Reviewerly/exHarmony huggingface dataset:
A generative method for steganography by cover synthesis with auxiliary semantics

Traditional steganography is the practice of embedding a secret message into an image by modifying the information in the spatial or frequency domain of the cover image. Although this method has a large embedding capacity, it inevitably leaves traces of rewriting that can eventually be discovered by the enemy. The method of Steganography by Cover Synthesis (SCS) attempts to construct a natural stego image, so that the cover image is not modified; thus, it can overcome detection by a steganographic analyzer. Due to the difficulty in constructing natural stego images, the development of SCS is limited. In this paper, a novel generative SCS method based on a Generative Adversarial Network (GAN) for image steganography is proposed. In our method, we design a GAN model called Synthetic Semantics Stego Generative Adversarial Network (SSS-GAN) to generate stego images from secret messages. By establishing a mapping relationship between secret messages and semantic category information, category labels can generate pseudo-real images via the generative model. Then, the receiver can recognize the labels via the classifier network to restore the concealed information in communications. We trained the model on the MINIST, CIFAR-10, and CIFAR-100 image datasets. Experiments show the feasibility of this method. The security, capacity, and robustness of the method are analyzed.
Intelligent Question Answering in Restricted Domains Using Deep Learning and Question Pair Matching

With the rapid expansion of the Internet, intelligent question answering for information retrieval has once again gained widespread attention. However, current question answering models mainly focus on the general and common-sense questions in open domains and are incapable to effectively solve more complex professional domain questions. This paper proposed an integrated framework for Chinese intelligent question answering in restricted domains. The proposed model fused convolutional neural network and bidirectional long short-term memory network which performs efficient semantic analysis on the question pairs to extract more effective features of the text. Meanwhile, the coattention mechanism and attention mechanism were combined to obtain the semantic interaction and feature representation of the question pair for providing complete information for subsequent calculations. In addition, we introduced the method of question pair matching to implement the Chinese intelligent question answering in a restricted domain. Experiments were tested and evaluated on the open-source CCKS2018 dataset and our private self-built inverted pendulum control question answering (IPC-QA) dataset for automation control virtual learning environment. Experimental results confirm that the proposed models are efficient and achieve a high precision of 0.86042 and 0.8031 on CCKS2018 and IPC-QA respectively.
Breaking limitation of quantum annealer in solving optimization problems under constraints

Quantum annealing is a generic solver for optimization problems that uses fictitious quantum fluctuation. The most groundbreaking progress in the research field of quantum annealing is its hardware implementation, i.e., the so-called quantum annealer, using artificial spins. However, the connectivity between the artificial spins is sparse and limited on a special network known as the chimera graph. Several embedding techniques have been proposed, but the number of logical spins, which represents the optimization problems to be solved, is drastically reduced. In particular, an optimization problem including fully or even partly connected spins suffers from low embeddable size on the chimera graph. In the present study, we propose an alternative approach to solve a large-scale optimization problem on the chimera graph via a well-known method in statistical mechanics called the Hubbard-Stratonovich transformation or its variants. The proposed method can be used to deal with a fully connected Ising model without embedding on the chimera graph and leads to nontrivial results of the optimization problem. We tested the proposed method with a number of partition problems involving solving linear equations and the traffic flow optimization problem in Sendai and Kyoto cities in Japan.
PENDIDIKAN LITERASI FINANSIAL MELALUI PEMBELAJARAN FIQH MU’ĀMALĀT BERBASIS KITAB KUNING

This research goals to gain the design for a learning program to guide students in pondok pesantren on achieving financial literacy throughout fiqh mu’āmalāt learning that is based on kitab kuning. We used R&amp;D approach 4D model that reduced into three stages: define, design, and develop. It was gained a syllabus that completed by lesson plan, student worksheets, nor assessment instrument as well, that validated by experts and practitioners and reliability counted based on test.
Variational Autoencoder With Optimizing Gaussian Mixture Model Priors

The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem.This paper proposes a variational autoencoder with optimizing Gaussian mixture model priors.This method utilizes a Gaussian mixture model to construct prior distribution, and utilizes the Kullback-Leibler (KL) distance between posterior and prior distribution to implement an iterative optimization of the prior distribution based on the data.The greedy algorithm is used to solve the KL distance for defining the approximate variational lower bound solution of the loss function, and for realizing the VAE with optimizing Gaussian mixture model priors.Compared with the standard VAE method, the proposed method obtains state-of-the-art results on MNIST, Omniglot, and Frey Face datasets, which shows that the VAE with optimizing Gaussian mixture model priors can learn a better model.
Predicting beam and diffuse horizontal irradiance using Fourier expansions

A new method for obtaining very accurate models to predict the Beam and Diffuse Horizontal Irradiance is proposed, using two sources of data: satellite irradiation estimations and two clear-sky models (one for Beam and one for Diffuse Irradiance). By means of a Fourier Series approximation, we correct the clear-sky models and adapt them to the climatological conditions of a specific location. Applying it to six cities in latitudes ranging from 36∘ to 60∘ degrees North, we compare our predictions with real ground-level data obtained (in this work) from the WRDC database. Results show that the proposed model is both accurate and applicable to different climates.
Is There Anything New to Say About SIFT Matching?

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. The paper then undertakes a comprehensive experimental evaluation of state-of-the-art hand-crafted and data-driven descriptors, also including the most recent deep descriptors. Comparisons are carried out according to several performance parameters, among which accuracy and space-time efficiency. Results are provided for both planar and non-planar scenes, the latter being evaluated with a new benchmark based on the concept of approximated patch overlap. Experimental evidence shows that, despite their age, SIFT and other hand-crafted descriptors, once enhanced through the proposed strategies, are ready to meet the future image matching challenges. We also believe that the lessons learned from this work will inspire the design of better hand-crafted and data-driven descriptors.
Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios.
Which BM25 Do You Mean? A Large-Scale Reproducibility Study of Scoring Variants

When researchers speak of BM25, it is not entirely clear which variant they mean, since many tweaks to Robertson et al.’s original formulation have been proposed. When practitioners speak of BM25, they most likely refer to the implementation in the Lucene open-source search library. Does this ambiguity “matter”? We attempt to answer this question with a large-scale reproducibility study of BM25, considering eight variants. Experiments on three newswire collections show that there are no significant effectiveness differences between them, including Lucene’s often maligned approximation of document length. As an added benefit, our empirical approach takes advantage of databases for rapid IR prototyping, which validates both the feasibility and methodological advantages claimed in previous work.
BERT-ATTACK: Adversarial Attack Against BERT Using BERT

Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate adversarial samples with gradient-based methods. Current successful attack methods for texts usually adopt heuristic replacement strategies on the character or word level, which remains challenging to find the optimal solution in the massive space of possible combinations of replacements while preserving semantic consistency and language fluency. In this paper, we propose \textbf{BERT-Attack}, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT. We turn BERT against its fine-tuned models and other deep neural models in downstream tasks so that we can successfully mislead the target models to predict incorrectly. Our method outperforms state-of-the-art attack strategies in both success rate and perturb percentage, while the generated adversarial samples are fluent and semantically preserved. Also, the cost of calculation is low, thus possible for large-scale generations. The code is available at https://github.com/LinyangLee/BERT-Attack.
Agro-industrialisation and financial intermediation in Nigeria

Purpose The study aims to analyse the role of finance in the agro-industrialisation nexus in Nigeria using annual data on manufacturing value added, agricultural value added and volume of finance availed to the agricultural sector from 1981 to 2015. Design/methodology/approach To establish the presence of a long-run relationship, the error correction model and bounds cointegration techniques are employed. Likewise, the model is augmented to test whether the associated relationship between industrial output and agricultural output depends on access to finance by farmers with the inclusion of an interaction term. Findings Some salient contributions to the literature are as follows: agriculture and finance are strong and positive predictors of industrialisation in the long run; in the short run, past realisations of industrial output and finance have significant asymmetric effects on industrial output; the explanatory power of agriculture decreases with the growth of the financial system; and the long-run results validate the role of finance in the agro-industrialisation nexus. Originality/value Given these findings, achieving growth in the agricultural sector that will induce desired industrialisation should be prioritised by the government through agencies such as the central bank, financial intermediaries and other stakeholders with a view to making agricultural financing a major concern for sustainable domestic consumption and industrial growth.
AN USER INTENTION MINING MODEL BASED ON FRACTAL TIME SERIES PATTERN

Users use the network more and more frequently, and more and more data is published on the network. Therefore, how to find, organize, and use the useful information behind these massive data through effective means, and analyze user intentions is a huge challenge. There are many time series problems in user intentions. Time series have complex characteristics such as randomness and multi-scale variability. Effectively identifying the inherent laws and objective phenomena contained in time series is the purpose of analyzing and processing time series data. Fractal theory provides a new way to analyze time series, and obtains the characteristics and rules of time series from a new perspective. Therefore, this paper introduces the fractal theory to analyze the time series problem, and proposes an improved G-P algorithm to realize the prediction and mining of user intentions. First, the method of array storage instead of repeated calculations is used to improve the method of saturated correlation dimension. Second, the Hurst exponent of the time series is obtained by the variable scale range analysis method. Finally, a fractal model for predicting user intent in short time series is established using the accumulation and transformation method. The experimental results show that the use of fractal theory can effectively describe the relevant characteristics of time series, the development trend of user intentions can be mined from big data, and the prediction model for short time series can be established to achieve information mining of user intentions.
Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing bias in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing bias is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating bias are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing bias in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of bias---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.
Computing Server Power Modeling in a Data Center

Data centers are large-scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT), and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware-level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power-measurement techniques, and error-calculation formulas on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power-measurement techniques, and error formulas, with the aim of achieving an objective comparison. We use different server architectures to assess the impact of heterogeneity on the models’ comparison. The performance analysis of these models is elaborated in the article.
Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

Keeping in view the variety of the applications, image denoising still remains the unexplored territory for the researchers. There are many pros and cons in existing denoising algorithms. The two prime cons of image denoising algorithms are (i) Over and under detection of noisy pixels (ii) Low performance at high noise levels. So, in order to overcome these existing issues, a spatially adaptive image denoising via enhanced noise detection method (SAID-END) is proposed for grayscale and color images. The denoising is achieved using a two-stage sequential algorithm, the first stage ensures accurate noise estimation by eliminating over and under detection of noisy pixels. The second stage performs image restoration by considering non-noisy pixels in estimation of the original pixel value. To enhance the accuracy while denoising high-density impulse noise and artifacts, both noise estimation and restoration stages are using a spatially adaptive window (window expands to spatially connected area), the size of the window depends upon the noise level in the vicinity of the reference noisy pixel. The two stages of the proposed method are referred to as (i) Enhanced adaptive noise detection (ii) Non-corrupted pixel sensitive adaptive image restoration. The proposed method is evaluated by two test steps to ensure its versatility and robustness. In the first step, the proposed method is tested on a wide standard data set of color and grayscale images affected by impulse noise and artifacts. The results of proposed method are compared with well-known methods compatible for denoising impulse noise and artifacts. In the second step, the results of proposed method are compared with the recent state of the art algorithms for traditional test images. The result shows that the proposed method outperforms the existing denoising methods when applied to grayscale and color images.
NanoLEDs for energy-efficient and gigahertz-speed spike-based sub-λ neuromorphic nanophotonic computing

Abstract Event-activated biological-inspired subwavelength (sub- λ ) photonic neural networks are of key importance for future energy-efficient and high-bandwidth artificial intelligence systems. However, a miniaturized light-emitting nanosource for spike-based operation of interest for neuromorphic optical computing is still lacking. In this work, we propose and theoretically analyze a novel nanoscale nanophotonic neuron circuit. It is formed by a quantum resonant tunneling (QRT) nanostructure monolithic integrated into a sub- λ metal-cavity nanolight-emitting diode (nanoLED). The resulting optical nanosource displays a negative differential conductance which controls the all-or-nothing optical spiking response of the nanoLED. Here we demonstrate efficient activation of the spiking response via high-speed nonlinear electrical modulation of the nanoLED. A model that combines the dynamical equations of the circuit which considers the nonlinear voltage-controlled current characteristic, and rate equations that takes into account the Purcell enhancement of the spontaneous emission, is used to provide a theoretical framework to investigate the optical spiking dynamic properties of the neuromorphic nanoLED. We show inhibitory- and excitatory-like optical spikes at multi-gigahertz speeds can be achieved upon receiving exceptionally low (sub-10 mV) synaptic-like electrical activation signals, lower than biological voltages of 100 mV, and with remarkably low energy consumption, in the range of 10–100 fJ per emitted spike. Importantly, the energy per spike is roughly constant and almost independent of the incoming modulating frequency signal, which is markedly different from conventional current modulation schemes. This method of spike generation in neuromorphic nanoLED devices paves the way for sub- λ incoherent neural elements for fast and efficient asynchronous neural computation in photonic spiking neural networks.
Personalizing symptom monitoring and contact tracing efforts through a COVID-19 web-app

The Coronavirus Disease 2019 (COVID-19) pandemic has sparked attention in many countries, especially those that have experienced a steep spike in the number of identified cases. The continued spread of the coronavirus suggests that this situation may be here to stay for a while. Contact tracing is a time-consuming and resource-intensive process, which taps on the already lean healthcare resource in certain countries. Furthermore, the massive infodemic on COVID-19 on the Internet has also resulted in widespread circulation of misinformation online. This outbreak has evoked irrational fear and anxiety from the public, which has resulted in destabilizing of societal norms, such as panic buying and hoarding of daily necessities, and can potentially pose serious health risks to the public. The aim of this paper is to present a COVID-19 Symptom Monitoring and Contact Tracking Record (CoV-SCR) web-app as a bottom-up, proactive approach to supplement the current management strategies for COVID-19.The CoV-SCR web-app ( http://bit.ly/covscrapp ) enables individuals to keep a personal record of their close contacts and monitor their symptoms on a daily basis, so that they can provide relevant and accurate details when they see the doctor and during the contact tracing process. Individuals can record their temperature and rate their symptoms on a 5-point severity scale, as well as record details of their travel and contact history for the last 14 days. The recorded information will be sent to their e-mail address for potential symptom monitoring and contact tracing purposes. In addition, this web-app consolidates evidence-based information on the coronavirus from credible sources, such as the World Health Organization, countries' health authorities, and PubMed literature.A COVID-19 Symptom Monitoring and Contact Tracking web-app has been developed to facilitate contact tracing efforts through public engagement. This app serves an additional purpose of providing information about COVID-19 from reliable resources.