Reviewerly/exHarmony huggingface dataset:
Distinguishing computer-generated images from photographic images using two-stream convolutional neural network

Today’s advanced multimedia tools allow us to create photorealistic computer graphic images, effortlessly. There are various fields such as the film industry, virtual reality, video games where computer-generated (CG) images are used widely. CG images can also be misused in many ways. Therefore, there is a need of distinguishing CG images from real photographic (PG) images. This paper proposes a method to distinguish CG images from PG images using a two-stream convolutional neural network (CNN). In the proposed method, the first stream takes the advantage of the knowledge learned by the pre-trained VGG-19 network, and then this knowledge is transferred to learn the distinct features of CG and PG images. Here, we propose a second stream, that preprocesses the images using three high-pass filters which aim to help the network to focus on noise-based distinct features of CG and PG images. Finally, we propose an ensemble model to merge the outcomes of the proposed two streams. Comparative and self-analysis experiments demonstrates that the proposed method outperforms the state-of-the-art methods in terms of classification accuracy. The experimental results also show that the proposed method performs satisfactorily under the additive white Gaussian noise postprocessing operation in the images.
Formal institutions and the development of entrepreneurial activity – the contingent role of corruption in emerging economies

Purpose The paper aims to analyse the interplay between formal and informal institutions' and their impact on entrepreneurship rates in emerging economies. Design/methodology/approach This study expands previous research in examining the moderating effect of control of corruption on the relationship between formal institutions and the development of the entrepreneurial activity. The study utilizes longitudinal analyses of a dataset from 41 emerging economies over 11 years (2006–2016). Findings Findings provided robust support for the study's hypotheses. The results suggested lower levels of corruption positively moderate the effects of a country's number of procedures and education and training on the rates of entrepreneurial activity, while negatively moderating the effects of firm-level technology absorption on the rates of entrepreneurial activity. Research limitations/implications The study has considered only one particular aspect of high-growth entrepreneurship, which is newly registered firms with limited liability. Although newly registered firms are recognized as one of the critical drivers of entrepreneurial activity. Future research should seek to examine other aspects of growth-oriented entrepreneurship such as activities involving a high level of innovation, corporate entrepreneurship or technology developments. Practical implications This study advanced the existing theories in the field of entrepreneurship and institutional economics as it merged the two theories as a driving framework in the design of the study in the context of emerging economies. Social implications The study tested a theoretical model by expanding the number of emerging economies in the study and found comparable findings that explain factors that may influence the likelihood of individuals entering entrepreneurship. Originality/value This article adds to the current literature as it highlights the importance of the interplay of formal and informal institutions in determining their impact on entrepreneurship rates in emerging economies. This is of particular importance to policy-makers, and the business world as the empirical results of this study show the benefits of control of corruption in boosting entrepreneurial rates in these economies, which strive for economic diversification in their developmental endeavours.
Candidate fusion: Integrating language modelling into a sequence-to-sequence handwritten word recognition architecture

Sequence-to-sequence models have recently become very popular for tackling handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging problem. The main challenge while training a language model is to deal with the language model corpus which is usually different to the one used for training the handwritten word recognition system. Thus, the bias between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this work, we introduce Candidate Fusion, a novel way to integrate an external language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two improvements. On the one hand, the sequence-to-sequence recognizer has the flexibility to not only combine the information from itself and the language model, but also choose the importance of the information provided by the language model. On the other hand, the external language model has the ability to adapt itself to the training corpus and even learn the most common errors produced from the recognizer. Finally, by conducting comprehensive experiments, the Candidate Fusion proves to outperform the state-of-the-art language models for handwritten word recognition tasks.
Determinants of Lecturer Performance to Enhance Accreditation in Higher Education

The current phenomenon is there are still many institutions that do not have institutional accreditation. Therefore, this study aims to be a solution in maximizing higher education achievement according to the standards set by BAN-PT. Lecturers as the spearhead of the teaching and learning process movement are expected to perform well. This research was conducted with the final aim to determine the factors that affect the performance of college lecturers with accreditation “B” in LLDIKTI Region IV Banten Province. This research approach is descriptive and precise. The method used is observation and distribution of online questionnaires. Based on the characteristics of the program to be studied, the analysis of the factors that influence the process uses the SEM (Structural Equation Modeling) method using the Lisrel (Linear Structural Relationship) version 8.80 program. Based on research results, leadership, competence, and organizational climate partially and simultaneously have a positive and significant effect on work motivation with a contribution of 51%. Leadership is the most dominant variable affecting work motivation. Meanwhile, leadership, competence, organizational climate, and work motivation either partially or simultaneously have a positive and significant effect on lecturer performance with a contribution of 75%. Organizational climate is partly the most dominant variable affecting lecturer performance. The novelty of this research to improve the performance of lecturers in private tertiary institutions accredited “B” in the IV environment of Banten Province, it is necessary to improve the organizational climate which is reflected in the dimension of responsibility while improving the work motivation performance it is necessary to increase the leadership.
Effect of Bitcoin Volatility on Altcoins Pricing

The study presents collected data on cryptocurrency prices’ average growth rates and average rates of decline in relation to Bitcoin price volatility. An analysis was conducted, which researched the correlation of Bitcoin price in conjunction with other cryptocurrencies, such as Litecoin, Ethereum, Bitcoin Cash, Dash and Monero. This analysis confirmed the absolute correlation of Bitcoin and its prices with altcoins. Average positive and negative growth rates of altcoins in relation to Bitcoin were determined and identified. Results illustrate that out of the sampled five cryptocurrencies, only three demonstrate substantial price growth rates in comparison with Bitcoin. This research can have a significant impact on the formation of diversified portfolios of crypto-assets, increasing the awareness and knowledge of market participants, and, furthermore, allow them to more effectively control investment risks. #COMESYSO1120.
End-to-End Recurrent Generative Adversarial Network for Traffic and Surveillance Applications

In video frame segmentation, many existing deep networks and contemporary approaches give a remarkable performance with the assumption that the only foreground is moving, and the background is stationary. However, in the presence of infrequent motion of foreground objects, sudden illumination changes in the background, bad weather, and dynamic background, the accurate foreground object(s) segmentation is a challenging task. Generative adversarial networks (GAN) based training shows fruitful results in various fields like image-to-image style transfer, image enhancement, semantic segmentation, image super-resolution, etc. The limited results of hand-crafted approaches for moving object segmentation (MOS) and the robustness of adversarial training for a given task inspired us to propose a novel approach for moving object segmentation (MOS). In this context, an end-to-end generative adversarial network (two generators) with recurrent technique is proposed for MOS and is named as RMS-GAN. The proposed RMS-GAN is able to incorporate foreground probability knowledge with residual and weight sharing based recurrent technique for accurate segmentation. The recurrent technique helps us to exhibit the temporal behavior between successive video frames, which is more prominent for any video processing applications. Also, to enhance the spatial coherence of the obtained foreground probability map using the generator-1 network, the cascaded architecture of two generators is proposed. The effectiveness of the proposed approach is evaluated both qualitatively and quantitatively on three benchmark video datasets for MOS. Experimental result analysis shows that the proposed network outperforms the existing state-of-the-art methods on three benchmark datasets for MOS.
PFLU and FPFLU: Two novel non-monotonic activation functions in convolutional neural networks

The choice of activation functions in Convolutional Neural Networks (CNNs) is very important. Rectified Linear Unit (ReLU) has been widely-used in most CNNs. Recently, a series of non-monotonic activation functions gradually become the new standard to enhance performance of CNNs. Inspired by them, this paper firstly proposes a novel non-monotonic activation function called Power Function Linear Unit (PFLU). The negative part of PFLU is non-monotonic and closer to zero with the negative input decreasing, which can maintain sparsity of the negative part while introducing negative activation values and non-zero derivative values for the negative part. The positive part of PFLU does not use identity mapping but is closer to identity mapping with the positive input increasing, which can bring non-linearity property for the positive part. Next, this paper proposes faster PFLU (FPFLU). A wide range of classification experiments show that PFLU tends to work better than current state-of-the-art non-monotonic activation functions, and FPFLU can run faster than most non-monotonic activation functions.
Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver Using Multi-Labelled Content-Based Image Retrieval

Laparoscopic Ultrasound (LUS) is recommended as a standard-of-care when performing laparoscopic liver resections as it images sub-surface structures such as tumours and major vessels. Given that LUS probes are difficult to handle and some tumours are iso-echoic, registration of LUS images to a pre-operative CT has been proposed as an image-guidance method. This registration problem is particularly challenging due to the small field of view of LUS, and usually depends on both a manual initialisation and tracking to compose a volume, hindering clinical translation. In this paper, we extend a proposed registration approach using Content-Based Image Retrieval (CBIR), removing the requirement for tracking or manual initialisation. Pre-operatively, a set of possible LUS planes is simulated from CT and a descriptor generated for each image. Then, a Bayesian framework is employed to estimate the most likely sequence of CT simulations that matches a series of LUS images. We extend our CBIR formulation to use multiple labelled objects and constrain the registration by separating liver vessels into portal vein and hepatic vein branches. The value of this new labeled approach is demonstrated in retrospective data from 5 patients. Results show that, by including a series of 5 untracked images in time, a single LUS image can be registered with accuracies ranging from 5.7 to 16.4 mm with a success rate of 78%. Initialisation of the LUS to CT registration with the proposed framework could potentially enable the clinical translation of these image fusion techniques.
Tourism Enhancer App: User-Friendliness of a Map with Relevant Features

Abstract There is many things to extract over a map that is dynamically available to the users. There are varieties of areas over a region in a map and are in traditional representation. To customize certain areas with own symbols from a pallet by providing prior guide, an environment that supports in user-friendly manner as well as rich built in libraries and simple graphical user interface is the python. With lot of built in libraries for this complex task, python takes less amount of code to bring the real time application compared with many other traditional programming environments. The guide to be intimated before using customized symbols for varieties of things in a particular location. This guide will easily help out how many are there according to category in that particular location. The end user might be aware of which country will be having highest number of that particular asset compared to other countries over a map. The information is worthy and is dynamic so, anybody will have accurate number of specific asset that required further analysis for their works. This information is similar to few apps like taking cursor over a region in a map, there playing radio or showing number of corona cases that are active. Information is wealth is the motto to make this paper as possible that demands the user to know anything over a region. So that, that region will be tagged with that specific asset which makes that location is popular and makes more tourism.
Self-Training for Class-Incremental Semantic Segmentation

In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods.
ADDRESSING IMBALANCED INSURANCE DATA THROUGH ZERO-INFLATED POISSON REGRESSION WITH BOOSTING

Abstract A machine learning approach to zero-inflated Poisson (ZIP) regression is introduced to address common difficulty arising from imbalanced financial data. The suggested ZIP can be interpreted as an adaptive weight adjustment procedure that removes the need for post-modeling re-calibration and results in a substantial enhancement of predictive accuracy. Notwithstanding the increased complexity due to the expanded parameter set, we utilize a cyclic coordinate descent optimization to implement the ZIP regression, with adjustments made to address saddle points. We also study how various approaches alleviate the potential drawbacks of incomplete exposures in insurance applications. The procedure is tested on real-life data. We demonstrate a significant improvement in performance relative to other popular alternatives, which justifies our modeling techniques.
Improved generative adversarial network and its application in image oil painting style transfer

In view of the difficulty in training the algorithm of image oil painting style migration and reconstruction based on the generative adversarial network, and the loss gradient of generator and discriminator disappears, this paper proposes an improved generative adversarial network based on gradient penalty, and constructs the total variance loss function to carry out the research of image oil painting style migration and reconstruction. Firstly, the Wasserstein distance (WGAN) is added to the loss function of the generative adversarial network to improve the stability of the alternative iterative training; secondly, the gradient penalty (WGAN-GP) is added to the loss function to deal with the problem of gradient disappearance in the training; finally, the LBP texture feature and total variation of the prototype are introduced based on the CycleGAN Loss noise constraint is used to improve the edge and texture strength of the image after migration of oil painting style. The experimental results show that the WGAN-GP algorithm constructed in this study has the ability of stable gradient and alternating iterative convergence, and the total variation loss noise constraint can provide good edge and texture details for the migration process of image oil painting style. Compared with the existing mainstream algorithm, the algorithm proposed in this study has better performance of image oil painting style migration and reconstruction, and better effect of image oil painting style migration and reconstruction.
Making it Real: A Study of Augmented Virtuality on Presence and Enhanced Benefits of Study Stress Reduction Sessions

Within the Virtuality Continuum from completely real to completely virtual, Augmented Virtuality (AV) has been much less explored in comparison to studies on Virtual Reality and Augmented Reality. This project aims to understand whether adding artefacts from the physical world into the virtual world (Augmented-virtuality) increases sense of presence and enhances the efficacy of an Intelligent Virtual Agent (IVA) to achieve its intended purpose. A study was designed where the purpose of the IVA was to reduce study stress in students. The between-subjects experiment involved 50 participants in two groups: The Augmented-virtual group and the Virtual group. The Augmented-virtual group experienced a Virtual Reality (VR) environment augmented with a chair and coffee table also experienced in the real world. The Virtual group experienced the same VR environment without augmentation; the real chair does not match the virtual chair and the virtual coffee table does not exist in the real world. Self-reporting and heart rate were analysed to measure stress levels. Self-reporting indicated significant changes in study stress (participants’ cognitive level) for both groups. However, measurement of stress via heart rate (participants’ physical level) was significantly reduced for the Augmented-virtual group, who also reported higher presence. Presence instruments only revealed significant differences for items relevant to visual aspects, as other aspects were not manipulated. We conclude that augmenting virtual worlds with elements concurrently experienced in the real world may increase sense of presence and enhance simulation efficacy, e.g. increased stress reduction, when compared to the use of VR only.
Learning an animatable detailed 3D face model from in-the-wild images

While current monocular 3D face reconstruction methods can recover fine geometric details, they suffer several limitations. Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression. Other methods are trained on high-quality face scans and do not generalize well to in-the-wild images. We present the first approach that regresses 3D face shape and animatable details that are specific to an individual but change with expression. Our model, DECA (Detailed Expression Capture and Animation), is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to predict detail, shape, albedo, expression, pose and illumination parameters from a single image. To enable this, we introduce a novel detail-consistency loss that disentangles person-specific details from expression-dependent wrinkles. This disentanglement allows us to synthesize realistic person-specific wrinkles by controlling expression parameters while keeping person-specific details unchanged. DECA is learned from in-the-wild images with no paired 3D supervision and achieves state-of-the-art shape reconstruction accuracy on two benchmarks. Qualitative results on in-the-wild data demonstrate DECA's robustness and its ability to disentangle identity- and expression-dependent details enabling animation of reconstructed faces. The model and code are publicly available at https://deca.is.tue.mpg.de.
Deep neural review text interaction for recommendation systems

Users' reviews contain valuable information which are not taken into account in most recommender systems. According to the latest studies in this field, using review texts could not only improve the performance of recommendation, but it can also alleviate the impact of data sparsity and help to tackle this problem. In this paper, we present a neural recommender model which recommends items by leveraging user reviews. In order to predict user rating for each item, our proposed model, named MatchPyramidRecommenderSystem (MPRS), represents each user and item with their corresponding review texts. Thus, the problem of recommendation is viewed as a text matching problem such that the matching score obtained from matching user and item texts could be considered as a good representative of their joint extent of similarity. To solve the text matching problem, inspired by MatchPyramid (Pang et al., 2016), we employed an interaction-based approach according to which a matching matrix is constructed given a pair of input texts. The matching matrix, which has the property of hierarchical matching patterns, is then fed into a Convolutional Neural Network (CNN) to compute the matching score for the given user–item pair. Our experiments on the small data categories of Amazon review dataset show that our proposed model gains from 1.76% to 21.72% relative improvement compared to DeepCoNN model (Zheng et al., 2017), and from 0.83% to 3.15% relative improvement compared to TransNets model (Catherine and Cohen, 2017). Also, on two large categories, namely AZ-CSJ and AZ-Mov, our model achieves relative improvements of 8.08% and 7.56% compared to the DeepCoNN model, and relative improvements of 1.74% and 0.86% compared to the TransNets model, respectively.