Reviewerly/exHarmony huggingface dataset:
Analytical Reseach of P-TECH Outcomes and Effective Course Working Plan (Case-Oriented of Korea Polytechnic College)

The industry-academic- integrated apprenticeship school project, which is a high school-level work based learning, was expanded to 198 schools in 2017, and the number of trained graduates was expected to increase rapidly to 2,500 peoples in 60 schools in 2018 and 7,000 peoples in 200 schools after 2019. Therefore, College-level work based learning was demanded after High school-level work based learning. The government has developed working model of The Pathways in Technical Education Convergent High-Technology(P-TECH) which apprentices participate in training program for up to two years by going back and forth between training center in college that was recommended by a company and theirs company after graduated apprenticeship school in high school. In 2017, The P-TECH course was firstly piloted in 6 campuses and 8 departments of Korea Polytechnic College. Through the government’s continuous policy to expand the P-TECH course, Korea Polytechnics are running currently 20 campuses and 28 departments in 2021. In particular, the employment rate of graduates shows an average of 88.9%, which is higher than general degree course.
Pseudo Label based Contrastive Sampling for Long Text Retrieval

Applying BERT to text retrieval brings great success recently, however, the internal limit of input length downgrades the performance on longer texts when using BERT. To address this issue, we split the long text into paragraphs as basic retrieval units. Then we explore several ways to calculate the pseudo labels for each query-paragraph pair: Inherit, BM25 and Vector inner product. With the annotated pseudo labels, contrastive sampling will be adopted to distinguish positive/negative examples to feed BERT for evaluating the relevance. Experiments show that our approach is effective on TREC 2020.
Small Target Detection Method Based on a Multilayer Recursive Pyramid Network

Aiming at the problem of low precision and inaccurate positioning of small targets in SSD detection, a multilayer recursive pyramid network (MRPN) is proposed based on SSD model. Firstly, the default frame size of the basic feature diagram is designed to obtain the different size of the feature map. The multi-layer recursive pyramid network with different levels of feature input band is improved to enhance the feature and integrates the multi-scale features. Then, the combined semantic features are fed back to each feature layer, and then the feature weights are redistributed through the SENet module, and the focus is on the effective features. Finally, in the process of loss function design, focal loss function is used instead of the two-classification cross entropy loss function to solve the problem of uneven number of positive and negative samples in the detection process. The experimental results show that the detection accuracy of Pascal VOC data set is improved by 4.9% compared with the original algorithm, and 37.6% on the COCO data set.
Impact of Mechanism and Influencing Factors of Radio Frequency Fingerprint on Wireless Devices

In recent years, with the continuous development of Internet of Things technology, the number of connected devices has gradually increased, which has brought new challenges to wireless network security. Due to the device tolerances introduced in the manufacturing process of wireless devices, there are hardware differences between individual devices. The radio frequency fingerprint identification technology uses this physical layer hardware difference as the fingerprint feature between different devices to realize device identification. The hardware difference of the device is the main source of the radio frequency fingerprint of the wireless device. Therefore, this article analyzes the mechanism and influencing factors of the radio frequency fingerprint of the wireless device.
Latent semantic analysis for tagging activation states and identifiability in northwestern Mexican news outlets

The present work aims to study the relationship between measures, obtained from Latent Semantic Analysis (LSA) and a variant known as SPAN, and activation and identifiability states (Informative States) of referents in noun phrases present in journalistic notes from Northwestern Mexican news outlets written in Spanish. The aim and challenge is to find a strategy to achieve labelling of new / given information in the discourse rooted in a theoretically linguistic stance. The new / given distinction can be defined from different perspectives in which it varies what linguistic forms are taken into account. Thus, the focus in this work is to work with full referential devices (n = 2 388). Pearson’s R correlation tests, analysis of variance, graphical exploration of the clustering of labels, and a classification experiment with random forests are performed. For the experiment, two groups were used: noun phrases labeled with all 10 tags of informative states and a binary labelling, as well as the use of two bags-of-words for each noun phrase: the interior and the exterior. It was found that using LSA in conjunction with the inner bag of words can be used to classify certain informational states. This same measure showed good results for the binary division, detecting which sentences introduce new referents in discourse. In previous work using a similar method in noun phrases in English, 80% accuracy (n = 478) was reached in their classification exercise. Our best test for Spanish reached 79%. No work on Spanish using this method has been done before and this kind of experiment is important because Spanish exhibits a more complex inflectional morphology.
Efficient Scalable Temporal Web Graph Store

Temporal web graphs have been attracting much attention recently due to their important applications in web search, data mining, and social network analysis. Accumulated over long periods, those graphs have grown gigantic in size and rich in temporal evolution, which poses tough challenges for data storage and management. Though a few temporal graph management systems were previously proposed, none of them can simultaneously satisfy both essential requirements when retrieving on temporal web graphs: very large data scalability and very low querying latency.In this work, we address the above gap in existing works by developing a highly efficient temporal graph management system which is dedicated to web graphs. To this end, we greatly extend the most efficient framework for managing large static web graphs to handle temporal information using the property matrix while preserving most of the outstanding features of the base framework. Ultimately, our proposed system can achieve a nearly instant response for vertex-centric temporal retrieval while still being scalable to huge datasets. Experiments on a real-world dataset with more than 43B nodes and 317B links show that using a small non-dedicated cluster, our system can reach a reduction of data storage space up to 88% of raw data size and reduce the retrieval time by 20%, compared to the baselines. We also demonstrate that our system also yields a significant reduction of computational costs for many graph ranking algorithms.
LUMOZ – A Real Time Augmented Reality Based Tool for Media Production

Globalization has created vast competition among media content productions. Gaining an audience for these products depends on the quality, correctness, timely content and should be produced according to the target audience’s comprehension level. The aforementioned factors can be achieved by using new technology in the process of content production. This trend also affects the local news productions. At present, news delivery and other digital media creations are transforming from traditional delivery methods into more advanced technology-based delivery. For this transformation, Augmented Reality and 3D technologies play significant roles which help improving content’s attractiveness and correctness. And using this new technology can create a new fan base and increase the popularity of the content. The use of these technologies in an industrial base or startup base still provides many challenges. LUMOZ provides a solution for these challenges. This tool provides easier access to point tracking,3D object placement, gesture controls, 3D data visualization, and 3D model library. Simply LUMOZ can be considered as a budget-friendly and time-efficient tool that can handle 3D objects in real-time in a live streaming environment.
Single Image Re ection Removal via Deep Feature Contrast

Removing undesired re ection from a single image is in demand for computational photography. Re ection removal methods are gradually effective because of the fast development of deep neural networks. However, current results of re ection removal methods usually leave salient re ection residues due to the challenge of recognizing diverse re ection patterns. In this paper, we present a one-stage re ection removal framework with an end-to-end manner that considers both low-level information correlation and efficient feature separation. Our approach employs the criss-cross attention mechanism to extract low-level features and to efficiently enhance contextual correlation. To thoroughly remove re ection residues in the background image, we punish the similar texture feature by contrasting the parallel feature separa- tion networks, and thus unrelated textures in the background image could be progressively separated during model training. Experiments on both real-world and synthetic datasets manifest our approach can reach the state-of-the-art effect quantitatively and qualitatively.
Blind Recognition of Channel Coding Based on Deep Learning

Blind recognition of channel coding means that the receiver can identify which channel coding type is used by the transmitter without any prior knowledge of channel coding. Traditional blind recognition algorithms for channel coding have high computational complexity and can only recognize specific coding type. In this paper, an algorithm to blindly identify channel coding types via deep learning is proposed, which can automatically extract the signal features and avoid complicated calculation. In particular, quasi-cyclic low-density parity-check (QC-LDPC) code and spatially coupled low-density parity-check (SC-LDPC) code are used as candidate codewords, and these two coding types are identified from noisy signals. Two kinds of recognizers based on long short-term memory (LSTM) network and convolutional neural network (CNN) are proposed, and the channel coding types are recognized through the classification process of neural network. Experimental results show that the proposed blind recognition method is able to realize high recognition accuracy even in poor channel conditions.
In-Depth Métan- Search Engine

The use of search engines is one of the most popular online activities. In addition, users generally receive good results and relatively high confidence in the ability of search engines.The primary objective of this paper is to provide a model for researching and collecting web pages and relevant information at the user’s request.Sometimes when a search query is sent by a user, the use of several search engines creates a difference in the search results on the one hand and a wealth of quality and the quantity of these results at the same time.Experts in this field have confirmed that the relevant search results obtained from a meta-search engine has made substantial progress.In order to improve these meta-search engines, we propose a new model by detailing and optimizing the results of a metasearch (the search for a search) by the duplicate of the search in a precise way and by dissecting a result in several search results until exhaustion of this research search.Repeating this search of search gives birth to a new concept that is «Méta <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -Search».
Cyber Security Awareness Requirements for Operational Technology Systems

Recent events have demonstrated that critical infrastructure assets operated by networked industrial control systems are vulnerable to cyber attacks. The first step to addressing the threats is to establish and maintain reliable monitoring of the cyber security health of systems so that their true cyber security states are known. For current operational technology systems, there is growing, but limited, availability of technologies and tools that provide the needed cyber security awareness. This chapter summarizes recent efforts conducted in collaboration with members of the U.S. natural gas distribution sector to develop a set of recommended functional requirements for cyber security health monitoring and awareness of operational technology systems. The design-driven process is described and the resulting nine key recommendations for securing operational technology systems are presented.
Application Research of Ship Maritime Safety Decision System Based on Big Data and Artificial Intelligence

With the continuous development of global economic integration, international shipping, as a cheap and large-capacity transportation method, has also developed rapidly. However, marine safety accidents occur from time to time. In the current economic situation, ship safety issues are particularly important. The purpose of this article is to study the application of marine safety decision-making system based on big data and artificial intelligence. This article first analyzes the concepts of big data Hadoop platform and artificial intelligence, and then this article analyzes the current safety problems in marine shipping. In the experimental part, this paper designs a maritime safety decision system. The goal of the system is to analyze the ship’s navigation status, and use artificial intelligence, machine learning and other advanced technical methods to replace the original artificial experience prediction method to predict the ship’s operating data. In terms of the specific model selection of the system, this paper selects the multi-layer perceptron which is more suitable for ship navigation data with greater volatility as the core model for predictive model training. Experimental results show that the performance of the system can meet the application requirements. In this paper, by analyzing the forecast error of the system, the error is controlled within 15%.
Blockchain For Secure and Proper Management of Medical Data and Records

Blockchain recently began to grow in the healthcare sector. A low level of health sensitive information can lead to situations where timely communication is not available, with serious health consequences. Besides, as patient involvement in healthcare grows, there is a growing need for patients to access and manage their data. Confidentiality in the healthcare industry refers to the” professional obligation”; the World Health Organization can access patient records or exchange information to carry that information with confidence. Managing electronic health data presents different challenges for compliance, ethical concerns, and ultimately the quality of care. Access to patient medical records in a hospital software package should be by a physician/conservative team. The abuse of digital signatures on information based on Blockchain allows mass access to control the provision and maintain health records' safety. Besides, the healthcare industry's public and stakeholders may be part of the Blockchain, reducing payment fraud. Applications can be designed to administer and exchange stable, open, and unbreakable audit processes by systemic fraud using this technology. This paper examines the literature to describe important challenges with different health-care stakeholders, as well as the capabilities of blockchain technology to address those concerns.
A Blockchain Electronic Bidding Scheme Based on Homomorphic Encryption

Proposes a block chain-based bidding solution. By recording all bidding processes, all quotation information uses additive homomorphism and multiplicative homomorphism encryption, combined with the application of random numbers, to prevent the encryption party from deducing other participating party's information. Using the homomorphic encryption-based blockchain electronic bidding scheme proposed in this article, using a decentralized solution, protects the security of bidders, improves the fairness and reliability of bidding, and the bidding process can be traced, and block chain ledger is stored distributed at every node of the blockchain, to prevent hackers from attacking a centralized node to arbitrarily tamper or illegally obtain bidding data, and the security is greatly improved.
MRRVOS: Modular Refinement Referring Video Object Segmentation

The previous referring video object segmentation method only focuses on the final prediction result. When the user input linguistic query does not match the information in the video, the video still segments error information. We propose a new reference video object segmentation framework. In our model, which we call the Modular Refinement Referring Video Object Segmentation (MRRVOS), when the objects in the referring linguistic query do not match the video frame, stop segmentation and feedback error information. Firstly, given a video clip and a linguistic query. Our method segments the specified object in a video frame automatically. Then our method matches linguistic query with video frames and implements object segmentation on other frames through a recursive three-module model: (1) Referring Video Object Segmentation module: we consider the referring video object segmentation task as a joint problem of referring object segmentation in the image and mask propagation in the video. (2) Image caption module: using recurrent neural networks (RNNs), and deep convolutional neural network (CNN) to encode every frame except the first frame, and a Long Short Term Memory (LSTM) RNN decoder to generate the output caption and put it into the corpus. (3) Semantic dissimilarity module: put all the text results into the corpus and embed vector space, our linguistic query of the input to perform a semantic dissimilarity search. We show that our approach is competitive to the state-of-the-art method.
Template Clustering for the Foundational Analysis of the Dark Web

The rapid rise of the Dark Web and supportive technologies has served as the backbone facilitating online illegal activity worldwide. These illegal activities supported by anonymisation technologies such as Tor has made it increasingly elusive to law enforcement agencies. Despite several successful law enforcement operations, illegal activity on the Dark Web is still growing. There are approaches to monitor, mine, and research the Dark Web, all with varying degrees of success. Given the complexity and dynamics of the services offered, we recognize the need for in depth analysis of the Dark Web with regard to its infrastructures, actors, types of abuse and their relationships. This involves the challenging task of information extraction from the very heterogeneous collection of web pages that make up the Dark Web. Most providers develop their services on top of standard frameworks such as WordPress, Simple Machine Forum, phpBB and several other frameworks to deploy their services. As a result, these service providers publish significant number of pages based on similar structural and stylistic templates. We propose an efficient, scalable, repeatable and accurate approach to cluster Dark Web pages based on those structural and stylistic features. Extracting relevant information from those clusters should make it feasible to conduct in depth Dark Web analysis. This paper presents our clustering algorithm to accelerate information extraction, and as a result improve attribution of digital traces to infrastructures or individuals in the fight against cyber crime.
Multimodal Query-Guided Object Localization

Consider a scenario in one-shot query-guided object localization where neither an image of the object nor the object category name is available as a query. In such a scenario, a hand-drawn sketch of the object could be a choice for a query. However, hand-drawn crude sketches alone, when used as queries, might be ambiguous for object localization, e.g. a sketch of a laptop could be confused for a sofa. On the other hand,Â a linguistic definition of the category, e.g., ``a small portable computer small enough to use in your lap" along with the sketch query gives better visual and semantic cues for the object localization. In this work, we present a multimodal query-guided object localization approach under the challenging open-set setting. In particular, we use queries from two modalities, namely, hand-drawn sketch and description of the object (also known as gloss), to perform object localization. Multimodal query-guided object localization is a challenging task especially when the large domain gap exists between the queries and the natural images, as well as due to the challenge in optimally combining the complementary and minimal information present across the queries. For example, hand-drawn crude sketches contain abstract shape information of an object while the text descriptions often capture partial semantic information of a given object category. To address the aforementioned challenges, we present a novel cross-modal attention scheme that guides the region proposal network to generate object proposals relevant to the input queries and a novel orthogonal projection-based proposal scoring technique that scores each proposal with respect to the queries, thereby yielding the final localization results. Our method is useful towards effectively localizing multiple object instances present in the target image not only in the challenging open-set setting (when some object categories are not seen during training), but also the traditional closed-set case, where samples from all the object categories are available during training. In our experiments, hand-drawn sketches from `Quick, Draw!' dataset and gloss retrieved from `WordNet' are used as queries, to evaluate the localization performance of our approach on widely-used object localization public benchmark MS-COCO. The proposed method clearly outperforms all related baseline approaches across both closed-set and open-set object localization tasks.
Expeditious Video Super Resolution Using Convolutional Neural Network

Advancements in deep learning techniques have paved a way for efficient up scaling of images and videos. Similar to up scaling an image, we can reach upto a higher resolution by the process of video super resolution. Various existing methods and technologies for achieving a higher resolution are briefly surveyed in this paper and compared to analyze the downfall of the existing approach and proposing a solution. It was ascertained that deep learning approach of Convolutional Neural Network (CNN) is favorable solution to carry out video super resolution. It was also noted that most of the existing techniques focused on either of accuracy or on decreasing complexity, wherein the question of audio was also neglected. Considering the audio factor a innovative video embellished technique is recommended to overcome the balance needed in precision and complexity.