Reviewerly/exHarmony huggingface dataset:
An Improved UKFNN Based on Iterative Renewal for Intelligent Modeling of Aluminum Electrolysis

Establishing an intelligent production model for aluminum electrolysis has always been a technical problem due to the existence of more parameters and multiple chemical and physical reactions in the manufacturing system of aluminum electrolysis. An iterative renewal approach tagged with Iterated Unscented Kalman Neural Networks (IUKFNN) is developed to approximate the intelligent modelling of direct current consumption from the electrolytic aluminum industry in this study. In the propose model, iterative idea and UKFNN are integrated. And the new idea of iterative updating is adopted to improve estimator precision of the model. To be more precise, after estimated states are obtained in the UKFNN algorithm, the estimated value is returned to the measurement updating to re-sample, so that the estimated value is more accurate. The experimental results indicate that the model can significantly predict the direct current consumption in the manufacturing system of aluminum electrolysis, and the precision of the model IUKFNN is higher than UKFNN. Consequently, this paper presents a better intelligent modeling method for further approaching the real energy consumption model in electrolytic aluminum manufacturing process.
A Meta-Strategy Enhancement for Network Embedding

Network embedding, which learns continuous low-dimension representations of nodes, provides an effective way for many network analysis tasks, such as node classification and link prediction. Most existing models are time-consuming and cannot be applied to dynamic networks. To alleviate these issues, we apply Arora's sentence2vec model to network embedding to enhance the performance of existing network embedding methods. Under the same framework of the sentence2vec, we name the network embedding method MNE^2, which allows us to leverage the latent representation obtained from a given embedding approach to learn enhanced node embeddings in a network. Taking into account interactions between nodes in the network, the enhanced network embedding can be viewed as the latent factors for generating embedding representations of neighbor nodes. Then, PCA is applied to modify the embedding results to make the enhanced embeddings more expressive. We evaluate MNE^2 on three real-world social network datasets for node classification and link prediction tasks. The results show that MNE^2 can outperform state-of-the-art network embedding learning methods in both tasks.
Research and Analysis of Learning Rate on Face Expression Recognition

As an important hyperparameter in facial expression recognition, learning rate determines whether the facial expression recognition function can converge to the local minimum and when it converges to the minimum value. With the increasing complexity of the facial expression recognition network model, it is particularly difficult to find a suitable learning rate, because the size of different parameters varies greatly and needs to be adjusted throughout the training process, which not only consumes a lot of time. Sometimes, it is not possible to adjust to the optimal, which makes it difficult to improve the accuracy of facial expression recognition. Therefore, in this paper, through the research and analysis of the facial expression recognition rate, we propose face expression recognition based on the Adabound algorithm and compare it with SGD (Stochastic Gradient Descent) and Adam on the face expression recognition network model use CK+ (Cohn-Kanade) dataset. The Adabound adaptive optimization algorithm achieved high accuracy of 96.97% and 100% on ResNet18 and ResNet34 respectively. Compared with recent articles, our model based on the Adabound algorithm has reached a very high level in the CK+ dataset in terms of the effect of a single model.
Construction of primitive polynomials over finite fields

In this paper, using the polynomial composition methods some computationally simple and explicit ways for constructing higher degrees primitive polynomials from a given primitive polynomial over [Formula: see text] are given.
Domain Information Acquiring with A Text Analysis System On Distributed Computing

Large amounts of unstructured documents are collected for data tasks or information studies in many fields. The analysis and study for these domain texts become the fundamental issue for domain information acquiring. In this paper, a web-based platform for text distributed computing of Hadoop are constructed, technologies of Spring Boot framework and node.js etc. web front-end techniques, distributing computing and text analysis are integrated for domain text processing. Documents are pre-processing for text analysis, Chinese segmented with the open source tool of IKAnalyzer is implemented. Our system is a good practice for the process of text gathering, filtering, distributed computing, analysis and results visualization. Documents from an educational investigation upload in the system, thousands of school office documents in Guizhou rural villages and towns can be dealing directly and get a quick look for the issue. Meanwhile, other documents such as EMC conference proceedings can follow the pipeline for knowledge discovery with this platform.
Solving the Defect in Application of Compact Abating Probability to Convolutional Neural Network Based Open Set Recognition

Close set is a hypothesis utilized by the majority of machine-learning-based (ML-based) recognition algorithms, assuming all testing classes are known at training time. In real world, the more practical model is Open Set Recognition (OSR), which allows the presence of unknown classes at testing time, but requires the rejection ability of the model. The compact abating probability (CAP) model, which assumes the probability of class membership decreases in value (abates) as points move from known data toward open space, is first raised in traditional ML-based OSR method and soon become the basis of majority of later developed works. Most of convolutional-neural-network-based (CNN-based) OSR methods also adopted this model as their basis. During our exploration, however, we find that the application of CAP model to the CNN-based OSR method is restricted by the difference of its feature space from that of ML-based method. To the best of our knowledge, we are the first group who find this gap. To fill this gap, we propose a method called OpenSoftMax to transform the CNN-based methods' features by the process of SoftMax. In order to investigate performance, we further implement quantitative comparison between our OpenSoftMax method and the well-known CNN-based method OpenMax on caltech256 datasets. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposals.
Almost-Zero Logic Implementation of Troika Hash Function on Reconfigurable Devices

Blockchain technology has gained immense popularity in the recent years due to its decentralized computing architecture. While it originally emerged as a technology for (crypto)currencies, it has since found many different application areas including (but not limited to) payments, money transfers, smart contracts, supply-chain management, networking, IoT, etc. Initially, it was only Bitcoin, the de facto standard for cryptocurrencies, but then it was followed by several (in fact hundreds of) others. Each new cryptocurrency had or claimed to have certain advantages over Bitcoin, such as transaction speed and cost. However, they all relied on the original idea of distributed ledger where each block has maintained a complete history of each transaction in the network. Blockchain technology has more recently been challenged by two new technologies called Tangle and Hashgraph, which are “directed acyclic graphs”, i.e. in layman's terms blockchains without blocks and chains. IOTA network is the original Tangle technology, which relies on ternary arithmetic architecture and uses ternary hash function “Troika”. It works on GF(3) and its design follows the sponge construction. Two of the main claims of IOTA are scalability and micro-transitions, both of which are likely to utilize compact hardware platforms in practical implementations. In this paper, an almost-zero logic compact and yet adequately fast hardware architectures of Troika hash function targeting reconfigurable devices are presented. The proposed architectures mainly depend on the utilization of BRAMs on FPGAs. Three different RAM-based hardware implementations have been realized on Xilinx Artix-7xc7a12tcpg238-3 device; all using only a single BRAM tile with minimal number of LUTs and FFs. The proposed architectures can easily be implemented on different reconfigurable devices with similar efficiency. To the best of our knowledge, this is the first reported hardware implementation of Troika hash function on reconfigurable devices which is also compact and fast.
New orthogonality criterion for shortest vector of lattices and its applications

The security of most lattice based cryptography relies on the hardness of computing a shortest nonzero vector of lattices. We say that a lattice basis is SV-reduced if it contains a shortest nonzero vector of the lattice. In this paper, we prove that, π∕6 orthogonality between the shortest vector of the basis and the vector space spanned by other vectors of the basis is enough to be SV-reduced under the assumption that a plausible condition Cn holds. By using the π∕6 orthogonality under C2, we prove a new complexity bound log3∥x∥23detL+1 for Gauss–Lagrange algorithm which clarifies why the currently known complexity is so far fall short to expose the efficiency of the algorithm we experience in practice. Our experiments suggest that our complexity bound of Gauss–Lagrange algorithm is somewhat close to actual efficiency of the algorithm. We also show that LLL(δ) algorithm outputs a SV-reduced basis if δ≥1∕3 for two dimensional lattice. We present an efficient three dimensional SV-reduction algorithm by using the condition C3 and π∕6 orthogonality and how to generalize the algorithm for higher dimension.
Using Use Case Points Method In a Health Surveillance Application Estimation

Foreseeing what number of assets and to what extent it takes to finish a venture, a few strategies can be utilized to make gauges, one of which is the Use of Case Point (UCP) technique. The evaluated exertion in the UCP method gets from the increase between the UCP esteem and the Effort Value (ER). The expense of ER is the extent of structure of working hours required for each utilization. Determination of ER values only uses data software development projects with simple linear regression analysis. However, most researchers refer to the proposed ER value without questioning or reviewing the ER value. This study evaluates the ER values for software development projects in the business field, namely the Health Surveillance Application. In conducting this research, we need several data on software development projects in the Health Surveillance field from this data. We can find the ER value by analyzing correlation and linear equations. The final results of this study get the benefit of ER 8.0. This price is lower than the quantity suggested by past scientists. Decreasing the ER value can reduce the estimated amount of the effort, then the decrease in estimated business value will reduce the resources needed for software development. May be due to a development project as a component or framework that facilitates project developmen, this is possible for several reasons, including more advanced software engineering technology, rapid growth.
Third-Party Doctrine Principles and the Fourth Amendment: Challenges and Opportunities for First Responder Emergency Officials

The unresolved issues between the Fourth Amendment and the third-party doctrine provide first responders with challenges in their approach to meet the needs of any emergency they may be called for. A first responder needs to provide help quickly, and often this does not leave much time to think about the legal implications of some of their actions. With the rise of the Internet, the challenges of terrorism, and WikiLeaks, first responders are no longer sheltered from the legal implications that the use of information from online and other secondary sources may have. Specifically, privacy concerns may be raised when first responders use social media either as a tool to gather information about evolving emergencies, or to engage in the process of monitoring those media to detect potential threats to the safety of the country and its citizens. This paper will address some challenges first responders face when considering the third-party doctrine principles and the Fourth Amendment in their rescue efforts. What are some liability and legal concerns in the context of what first responders encounter when responding to potential threats? The paper will also include a discussion of practical experiences with the Fourth Amendment and third-party doctrine principles and explore liability issues related to first responders’ use of information.
A Novel Method For Haze Detection In Color Images

Nowadays quality of outdoor as well as indoor images are very important. But outdoor images are deteriorated by haze, fog, rain etc. These atmosphere conditions are adversely affects the visibility of the images and reduces the contrast. It is very much adverse in applications especially in military. Image dehazing algorithm are commonly used to restore the original image which is affected by haze. Fine details in the images are lost due to haze Herein, an algorithm for detection of haze using different classifiers has been implemented. In this work we used Dark channel prior technique for haze detection. In this work initially a number of image features are estimated to train different classifiers such as support vector machine, logistic regression and naïve Bayes Classifiers. In this work we compared Area under the curve of different classifiers.
Multimodal Continual Learning Using Online Dictionary Updating

This article studies the continual learning task from the perspective of multimodal fusion. In the multimodal fusion problem, the unified representations of heterogeneous modalities are continuously updated, while the classifier is also constantly refreshed for each multimodal learning task. To tackle this point, we establish a continual learning framework for multimodal learning and design an effective online dictionary updating method. Finally, we experimentally verify a complex material identification task and obtain promising results.
Analysis of Facial Image by Gradient Boosting Locally Extracted Features

Face recognition systems running on commodity hardware arguably play the most important role as reinforcement for human computer interfacing. This paper intends to tackle the specific hurdle of labeling the person present in the given facial image, pertaining to different situations such as varying illumination, distinctive expressions, and noisy background. A novel pipeline that contemplates to solve the given complication is presented using a comparative study. Locally extracted descriptors are subjected to dimensionality reduction to structure the feature set. Compaction of features is achieved through aggregators such as VLAD, Fischer vectors and Bag of LARK features. Aggregated vectors are classified using gradient boosting techniques. The stack is tested using multiple benchmark datasets with appropriate splits to cross validate the results. It was observed that the model performed at its best when there were changes in the facial expressions of the subjects. Comparative paradigm helped to accurately examine the several approaches proposed by the paper.
Voice based Home Behavior Self-Management System for The Elderly

Aiming at the serious social problems of increasingly population aging, we designed a system to identify users' daily behavior, which based on various low power sensors as well as speech recognition and synthesis technology. The test in the laboratory shows that the system can perform the corresponding operation well and is more practical.
Optimizing the Parameters for Post-Processing Consumer Photos via Machine Learning

Photo sharing in social media is a part of everyday life for many, as inexpensive cameras integrated in smartphones are widely available. Unfortunately, low cost consumer devices are often prone to capture artifacts, and this is why there is a growing demand for automatic post-processing to enhance the image quality. Due to the wide range of distortions in non-professional photography, automatic selection of the post-processing methods and parameters is a challenging problem. In this paper, we present a subjective study based on rank-ordering method, comparing the subjective preferences between photos processed with different parameters for image sharpening and denoising. The subjective results are used as a basis to derive the ground truth values for the post-processing parameters for different photos. Then, we apply a pre-trained convolutional neural network (CNN) to extract a set of features from photos, used as input to a regression model to predict the optimal post-processing parameters. Test results show that the learning-based approach can predict post-processing parameters with a satisfactory accuracy.
Outliers Identification Model in Point-of-Sales Data Using Enhanced Normal Distribution Method

Data Mining extrapolates patterns drawing conclusions from data. Outliers detection identifies those objects that fall some standard deviations away from the mean and is an important tool of commercial data mining. Characterizing the manner of outliers can lead to new knowledge, such as the manner of fraudulent transactions. However, outliers may represent meaningless aberrations and hence there is no rigid mathematical or statistical definition of what constitutes an outlier, and, in many scenarios, determination of the outlier is ultimately a subjective exercise. Standard deviation is a central actor in outlier detection and yet exhibits sensitivity to values and can be distorted, inflated, by a single or even a few observations of borderline and extreme values. It can mask the situation where less extreme outliers or anomalies go undetected because of the existence of the most extreme outliers. This study proposes a novel outlier identification model using an enhanced normal distribution method. The model can explore different types of outliers giving an end-user the ability to fully or partially eliminate outliers found in a retail point of sale (POS) dataset. Experiments revealed that the enhanced normal distribution method appeared more accurate than the standard normal distribution method, and results were also evaluated subjectively by the client, who found most of the outliers to be truly outliers and some representing potentially fraudulent transactions.
On Nature-Inspired Dynamic Route Planning: Hammerhead Shark Optimization Algorithm

Dynamic route planning is a classical problem with interesting applications in optimizing car navigation, fleet management, urban evacuation planning, unmanned ground and airborne vehicle movement, and maritime route planning. While numerous traditional and heuristic-based algorithms have been proposed to address deficiencies in dynamic route planning, ongoing efforts are increasingly focusing on deriving scalable solutions from nature motivated schemes. The present work inspired by the behavior and prey hunting skills of sea predator, the hammerhead shark, seeks to devise a new dynamic route optimization algorithm. The proposed hammerhead shark optimization algorithm (HOA) finds destination in an unknown solution space by applying the natural route optimization behavior inherent in the hammerhead shark. Once the destination location is identified, HOA attempts to determine the path starting from source employing targeted movement towards the destination. The proposed algorithm can deal with static as well as dynamic environments. During validation, the proposed algorithm is performance tested against two state of the art algorithms A* and ant colony optimization (ACO). Attributes including the iteration count, path length (cells) and time complexity are compared. The results show that proposed algorithm outperforms A* in terms of latency, and ACO in terms of path optimality. The highly satisfactory performance of HOA leads us to recommend it as a suitable candidate for further adoption in optimal, scalable and dynamic path calculation concerns.
Video steganography Technique in Uncompressed Raw Domain based on Transform Domain and using SSVLCs

In this paper, we present a video steganography in uncompressed raw domain based on transform domain and using self-synchronizing variable length codes (SSVLCs). The proposed algorithm is tested on Audio Video Interleave (AVI) carrier files using Matlab R2015b. The wavelet-like transform, viz., Slantlet transform (SLT) is known for retaining a higher percentage of energy after compression as compared to the DWT. We have applied SLT to each of the frames extracted from the cover video for obtaining the four frequency subbands. Then, the message is hidden in the frequency coefficients of these bands in encoded form using the SSVLCs. We have used the modified LSB technique as the embedding process. The experimental analysis has shown that SLT is a better option than the DWT for video steganography. We observe no noticeable degradation in the visual quality of stego-video after embedding the secret message in the cover-video.
Design and Implementation of GPS Data Inspection System Based on Desktop Virtualization Technology

In view of the shortcomings of the traditional manual means of GPS field mapping data in the earthquake field, the data quality management is inefficient. This paper develops the GPS original platform based on the desktop virtual observation data management, reporting, quality management and archiving. Based on the research content of this paper, a set of seismic field data management system software based on desktop virtualization platform was designed and developed to solve the inconvenience caused by the complex operation process of GPS data service. The results in this paper will effectively improve the efficiency of GPS measurement data management and quality inspection.
Proxy Re-Encryption using MLBC (Modified Lattice Based Cryptography)

In last few years, Proxy Re-Encryption has been used for forwarding the encrypted message to the user, these users are the one who has not been a part of encryption. In the past several scheme were developed in order to provide the efficient and secure proxy re-encryption. However, these methodology mainly focused on features like maximum key privacy, minimal trust proxy and others. In such cases the efficiency and security was mainly ignored. Hence, in order to provide the efficient and secure proxy re-encryption, we proposed an algorithm named as MLBC (Modified Lattice Based Cryptography) is proposed. Our method is based on the PKE (Public Key Encryption) and it provides more efficiency when compared to the other cryptography technique. Later in order to evaluate the algorithm simulation is done based on several parameter such as encryption time, proxy key generation time, Re-encryption time and Total computation time. Later, it is compared with the existing algorithm and the plotted graph clearly shows that our algorithm outperforms the existing algorithm.