Reviewerly/exHarmony huggingface dataset:
Research On 5G Encryption Algorithm Identification Scheme Based On Adaboost

5G network is a new type of network method that is being popularized, and the study of identifying 5G encryption algorithms helps ciphertext analysis and improves 5G network security. In this paper, the feature extraction of ciphertext is carried out by means of information entropy and feature construction and feature screening in feature engineering. This uses the AdaBoost method in integrated learning to construct the encryption algorithm recognition scheme, analyze and recognize the data encrypted by AES and SM4 in four working modes, and obtain high recognition accuracy in some situations.
Application of Uncertainty Analysis in Evaluation of Marine Engine Simulator

Abstract Aiming at the problem of fuzziness and randomness in the evaluation process of marine engine simulator, a comprehensive evaluation model based on subjective and objective combination weighting method and cloud model method is established on the basis of single subjective and objective combination weighting method. Firstly, the combined weighting method is used to determine the comprehensive weight; Then, the standard cloud model is generated by using the cloud model method, and the digital characteristics of the evaluation cloud are obtained through the sample data and cloud computing rules. The digital characteristics of the evaluation cloud are obtained by the backward cloud generator and comprehensive weight; Finally, the similarity between the generated evaluation cloud and the standard cloud model is calculated, so as to evaluate the operation of the student marine engine simulator. The example analysis shows that the evaluation method can scientifically and reasonably evaluate the crew’s operation ability, and is feasible and effective.
Semantic user profile enrichment in collective intelligence context: a Healthcare case study

The Personalized systems are generally based on collecting and exploiting users’ preferences by exploring their traces’ data. Actually, they find users who have similar attributes, cluster them, and then applying algorithms using the subnets. The similarity between users compares their profiles including their attributes. The adding of tags to enrich the user profile must take into consideration the long and short term criteria of the user’s attributes that change over time. In this paper, we present a tag-based profile enrichment approach by adding a time score describing the short and long term criteria of the attribute. Then we use graph analytics to draw clusters of users by inspecting similar tags. Our approach helps companies to make their predictions and conclusions. The datasets of patients’ images ChestX-Ray14 have been conducted to evaluate the effectiveness of our approach.
Short Utterances based real time on-Device Spoken Language Identification

Speech based tasks on mobile devices such as voice based interactions with virtual personal assistants, speech translation etc. have become commonplace today. For such tasks, real-time language identification can serve as an important first step. However, due to the less speech content in short utterances, this is a challenging problem. In this paper, we present Short Utterances Language Identification (SULInet) which is an attention-based light-weight CNN model. It uses 3 seconds utterance and is fit for real time analysis for resource constraint environment like mobile devices. We used state of the art systems trained on Voxforge and EuroSpeech datasets as baseline. We showcase the results at frame-level (3 seconds) and file-level (complete audio file) on PC and an Android based mobile device. We significantly improved our accuracy and achieved SOTA results by proposing a voice activity detector (VAD) as a preprocessing step. On Voxforge dataset, we surpassed SOTA system by 2.36% with 65% less training parameters and 70% smaller input. Similarly, on EuroSpeech dataset, we achieved comparable results with 42% less parameters. Our approach achieved on-Device frame level accuracy as 92%, 80% on Voxforge and EuroSpeech respectively with just one-tenth of PC model size.
An Evaluation Benchmark for Automatic Speech Recognition of German-English Code-Switching

Code-switching arises when a (typically multilingual) speaker changes language during an utterance. This linguistic phenomenon causes problems for automatic speech recognition as the models are typically monolingual. In this work, we present a code-switching evaluation scenario for German-English that is created by resegmenting the German Spoken Wikipedia Corpus. Since these articles span a wide variety of (often technical) topics, they include a lot of borrowing and code-switching phenomena. The resulting corpus consists of around 34 hours of intra-sentential switches. We investigate end-to-end approaches using both monolingual and multilingual automatic speech recognition as well as language modeling to address the code-switching scenario. Results suggest that multilingual sequence-to-sequence approaches are to be preferred for code-switching thanks to the power of the attention mechanism. The segments are made available to the community as a benchmark.
Dominant subject recognition by Bayesian learning

We tackle the problem of dominant subject recognition (DSR), which aims at identifying the faces of the subject whose faces appear most frequently in a given collection of images. We propose a simple algorithm solving the DSR problem in a principled way via Bayesian learning. The proposed algorithm has complexity quadratic in the number of detected faces, and it provides labeling of images along with an accurate estimate of the prediction confidence. The prediction confidence permits using the algorithm in semiautomatic mode when only a subset of images with uncertain labels are corrected manually. We demonstrate on a challenging IJB-B database, that the algorithm significantly reduces the number of images that need to be manually annotated to get the perfect performance of face verification and face identification systems using the face database created by the method.
An Efficient Predictive Model for High Dimensional Data

Analyzing high dimensional data is a challenging task. Sometimes, it becomes extremely difficult to work with a large number of features as these result in a complex model. High dimensional data hampers the efficiency of any machine learning model because it increases overfitting and computational costs. It can cause difficulty in prediction as all data points may locate near to one another and less distinction will be there, which increases redundancy. So, in order to tackle such data, certain methods for reducing dataset are used like feature selection and dimensionality reduction. These methods improve the data mining processes by reducing the training time and making data analysis easier. This paper focuses on how to deal with high dimensional data with a step by step procedure to incorporate various known methodologies to generate an efficient model. Furthermore, various ensemble methods are used and their importance will be clearly visible in performance evaluation.
MPTCP under Virtual Machine Scheduling Impact

Multipath TCP (MPTCP) has captured the networking community's attention in recent years since it simultaneously transfers data over multiple network interfaces, thus increases the performance and stability. Existing works on MPTCP study its performance only in traditional wired and wireless networks. Meanwhile, cloud computing has been growing rapidly with lots of applications deployed in private and public clouds, where virtual machine (VM) scheduling techniques are often adopted to share physical CPUs among VMs. This motivates us to study MPTCP's performance under VM scheduling impact. For the first time, we show that VM scheduling negatively impacts all MPTCP subflows' throughput. Specifically, VM scheduling causes the inaccuracy in computing the overall aggressiveness parameter of MPTCP congestion control, which leads to the slow increment of the congestion windows of all MPTCP subflows instead of just a single subflow. This finally results in a poor overall performance of MPTCP in cloud networks. We propose a modified version for MPTCP, which considers VM scheduling noises when MPTCP computes its overall aggressiveness parameter and its congestion windows. Experimental results show that our modified MPTCP performs considerably better (with up to 80% throughput improvement) than the original MPTCP in cloud networks.
Research on the Architecture of Shipborne UAV Target Indication Command and Control System based on DoDAF

The latest Department of Defense Architecture Framework (DoDAF) emphasizes the consistency of data, which requires to ensure that the models are related to each other and the data contained can be reused repeatedly. Therefore, a model relevance judgment principle is proposed. Based on DM2 related concepts, this principle determines whether there is an association relationship between models required for modeling according to the number of common data element types included and whether views can iterate with each other, so as to optimize modeling steps and find out the sequence of model generation. The relevance principle is applied to the development of shipborne UAV target command and control system architecture, and a visual description is made for organizational relations, resource interaction and combat activities.
A Deep Learning Classification Method for Early Endometrial Cancer on MRI Images

Early detection benefits patients with endometrial cancer (EC). In order to detect and diagnose early endometrial cancer from an image with FIGO stage I endometrial cancer from an image without endometrial cancer on magnetic resonant image, a deep learning endometrial cancer classifier based on deep learning was presented. Data augmentation was used in the classifier training to address the data imbalance problem. The experimental results show the model test accuracy for axial DWI images is 0.870 and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 0.8611 in original data and 0.9764 in augmented data. The model test accuracy for axial T2WI images is 0.810 and the AUC of the ROC curve is 0.9342 in original data and 0.9389 in augmented data. The model test accuracy for sagittal T2WI images is 0.677 and the AUC of the ROC curve is 0.7919 in original data and 0.9294 in augmented data. The deep learning EC classifier is potential for use in the computer-aided diagnosis for endometrial cancer on MRI images.
Lost in Translation: What Linguistic Measurements Best Measure Text Quality of Online Listings

Ecommerce websites are filled with international sellers. Product descriptions on these sites are often written in English by non-native speakers. Linguistic imperfections in these descriptions confuse consumers, which may further attenuate their purchase intentions. How descriptive quality/efficacy can be defined and then improved shall be of great interest to all sellers and their consumers. In this research, we attempt to evaluate online product description quality using lexical measurements from linguistics studies. Linguistics measurements of writing quality were mostly developed in pure academic settings. We test and analyze these measurements’ applicability in defining and contrasting business description quality using Amazon.com data. Modern classification techniques in the artificial intelligence and machine learning field are deployed in identifying measurement applicability and assessing computational efficiency. Our findings enable automatic identification of descriptive efficacy through artificial intelligence methods on real ecommerce text data.
Language Analysis &amp; Grammar in Use

This chapter outlines two introductory lectures on English grammar, Language Analysis (LA) and Grammar in Use (GIU), which are part of the English Language Competence (ELC) programme offered by the Department of English and American Studies at the University of Vienna. Although LA is for students pursuing a Bachelor of Arts (BA) in English and American Studies and GIU is for students in the Bachelor of Education (BEd) programme, they both share the same overall objectives and are therefore discussed together here. As the titles suggest, they are both about grammar and analysing English sentences with respect to their component parts. More specifically, they provide an overview of fundamental grammatical terms and concepts used for describing English sentences and texts. In doing so, the lectures demonstrate how grammar is used for communication and how language structures can be explained and evaluated in context, reflecting the general goal of giving students a better understanding of the concept of grammar. Other aims include students being able to (i) identify and name individual parts of syntactic structures, (ii) talk informedly about key grammatical categories and explain their use in a given context, (iii) evaluate the appropriate use of grammar in specific contexts, and (iv) make independent and judicious use of a variety of resources to find answers to grammar issues. These introductory lectures thus provide an important foundation for language classes and linguistics courses alike.
An adaptive learning-based weakly supervised object detection via context awareness

Weakly supervised object detection (WSOD) methods have become a powerful tool without fully labeled bounding boxes. In the field of object detection, however, there is still a certain gap in performance between the existing WSOD technique and the fully supervised object detection method. There exist two problems that are not common in fully supervised object detection including object ambiguity and falling into the local optima. To solve the object ambiguity problem, multiple instance learning with the self-training method is proposed in this paper, from which pseudo labels will be generated and gradually replace the training labels to locate objects more accurately during the training process. For the problem of falling into the local optima, a context awareness block is added into our module, which makes our network pay more attention to the background and context of our region of interest (ROI) object. Experiment results based on the dataset PASCAL VOC2007 and VOC2012 are carried out to demonstrate the effectiveness of the proposed approach.
A New RGB-D Gesture Video Dataset for Real-life Scenarios

In the field of human-computer interaction, video-based gesture detection has already sparked a lot of attention. Unlike typical RGB gesture movies, RGB-D gesture videos also record the depth information corresponding to all of the pixels in each frame, potentially reducing the impact of lighting and background fluctuations. To the best of our knowledge, current RGB-D gesture video datasets primarily focus on high classification accuracy while ignoring adequate illumination and backdrop variations that occur in real life. These will stifle the advancement of gesture recognition algorithms in some way. Instead, we present DG-13, an RGB-D gesture video dataset that properly accounts for various brightness and backdrops. On the proposed DG-13 dataset, benchmark evaluations of five exemplary light-weighted 3D CNN networks are also provided. Experiment results reveal that when there are significant illumination and backdrop differences, RGB-D gesture video can successfully aid increase classification accuracy and numerous other classification metrics. The DG-13 dataset and benchmark codes are public at https://github.com/xiaooo-jian/DG-13.
Design and Development of Privacy Preservation Approach in Data Mining: A Literature Review Paper

In our daily life a huge amount of data is being created that has to be mined to extract the significant information without loss of any sensitive data. There is a major concern to preserve privacy during the mining of big data sets. Since every data set has a significant amount of frequent data items, our aim is to establish the association among the frequent item sets that can be efficiently done. Although the association rule mining is an algorithm that is implemented with more parameters, it is still a complex task to make the association among the frequent data items. As we know that the data can float through the centralized database or it can float through the distributed database so to preserve the privacy in both the models is a major concern. In this research work the association rule mining and apriori algorithm will help to satisfy the objective and a hybrid approach of data mining with the machine learning can overcome the research gaps to preserve the privacy and to optimize the result.
Development of the culture of continuing education

The subject of this research is the basic models of continuing education used by the representatives of business, higher education and the government. The object of this research is the process of continuing education in Russia as the response to the challenges of the new job market. Analysis is conducted on the context of the emergence of the need for continuing education of the professionals alongside the currently implemented models of such education in Russia and worldwide. The data acquired in the authorial quantitative research conducted in September 2021 in the format of online questionnaire served as the empirical basis for this research. The survey involved 211 specialists from 26 different fields of activity responsible for staff training in Russian organizations. The analytical research is based on the data of the VI International Conference &amp;ldquo;Beyond Training: Continuing Education for the New Job Market&amp;rdquo; held jointly by SberUniversity and the European Foundation for Management Development (EFMD) with the participation of the Sberbank Charitable Foundation &amp;ldquo;Contribution to the Future&amp;rdquo;. The author seeks to scientifically substantiate the question of why continuing education of the professional has become a topical issue for the organizations. A list of cyclical and sudden trends affecting the organizations, their strategies and business models is explored. The author traces the changes in professions and skills essential for the future specialists. The practical importance of this work lies in description of the new models of continuing education in the organization, which would allow the companies to set the new skill sets that can be acquired in the course of continuing education system the new paradigm in the conditions of new reality.
Writer-Designer Intersections at MODULE Digital Alchemy Creation Lab: A Conversation between Taylor Marie Graham and Beth Kates

Playwright Taylor Marie Graham and theatre/XR designer Beth Kates reflect on their collaborative digital theatre experiments throughout the week-long digital dramaturgy intensive Digital Alchemy Creation Lab: MODULE, funded by a Canada Council Digital Strategy Fund grant. MODULE was an experiment designed to provide playwrights and theatre artists with hands-on exposure to available and emerging technologies (projection design tools, virtual reality tools, etc.). The two discuss the need to dismantle rehearsal-room hierarchies, needed complications to the problematic binary of rural storytelling versus technology, creative process overlap between writers and designers, and theatremaking in virtual reality, as well as the expansive storytelling potential of writer-designer intersections at project inception.
Functional Programming

In the non-fiction work Old Times on the Mississippi, Mark Twain wrote, “When I was a boy of 14, my father was so ignorant I could hardly stand to have the old man around. But when I was 21, I was astonished at how much the old man had learned in seven years.” Functional programming is the old man who comes to the rescue when writing robust concurrent software. Functional programming treats computation as the evaluation of mathematical functions and avoids state and mutable data. It is a declarative programming paradigm, in which programming is done with expressions. The imperative style of programming emphasizes a sequence of operations characterized by iteration with loops, mutating data in place, and methods with side effects where the order of side effects is critical for the right effect.
Online Semi-supervised Learning from Evolving Data Streams with Meta-features and Deep Reinforcement Learning

Online semi-supervised learning (SSL) from data streams is an emerging area of research with many applications due to the fact that it is often expensive, time-consuming, and sometimes even unfeasible to collect labelled data from streaming domains. State-of-the-art online SSL algorithms use clustering techniques to maintain micro-clusters, or, alternatively, employ wrapper methods that utilize pseudo-labeling based on confidence scores. Current approaches may introduce false behaviour or make limited use of labelled instances, thus potentially leading to important information being overlooked. In this paper, we introduce the novel Online Reinforce SSL algorithm that uses various K Nearest Neighbour (KNN) classifiers to learn meta-features across diverse domains. Our Online Reinforce SSL algorithm features a meta-reinforcement learning agent trained on multiple-source streams obtained by extracting meta-features and subsequently transferring this meta-knowledge to our target domain. That is, the predictions of the KNN learners are used to select pseudo-labels for the target domain as instances arrive via an incremental learning paradigm. Extensive experiments on benchmark datasets demonstrate the value of our approach and confirm that Online Reinforce SSL outperforms both the state-of-the-art and a self-training baseline.
Drivers of Changing Green Innovation in China: A Comparative Analysis of Invention and Utility Model Patents Granted

As the most important type of intellectual property rights, invention patents granted (IPG) constitutes an excellent indicator of technological change and is a critical index for reflecting the accumulation of technology. This study comparatively analyses the key drivers of changes in China’s green invention and green utility model patents, based on data from 18 categories of industries from 2011 to 2018 using decomposition analysis. We find that the efficiency of patents granted relative to research and development expenditure had a decisive effect on decreasing IPG in most industries, while it strongly contributed to the dramatic increase in utility model patents granted. The importance of green technology (GT) cannot improve overall green innovation effectively as expected, inventors are mainly concentrating their research resources on administration, regulation, and design aspects technology. The prioritization of specific GT has a considerable positive effect on current energy conversion technology, which drives a cumulative increase of 583 patents granted and the cumulative contribution rate reached up to 30.2%. Finally, the paper analyzes different priority trends for seven types of GTs among each industry that can be utilized to prepare practical guidelines that boost sustainable green development.