Reviewerly/exHarmony huggingface dataset:
High-efficiency and visual-usability image encryption based on thumbnail preserving and chaotic system

Privacy concerns may be caused after uploading images into image hosting platforms. For traditional image encryption, images with rich content and meaning are transformed into noise-like encrypted images without any visual information, which can protect privacy from infringement but at the expense of visual usability. Recently, thumbnail-preserving encryption (TPE) was proposed to balance privacy and usability by preserving the thumbnail unchanged after encrypting. On the other hand, a large number of pseudo-random functions with high computational complexity are utilized in the existing TPE schemes, which makes the encryption and decryption time too long. Motivated by this, we deeply analyze the characteristics of TPE and propose a scheme to organically combine TPE with chaotic systems, which reduces the encryption and decryption time exponentially besides ensuring security. The experiments show that the time cost is reduced dozens of times as well as relatively stable compared with the existing schemes. Meanwhile, encrypted images have high visual quality and can resist some commonly used attacks including differential attack and face detection and the balance between privacy and visual usability is achieved.
Hidden Single‐Qubit Topological Phase Transition without Gap Closing in Anisotropic Light‐Matter Interactions

Conventionally the occurrence of topological phase transitions (TPTs) requires gap closing, whereas there are also unconventional cases without need of gap closing. Although traditionally TPTs lie in many-body systems in condensed matter, both cases of TPTs may find analogs in few-body systems. Indeed, the ground-state node number provides a topological classification for single-qubit systems. While the no-node theorem of spinless systems is shown to also restrict the fundamental quantum Rabi model in light-matter interactions, it is demonstrated that the limitation of the no-node theorem can be broken not only in a small counter-rotating term (CRT) but also in the large-CRT regime, which striates a rich phase diagram with different TPTs. While these transitions are mostly accompanied with gap closing and parity reversal, a hidden node-phase transition is revealed that has neither gap closing nor parity change, which turns out to be an analog of the unconventional TPT in condensed matter. A hysteresis sign for the unconventional TPT is unveiled via the transition from amplitude squeezing to phase squeezing in the gapped phase. The imprints in the Wigner function are also addressed. The clarified mechanisms provide some special insights for the subtle role of the CRT.
Explosion of digital resources and its effects on the development of digital reading culture in Africa

Purpose The purpose of this paper was to highlight pertinent issues relating to digital access and the development of digital reading culture in Africa. This study established the appropriate strategies of promoting digital reading culture in African libraries and also the roles that libraries use in promoting the adoption of digital reading culture amidst various challenges and obstacles. Design/methodology/approach This paper examined an extensive empirical and theoretical literature review. A thorough analysis of the literature was done, focusing on combining empirical studies and theoretical ones, to bring out the importance of the reading process and how it can be adapted to digital context to overcome the access issues in the digital environment. Findings The findings indicated the advantages of digital reading in the present era. Additionally, the results pointed out the challenges faced in developing countries in Africa when promoting a digital reading culture and proposed the important role of librarians in helping the population through digital literacy. Originality/value This paper provides significant approaches on promoting digital reading in developing countries. To the best of the authors’ knowledge, this is the first study to review the literature from different aspects and perspectives on this topic in the context of developing countries in Africa.
Cost Minimization of Cloud Services for On-Demand Video Streaming

Cloud Technology is adopted to process video streams because of the great features provided to video stream providers such as the high flexibility of using virtual machines and storage servers at low rates. Video stream providers prepare several formats of the same video to satisfy all users’ devices specification. Video streams in the cloud are either transcoded or stored. However, storing all formats of videos is still costly. In this research, we develop an approach that optimizes cloud storage. Particularly, we propose a method that decides which video in which cloud storage should be stored to minimize the overall cost of cloud services. The results of the proposed approach are promising, it shows effectiveness when the number of frequently accessed video grow in a repository, and when the views of videos increases. The proposed method decreases the cost of using cloud services by up to 22%.
Argument for Improved Security in Local Governments Within the Economic Community of West African States

The Economic Community of West African States is an economic region located in West Africa. This region has a population of over 349 million and representation for approximately 15 countries. With the explosion of technological advances in agriculture, healthcare, and personal device use, cybersecurity has become an important issue. Coupled with dictatorships, corrupt regimes, religious extremists, and other illicit activities, it is imperative that cybersecurity become a cornerstone in local governments to ensure the safety of citizens. This chapter reviews recent literature that surrounds West African states to present an argument on why cybersecurity must be considered essential for local governments. Observations and interviews were conducted at a government facility concerning its security posture as it relates to physical and cybersecurity. This activity included interviews with senior government officials and employees to understand the state of affairs at the organization.
A hybrid adaptive grey wolf levenberg-marquardt (GWLM) and nonlinear autoregressive with exogenous input (NARX) neural network model for the prediction of rainfall

The climate change study looks at how the weather has changed over time.Rainfall forecasting is one of the most significant climatic change activities.It involves specific features like humidity, temperature, etc. to predict rainfall in a certain region.Predicting rainfall has been the most difficult task in weather analysis for every researcher in the contemporary climate.Precision, accuracy, and timely rainfall prediction have been impossible due to the complexity of the measures.
Machine-learning potential of a single pendulum

Reservoir computing offers a great computational framework where a physical system can directly be used as computational substrate. Typically a "reservoir" is comprised of a large number of dynamical systems, and is consequently high dimensional. In this work, we use just a single simple low-dimensional dynamical system, namely, a driven pendulum, as a potential reservoir to implement reservoir computing. Remarkably we demonstrate, through numerical simulations as well as a proof-of-principle experimental realization, that one can successfully perform learning tasks using this single system. The underlying idea is to utilize the rich intrinsic dynamical patterns of the driven pendulum, especially the transient dynamics which has so far been an untapped resource. This allows even a single system to serve as a suitable candidate for a reservoir. Specifically, we analyze the performance of the single pendulum reservoir for two classes of tasks: temporal and nontemporal data processing. The accuracy and robustness of the performance exhibited by this minimal one-node reservoir in implementing these tasks strongly suggest an alternative direction in designing the reservoir layer from the point of view of efficient applications. Further, the simplicity of our learning system offers an opportunity to better understand the framework of reservoir computing in general and indicates the remarkable machine-learning potential of even a single simple nonlinear system.
Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems

Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article proposes a bit fusion ensemble algorithm that minimizes the classification error rate and has been tested on various datasets. Five diversified base classifiers k- nearest neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (D.T.), and Naïve Bayesian Classifier (N.B.), are used in the implementation model. Bit fusion algorithm works on the individual input from the classifiers. Decision vectors of the base classifier are weighted transformed into binary bits by comparing with high-reliability threshold parameters. The output of each base classifier is considered as soft class vectors (CV). These vectors are weighted, transformed and compared with a high threshold value of initialized δ = 0.9 for reliability. Binary patterns are extracted, and the model is trained and tested again. The standard fusion approach and proposed bit fusion algorithm have been compared by average error rate. The error rate of the Bit-fusion algorithm has been observed with the values 5.97, 12.6, 4.64, 0, 0, 27.28 for Leukemia, Breast cancer, Lung Cancer, Hepatitis, Lymphoma, Embryonal Tumors, respectively. The model is trained and tested over datasets from UCI, UEA, and UCR repositories as well which also have shown reduction in the error rates.
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation

Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved performance.However, deep architectures trained with gradientbased techniques suffer from catastrophic forgetting, which is the tendency to forget previously learned knowledge while learning new tasks.Aiming at devising strategies to counteract this effect, incremental learning approaches have gained popularity over the past years.However, the first incremental learning methods for semantic segmentation appeared only recently.While effective, these approaches do not account for a crucial aspect in pixel-level dense prediction problems, i.e. the role of attention mechanisms.To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial-and channel-level dependencies.Furthermore, we propose a continual attentive fusion structure, which takes advantage of the attention learned from the new and the old tasks while learning features for the new task.Finally, we also introduce a novel strategy to account for the background class in the distillation loss, thus preventing biased predictions.We demonstrate the effectiveness of our approach with an extensive evaluation on Pascal-VOC 2012 and ADE20K, setting a new state of the art.
MIA-Former: Efficient and Robust Vision Transformers via Multi-Grained Input-Adaptation

Vision transformers have recently demonstrated great success in various computer vision tasks, motivating a tremendously increased interest in their deployment into many real-world IoT applications. However, powerful ViTs are often too computationally expensive to be fitted onto real-world resource-constrained platforms, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth. In parallel, different images are of varied complexity and their different regions can contain various levels of visual information, e.g., a sky background is not as informative as a foreground object in object classification tasks, indicating that treating those regions equally in terms of model complexity is unnecessary while such opportunities for trimming down ViTs' complexity have not been fully exploited. To this end, we propose a Multi-grained Input-Adaptive Vision Transformer framework dubbed MIA-Former that can input-adaptively adjust the structure of ViTs at three coarse-to-fine-grained granularities (i.e., model depth and the number of model heads/tokens). In particular, our MIA-Former adopts a low-cost network trained with a hybrid supervised and reinforcement learning method to skip the unnecessary layers, heads, and tokens in an input adaptive manner, reducing the overall computational cost. Furthermore, an interesting side effect of our MIA-Former is that its resulting ViTs are naturally equipped with improved robustness against adversarial attacks over their static counterparts, because MIA-Former's multi-grained dynamic control improves the model diversity similar to the effect of ensemble and thus increases the difficulty of adversarial attacks against all its sub-models. Extensive experiments and ablation studies validate that the proposed MIA-Former framework can (1) effectively allocate adaptive computation budgets to the difficulty of input images, achieving state-of-the-art (SOTA) accuracy-efficiency trade-offs, e.g., up to 16.5\% computation savings with the same or even a higher accuracy compared with the SOTA dynamic transformer models, and (2) boost ViTs' robustness accuracy under various adversarial attacks over their vanilla counterparts by 2.4\% and 3.0\%, respectively. Our code is available at https://github.com/RICE-EIC/MIA-Former.
A lightweight three-user secure quantum summation protocol without a third party based on single-particle states

In this paper, a lightweight three-user secure quantum summation protocol is put forward by using single-particle states, which can accomplish the goal that three users cooperate together to calculate the modulo 2 addition of their private messages without the help of a third party. This protocol only requires single-particle states rather than quantum entangled states as the initial quantum resource, and only needs single-particle measurements and Bell basis measurements. This protocol needs none of quantum entanglement swapping, the Pauli operations, the controlled-not (CNOT) operation, the Hadamard gate or a pre-shared private key sequence. Security analysis proves that this protocol is secure against both the outside attacks and the participant attacks. Compared with the existing two-dimensional three-user quantum summation protocols, this protocol more or less takes advantage over them on the aspects of the initial quantum resource, users' quantum measurement, the usage of quantum entanglement swapping, the usage of Pauli operations, the usage of CNOT operation or the usage of Hadamard gate.
Multispace Evolutionary Search for Large-Scale Optimization With Applications to Recommender Systems

Large-scale optimization is vital in today’s artificial intelligence (AI) applications for extracting essential knowledge from huge volumes of data. In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify the problem solution space of a given problem for the evolutionary search. In the literature, the existing approaches can generally be categorized as decomposition-based methods and dimension-reduction-based methods. The former decomposes a large-scale problem into several smaller subproblems, while the latter transforms the original high-dimensional solution space into a low-dimensional space. However, it is worth noting that a given large-scale optimization problem may not always be decomposable, and it is also difficult to guarantee that the global optimum of the original problem is preserved in the reduced low-dimensional problem space. This article, thus, proposes a new search paradigm, namely the multispace evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems. In contrast to existing approaches that perform an evolutionary search in a single search space, the proposed paradigm is designed to conduct a search in multiple solution spaces that are derived from the given problem, each possessing a unique landscape. The proposed paradigm makes no assumptions about the large-scale optimization problem of interest, such as that the problem is decomposable or that a certain relationship exists among the decision variables. To verify the efficacy of the proposed paradigm, comprehensive empirical studies in comparison to five state-of-the-art algorithms were conducted using the CEC2013 large-scale benchmark problems as well as an AI application in e-commerce, i.e., movie recommendation.
PromptMNER: Prompt-Based Entity-Related Visual Clue Extraction and Integration for Multimodal Named Entity Recognition

Multimodal named entity recognition (MNER) is an emerging task that incorporates visual and textual inputs to detect named entities and predicts their corresponding entity types. However, existing MNER methods often fail to capture certain entity-related but text-loosely-related visual clues from the image, which may introduce task-irrelevant noises or even errors. To address this problem, we propose to utilize entity-related prompts for extracting proper visual clues with a pre-trained vision-language model. To better integrate different modalities and address the popular semantic gap problem, we further propose a modality-aware attention mechanism for better cross-modal fusion. Experimental results on two benchmarks show that our MNER approach outperforms the state-of-the-art MNER approaches with a large margin.
Frequency-Aware Contrastive Learning for Neural Machine Translation

Low-frequency word prediction remains a challenge in modern neural machine translation (NMT) systems. Recent adaptive training methods promote the output of infrequent words by emphasizing their weights in the overall training objectives. Despite the improved recall of low-frequency words, their prediction precision is unexpectedly hindered by the adaptive objectives. Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective. Specifically, we propose a frequency-aware token-level contrastive learning method, in which the hidden state of each decoding step is pushed away from the counterparts of other target words, in a soft contrastive way based on the corresponding word frequencies. We conduct experiments on widely used NIST Chinese-English and WMT14 English-German translation tasks. Empirical results show that our proposed methods can not only significantly improve the translation quality but also enhance lexical diversity and optimize word representation space. Further investigation reveals that, comparing with related adaptive training strategies, the superiority of our method on low-frequency word prediction lies in the robustness of token-level recall across different frequencies without sacrificing precision.
Detecting return-oriented programming on firmware-only embedded devices using hardware performance counters

Return-oriented programming (ROP) relies on in-memory code sequences ending in return instructions to chain together arbitrary malware. ROP is one of the most dangerous security exploits because, if wittingly crafted, it can be used to wreak havoc on the system, network, and nodes connected to it. It is not surprising that ROP has been studied on architectures such as x86 and ARM, mostly with an operating system (OS). Xtensa is one of the most popular industry standards for digital signal processors and it is present in many resource-constrained firmware-based embedded WiFi home automation devices, which operate by reading instructions directly from flash memory. Despite leveraging no real OS, Xtensa is not immune to ROP, and there have been reports of buffer overflow vulnerability exploitations leading to ROP in Xtensa.
An Improved Pseudorandom Sequence Generator and its Application to Image Encryption

This paper proposes an improved Pseudorandom Sequence Generator (PRSG) based on the concept of modular arithmetic systems with non-integral numbers.The generated random sequence use in various cryptographic applications due to its unpredictability.Here the mathematical model is designed to solve the problem of the non-uniform distribution of the sequences.In addition, PRSG has passed the standard statistical and empirical tests, which shows that the proposed generator has good statistical characteristics.Finally, image encryption has been performed based on the sort-index method and diffusion processing to obtain the encrypted image.After a thorough evaluation of encryption performance, there has been no direct association between the original and encrypted images.The results show that the proposed PRSG has good statistical characteristics and security performance in cryptographic applications.
A new method using knowledge reasoning techniques for improving robot performance in coverage path planning

Robots are a rapidly evolving development field encompassing variable domains ranging from industrial robots to empathetic robots for human companions. Future robots will be highly dependent on the ability to understand, interpret, and generate a representation of the environment in which they are operating, ideally in both a human and machine-readable formalism. An important element in this process lies in Path Planning (PP) with obstacle avoidance in dynamic environments (including cleaning and monitoring in robotics) to identify optimal coverage paths. The study in this paper presents a new approach which combines knowledge reasoning techniques with Breadth First Search to find the optimal path for a cleaning robot in a dynamic environment. This approach is used to apply knowledge inference with conventional coverage PP algorithms to enable robot control and avoid obstacles with optimal coverage PP. The experimental results show that using the proposed approach a robot avoids fixed and mobile obstacles, optimal PP reducing both computational cost and time. When compared to other current approaches, the proposed approach with high-coverage rate and low-repetition rate in coverage performs better than the conventional robot algorithms.