Reviewerly/exHarmony huggingface dataset:
Random search with adaptive boundaries algorithm for obtaining better initial solutions

Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, random search techniques with advanced competencies play an essential role in algorithms. In this study, we develop an algorithm that provides an adaptive initial solution, to some extent reducing the diversity of randomness in the initialization of the algorithms for continuous unconstrained/bounded nonlinear optimization problems. The algorithm meets this expectation by narrowing search space adaptively without trapping into local optimums. It also escapes from eliminating accidentally global optimum in multi-modal problems. For this reason, we configure the proposed algorithm on the principle of updating given upper-lower boundaries dynamically. It is worth mentioning that this procedure does not add an additional burden to existing solution methods; on the contrary, it contributes to problem-solving in terms of time and efficiency. To show its performance, we have incorporated with most frequently used unconstrained/bounded benchmarks and compared them with the solutions in the literature. In conclusion, the proposed algorithm converges solutions quickly and is applicable for later usage in further studies.
Confinement <scp>forest‐based</scp> enhanced <scp>min‐min</scp> and <scp>max‐min</scp> technique for secure multicloud task scheduling

Abstract Task scheduling is the most important issue in the cloud computing environment since its implementation is largely dependent on it. However, due to its weaknesses in energy consumption for resource usage, high computational complexity, and memory needs, Virtual Machine (VM) cannot manage large amounts of workload. Furthermore, by reducing the number of active hosts' data assignments while utilizing the resources, a security risk occurs. To address these challenges, a Grey‐wolf with Deadline Restrictions approach was developed to allocate tasks and resources in less time while maintaining fixed interval constraints. Moreover, the Cognizing Anomaly detection method is utilized to find even the most recent abnormalities in the data. Furthermore, using the Confinement Forest‐based augmented Min‐Min and Max‐Min approach, the task scheduling process provides an efficient workload balancing mechanism by restricting makespan, energy consumption, load balancing, and fault tolerance. In contrast, enabling a security‐aware multicharacteristic‐based heuristic technique is employed to provide secure placement of data in the appropriate location. The proposed technique outperforms existing methods in terms of makespan and memory consumption.
A Flash-based Current-mode IC to Realize Quantized Neural Networks

This paper presents a mixed-signal architecture for implementing Quantized Neural Networks (QNNs) using flash transistors to achieve extremely high throughput with extremely low power, energy and memory requirements. Its low resource consumption makes our design especially suited for use in edge devices. The network weights are stored in-memory using flash transistors, and nodes perform operations in the analog current domain. Our design can be programmed with any QNN whose hyperparameters (the number of layers, filters, or filter size, etc) do not exceed the maximum provisioned. Once the flash devices are programmed with a trained model and the IC is given an input, our architecture performs inference with zero access to off-chip memory. We demonstrate the robustness of our design under current-mode non-linearities arising from process and voltage variations. We test validation accuracy on the ImageNet dataset, and show that our IC suffers only 0.6% and 1.0% reduction in classification accuracy for Top-1 and Top-5 outputs, respectively. Our implementation results in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim \boldsymbol {50}\times$</tex> reduction in latency and energy when compared to a recently published mixed-signal ASIC implementation, with similar power characteristics. Our approach provides layer partitioning and node sharing possibilities, which allow us to trade off latency, power, and area amongst each other.
Can Bitcoin be Trusted? Quantifying the economic value of blockchain transactions

Bitcoin is the largest blockchain, and provides the underlying UTXO architecture used by many other cryptocurrencies. We identify an inherent bias embedded in this architecture (the Notebreaker mechanism) which forces users to ‘spend’ the entire content of a wallet address in order to make a payment, receiving ‘change’ into a unique new address. This inflates both the apparent volume transacted and network users, as well as minimizing the apparent fees of transacting. We develop an innovative Transaction Identification Methodology (TIM) to quantify the economic value of transactions from raw blockchain data. Using four different algorithms across three stages, we achieve 95% accuracy in quantifying the degree of bias in these measures. Validated across more than 430 million Bitcoin transactions involving 600 million wallet addresses, our methodology reveals that the Notebreaker mechanism inflates transaction volumes 8 times, makes the actual costs of blockchain transactions appear 3–7 times more expensive than what is commonly reported, and inflates wallet counts – a common heuristic of unique adopter counts. We provide a remediation strategy to make Bitcoin blockchain data a more accurate representation of reality, and provide a daily data set of these remediated volumes and transaction fees.
A Triple Relation Network for Joint Entity and Relation Extraction

Recent methods of extracting relational triples mainly focus on the overlapping problem and achieve considerable performance. Most previous approaches extract triples solely conditioned on context words, but ignore the potential relations among the extracted entities, which will cause incompleteness in succeeding Knowledge Graphs’ (KGs) construction. Since relevant triples give a clue for establishing implicit connections among entities, we propose a Triple Relation Network (Trn) to jointly extract triples, especially handling extracting implicit triples. Specifically, we design an attention-based entity pair encoding module to identify all normal entity pairs directly. To construct implicit connections among these extracted entities in triples, we utilize our triple reasoning module to calculate relevance between two triples. Then, we select the top-K relevant triple pairs and transform them into implicit entity pairs to predict the corresponding implicit relations. We utilize a bipartite matching objective to match normal triples and implicit triples with the corresponding labels. Extensive experiments demonstrate the effectiveness of the proposed method on two public benchmarks, and our proposed model significantly outperforms previous strong baselines.
Exploring the Perception of Additional Information Content in 360° 3D VR Video for Teaching and Learning

360° 3D virtual reality (VR) video is used in education to bring immersive environments into a teaching space for learners to experience in a safe and controlled way. Within 360° 3D VR video, informational elements such as additional text, labelling and directions can be easily incorporated to augment such content. Despite this, the usefulness of this information for learners has not yet been determined. This article presents a study which aims to explore the usefulness of labelling and text within 360° stereoscopic 3D VR video content and how this contributes to the user experience. Postgraduate students from a university in the UK (n = 30) were invited to take part in the study to evaluate VR video content augmented with labels and summary text or neither of these elements. Interconnected themes associated with the user experience were identified from semi-structured interviews. From this, it was established that the incorporation of informational elements resulted in the expansion of the field of view experienced by participants. This “augmented signposting” may facilitate a greater spatial awareness of the virtual environment. Four recommendations for educators developing 360° stereoscopic 3D VR video content are presented.
Contrasting mineral dust abundances from X-ray diffraction and reflectance spectroscopy

Abstract. Mineral dust particles dominate aerosol mass in the atmosphere and directly modify Earth's radiative balance through absorption and scattering. This radiative forcing varies strongly with mineral composition, yet there is still limited knowledge on the mineralogy of atmospheric dust. In this study, we performed X-ray diffraction (XRD) and reflectance spectroscopy measurements on 37 different dust deposition samples collected as airfall in an urban setting to determine mineralogy and the relative proportions of minerals in the dust mixture. Most commonly, XRD has been used to characterize dust mineralogy; however, without prior special sample preparation, this technique is less effective for identifying poorly crystalline or amorphous phases. In addition to XRD measurements, we performed visible and short-wave infrared (VSWIR) reflectance spectroscopy for these natural dust samples as a complementary technique to determine mineralogy and mineral abundances. Reflectance spectra of dust particles are a function of a nonlinear combination of mineral abundances in the mixture. Therefore, we used a Hapke radiative transfer model along with a linear spectral mixing approach to derive relative mineral abundances from reflectance spectroscopy. We compared spectrally derived abundances with those determined semi-quantitatively from XRD. Our results demonstrate that total clay mineral abundances from XRD are correlated with those from reflectance spectroscopy and follow similar trends; however, XRD underpredicts the total amount of clay for many of the samples. On the other hand, calcite abundances are significantly underpredicted by SWIR compared to XRD. This is caused by the weakening of absorption features associated with the fine particle size of the samples, as well as the presence of dark non-mineral materials (e.g., asphalt) in these samples. Another possible explanation for abundance discrepancies between XRD and SWIR is related to the differing sensitivity of the two techniques (crystal structure vs. chemical bonds). Our results indicate that it is beneficial to use both XRD and reflectance spectroscopy to characterize airfall dust because the former technique is good at identifying and quantifying the SWIR-transparent minerals (e.g., quartz, albite, and microcline), while the latter technique is superior for determining abundances for clays and non-mineral components.
FGAM: A pluggable light-weight attention module for medical image segmentation

Medical image segmentation is fundamental for computer-aided diagnosis or surgery. Various attention modules are proposed to improve segmentation results, which exist some limitations for medical image segmentation, such as large computations, weak framework applicability, etc. To solve the problems, we propose a new attention module named FGAM, short for Feature Guided Attention Module, which is a simple but pluggable and effective module for medical image segmentation. The FGAM tries to dig out the feature representation ability in the encoder and decoder features. Specifically, the decoder shallow layer always contains abundant information, which is taken as a queryable feature dictionary in the FGAM. The module contains a parameter-free activator and can be deleted after various encoder-decoder networks' training. The efficacy of the FGAM is proved on various encoder-decoder models based on five datasets, including four publicly available datasets and one in-house dataset.
Detailed feature extraction network-based fine-grained face segmentation

Face parsing refers to the labeling of each facial component in a face image and has been employed in facial stimulation, expression recognition, and makeup use, effectively providing a basis for further analysis, computations, animation, modification, and numerous other applications. Although existing face parsing methods have demonstrated good performance, they fail to extract rich features and recover accurate segmentation maps, particularly for faces with high variations in expression and sufficiently similar appearances. Moreover, these approaches neglect the semantic gaps and dependencies between facial categories and their boundaries. To address these drawbacks, we propose an efficient dilated convolution network with different aspect ratios to attain accurate face parsing of the output by applying the feature extraction capability. The proposed network-structured multiscale dilated encoder–decoder convolution model obtains rich component information and efficiently improves the capture of global information by obtaining low- and high-level semantic features. To achieve a delicate parsing output of the face components along the borders and analyze the connections between the face categories and their border edges, the semantic edge map is learned using a conditional random field, which aims to distinguish border and non-border pixels during the modeling. We conducted experiments using three well-known publicly available face databases. The recorded results demonstrate the high accuracy and capacity of the proposed method in comparison to previous state-of-art methods. Our proposed model achieved a mean accuracy of 90% on the CelebAMask-HQ dataset for the category case and 81.43% for the accessory case, and achieved accuracies of 91.58% and 92.44% on the HELEN and LaPa datasets, respectively, thereby demonstrating its effectiveness.
Implementing Converged Security Risk Management: Drivers, Barriers, and Facilitators

Converged security risk management is an approach that addresses interdependencies between security-related business functions that have traditionally been managed by separate departments within organizations. It is a more effective means of addressing organizational security risks and threats than tackling physical and information security challenges separately, given that the boundaries between the two are frequently blurred. However, fully converged security remains the exception rather than the rule, leaving organizations increasingly vulnerable as their adoption and reliance on digital technologies accelerates. Through interviews with eight senior security professionals, this research identified key factors critical to effective converged security risk management, expressed as 'drivers,' 'barriers,' and 'facilitators.' The practitioners' accounts illuminated how the modern threat landscape continues to drive further the need for such an approach, while the traditional separation of corporate security departments from the information security function in organizations remains a barrier. A greater focus on training and education, as well as soft skills, were identified as key priorities in the drive for an effective converged approach.
A two‐stage method for single image de‐raining based on attention smoothed dilated network

Rain can severely hamper the visibility of scene objects. Although existing deep learning methods have reported promising performance, they often fail to obtain satisfactory results in many practical situations, especially when the input image contains both rain streaks and haze-like degradation. In this paper, a new two-stage method based on attention smoothed dilated network (SDN) is proposed. Unlike most fully-supervised methods, the mixture of rain streaks and haze-like effects is considered in the model. The proposed method consists of two stages. First, a generative adversarial network guided by the rain-streak attention map is proposed to remove rain streaks, where a multi-stage attention module is used to accurately locate rain streaks in the generator. Second, haze-like effects are further removed through SDN with the same structure as the generator. Extensive experiments on multiple datasets show that the method outperforms the state-of-the-art in both objective evaluation and visual quality.
The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity

Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter users’ profiles in predicting a cryptocurrency’s popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity.
HFF-SRGAN: super-resolution generative adversarial network based on high-frequency feature fusion

At present, generative adversarial network (GAN) has made outstanding progress for image super-resolution (SR), solving the problem of edge smoothing in SR images. Nevertheless, there are also problems with a lack of texture details, noise, and artifacts. The high-frequency (HF) information of an image is significant for keeping the texture part of the reconstructed image, and the introduction of HF information can increase the learning ability of super-resolution generative adversarial network (SRGAN) for the image features, so we propose a super-resolution GAN based on high-frequency feature fusion (HFF-SRGAN), which can increase the texture detail of the reconstructed image and reduce the noise and artifacts with less computational cost. Experimental results show that our algorithm evaluates significantly better than SRGAN and has significant advantages in texture details and noise compared with other SR algorithms.
Decentralized Stochastic Optimization With Inherent Privacy Protection

Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data usually contain sensitive information like user locations, healthcare records, and financial transactions, privacy protection has become an increasingly pressing need in the implementation of decentralized stochastic optimization algorithms. In this article, we propose a decentralized stochastic gradient descent (SGD) algorithm, which is embedded with inherent privacy protection for every participating agent against other participating agents and external eavesdroppers. This proposed algorithm builds in a dynamics based gradient-obfuscation mechanism to enable privacy protection without compromising optimization accuracy, which is in significant difference from differential-privacy based privacy solutions for decentralized optimization that have to trade optimization accuracy for privacy. The dynamics based privacy approach is encryption-free, and hence avoids incurring heavy communication or computation overhead, which is a common problem with encryption based privacy solutions for decentralized stochastic optimization. Besides rigorously characterizing the convergence performance of the proposed decentralized SGD algorithm under both convex objective functions and nonconvex objective functions, we also provide rigorous information-theoretic analysis of its strength of privacy protection. Simulation results for a distributed estimation problem as well as numerical experiments for decentralized learning on a benchmark machine learning dataset confirm the effectiveness of the proposed approach.
Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investigate the effectiveness of REGWO, it is evaluated on 21 widely used benchmark functions and IEEE CEC 2019 test functions. Experimental results show/ that REGWO performs better than the other competitors on most benchmarks.
A Method of Digital English Teaching Resource Sharing Based on Artificial Intelligence

In order to better improve the quality of English teaching, this paper studies the sharing method of English digital teaching resources combined with modern digital technology. Through the collection and management of English digital teaching resources, it constructs the evaluation index system of teaching resources, so as to comprehensively and objectively evaluate the digital teaching resources and promote the construction and development of digital teaching resource and to effectively promote and improve the teaching effect, and realise the research requirements of effective sharing of massive teaching resources in complex environment.
Hierarchical goals contextualize local reward decomposition explanations

Abstract One-step reinforcement learning explanation methods account for individual actions but fail to consider the agent’s future behavior, which can make their interpretation ambiguous. We propose to address this limitation by providing hierarchical goals as context for one-step explanations. By considering the current hierarchical goal as a context, one-step explanations can be interpreted with higher certainty, as the agent’s future behavior is more predictable. We combine reward decomposition with hierarchical reinforcement learning into a novel explainable reinforcement learning framework, which yields more interpretable, goal-contextualized one-step explanations. With a qualitative analysis of one-step reward decomposition explanations, we first show that their interpretability is indeed limited in scenarios with multiple, different optimal policies—a characteristic shared by other one-step explanation methods. Then, we show that our framework retains high interpretability in such cases, as the hierarchical goal can be considered as context for the explanation. To the best of our knowledge, our work is the first to investigate hierarchical goals not as an explanation directly but as additional context for one-step reinforcement learning explanations.