Reviewerly/exHarmony huggingface dataset:
Implementation of Artificial Intelligence Based Chatbot System With Long Term Memory

This paper mainly explores a specific deep learning method to build a conversational agent. Nowadays the popularity of chatbot systems is on rise as they attempt to get into daily life and achieve some commercial success. Previous approaches used simple keywords & pattern matching methodologies, answering in a static manner irrespective of previous conversions. As an improvement to this technology would be a system that will work with sequence to sequence framework. Our proposed model makes use of this framework. Given the previous sentence or sentences and the next sentence in a conversation, the model converses by predicting the next sentence. The distinctive feature of our model is that it can be trained end-to-end hence requires much fewer hand-crafted rules. This straight forward model can generate simple conversations given a large conversational training dataset.
The facial features analysis method based on human star-structured model

This paper proposes a face detection and facial feature points location algorithm based on the human starstructured model. The head model of human star-structured model based on deformable part models (DPM) can locate the pedestrian’s head area. Haar feature and Adaboost cascaded classifier are used to detect face on the head area. Then, local binary features from face are extracted based on random forest. Global linear regression model are learned from all the local binary features and then the facial feature points are located. The method for face detection and feature points location based on human star-structured model reduces the time from scale and iterate through the images, therefore reduces the time of face detecting and feature points locating.
Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior

Digital restoration of image with missing data is a basic need for visual communication and industrial applications. In this paper, making full use of priors of low rank and nonlocal self-similarity a gradual reweighted regularization is proposed for matrix completion and image restoration. Sparsity-promoting regularization produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternation direction method of multipliers algorithm is developed to speed up iterative solving of the above problem. Image block classification further enhances the adaptivity of the proposed method. Experiments on simulated matrix and natural image show that the proposed method obtains better image restoration results, where most lost information is reorcovered and few artifacts are produced.
Matrix factorization recommendation algorithm based on deep neural network

Collaborative filtering is the most classical technology in recommendation system. Compared with memory-based collaborative filtering technology, matrix factorization has good scalability and recommendation effect, which makes it widely used. On the basis of matrix factorization model, deep neural network is introduced to improve the accuracy of scoring prediction and the quality of recommendation. Experiments on MovieLens dataset show that the proposed method improves the accuracy and quality of recommendation algorithm.
Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis

The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evaluations.
Incorporating Association Patterns into Manifold Clustering for Enabling Predictive Analytics

The goal of this research is to develop a predictive analytics technique based on manifold clustering of mixed data type. In this research, we explore the concept of statistically significant association patterns to induce an initial partition on data for deriving manifolds. Manifolds are hyperplanes embedded in low dimensions. The advantage of this novel technique is a bootstrap on data clusters that reveals statistical associations from the information-theoretic perspective. As an illustration, the proposed technique is applied to a real data set of diabetes patients. An assessment on the proposed technique is performed to investigate the effect of bootstrap based on association patterns. Results of the preliminary study demonstrate the feasibility of applying the proposed technique to real-world data.
An effective HPSO-MGA Optimization Algorithm for Demand based Resource Allocation in Cloud Environment

Cloud computing (CC) is the growing technology for providing multiple virtual infrastructures. Nowadays it is becoming and providing dynamics scaling of resources that are accessible to users as required. Dynamic Resource allocation means allotting the computing resources to the various services needed by the user without any time constraint with the help of virtual machines. It is also known as demand-based resource allocation. In the dynamic resource allocation method, the number of users remotely requesting the computing Resources. So resources are allocated to virtual machines on-demand basis. Here feature prediction is the important part by using the streaming data. So both the process was concentrate on this work. As a result, we propose a hybridization of Hybrid Particle Swarm Optimization and the Modified Genetic Algorithm (HPSO-MGA), which are developed to make the entire process for allocating resources dynamically by scattering tasks or requests in VMs. By using the rule generation process the feature outcome also predicted. The process begins from the User or Consumer by getting data from various resources through on-line. These data will be given to task manager. From the task manager, we can extract parameters like Cost of the task, Speed, Size of data and weight, etc. Similarly, we can extract the details of system resources like CPU Utilization, Memory Usage, Processing speed, and Process Cycle, Bandwidth, Number of requests, load on VM, Disk space from the cloud storage. Here the required features are selected by using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA) to decrease the processing time required for allocating resources dynamically. From all the features only necessary features will be used. Hence time taken for resource allocation is reduced and accuracy becomes low. So our proposed Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA) reduce the execution time and increase the performance of resource allocation. Our proposed RA methodology shows better performance when compared with the existing methods.
Automatic Modulation Recognition Method for Multiple Antenna System Based on Convolutional Neural Network

In this paper, we propose a convolutional neural network (CNN) aided automatic modulation recognition (AMR) method for a multiple antenna system. We also present two specific combination strategies, such as the relative majority voting method and arithmetic mean method to improve the classification performance in comparison with the state of the art. Our results are given to verify that the proposed method dominant exploits features and classify the modulation types with higher accuracy in comparison with the AMR employing high order cumulants (HOC) and artificial neural networks (ANN).
Optimal functional supervised classification with separation condition

We consider the binary supervised classification problem with the Gaussian functional model introduced in (Math.Methods Statist.22 (2013) 213-225).Taking advantage of the Gaussian structure, we design a natural plug-in classifier and derive a family of upper bounds on its worst-case excess risk over Sobolev spaces.These bounds are parametrized by a separation distance quantifying the difficulty of the problem, and are proved to be optimal (up to logarithmic factors) through matching minimax lower bounds.Using the recent works of (In Advances in Neural Information Processing Systems (2014) 3437-3445 Curran Associates) and (Ann.Statist.44 (2016) 982-1009), we also derive a logarithmic lower bound showing that the popular k-nearest neighbors classifier is far from optimality in this specific functional setting.
Real-time Class Attendance Monitoring using Smart Face Recognition

Student attendance in classroom-based learning usually becomes a pain stake for teachers in terms of monitoring and records keeping. The study provides a solution to this by using the technology of image processing applied in facial recognition. A software was developed to check the student attendance in real-time. The ISO 9126 gauges for functional stability, usability, reliability, portability, efficiency, and maintainability. The functionalities and user acceptance were measured within the bounds of software engineering and measured acceptable to a mean very acceptable rating of 3.74.
SECRET: Semantically Enhanced Classification of Real-World Tasks

Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to the corresponding labels while ensuring generalizability to unseen data. However, they do not integrate meaning-based relationships among labels in the decision process. On the other hand, natural language processing (NLP) algorithms emphasize the importance of semantic information. In this article, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks). SECRET performs classifications by fusing the semantic information of the labels with the available data: it combines the feature space of the supervised algorithms with the semantic space of the NLP algorithms and predicts labels based on this joint space. Experimental results indicate that, compared to traditional supervised learning, SECRET achieves up to 14.0 percent accuracy and 13.1 percent F1 score improvements. Moreover, compared to ensemble methods, SECRET achieves up to 12.7 percent accuracy and 13.3 percent F1 score improvements. This points to a new research direction for supervised classification based on incorporation of semantic information.
Towards a Theory for Explaining Socially-Engineered Cyber Deception and Theft

Socially engineered cyber deception and theft seems to have gained prominence in cybercrime. Given the contextual background of inadequate theoretical explanations of socially engineered cyber deception and theft cybercrime, there is the need for theory to better explain and possibly predict activities involved in socially engineered cyber deception and theft. This chapter proposes a theory of socially engineered cyber deception and theft (SECT), with routine activity theory, crime displacement theory, the space transition theory, and empirical review as its foundation. It iteratively combines deductive and inductive approaches to infer the occurrence of socially engineered cyber deception and theft. While the deductive approach serves the deduction leading to the inference, the inductive approach extracts and suggests empirical evidence for a deterministic prediction of the crime occurrence. It is recommended that the theory is further validated to test its applicability.
A 1.15-TOPS 6.57-TOPS/W DNN Processor for Multi-Scale Object Detection

We present a 40-nm multi-scale object detection processor with only three operations: 3 × 3 convolution, 1 × 1 convolution, and 4 × 4 deconvolution. The multi-scale object detection at high accuracy is possible by virtue of the deconvolution feature. Input memory for a feature map has 8-bit width as well as a multiplier for the inputs has 8-bit precision. Partial-sum memory, however, has 16-bit width to suppress detection accuracy deterioration in a layer with 5 12 channels or more. By fixed-point bit precision, the external memory bandwidth and internal memory capacity are reduced. Optimized parallelization in input and output channels reduces the external memory bandwidth to 0.50 GB per 1280 × 384 image with internal memory capacity of 400 kB. The detection error is 1.9% of that using single-precision floating point. The maximum operating frequency is 500 MHz at a supply voltage of 1 V. Its peak performance is 1. 15 TOPS. The maximum energy efficiency is 6.57 TOPS/W at 174 MHz and 0.6 V.
UAV-based Regional Environmental Monitoring IoT System

Abstract The UAV regional environmental monitoring system has the characteristics of various functions and simple operation. Relevant staff can ensure effective monitoring of regional temperature and humidity by means of specific mobile networks and flight control of drones. Based on past work experience, this paper summarizes the content of program demonstration and system design, and discusses the software flow design of regional environmental monitoring IoT system from five aspects: process design review, overall structure, topology diagram module, information query display module and system test.
Synchronization Safety Problem in Quantum Key Distribution System

The problem of providing secrecy at quantum key distribution was analyzed. A two-pass self-compensation fiber-optic quantum key distribution system (QKDS) with phase coding photons states that operate on the BB84 protocol and implemented by the «plug&play» technology are reviewed. The synchronization process of quantum key distribution system and field test results of quantum cryptographic network basis on the QKDS Clavis2 are analyzed. Abstract strategies of unauthorized access to information in a quantum key distribution system by attacks such as “Trojan horse” and “Brute force” are described. One of the principles of protection the synchronization process of self-compensation quantum key distribution system from unauthorized access was described.
Feasibility/Design Study for a Cloud Seeding Program in the Yadkin River Basin, North Carolina

North American Weather Consultants (NAWC) was contracted by Cube Hydro Partners, LLC with headquarters in Bethesda, Maryland, to conduct a cloud seeding feasibility/design study for the Yadkin River Basin located in North Carolina. This study was prepared in conformance with American Society of Civil Engineer’s Guidelines and Standards. The goal of the program would be to augment precipitation over the Yadkin River Basin to increase streamflow into a series of Cube Hydro reservoirs along the Yadkin River. This increased streamflow would then be used to generate additional hydropower (clean energy) from facilities installed on project dams. NAWC compiled a site-specific climatology for this Basin. Both ground-based and airborne seeding was considered, but only airborne seeding was recommended due to the lack of significant topographic features in the Basin and the rather high average height of the -5° C level in potentially “seedable” storms (630 mb), as well as other meteorological factors. Regression equations relating precipitation to streamflow suggest that seeding only during the cool season produces streamflow increases not only during the cool season, but would also have a residual effect that increases streamflow throughout the remainder of the year. Overall, cool-season seeding is likely to have about 1.4 times as much impact on total streamflow as warm-season seeding (assuming the same increase percentage-wise in precipitation), with the cool season increase more evenly distributed throughout the year in terms of the resultant streamflow. Therefore, only a cool season program was recommended. Detailed examination of a 3-year cool season storm climatology in the Yadkin Basin suggest that aircraft-based cloud seeding during the cool season could enhance precipitation by 4.1%, resulting in an estimated 5.2% increase in annual streamflow (109,200 acre-feet). Cost estimates were prepared for a seasonal program. Once the estimated costs were established, NAWC was able to develop benefit/cost ratio estimates. Using information provided by Cube Hydro, NAWC calculated a potential benefit/cost ratio of 5/1. NAWC concluded that the proposed program was feasible based upon both technical and economic considerations as recommended in the “Guidelines for Cloud Seeding to Augment Precipitation”, Third Edition by the American Society of Civil Engineers (ASCE,2016).
Learning genetic and environmental graphical models from family data

Many challenging problems in biomedical research rely on understanding how variables are associated with each other and influenced by genetic and environmental factors. Probabilistic graphical models (PGMs) are widely acknowledged as a very natural and formal language to describe relationships among variables and have been extensively used for studying complex diseases and traits. In this work, we propose methods that leverage observational Gaussian family data for learning a decomposition of undirected and directed acyclic PGMs according to the influence of genetic and environmental factors. Many structure learning algorithms are strongly based on a conditional independence test. For independent measurements of normally distributed variables, conditional independence can be tested through standard tests for zero partial correlation. In family data, the assumption of independent measurements does not hold since related individuals are correlated due to mainly genetic factors. Based on univariate polygenic linear mixed models, we propose tests that account for the familial dependence structure and allow us to assess the significance of the partial correlation due to genetic (between‐family) factors and due to other factors, denoted here as environmental (within‐family) factors, separately. Then, we extend standard structure learning algorithms, including the IC/PC and the really fast causal inference (RFCI) algorithms, to Gaussian family data. The algorithms learn the most likely PGM and its decomposition into two components, one explained by genetic factors and the other by environmental factors. The proposed methods are evaluated by simulation studies and applied to the Genetic Analysis Workshop 13 simulated dataset, which captures significant features of the Framingham Heart Study.
A SDN-based QoE-aware Routing Algorithm on Video

In recent years, with the vigorous development of the video business market, the competition among service providers has become increasingly fierce. The key to winning the competition depends on the Quality of Experience (QoE). Faced with the increasing video traffic, how to use the limited infrastructure resources to ensure the good user experience is a difficult problem. Recent studies unilaterally focus on guaranteeing QoE, which may lead to sub-optimal resource utilization. To solve this problem, this paper proposes a QoE-centric routing algorithm based on Yen K-shortest paths (YenKSP), which pays attention to the utilization of limited resources while guaranteeing the quality of user experience.
Development of a Weather Mobile Application in Android Operating System

The goal of this paper is to find out what are the steps in developing an application, the difficulty of it and future improvements. With this occasion, we chose to develop a weather application. A weather application is a very handy one, with daily usability and can be constantly updated to fit certain needs. Many people now rely on data directly from their smartphones and the weather forecast is something indispensable to have. The most commonly used development environment is Android Studio, the all in one developing tool which ensures full control on the application development.
A Distributed Intelligent Algorithm Applied to Imbalanced Data

Data mining means to find valuable information in database or data sets. For imbalanced data, there are extremely low number of samples in database or data sets and it is not easy to solve these problems by traditional methods of data mining. In this paper, a distributed intelligent algorithm is proposed to imbalanced data. Apache Spark is implemented as the distributed framework in the proposed distributed intelligent algorithm, and its cluster computing framework with in-memory data processing engine can do analytic on large volumes of data. In the distributed framework, Apache Spark with synthetic minority oversampling technique (SMOTE) is proposed to process imbalanced data first. Thereafter, the support vector machine (SVM) is used to classify imbalanced data. The zoo data set from UCI repository is used to verify the correctness of the proposed algorithm. The results of the proposed distributed intelligent algorithm can get better performance than these compared traditional classifiers.
Multi -acoustic Array Localization and Tracking Method based on Gibbs-GLMB

We consider an acoustic array of a specific arrangement for the tracking of targets by time difference of arrival (TDOA) measurement and angle of arrival (AOA) measurement. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a multi-acoustic array localization tracking method based on the Gibbs-generalized label multi-Bernoulli (Gibbs-GLMB) filter. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC), and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets, and fuse the measurements which are then used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.
Genetic Algorithm Based 3D Motion Planning for Unmanned Aerial Vehicle

Development of Unmanned Aerial Vehicle (UAV) is now a popular field in research. In most of its applications, a pathfinding algorithm is needed in order to find the optimal path and avoid obstacles. In this paper, a genetic algorithm is implemented in order to determine the optimal path for a UAV that will avoid obstacles along the way. The genetic algorithm implemented uses variable-length chromosomes to solve the problem. The results of the simulation of the system yield an average of 29 generations and avoided 53, 500 collisions to find the best path.
Internal Governance of the IETF, W3C, OASIS, and IEEE

Abstract Chapter 3 explains the internal organization of SDOs. It outlines the four main layers which make up the Internet and focuses on the principal SDOs associated with them: the IETF, W3C, the Organization for the Advancement of Structured Information Standards (OASIS), and IEEE, analysing their governance structures and most salient areas of work. Standards are important for interoperability and to prevent lock-in to single company technology. SDOs enable a wider technical community to scrutinize proposals for errors and security. Nonetheless, procedures for decision-making are complex and often opaque. The chapter explores decision-making in a governance context. It charts the evolution of each SDO, and its main purpose, functions, and central decision-making processes. It highlights the differences as well as the degree of synergy and collaboration between each and then explains how procedures vary between the different fora and how moving goalposts and high barriers to entry make it difficult for civil society to participate.