Reviewerly/exHarmony huggingface dataset:
Controlling Neural Machine Translation Formality with Synthetic Supervision

This work aims to produce translations that convey source language content at a formality level that is appropriate for a particular audience. Framing this problem as a neural sequence-to-sequence task ideally requires training triplets consisting of a bilingual sentence pair labeled with target language formality. However, in practice, available training examples are limited to English sentence pairs of different styles, and bilingual parallel sentences of unknown formality. We introduce a novel training scheme for multi-task models that automatically generates synthetic training triplets by inferring the missing element on the fly, thus enabling end-to-end training. Comprehensive automatic and human assessments show that our best model outperforms existing models by producing translations that better match desired formality levels while preserving the source meaning.1
Review of in situ X‐ray fluorescence analysis technology in China

In the past two decades, the in situ X‐ray fluorescence (XRF) technology has been developed rapidly in China, which is mainly due to the rapid development of China's economy and the great demand for rapid qualitative and quantitative analysis of elements in geological exploration, environmental protection, and industrial process analysis. In this article, the development of in situ XRF analysis technology in China is reviewed from three aspects, namely in situ XRF analyzers, in situ XRF analysis technology, and applications of in situ XRF analysis technology. The in situ XRF analyzers are divided into four generations, and the technical characteristics of each generation of analyzers are discussed from the perspectives of X‐ray excitation source, detector, electronic circuit unit, and digital signal processing. The progress of X‐ray spectrum analysis, matrix effect correction, and correction of uneven effect and humidity effect is reviewed. The representative applications of XRF analysis technology in geological and mineral survey, environmental pollution investigation, cultural relic identification, and alloy analysis are introduced.
Exposing Face-Swap Images Based on Deep Learning and ELA Detection

New developments in artificial intelligence (AI) have significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake are so realistic that it is difficult to distinguish their authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial images, a novel model has been developed based on deep learning and error level analysis (ELA) detection, which is related to entropy and information theory, such as cross-entropy loss function in the final Softmax layer, normalized mutual information in image preprocessing, and some applications of an encoder based on information theory. Due to the limitations of computing resources and production time, the DeepFake algorithm can only generate limited resolutions, resulting in two different image compression ratios between the fake face area as the foreground and the original area as the background, which leaves distinctive artifacts. By using the error level analysis detection method, we can detect the presence or absence of different image compression ratios and then use Convolution neural network (CNN) to detect whether the image is fake. Experiments show that the training efficiency of the CNN model can be significantly improved by using the ELA method. And the detection accuracy rate can reach more than 97% based on CNN architecture of this method. Compared to the state-of-the-art models, the proposed model has the advantages such as fewer layers, shorter training time, and higher efficiency.
ASGOP: An aggregated similarity-based greedy-oriented approach for relational DDBSs design

<h2>Abstract</h2> In the literature of distributed database system (DDBS), several methods sought to meet the satisfactory reduction on transmission cost (TC) and were seen substantially effective. Data Fragmentation, site clustering, and data distribution have been considered the major leading TC-mitigating influencers. Sites clustering, on one hand, aims at grouping sites appropriately according to certain similarity metrics. On the other hand, data distribution seeks to allocate the fragmented data into clusters/sites properly. The combination of these methods, however, has been shown fruitful concerning TC reduction along with network overheads. In this work, hence, a heuristic clustering-based approach for vertical fragmentation and data allocation is meticulously designed. The focus is directed on proposing an influential solution for improving relational DDBS throughputs across an aggregated similarity-based fragmentation procedure, an effective site clustering and a greedy algorithm-driven data allocation model. Moreover, the data replication is also considered so TC is further minimized. Through the delineated-below evaluation, the findings of experimental implementation have been observed to be promising.
Identification of hate speech and abusive language on indonesian Twitter using the Word2vec, part of speech and emoji features

Freedom of speech for the people of Indonesia on social media makes the spread of hate speech and abusive language inevitable. If there is no proper handling, this will lead to social disharmony between individuals and communities. The identification of hate speech and abusive language on Twitter in the Indonesian language is quite challenging. Because of its ability to understand the meaning of a sentence, semantic features such as word embedding can be relied on to understand tweets that contain hateful and abusive words. In this study, word embedding (word2vec) feature and its combinations with part of speech and/or emoji were used to identify hate speech and abusive language on Twitter in the Indonesian language. Furthermore, some combinations of unigram with part of speech and/or emojis were also utilized during the experiment and the results were studied. The classification algorithms used in this study were Support Vector Machine, Random Forest Decision Tree, and Logistic Regression. The combination of unigram features, part of speech and emoji obtained the highest accuracy value of 79.85% with F-Measure of 87.51%.
A New Stitching Method for Dark-Field Surface Defects Inspection Based on Simplified Target-Tracking and Path Correction

A camera-based dark-field imaging system can effectively detect defects of microns on large optics by scanning and stitching sub-apertures with a small field of view. However, conventional stitching methods encounter problems of mismatches and location deviations, since few defects exist on the tested fine surface. In this paper, a highly efficient stitching method is proposed, based on a simplified target-tracking and adaptive scanning path correction. By increasing the number of sub-apertures and switching to camera perspective, the defects can be regarded as moving targets. A target-tracking procedure is firstly performed to obtain the marked targets. Then, the scanning path is corrected by minimizing the sum of deviations. The final stitching results are updated by re-using the target-tracking method. An experiment was carried out on an inspection of our specially designed testing sample. Subsequently, 118 defects were identified out of 120 truly existing defects, without stitching mismatches. The experiment results show that this method can help to reduce mismatches and location deviations of defects, and it was also effective in increasing the detectability for weak defects.
A Novel Enhanced Lightweight Node for Blockchain

Blockchain is a single linked list and its data volume grows endlessly. Each node in the Blockchain network keeps the entire data, which not only waste storage, but also requires a vast amount of computation which is wasteful and scales poorly. The SPV (simplified payment verification) node simplifies node storage, but decentralization, the most important feature of Blockchain, is compromised. To address the issue, this paper proposes a novel enhanced lightweight node called ESPV (Enhanced SPV) based on the analysis of the characteristics of Blockchain data. ESPV nodes label blocks as new and old, and keep all the new blocks while storing partial old blocks according their capacity. A hierarchical block partition routing table is established to ensure data availability when ESPV nodes access old blocks they do not have. An ESPV node has all the functions of a full node to ensure the decentralization and stability of a Blockchain system.
How hospitals can protect themselves from cyber attack

Hospitals and health care systems have become a major target for hackers. The announcement that LifeLabs, Canada’s largest medical testing company, paid a ransom to retrieve the data of 15 million patients is just the latest in a string of cyber attacks aimed at stealing data or extracting money
Digital Video Summarization Techniques: A Survey

Video summarization which gives a short and precise representation of original video clips by showing the most representative synopsis is gaining more attention.The main objective of Video summarization is to provide a clear analysis of the video by removing redundant and extracting key frames contents from the video.The architecture in video summarization shows how a large video skims in to short and story contents.Many types of research were done in the past and ongoing until now.Therefore, multiple methods and techniques proposed by researchers from classical computer vision until the recent deep learning approaches.Most literature shows that most of the video generation and summarization approaches shift into deep generative models and variational auto encoders.These techniques may fall into summarized, unsupervised and deep reinforcement learning approaches.Video representation categorized in static and dynamic summarization ways.But video summarization still challenging with different problems, these are computational devices, complexity, and lack of dataset are some them.The effective implementation of video summarization applied in different real-world scenarios like movies tailor in the film industry, highlight in football soccer, anomaly detection video surveillance system.
Structure, Challenges and Opportunities for Development of Entrepreneurial Education in Russian Universities

This study explores the creation and development of entrepreneurial education tracks in the formation of a University Entrepreneurial Ecosystem (UEE) in certain Russian universities.In particular, the ways in which these tracks promote new venture launches, the commercialization of technologies, and the development of entrepreneurial mindsets and skillsets will be explored.A panel of 21 Russian Universities was used to verify the integrated UEE model using the method of co-operative inquiry.The role of entrepreneurial courses in UEEs is illustrated herein with the use of 4 cases of Russian universities.Among the key findings of this research is that the implementation of entrepreneurship education courses configures the UEE development model centered around the education course.UEE formation begins with the personal development of individuals as the course ingrains an entrepreneurial mindset and related skills in students, and attracts entrepreneurs and business angels for mentoring
Information System of Catering Selection by Using Clustering Analysis.

The topic of tourism is up-to-date because everyone wants to broaden their mind. An integral part of all trips is food, the difference is only in price and quality. Moreover, today is popular gastr ...
Neural Network-based Equalizer by Utilizing Coding Gain in Advance

Recently, deep learning has been exploited in many fields with revolutionary breakthroughs. In the light of this, deep learning-assisted communication systems have also attracted much attention in recent years and have potential to break down the conventional design rule for communication systems. In this work, we propose two kinds of neural network-based equalizers to exploit different characteristics between convolutional neural networks and recurrent neural networks. The equalizer in conventional block-based design may destroy the code structure and degrade the capacity of coding gain for decoder. On the contrary, our proposed approach not only eliminates channel fading, but also exploits the code structure with utilization of coding gain in advance, which can effectively increase the overall utilization of coding gain with more than 1.5 dB gain.
Teaching Operations Management With Virtual Reality: Bringing the Factory to the Students

Recent developments in virtual reality (VR) technologies offer new opportunities for teaching management courses. The objective of this article is to present one way to use VR to teach operations management. In partnership with a global manufacturer, we integrate virtual environments of the manufacturers’ real factories in a course assignment. The assignment was used in two graduate operations management courses. Theoretically, we draw on the concept of immersion. To evaluate the effects of VR on students’ learning experiences, we use focus groups and a survey. We find that VR can be implemented cost-efficiently in operations management courses and present one way to do it. Considering effectiveness, we find that students generally perceive that VR improves their learning experience. The presented VR assignment provides students a guided discovery learning, which is active. However, we also find several limitations with the current technology, which can be overcome in future implementations. Teachers can use the idea and findings presented here to innovate their own teaching by the means of readily available and low-cost VR technologies.
Towards a new template for the specification of requirements in semi-structured natural language

Requirements engineering is a systematic and disciplined approach for the specification and management of software requirements; one of its objectives is to transform the requirements of the stakeholders into formal spec-ifications in order to analyze and implement a system. These requirements are usually expressed and articulated in natural language, this due to the universality and facility that natural language presents for communicating them. To facilitate the transformation processes and to improve the quality of the resulting requirements, several authors have proposed templates for writing requirements in structured natural language. However, these templates do not allow writing certain functional requirements, non-functional requirements and constraints, and they do not adapt correctly to certain types of systems such as self-adaptive, product line-based and embedded systems. This paper (i) presents evidence of the weaknesses of the template recommended by the IREB® (International Requirements Engineering Institute), and (ii) lays the foundations, through a new template, for facilitating the work of the re-quirements engineers and therefore improving the quality of the products specified with the new template. This new template was built and evaluated through two active research cycles. In each cycle we identified the problems specifying the requirements of the corresponding industrial case with the corresponding base-line template, pro-pose some improvements to address these problems and analyze the results of using the new template to specify the requirements of each case. Thus, the resulting template was able to correctly write all requirements of both industrial cases. Despite the promising results of this new template, it is still preliminary work regarding its cov-erage and the quality level of the requirements that can be written with it.
DCNN-BiGRU Text Classification Model Based on BERT Embedding

Text Classification is a hot topic in natural language processing. In view of the strong correlation the structure of natural language, direct translation the text into vector will lead to too high dimension. In addition, traditional word vector usually maps words with a single vector, which cannot represent the polyseme of words and affect the accuracy of the final classification. In this paper, we propose a novel DCNN-BiGRU (Deep Convolutional Neural Network Bidirection Gated Recurrent) text classification model based on BERT(Bidirectional Encoder Representations from Transformer) embedding. The model adopts the BERT to train the language model of word semantic representation. The semantic vector is generated dynamically according to the context of the word, and then it is put into the DCNN-BiGRU hybrid model. By doing so, the semantic vector not only contains the local features of text but also the context features of text. Experiments on CCERT Chinese email sample set and movie comment data set verify the validity of this model.
Hate Speech Identification in Text Written in Indonesian with Recurrent Neural Network

Some researches had succeeded in doing hate speech identification automatically from text with machine learning and deep learning approaches. However, it was still unclear how adaptive is a deep learning-based model if it is tested on a different set of text data with different domain. To address this issue, this research proposed some deep learning-based methods, using some variants of Recurrent Neural Network to identify hate speech in texts sourced from Twitter, and then used to predict other set of text data sourced from Facebook and Twitter. The experiment was done in order to measure the difference of model performance between training phase and testing phase. Experiment results showed that the proposed method outperformed the machine learning based methods, both in training phase, by GRU algorithm with 85.37% F1-score, and in testing phase, by LSTM algorithm with 76.30% F1-score. Then, in terms of adaptability of model performance, the proposed method gave comparable result against the baseline method.
A Deep Learning Approach for Handwritten Arabic Names Recognition

Optical Character recognition (OCR) has enabled many applications as it has attained high accuracy for all printing documents and also for handwriting of many languages.However, the state-of-the-art accuracy of Arabic handwritten word recognition is far behind.Arabic script is cursive (both printed and handwritten).Therefore, traditionally Arabic recognition systems segment a word to characters first before recognizing its characters.Arabic word segmentation is very difficult because Arabic letters contain many dots.Moreover, Arabic letters are context sensitive and some letters overlapped vertically.A holistic recognizer that recognizes common words directly (without segmentation) seems the plausible model for recognizing Arabic common words.This paper presents the result of training a Conventional Neural Network (CNN), holistically, to recognize Arabic names.Experiments result shows that the proposed CNN is distinct and significantly superior to other recognizers that were used with the same dataset.
Evolving Ensembles of Routing Policies using Genetic Programming for Uncertain Capacitated Arc Routing Problem

The Uncertain Capacitated Arc Routing Problem (UCARP) has a wide range of real-world applications. Genetic Programming Hyper-heuristic (GPHH) approaches have shown success in solving UCARP to evolve routing policies that generate routes in real time. However, existing GPHH approaches still have a drawback. Despite the effectiveness in many benchmarks, the single routing policy evolved by GPHH is too complex to interpret. On the other hand, the users need to be able to understand the evolved routing policies to feel confident to use them. In this paper, we aim to employ three ensemble methods, BaggingGP, BoostingGP and Cooperative Co-evolution GP (CCGP) to evolve a group of interpretable routing policies. The ensemble can be used to compare with single complex routing policy from GPHH. Experiment studies show that CCGP significantly outperformed BaggingGP and BoostingGP, and can generate much smaller and simpler routing policies to form ensembles with comparable test performance as the routing policy evolved by SimpleGP. This demonstrates the potential of improving the interpretability issue of GPHH using ensemble methods.
Toward a Hybrid Agile Product Development Process

Both startups and traditional space industry are exploring new business opportunities and new engineering approaches in what is becoming known now as New Space.Organizations started accelerating project schedules and challenging the V-model typically used in space product development. The need of a faster and more adaptive response to changing customer needs within an improved development productivity made Agile process a potential key enabler of New Space sector.On the other hand, space system projects are typically executed in multi-party consortia. Each organization in a consortium adopts its own product development process and interprets “New Space” differently. For this reason, the implementation of Agile is not seamless as it requires coordination with traditional systems engineering approaches. This setup is what we refer to as “hybrid Agile product development process”.This paper provides a first definition of the architecture of hybrid product development process targeted toward systems development and lifecycle management of hardware projects developed by multi-party consortia. We consider this discussion in the context of the development of spaceflight hardware in the New Space industry. We identify the main challenges in adopting such a methodology in developing hardware systems. This work identifies opportunities of future work for defining coordination approaches in hybrid product development settings, and improved organization structures of hardware projects in hybrid development contexts.
Key issues and technologies for AR/VR head-mounted displays

We first discuss the key factors of augmented reality (AR) and virtual reality (VR) displays. Various requirements for immersive experiences are categorized as six factors that must be considered when designing the AR/VR head-mounted displays (HMDs). These factors have a strong correlation with other factors and should maintain a moderate balance between them. Based on recent researches we second introduce various technologies for AR/VR. By comparing the pros and cons of each method, we discuss the progression of AR/VR devices that can provide more affordable HMD devices for the public.
Adaptive Model for Credit Card Fraud Detection

&lt;p&gt;While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.&lt;/p&gt;