Reviewerly/exHarmony huggingface dataset:
An attention network based on feature sequences for cross-domain sentiment classification

The difficulty of cross-domain text sentiment classification is that the data distributions in the source domain and the target domain are inconsistent. This paper proposes an attention network based on feature sequences (ANFS) for cross-domain sentiment classification, which focuses on important semantic features by using the attention mechanism. Particularly, ANFS uses a three-layer convolutional neural network (CNN) to perform deep feature extraction on the text, and then uses a bidirectional long short-term memory (BiLSTM) to capture the long-term dependency relationship among the text feature sequences. We first transfer the ANFS model trained on the source domain to the target domain and share the parameters of the convolutional layer; then we use a small amount of labeled target domain data to fine-tune the model of the BiLSTM layer and the attention layer. The experimental results on cross-domain sentiment analysis tasks demonstrate that ANFS can significantly outperform the state-of-the-art methods for cross-domain sentiment classification problems.
IIOT for Monitoring Oil Well Production and Ensure Reliability

IIOT integration has been a ultimate growth factor for multinational companies especially in Oil and Gas industry. Using IIOT and digital analysis of raw data, the industry leaders can beforehand know the overview architecture and the technologically complex frameworks with ease and whether the equipments work in a proper condition without having to go onsite production location. As a result, we a robust connectivity system that manages and interconnects all the sensor statistics which are already installed onsite, without having to visit manually by any site engineer. We need robust and reliable connectivity to ensure the live time series data can be fetched and analyzed without any unforeseen delay. Due to this framework, the owner of the oil and gas sites can readily the performance and monitor the equipments visually and can focus on degradation of any specific module of framework. This study presents an application of a multi- variate regression model to estimate the future production performance of oil wells based on monthly-production time series data. Several engineering techniques that require expertise and data that are difficult to obtain are used currently to predict well performance. In this report, focus is on how innovative networking standards and protocols are enabling revolutionary system-building approaches that greatly ease field operations, how to predict, forecast the value of oil production over time under different condition.
RETRACTED CHAPTER: A Computer Vision-Based Approach for Subspace Clustering and Lagrange Multiplier Optimization in High-Dimensional Data

In this work, we discuss the issues raised due to the high-dimensionality data in real-life scenario and present a novel approach to overcome the high-dimensionality issue. Principal component analysis (PCA)-based dimension reduction and clustering are considered as promising techniques in this field. Due to computational complexities, PCA fails to achieve the desired performance for high-dimensional data, whereas subspace clustering has gained huge attraction from research community due to its nature of handling the high-dimensional data. Here, we present a new approach for subspace clustering for computer vision-based applications. According to the proposed approach, first all subspace clustering problem is formulated which is later converted into an optimization problem. This optimization problem is resolved using a diagonal optimization. Further, we present a Lagrange multiplier-based optimization strategy to reduce the error during reconstruction of low-level data from high-dimensional input data. Proposed approach is validated through experiments where face clustering and motion segmentation experiments are conducted using MATLAB simulation tool. A comparative analysis is presented which shows that the proposed approach achieves better performance when compared with the existing subspace clustering techniques.
Clustering analysis method of power grid company based on K-means

Abstract With the development of network and the expansion of power grid business, the power grid traffic becomes more and more complex and huge. It is more and more difficult to identify the power grid business through the traffic, and the identification accuracy also needs to be improved. In order to solve this problem, this paper proposes a clustering scheme based on K-means to classify the power grid business through the grid traffic, using elkan K-means algorithm improves the efficiency of the algorithm and finds the most suitable K value for power grid business data.
Reinforcement Learning based Heterogeneous Resource Provisioning for Cloud Web Applications

Cloud computing enables enterprises to deploy Web applications on elastically rented Virtual Machines (VMs). In order to minimize VM rental costs while fulfilling Service Level Agreements (SLAs), it is crucial to design effective VM provisioning algorithms. Queueing and control theories based methods are widely used in existing works. However, complex nonlinear system characteristics restrict these methods from getting well performance and most of them are tailored for homogeneous resources. In this paper, a deep reinforcement learning method is proposed for provisioning heterogeneous multi-type VMs which takes advantage of the deep-learning-network's nonlinear modeling ability, incremental-VM-capacity-based action space definition and Integer Linear Programming based mapping from the incremental capacity to the multi-type VM renting plan. Experimental results demonstrate that our approach achieves the fastest convergence speed and highest total rewards.
A domain independent semantic measure for keyword sense disambiguation

Understanding the user's intention is crucial in human-machine interaction. When dealing with text input, Word Sense Disambiguation (WSD) techniques play an important role. WSD techniques typically require well-formed sentences as context to operate, and predefined catalogues of word senses. However, such conditions do not always apply, such as when there is a need to disambiguate keywords from a query, or sets of tags describing any Web resource.
Ensemble Classifier Based Modulation Recognition for Beyond 5G Massive MIMO (mMIMO) Communication

Massive MIMO (mMIMO) communication has been considered as pivotal and significant technology to furnish the expected demand of Fifth-generation (5G) and beyond 5G systems. It provides enormous potentiality to 5G systems by the deployment of excessive antennas at base station. Therefore, the probability of using different modulation by different transmitters has been increased aiming to reduce bit error rate as well as enhance data rate. It leads to develop intelligent receiver that can efficiently classify and recognize accurate modulation and decodes data. To shed light on this issue, automatic modulation recognition scheme based on ensemble classifier has been studied in this paper. The overall performance analysis of the ensemble classifier for several modulated radio signal has been presented in this article. The simulation results justify the exactitude of ensemble classifier as efficient one for modulation classification.
Analysis of wavelet transform de-noising technique based on measured lightning return stroke electric field

This paper presents a difference type of wavelet transform de-noising technique based on measured lightning electric field. The lightning electric fields captured by two parallel plats sensor and the signal were analyses accordingly. In engineering application, lightning stroke current parameter will relate with electric fields and these parameters can be recognized by direct and in-direct measuring method. Therefore, measuring lightning electric fields (LEF) using parallel plate was done by many researchers. However, measuring LEF has many disturbances such as noise from other apparatus and other device. Therefore, de-noising of LEF signal is important to get useful signal for further research. For this reason, the Centre for Electromagnetic and Lighting Protection Research (CELP) group has been working on a research program to study and characteristic lightning using de-noising technique in order to process and analyse the radiated LEF produced during flash happens. Only three types of wavelet transform are selected and there are Daubechies, Symlets and Coiflets wavelet. The result will be discussed and the best will be selected based on the measured signal.
Balancing the formal and the informal in user-centred design

Abstract This paper explores the role of formal methods as part of the user-centred design of interactive systems. An iterative process is described, developing prototypes incrementally, proving user-centred requirements while at the same time evaluating the prototypes that are executable forms of the developed models using ‘traditional’ techniques for user evaluation. A formal analysis complements user evaluations. This approach enriches user-centred design that typically focuses understanding on context and producing sketch designs. These sketches are often non-functional (e.g. paper) prototypes. They provide a means of exploring candidate design possibilities using techniques such as cooperative evaluation. This paper describes a further step in the process using formal analysis techniques. The use of formal methods provides a systematic approach to checking plausibility and consistency during early design stages, while at the same time enabling the generation of executable prototypes. The technique is illustrated through an example based on a pill dispenser.
Using reflectance spectroscopy and Advanced Spaceborne Thermal Emission and Reflection Radiometer data to identify bauxite deposits in vicinity of Az Zabirah, northern Saudi Arabia

Bauxite samples from deposits in northern Saudi Arabia were identified in the laboratory using spectral reflectance measurements (at 0.3–2.5 μm) and X-ray diffraction (XRD). The XRD results revealed that the bauxite deposits are composed mainly of goethite, gibbsite, and boehmite with small amounts of kaolinite, hematite, and quartz. The bauxite spectra revealed the presence of significant iron oxides (at 0.5 and 0.87 μm) accompanied by water (at 1.4 and 1.9 μm) and aluminum hydroxide (at 2.2 μm). The convolved Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectra of the bauxite samples were characterized by an aluminum hydroxide feature in ASTER band 6 (at 2.2 μm). The results demonstrated that principal components analysis band PC2 is the best component for delineating the bauxite deposits. Fractional abundances of bauxite were derived by using a matched-filtering method. This study demonstrates the applicability of reflectance spectroscopy and ASTER data to provide spectral information for distinguishing economically important minerals from visible and near-infrared and shortwave-infrared spectra in arid and semiarid environments such as in Saudi Arabia.
Iterative Multiple-Path One-Shot NAS for the Optimized Performance

In this work, we develop the Iterative Multiple-path One-shot Network Architecture Search (NAS), (IMO-NAS), to effectively look for preferable neural network architectures in terms of accuracy, computational complexity, and parameter quantity. Our IMO-NAS includes three steps of full search, random search, and candidate retraining at an iterative operation manner. At each iteration, multiple scaling ratios are employed to multiply the channel numbers at all layers to get many combinations for channel searching where the conventional one-shot NAS cannot alter channel numbers. Based on the super neural network using the mixed depthwise convolutional kernels, IMO-NAS is employed to seek outstanding architectures using Cifar-10 and Cifar-100 datasets where the depthwise convolution layer has multiple blocks to establish multiple paths for search. Simulation results reveal that the architectures found by IMO-NAS can have advantageous performance on accuracy, computational complexity, and memory size, comparing to the conventional deep neural network architectures. Therefore, the proposed IMO-NAS can successfully discover new architectures or fine adjust the popularly-used architectures at fair search time for various artificial intelligence applications.
On Z₂Z₂[u³]-Additive Cyclic and Complementary Dual Codes

Aydogdu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> studied the standard forms of generator and parity-check matrices of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{2}\mathbb {Z}_{2}[u^{3}]$ </tex-math></inline-formula> -additive codes, and presented generators of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{2}\mathbb {Z}_{2}[u^{3}]$ </tex-math></inline-formula> -additive cyclic codes (Finite Fields Appl. 48: 241-260, 2017). In this paper, we investigate some other useful properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{2}\mathbb {Z}_{2}[u^{3}]$ </tex-math></inline-formula> -additive codes, including asymptotically good <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{2}\mathbb {Z}_{2}[u^{3}]$ </tex-math></inline-formula> -additive cyclic codes and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {Z}_{2}\mathbb {Z}_{2}[u^{3}]$ </tex-math></inline-formula> -additive complementary dual codes. The present paper can be viewed as a necessary complementary part of Aydogdu’s work.
Envision Foundational of Convolution Neural Network

Profound learning’s goes to the achievement of spurs in a large number and understudies to find out about the energizing innovation. At this regular process of novices to venture the multifaceted nature of comprehension and applying profound learning. We present Convolution Neural Network (CNN) EXPLAINER, an intelligent representation instrument intended for non-specialists to learn and inspect (CNN)-Convolution Neural Network a fundamental profound learning model engineering. Our apparatus tends to key difficulties that fledglings face in finding out about Convolution Neural Network, it can be distinguish from pointing with educators and input with past understudies. Convolution Neural Network firmly incorporates representation outline that sums up the construction of CNN, and on-request, dynamic visual clarification sees that assist clients with understanding the hidden parts of CNNs. Constantly polished changes across levels of deliberation, our device empowers clients to examine the exchange between low-level numerical activities and undeniable level model designs. A subjective client study shows that Convolution Neural Network EXPLAINER helps clients all the more effectively comprehend the inward operations of CNNs, and is drawing in and agreeable to utilize. We additionally determine plan exercises from our examination. Created utilizing current web innovations, CNN EXPLAINER runs locally in clients’ internet browsers without the requirement of establishment or particular equipment, widening the general preparation with current profound learning strategies.
Building the Korean Sentiment Lexicon for Finance (KOSELF)

This study aims to verify and establish a Korean sentiment lexicon suitable for corporate financial analysis. When analyzing existing sentiment lexicons, the KOSAC and KNU (Kunsan University) dictionaries developed based on Korean are weak because they are used for general purposes. The Harvard IV and Loughran and McDonald (2011) have the disadvantage of being translated from English. In this study, the Korean Sentiment Lexicon for Finance (KOSELF) is constructed and presented. To verify its usefulness, text data from about 20,000 analyst reports published in Korea from 2016 to 2018 are collected from the Hankyung Consensus web page. After calculating the sentiment variables of negative and positive word frequency using five sentiment lexicons for each report, the recommendation and target price changes are regressed on these sentiment variables. The sentiment variables from the newly-constructed KOSELF in this study have a significant relationship with the analyst’s recommendation and target price change. Even when the sentiment variables calculated through the other four sentiment lexicons are added, it shows better performance. Our work has practical significance in that it proposes a Korean sentiment dictionary that can be used for finance.
Performance-Portable Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL

In the age of data collection, machine learning algorithms have to be able to efficiently cope with vast data sets. This requires scalable algorithms and efficient implementations that can cope with heterogeneous hardware. We propose a new, performance-portable implementation of a well-known, robust, and versatile multi-class classification method that supports multiple Graphics Processing Units (GPUs) from different vendors. It is based on a performance-portable implementation of the approximate k-nearest neighbors (k-NN) algorithm in SYCL. The k-NN assigns a class to a data point based on a majority vote of its neighborhood. The naive approach compares a data point x to all other data points in the training data to identify the k nearest ones. However, this has quadratic runtime and is infeasible for large data sets. Therefore, approximate variants have been developed. Such an algorithm is the Locality-Sensitive Hashing (LSH) algorithm, which uses hash tables together with locality-sensitive hash functions to reduce the data points that have to be examined to compute the k-NN.
Learning intra-inter semantic aggregation for video object detection

Video object detection is a challenging task due to the appearance deterioration problems in video frames. Thus, object features extracted from different frames of a video are usually deteriorated in varying degrees. Currently, some state-of-the-art methods enhance the deteriorated object features in a reference frame by aggregating the undeteriorated object features extracted from other frames, simply based on their learned appearance relation among object features. In this paper, we propose a novel intra-inter semantic aggregation method (ISA) to learn more effective intra and inter relations for semantically aggregating object features. Specifically, in the proposed ISA, we first introduce an intra semantic aggregation module (Intra-SAM) to enhance the deteriorated spatial features based on the learned intra relation among the features at different positions of an individual object. Then, we present an inter semantic aggregation module (Inter-SAM) to enhance the deteriorated object features in the temporal domain based on the learned inter relation among object features. As a result, by leveraging Intra-SAM and Inter-SAM, the proposed ISA can generate discriminative features from the novel perspective of intra-inter semantic aggregation for robust video object detection. We conduct extensive experiments on the ImageNet VID dataset to evaluate ISA. The proposed ISA obtains 84.5% mAP and 85.2% mAP with ResNet-101 and ResNeXt-101, and it achieves superior performance compared with several state-of-the-art video object detectors.
Securely Transfer Information with RSA and Digital Signature by using the concept of Fog Computing and Blockchain

In the modern era of technology, cloud computing has been on the rise in the office management system. However, due to some concerning issues regarding provided security by the cloud, it has been losing its publicity. With the introduction of fog computing with its advantageous feature of decentralization, this system has replaced traditional cloud systems in various organizations. In this paper, we propose a smart office system which is based on fog computing. Blockchain is implemented to ensure overall system security and user authentication has been conducted by creating wallets in the blockchain system. The overall transaction process has been carried out with the popular bitcoin method. Information sharing has been implemented with the help of RSA algorithm and digital signature scheme.
Improved Visual Analytic Process Under Cognitive Aspects

The Visual Analytics (VA) systems aids to ameliorate the decision-making activity, since these systems relates visual to automatic analysis to extract knowledge and explore data. In this paper, we present an adaptation of a cognitive Visual Analytics with a well-known Visual Analytics process under cognitive aspects: intelligent data collection, fusion and synchronization and their analysis using the adapted cognitive data mining technique. To prove the effectiveness of our approach, we have applied it during a formative assessment. We have used the Eye Tracking as well as the Electroencephalogram (EEG) as an intelligent data collection source and the Fuzzy Logic as data mining technique. We performed an evaluation for proving the utility and usability of this developed system.
Research of College Computer Laboratory Based on Cloud Computing Technology

With the development of big data and artificial intelligence, more and more colleges establish new courses about related domains. Innovation experiments and entrepreneurial practices for college students also require more computing and storage resources in computer laboratory. Traditional management mode for computer laboratory could not meet the requirement of the courses and experiments. And increasingly enriched experiments lead to new challenges to laboratory operation and maintenance personnel. In this paper, a platform based on cloud computing was designed for the college computer laboratory. We applied VMware Horizon to build cloud desktop to solve software compatibility issues of difference courses. For big data and artificial intelligence courses, innovation experiments and entrepreneurial practices, we designed the program based on OpenShift to build cloud computing platform for service, which could integrate existing resources and improve management efficiency, provide a better platform for students to strengthen their creativity.