Reviewerly/exHarmony huggingface dataset:
A Very Deep Spatial Transformer Towards Robust Single Image Super-Resolution

In general, existing research on single image super-resolution does not consider the practical application that, when image transmission is over noisy channels, the effect of any possible geometric transformations could incur significant quality loss and distortions. To address this problem, we present a new and robust super-resolution method in this paper, where a robust spatially-transformed deep learning framework is established to simultaneously perform both the geometric transformation and the single image super-resolution. The proposed seamlessly integrates deep residual learning based spatial transform module with a very deep super-resolution module to achieve a robust and improved single image super-resolution. In comparison with the existing state of the arts, our proposed robust single image super-resolution has a number of novel features, including 1) content-characterized deep features are extracted out of the input LR images to identify the incurred geometric transformations, and hence transformation parameters can be optimized to influence and control the super-resolution process; 2) the effects of any geometric transformations can be automatically corrected at the output without compromise on the quality of final super-resolved images; and 3) compared with the existing research reported in the literature, our proposed achieves the advantage that HR images can be recovered from those down-sampled LR images corrupted by a number of different geometric transformations. The extensive experiments, measured by both the peak-signal-to-noise-ratio and the similar structure index measurement, show that our proposed method achieves a high level of robustness against a number of geometric transformations, including scaling, translations, and rotations. Benchmarked by the existing state-of-the-arts SR methods, our proposed delivers superior performances on a wide range of datasets which are publicly available and widely adopted across relevant research communities.
Exploring Preliminary Challenges and Emerging Best Practices in the Use of Enterprise Blockchains Applications

Enterprise blockchain applications can allow trading partners to transact directly without relying on trusted third parties and promise to: eliminate the need for reconciliations, instantly track and trace assets through a supply chain, provide unbeatable data provenance, settle transactions quickly and cheaply, and enable an information security model that is fault tolerant, resilient, and available.Many of these promised benefits seemingly address the challenges of non-blockchain based interorganizational systems.However, this research indicates that blockchain based inter-organizational applications pose significant challenges of their own.Based on interview and participant observation data, we identified five challenges: (1) competing blockchain standards, (2) adjusting to different shared governance models, (3) intellectual property concerns (4) industrial espionage risks, and (5) regulatory uncertainty.We also identified emerging practices stakeholders are using to address those challenges when considering enterprise blockchain applications.
Multistructure-Based Collaborative Online Distillation

Recently, deep learning has achieved state-of-the-art performance in more aspects than traditional shallow architecture-based machine-learning methods. However, in order to achieve higher accuracy, it is usually necessary to extend the network depth or ensemble the results of different neural networks. Increasing network depth or ensembling different networks increases the demand for memory resources and computing resources. This leads to difficulties in deploying depth-learning models in resource-constrained scenarios such as drones, mobile phones, and autonomous driving. Improving network performance without expanding the network scale has become a hot topic for research. In this paper, we propose a cross-architecture online-distillation approach to solve this problem by transmitting supplementary information on different networks. We use the ensemble method to aggregate networks of different structures, thus forming better teachers than traditional distillation methods. In addition, discontinuous distillation with progressively enhanced constraints is used to replace fixed distillation in order to reduce loss of information diversity in the distillation process. Our training method improves the distillation effect and achieves strong network-performance improvement. We used some popular models to validate the results. On the CIFAR100 dataset, AlexNet’s accuracy was improved by 5.94%, VGG by 2.88%, ResNet by 5.07%, and DenseNet by 1.28%. Extensive experiments were conducted to demonstrate the effectiveness of the proposed method. On the CIFAR10, CIFAR100, and ImageNet datasets, we observed significant improvements over traditional knowledge distillation.
The Bernays-Schönfinkel-Ramsey Class of Separation Logic on Arbitrary Domains

Abstract This paper investigates the satisfiability problem for Separation Logic with k record fields, with unrestricted nesting of separating conjunctions and implications, for prenex formulæ with quantifier prefix $$\exists ^*\forall ^*$$ . In analogy with first-order logic, we call this fragment Bernays-Schönfinkel-Ramsey Separation Logic [ $$\mathsf {BSR}(\mathsf {SL}^{\!\scriptstyle {k}})$$ ]. In contrast to existing work in Separation Logic, in which the universe of possible locations is assumed to be infinite, both finite and infinite universes are considered. We show that, unlike in first-order logic, the (in)finite satisfiability problem is undecidable for $$\mathsf {BSR}(\mathsf {SL}^{\!\scriptstyle {k}})$$ . Then we define two non-trivial subsets thereof, that are decidable for finite and infinite satisfiability respectively, by controlling the occurrences of universally quantified variables within the scope of separating implications, as well as the polarity of the occurrences of the latter. Beside the theoretical interest, our work has natural applications in program verification, for checking that constraints on the shape of a data-structure are preserved by a sequence of transformations.
Impact of Agricultural Financing on Nigeria Economy

The importance of agricultural surplus for the structural transformation accompanying economic growth is often addressed by development economists. In view of this, the study empirically assesses the impact of agricultural finance on the growth of Nigerian economy.&#x0D; This paper employed secondary data and econometric techniques of Ordinary Least Square (OLS) of multiple regression estimates. The result of the model used suggests that the productivity of investment will be more appropriately financed with resources administered by the commercial and specialized financial institutions. And also, that there are an urgent and sincere needs to expand the credit size to the agricultural sector in order to enhance the productivity growth of the sector. It is recommended that maintenance of credible macroeconomic policies that is pro-investment in overhauling the Agricultural Sector and debt-equity swap option are necessary for an agricultural-led economic growth.
New Approximation to Distribution of Positive RVs Applied to Gaussian Quadratic Forms

This letter introduces a new approach to the problem of approximating the probability density function (PDF) and the cumulative distribution function (CDF) of a positive random variable. The novel approximation strategy is based on the analysis of a suitably defined sequence of auxiliary variables which converges in distribution to the target variable. By leveraging such convergence, simple approximations for both the CDF and PDF of the target variable are given in terms of the derivatives of its moment generating function (MGF). In contrast to classical approximation methods based on truncated series of moments or cumulants, our approximations always represent a valid distribution and the relative error between variables is independent of the variable under analysis. The derived results are then used to approximate the statistics of positive-definite real Gaussian quadratic forms, comparing our proposed approach with other existing approximations in the literature.
Role and Performance of Different Traditional Classification and Nature-Inspired Computing Techniques in Major Research Areas

In the last few years, different machine learning techniques such as supervised, unsupervised, and reinforcement learning have been effectively employed to solve distinct real-life multidisciplinary problems.These techniques have been effectively applied to accurately predict the problems related to stock values, disease diagnosis, sentiment analysis, text processing, gene classification, crop prediction, and weather forecasting.The objective of this manuscript is to present the systematic review on the use of these techniques in five major domains i.e. agriculture, finance, healthcare, education and engineering.A standard review methodology has been adapted to include and exclude the related literature.The performance of different supervised and nature-inspired computing techniques have been accessed on the basis of different performance metrics.The publication trend on the use of machine learning techniques in these five research areas has been also explored.Finally, the gaps in the study have been identified that will assist prospective researchers who want to pursue their research in these areas.
The role of speech technology in biometrics, forensics and man-machine interface

&lt;span lang="EN-US"&gt;Day by day Optimism is growing that in the near future our society will witness the Man-Machine Interface (MMI) using voice technology. Computer manufacturers are building voice recognition sub-systems in their new product lines. Although, speech technology based MMI technique is widely used before, needs to gather and apply the deep knowledge of spoken language and performance during the electronic machine-based interaction. Biometric recognition refers to a system that is able to identify individuals based on their own behavior and biological characteristics. Fingerprint success in forensic science and law enforcement applications with growing concerns relating to border control, banking access fraud, machine access control and IT security, there has been great interest in the use of fingerprints and other biological symptoms for the automatic recognition. It is not surprising to see that the application of biometric systems is playing an important role in all areas of our society. Biometric applications include access to smartphone security, mobile payment, the international border, national citizen register and reserve facilities. The use of MMI by speech technology, which includes automated speech/speaker recognition and natural language processing, has the significant impact on all existing businesses based on personal computer applications. With the help of powerful and affordable microprocessors and artificial intelligence algorithms, the human being can talk to the machine to drive and control all computer-based applications. Today's applications show a small preview of a rich future for MMI based on voice technology, which will ultimately replace the keyboard and mouse with the microphone for easy access and make the machine more intelligent.&lt;/span&gt;
The role of social media in tourism marketing in Jordan

Currently, the world is witnessing a remarkable development in the modern means and electronics techniques, especially the social networking sites which became available in many sectors including the tourism sector. This increases the competition between the tourism companies to provide the best offers to the individuals about the tourists’ sites. Due to the reason that few studies investigated the effect of social media networks in encouraging tourism, this has created the need to conduct this study to reach results about the extent of social media networks in promoting tourism in Jordan. The significance of the study stems from being treating a new topic with a significant effect on the electronic market. The study also addressed the important side of improving the local income resulting from the tourism sector since this study is focusing on vital and essential topics in modern marketing.
Cleaning Adversarial Perturbations via Residual Generative Network for Face Verification

Deep neural networks (DNNs) have recently achieved impressive performances on various applications. However, recent researches show that DNNs are vulnerable to adversarial perturbations injected into input samples. In this paper, we investigate a defense method for face verification: a deep residual generative network (ResGN) is learned to clean adversarial perturbations. We propose a novel training framework composed of ResGN, pre-trained VGG-Face network and FaceNet network. The parameters of ResGN are optimized by minimizing a joint loss consisting of a pixel loss, a texture loss and a verification loss, in which they measure content errors, subjective visual perception errors and verification task errors between cleaned image and legitimate image respectively. Specially, the latter two are provided by VGG-Face and FaceNet respectively and have essential contributions for improving verification performance of cleaned image. Empirical experiment results validate the effectiveness of the proposed defense method on the Labeled Faces in the Wild (LFW) benchmark dataset.
Classification of Ontological Violence Content Detection through Audio Features and Supervised Learning

Violence detection is one of the important aspects, which can be used in different applications.Based on the data format, the violence can be defined in many ways.This paper focused to develop an automatic violence detection framework from audio type data.To do this, a new and efficient set of features are extracted from the audio signals, which provides more discrimination between different types of violence types in audio signals.Considering both spatial and Mel frequency characteristics of audio signals, totally 12 statistical functionals are accomplished to define every signal.Furthermore, the violence is defined in an ontological fashion, such that the all possible violence types which signify the violent behavior are detected.Extensive simulations are carried out over the proposed detection framework by considering the audio signals extracted from different video clips ripped from different movies.The performance is analyzed through the Receiver Operating Characteristics like, Accuracy, Precision, Recall, and False Positive Rate and the obtained results verify the performance enhancement and show a better performance than the conventional approaches.
Equilibrium Strategies for Alpha-Maxmin Expected Utility Maximization

In the existing literature of robust utility maximization with ambiguity, agents are generally assumed to be extremely ambiguity-averse as they tend to only consider expected payoffs in the worst-case scenario. However, experimental studies have shown that agents' attitude to ambiguity is not systematically negative and can even be ambiguity-seeking when they consider themselves knowledgeable or competent. To conceptually distinguish between an agent's perception of ambiguity and ambiguity aversion, the so-called $\alpha$-maxmin expected utility ($\alpha$-MEU) was proposed in the economics literature as a linear aggregation of the most and least favorable prior beliefs. Although the axiomatic characterization of $\alpha$-MEU has been well studied, there has been little work on the benchmark maximization problem for $\alpha$-MEU. The main difficulty stems from the dynamic inconsistency of two distinct extreme priors and nonconvexity (and nonconcavity) of the value function. In this paper, we study the maximization problem for $\alpha$-MEU and solve for the equilibrium strategies of open-loop type. Under logarithmic risk preference, we obtain the explicit form of equilibrium investment strategies, which involves a two-dimensional system of fully coupled quadratic backward stochastic differential equations (BSDE). The main challenge in completing the verification theorem is to study the existence, uniqueness, and stability of this system of BSDE. For this purpose, we consider a general Markov model of the financial market, which leads to a system of quadratic Markovian BSDE. We find that the equilibrium investment strategy becomes more conservative if the agent is more ambiguity-averse or the agent perceives more ambiguity in the financial market. The equilibrium strategy is close to the classical nonrobust strategy (without ambiguity) if the agent is ambiguity-neutral. As time approaches maturity, ambiguity-seeking (ambiguity-averse) agents adopt more aggressive (conservative) equilibrium strategies. Additionally, the equilibrium strategy of an ambiguity-neutral agent converges to the classical nonrobust strategy at maturity.
A Design review of Smart Stick for the Blind Equipped with Obstacle Detection and Identification using Artificial Intelligence

Visually impaired individuals find it more challenging to move about independently because of their compromised vision.Moreover, a blind person's capacity to navigate in a given setting, along with their ability to organize their daily activities are vital to their health and wellbeing.Organizing any commonplace activity can be especially difficult for a blind man/woman if he/she has not learned to distinguish between different items like drug containers and packaged goods, just by feeling with the hands.The more saddening fact is that there are tens of millions of visually impaired persons worldwide who have to go through such experience and are dependent on others for their wellbeing and happiness.The encouraging news, however, is that the rapid advancement in technology has seen the innovation of better systems for assisting the disabled, including the blind, such as the AI glasses, which can provide intelligent navigation capabilities to the blind.This paper reviews the design of a smart cane, i.e., A smart stick for the blind, equipped with obstacle recognition using AI Technologies adds more virtual visibility in their journey.It shows that such a stick can be a significant boon to the blind.
SLiQA-I: Towards Cold-start Development of End-to-end Spoken Language Interface for Question Answering

Question answering (QA) has become a key capability for voice enabled personal assistants to automatically answer various user questions. However, the development of a spoken language interface for QA in a new domain is time consuming and requires a lot of human labors. Thus, it is crucially desirable to design an end-to-end system, referred to as SliQA, that can facilitate developers to easily and quickly build a QA interface from scratch and output a high quality plug-and-play QA engine. In this paper, we take the first step of SliQA system design, named SliQA-I, to support answering factoid questions regarding an entity over existing knowledge graphs. SliQA-I incorporates a novel iterative human-in-the-loop question generator and an enhanced deep coupled QA engine, thereby requiring light human workload. We implement the real system and evaluate it on three domains from different aspects. The results show that the QA performance of SliQA-I achieves up to 3.58% accuracy gain compared with baseline approaches which use existing QA engine on human generated data. More importantly, we show that SliQA-I only takes as low as 0.025 second to generate a question which has similar quality as human generated ones in terms of both naturalness and grammatical correctness.
Low-Rank Deep Convolutional Neural Network for Multitask Learning

In this paper, we propose a novel multitask learning method based on the deep convolutional network. The proposed deep network has four convolutional layers, three max-pooling layers, and two parallel fully connected layers. To adjust the deep network to multitask learning problem, we propose to learn a low-rank deep network so that the relation among different tasks can be explored. We proposed to minimize the number of independent parameter rows of one fully connected layer to explore the relations among different tasks, which is measured by the nuclear norm of the parameter of one fully connected layer, and seek a low-rank parameter matrix. Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty so that the useful features learned by the lower layers can be selected. The learning problem is solved by an iterative algorithm based on gradient descent and back-propagation algorithms. The proposed algorithm is evaluated over benchmark datasets of multiple face attribute prediction, multitask natural language processing, and joint economics index predictions. The evaluation results show the advantage of the low-rank deep CNN model over multitask problems.
Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.