Reviewerly/exHarmony huggingface dataset:
Teaching Mode of English Language and Literature Based on Artificial Intelligence Technology in the Context of Big Data

Widespread problems exist in English major education, which directly or indirectly restrict the development of English major education. The teaching mode of English language and literature is considered in the context of big data. This paper designs an online English learning system platform based on the BP neural network algorithm under the big data network and uses the BP neural network for learning to improve the learning level of the platform. This article adds a course of Chinese and Western cultural comparison in the process of education and teaching. Through Chinese literature and culture courses, it is conducive to learning and inheriting Chinese civilization, training students' ability to understand and communicate Chinese and Western civilizations, and also training students' thinking skills. The teaching plan introduces courses with Chinese elements such as the history of Chinese philosophy and the introduction of Chinese literary classics. Through the courses of Chinese literature and culture, students are guided to ask questions, apply theory to practice, conduct experiments, obtain data, and then analyze problems and draw conclusions according to certain research methods, which exercises students' thinking ability. This also improves students' language application ability through English writing courses. In addition, this paper designs an English language and literature teaching system. With the support and guidance of the English intelligent teaching system, it analyzes the learning level and weaknesses and puts forward teaching suggestions. Then, data mining technology is used to find the key information in the answers from the massive data and is applied to English teaching. In the English learning test, the average attitude of the experimental class was 1.3314 points. This research will effectively solve the problem of English major courses.
Deep High Dynamic Range Imaging without Motion Artifacts Using Global and Local Skip Connections

This paper proposes a new HDR image generation method using a deep residual network. The proposed method aligns input images of three low dynamic range (LDR) images with different exposure times for the same scene based on the images with intermediate exposure times and uses them as input to the network. In the network, three LDR images and three images converted into high dynamic range (HDR) regions are input, and three LDR images with improved artifacts and an alpha map are output to generate HDR images. The method proposed through the experimental results can show HDR images without color distortion or ghosting artifact when compared to the existing method, and good performance compared to the HDR function used in consumer imaging systems.
Smart Web Dictionary using OCR

Language and linguistics play an important part in our daily lives more than we realize. Language is a system of communication which we use to express our thoughts and ideas. As the times have progressed language and communication has played a vital role in shaping our societies allowing us to work together and coexist with each otherDue to diverse nature of the geography and climate of ur planet it has resulted in varying language systems each vastly different from one another making it harder for a person knowing one language to understand and another person knowing another language this leads to miscommunication. Miscommunication can lead to misunderstandings and complicate problems which could be solved simply. Hence understanding each other and communicating are extremely important especially in this modern day and age. Our pipeline approach which uses a combination of CNN, NLP and iNLTK and yielding a global accuracy of 94&#x0025; Hence its essential people understand each others language and linguistic for smooth communication and dialogue which can lead to solving of disputes and exchange of ideas
Exploiting LSB Self-quantization for Plaintext-related Image Encryption in the Zero-trust Cloud

The zero-trust (ZT) principle is a promising way to ensure data security in cloud storage. This principle stated that data confidentiality is guaranteed by encryption. However, in ZT image cloud storage scenarios, traditional symmetric cryptosystems and image encryption schemes based on the permutation-diffusion structure cannot balance between efficiency and security against some cryptanalysis. Existing plaintext-related image encryption schemes improve such problems but cause additional keys overhead for different plaintexts, additional ciphertexts overhead, or decryption failure under clipping and noise attacks. Thus, these encryption schemes cannot meet the requirements of ZT in both reducing implementation overhead and resisting multiple attacks. By using least significant bit (LSB) embedding in pixels, we propose a self-adaptive process driven by LSB self-quantization, which forms a novel plaintext-related encryption scheme in a provided ZT model. In our analysis, two pixel-level properties can be obtained through LSB repetitive embedding, LSB extraction, bitwise-XOR, and cyclic-shift operations. With these properties, the plaintext-related key stream is generated to encrypt pixel positions and pixel values, and its disturbance value can be spontaneously extracted from the LSB for decryption. As a result, the proposed scheme needs only one-round execution, resists multiple cryptanalysis without additional keys and ciphertexts, and ensures decryption under clipping or noise attacks. Further simulation results demonstrate that the scheme is efficient and has good resistance to these attacks, which indicates its good application potential for ZT image cloud storage scenarios.
Dual integration of multi‐model with spatial‐temporal occlusion‐awareness for visual object tracking

Visual object tracking is a complicated problem due to varied challenges such as occlusion, background clutter, target appearance changes, abrupt target motion, and illumination variations. Different tracking algorithms perform well on different challenges because of their unique strengths. In this paper, a dual-integration framework that integrates the strengths of multiple models while avoiding their weaknesses is proposed. In the proposed framework, several evaluation criteria and multiple models updated via different processes are combined. To distinguish the target from the distractors caused by different criteria, the motion dynamic model and the forward-backward analysis is introduced to provide information that is complementary to appearance information. In addition, for discriminating occlusion, a spatial-temporal occlusion-aware approach is further proposed. The detected occlusion results are applied in avoiding contamination of the appearance model. Extensive experiments on multiple benchmarks demonstrate that this proposed method improves the overlap rate of a hand-crafted feature-based tracker with relative gains of 4%, 1.6%, 1.9%, 2.2% and 8.5% on OTB-2015, OTB-2013, Temple-Color, UAV123 and UAV20L, respectively. Also, the experimental results demonstrate that our approach outperforms a deep feature-based tracker in overlap rate by 1.4%, 1.6%, 2.3%, 2.5% and 3.5% on OTB-2015, OTB-2013, Temple-Color, UAV123 and UAV20L, respectively.
Enhancing the Performance of Classifiers in Detecting Abnormalities in Medical Data using Nature Inspired Optimization Techniques

A significant variety of nature-inspired techniques are used in the field of Machine Learning (ML) to handle a wide range of problems. The term &#x201C;evolutionary computation&#x201D; refers to a group of global optimization algorithms inspired by biological evolution. They&#x0027;re a type of population-based trial-and-error problem solver that includes a metaheuristic or stochastic optimization function. The aim of this work is to create a model that can effectively detect diabetes from the given dataset using nature inspired algorithms. This work implants the usage of nature inspired optimization techniques for predicting diabetes. The nature inspired algorithms such as Bat Algorithm, Hybrid Bat Algorithm, Grey Wolf optimization algorithm, and Firefly algorithm were mostly used for numerical data and also used for improving the accuracy and other performance metrics. Seven classification algorithms were used in detection of diabetes and the Nature inspired algorithms are used to tune the hyperparameters of the classifiers in order to get an enhanced accuracy. Majority of the used classifiers&#x0027; performance was enhanced with the use of nature inspired algorithms. Firefly algorithm and hybrid bat algorithm outperforms the other algorithms with the highest accuracy of 77.2&#x0025; in the case of random forest classifier.
SAMoA: A Self-adaptive Mobility Monitoring Framework for MCI Elderly People and Their Caregivers

People with Mild Cognitive Impairment (MCI) often experience a slight -but noticeable- decrease in their mental abilities, greater than other people of the same age, but not so severe as to interfere with their normal daily life and activities. MCI currently affects between 8 and 37% of people over 65 and, according to the latest findings, people with MCI are more likely to develop dementia. This paper introduces SAMoA: a self-adaptive framework aimed at helping elderly people suffering from MCI and their caregivers. SAMoA includes two self-adaptive mobile applications allowing to monitor the mobility of MCI elderly people, warning their caregivers when they leave their (previously defined) safe zones or change their daily mobility routines.
Generating class name in sequential manner using convolution attention neural network

Software code comprehension is strongly dependent on identifier names; therefore, software developers spend a lot of time assigning suitable names to identifiers. Manually suggesting a good name is a time taking and hard problem for developers. For automatic identifiers name recommendation, various techniques have been proposed. Most of the work has been done for method name prediction. We found very few research works on class name recommendation. A good class name communicates the class’s intent, whereas bad ones create confusion and frustration in the developer’s mind. In this paper, we first analyze the existing class name recommendation approach for dynamically typed language. In this approach, we represent the nature or behavior of python classes in quantitative form using the embedding concept for heterogeneous graphs. Next, we use these embeddings to suggest class names. The first approach can only suggest the existing class names. Therefore, we propose a new approach, which is based on the convolution attention model. In the new approach, we try to generate class name as a token sequence, instead of whole class name at once. We use two variants of the attention mechanism: simple attention and copy attention. Copy attention based model is able to predict out-of-vocab tokens during prediction. Experimental results suggest that the convolution attention model can predict accurate class name tokens.
Drivers of Users’ Embracement of 3D Digital Educational Spaces in Higher Education: A Qualitative Approach

Abstract Three-Dimensional Virtual Worlds (3DVW) have significant promise for the development of teaching and learning environments. Educational communities have become interested in such virtual platforms, and this technology has widely benefited educational settings. This study aims to identify the factors that drive the acceptance of 3DVWs as means for distance learning by higher education students, and provides a taxonomy of such drivers. A qualitative approach was employed for data collection to enable a detailed exploration. Two data collection instruments were used in the experiment: a virtual synchronous focus group and an open-ended question. One hundred twenty-one students participated in 13 virtual focus groups, and 124 students responded to the open-ended questions. Thematic analysis was used to analyse data, and as a result, 26 factors were identified as antecedents to the acceptance of 3DVW in higher education. Those factors are then classified into 12 categories based on their definition and similarities. Twenty-one out of the 26 factors impact the user acceptance of 3DVW positively, and five factors impact it negatively. The findings of this study indicate advances in theory, methodology, and pedagogical practice.
Assessing Corpus Evidence for Formal and Psycholinguistic Constraints on Nonprojectivity

Abstract Formal constraints on crossing dependencies have played a large role in research on the formal complexity of natural language grammars and parsing. Here we ask whether the apparent evidence for constraints on crossing dependencies in treebanks might arise because of independent constraints on trees, such as low arity and dependency length minimization. We address this question using two sets of experiments. In Experiment 1, we compare the distribution of formal properties of crossing dependencies, such as gap degree, between real trees and baseline trees matched for rate of crossing dependencies and various other properties. In Experiment 2, we model whether two dependencies cross, given certain psycholinguistic properties of the dependencies. We find surprisingly weak evidence for constraints originating from the mild context-sensitivity literature (gap degree and well-nestedness) beyond what can be explained by constraints on rate of crossing dependencies, topological properties of the trees, and dependency length. However, measures that have emerged from the parsing literature (e.g., edge degree, end-point crossings, and heads’ depth difference) differ strongly between real and random trees. Modeling results show that cognitive metrics relating to information locality and working-memory limitations affect whether two dependencies cross or not, but they do not fully explain the distribution of crossing dependencies in natural languages. Together these results suggest that crossing constraints are better characterized by processing pressures than by mildly context-sensitive constraints.
Artificial Intelligence and Technological Development in Behavioral and Mental Healthcare

The world is witnessing an increase in mental disorders and with the current rate, this is expected to rise. Physiological, environmental, and biological factors combined play an essential function in causing mental illnesses. As a matter of fact, if mental health problems are not addressed properly, it will give rise to a tremendous burden of diseases around the globe because with estimated surveys, one in five adults worldwide is already suffering from mental disorders. Leveraging artificial intelligence techniques allows formulating risk models for determining an individual&#x0027;s risk of developing mental illness and provides the potential for the betterment of pre-diagnosis screening tools. People who are unaware of the term AI may picture intelligent machines as driverless cars, drones, or ironman suits while others might imagine it as some sort of mysterious robotic computer confined to scientific advancement which will emerge in the future eventually. But the psychological and mental healthcare are also getting benefits from the development in AI such as computer work for studying, recognizing and analyzing that can help doctors with identifying the diseases and treat the patient accordingly. Instead of therapists, artificially intelligent virtual humans are also being used nowadays that can communicate with care searchers and come up with treatment solutions. In the review, modern hi tech advancements are highlighted in order to display appearing potentiality and to furnish a glance of innovations on the outlook. Many practical benefits have also been discussed afterward which machine technology introduce to psychological well-being care accompanying further deliberations.
Dependency Factors in Evidence Theory: An Analysis in an Information Fusion Scenario Applied in Adverse Drug Reactions

Multisensor information fusion brings challenges such as data heterogeneity, source precision, and the merger of uncertainties that impact the quality of classifiers. A widely used approach for classification problems in a multisensor context is the Dempster-Shafer Theory. This approach considers the beliefs attached to each source to consolidate the information concerning the hypotheses to come up with a classifier with higher precision. Nevertheless, the fundamental premise for using the approach is that sources are independent and that the classification hypotheses are mutually exclusive. Some approaches ignore this premise, which can lead to unreliable results. There are other approaches, based on statistics and machine learning techniques, that expurgate the dependencies or include a discount factor to mitigate the risk of dependencies. We propose a novel approach based on Bayesian net, Pearson's test, and linear regression to adjust the beliefs for more accurate data fusion, mitigating possible correlations or dependencies. We tested our approach by applying it in the domain of adverse drug reactions discovery. The experiment used nine databases containing data from 50,000 active patients of a Brazilian cancer hospital, including clinical exams, laboratory tests, physicians' anamnesis, medical prescriptions, clinical notes, medicine leaflets packages, international classification of disease, and sickness diagnosis models. This study had the hospital's ethical committee approval. A statistically significant improvement in the precision and recall of the results was obtained compared with existing approaches. The results obtained show that the credibility index proposed by the model significantly increases the quality of the evidence generated with the algorithm Random Forest. A benchmark was performed between three datasets, incremented gradually with attributes of a credibility index, obtaining a precision of 92%. Finally, we performed a benchmark with a public base of heart disease, achieving good results.
Secured platform for healthcare data: privacy preservation based blockchain environment

Purpose To make more smart health-care system, the health-care data should be shared in the secure manner, and it improves health-care service quality. This paper aims to implement a modern decentralized blockchain, safe and easy-to-use health-care technology application in the cloud. Findings On observing the graph, the convergence analysis of proposed Levy Flight-integrated moth flame optimization method at 80th iteration was 4.59%, 2.80%, 3.316%, 8.92% and 2.55% higher than the traditional models MFO, artificial bee colony (ABC), particle swarm optimization (PSO), moth search algorithm (MSA) and glow worm swarm optimization (GWSO), respectively, for Hungarian data set. Particularly, in best case scenario, the adopted method attains low cost value (5.672671) when compared to all other traditional models such as MFO (5.727314), ABC (5.711577), PSO (5.706499), MSA (5.764517) and GWSO (5.723353). Originality/value The proposed method achieved effective performance in terms of key sensitivity, sanitization effectiveness, restoration effectiveness, etc.
Using measurement induced disturbance to characterize correlations of multipartite non-k-classical states

In [S. L. Luo, Using measurement induced disturbance to characterize correlations as classical or quantum, Phys. Rev. A77 (2008) 022301], the author presented a statistical correlation measure for bipartite quantum states using measurement induced disturbance. In this paper, motivated by this correlation measure, we obtain some necessary conditions for [Formula: see text]-classical states in multipartite quantum systems, and obtain a relation between the set of [Formula: see text]-classical states and the set of [Formula: see text]-product states; Secondly, we introduce a class of correlation measures based on trace distance using measurement induced disturbance in order to quantify the strength of correlation for non-[Formula: see text]-classical states; Lastly, we introduce the concept of quantum channel with respect to [Formula: see text]-partition, it is simply denoted by identical-[Formula: see text]-channel, and furthermore discuss the influences of the correlation measure under identical-[Formula: see text]-channel.
Systematically Understanding Graph Accelerator Dimensions and the Value of Hardware Flexibility

Because of the importance of graph workloads and the limitations of central processing units/graphics processing units (CPUs/GPUs), many graph-processing accelerators have been proposed. Most prior such accelerators adopt a single fixed algorithm. While helpful for specialization, this leaves performance potential from flexibility on the table and also complicates understanding the relationship between graph types, workloads, algorithms, and specialization. In this work, we explore the value of flexibility in graph-processing accelerators. Our approach is to identify a taxonomy of key algorithm variants, and develop a modular architecture, PolyGraph, which is flexible across them. The key to flexibility is our novel Taskflow execution model, which unifies task and dataflow parallelism. Overall, we find that flexibility is essential; PolyGraph outperforms similarly provisioned GPUs by mean 49.6× (up to 275×), and the best prior accelerator by mean 5.7×.
A Fully Bayesian Inference with Gibbs Sampling for Finite and Infinite Discrete Exponential Mixture Models

In this paper, we propose clustering algorithms based on finite mixture and infinite mixture models of exponential approximation to the Multinomial Generalized Dirichlet (EMGD), Multinomial Beta-Liouville (EMBL) and Multinomial Shifted-Scaled Dirichlet (EMSSD) with Bayesian inference. The finite mixtures have already shown superior performance in real data sets clustering using the Expectation–Maximization approach. The proposed approaches in this paper are based on a Monte Carlo simulation technique namely Gibbs sampling algorithm including an additional Metropolis–Hastings step, and we utilize exponential family conjugate prior information to construct their posterior relying on Bayesian theory. Furthermore, we also present the infinite models based on Dirichlet processes, which results in clustering algorithms that do not require the specification of the number of mixture components to be given in advance and selects it in a principled manner. The performance of our Bayesian approaches was evaluated in some challenging real-world applications concerning text sentiment analysis, fake news detection, and human face gender recognition.