Reviewerly/exHarmony huggingface dataset:
Image Style Transfer Algorithm Based on Semantic Segmentation

Most of the existing image style transfer algorithms transfer the whole image style as a whole.Style feature is a set of correlation matrix based on style image, namely Gram matrix.Each matrix is a global description of the style image.This kind of methods can perform well in the insensitive semantic scenes (such as the style transfer between landscape photos), but in the sensitive semantic scenes (such as the style transfer between portrait photos), the problem of semantic mismatch will be highlighted, such as transferring the background texture of the style image to the foreground of the target image.Although the existing research takes the manually annotated semantic image as an input of the algorithm, and then guides the style transfer based on the semantic information, and finally achieves good results in the style transfer between portraits.But there are still two problems: first, semantic images need to be manually annotated, which costs human resources.In practical applications, large-scale image style transfer is often needed.Second, the details of the synthesized image are fuzzy, and the definition is not enough.We propose an image style transfer algorithm based on semantic segmentation to resolve semantic mismatching in image style transfer.Our algorithm extracts the semantic information of style image and content image automatically through a semantic segmentation network and uses the semantic information to guide the style transfer.Our algorithm builds a semantic segmentation network based on mask R-CNN, introduces semantic information, and then makes style transfer on the patch level, realizes the style transfer between similar objects (consistent semantic information).Experiments on Celeba and Wikiart show that our method could automatically extract the semantic information of style image and content image.Compared with the state-of-art approaches in this field, our method can effectively avoid semantic mismatch in the process of image style transfer.That is, it can maintain semantic consistency in the process of style transfer.
Comparative Analysis of Detection of Email Spam With the Aid of Machine Learning Approaches

Abstract Over the past few decades, Technology has gained a rapid pace in its development making communication easier. Considering several modes of communication, E-mails(Electronic mails) are the best means for both informal and formal conversations. Some also use e-mails to store and share important information in the form of text, images, documents, etc. between people using electronic devices. Besides, some people improperly use this means of communication by sending useless or unwanted e-mails in bulk i.e., spammed emails which could result in disproportionate usage of memory in the mailbox. There are many suggested approaches in practice that could identify spam emails from the mailbox using machine learning methods. This paper mainly deals with the comparative analysis of detecting Spam Emails by various machine learning methodologies along with the proposed methodology. Considering various evaluation metrics such as Accuracy, Error, Evaluation time, Efficiency, and so on for the evaluation of models. This document draws the contrast on strengths, drawbacks, and limitations of some of the existing techniques that use the approaches of machine learning to detect spam emails. The machine learning method is further resourceful than the acquaintance approach of engineering which does not involve the specifications of any instructions. Considering various evaluation metrics such as Accuracy, Error, Evaluation time, Efficiency, and so on for the evaluation of models. The various accuracies obtained in this framework are KNN – 96.20%, Naïve Bayes – 99.46%, SVM – 96.90, Rough Sets Classifiers – 97.42%.
Deep Full-Body HPE for Activity Recognition from RGB Frames Only

Human Pose Estimation (HPE) is defined as the problem of human joints’ localization (also known as keypoints: elbows, wrists, etc.) in images or videos. It is also defined as the search for a specific pose in space of all articulated joints. HPE has recently received significant attention from the scientific community. The main reason behind this trend is that pose estimation is considered as a key step for many computer vision tasks. Although many approaches have reported promising results, this domain remains largely unsolved due to several challenges such as occlusions, small and barely visible joints, and variations in clothing and lighting. In the last few years, the power of deep neural networks has been demonstrated in a wide variety of computer vision problems and especially the HPE task. In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only. Based on ConvNets, fifteen human joint positions are predicted and can be further exploited for a large range of applications such as gesture recognition, sports performance analysis, or human-robot interaction. To evaluate the proposed deep pose estimation model, we apply it to recognize the daily activities of a person in an unconstrained environment. Therefore, the extracted features, represented by deep estimated poses, are fed to an SVM classifier. To validate the proposed architecture, our approach is tested on two publicly available benchmarks for pose estimation and activity recognition, namely the J-HMDBand CAD-60datasets. The obtained results demonstrate the efficiency of the proposed method based on ConvNets and SVM and prove how deep pose estimation can improve the recognition accuracy. By means of comparison with state-of-the-art methods, we achieve the best HPE performance, as well as the best activity recognition precision on the CAD-60 dataset.
DDLBench: Towards a Scalable Benchmarking Infrastructure for Distributed Deep Learning

Due to its many applications across various fields of research, engineering, and daily life, deep learning has seen a surge in popularity. Therefore, larger and more expressive models have been proposed, with examples like Turing-NLG using as many as 17 billion parameters. Training these very large models becomes increasingly difficult due to the high computational costs and large memory footprint. Therefore, several approaches for distributed training based on data parallelism (e.g., Horovod) and model/pipeline parallelism (e.g., GPipe, PipeDream) have emerged. In this work, we focus on an in-depth comparison of three different parallelism models that address these needs: data, model and pipeline parallelism. To this end, we provide an analytical comparison of the three, both in terms of computation time and memory usage, and introduce DDLBench, a comprehensive (open-source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , ready-to-use) benchmark suite to quantify these differences in practice. Through in-depth performance analysis and experimentation with various models, datasets, distribution models and hardware systems, we demonstrate that DDLBench can accurately quantify the capability of a given system to perform distributed deep learning (DDL). By comparing our analytical models with the benchmarking results, we show how the performance of real-life implementations diverges from these analytical models, thus requiring benchmarking to capture the in-depth complexity of the frameworks themselves.
Twitter Sentiment Analysis During Unlock Period of COVID-19

The pandemic has hit the individuals at both personal, social and professional front triggering emotional crisis leading to stress, anxiety and other related problems.However, some countries are now easing down on restrictions by going from lock down to unlocking in a phased manner.As life springs back to action the sentiments and emotions of people are bound to change.It therefore becomes imperative to understand the emotions and sentiments of people after seven months of outbreak when the people are more informed about the nature of disease, steps for prevention and also have hope for a vaccine coming up in near future.The study analyses the sentiments of the people from the USA and India by text mining using R Studio.The study has various implications for academicians as it adds to the existing knowledge pool.The findings provide guidance to the policy makers to tailor their support policies in response to the emotional state of their people and also assists the marketers to tailor the communication strategies in the light of the emotional state of the target market.
Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Summary Workflow scheduling is the most focused research issue in the on‐demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced Artificial Fish Swarm Algorithm (EAFSA)‐based IaaS Cloud Partial Critical Path (IC‐PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.
Digital Transformation of Land Services in Indonesia: A Readiness Assessment

In 2020, digital transformation was a major theme to commemorate Indonesia’s main agrarian law’s anniversary. This theme is a reminder of the need to fully implement digital services to improve the quality of land registration products that are cheap, easy to operate, perform quickly, and are trusted by the community. However, no research has comprehensively assessed the readiness of the digital transformation of land services in Indonesia. This paper aims to evaluate the readiness of a land office to achieve digital transformation visions. Here, we apply the Digital Governance Assessment Framework (DGRA), adapted to the land service sector, as the basis for conducting this evaluation. The nine core indicators of the DGRA toolkit are used as a basis for assessment. Desk studies were conducted to identify formal legislation and to find the technical specifications. Direct observations and in-depth interviews were conducted with stakeholders to find user needs and evaluate the implementation of current regulations on the land service business process. Quality assessment was carried out on land registration data at the Land Office of Yogyakarta City as a sample. The quality assessment results indicate a problem with completeness, conformity, consistency, accuracy, duplication, and integrity. In conclusion, the readiness level still needs improvement, especially in the indicator related to Cyber Security, Privacy, and Resilience (1.0). Even though the Leadership and Governance, User-Centered Design, and Public Administration Reforms and Change Management sections shows a reasonably high score (≥2.0), other core sections, namely Technology Infrastructure (1.7), Legislation and Regulation (1.4), Data Infrastructure, Strategies, and Governance (1.8) are mediocre, and therefore they need improvement.
A semi-centralized blockchain system with multi-chain for auditing communications of Wide Area Protection System

Wide Area Protection System (WAPS) undertakes the important task of maintaining system reliability and stability when the power system is subject to abnormal or predetermined unstable conditions. The existing WAPS adopts a centralized mechanism to record and audit communication messages, which faces the risk of excessive authority and tampering with communication records and audit logs, thus making it impossible to achieve true transparency and fairness. Due to the involvement of multiple parties and equipment maintained by different manufacturers in the communication of WAPS, there are difficulties in tracing the cause of the accident and determining the at-fault party following misoperations and miss trips. To address this issue, we propose a semi-centralized blockchain system with multi-chain for auditing communications of WAPS. We first propose a semi-centralized system architecture according to the system architecture and management requirements of WAPS. Then, we utilize the blockchain network as a self-recording channel to achieve tamper-proof and non-repudiation verification interaction. We also design a multi-chain structure and classification node mechanism to meet the communication auditing requirements of multiple WAPS. We have designed a new block structure that conforms to the communication protocol of WAPS. To reduce the storage burden caused by the ever-expanding blockchain ledger, we propose a deletable blockchain scheme while maintaining the integrity and security of blockchain. Analysis and experiments show that the proposed blockchain system can support the secure, transparent, tamper-proof and traceable communication recording and auditing of WAPS along with high performance.
BugPecker

Given a bug report of a project, the task of locating the faults of the bug report is called fault localization. To help programmers in the fault localization process, many approaches have been proposed, and have achieved promising results to locate faulty files. However, it is still challenging to locate faulty methods, because many methods are short and do not have sufficient details to determine whether they are faulty. In this paper, we present BugPecker, a novel approach to locate faulty methods based on its deep learning on revision graphs. Its key idea includes (1) building revision graphs and capturing the details of past fixes as much as possible, and (2) discovering relations inside our revision graphs to expand the details for methods and calculating various features to assist our ranking. We have implemented BugPecker, and evaluated it on three open source projects. The early results show that BugPecker achieves a mean average precision (MAP) of 0.263 and mean reciprocal rank (MRR) of 0.291, which improve the prior approaches significantly. For example, BugPecker improves the MAP values of all three projects by five times, compared with two recent approaches such as DNNLoc-m and BLIA 1.5.
Privacy-preserving string search on encrypted genomic data using a generalized suffix tree

Efficient sequencing technologies generate a plethora of genomic data and make it available to researchers. To compute a massive genomic dataset, outsourcing the data to the cloud is often required. Before outsourcing, data owners encrypt sensitive data to ensure data confidentiality. Outsourcing helps data owners to eliminate the local storage management problem. Since genome data is large in volume, executing researchers’ queries securely and efficiently is challenging. In this paper, we propose a method to securely perform substring search and set-maximal search on a SNPs dataset using a generalized suffix tree. The proposed method guarantees the following: (1) data privacy, (2) query privacy, and (3) output privacy. It adopts the semi-honest adversary model, and the security of the data is guaranteed through encryption and garbled circuits. Our experimental results demonstrate that our proposed method can compute secure substring and set-maximal searches against a single-nucleotide polymorphism (SNPs) dataset of 2184 records (each record contains 10000 SNPs) in 2.3 and 2 s, respectively. Furthermore, we compared our results with existing techniques of secure substring and set-maximal search (Ishimaki et al., 2016; Shimizu et al., 2016) [1,2], and we achieved a 400 and 2 times improvement, respectively (Table 5).
Governance of the Internet of Things (IoT)

Today’s increasing rate of technological change results from the rapid growth in computer processing speed, when combined with the cost decline of processing capacity, and is of historical import. The daily life of billions of individuals worldwide has been forever changed by technology in just the last few years. Costly data breaches continue at an alarming rate. The challenge facing humans as they attempt to govern the process of artificial intelligence, machine learning, and the impact of billions of sensory devices connected to the Internet is the subject of this Article. We proceed in six sections. First, we define the Internet of Things (IoT), comment on the explosive growth in sensory devices connected to the Internet, provide examples of IoT devices, and speak to the promise of the IoT. Second, we discuss the challenge of governing the IoT. Third, we look at potential IoT threats. Fourth, we discuss Manufactured Usage Description (MUD) methodology. Fifth, is a look at recent trends in regulatory developments. And finally, we conclude. We believe this Article contributes to our understanding of the widespread exposure to malware associated with IoT and adds to the nascent but emerging literature on governance of enterprise risk, a subject of vital societal importance.
Quantitative analysis of the generalization ability of deep feedforward neural networks

Generalization ability is known as an important performance index of artificial neural networks (ANNs). The generalization ability of an ANN usually refers to its ability to recognize untrained samples, but it lacks quantitative analysis. A method is designed by using frequency-domain signals to observe the generalization ability of deep feedforward neural networks (DFFNNs) which are popular ANN models. This method allows us to observe that the generalization ability of DFFNNs is limited to a small neighborhood around the trained samples. Then, the relationship between sample similarity and the DFFNN’s generalization performance is further analyzed. The analysis results show that the correlation coefficient between samples has a certain positive correlation with the DFFNN’s generalization performance. Based on this new understanding, an algorithm in which shadows of the trained samples are added into the training set is proposed to improve the generalization ability of DFFNNs. The proposed algorithm is tested with some simulated signals and some real-world data. The tests show that the proposed method can indeed improve the DFFNN’s generalization ability by only changing the training sample set.
Neuronized Priors for Bayesian Sparse Linear Regression

Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the product of a Gaussian weight variable and a scale variable transformed from Gaussian via an activation function. Compared with classic spike-and-slab priors, the neuronized priors achieve the same explicit variable selection without employing any latent indicator variables, which results in both more efficient and flexible posterior sampling and more effective posterior modal estimation. Theoretically, we provide specific conditions on the neuronized formulation to achieve the optimal posterior contraction rate, and show that a broadly applicable MCMC algorithm achieves an exponentially fast convergence rate under the neuronized formulation. We also examine various simulated and real data examples and demonstrate that using the neuronization representation is computationally more or comparably efficient than its standard counterpart in all well-known cases. An R package NPrior is provided for using neuronized priors in Bayesian linear regression.
Patient similarity analytics for explainable clinical risk prediction

Abstract Background Clinical risk prediction models (CRPMs) use patient characteristics to estimate the probability of having or developing a particular disease and/or outcome. While CRPMs are gaining in popularity, they have yet to be widely adopted in clinical practice. The lack of explainability and interpretability has limited their utility. Explainability is the extent of which a model’s prediction process can be described. Interpretability is the degree to which a user can understand the predictions made by a model. Methods The study aimed to demonstrate utility of patient similarity analytics in developing an explainable and interpretable CRPM. Data was extracted from the electronic medical records of patients with type-2 diabetes mellitus, hypertension and dyslipidaemia in a Singapore public primary care clinic. We used modified K-nearest neighbour which incorporated expert input, to develop a patient similarity model on this real-world training dataset (n = 7,041) and validated it on a testing dataset (n = 3,018). The results were compared using logistic regression, random forest (RF) and support vector machine (SVM) models from the same dataset. The patient similarity model was then implemented in a prototype system to demonstrate the identification, explainability and interpretability of similar patients and the prediction process. Results The patient similarity model (AUROC = 0.718) was comparable to the logistic regression (AUROC = 0.695), RF (AUROC = 0.764) and SVM models (AUROC = 0.766). We packaged the patient similarity model in a prototype web application. A proof of concept demonstrated how the application provided both quantitative and qualitative information, in the form of patient narratives. This information was used to better inform and influence clinical decision-making, such as getting a patient to agree to start insulin therapy. Conclusions Patient similarity analytics is a feasible approach to develop an explainable and interpretable CRPM. While the approach is generalizable, it can be used to develop locally relevant information, based on the database it searches. Ultimately, such an approach can generate a more informative CRPMs which can be deployed as part of clinical decision support tools to better facilitate shared decision-making in clinical practice.