Reviewerly/exHarmony huggingface dataset:
Design of an Expert System for Decision Making in Complex Regulatory and Technology Implementation Projects

Project planning is a critical event for overall success of a project. Project planning in business technology projects is a multidisciplinary activity. Many times, project planning overlooks interdependencies and fails to utilize the historical knowledge and best practices, resulting in re-work. To address this gap, an AI expert system was designed that can facilitate flawless intake and planning. This system ensures work “starts right” by enforcing the entry criteria and allows tailoring of the project plan based on project type and complexity. This technology system is built using a rules-based design engine and optimized search algorithm that covers multiple domains like software engineering, regulations and risk management, and computer system validation. This system aligns very closely with the software development best practices of industry standards such as CMMi and GAMP. This system also seamlessly interfaces with project management systems to enable stage gate reviews and track project outcomes at the later stages of execution and project close out.
Privacy Attributes-aware Message Passing Neural Network for Visual Privacy Attributes Classification

Visual Privacy Attribute Classification (VPAC) identifies privacy information leakage via social media images. These images containing privacy attributes such as skin color, face or gender are classified into multiple privacy attribute categories in VPAC. With limited works in this task, current methods often extract features from images and simply classify the extracted feature into multiple privacy attribute classes. The dependencies between privacy attributes, e.g., skin color and face typically coexist in the same image, are usually ignored in classification, which causes performance degradation in VPAC. In this paper, we propose a novel end-to-end Privacy Attributes-aware Message Passing Neural Network (PA-MPNN) to address VPAC. Privacy attributes are considered as nodes on a graph and an MPNN is introduced to model the privacy attribute dependencies. To generate representative features for privacy attribute nodes, a class-wise encoder-decoder is proposed to learn a latent space for each attribute. An attention mechanism with multiple correlation matrices is also introduced in MPNN to learn the privacy attributes graph automatically. Experimental results on the Privacy Attribute Dataset demonstrate that our framework achieves better performance than state-of-the-art methods for visual privacy attributes classification.
Exorcising the phantom zone

In this paper we introduce a language of first-order hybrid logic in which function symbols are interpreted by partial functions and prove a number of completeness results. Syntactically, our language builds on the basic propositional hybrid language, has a primitive unary predicate symbol DEN which tests whether a term denotes or not, and permits satisfaction operators to rigidify predicate and function symbols. Semantically, our system is actualist, allows terms to be undefined, and has no truth-value gaps. But should we follow Fitting and Mendelsohn and rule out domain elements not belonging to any world, or should we tolerate them? Here we explore both options. As we shall see, while the choice makes no difference when it comes to validity, it has consequences for richer logics.
A design methodology for web-based services to detect fraudulent transactions in credit card

Financial fraud associated with the transactions of credit card leads to unauthorized access of performing credit card transactions in different platforms by intercepting important card credentials. In order to curb this problem, an effective fraud detection system is of primary importance for any financial institution. In the proposed methodology, a web-based fraud detection system has been designed considering two different protocols for the web-based services such as simple object access protocol (SOAP) and representational state transfer (REST). Further, for detecting the fraudulent transactions, these services are associated with five different machine learning techniques such as support vector machine (SVM), multilayer perceptron (MLP), random forest regression, autoencoder and isolation forest. The performance analysis of each machine learning algorithm associated with SOAP and REST services are critically assessed. The web services have been designed based on concepts of service oriented architecture (SOA) by considering a middleware family of software products i.e., Oracle SOA suite which is very often used by the software architects.
Application of Internet of Things Compressed Sensing and Information Interaction Technology in Intelligent Transportation Layout

With the continuous development of economy, the number of motor vehicles has increased sharply, and urban traffic congestion has become more and more frequent. Urban traffic congestion has become one of the important reasons that hinder the continuous development of major cities. In order to better solve the problem of urban traffic congestion, it is more urgent to build urban intelligent transportation system. The technology related to Internet of things is becoming more and more mature, which has become a new idea of collecting traffic information, providing convenience for traffic, and injecting vitality into the study of intelligent transportation layout. The traffic system in the city or the main traffic intersection, through the traffic guidance layout road, provides the traffic instruction for the passenger on the highway network, enables the driver to choose the suitable driving road, can provide the travel guidance service for the driver, adjusts the flow distribution, and improves the traffic condition. This paper describes the research status of intelligent transportation layout and uses the basic theory of compressed sensing and information interaction technology to carry out data fusion and reconstruction algorithm in intelligent transportation. Finally, the application of compressed sensing in intelligent transportation layout is summarized to realize data collection in intelligent transportation network. In this paper, compressed sensing theory is applied to the data acquisition of intelligent transportation network to reduce the amount of data and improve the effective data acquisition. The reconstruction algorithm is used to realize the data reconstruction to ensure the accuracy and stability of signal reconstruction and the accuracy of network transmission information.
Research on cloud computing task scheduling based on evolutionary algorithm

Two improved schemes of evolutionary algorithm are proposed, which are linear changing population size and nonlinear changing population size. And in this paper, we apply them to simulate the task scheduling process in cloud computing with Cloudsim platform. Simulation results show that the improved scheme has certain advantages over the traditional evolutionary algorithm.
A Survey on Event Relation Identification

Event relation identification aims to identify relations between events in texts, including causal relation, temporal relation, sub-class relation and so on. Most of the research focuses on temporal relation and causal relation. Extracting events and the relation between events is an essential step to build an event-centric knowledge graph, which plays an important role in story ending prediction and decision-making. The form of causal and temporal relation in natural language text is diverse, sparse and complex which brings challenges to relation identification. In recent years, the integration of deep learning and knowledge has promoted the relation identification progress. This paper describes in detail the characteristics of causal and temporal relations in natural language texts and their connections. What is more, this paper surveys existing approaches based on pattern matching, machine learning and deep learning. Besides, this paper analyzes corpus and points out the future development direction and contributes ideas to further improve relation identification between events. To our knowledge, this is the first paper to survey the event relation identification.
Bucket MapReduce: Relieving the Disk I/O Intensity of Data-Intensive Applications in MapReduce Frameworks

Hadoop MapReduce is a software framework for processing vast amounts of data in parallel on large clusters. As data size increases, a need arises to resolve the significant increase in the disk I/O of reducer nodes, which may cause runtime bottlenecks. In response, we propose Bucket MapReduce, a system that utilizes bucket sort and pipeline parallelism to improve the performance of Hadoop MapReduce. Several parameters may influence the performance in Bucket MapReduce, and thus we will further discuss parameter tuning. In this paper, we perform experiments on TeraSort benchmark. Bucket MapReduce successfully reduces local disk I/O by 61% and improves the runtime by 1. 39× in 800GB TeraSort benchmark.
Robust pencil drawing generation via fast Retinex decomposition

As imaging devices have been rapidly developed, it is very convenient to obtain natural images at all times and places. As a step further, generating the visual effect of artistic paintings from natural images has become an attractive image processing task, such as pencil drawing generation. However, current pencil drawing generation methods mainly concentrate on simulating the pencil-drawing patterns, while neglecting the complex illumination variations of an image. Therefore, various unsatisfying effects can be inevitably introduced, in turn weakening the quality of the generated pencil drawing patterns. To address this problem, we present a novel pencil drawing generation method based on Retinex decomposition. A mixed-norm Retinex image decomposition model is firstly proposed, which decomposes an image into an illumination layer and a reflectance layer. Then, with the obtained layers, the pencil line layer and tone layer are produced based on our simple but effective models, respectively. In experiments, the results show that our method consistently generates high-quality pencil drawing patterns from images with different illumination conditions. For example, as for the images with low light or back light, our method still ensures the visual quality of the obtained pencil drawings.
Eigenbackground Revisited: Can We Model the Background with Eigenvectors?

Using dominant eigenvectors for background modeling (usually known as Eigenbackground) is a common technique in the literature. However, its results suffer from noticeable artifacts. Thus, there have been many attempts to reduce the artifacts by making some improvements/enhancements in the Eigenbackground algorithm. In this paper, we show the main problem of the Eigenbackground is at its own core and in fact, it may not be a good idea to use the strongest eigenvectors for modeling the background. Instead, we propose an alternative solution by exploiting the weakest eigenvectors (which are usually thrown away and treated as garbage data) for background modeling. MATLAB codes are available at the GitHub of the paper.
MDA-Network: Mask and Dual Attention Network for Handwritten Mathematical Expression Recognition

Building a system for automatic handwritten mathematical expressions recognition (HMER) has received considerable attention for its extensive applications. However, HMER remains challenging due to its own many characteristics such as the ambiguity of handwritten symbol, the two-dimensional characteristics of expression structure, and a large amount of context information. Inspired by research on machine translation and image caption, we proposed an Encoder-Decoder structure to recognize the handwritten mathematical expression. Encoder based dual attention is used to extract the features of the expression image and attention-decoder achieves symbol recognition and structural analysis. The mask information is added to the input data allows the model to better focus on the region of interest. In order to verify the effectiveness of our method, we train the model on the CROHME-2016 train set and use the CROHME-2014 test set as the validation set, the CROHME-2016 test set as the test set. The experimental results show that our method is greatly improved compared with other recognition methods, achieved respectively 47.49% and 45.10% ExpRate in the two test sets.
Pitfalls of InfiniBand with On-Demand Paging

InfiniBand is a popular high-performance interconnect and offers Remote Direct Memory Access (RDMA), which enables low-latency communication based on kernel bypassing. Although the conventional RDMA technology necessitates manual physical memory management, an emerging extension, On-Demand Paging (ODP), implements automatic memory management based on RDMA-triggered page faults, which benefits productivity. Although the existing studies said the overhead of a page fault of ODP to be small enough, an in-depth investigation in various network situations including retransmission and timeout is missing. In this work, we conduct a comprehensive analysis of the actual behaviors of ODP on different devices and reveal two awful performance pitfalls, which incur longer latencies 3-4 orders of magnitude than a common-case page fault does. We also experimentally demonstrate that the revealed pitfalls are harmful to existing software systems. This paper presents our experimental analysis and lessons learned therefrom.
Robust Digital Image Watermarking Approach for Secure Medical Data Transmission in Smart Cities

Smart cities intend to effectively manage the rising urbanization, energy utilization, and help maintain a green environment for improving the financial and living standards of people. Many sources of data generation by Cloud-based Internet of Things (IoT) devices play a crucial part in the diverse applications of smart cities, especially healthcare. The generation of medical data from the IoT devices and communication with the healthcare center needs to be secure. Therefore, this chapter introduces a robust digital watermarking approach for secure transmission of medical images using the firefly algorithm, named the RDW-FF algorithm. The proposed RDW-FF algorithm makes use of stationary wavelet transform (SWT) with discrete cosine transform (DCT) called the SWT-DCT model for the extraction of the visual feature vector of the medicinal image for embedding and extracting the watermark image. The RDW-FF algorithm chooses a set of four sub-bands of an original image for embedding watermarks with appropriate SFs using the FF algorithm. The choice of SFs using the FF algorithm ensures the optimal trade-off between invisibility and robustness. A detailed experimental process occurs and the attained outcome indicates the enhanced performance of the RDW-FF algorithm.
A Cloud Storage Framework for Massive Meteorological and Oceanographic Data and the Application of Virtualization Technology

With the development of observation and forecasting technologies, there are more and more high-accuracy and high-resolution meteorological and oceanographic observation and model data. Existing RDBMS-based (RDBMS, Relational Database Management System) frameworks of data storage and retrieval cannot store and retrieve such massive meteorological and oceanographic data (MOD) efficiently. In this paper, we propose a cloud storage framework for massive MOD based on recently developed big data storage technologies, We also give a preliminary study of incorporating virtualization technologies into the framework. Experimental results show that virtualization technologies can be applied in the framework but bring a little performance penalty.
Privacy Preserving Bloom Recommender System

Recommender Systems depend on massive amount of user data to provide accurate results. Such dependency creates a threat to individual user privacy. In this paper, a differential privacy based method is proposed to prevent the privacy attack. The Recommendation system performs predictions based on the private aggregated data from multiple clients instead of individual data. This ensures a high privacy guarantee for clients and also provides an effective recommendation. The private aggregated data from the client is collected with Local Differential Privacy (LDP). Using LDP, every client perturbs the data in their device and sends noisy data to the server. Then the Recommendation system aggregates perturbed data from all clients and computes the Recommendation. The proposed method is evaluated with a benchmarked dataset, and the results are evaluated using Precision@k, Recall@k, and nDCG@k. The experimental results confirm that it shows improved accuracy on the MovieLens 100k dataset over the existing private algorithms.
Job Placement Strategy with Opportunistic Resource Sharing for Distributed Deep Learning Clusters

Distributed deep learning frameworks train large deep leaning workload with multiple training jobs on shared distributed GPU servers. There are new challenges when scheduling resources for these systems. Modern deep learning training jobs tend to consume large amount of GPU memory. A training job has an iterative nature that causes the memory usage fluctuate overtime. Jobs sharing a host may suffer from significant performance degradation caused by memory overload in runtime. Moreover, even without memory overloads, deep learning training jobs still experience different levels of performance interference when sharing a GPU device. This paper studies these two issues. We introduced an opportunistic memory sharing model to allocate resources for training jobs with time-varying memory requirements. Based on this model, we introduced an opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seeks job placement configurations using minimum number of GPU devices and guarantees user-defined performance requirements. We proposed a greedy algorithm and a heuristic algorithm with computational complexities of $O(n\log n)$ and $O(n^{2}\log n)$, respectively, to solve the problem. Extensive experiments are conducted using a GPU cluster to verify the correctness, effectiveness, and the scalability of our approaches. The proposed approach achieved over 80% percent of the standalone performance, in term of average job completion time, with less than 30% extra resources consumption.
COVID 19 Visitor Management System

Most of the world's countries are in the grip of coronavirus pandemic.COVID -19 is spread primarily by respiratory droplets contaminating surfaces and hands that then transmit the virus to another person's respiratory system.Older people and those with cardiopulmonary co-morbidities or immunological deficiency will be more at risk of severe disease.Human lifestyle is adversely affected.Most of the people are afraid of visiting or welcoming people into their respective houses.There is need of better sanitization of homes and keeping us safe from infection.To tackle this problem, we thought a new smart system to handle the visitor's entry in houses or premise automatically.This system consists of different devices coupled together to work with each other in order to provide utmost protection from infection.It keeps track of people visiting, their condition while entering premise, safety gear equipment etc.In this research paper we have briefly explained architecture, working and software implementation of this system.
Trust Prediction Based on Extreme Learning Machine and Asymmetric Tri-Training

Currently, more and more people communicate and trade through online social networks, so it is necessary to predict the trust between users. In order to predict the trust between users through a social network requires some other factors, such as user similarity, topic relevance, and the number of common neighbors should be considered. Some scholars use BP neural network based on the asymmetric tri-training model to train one social network and use transfer learning to predict the social relationship of another social network. However, this method has high computational complexity and slow operation speed. To solve the problem, the three classifiers are expanded into four classifiers on the basis of asymmetric tri-training, and the extreme learning machine classifier is used to replace the BP neural network classifier. The specific steps are: we first obtain the common features between different networks, use source data samples to train the first three classifiers, save the model, and train target samples to generate pseudo labels; then we use standard pseudo label samples to train the fourth classifier and use it to predict the social relations of the target network. Finally, compare with the existing evaluation methods, we proposed an algorithm on six online social networks. The experimental results show that the model in this paper is superior to other evaluation methods in recall and stability.
Twitter Spammer Identification using URL based Detection

Abstract Millions of users worldwide participate in social networking sites. The interactions between users and those social sites, as Facebook and Twitter, have an enormous effect as well as sometimes unwanted impact on daily life. Spammers have become a target platform on the prominent social networking sites to scatter huge amounts of damaging and irrelevant information. Twitter has become one of the best-used platforms of all times also thus permits unreasoning spamming. Fake users send unwanted tweets to users to encourage services or sites that affect not just genuine users, but also disrupt the consumption of resources. The main aim of this study is to identify forged users who harm real users, threaten users’ identity and therefore their safety and privacy. In this paper, suggest to URL based detection plus machine learning techniques has been developed for the detection of malicious users, celebrities as well as malicious users, based on user characteristics. A crawler is developed for Twitter for the purpose of detecting malicious users, celebrities and non-malicious users and celebrities, and data from around 22 K users were collected from public information. For training and testing purposes, data from around 7,500 users were used in machine learning classifications to classify users. On basis of performance metrics like accuracy, recall, F-measurement also accuracy 5 classifiers were used and compared. With a precision of 99.8 percent, Random Forest is above all classifiers.