Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis using Deep Belief Network for User Rating Classification

People’s attitudes, opinions, feelings and sentiments which are usually expressed in the written languages are studied by using a well known concept called the sentiment analysis. The emotions are expressed at various different levels like document, sentence and phrase level are studied by using the sentiment analysis approach. The sentiment analysis combined with the Deep learning methodologies achieves the greater classification in a larger dataset. The proposed approach and methods are Sentiment Analysis and deep belief networks, these are used to process the user reviews and to give rise to a possible classification for recommendations system for the user. The user assessment classification can be progressed by applying noise reduction or pre-processing to the system dataset. Further by the input nodes the system uses an exploration of user’s sentiments to build a feature vector. Finally, the data learning is achieved for the suggestions; by using deep belief network. The prototypical achieves superior precision and accuracy when compared with the LSTM and SVM algorithms.
Accelerating Spark-Based Applications with MPI and OpenACC

The amount of data produced in scientific and commercial fields is growing dramatically. Correspondingly, big data technologies, such as Hadoop and Spark, have emerged to tackle the challenges of collecting, processing, and storing such large-scale data. Unfortunately, big data applications usually have performance issues and do not fully exploit a hardware infrastructure. One reason is that applications are developed using high-level programming languages that do not provide low-level system control in terms of performance of highly parallel programming models like message passing interface (MPI). Moreover, big data is considered a barrier of parallel programming models or accelerators (e.g., CUDA and OpenCL). Therefore, the aim of this study is to investigate how the performance of big data applications can be enhanced without sacrificing the power consumption of a hardware infrastructure. A Hybrid Spark MPI OpenACC (HSMO) system is proposed for integrating Spark as a big data programming model, with MPI and OpenACC as parallel programming models. Such integration brings together the advantages of each programming model and provides greater effectiveness. To enhance performance without sacrificing power consumption, the integration approach needs to exploit the hardware infrastructure in an intelligent manner. For achieving this performance enhancement, a mapping technique is proposed that is built based on the application’s virtual topology as well as the physical topology of the undelaying resources. To the best of our knowledge, there is no existing method in big data applications related to utilizing graphics processing units (GPUs), which are now an essential part of high-performance computing (HPC) as a powerful resource for fast computation.
Platforms, Blockchains, and Hybrids: Digital Governance of Interorganizational Networks

Digital platforms and blockchains have gained increasing attention as viable governance instruments for interorganizational networks. Research has juxtaposed these two instruments by emphasizing that platforms achieve network governance through intermediation, whereas blockchains do so through disintermediation. Extending this theoretical groundwork, we propose that platforms and blockchains can blend into hybrid forms with characteristics of both intermediation and disintermediation. Since this phenomenon has received scarce scholarly attention, we develop a rich, grounded theory of network-level interorganizational governance choices and transitions. We seek to better understand these complex governance phenomena with a focus on supporting trust and control mechanisms. Our model emerges from a longitudinal, qualitative analysis of Global Commerce Portal (pseudonym), a world-leading, industry-spanning global trade network. By highlighting how network governance transitioned from an intermediated, platform-based governance mode to a disintermediated, blockchain-based governance mode, we integrate and extend previously disparate scholarly conversations.
Fractional pseudorandom generators from any fourier level

We prove new results on the polarizing random walk framework introduced in recent works of Chattopadhyay et al. [4, 6] that exploit L1 Fourier tail bounds for classes of Boolean functions to construct pseudorandom generators (PRGs). We show that given a bound on the k-th level of the Fourier spectrum, one can construct a PRG with a seed length whose quality scales with k. This interpolates previous works, which either require Fourier bounds on all levels [4], or have polynomial dependence on the error parameter in the seed length [6], and thus answers an open question in [6]. As an example, we show that for polynomial error, Fourier bounds on the first O(log n) levels is sufficient to recover the seed length in [4], which requires bounds on the entire tail. We obtain our results by an alternate analysis of fractional PRGs using Taylor's theorem and bounding the degree-k Lagrange remainder term using multilinearity and random restrictions. Interestingly, our analysis relies only on the level-k unsigned Fourier sum, which is potentially a much smaller quantity than the L1 notion in previous works. By generalizing a connection established in [5], we give a new reduction from constructing PRGs to proving correlation bounds. Finally, using these improvements we show how to obtain a PRG for F2 polynomials with seed length close to the state-of-the-art construction due to Viola [26].
A Dynamic Recurrent Neural Networks-Based Recommendation System for Banking Customers

In recent years, machine learning approaches are replacing traditional methods in many industries. In parallel with these developments, marketing operations in banking started to be supported with machine learning based applications. In this study, a highly applicable product recommendation approach for banking customers was implemented using deep learning techniques. For this purpose, the dynamic recurrent neural network (DREAM) architecture, which was previously applied to e-commerce data, has been applied to banking customer data for recommendation system design. Comparative experiments with long short term memory and multilayer perceptron-based solutions have shown that the DREAM based recommendation approach has significant potential to be used in product proposal in the banking sector.
Revenue Maximization from Finite Samples

In the present paper, we study the following fundamental problem: how should a decision-maker price based on a finite and limited number of samples from the distribution of values of customers. The decision-maker's objective is to select a pricing policy with maximum competitive ratio when the value distribution is only known to belong to some general non-parametric class. We study achievable performance for two central classes, regular and monotone hazard rate (mhr) distributions, through a general framework. To date, only results are available for a single sample and two samples. We improve existing results but also obtain the first results on achievable performance as the number of samples increases. At a higher level, this work also provides insights on the value of samples for pricing purposes. For example, against mhr distributions (resp. regular), two samples suffice to ensure 71% (resp. 61%) of optimal oracle performance, and ten samples guarantee $80%$ (resp. $65%$) of such performance. Our analysis relies on the introduction of a new (simple) class of policies and the derivation of tractable lower bounds on their performance through factor revealing dynamic programs.
Research on Blending learning Design for Promoting Deep Learning

Under the background of further promoting the construction of "golden class" in universities, how to use teaching innovation and promote the cultivation of learners is the focus of educational researchers. Guided by the theory of deep learning, based on MOOC resource platform and teaching management platform, this study constructs a "3-stage-2 platform" blended learning mode for deep learning in three stages: before class, in class and after class, puts forward teaching strategies to promote deep learning, and based on this mode, takes the computer network course as an example to carry out the blended teaching design and practice. The results show that the model has a certain role in promoting learners' knowledge mastery and ability training, and good emotional experience is conducive to learners' deep learning. Keywords: Deep learning, mixed teaching, teaching design.
Real Time Face Mask Detection and Thermal Screening with Audio Response for COVID-19

The coronavirus COVID-19 pandemic is continuously spreading until now everywhere on the earth, and causing a severe health crisis. So the helpful and safe-keeping method is wearing a face mask in all areas where people are gathered, according to the World Health Organization (WHO). Along with the face mask, body temperature and sanitization also plays a vital role in being safer. Thus, monitoring the individuals that are wearing the mask or not is more significant. In this paper, we propose a system that uses TensorFlow, Keras, MobileNetV2, and OpenCV to detect the face mask. A dataset contains images of persons with and without masks obtained from multiple sources and trained on a deep learning model. Then the automatic temperature checking and Sanitation are done. Finally, the proposed system gives an audio/voice output whether the face mask is present or not, the person's body temperature. Our approach would be beneficial in reducing the spread of this infectious disease and will encourage people to use face masks, getting regularly sanitized and monitoring the temperature can keep the workplace safe.
DIRECTED READING ACTIVITY (DRA) APPLICATION IN ENGLISH READING COMPREHENSION CLASSROOM

This research manages to analyze the effectiveness of DRA in enhancing students ‘skill in reading comprehension. The researchers carried out the research at SMP N 4 Takengon, in which they found that teachers faced some problems in teaching reading comprehension to students. To figure out the answer, the research applies quantitative research with experimental approach. After statistically obtaining and interpreting the data, the study concludes that Directed Reading Activity (DRA) method is able to increase students reading comprehension skill. This is because that Directed Reading Activity (DRA) is a teacher strategy in reading comprehension that emphasizes on instructional support before, during, and aftermath the reading. Meanwhile, the research found that this strategy is also able to increase and build stronger student vocabulary and reading skill. In conclusion, the DRA strategy is effective in improving students’ ability in reading comprehension, and also building their vocabulary.
Trust, Trustworthiness, and Trust-Free Technologies

The technology underlying most cryptocurrencies, blockchain, is well known for enabling “trust-free” financial transactions and for transforming the role of trust in financial transactions (Nakamoto, 2008). Since blockchains typically depend on the cryptocurrencies they underlie to pay users for monitoring and updating them, I investigate whether cryptocurrencies are also trust free. Based on a classic conception of trust (Rousseau, Sitkin, Burt, & Camerer, 1998), I define what it means for technologies to be trust dependent and trust free. I argue that blockchain is trust free, but that conventional currencies and cryptocurrencies are trust dependent. Then, I introduce a novel, probabilistic account of trustworthiness, adapted from Gambetta (1988b) to conform to Rousseau and colleagues’ (1998) account of trust and the research streams to which it has given rise. Based on this account, I argue that most conventional currencies are trustworthy but that most cryptocurrencies are untrustworthy. Finally, I investigate what my argument that most cryptocurrencies are untrustworthy entails regarding the question of whether cryptocurrencies are ethical. Drawing upon research regarding when cryptocurrencies are ethical and when a trustee’s trustworthiness demonstrates that the trustee is ethical, I argue that whether cryptocurrencies are ethical is independent of whether they are trustworthy.
Panic Disorder Intelligent Health System based on IoT and Context-aware

With the rapid development of artificial intelligence and big data, a lot of medical data is effectively used, and the diagnosis and analysis of diseases has entered the era of intelligence. With the increasing public health awareness, ordinary citizens have also put forward new demands for panic disorder health services. Specifically, people hope to predict the risk of panic disorder as soon as possible and grasp their own condition without leaving home. Against this backdrop, the smart health industry comes into being. In the Internet age, a lot of panic disorder health data has been accumulated, such as diagnostic records, medical record information and electronic files. At the same time, various health monitoring devices emerge one after another, enabling the collection and storage of personal daily health information at any time. How to use the above data to provide people with convenient panic disorder self-assessment services and reduce the incidence of panic disorder in China has become an urgent problem to be solved. In order to solve this problem, this research applies the context awareness to the automatic diagnosis of human diseases. While helping patients find diseases early and get treatment timely, it can effectively assist doctors in making correct diagnosis of diseases and reduce the probability of misdiagnosis and missed diagnosis.
Nature of Ore Fluid at the Sopokomil Zn-Pb Deposit, North Sumatra, Indonesia: Implications for Metal Transport and Sulfide Deposition

Little is known about the nature of ore fluid at the Sopokomil shale-hosted massive sulfide Zn-Pb deposit (North Sumatra, Indonesia). We therefore investigated its ore-fluid salinities, temperatures, densities, redox state, and pH using fluid inclusion microthermometry, sphalerite composition, and thermodynamic modelling. The fluid salinities and temperatures were ≈6 wt.% NaCl equiv and ≈165 °C, respectively, corresponding to an ore fluid less dense than seawater (≈0.96 g/mL). Sphalerite contains ≈9.9 mole% FeS in the stratiform ore and ≈3.4 mole% FeS in the feeder ore, suggesting a reduced fluid, which must have been acidic to be fertile. Such redox state and acidity invoke fluid dilution as the sulfide depositional mechanism. The bulk of the sulfides were precipitated in the early stage of mixing, within T = 165–155 °C. Key ingredients of sphalerite and galena at Sopokomil include (1) Zn that was primarily transported as ZnCl+, (2) Pb that predominantly occurred as PbCl2(aq), and (3) S that was largely supplied by marine sediment porewater. This study highlights the significance of a dramatic shift in thermal and chemical equilibrium induced by fluid dilution in the making of the first significant shale-hosted massive sulfide Zn-Pb deposit in Indonesia.
Touchscreen-based Smartphone Continuous Authentication System (SCAS) using Deep Neural Network

Due to the huge increasing usage of mobile devices and applications, basic authentication is not a secure choice for a long user session. Therefore, it needs a second layer of protection developed as a seamless and non-intervention security procedure, which is the continuous authentication. A framework analyses behavioral biometrics dataset was designed based on the touch-screen and a user continuous authentication was applied depending on pre-specified acceptance ratio to the portions of the stroke. In this framework, an approach based on a deep learning technique was adopted to train a model able to classify these behavioral data and achieve high accuracy. By comparing with the traditional use of machine learning algorithms that required more processing actions, which it is considered costly from the perspective of execution and the complexity of processing. In general, machine-learning algorithms need to extract statistical features from the raw data before they can work on it directly. The experiences of using machine learning classification algorithms have proven unreliable results when they are using on one behavioral biometrics modality, comparison with our framework that are based on a deep learning technique. The approach designed to apply a deep neural network on behavioral biometric data such as X, Y coordinates; area covered and phone orientation …etc. These features are captured directly from the touchscreen sensor of the smartphone during the user’s sessions, to train system able to classify new input data with high accuracy if it is coming from a genuine or fraudulent user, which already was achieved and obtained test accuracy 94.2% with equal error rate (EER 3.46%).
Reliability of YouTube videos on knee injection

Intra-articular injection requires a visual learning and the knee is the most commonly used joint for t his application.YouTube videos have become a source of visual learning.However, there is a obscurity whether YouTube videos on medical education provide safe and useful information.Therefore, this study aim to evaluate the reliability of YouTube videos on knee joint injection according to the current approach.The term ʺknee joint injection techniqueʺ was used to search the related YouTube videos.The videos were grouped in terms of the uploader, uploading years, and the number of views.The procedure was evaluated and scored for positioning of the patient, palpation of the landmarks, washing hands and wearing gloves, needle approach and entry, drug injection, removing the needle and placing the sponge and finally bandaging.Of the 61 videos, only 11 (18%) had enough quality.The score of videos uploaded by an institution was significantly higher than others (p=0.006).The score of videos viewed over (n=31) and lower (n=30) than 10000 times was 4.5 (IQR 2.0) and 3.5 (IQR 2.0), respectively (p=0.018).There was no statistical significance between the groups according to uploading years (p=0.694).YouTube videos are not enough qualified enaugh reliable visual learning of knee joint injection.The institutional YouTube videos are more educational sources than others.YouTube videos with higher views count also have higher reli ability.
Overview of IoT and Blockchain Technology in Smart Agriculture

The digital breach between applications of IoT and agriculture decaying from the last few years. As we know nowadays the latest technologies such as Blockchain technology etc. are blooming. And the combination of IoT and these blooming technologies will allow in improvisation of productivity through the sustainable cultivation of food, efficient use of water, food security, food chain supply, transactions efficiency, transparency of food safety, e.t.c. Thus, in this paper, applications of “ IoT” in agriculture have been analyzed and studied, also this paper concisely introduced the technology IoT, agriculture sector, benefits of IoT in agriculture, and presents a review of some literature. The purpose of this paper is to elaborate the ongoing studies and research on smart agriculture using the recent combination of IoT, Blockchain Technologies to fix a variety of agriculture’s problems.
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings

Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora.This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general.In this paper, we argue that using a pseudo-parallel corpus generated by an unsupervised machine translation model facilitates the structural similarity of the two embedding spaces and improves the quality of CLWEs in the unsupervised mapping method.We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mappingbased CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel; (2) the pseudo data contains information on the original language that helps to learn similar embedding spaces between the source and target languages.
CSI intelligent feedback for massive MIMO systems in V2I scenarios

With the rapid development of the Internet of vehicles (IoV), vehicle to everything (V2X) has strict requirements for ultra-reliable and low latency communications (URLLC), and massive multi-input multi-output (MIMO) channel state information (CSI) feedback can effectively support URLLC communication in 5G vehicle to infrastructure (V2I) scenarios. Existing research applies deep learning (DL) to CSI feedback, but most of its algorithms are based on low-speed outdoor or indoor environments and assume that the feedback link is perfect. However, the actual channel still has the influence of additive noise and nonlinear effects, especially in the high-speed V2I scene, the channel characteristics are more complex and time-varying. In response to the above problems, this paper proposes a CSI intelligent feedback network model for V2I scenarios, named residual mixnet (RM-Net). The network learns the channel characteristics in the V2I scenario at the vehicle user (User Equipment, UE), compresses the CSI and sends it to the channel; the roadside base station (Base Station, BS) receives the data and learns the compressed data characteristics, and then restore the original CSI. The system simulation results show that the RM-Net training speed is fast, requires fewer training samples, and its performance is significantly better than the existing DL-based CSI feedback algorithm. It can learn channel characteristics in high-speed mobile V2I scenarios and overcome the influence of additive noise. At the same time, the network still has good performance under high compression ratio and low signal-to-noise ratio (SNR).
Octane Optimization with a Combined Machine Learning and Optimization Approach

Refinery operations are always sensitive to optimization, and due to the increasingly adverse effects of COVID-19 on energy sectors, its importance had increased significantly. This thesis aims to analyze the reactor temperature that yields a higher RON (octane measurement) value in isomerate product using all available information in the isomerate production network. The main explanatory variables that can affect the RON value can be divided into three categories: feed impurities, isomerization reactor operations, deizohexanizer column operations. Isomerate feed network is quite complex and fed by different crude distillation units and cracker units. Various reactions occur in the isomerization reactors, and depending on the feed content, the reaction mechanism changes. This thesis applies machine learning algorithms to build a model that can capture the relationship between RON and reactor temperature with the other explanatory variables. We implemented a number of machine learning algorithms to assess their performance on the problem, specifically Linear Regression, Decision Tree, Random Forest, XGBoost, Support Vector Regression, and KNN. Comparing with the linear regression, we achieved 0.82 decreases in the mean absolute error. The mean absolute error of the XGBoost model is 0.08 RON. We find a temperature value with the selected model that yields a higher RON number by trying different temperature values while keeping the same values for the other variables. If we used the suggested temperature by our model, we predict that we could obtain a 0.2 RON increase in the validation zone resulting in an annual profit increase of 528 000 USD Dollar.