Reviewerly/exHarmony huggingface dataset:
A Convolutional Neural Network for Image Super-Resolution Using Internal Dataset

Deep convolutional neural networks have recently achieved dramatic success in super-resolution (SR) performance in the past few years. However, the parameters of the mapping functions of these networks require an external dataset for training. In this paper, we propose a convolutional network for image super-resolution reconstruction that can be trained using an internal dataset constructed using a single image. The proposed single image convolutional neural network (SICNN) is designed with two branches. First, a large scale-feature branch trains the feature mappings that are from the low resolution (LR) image patches to the high- resolution image (HR) patches. The LR image patches are the enlarged image patches via bicubic interpolation. Second, the small scale-feature branch trains the feature mappings that are from the down-sampling image patches to the enlarged image patches. In contrast to the existing SR networks, the SICNN enjoys two desirable properties: 1) it does not require external datasets to conduct training, and 2) it enlarges an SR image at an arbitrary scale while restoring the clear edges and textures. The results of evaluations on a wide variety of images show that the proposed SICNN achieves advantages over the state-of-the-art methods in terms of both numerical results and visual quality.
Fraud detection in credit card transaction using hybrid model

Credit card payment is a popular mode of online transaction. It is one of the simplest and easiest mode of payment across the internet. However, with growing popularity of credit card transactions, there is an exponential growth in fraudulent payments. Every year we lose billions of dollars due to fraudulent acts. These activities look like a genuine transaction; hence, simple pattern techniques and less complex methods don’t notgo to work to minimize the fraudulent act and minimize the chaos we are proposing a Hybrid model consisting of Deep Learning Algorithm of Convolutional Neural Network followed by K- Nearest Neighbors Classification. These two approaches are proved to decrease the false alarm rates and increase the fraud detection rate and expected to be more efficient than other relevant algorithms.
Incremental predictive clustering trees for online semi-supervised multi-target regression

Abstract In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common.
Explicit-implicit dual stream network for image quality assessment

Abstract Communications industry has remarkably changed with the development of fifth-generation cellular networks. Image, as an indispensable component of communication, has attracted wide attention. Thus, finding a suitable approach to assess image quality is important. Therefore, we propose a deep learning model for image quality assessment (IQA) based on explicit-implicit dual stream network. We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features. Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features. On this basis, the number of network layers of VGGNet is reduced by adding the parallel wavelet kurtosis value frequency domain features. Thus, the training parameters and the sample requirements decline. We verified, by cross-validation of different databases, that the wavelet kurtosis feature fusion method based on deep learning has a more complete feature extraction effect and a better generalisation ability. Thus, the method can simulate the human visual perception system better, and subjective feelings become closer to the human eye. The source code about the proposed EI-IQA model is available on github https://github.com/jacob6/EI-IQA .
The mediating effect of employee happiness on the relationship between quality of work-life and employee intention to quit: A study on fast-food restaurants in Jordan

This study aims to investigate the mediating role of employee happiness in the effect of the quality of work-life on employee intention to quit. Data were collected using a questionnaire developed regarding prior related works. It was administered to a sample consisted of 122 employees selected from four fast-food restaurants in Jordan. Data analysis was conducted using structural equation modelling via Smart-PLS3; the results revealed that the quality of life had significant direct effects on both employee happiness and employee intention to quit. The first effect was positive, while the second was negative. Employee happiness had a significant impact on employee intention to quit. Consequently, the results indicated that employee happiness played a significant mediating role in the effect of the quality of work-life on employee intention to quit. It was concluded that the quality of work-life is not enough to reduce employee intention to quit, which means that organization should pay great attention to employee happiness to ensure the positive effect of the quality of life and to reduce employee intention to quit.
Detecting DNS covert channels using stacking model

A covert channel is an information channel that is used by the computer process to exfiltrate data through bypassing security policies. The DNS protocol is one of the important ways to implement a covert channel. DNS covert channels are easily used by attackers for malicious purposes. Therefore, an effective detection approach of the DNS covert channels is significant for computer systems and network securities. Aiming at the difficulty of the DNS covert channel identification, we propose a DNS covert channel detection method based on a stacking model. The stacking model is evaluated on a campus network and the experimental results show that the detection based on the stacking model can detect the DNS covert channels effectively. Besides, it can identify unknown covert channel traffic. The area under the curve (AUC) of the proposed method reaches 0.9901, which outperforms existing detection methods.
Length- and Noise-Aware Training Techniques for Short-Utterance Speaker Recognition

Speaker recognition performance has been greatly improved with the emergence of deep learning. Deep neural networks show the capacity to effectively deal with impacts of noise and reverberation, making them attractive to far-field speaker recognition systems. The x-vector framework is a popular choice for generating speaker embeddings in recent literature due to its robust training mechanism and excellent performance in various test sets. In this paper, we start with early work on including invariant representation learning (IRL) to the loss function and modify the approach with centroid alignment (CA) and length variability cost (LVC) techniques to further improve robustness in noisy, far-field applications. This work mainly focuses on improvements for short-duration test utterances (1-8s). We also present improved results on long-duration tasks. In addition, this work discusses a novel self-attention mechanism. On the VOiCES far-field corpus, the combination of the proposed techniques achieves relative improvements of 7.0% for extremely short and 8.2% for full-duration test utterances on equal error rate (EER) over our baseline system.
Open Spatiotemporal Data Warehouse for Agriculture Production Analytics

Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative.
Compact Speaker Embedding: lrx-Vector

Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).
Quantum aspects of evolution: a contribution towards evolutionary explorations of genotype networks via quantum walks

Quantum biology seeks to explain biological phenomena via quantum mechanisms, such as enzyme reaction rates via tunnelling and photosynthesis energy efficiency via coherent superposition of states. However, less effort has been devoted to study the role of quantum mechanisms in biological evolution. In this paper, we used transcription factor networks with two and four different phenotypes, and used classical random walks (CRW) and quantum walks (QW) to compare network search behaviour and efficiency at finding novel phenotypes between CRW and QW. In the network with two phenotypes, at temporal scales comparable to decoherence time TD, QW are as efficient as CRW at finding new phenotypes. In the case of the network with four phenotypes, the QW had a higher probability of mutating to a novel phenotype than the CRW, regardless of the number of mutational steps (i.e. 1, 2 or 3) away from the new phenotype. Before quantum decoherence, the QW probabilities become higher turning the QW effectively more efficient than CRW at finding novel phenotypes under different starting conditions. Thus, our results warrant further exploration of the QW under more realistic network scenarios (i.e. larger genotype networks) in both closed and open systems (e.g. by considering Lindblad terms).
The bimodality of saccade duration during the exploration of visual scenes

Eye movement parameters are consistently investigated, and the distribution of these parameters are well known. Whereas saccade duration has been studied along with saccade amplitude , the distribution of saccade duration has not yet been reported. We aim to investigate the distribution of saccade duration in several eye movement datasets from the literature and from our own data to confirm the common, but never reported, observation that the distribution of saccade duration is bimodal. We consistently observed the bimodality of saccade durations, not task- or stimuli-dependent. We created two groups of saccades based on the saccade duration distribution. Our results suggest that short duration saccades could be partly linked to bottom-up processes and long duration saccades to top-down processes. This study highlights the importance of reporting the distribution of eye movement data, in addition to means, which do not allow a correct and representative analysis in the case of bimodal distributions.
Priorless Recurrent Networks Learn Curiously

Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs.We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and which humans struggle to process.These results suggest that the models are learning from their input in a manner that is substantially different from human language acquisition, and we undertake an analysis of how the learned knowledge is stored in the weights of the network.We find that while the model has an effective understanding of singular versus plural for individual sentences, there is a lack of a unified concept of number agreement connecting these processes across the full range of inputs.Moreover, the weights handling natural and unnatural structures overlap substantially, in a way that underlines the non-human-like nature of the knowledge learned by the network.
Survive IT! Survival analysis of IT project planning approaches

Despite the emerging importance of IT projects and the application of new flexible project management approaches, such as agile and hybrid methodologies, there are still many unsuccessful projects. Therefore, it is important to study which risk factors have the highest influence on the success and survivability of IT projects, how they are related to each other, and which project planning and management approaches can mitigate project risk the most. The goal of the study is twofold. On the one hand, a new risk analysis tool is proposed that handles the project flexibility and the dependency of the risk factors at the same time. On the other hand, with the proposed risk analysis tool, the effects of project management approaches for scheduling performance and risk mitigation are analyzed. With the proposed risk management tool, we determined the level of flexibility that allows flexible project management methods to gain advantage over traditional methods. In addition, we answer whether it makes sense to apply flexible planning for non-IT projects.
Inserting Information Bottlenecks for Attribution in Transformers

Pretrained transformers achieve the state of the art across tasks in natural language processing, motivating researchers to investigate their inner mechanisms.One common direction is to understand what features are important for prediction.In this paper, we apply information bottlenecks to analyze the attribution of each feature for prediction on a black-box model.We use BERT as the example and evaluate our approach both quantitatively and qualitatively.We show the effectiveness of our method in terms of attribution and the ability to provide insight into how information flows through layers.We demonstrate that our technique outperforms two competitive methods in degradation tests on four datasets.Code is available at
Higher-Order Certification For Randomized Smoothing

Randomized smoothing is a recently proposed defense against adversarial attacks that has achieved SOTA provable robustness against $\ell_2$ perturbations. A number of publications have extended the guarantees to other metrics, such as $\ell_1$ or $\ell_\infty$, by using different smoothing measures. Although the current framework has been shown to yield near-optimal $\ell_p$ radii, the total safety region certified by the current framework can be arbitrarily small compared to the optimal. In this work, we propose a framework to improve the certified safety region for these smoothed classifiers without changing the underlying smoothing scheme. The theoretical contributions are as follows: 1) We generalize the certification for randomized smoothing by reformulating certified radius calculation as a nested optimization problem over a class of functions. 2) We provide a method to calculate the certified safety region using $0^{th}$-order and $1^{st}$-order information for Gaussian-smoothed classifiers. We also provide a framework that generalizes the calculation for certification using higher-order information. 3) We design efficient, high-confidence estimators for the relevant statistics of the first-order information. Combining the theoretical contribution 2) and 3) allows us to certify safety region that are significantly larger than the ones provided by the current methods. On CIFAR10 and Imagenet datasets, the new regions certified by our approach achieve significant improvements on general $\ell_1$ certified radii and on the $\ell_2$ certified radii for color-space attacks ($\ell_2$ restricted to 1 channel) while also achieving smaller improvements on the general $\ell_2$ certified radii. Our framework can also provide a way to circumvent the current impossibility results on achieving higher magnitude of certified radii without requiring the use of data-dependent smoothing techniques.
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond

Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed impressive progress propelled by deep learning methods. However, one critical challenge faced by existing methods is to strike a sweet spot of deep model complexity and resulting SISR quality. This paper addresses this pain point by proposing a linearly-assembled pixel-adaptive regression network (LAPAR), which casts the direct LR to HR mapping learning into a linear coefficient regression task over a dictionary of multiple predefined filter bases. Such a parametric representation renders our model highly lightweight and easy to optimize while achieving state-of-the-art results on SISR benchmarks. Moreover, based on the same idea, LAPAR is extended to tackle other restoration tasks, e.g., image denoising and JPEG image deblocking, and again, yields strong performance. The code is available at this https URL.
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations

Representing, and reasoning over, long narratives requires models that can deal with complex event structures connected through multiple relationship types.This paper suggests to represent this type of information as a narrative graph and learn contextualized event representations over it using a relational graph neural network model.We train our model to capture event relations, derived from the Penn Discourse Tree Bank, on a huge corpus, and show that our multi-relational contextualized event representation can improve performance when learning script knowledge without direct supervision and provide a better representation for the implicit discourse sense classification task.
IoT Adaptive Dynamic Blockchain Networking Method Based on Discrete Heartbeat Signals

The combination of blockchain technology and Internet of Things (IoT) technology has brought many significant advantages and new development directions. With the development of embedded technology and 5G communication technology, the performance limitations and network limitations that are traditionally believed to restrict the application of blockchain technology to IoT devices have been broken. The development of “blockchain + 5G + IoT” provides reliable data from the source for the blockchain, linking the credible mapping of physical assets and digital assets. However, at the beginning of the blockchain design, the application of the IoT was not fully considered, so there have been some obvious defects in applying the blockchain technology in the IoT. In the Byzantine fault tolerance (BFT) consensus algorithm of traditional blockchain, the entire blockchain network will become paralyzed when more than 1/3 of the nodes in the network are offline. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of the application of blockchain technology in the IoT. In order to solve this problem, we proposed an IoT adaptive dynamic blockchain networking method based on discrete heartbeat signals. The feature of the method is to set a different monitoring time for each group of nodes, that is, discrete heartbeat signals monitoring. When the number of nodes gradually decreases, the IoT adaptive dynamic blockchain network can dynamically adapt to this process. Even when more than 1/3 of the IoT are offline, the adaptive dynamic IoT blockchain network can maintain stable running. This method also has the advantages of a short network expectation recovery time and avoids instantaneous system paralysis caused by the thundering herd effect. This research improves the security and stability of the application of blockchain technology in the IoT, and provides the necessary technical foundation for the better combination of blockchain technology and IoT technology.