Reviewerly/exHarmony huggingface dataset:
Testing the Relationship between Word Length, Frequency, and Predictability Based on the German Reference Corpus

In a recent article, Meylan and Griffiths (Meylan & Griffiths, 2021, henceforth, M&G) focus their attention on the significant methodological challenges that can arise when using large-scale linguistic corpora. To this end, M&G revisit a well-known result of Piantadosi, Tily, and Gibson (2011, henceforth, PT&G) who argue that average information content is a better predictor of word length than word frequency. We applaud M&G who conducted a very important study that should be read by any researcher interested in working with large-scale corpora. The fact that M&G mostly failed to find clear evidence in favor of PT&G's main finding motivated us to test PT&G's idea on a subset of the largest archive of German language texts designed for linguistic research, the German Reference Corpus consisting of ∼43 billion words. We only find very little support for the primary data point reported by PT&G.
Finding the Dwarf: Recovering Precise Types from WebAssembly Binaries

The increasing popularity of WebAssembly creates a demand for understanding and reverse engineering WebAssembly binaries. Recovering high-level function types is an important part of this process. One method to recover types is data-flow analysis, but it is complex to implement and may require manual heuristics when logical constraints fall short. In contrast, this paper presents SnowWhite, a learning-based approach for recovering precise, high-level parameter and return types for WebAssembly functions. It improves over prior work on learning-based type recovery by representing the types-to-predict in an expressive type language, which can describe a large number of complex types, instead of the fixed, and usually small type vocabulary used previously. Thus, recovery of a single type is no longer a classification task but sequence prediction, for which we build on the success of neural sequence-to-sequence models. We evaluate SnowWhite on a new, large-scale dataset of 6.3 million type samples extracted from 300,905 WebAssembly object files. The results show the type language is expressive, precisely describing 1,225 types instead the 7 to 35 types considered in previous learning-based approaches. Despite this expressiveness, our type recovery has high accuracy, exactly matching 44.5% (75.2%) of all parameter types and 57.7% (80.5%) of all return types within the top-1 (top-5) predictions.
Taxonomy of machine learning paradigms: A data‐centric perspective

Abstract Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi‐label learning (MLL), semi‐supervised learning (SSL), one‐class classification (OCC), positive‐unlabeled learning (PUL), transfer learning (TL), multi‐task learning (MTL), and one‐shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data‐driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning‐paradigm graph (LP‐graph). Overall, the LP‐graph establishes a taxonomy among 10 different learning paradigms. This article is categorized under: Technologies &gt; Machine Learning Application Areas &gt; Science and Technology Fundamental Concepts of Data and Knowledge &gt; Key Design Issues in Data Mining
TANA: The amalgam neural architecture for sarcasm detection in indian indigenous language combining LSTM and SVM with word-emoji embeddings

Sentiment analysis is indeed a difficult task owing to the playful language mannerism, altered vocabulary and speak-text used on online forums. Humans tend to use words and phrases in ways that are incomprehensible to those who are not involved in the discourse. Sarcastic remarks in conversations are often utilized to mock others by saying something that isn't pleasant. Sardonic or humorous statements/ tones are used to insult or make others appear puerile. Automated sarcasm detection is considered as one of the key tasks to tweak sentiment analysis and extending it to a morphologically rich and free-order dominant indigenous Indian language Hindi is another challenge. This research puts forward ‘The Amalgam Neural Architecture’, TANA, to detect sarcasm in Hindi tweets. The architecture is trained using two embeddings, namely word and emoji embeddings and combines an LSTM with the loss function of SVM for sarcasm detection. We use the Sarc-H dataset, which is built by scrapping Hindi language tweets and manually annotating based on the hashtags ‘’ (pronounced as kataaksh, which means sarcasm in Hindi) and ‘’ (pronounced as vyangya, another word for sarcasm in Hindi) used by the tweeters and the results are evaluated using various classification performance metrics and achieves a F-score of 0.9675 outperforming LSTM using last layer as softmax as well as the existing works.
Enhancing quantum teleportation fidelity under decoherence via weak measurement with flips

Abstract Noiseless quantum channels are critical to share a pure maximally entangled state for performing an ideal teleportation protocol. However, in reality the shared entanglement severely degraded due to decoherence. In this paper, we propose a quantum teleportation channel protection scheme to enhance the teleportation fidelity in presence of decoherence. Before the entangled pair enters the decoherence channel, the weak measurement and flip operations are applied to transfer the qubit to a more robust state to the effects of the noise. After the decoherence channel the reversed flip operations and weak measurement reversal are applied to recover the initial state. We illustrate our protected teleportation scheme and compare it with a protocol based on weak measurement reversal. The numerical results show that the average teleportation fidelity of our proposed scheme can be significantly improved. Although the proposed entanglement protection scheme is probabilistic, after a successful entanglement transmission, we use the standard teleportation protocol which has probability one.
Episodic Memory and Information Recognition Using Solitonic Neural Networks Based on Photorefractive Plasticity

Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior of networks based on these solitonic neurons, which are capable of performing complex tasks such as bit-to-bit information memorization and recognition. By exploiting photorefractive nonlinearity as if it were a biological neuroplasticity, the network modifies and adapts to the incoming signals, memorizing and recognizing them (photorefractive plasticity). The information processing and storage result in a plastic modification of the network interconnections. Theoretical description and numerical simulation of solitonic networks are reported and applied to the processing of 4-bit information.
Machine Learning for Solar Resource Assessment Using Satellite Images

Understanding solar energy has become crucial for the development of modern societies. For this reason, significant effort has been placed on building models of solar resource assessment. Here, we analyzed satellite imagery and solar radiation data of three years (2012, 2013, and 2014) to build seven predictive models of the solar energy obtained at different altitudes above sea level. The performance of four machine learning algorithms was evaluated using four evaluation metrics, MBE, R2, RMSE, and MAPE. Random Forest showed the best performance in the model with data obtained at altitudes below 800 m.a.s.l. The results achieved by the algorithm were: 4.89, 0.82, 107.25, and 41.08%, respectively. In general, the differences in the results of the machine learning algorithms in the different models were not very significant; however, the results provide evidence showing that the estimation of solar radiation from satellite images anywhere on the planet is feasible.
An Adaptive Bi-Mutation-Based Differential Evolution Algorithm for Multi-Threshold Image Segmentation

Aiming at solving the problems of large calculation, time-consuming, and low segmentation accuracy of multi-threshold image segmentation, an adaptive threshold value based differential evolution algorithm is proposed in this paper. Firstly, an opposite learning strategy is introduced into the initial population to improve the quality of the initial population; secondly, a threshold-value-based mutation strategy is proposed to balance the exploration and development capabilities of the algorithm, and the number of successfully evolved individuals is considered as a threshold value to adaptively adjust the evolution of superior and inferior individuals. Experiments demonstrate that the proposed algorithm has better performance in enhancing accuracy and speeding up the convergence.
Detecting Li-bearing pegmatites using geospatial technology: the case of SW Konar Province, Eastern Afghanistan

Lithium-cesium-tantalum (LCT) pegmatites are widely distributed in northeastern, eastern, and central Afghanistan. The largest rare-metal pegmatites having Lithium (Li)-bearing minerals occur in the Nuristan pegmatite belt in Eastern Afghanistan. The main aim of this study is to detect and highlight the Li-bearing pegmatites using spectral analysis within the Southwest Konar province in the east of Afghanistan and to establish self-proposed spectral indices for the detection of Li-rich minerals. To achieve the study’s objective, we apply several algorithms, for example, false color composite, band ratio, and spectral angle mapper on ASTER, Landsat 8 OLI/TIRS, and Sentinel-2MSI data. Considering the spectral characteristics of key mineral spodumene and other diagnostic minerals, for example, lepidolite, amblygonite, and cookeite, which are considered diagnostic minerals of LCT pegmatites, particularly lithium, we utilize the ECOSTRESS Spectral Library-Version 1.0 for the interpretation of reflectance and absorption determinants of these minerals. Based on the diagnostic characteristics of the selected Li-bearing minerals, we examine several BRs and thresholding and consequently, propose new spectral indices which show a high concentration of spodumene, cookeite, and lepidolite over the northern regions of the study area. According to the obtained results, ASTER data and thresholded BRs present effective results compared to Landsat 8 OLI/TIRS. Based on the final outputs, we define nine zones having Li-bearing pegmatites, particularly over the Chapa Dara and Dara-e-Pech districts of southwestern Konar province, which could be promising areas of interest for further detailed exploration. Lastly, we compare the 118 known Li-bearing points collected from previous studies and field observations with the newly-defined zones to validate accuracy. The study’s overall accuracy is 71.1%, indicating a good match between our findings and the reference points.
The epidemiological impact of the Canadian COVID Alert app

Abstract Objectives We analyzed the effectiveness of the Canadian COVID Alert app on reducing COVID-19 infections and deaths due to the COVID-19 virus. Methods Two separate but complementary approaches were taken. First, we undertook a comparative study to assess how the adoption and usage of the COVID Alert app compared to those of similar apps deployed in other regions. Next, we used data from the COVID Alert server and a range of plausible parameter values to estimate the numbers of infections and deaths averted in Canada using a model that combines information on number of notifications, secondary attack rate, expected fraction of transmissions that could be prevented, quarantine effectiveness, and expected size of the full transmission chain in the absence of exposure notification. Results The comparative analysis revealed that the COVID Alert app had among the lowest adoption levels among apps that reported usage. Our model indicates that use of the COVID Alert app averted between 6284 and 10,894 infections across the six Canadian provinces where app usage was highest during the March–July 2021 period. This range is equivalent to 1.6–2.9% of the total recorded infections across Canada in that time. Using province-specific case fatality rates, 57–101 deaths were averted during the same period. The number of cases and deaths averted was greatest in Ontario, whereas the proportion of cases and deaths averted was greatest in Newfoundland and Labrador. App impact measures were reported so rarely and so inconsistently by other regions that the relative assessment of impact is inconclusive. Conclusion While the nationwide rates are low, provinces with widespread adoption of the app showed high ratios of averted cases and deaths (upper bound was greater than 60% of averted cases). This finding suggests that the COVID Alert app, when adopted at sufficient levels, can be an effective public health tool for combatting a pandemic such as COVID-19.
Learning to resolve uncertainties for large-scale face recognition

Facial recognition is a category of biometric security, used widely in various industries where we identify and authenticate an individuals identity using their face. In the modern deep learning era, face recognition datasets are playing a significant role in achieving state-of-the-art accuracy by acquiring and training millions of face images. Annotating such a large-scale face recognition dataset is challenging due to low-quality face images, and incorrect annotations unknowingly made by annotators. Training a deep learning model with such uncertainties leads to deep model overfitting on noisy uncertain samples and degradation of the discriminative ability of the model. To address these issues, we propose a simple yet effective uncertainty learning network that efficiently reduces over-fitting caused by uncertain face images. More specifically, our FC module weights each sample in the mini-batch at the decision layer, and relabeling mechanism carefully modify the labels of incorrect samples in the mini- batch. Results on IJB-B, IJB-C, LFW, AgeDB30, CFP-FP, CALFW and CPLFW public datasets demonstrate that our approach achieves state-of-the-art performance
Dual-frame spatio-temporal feature modulation for video enhancement

Current video enhancement approaches have achieved good performance in specific rainy, hazy, foggy, and snowy weather conditions. However, they currently suffer from two important limitations. First, they can only handle degradation caused by single weather. Second, they use large, complex models with 10–50 millions of parameters needing high computing resources. As video enhancement is a pre-processing step for applications like video surveillance, traffic monitoring, autonomous driving, etc., it is necessary to have a lightweight enhancement module. Therefore, we propose a dual-frame spatio-temporal feature modulation architecture to handle the degradation caused by diverse weather conditions. The proposed architecture combines the concept of spatio-temporal multi-resolution feature modulation with a multi-receptive parallel encoders and domain-based feature filtering modules to learn domain-specific features. Further, the architecture provides temporal consistency with recurrent feature merging, achieved by providing feedback of the previous frame output. The indoor (REVIDE, NYUDepth), synthetically generated outdoor weather degraded video de-hazing, and de-raining with veiling effect databases are used for experimentation. Also, the performance of the proposed method is analyzed for night-time de-hazing and de-raining with veiling effect weather conditions. Experimental results show the superior performance of our framework compared to existing state-of-the-art methods used for video de-hazing (indoor/outdoor) and de-raining with veiling effect weather conditions. The code is available at https://github.com/pwp1208/PR2022
Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data Applications

Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable datasets in memory rather than recomputing them in each iteration. Selecting appropriate datasets to cache as well as allocating a suitable cluster configuration for caching these datasets play a crucial role in achieving optimal performance. In practice, both are tedious, time-consuming tasks and are often neglected by end users, who are typically not aware of workload semantics, sizes of intermediate data, and cluster specification.
Analysis and Ranking of IT Risk Factors Using Fuzzy TOPSIS-Based Approach

In today’s age of information and communication technology (ICT), many companies are using advanced digital technologies as well as the application of information technology to grow the company and effectively manage their processes. The risk management of information technology plays a crucial role in protecting the important information and data assets of an enterprise. The key objective of risk management in information technology is to safeguard the digital infrastructure from ICT-related harm. An efficient as well as cost effective risk managing mechanism is an integral aspect of an extensive safety system for information technology. A successful approach to IT risk management would strive to protect the company and its infrastructure, not just its digital assets, to conduct their process. Subsequently, the risk managing mechanism must not be viewed solely for instance as a procedural task performed by the IT specialists who run and administer the IT program but as the organization’s critical management task. The risks of information technology assets are of a dynamic nature; different strategies tackle the management of information security risk. This research paper is intended to review and discuss information technology risk managing procedures. We also carried out a multi-criteria decision-making (MCDM)-based empirical investigation to analyses and prioritized different IT risk factors. This has recognized that there are many reports on the techniques, and that various approaches to risk management exist.
Methods for improving the availability of spot instances: A survey

The burgeoning development of the cloud market has promoted the expansion of resources held by cloud providers, but the resulting underutilization caused by the over-provisioned resources has become a challenge. To improve the utilization of these resources, in November 2009, AWS (Amazon Web services) first introduces an auction mechanism to provide users with these temporarily idle resources, which are named spot instances. After decades of development, AWS spot instance has become the largest public infrastructure with dynamic pricing based on the supply and demand of instance market resources. Other major cloud providers such as Microsoft Azure and GCP (Google Cloud Platform) have also introduced their spot-like instances. These instances are sold in a pay-as-you-go way at much lower prices than on-demand instances with the same configuration. Such price advantages have quickly attracted the attention of users needing a lot of computing resources. However, the availability of spot instances cannot be guaranteed. Therefore, methods for improving the availability of spot instances have been widely discussed. This paper presents a survey on the work related to spot instances, by first introducing the development history of spot instance pricing models, then summarizing the methods that can improve the availability of spot instances, and finally discussing how to better understand and use spot instances. We hope that cloud users can obtain enough knowledge in this article to be able to use spot instances from various cloud providers to provide themselves with cheap and stable computing resources.
Uncertainty‐aware convolutional neural network for explainable artificial intelligence‐assisted disaster damage assessment

Accurate damage assessment is a critical step in post-disaster risk assessment, mitigation, and recovery. Current practices performed by experts and reconnaissance teams in the form of field evaluation require considerable time and resources. Recent advances in remote sensing imagery, artificial intelligence (AI), and computer vision have enhanced automated and rapid disaster damage assessment. Recent literature has shown promising progress in AI-assisted aerial damage assessment. However, accounting for the uncertainty in the outcome for improved quantification of confidence and enhanced model explainability for human decision-makers remains one of the key challenges. Overlooking uncertainty can lead to erroneous decisions, especially in highly-consequential tasks such as damage assessment. The aim of this study is to develop uncertainty-aware deep learning models for the assessment of post-disaster damage using aerial imaging. Within the framework of variational Bayesian inference, Monte Carlo dropout sampling technique is used to propagate epistemic uncertainty in model predictions. With this stochastic setting, the model produces damage prediction labels with softmax as random variables, which helps quantify confidence in the model outcome using appropriate measures of uncertainty. Two networks are implemented and trained separately on two different disaster damage datasets consisting of unmanned aerial vehicle building footage as well as satellite-captured post-disaster imagery. The first network attains 59.4% accuracy in building classification, and the second network gives an accuracy of 55.1%. Results from uncertainty analysis, model confidence quantification, and analyzing model attention zone can lead to more explainable and risk-informed automated damage assessment outcomes using AI technology.