Northeastern State University

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature extraction, and we empirically prove that the preprocessing of the two-stage sliding window will be useful for high-level feature extraction in the integrated model. After data preprocessing, spatial and temporal features are extracted in CNN and recurrent autoencoder for the classification in fully connected networks. Empiric results show that the proposed model has better performances on multiple classification metrics and achieves preferable effect on anomaly detection.

With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and inference attacks), transparency, scalability, and verifiability limits faster adaptations of smart cities. Motivated by the aforementioned discussions, we present a Privacy-Preserving and Secure Framework (PPSF) for IoT-driven smart cities. The proposed PPSF is based on two key mechanisms: a two-level privacy scheme and an intrusion detection scheme. First, in a two-level privacy scheme, a blockchain module is designed to securely transmit the IoT data and Principal Component Analysis (PCA) technique is applied to transform raw IoT information into a new shape. In the intrusion detection scheme, a Gradient Boosting Anomaly Detector (GBAD) is applied for training and evaluating the proposed two-level privacy scheme based on two IoT network datasets, namely ToN-IoT and BoT-IoT. We also suggest a blockchain-InterPlanetary File System (IPFS) integrated Fog-Cloud architecture to deploy the proposed PPSF framework. Experimental results demonstrate the superiority of the PPSF framework over some recent approaches in blockchain and non-blockchain systems.

Abstract The growing availability of mobile technologies has contributed to an increase in mobile-assisted language learning in which learners can autonomously study a second language (L2) anytime or anywhere (e.g. Kukulska-Hulme, Lee & Norris, 2017; Reinders & Benson, 2017). Research investigating the effectiveness of such study for L2 learning, however, has been limited, especially regarding large-scale commercial L2 learning apps, such as Duolingo. Although one commissioned research study found favorable language learning outcomes (Vesselinov & Grego, 2012), limited independent research has reported issues related to learner persistence, motivation, and program efficacy (Lord, 2015; Nielson, 2011). The current study investigates the semester-long learning experiences and results of nine participants learning Turkish on Duolingo. The participants showed improvement on L2 measures at the end of the study, and results indicate a positive, moderate correlation between the amount of time spent on Duolingo and learning gains. In terms of perceptions of their experiences, the participants generally viewed Duolingo’s flexibility and gamification aspects positively; however, variability in motivation to study and frustration with instructional materials were also expressed.

This paper outlines the design features, data collection methods and analytic strategies of the International Tobacco Control (ITC) Four Country Survey, a prospective study of more than 2000 longitudinal respondents per country with yearly replenishments. This survey possesses unique features that sets it apart among surveys on tobacco use and cessation. One of these features is the use of theory-driven conceptual models. In this paper, however, the focus is on the two key statistical features of the survey: longitudinal and "quasi-experimental" designs. Although it is often possible to address the same scientific questions with a cross-sectional or a longitudinal study, the latter has the major advantage of being able to distinguish changes over time within individuals from differences among people at baseline (that is, differences between age and cohort effects). Furthermore, quasi-experiments, where countries not implementing a given new tobacco control policy act as the control group to which the country implementing such a policy will be compared, provide much stronger evidence than observational studies on the effects of national-level tobacco control policies. In summary, application of rigorous research methods enables this survey to be a rich data resource, not only to evaluate policies, but also to gain new insights into the natural history of smoking cessation, through longitudinal analyses of smoker behaviour.

Knowledge graph (KG) embedding aims to study the embedding representation to retain the inherent structure of KGs. Graph neural networks (GNNs), as an effective graph representation technique, have shown impressive performance in learning graph embedding. However, KGs have an intrinsic property of heterogeneity, which contains various types of entities and relations. How to address complex graph data and aggregate multiple types of semantic information simultaneously is a critical issue. In this article, a novel heterogeneous GNNs framework based on attention mechanism is proposed. Specifically, the neighbor features of an entity are first aggregated under each relation-path. Then the importance of different relation-paths is learned through the relation features. Finally, each relation-path-based features with the learned weight values are aggregated to generate the embedding representation. Thus, the proposed method not only aggregates entity features from different semantic aspects but also allocates appropriate weights to them. This method can capture various types of semantic information and selectively aggregate informative features. The experiment results on three real-world KGs demonstrate superior performance when compared with several state-of-the-art methods.

Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial Internet of Things (IoT). However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing research works reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this article, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula>-2 of <inline-formula><tex-math notation="LaTeX">$\boldsymbol{n}$</tex-math></inline-formula> participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network structure in which the node indicates entity and the edge represent relation. However, with the explosion of network volume, the problem of data sparsity that causes large-scale KG systems to calculate and manage difficultly has become more significant. For alleviating the issue, knowledge graph embedding is proposed to embed entities and relations in a KG to a low-, dense and continuous feature space, and endow the yield model with abilities of knowledge inference and fusion. In recent years, many researchers have poured much attention in this approach, and we will systematically introduce the existing state-of-the-art approaches and a variety of applications that benefit from these methods in this paper. In addition, we discuss future prospects for the development of techniques and application trends. Specifically, we first introduce the embedding models that only leverage the information of observed triplets in the KG. We illustrate the overall framework and specific idea and compare the advantages and disadvantages of such approaches. Next, we introduce the advanced models that utilize additional semantic information to improve the performance of the original methods. We divide the additional information into two categories, including textual descriptions and relation paths. The extension approaches in each category are described, following the same classification criteria as those defined for the triplet fact-based models. We then describe two experiments for comparing the performance of listed methods and mention some broader domain tasks such as question answering, recommender systems, and so forth. Finally, we collect several hurdles that need to be overcome and provide a few future research directions for knowledge graph embedding.

Plant molecular farming (PMF), defined as the practice of using plants to produce human therapeutic proteins, has received worldwide interest. PMF has grown and advanced considerably over the past two decades. A number of therapeutic proteins have been produced in plants, some of which have been through pre-clinical or clinical trials and are close to commercialization. Plants have the potential to mass-produce pharmaceutical products with less cost than traditional methods. Tobacco-derived antibodies have been tested and used to combat the Ebola outbreak in Africa. Genetically engineered immunoadhesin (DPP4-Fc) produced in green plants has been shown to be able to bind to MERS-CoV (Middle East Respiratory Syndrome), preventing the virus from infecting lung cells. Biosafety concerns (such as pollen contamination and immunogenicity of plant-specific glycans) and costly downstream extraction and purification requirements, however, have hampered PMF production from moving from the laboratory to industrial application. In this review, the challenges and opportunities of PMF are discussed. Topics addressed include; transformation and expression systems, plant bioreactors, safety concerns, and various opportunities to produce topical applications and health supplements.

IMPORTANCE: Administration of pembrolizumab plus concurrent chemoradiation therapy (cCRT) may provide treatment benefit to patients with locally advanced, stage III non-small cell lung cancer (NSCLC). OBJECTIVE: To evaluate treatment outcomes and safety of pembrolizumab plus cCRT in stage III NSCLC. DESIGN, SETTING, AND PARTICIPANTS: The phase 2, nonrandomized, 2-cohort, open-label KEYNOTE-799 study enrolled patients between November 5, 2018, and July 31, 2020, from 52 academic facilities and community-based institutions across 10 countries. As of October 28, 2020, median (range) follow-up was 18.5 (13.6-23.8) months in cohort A and 13.7 (2.9-23.5) months in cohort B. Of 301 patients screened, 216 eligible patients with previously untreated, unresectable, and pathologically/radiologically confirmed stage IIIA/IIIB/IIIC NSCLC with measurable disease per Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST v1.1) were enrolled. INTERVENTIONS: Patients in cohort A (squamous/nonsquamous) received 1 cycle (3 weeks) of carboplatin (area under the curve [AUC] 6 mg/mL/min), paclitaxel (200 mg/m2), and pembrolizumab (200 mg), followed by carboplatin (AUC 2 mg/mL/min) and paclitaxel (45 mg/m2) once weekly for 6 weeks and 2 cycles of pembrolizumab plus standard thoracic radiotherapy. Patients in cohort B (nonsquamous) received 3 cycles of cisplatin (75 mg/m2), pemetrexed (500 mg/m2), and pembrolizumab (200 mg) every 3 weeks and thoracic radiotherapy in cycles 2 and 3. Patients received 14 additional cycles of pembrolizumab. MAIN OUTCOMES AND MEASURES: Coprimary end points were objective response rate per RECIST v1.1 by blinded independent central review and incidence of grade 3 to 5 pneumonitis. RESULTS: A total of 112 patients received treatment in cohort A (76 men [67.9%]; median [range] age, 66.0 [46-90] years; 66 patients [58.9%] with programmed cell death ligand 1 [PD-L1] tumor proportion score ≥1%) and 102 patients received treatment in cohort B (62 men [60.8%]; median [range] age, 64.0 [35-81] years; 40 patients [39.2%] with PD-L1 tumor proportion score ≥1%). Objective response rate was 70.5% (79 of 112; 95% CI, 61.2%-78.8%) in cohort A and 70.6% (72 of 102; 95% CI, 60.7%-79.2%) in cohort B. Median duration of response was not reached, but 79.7% and 75.6%, respectively, had response duration of 12 months or longer. Grade 3 or higher pneumonitis occurred in 9 of 112 patients (8.0%) in cohort A and 7 of 102 (6.9%) in cohort B. Grade 3 to 5 treatment-related adverse events occurred in 72 of 112 (64.3%) and 51 of 102 (50.0%) patients, respectively. CONCLUSIONS AND RELEVANCE: The findings of this phase 2, nonrandomized, 2-cohort study suggest promising antitumor activity of pembrolizumab plus cCRT and manageable safety in patients with previously untreated, locally advanced, stage III NSCLC.

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

PURPOSE: To compare aberrometry measurements from multiple sites and compute mean Zernike coefficients and root-mean-square (RMS) values for the entire data pool to serve as a reference set for normal, healthy adult eyes. SETTING: Northeastern State University, Tahlequah, Oklahoma, USA. METHODS: Data were collected from 10 laboratories that measured higher-order aberrations (HOAs) in normal, healthy adult eyes using Shack-Hartmann aberrometry (2560 eyes of 1433 subjects). Signed Zernike coefficients were scaled to pupil diameters of 6.0 mm, 5.0 mm, 4.0 mm, and 3.0 mm and corrected to a common wavelength of 550 nm. The mean signed and absolute Zernike coefficients across data sets were compared. Then, the following were computed: overall mean values for signed and absolute Zernike coefficients; polar Zernike magnitudes and RMS values for coma-like aberrations (Z(3)(+/-1) and Z(5)(+/-1) combined); spherical-like aberrations (Z(4)(0) and Z(6)(0) combined); and 3rd-, 4th-, 5th-, and 6th-order, and higher-order aberrations (orders 3 to 6). RESULTS: The different data sets showed good agreement for Zernike coefficients values across most higher-order modes, with greater variability for Z(4)(0) and Z(3)(-1). The most prominent modes and their mean absolute values (6.0-mm pupil) were, respectively, Z(3)(-1) and 0.14 microm, Z(4)(0) and 0.13 microm, and Z(3)(-3) and 0.11 microm. The mean total higher-order RMS was 0.33 microm. CONCLUSIONS: There was a general consensus for the magnitude of HOAs expected in normal adult human eyes. At least 90% of the sample had aberrations less than double the mean values reported here. These values can serve as a set of reference norms.

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.

Regret may be a key variable in understanding the experience of smokers, the vast majority of whom continue to smoke while desiring to quit. We present data from the baseline wave (October-December 2002) of the International Tobacco Control Policy Evaluation Survey, a random-digit-dialed telephone survey of a cohort of over 8,000 adult smokers across four countries--Canada, the United States, the United Kingdom, and Australia--to estimate the prevalence of regret and to identify its predictors. The proportion of smokers who agreed or agreed strongly with the statement "If you had to do it over again, you would not have started smoking" was extremely high--about 90%--and nearly identical across the four countries. Regret was more likely to be experienced by older smokers, women, those who had tried to quit more often, those who perceived quitting as conferring benefits, those with higher levels of perceived addiction, those who worried about future damage to health, those who perceived smoking as lowering their quality of life, those who perceived higher monetary costs of smoking, and those who believed that smoking is not socially acceptable. This predictive model was the same in all four countries. Regret is thus a near-universal experience among smokers in all four countries, and the factors that predict regret are universal across these four countries. Among other implications for cessation treatment and smoking prevention, this near universality of regret casts doubt on the view of some policy analysts and economists that the decisions to take up and continue smoking are welfare-maximizing for the consumer.

BACKGROUND: Bifocals have long been thought to reduce progression of childhood myopia. However, this hypothesis has not been definitively evaluated. METHODS: We conducted a randomized clinical trial to test the hypothesis that bifocals slow myopia progression in children with near-point esophoria. Eighty-two myopic children were randomized to single-vision glasses (n = 40) or to bifocals with a +1.50 D add (n = 42) and were followed for 30 months. Refraction was measured by an automated refractor after cycloplegia. The primary outcome was myopia progression defined as the difference between the spherical equivalent at baseline and at the 30-month examination, averaged over both eyes. RESULTS: Follow-up was incomplete for six children in the bifocal group and one child in the single-vision group. Among the children completing the 30 months of follow up, myopia progression (mean spherical equivalent of the two eyes) averaged 0.99 D for bifocals and 1.24 D for single vision (unadjusted, p = 0.106; adjusted for age, p = 0.046). Treatment groups differed in their cumulative distributions (Kolmogorov-Smirnov procedure, p = 0.031). Evidence for a treatment effect on growth in vitreous chamber depth was similar (p = 0.046 by K.S.). CONCLUSION: Use of bifocals, instead of single-vision glasses, by children with near-point esophoria seemed to slow myopia progression to a slight degree.

This study examined the effectiveness of the Self-Directed IEP to teach individualized education program (IEP) meeting skills. One hundred and thirty secondary students were randomly assigned to the treatment or control group. Observations of 130 meetings and 764 IEP team members were performed using 10-s momentary time sampling to determine the percentage of intervals team members talked and the percentage of time they discussed transition. Special education teachers completed a pre/post ChoiceMaker self-determination student skill and opportunity assessment, and meeting participants answered postmeeting surveys. The Self-Directed IEP had a strong effect on increasing the percentage of time students talked, started, and led the meetings. This was verified by survey results. These findings add to the growing literature demonstrating the effectiveness of the Self-Directed IEP.

Comparative genomic analyses among closely related species can greatly enhance our understanding of plant gene and genome evolution. We report de novo-assembled AA-genome sequences for Oryza nivara, Oryza glaberrima, Oryza barthii, Oryza glumaepatula, and Oryza meridionalis. Our analyses reveal massive levels of genomic structural variation, including segmental duplication and rapid gene family turnover, with particularly high instability in defense-related genes. We show, on a genomic scale, how lineage-specific expansion or contraction of gene families has led to their morphological and reproductive diversification, thus enlightening the evolutionary process of speciation and adaptation. Despite strong purifying selective pressures on most Oryza genes, we documented a large number of positively selected genes, especially those genes involved in flower development, reproduction, and resistance-related processes. These diversifying genes are expected to have played key roles in adaptations to their ecological niches in Asia, South America, Africa and Australia. Extensive variation in noncoding RNA gene numbers, function enrichment, and rates of sequence divergence might also help account for the different genetic adaptations of these rice species. Collectively, these resources provide new opportunities for evolutionary genomics, numerous insights into recent speciation, a valuable database of functional variation for crop improvement, and tools for efficient conservation of wild rice germplasm.

In the digital environment, steganography has increasingly received attention over the last decade. Steganography, which literally means “covered writing,” includes any process that conceals data or information within other data or conceals the fact that a message is being sent. Though the focus on use of steganography for criminal and terrorist purposes detracts from the potential use for legitimate purposes, the focus in this chapter is on its role as a security threat. The history of stenography as a tool for covert purposes is addressed. Recent technical innovations in computerized steganography are presented, and selected widely available steganography tools are presented. Finally, a brief discussion of the role of steganalysis is presented.

Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great challenge. In this article, a simple but fast approximate DBSCAN, namely, KNN-BLOCK DBSCAN, is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise). KNN-BLOCK DBSCAN uses a fast approximate kNN algorithm, namely, FLANN, to detect core-blocks (CBs), noncore-blocks, and noise-blocks within which all points have the same type, then a fast algorithm for merging CBs and assigning noncore points to proper clusters is also invented to speedup the clustering process. The experimental results show that KNN-BLOCK DBSCAN is an effective approximate DBSCAN algorithm with high accuracy, and outperforms other current variants of DBSCAN, including ρ-approximate DBSCAN and AnyDBC.

Natto, a fermented soybean product, has been consumed as a traditional food in Japan for thousands of years. Nattokinase (NK), a potent blood-clot dissolving protein used for the treatment of cardiovascular diseases, is produced by the bacterium Bacillus subtilis during the fermentation of soybeans to produce Natto. NK has been extensively studied in Japan, Korea, and China. Recently, the fibrinolytic (anti-clotting) capacity of NK has been recognized by Western medicine. The National Science Foundation in the United States has investigated and evaluated the safety of NK. NK is currently undergoing a clinical trial study (Phase II) in the USA for atherothrombotic prevention. Multiple NK genes have been cloned, characterized, and produced in various expression system studies. Recombinant technology represents a promising approach for the production of NK with high purity for its use in antithrombotic applications. This review covers the history, benefit, safety, and production of NK. Opportunities for utilizing plant systems for the large-scale production of NK, or for the production of edible plants that can be used to provide oral delivery of NK without extraction and purification are also discussed.