Reviewerly/exHarmony huggingface dataset:
Indoor scene classification model based on multi-modal fusion

Aiming at the classification of indoor scene images, a multi-modal fusion model is proposed. Firstly, based on the scene image and its semantic description, a single-modal classification model is constructed. For scene images, a convolutional neural network is used to extract features and train classification models. For scene semantic descriptions, a recurrent neural network is used to extract text features. A scene feature space is then constructed and the semantic descriptions are embedded to it to obtain classification results. Secondly, these two kinds of single-modal features are fused and input to a deep neural network after dimensionality reduction, a feature-level fusion model is trained. Finally, two single-modal models and the feature-level fusion model are given different weights to construct a hybrid fusion model, the weights are constantly adjusted to get the best classification accuracy.
Resource Prediction Service for Efficient Execution of Bioinformatics Workflows in Federated Cloud with Machine Learning

Cloud federation emerged to extend the resources available between different interconnected cloud providers for transparent and unlimited availability to the end-user. Cloud orchestration platforms have become a way to centralize demands for high computational power in applications such as Bioinformatics workflows. The large quantity of resources available among several providers in a federation makes it challenging to choose a suitable one for particular workflows. This work proposes a Machine Learning Resource Prediction Service called sPCRAM. sPCRAM uses a machine learning model combined with a GRASP metaheuristic to transparently and adequately dimension the resources, determining the monetary cost and the runtime before the workflow execution. sPCRAM interactively allows the user to set the execution type, calibrate time and cost. Such executions can have, for example, long duration and low cost, as well as a shorter duration and a higher cost. The results demonstrate that sPCRAM can appropriately estimate runtime and cost for cloud federation resources on average 97,70% faster than the brute force technique for resource selection.
Intelligence e-market for farmers using block-chain approach

The terminology internet-based e-market implies a platform that is committed to combine farmer, authorities, and consumer and by this means bridge the gap between them. The main idea is for every stakeholder to be informed with the varying consumer scenario. Indian farmers encountered many challenges and one of the major challenges is to make substantial profit for thehard work and financing that they had invested in their agriculture. The proposed e-market allows the farmers to bypass agents and boosting the farmers to consumer chain and guaranteeing traceability to produce benefit for all participants. The Internet based e-marketplace also offers end-to-end traceability and openness all through last-mile authentication and expansion foundation in a block chain technology.
A Blockchain Based Automatic Access Scheme Design and Implement for Small Cell Base Station

5G technology is constrained by its higher frequency band and smaller coverage area, which leads to the need for operators to use technologies such as small cell base stations to increase the density of base station deployment to ensure signal coverage quality, which leads to more enormous construction costs. Therefore, it is urgent to find a safe and reliable solution that can use the existing public network to realize small cell base stations with automatic access. Based on this demand, we summarize various existing small cell base station automatic access technology solutions and their advantages and disadvantages while combing the characteristics of blockchain technology and its application solutions in similar scenarios. And then, we propose a new blockchain-based base station automatic access solution and implements the system in a practical scenario. In our solution, we innovatively introduce blockchain as an intermediate manager for small base stations and core networks, which solves traditional solutions requiring customized equipment while ensuring safety and reliability. It reduces costs and improves efficiency, but some problems are brought by the “decentralization” of blockchain waiting to be solved.
Distantly Supervised Relation Extraction Based on Multi-head Self-attention and Gate Mechanism

In the field of distant supervision relation extraction, PCNN (piecewise convolution neural network) is normally involved to trap local features of sentences, and has achieved good results. However, the existing PCNN-based methods are unable to capture the features of long-distance interdependence in sentences and cannot distinguish the influence of the three segments of PCNN on relation classification, which probably results in missing some vital information. To address the above two issues, we come up with a new model with multi-head self-attention and gate mechanism for distant supervised relation extraction. Firstly, to capture the features of long-distance interdependence in sentences, we employ an internal multi-head self-attention in PCNN model, which can grab information in different representation subspaces. Secondly, to distinguish the influence of the three segments of the piecewise max-pooling output on the relation classification, the gate mechanism is introduced to assign different weights to the three segments and highlight important segments. After a series of experiments, it is proven that the model we presented is better than the previous approaches on each evaluation criterion.
Scalable l-Diversity

Privacy preserving data publishing is one of the most demanding research areas in the recent few years. There are more than billions of devices capable to collect the data from various sources. To preserve the privacy while publishing data, algorithms for equivalence class generation and scalable anonymization with k-anonymity and l-diversity using MapReduce programming paradigm are proposed in this article. Equivalence class generation algorithms divide the datasets into equivalence classes for Scalable k-Anonymity (SKA) and Scalable l-Diversity (SLD) separately. These equivalence classes are finally fed to the anonymization algorithm that calculates the Gross Cost Penalty (GCP) for the complete dataset. The value of GCP gives information loss in input dataset after anonymization.
<i>LinkMan</i>

How can we detect and analyze hyperlink-driven misbehavior in online forums? Online forums contain enormous amounts of user-generated content, with threads and comments frequently supplemented by hyperlinks. These hyperlinks are often posted with malicious intention and we refer to this as 'hyperlink-driven misbehavior'. We present LinkMan, a systematic suite of capabilities, to detect and analyze hyperlink-driven misbehavior in online forums. We take a unique perspective focusing on hyperlink sharing practices of the users to spot misbehavior. LinkMan can categorize these hyperlinks as: a) phishing, b) spamming, and b) promoting malicious products. Our approach consists of three high-level phases: (a) extracting hyperlinks from the textual data, (b) identifying misbehaving hyperlinks, and (c) modeling the behavioral patterns of hyperlink sharing, where we identify key hyperlinks and analyze the collaboration dynamics of hyperlink sharing. In addition, we implement our approach as a powerful and easy-to-use open platform for practitioners. We apply LinkMan to spot misbehavior from three online security forums, where we expect the users to be more security-aware. We show that our approach works very well in terms of retrieving and classifying hyperlinks compared to previous solutions. Furthermore, we find non-trivial and often systematic misbehavior: (a) we find a total of 637 misbehaving hyperlinks, and (b) we identify 30 colluding groups of users in terms of promoting hyperlinks. Our work is a significant step towards mining online forums and detecting misbehaving users comprehensively.
Image Searching System

Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.
Study on the design of graduate education management system based on design thinking

This paper analyzes the deficiencies in the design of the current postgraduate education management system, proposes new ideas for the interactive design of the postgraduate education management system under the guidance of the principle of design thinking. Data collection and comparative analysis are carried out on the current situation of the Chinese graduate education management system, and through interviews and questionnaires we collected information about teachers' and students' opinion of the current system. Through SPSS regression analysis, combined with interview records, we found the main problems of the current system on interactive design and function design. Based on the design thinking theory, the improvement method of the system is proposed, which can make the process of communication and interaction between teachers and students more effective, with the goal to improve the quality of postgraduate education as a tool of academic research.
Quantum Switchboard with Coupled-Cavity Array

The ability to control the flow of quantum information is deterministically useful for scaling up quantum computation. In this paper, we demonstrate a controllable quantum switchboard which directs the teleportation protocol to one of two targets, fully dependent on the sender's choice. Importantly, the quantum switchboard also acts as a optimal quantum cloning machine, which allows the receivers to recover the unknown quantum state with a maximal fidelity of 56. This protects the system from the complete loss of quantum information in the event that the teleportation protocol fails. We also provide an experimentally feasible physical implementation of the proposal using a coupled-cavity array. The proposed switchboard can be utilized for the efficient routing of quantum information in a large quantum network.
Blockchain Based Employee Rewarding System

In this technical era which changes every second, Blockchain plays an advanced role by extending its application in several areas such as telecommunication, networking, and mobile applications, so, now blockchain technology as a decentralized application is made an efficient, immutable, and secured platform to economical society in many aspects specially for database analysis and networking purposes. Recently, many organizations all around the world are using this technology to meet with the competitive business society because they understand the potential of this decentralized management platform. Although there are many systems existing and developing for bracing the bond between organization and customers, there is a lack of systems for employee evaluation. Decentralized Ethereum private blockchain network which this paper has described will enable company generated token transactions among the employees in the organization. The system allows an employee to reward a number of tokens to his subordinates on their performance base.
MOTUNER: An Online Automatic Container Tuning System for Spark on YARN

Nowadays Spark has become one of the most popular big data frameworks. In the deploy mode of Spark on YARN, applications apply for resources in the form of "container". The size of the container is set by parameter and will affect the running efficiency, but due to various application characteristics and different cluster scale it’s difficult for users to set the parameter properly. To solve this problem, in this paper we present MOTUNER, an online auto-tune system for Spark on YARN, which dynamically adjusts the container size while the application is running. It consists of two parts: monitor and tuner. The monitor collects metrics of allocated containers and the tuner dynamically tunes the size of newly coming containers according to a search algorithm. By adjusting memory of containers to an optimal value adaptively, it provides an automatic, low-overhead and efficient way of container parameter tuning. The performance of MOTUNER was evaluated by HiBench benchmark, and results showed that it effectively reduces application’s cost by up to 31% and improves memory utilization, alleviating over or under utilization.
Generation of One-Time Password for the Authentication of Software Requirements Using Secure Hash Algorithms

Security requirement is a non-functional requirement whose objective is to safeguard the data from misusers. Different algorithms have been developed to provide security in software by using various hash algorithms like message digest, secure hash algorithm, and race integrity primitives’ evaluation message digest. Based on our literature review, we found that during the generation of one-time password (OTP) numeric values are mostly used for the authentication purpose. One of the limitations of such types of passwords is that they can be easily remembered or guessed by the misusers. Therefore, to address this, in this paper, we present a method for the generation of the OTPs in which alphanumeric values will be generated for authentication purpose. Finally, an example is given to explain the steps of the proposed method.
QoE Assessment Model Based on Continuous Deep Learning for Video in Wireless Networks

Quality of experience (QoE) is a vital metric that indicates how well the wireless network provides transmission services to users, while quality of service (QoS) help better configure the network parameters for higher performance. The evaluation time of QoE is usually several orders of magnitude larger than that of QoS, because QoE is the perception of users over a period of time, but QoS can be collected every millisecond. Therefore, the implementation of QoE/QoS mapping model can help us obtain QoE by collecting the QoS measurements, and perform QoE-based network configurations with smaller time granularity. Many studies are made to obtain the QoS to QoE mapping, including the use of machine learning (ML) methods. However, traditional ML-based regression methods for QoE/QoS mapping face the challenge of high regression error and catastrophic forgetting in dealing with continuously arriving data. In this paper, we propose a novel QoE model based on continual deep learning in wireless network. This model is formed with two deep neural networks (DNNs) concatenated. The first DNN classifies data into different subsets, which are then fed into the second DNN for regression. The second DNN dynamically form the corresponding subnets, each with nodes and connections adaptively selected in each new time period with new arriving data. We solve the catastrophic forgetting problem with the use of node splitting and hidden state augmentation. Our proposed learning framework greatly reduces the regression error to as low as 0.9314%. The experimental results demonstrate that our proposed model reduces the root mean square error (RMSE) by <inline-formula><tex-math notation="LaTeX">$21 \sim 86$</tex-math></inline-formula> times compared with several existing approaches, specially, the testing error of our proposed model is more than 80 times lower than that of traditional DNN. Compared with other DNN-based cascade models, our proposed method provides good performance in both training time and RMSE.
Reversible Data Hiding in Encrypted Images Using Histogram Modification and Msbs Integration

This paper presents a reversible data hiding in an encrypted image that employs some notions of reversible data hiding (RDH) in a plain-image such as histogram modification and prediction-error computation. In the proposed method, the original image may be encrypted by stream cipher. The most significant bit (MSB) of the encrypted pixels are integrated to vacate room for embedding data bits. The integrated MSBs would be more robust against failure of lossless reconstruction if modified for data embedding. Meanwhile, a data hider who embeds data is completely blind to the original content. At the recipient side, data may error-freely be extracted and in a separable manner, original image may losslessly be reconstructed. We employ the chess-board predictor for lossless reconstruction of the original image through prediction-error analysis. Experimental results confirm that the proposed method outperforms state of the art methods.
When Lightweight Network Meet Siamese Structure for Real-Time Object Tracking

In recent years, Internet of things devices are more and more widely used, and real-time visual information processing is an important part of the Internet of things. The current visual tracking has the problem that the network model is too large, so it cannot be well implanted in the mobile terminal, which leads to the inability to give full play to the advantages of the Internet of things. The proposal of the lightweight network alleviates the problem that the model is too large. Because the feature extraction based on Siamese tracker needs to meet the translation invariance, and the current popular lightweight networks all have padding structures, the spatial position information will be encoded when extracting features, which cannot meet the translation invariance requirements. In this paper, a new lightweight network tracking framework without padding is designed based on Siamese structure. After it is integrated with the SiamFC target tracking algorithm, the model size is reduced from 14.3MB to 3.94MB, the accuracy and success rate are increased by 0.34 and 0.12 respectively, and the tracking speed can reach 102fps. This fully proves the effectiveness of the lightweight network model designed by us in the field of target tracking.
The impact of intellectual property protection on imports : a comparison between OECD and non-OECD member countries

지식재산권보호가 무역에 미치는 영향은 시장확대효과(market expansion effect)와 시장지배력효과(market power effect)의 두 가지로 나눌 수 있다. 본 연구에서는 전세계 122개국가를 대상으로 지식재산권보호가 수입에 미치는 영향을 실증분석하였다. 분석연도는 2000년, 2005년, 2010년, 2015년이며, 전체국가를 OECD 회원국과 비OECD 회원국으로 구분하여 지식재산권의 보호효과에 차이가 있는지도 살펴보았다. 분석결과 전체국가를 대상으로 하였을 때는 국가의 지식재산권 보호의 수준이 높을수록 수입에 긍정적 영향을 미치는 것으로 나타나 지식재산권의 보호가 무역에 미치는 시장확대효과를 지지하는 것으로 나타났다. 국가의 규모(GDP), 개방도, R&D집중도를 모형에 추가하여 분석한 결과 국가의 규모와 개방도는 무역에 유의한 양의 영향을 미치는 것으로 나타났으나 R&D집중도의 영향은 유의하지 않은 것으로 나타났다. 이러한 효과는 OECD 회원국을 대상으로 했을 때 더욱 분명하게 나타나며, 비 OECD 회원국의 경우 지식재산권이 무역에 미치는 영향은 확실하지 않다. 또한 OECD회원국을 대상으로 규모(GDP)와 개방도와 R&D집중도 변수를 추가하여 분석한 결과 이들 변수는 모두 수입에 유의한 양의 영향을 미치는 것으로 나타났다.The impact of intellectual property right(IPR) protection on trade can be divided into two categories: the market expansion effect and the market power effect. This study empirically analyzed the impact of IPR protection on imports in 122 countries. The years of analysis are 2000, 2005, 2010, and 2015. We also examined whether there are differences in the protection effect of IPRs between OECD and non-OECD member countries. As a result of the analysis for all countries, the higher the level of national IPR protection, the more positive the impact on imports, thus supporting the market expansion effect of IPR protection on trade. As a result of analyzing the country s size(GDP), openness, and R&D intensity by adding it to the model, it was found that the country s size and openness had a significant effect on trade, but the effect of R&D intensity was not significant. This effect is more evident in OECD member countries, and the impact of intellectual property rights on trade in non-OECD member countries is unclear. In addition, as a result of analyzing the scale(GDP), degree of openness, and R&D intensity in OECD member countries, it was found that all of these variables had a significant positive effect on imports.
EskayApp: An Eskaya-Latin Script OCR Transliteration e-Learning Android Application using Supervised Machine Learning

A language dies when the population that speaks it fails to pass it to their future generations. This study aims to address such problems for the Eskayas and Boholanos. The proposed EskayApp is an OCR Transliteration e-Learning Android Application that will serve as a tool to learn Eskaya. The app uses the k-Nearest Neighbors Algorithm (k-NN) for its business logic layer or backend and Supervised Machine Learning to create the application programming interface (API) for the said application. The Android application accesses the camera to capture an image of an Eskaya character and returns the Latin transliteration and the equivalent Latin character. The application got a 4.524-star rating through Google Play Store. After conducting testing, the machine learning model accuracy was 89.93% based on a 2x2 confusion matrix. A usability test was also conducted with members of the tribe as respondents. The usability review has 3 measurement items in which users can respond from 1 (Very Poor) to 5 (Excellent). The average for all the measurement items gathered from the users is 4.3-star and all the respondents want to use the e-Learning application again.
Unsupervised Barcode Image Reconstruction Based on Knowledge Distillation

Due to the influence of the lighting and the focal length of the camera, the barcode images collected are degraded with low contrast, blur and insufficient resolution, which affects the barcode recognition. To solve the above problems, this paper proposes an unsupervised low-quality barcode image reconstruction method based on knowledge distillation by combining traditional image processing and deep learning technology. The method includes both teacher and student network, in the teachers' network, the first to use the traditional algorithm to enhance the visibility of the barcode image and edge information, and then the method of using migration study, using the barcode image super-resolution network training to blur and super resolution, the final barcode image reconstruction using the depth image prior to in addition to the noise in the image; In order to meet the real-time requirements of model deployment, the student network chooses a lightweight super-resolution network to learn the mapping between the input low quality barcode image and the output high quality barcode image of the teacher network. Experiment shows the proposed algorithm effectively improves the quality and the recognition rate of barcode image, under the premise of ensuring real-time performance.