the requirements of various devices and suggesting efficient and physically secure implementations that can be built into existing embedded applications as easily as traditional PKC. It focuses on the five types of post-quantum signature schemes and investigates their theoretical backgrounds, structures, state-of-the-art constructions and implementation aspects on various platforms raging from resource constrained IoT devices to powerful servers connected to the devices for secure communications in post-quantum era. It offers appropriate solutions to find tradeoffs between key sizes, signature lengths, performance, and security for practical applications.
Towards an Accurate Latency Model for Convolutional Neural Network Layers on GPUs

Convolutional Neural Networks (CNN) have shown great success in many sensing and recognition applications. However, the excessive resource demand remains a major barrier against their deployment on low-end devices. Optimizations, such as model compression, are thus a need for practical deployment. To fully exploit existing system resources, platform-aware optimizations emerged in recent years, where an execution-time model becomes a necessity. However, non-monotonicity over the network configuration space makes execution time modeling a challenging task. Data-driven approaches have the advantage of being portable over different platforms by treating the hardware and software stack as a black box but at the cost of extremely long profiling time. On the other hand, analytical models can be found in the architecture and system literature that do not need heavy profiling but require laborious analysis by domain experts. In this paper, we focus on building a general latency model for convolutional layers that account for the majority of the total execution time in CNN models. We identify two major non-linear modes in the relationship between latency and convolution parameters, and analyze the mechanism behind them. The resulting model has better interpretability and can reduce profiling workload. The evaluation results show that our model outperforms baselines on different platforms and CNN models.
Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.
Applying formal verification to microkernel IPC at meta

We use Iris, an implementation of concurrent separation logic in the Coq proof assistant, to verify two queue data structures used for inter-process communication in an operating system under development. Our motivations are twofold. First, we wish to leverage formal verification to boost confidence in a delicate piece of industrial code that was subject to numerous revisions. Second, we aim to gain information on the cost-benefit tradeoff of applying a state-of-the-art formal verification tool in our industrial setting. On both fronts, our endeavor has been a success. The verification effort proved that the queue algorithms are correct and uncovered four algorithmic simplifications as well as bugs in client code. The simplifications involve the removal of two memory barriers, one atomic load, and one boolean check, all in a performance-sensitive part of the OS. Removing the redundant boolean check revealed unintended uses of uninitialized memory in multiple device drivers, which were fixed. The proof work was completed in person months, not years, by engineers with no prior familiarity with Iris. These findings are spurring further use of verification at Meta.
A Privacy Protection Method Based on Key Encapsulation Mechanism in Medical Blockchain

At present, many blockchain technologies have played a role in the storage and sharing of medical data, functions such as on-chain operation and data encryption have not been fully realized in terms of privacy protection. To solve these problems, this paper proposes a medical privacy protection method based on key encapsulation mechanism (KEM) in blockchain. First, the medical data of the patient is symmetrically encrypted, and then the public key of the symmetric cipher is asymmetrically encrypted. Furthermore, the public key of the asymmetric cipher and the encapsulated symmetric key are stored in the smart contract of the blockchain, so as to achieve the purpose of privacy security protection and on-chain operation. Through the experiment of security and throughput, the decentralized medical privacy protection method combined with KEM can realize the security protection of medical data, which has good throughput performance, and good resistance effect in the face of block forgery attack.
An integrated surrogate model constructing method: Annealing combinable Gaussian process

Surrogate models are widely used to mimic complex systems to reduce the high experimental cost. As the system becomes high-dimensional and complex, there is an increasing demand for building relatively simplified surrogate models that capture key variables and represent complex interactions. This study proposes the annealing combinable Gaussian process, an integrated solution for identifying key variables and constructing the high-precision surrogate model. Firstly, to identify redundant variables, this study optimises variables selection with a modified simulated annealing algorithm over the complete model space. This process is called the outer loop. Secondly, to improve the model accuracy and structure, this study constructs a non-parametric Gaussian process model with additive or multiplicative kernel, effectively extracting high-order interactions. Simultaneously, a Markov chain is proposed to sample the model space. The conditional entropy is used as the scoring rule for the simulated annealing algorithm of the outer loop. It is named the inner loop. This study also discusses the rationality of conditional entropy as a criterion. The annealing combinable Gaussian process performs well in various application scenarios, including regression and classification problems. Finally, our method is implemented with the particle-in-cell simulation to find out the key physics parameters.
Analysis of noisy image lossy compression by BPG using visual quality metrics

Image lossy compression is widely used nowadays. A common assumption is that images subject to compression are noise-free. If intensive (visually noticeable) noise is present, lossy compression has several peculiarities that have to be taken into account. These peculiarities have been earlier studied for several coders, but they have not yet been analyzed for Better Portable Graphics (BPG) coder proposed recently and controlled by a quality parameter Q. In this paper, performance of this coder is analyzed for the case of additive white Gaussian noise using two known visual quality metrics, PSNR-HVS-M and MS-SSIM. It is demonstrated that for both metrics it is possible that optimal operation point exists. This usually happens for quite simple structure images and/or if noise intensity is high enough. Based on simulation results obtained for a set of grayscale images initial recommendations concerning Q setting are given for practice.
HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde–Buzo–Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.
Hybrid HOG-SVM encrypted face detection and recognition model

Security plays a major role in an individual’s life to win this world with highly secure and authentic lifestyle with the digital equipment’s. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time.
A deep contrastive learning approach to extremely-sparse disaster damage assessment in social sensing

Social sensing has emerged as a pervasive and scalable sensing paradigm to obtain timely information of the physical world from "human sensors". In this paper, we study a new extremely-sparse disaster damage assessment (DBA) problem in social sensing. The objective is to automatically assess the damage severity of affected areas in a disaster event by leveraging the imagery data reported on online social media with extremely sparse training data (e.g., only 1% of the data samples have labels). Our problem is motivated by the limitation of current DDA solutions that often require a significant amount of high-quality training data to learn an effective DDA model. We identify two critical challenges in solving our problem: i) it remains to be a fundamental challenge on how to effectively train a reliable DDA model given the lack of sufficient damage severity labels; ii) it is a difficult task to capture the excessive and fine-grained damage-related features in each image for accurate damage assessment. In this paper, we propose ContrastDDA, a deep contrastive learning approach to address the extremely-sparse DDA problem by designing an integrated contrastive and augmentative neural network architecture for accurate disaster damage assessment using the extremely sparse training samples. The evaluation results on two real-world DDA applications demonstrate that ContrastDDA clearly outperforms state-of-the-art deep learning and semi-supervised learning baselines with the highest DDA accuracy under different application scenarios.
UNESCO Strategy and Digital Policies for Teacher Training: The Deconstruction of Innovation in Spain

This paper examines initial teacher training policies in the context of youth digitalisation. Based on international ICT competency frameworks for teachers (UNESCO, 2008, 2019) and information literacy, MIL (UNESCO, 2011), we analyse how Spanish (public and private) universities represent digital innovation, and the identity practices of young people, linked to hyperconnectivity. Focusing on initial training received by future secondary teachers, the sample was selected according to the criteria of non-presential learning (online, distance, blended) applied in the teacher training process. Results confirm ongoing deficiencies in critical teacher digitalisation management, highlighting certain asymmetries between the categories constructed. Data reveals the prevalence of intervention more geared towards formal knowledge than towards decoding digital grammar (which is how digitalisation is used in education). The paper concludes by advocating convergence strategies as a circular mechanism that must underpin all teacher training policies in the age of hyperconnectivity.
A framework and comparative analysis of web-based climate change visualization tools

Interactive and web-based data visualizations are now widely adopted to make climate change information more accessible, actionable, and meaningful. Despite the rapid uptake of such climate visualization tools (CVTs), there is little research on the design considerations that underpin their efficacy in meeting needs for climate change science and communication. To address this gap, we present a framework and comparative analysis of 41 public facing CVTs, assessing their purpose, data content, visual representation, interactivity, and web technology. Our analysis reveals several trends, including a tendency to focus on meteorological datasets over climate impacts, the widespread use of interactive maps for displaying climate data and the growing use of interactive options to promote sharing of results and user configurations. We found that distinguishing CVTs as being either "exploratory" or "explanatory" in their goal was useful in characterizing their visual and interactive complexity. We archived the analysis framework and repository of 41 coded tools on GitHub (available at https://github.com/smlum/climate-vis) to support further analysis and improve the capacity of CVTs to reduce climate change and its impacts.
MLP-Based Efficient Stitching Method for UAV Images

Unmanned aerial vehicle (UAV) image stitching techniques based on position and attitude information have shown clear speed superiority over feature-based counterparts. However, how to improve stitching accuracy and robustness remains a great challenge since position and attitude parameters are sensitive to noise introduced by sensors and external environment. To mitigate this issue, this work presents a simple yet effective stitching algorithm for UAV images based on a coarse-to-fine strategy. Specifically, we first conduct coarse registration using the position and attitude information obtained from GPS, IMU, and altimeter. Then, we introduce a novel offline calibration phase that is designed to regress the obtained global transformation matrix to the optimal one computed from feature-based algorithms, by using multi-layer perceptron (MLP) neural networks for fast correction. Consequently, the proposed method well integrates the complementary strengths of both parameter and feature-based methods, achieving an ideal speed–accuracy tradeoff. Moreover, to facilitate research on this topic, we establish a new dataset, named UAV-AIRPAI, that comprises over 100 UAV image pairs with position and attitude annotations to the community, opening up a promising direction for UAV image stitching. Extensive experiments on the UAV-AIRPAI dataset show that our method achieves superior accuracy compared to priors while running at a real-time speed of 0.0124 s per image pair. Code and data will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dededust/UAV-AIRPAI</uri> .
Achieving data privacy for decision support systems in times of massive data sharing

The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records. The researchers access the patient's data through DSS and perform predictions on the severity and effect of the Covid-19 disease; in contrast, unauthorized users can also access the data for malicious purposes. For that reason, it is a challenging task to protect Covid-19 patient data. In this paper, we proposed a new technique for protecting Covid-19 patients' data. The proposed model consists of two folds. Firstly, Blowfish encryption uses to encrypt the identity attributes. Secondly, it uses Pseudonymization to mask identity and quasi-attributes, then all the data links with one another, such as the encrypted, masked, sensitive, and non-sensitive attributes. In this way, the data becomes more secure from unauthorized access.
Mixhead: Breaking the low-rank bottleneck in multi-head attention language models

The Transformer-based models have achieved significant advances in language modeling, while the multi-head attention mechanism in Transformers plays an indispensable part in their success. However, the too-small head size caused by the multi-head mechanism will lead to one problem called the low-rank bottleneck, which means that the rank of the attention weight matrix is too small to represent any desired attention. Naively increasing the head size is insufficient to solve the problem because it leads to severe parameter explosion and overfitting. To tackle this problem, we propose a mix-head attention (Mixhead) which mixes multiple attention heads by learnable mixing weights to improve the expressive power of the model. In contrast, Mixhead achieves a higher rank of the attention weight matrix while introducing a negligible number of parameters. Furthermore, Mixhead is quite general and can be easily adopted to most multi-head attention based models. We conduct extensive experiments including language modeling, machine translation, and finetuning BERT to demonstrate the effectiveness of our method.