Reviewerly/exHarmony huggingface dataset:
Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

Dipper throated optimization (DTO) algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird. DTO has its unique hunting technique by performing rapid bowing movements. To show the efficiency of the proposed algorithm, DTO is tested and compared to the algorithms of Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) based on the seven unimodal benchmark functions. Then, ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques. Additionally, to demonstrate the proposed algorithm's suitability for solving complex real-world issues, DTO is used to solve the feature selection problem. The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine (UCI) repository. The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues, demonstrating the proposed algorithm's capabilities to solve complex real-world situations.
A Comprehensive Review of Arabic Text Summarization

The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language’s morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic representations, the lack of using rouge metrics in abstractive text summarization, and using recent deep learning models to adopt them in Arabic summarization studies is an essential demand.
Effective prediction of bitcoin price using wolf search algorithm and bidirectional LSTM on internet of things data

Internet of things is the concept of establishing relationships and interactions with other connected devices through a network to reach a specific objective. The collected data from devices could be transformed into valuable insights by applying some intelligent learning algorithms. A distributed, permission less ledger called IOTA (MIOTA) manages micro transactions between multiple devices with the help of IoT. It provides the information about the transactions made with the specific type of crypto currency in the market. In this paper, an effective crypto currency price prediction model is proposed to identify the fluctuations in currency value from the past one-year data. Wolf search optimisation algorithm selects the best performing feature subset. Bidirectional long short-term memory (BiLSTM) model is employed to train and validate the data captured from the feature selection process. The proposed model attained 93% accuracy, significantly higher than the existing methods, portraysits significance and efficacy.
Digital competence of higher education learners in the context of COVID-19 triggered online learning

The spread of COVID-19 caused wide scale disruptions in the educational sector across the globe. Digital education, which involves the use of digital tools, virtual platforms, and online learning, is seen as one of the viable alternatives to continue academic activities in such an environment. Higher education institutions have largely switched to this new mode of learning and continue to rely on digital mode in many parts of the world, due to the ongoing pandemic threat. However, learners' competency to effectively engage in online courses and the impact of their socioeconomic background on this competency has not been adequately addressed in the literature. The present study was an attempt to explore these aspects, as they are crucial to the success of digital education. The study was conducted with 833 undergraduate, postgraduate, and doctoral students from an agricultural university to assess their digital competencies and factors that influence effective participation in online courses. The Digital Competence Framework 2.0 of EU Science Hub (DIGCOMP) was adapted and used for this study. Our findings suggest that the learners have a satisfactory level of competence in most of the aspects of digital competence. Majority of the participants were relying on smart phones both as the device for accessing internet as well as for their learning activities. The results of a Tukey's difference in the mean test reveals that learners' digital competence varies significantly by gender, economic profile, and academic level. This finding can be attributed to the difference in their socio-economic background, which confirms digital divide among learners. Our findings have implications for the design of digital higher education strategies and institutional management to ensure effective learner participation, especially for higher education institutions in developing countries.
Artificial intelligence chatbots in academic libraries: the rise of ChatGPT

Purpose The study aims to investigate the potential of artificial intelligence chatbots in academic libraries along with the dangers associated with the technology. Design/methodology/approach Literature were sourced from Google scholar and Scopus database-indexed journals to assess the potentials and drawbacks of using ChatGPT (generative pretrained transformer). Findings The study found that ChatGPT can aid with technical and reader services such as answering basic reference inquiries, navigating the library website and assisting with research, cataloguing, classification and collection development. However, due to the risk of inaccurate query responses, misuse, limited comprehension, input limitation and technological reliance, it should be a complementary technology rather than a replacement for human librarians. Originality/value To the best of the author’ knowledge, this is one of the first articles reviewing the potential of ChatGPT in academic libraries.
Secure distributed adaptive bin packing algorithm for cloud storage

Cloud computing delivers highly scalable and flexible computing and storage resources on pay-per-use policy. Cloud Computing's services for computation and storage are getting increasingly popular and many organizations are now moving their data from in-house data centers to the Cloud Storage Providers (CSPs). However, increasing user base and remote storage of data introduces challenges like inefficient usage of resources and insider threat to data at rest in the cloud storage. Time varying workload and data intensive applications are vulnerable to encounter such challenges while using cloud computing services. The consolidation of virtual machines to minimize the total number of active servers is a promising solution to conserve the energy but it requires efficient resource management strategies. Additionally, the encryption strategies and third party auditors to maintain data integrity are still in their developing stage and therefore the data at rest is still a concern for the cloud storage users. In this paper, we designed a distributed storage allocation architecture for fair utilization of storage resources and we also design an integrated end to end security framework for data at rest in cloud storage to eliminate insider threats.
Predator-Prey Pigeon-Inspired Optimization for UAV ALS Longitudinal Parameters Tuning

This paper presents a predator-prey pigeon-inspired optimization (PPPIO) algorithm for an automatic landing system of a fixed-wing unmanned aerial vehicle (UAV) in the longitudinal plane. A pitch command autopilot and two types of approach power compensators are presented and evaluated in automatic landing. The design of a glide slope command system and an approach power compensator are converted to a finite-dimensional optimization problem. The longitudinal flight controller parameters are optimized by the proposed PPPIO algorithm. The differences between the two proposed approach power compensators are also analyzed. The proposed optimization process can guarantee the control gains vector converge to an approximate optimal solution, and it is computationally much more efficient. Simulation results are presented to demonstrate that this approach helps solve control system optimization problems for different criteria and attains a rather accurate result. As verified by these simulation results, the proposed automatic landing system can help improve UAV's performance during the landing phase.
A Bayesian nonparametric causal inference model for synthesizing randomized clinical trial and real‐world evidence

With the wide availability of various real‐world data (RWD), there is an increasing interest in synthesizing information from both randomized clinical trials and RWD for health‐care decision makings. The task of addressing study‐specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical models with nonparametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the sources. In this paper, we propose a propensity score‐based Bayesian nonparametric Dirichlet process mixture model that summarizes subject‐level information from randomized and registry studies to draw inference on the causal treatment effect. Simulation studies are conducted to evaluate the model performance under different scenarios. In addition, we demonstrate the proposed method using data from a clinical study on angiotensin converting enzyme inhibitor for treating congestive heart failure.
Deep Learning Based Stair Detection and Statistical Image Filtering for Autonomous Stair Climbing

Mobile robots are widely used in the surveillance industry, for military and industrial applications. To carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning based approach for stair detection, statistical filtering on images for the estimation of stair alignment, and novel mechanical design for an autonomous stair climbing robot. The primary objective is to solve the problem of indoor locomotion over staircases with the proposed implementation. The detection of stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters. However, with the advent of deep learning methods, we could arrive at more scalable and robust detection schemes. The proposed statistical filtering eliminates the need for manual tuning of parameters of the edge detector and the Hough accumulator. The experimental results of stair detection and stair alignment algorithm are demonstrated in this paper.
Path identity as a source of high-dimensional entanglement

Significance Quantum entanglement amounts to an extremely strong link between two distant particles, a link so strong that it eludes any classical description and so unsettling that Albert Einstein described it as “spooky action at a distance.” Today, entanglement is not only a subject of fundamental research, but also a workhorse of emerging quantum technologies. In our current work we experimentally demonstrate a completely different method of entanglement generation. Unlike many traditional methods, where entanglement arises due to conservation of a physical quantity, such as momentum, in our method it is rather a consequence of indistinguishability of several particle-generating processes. This approach, where each process effectively adds one dimension to the entangled state, allows for a high degree of customizability.
BoSR: A CNN-based aurora image retrieval method

The deep learning models especially the CNN have achieved amazing performance on natural image retrieval. However, remote sensing images captured with anamorphic lens are still retrieved via manual selection or traditional SIFT-based methods. How to leverage the advanced CNN models for remote sensing image retrieval is a new task of significance. This paper focuses on the aurora images captured with all-sky-imagers (ASI). By analyzing the imaging principle of ASI and characteristics of aurora, a salient region determination (SRD) scheme is proposed and embedded into the Mask R-CNN framework. Thus, we can regard an image as a "bag" of salient regions (BoSR). In practice, each salient region is represented with a CNN feature extracted from the SRD embedded Mask R-CNN. After clustered to generate a visual vocabulary, each CNN feature is quantized to its nearest center for indexing. In the stage of online retrieval, by computing the similarity scores between query image and all images in the dataset, ranking results can be obtained and image with the highest value is exported as the top rank. Extensive experiments are conducted on the big aurora data, and the results demonstrate that the proposed method improves the retrieval accuracy and efficiency.
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

Jianmo Ni, Chenguang Zhu, Weizhu Chen, Julian McAuley. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Multiscale sparse microcanonical models

We study approximations of non-Gaussian stationary processes having long range correlations with microcanonical models. These models are conditioned by the empirical value of an energy vector, evaluated on a single realization. Asymptotic properties of maximum entropy microcanonical and macrocanonical processes and their convergence to Gibbs measures are reviewed. We show that the Jacobian of the energy vector controls the entropy rate of microcanonical processes. Sampling maximum entropy processes through MCMC algorithms require too many operations when the number of constraints is large. We define microcanonical gradient descent processes by transporting a maximum entropy measure with a gradient descent algorithm which enforces the energy conditions. Convergence and symmetries are analyzed. Approximations of non-Gaussian processes with long range interactions are defined with multiscale energy vectors computed with wavelet and scattering transforms. Sparsity properties are captured with $\mathbf{l}^1$ norms. Approximations of Gaussian, Ising and point processes are studied, as well as image and audio texture synthesis.
Review on Video Object Tracking Based on Deep Learning

Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework for the research of moving object tracking algorithm in this paper. In this paper, the existing deep tracking-based target tracking algorithms are classified and sorted out. Based on the previous knowledge and my own understanding, several solutions are proposed for the existing methods. In addition, the existing deep learning target tracking method is still difficult to meet the requirements of real-time, how to design the network and tracking process to achieve speed and effect improvement, there is still a lot of research space.
Reversible data hiding based on novel pairwise PVO and annular merging strategy

Reversible data hiding (RDH) methods aim to reduce the embedding distortion below the required embedding capacity. Concerning this problem, pixel value ordering (PVO)-based methods achieve remarkably good performance as they only modify the minimum and maximum of pixels in each block. In this paper, we further exploit the embedding potential of PVO by merging blocks that cannot be directly used in PVO with neighboring blocks, in order to generate a larger block. The mechanism of PVO is reconsidered and pixel distribution density (PDD) is defined as a metric to control both order and termination of merging strategy. Moreover, a novel two-dimensional prediction-error histogram is generated to enhance the embedding performance. Experimental results verify that the proposed method outperforms previous PVO-based schemes and state-of-the-art methods.
Delegated Proof of Reputation

Consensus mechanism is the heart of any blockchain network. Many projects have proposed alternative protocols to improve restricted scalability of Proof of Work originated since Bitcoin. As an improvement of Delegated Proof of Stake, in this paper, we introduce a novel consensus, namely, Delegated Proof of Reputation, which is scalable, secure with an acceptable decentralization. Our innovative idea is replacing pure coin-staking by a reputation ranking system essentially based on ranking theories (PageRank, NCDawareRank and HodgeRank).
Paraphrase Generation with Latent Bag of Words

Paraphrase generation is a longstanding important problem in natural language processing. Recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation. We ground the semantics of a discrete latent variable by the target BOW. We use this latent variable to build a fully differentiable content planning and surface realization pipeline. Specifically, we use source words to predict their neighbors and model the target BOW with a mixture of softmax. We use gumbel top-k reparameterization to perform differentiable subset sampling from the predicted BOW distribution. We retrieve the sampled word embeddings and use them to augment the decoder and guide its generation search space. Our latent BOW model not only enhances the decoder, but also exhibits clear interpretability. We show the model interpretability with regard to (1). unsupervised learning of word neighbors (2). the step-by-step generation procedure. Extensive experiments demonstrate the model's transparent and effective generation process.
Learning Reward Machines for Partially Observable Reinforcement Learning

Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into subproblems that can be efficiently learned using off-policy learning. Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems. We pose the task of learning RMs as a discrete optimization problem where the objective is to find an RM that decomposes the problem into a set of subproblems such that the combination of their optimal memoryless policies is an optimal policy for the original problem. We show the effectiveness of this approach on three partially observable domains, where it significantly outperforms A3C, PPO, and ACER, and discuss its advantages, limitations, and broader potential.
Uncertainty-based Continual Learning with Adaptive Regularization

We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learning framework with variational inference. We focus on two significant drawbacks of the recently proposed regularization-based methods: a) considerable additional memory cost for determining the per-weight regularization strengths and b) the absence of gracefully forgetting scheme, which can prevent performance degradation in learning new tasks. In this paper, we show UCL can solve these two problems by introducing a fresh interpretation on the Kullback-Leibler (KL) divergence term of the variational lower bound for Gaussian mean-field approximation. Based on the interpretation, we propose the notion of node-wise uncertainty, which drastically reduces the number of additional parameters for implementing per-weight regularization. Moreover, we devise two additional regularization terms that enforce \emph{stability} by freezing important parameters for past tasks and allow \emph{plasticity} by controlling the actively learning parameters for a new task. Through extensive experiments, we show UCL convincingly outperforms most of recent state-of-the-art baselines not only on popular supervised learning benchmarks, but also on challenging lifelong reinforcement learning tasks. The source code of our algorithm is available at https://github.com/csm9493/UCL.
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit~(LMU) is mathematically derived to orthogonalize its continuous-time history -- doing so by solving $d$ coupled ordinary differential equations~(ODEs), whose phase space linearly maps onto sliding windows of time via the Legendre polynomials up to degree $d - 1$. Backpropagation across LMUs outperforms equivalently-sized LSTMs on a chaotic time-series prediction task, improves memory capacity by two orders of magnitude, and significantly reduces training and inference times. LMUs can efficiently handle temporal dependencies spanning $100\text{,}000$ time-steps, converge rapidly, and use few internal state-variables to learn complex functions spanning long windows of time -- exceeding state-of-the-art performance among RNNs on permuted sequential MNIST. These results are due to the network's disposition to learn scale-invariant features independently of step size. Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales. We demonstrate that LMU memory cells can be implemented using $m$ recurrently-connected Poisson spiking neurons, $\mathcal{O}( m )$ time and memory, with error scaling as $\mathcal{O}( d / \sqrt{m} )$. We discuss implementations of LMUs on analog and digital neuromorphic hardware.