Reviewerly/exHarmony huggingface dataset:
Hessian averaging in stochastic Newton methods achieves superlinear convergence

Abstract We consider minimizing a smooth and strongly convex objective function using a stochastic Newton method. At each iteration, the algorithm is given an oracle access to a stochastic estimate of the Hessian matrix. The oracle model includes popular algorithms such as Subsampled Newton and Newton Sketch, which can efficiently construct stochastic Hessian estimates for many tasks, e.g., training machine learning models. Despite using second-order information, these existing methods do not exhibit superlinear convergence, unless the stochastic noise is gradually reduced to zero during the iteration, which would lead to a computational blow-up in the per-iteration cost. We propose to address this limitation with Hessian averaging: instead of using the most recent Hessian estimate, our algorithm maintains an average of all the past estimates. This reduces the stochastic noise while avoiding the computational blow-up. We show that this scheme exhibits local Q -superlinear convergence with a non-asymptotic rate of $$(\varUpsilon \sqrt{\log (t)/t}\,)^{t}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>Υ</mml:mi> <mml:msqrt> <mml:mrow> <mml:mo>log</mml:mo> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>)</mml:mo> <mml:mo>/</mml:mo> <mml:mi>t</mml:mi> </mml:mrow> </mml:msqrt> <mml:mspace /> <mml:mo>)</mml:mo> </mml:mrow> <mml:mi>t</mml:mi> </mml:msup> </mml:math> , where $$\varUpsilon $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Υ</mml:mi> </mml:math> is proportional to the level of stochastic noise in the Hessian oracle. A potential drawback of this (uniform averaging) approach is that the averaged estimates contain Hessian information from the global phase of the method, i.e., before the iterates converge to a local neighborhood. This leads to a distortion that may substantially delay the superlinear convergence until long after the local neighborhood is reached. To address this drawback, we study a number of weighted averaging schemes that assign larger weights to recent Hessians, so that the superlinear convergence arises sooner, albeit with a slightly slower rate. Remarkably, we show that there exists a universal weighted averaging scheme that transitions to local convergence at an optimal stage, and still exhibits a superlinear convergence rate nearly (up to a logarithmic factor) matching that of uniform Hessian averaging.
Entanglement and Fisher Information for Atoms–Field System in the Presence of Negative Binomial States

We developed a quantum scheme of two atoms (TAs) and field initially in a negative binomial state (NBS). We displayed and discussed the physical implications of the obtained results in terms of the physical parameters of the model. By considering that the TAs were initially prepared in a maximally entangled state, and that the single-mode field was in the NBS, the dynamics of quantum phenomena such TAs-field entanglement, TAs entanglement, and parameter estimation were examined. We found that the quantum quantifiers exhibited randomly quasi-periodic and periodic oscillations that depended on the success probability, photon number transition, and the intensity-dependent coupling effect. Furthermore, we analyzed the connection between the dynamical behavior of the quantifiers. This system can be compared with some other ones that are being discussed in the literature, in order to realize the quantum entanglement, and to control the precision of the parameter estimation.
Optical Remote Sensing for Fluorapatite Content Estimation

Remote sensing techniques are an interesting alternative to traditional methods for the rapid abundance prediction and mapping of phosphate mineralization surface states. In this context, a methodological approach based on hyperspectral spectroscopy and X-ray diffraction (XRD) method is proposed for the phosphate surface abundance prediction and exploration in a specific geological context in Tunisia. In this study, partial least square regression (PLSR) method was conducted on hyper-spectral visible-near infrared (VNIR) and short-wave infrared (SWIR) field reflectance spectra of the collected samples and XRD analysis results for phosphate content prediction. Results revealed that carbonate absorption features in SWIR region could be considered for an accurate estimation of phosphate contents. The generated model has shown an interesting performance with an R2 of 0.64, an RMSE of 5.52, and an RPIQ of 2.15, using the training samples set. Moreover, X-ray diffraction (XRD) analysis results were used for the validation purposes. The using validation samples set revealed an R2 of 0.42, an RMSE of 10.29, and an RPIQ of 1.74. All performance coefficients have shown that the generated model can be applied successfully for the content prediction of phosphates. The present study revealed, thus, the contribution of the proposed methodological approach for phosphate exploration in the Chaketma mine site in the Centre West of Tunisia, which can be improved in the future.
Automatic anomaly identification of wireless communication signal based on nonlinear analysis

In order to effectively reduce the risk of high-risk intrusion signals to wireless communication networks, an automatic anomaly identification of wireless communication signals based on nonlinear analysis is proposed. The fuzzy phase frequency feature of denoised abnormal signal is extracted and the wireless communication signal parameters are estimated by optimizing the fuzzy network abnormal signal feature extraction method of particle filter. The wireless communication signal is collected based on nonlinear analysis, the wireless communication signal network model is constructed, and the Back Propagation Neural Network—Dempster Shafer method is used to classify and fuse the results of feature extraction to realize the automatic identification of wireless communication signal anomalies. The experimental results show that the signal initial frequency estimation and signal slope estimation obtained by the research method have high accuracy, strong coverage performance, large total amount of automatic identification abnormal signals and high compatibility level.
Automatic Extraction of Ontological Explanation for Machine Learning-Based Systems

Machine learning has been implemented as a part of many software systems to support data-driven decisions and recommendations. The prominent machine learning technique is the artificial neural network, which lacks the explanation of how it produces the output. However, many application domains require algorithmic decision making to be transparent so explainability in these systems has been an important challenge. This paper proposes an automated framework that elicits the contributing rules describing how the neural network model makes decisions. The explainability of contributing rules can be measured and it is able to address issues in the training dataset. With the ontology representation of contributing rules, an individual decision can be automatically explained through ontology reasoning. We have developed a tool that supports applying our framework in practice. The evaluation has been conducted to assess the effectiveness of our framework using open datasets from different domains. The results prove that our framework performs well to explain the neural network models, as it can achieve the average accuracy of 81% to explain the subject models. Also, our framework takes significantly less time to process than the other technique.
A computer vision-based system for recognition and classification of Urdu sign language dataset

Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positions using a camera. This work provides a strategy for automated identification of USL numbers based on a bag-of-words (BoW) paradigm. For classification purposes, support vector machine (SVM), Random Forest, and K-nearest neighbor (K-NN) are used with the BoW histogram bin frequencies as characteristics. The proposed technique outperforms others in number classification, attaining the accuracies of 88%, 90%, and 84% for the random forest, SVM, and K-NN respectively.
A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran

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POVERTY ALLEVIATION IN DEVELOPING AND UNDERDEVELOPED COUNTRIES. DO FOREIGN CAPITAL AND ECONOMIC FREEDOM MATTER?

Our study focuses on the role of foreign capital which includes foreign direct investment, foreign aid, and economic freedom in poverty alleviation in developing and underdeveloped countries by using panel data from 1995 to 2018 for 71 countries. In the pursuit of achieving our objective, we employed several econometric techniques such as dynamic ordinary least square, fully modified ordinary least square, dynamic fixed effect, and pooled mean group regression methods. Furthermore, we performed the Granger causality test, impulse response function, and variance decomposition analysis. In our long-run estimations, we found that foreign direct investment could significantly alleviate poverty but increases poverty in the short run. Instead, foreign aid plays no significant role in poverty alleviation. Moreover, economic growth and economic freedom are essential as our findings consistently exhibited that they play a crucial role in poverty alleviation. We also found bidirectional causality between poverty alleviation and population growth, while a unidirectional causal linkage was found from poverty alleviation to foreign aid. We conclude that policymakers should look at a new paradigm of developmental assistance, and governments should also create an aiding environment for foreign investment to support their growth plan.
Which Exceptions Do We Have to Catch in the Python Code for AI Projects?

Recently, Python is the most-widely used language in artificial intelligence (AI) projects requiring huge amount of CPU and memory resources, and long execution time for training. For saving the project duration and making AI software systems more reliable, it is inevitable to handle exceptions appropriately at the code level. However, handling exceptions highly relies on developer’s experience. This is because, as an interpreter-based programming language, it does not force a developer to catch exceptions during development. In order to resolve this issue, we propose an approach to suggesting appropriate exceptions for the AI code segments during development after training exceptions from the existing handling statements in the AI projects. This approach learns the appropriate token units for the exception code and pretrains the embedding model to capture the semantic features of the code. Additionally, the attention mechanism learns to catch the salient features of the exception code. For evaluating our approach, we collected 32,771 AI projects using two popular AI frameworks (i.e. Pytorch and Tensorflow) and we obtained the 0.94 of Area under the Precision-Recall Curve (AUPRC) on average. Experimental results show that the proposed method can support the developer’s exception handling with better exception proposal performance than the compared models.
A novel double keys adapted elliptic curve cryptography and log normalized Gaussian sigmoid adaptive neuro‐fuzzy interference system based secure resource allocation system in decentralized cloud storage

Abstract Cloud computing has emerged as a promising platform that grants users direct yet shared access to computing resources and services without worrying about the internal complex infrastructure. However, secure data storage and data retrieval is the basic characteristics. Therefore, this paper proposes a novel double keys adapted elliptic curve cryptography (DKECC) and log normalized Gaussian sigmoid adaptive neuro‐fuzzy interference system (LGS‐ANFIS) based secure resource allocation system in decentralized cloud storage. Initially, the input data undergoes data fragmentation. After that, encryption is carried out using DKECC algorithm. Next, hash code generation using Pearson hash function is done. Then, resource availability is estimated via logistic sine chaotic mapping indulged rock hyraxes swarm optimization technique. Afterwards, data is checked for deduplication. Finally, resource allocation through LGS‐ANFIS takes place. The experimental outcomes demonstrated better results compared to baseline techniques. The modelling process showed that the suggested safe resource allocation model achieves the total security level of 96.27% while also having shorter reaction times (3018 ms), greater throughputs (1258), reduced load balancing (0.355874), and reduced latency (4372 ms).
A Systematic Review of Remote and Virtual Training for Safety Management

With the proliferation of personal computer systems in the workplaceWorkplaces across most fields and professions alongside the increasing geographical stratification of a given workforce, it has become necessary for onboarding and training activities to be distributed by virtual means. Online and remoteRemote training systems provide many possibilities for effective employeeEmployees engagement. This study will review such remoteRemote systems as online training, virtual training, and virtual reality (VR) through a systematicSystematic review with analytical tools including CiteSpaceCitespace, VOSviewer and MAXQDAMaxqda. After analyzing the results of the review, it was found that virtual and remoteRemote training systems are currently being leveraged to great success for a variety of applications not limited to Safety Management. However, it is also increasingly apparent that virtual reality and virtual training systems have many hurdles yet to cross before they can reachReach the full potential of their effectiveness. It was concluded that a variety of factors are critical to the successful development and deployment of remoteRemote and virtual training systems, including engagement, ergonomicErgonomics, immersionImmersion, and feedback considerations.
Cuteness Engineering and UX Design

The authors conducted initial research/analysis of cuteness, especially in east Asian countries, and its existing/potential use in computer-based products/services worldwide. They provide a taxonomy of cuteness to guide developersDevelopers, offer initial recommendationsRecommendations, and describe follow-on publications.
Shape modeling with spline partitions

Shape modelling (with methods that output shapes) is a new and important task in Bayesian nonparametrics and bioinformatics. In this work, we focus on Bayesian nonparametric methods for capturing shapes by partitioning a space using curves. In related work, the classical Mondrian process is used to partition spaces recursively with axis-aligned cuts, and is widely lied in multi-dimensional and relational data. The Mondrian process outputs hyper-rectangles. Recently, the random tessellation process was introduced as a generalization of the Mondrian process, partitioning a domain with non-axis aligned cuts in an arbitrary dimensional space, and outputting polytopes. Motivated by these processes, in this work, we propose a novel parallelized Bayesian nonparametric approach to partition a domain with curves, enabling complex data-shapes to be acquired. We apply our method to HIV-1-infected human macrophage image dataset, and also simulated datasets sets to illustrate our approach. We compare to support vector machines, random forests and state-of-the-art computer vision methods such as simple linear iterative clustering super pixel image segmentation. We develop an R package that is available at https://github.com/ShufeiGe/Shape-Modeling-with-Spline-Partitions .
Technology and Education Today

This essay probes the delusion that technology and education commonly tend to generate by projecting visions of transformation. Recognising that the total change they promise needs to be examined through extended social categories, the essay uses Ursula Franklin’s idea of the ‘real’ world of technology and investigates the implications of digitally-led education. Three spheres—work, knowledge and ethics—are identified for a focussed enquiry so as to obtain a critical view of the relationship between technology and education today. This enquiry enables us to grasp the political economy of technology-led educational planning. A brief attempt is made to indicate how the pervasive promotion of digital devices in the context of the COVID-19 pandemic has influenced institutionalised education.
Quantum Battery Based on Hybrid Field Charging

A quantum battery consisting of an ensemble two-level atom is investigated. The battery is charged simultaneously by a harmonic field and an electrostatic field. The results show that the hybrid charging is superior to the previous case of only harmonic field charging in terms of battery capacity and charging power, regardless of whether the interaction between atoms is considered or not. In addition, the repulsive interaction between atoms will increase the battery capacity and charging power, while the attractive interaction between atoms will reduce the battery capacity and discharge power.
Iterative Online 3D Reconstruction from RGB Images

3D reconstruction is the computer vision task of reconstructing the 3D shape of an object from multiple 2D images. Most existing algorithms for this task are designed for offline settings, producing a single reconstruction from a batch of images taken from diverse viewpoints. Alongside reconstruction accuracy, additional considerations arise when 3D reconstructions are used in real-time processing pipelines for applications such as robot navigation or manipulation. In these cases, an accurate 3D reconstruction is already required while the data gathering is still in progress. In this paper, we demonstrate how existing batch-based reconstruction algorithms lead to suboptimal reconstruction quality when used for online, iterative 3D reconstruction and propose appropriate modifications to the existing Pix2Vox++ architecture. When additional viewpoints become available at a high rate, e.g., from a camera mounted on a drone, selecting the most informative viewpoints is important in order to mitigate long term memory loss and to reduce the computational footprint. We present qualitative and quantitative results on the optimal selection of viewpoints and show that state-of-the-art reconstruction quality is already obtained with elementary selection algorithms.
Data-Driven Cyber Threat Intelligence: A Survey of Mexican Territory

Information technologies, as well as digital information and information assets, play a very important role today globally, which is why we have witnessed how publications related to cybersecurity incidents are increasing day by day; therefore, and in the face of the great growth of cyber threats, various researchers have dedicated a large part of their efforts to protect these information assets using intelligence sources to develop various techniques for understanding, evolving, detecting, and proactively responding against the cyber threats they face. For their part, companies, governments, and cybersecurity specialists have shown great interest in consuming these sources of intelligence called cyber threat intelligence (CTI), which consists of evidence-based knowledge, which includes context, mechanisms, actionable indicators, implications, and advice on an existing or emerging threat or danger to assets that can be used to inform decisions regarding the subject's response to that threat or danger. With a focus on the Mexican territory, this work aims to analyze the data obtained from CTI sources using the detection of perimeter cybersecurity devices (firewalls, intrusion prevention system, antivirus, honeypots, etc.), as well as the study of research related to cybersecurity predictions to point out the importance of having a model capable of making a possible prediction of cyber threats in Mexico. Challenges and future directions in this field are also discussed.
A Digital Denoising Method Based on Data Frequency Statistical Filtering

Noise amplitude in original time domain data is usually discrete and sparse. This article presents a digital filter denoising method based on statistical frequencies of the signal values. The effective signal and noise signal are identified by comparing the frequency of the value of each pixelin the original signal with the preset validity discrimination threshold. Signals recognized as valid will be output directly, while noise signals will be replaced by the mean value of their surrounding pixel values. Compared to classical digital filtering methods such as mean filtering and median filtering, this method may improve signal recognition accuracy and has the potential to remove random noise while retaining details. An image noise reduction software based on frequency statistics was developed in the MATLAB environment. Noise reduction based on this algorithm was implemented on a portrait image with a noise density of 5%~40%, and noise reduction efficiency was compared to the classical noise reduction algorithms. The experimental results show that the PSNR of the proposed new method exceeds 41, reaching the same level as switching median filtering and adaptive filtering and preceding mean filtering. The SSIM of the new method exceeds 0.97, which is better than other classical methods. Additionally, the higher the noise density, the more obvious the advantage of this method.
Solving Multidimensional Knapsack Problem with Bayesian Multiploid Genetic Algorithm

Solving optimization problems is still a big challenge in the area of optimization algorithms. Many proposed algorithms in the literature don’t consider the relations between the variables of the nature of the problem. However, a recently published algorithm, called “Bayesian Multiploid Genetic Algorithm” exploits the relations between the variables and then solves the given problem. It also uses more than one genotype unlike the simple Genetic Algorithm (GA) and it acts like an implicit memory in order to remember the old but good solutions. In this work, the well-known Multidimensional Knapsack Problem (MKP) is solved by the Bayesian Multiploid Genetic Algorithm. And the results show that exploiting relations between the variables gets a huge advantage in solving the given problem.