Reviewerly/exHarmony huggingface dataset:
Analyzing the Classification Techniques for Bulk of Cursive Languages Data: An Overview

The remarkable growth of texts both in online and offline is becoming a challenging issue which need exploration for further research. Diversities of regional and cultural changes have produced diverse languages as a source of communication. Variations of styles are existing for handwritten texts which is due to varying writing styles. The research area of text recognition is matured which has increased a number of directions in the area of research. A detail report of the existing literature is needed which can help practitioners and researchers to use the existing evidence and provide new solutions for identification of cursive languages and to optimize the ability of recognition for cursive text. For facilitating the researchers and practitioners by providing in-depth analysis of the existing literature, the proposed study provide a detail report through which researchers can get benefit of the literature and devise new solutions. This study is based on searching various popular libraries for identifying relevant materials associated with the proposed study.
Multi-label Long Short-Term Memory for construction vehicle activity recognition with imbalanced supervision

Sensor-based activity recognition for construction vehicles is useful for evaluating the skills of the operator, measuring work efficiency, and many other use cases. Therefore, many researches have explored robust activity-recognition models. However, it remains a challenge to apply the model to many construction sites because of the imbalance of the dataset. While it is natural to employ multi-label representation on imbalanced data with a large number of activity categories, multi-label robust classification for activity recognition has yet to be resolved because of the nature of the time-series property. In this work, we propose a novel multi-label long short-term memory (LSTM) model, which is effective for the sequence multi-labeling problem. The proposed model has connections to the temporal direction and attribute direction, which exploit both the temporal pattern and co-occurrence among attributes. In addition, by providing a bidirectional connection structure in the attribute direction, the model enables us to alleviate the dependency of the chain order in what we call "classifier chain", which is a classical approach to multi-label classification. To validate our methods, we conduct experiments using real-world construction-vehicle dataset.
User-centered Requirements for Patient Fluid Intake Monitoring Support

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis.
Bilinear Model for Knowledge Graph Completion with Hard Negative Examples Mining

Sparsity is a challenge for knowledge graph completion. The negative sample space, which is much larger than the positive sample space, has many negative samples similar to the positive samples or potential positive samples, which affects the performance of the knowledge graph completion model. We propose a novel knowledge graph completion method, hard negative examples mining bilinear model (HNEMB). This model uses two components to model entities and relations, namely models relations into relation matrices and inverse relation matrices to learn latent inverse semantics of relations, and models entities into head vectors and tail vectors to learn different representations of entity in different positions. Then we calculate the bilinear scores of triples, we choose correct candidates based on the scores. To address the problem of sparsity of the knowledge graph, we first propose a method for mining hard negative examples for knowledge graph completion, to make model effectively capture the associations between head entities, relations and tail entities. Finally, experiments on multiple knowledge graph completion datasets shows that, as compare with baselines, our model achieves significant improvements and shows its advantages to model sparse knowledge graph.
A Novel High-Precision Method Based on Adaptive Unscented Particle Filter for BeiDou/GPS Disciplined Crystal Oscillator

There is a random error in the pulse per second (PPS) signal output by BeiDou/GPS receiver. Meanwhile, the error of the frequency difference signal exists because of the frequency nonlinear drift of crystal oscillator caused by the module aging. The mathematical model of the frequency difference between the PPS signal and crystal oscillator signal is established, and the adaptive unscented particle filter(A-UPF) algorithm is proposed to process the noise contained in the frequency difference signal. In the importance sampling stage, the unscented Kalman filter is used to generate the optimal distribution function, which can reduce the estimation error and increase the robustness of the system. In the measurement update phase, the attenuation factor is introduced to reduce the impact of the uncertainty of noise statistical characteristics on the filtering result and overcome the phenomenon of particle degradation in UPF. When the statistical characteristics of the system noise are determined, the realtime performance of the adaptive UPF is also improved. Through simulation verification, the accuracy of output frequency can be improved by applying the A-UPF algorithm to BeiDou/GPS disciplining crystal oscillator system.
An Improved Salp Swarm Algorithm Based on Cosine Decline and Chaos Crossover Strategies

To solve the problem of low convergence precision and imbalance between exploitation and exploration abilities in the optimization process of salp swarm algorithm (SSA), an improved version based on the strategies of cosine decline and chaos crossover (CDCSSA) is proposed. The position updating of the leader in SSA is optimized by the cosine decline strategy, which enhances the local exploitation and global exploration abilities of the algorithm. The crossover strategy based on Logistic chaotic map helps followers to improve the precision of convergence and effectively jump out of the local optimal solution. The experimental results show that CDCSSA has a good performance in balancing exploitation and exploration abilities. Compared with other algorithms, CDCSSA achieves faster convergence speed and higher precision on 8 single-peak and multi-peak benchmark functions.
Parameterized Complexity of Logic-based Argumentation in Schaefer’s Framework

Argumentation is a well-established formalism dealing with conflicting information by generating and comparing arguments. It has been playing a major role in AI for decades. In logic-based argumentation, we explore the internal structure of an argument. Informally, a set of formulas is the support for a given claim if it is consistent, subset-minimal, and implies the claim. In such a case, the pair of the support and the claim together is called an argument. In this article, we study the propositional variants of the following three computational tasks studied in argumentation: ARG (exists a support for a given claim with respect to a given set of formulas), ARG-Check (is a given set a support for a given claim), and ARG-Rel (similarly as ARG plus requiring an additionally given formula to be contained in the support). ARG-Check is complete for the complexity class DP, and the other two problems are known to be complete for the second level of the polynomial hierarchy (Creignou et al. 2014 and Parson et al., 2003) and, accordingly, are highly intractable. Analyzing the reason for this intractability, we perform a two-dimensional classification: First, we consider all possible propositional fragments of the problem within Schaefer’s framework (STOC 1978) and then study different parameterizations for each of the fragments. We identify a list of reasonable structural parameters (size of the claim, support, knowledge base) that are connected to the aforementioned decision problems. Eventually, we thoroughly draw a fine border of parameterized intractability for each of the problems showing where the problems are fixed-parameter tractable and when this exactly stops. Surprisingly, several cases are of very high intractability (para-NP and beyond).
Invariance of Algebraic Immunity of Vectorial Boolean Functions under Equivalence Relations

Both of algebraic immunity and equivalence relations are of great cryptographic significance. But there are few researches on the properties of algebraic immunity of vectorial Boolean functions under equivalence relations. This paper defines three new notions, which are degree-rank, basic-algebraic-immunity-rank-set and component-algebraic-immunity-rank-set. This paper proves that the degree-rank of the graph of a function for any degree is Carlet-Charpin-Zinoviev (CCZ) equivalence invariant, the basic-algebraic-immunity-rank-set is affine equivalence invariant and the component-algebraic-immunity-rank-set is also affine equivalence invariant. Based on these analyses, this paper finds that the graph algebraic immunity is CCZ equivalence invariant, both of basic algebraic immunity and component algebraic immunity are affine equivalence invariant. This paper also finds that neither the basic algebraic immunity (basic-algebraic-immunity-rank-set) nor the component algebraic immunity (component-algebraic-immunity-rank-set) is extended affine equivalence invariant. It is also shown that the component algebraic immunity for a permutation is not invariant under inverse transformation. Last but not least, this paper investigates the graph algebraic immunity and the component algebraic immunity of optimal 4-bit permutations of all the affine equivalence classes.
Image Denoising for Efficient Anomaly Detection in Videos

Video anomaly detection is tasked with the identification of events that do not conform to expected events. Currently, most methods tackle this problem by mining common normal patterns from training data and minimizing the generative errors. In inference phase, a large generative error is assigned to an abnormal event and a small one is for a normal event. However, because these methods only focus on the error intensity but ignore the error pattern, partial abnormal events will own similar generative error intensities to the normal ones. Thus, we propose to tackle the anomaly detection within an efficient image denoising framework. In this framework, the generative errors are treated as a kind of artificial noise, which will be superimposed on the current frame. Then, the contaminated frame is fed into a denoising network, which is trained to output a frame close to the current frame. In the denoising network, the common patterns of training data and the error patterns of each training frame can be learned jointly. It will benefit anomaly detection by restraining the generative errors of normal frames. The results on several challenging benchmark datasets demonstrate the effectiveness of our proposed method.
A Novel SAR Image Ship Small Targets Detection Method

To satisfy practical requirements of high real-time accuracy and low computational complexity of synthetic aperture radar (SAR) image ship small target detection, this paper proposes a small ship target detection method based on the improved You Only Look Once Version 3 (YOLOv3).The main contributions of this study are threefold.First, the feature extraction network of the original YOLOV3 algorithm is replaced with the VGG16 network convolution layer.Second, general convolution is transformed into depthwise separable convolution, thereby reducing the computational cost of the algorithm.Third, a residual network structure is introduced into the feature extraction network to reuse the shallow target feature information, which enhances the detailed features of the target and ensures the improvement in accuracy of small target detection performance.To evaluate the performance of the proposed method, many experiments are conducted on public SAR image datasets.For ship targets with complex backgrounds and small ship targets in the SAR image, the effectiveness of the proposed algorithm is verified.Results show that the accuracy and recall rate improved by 5.31% and 2.77%, respectively, compared with the original YOLOV3.Furthermore, the proposed model not only significantly reduces the computational effort, but also improves the detection accuracy of ship small target.
Multi-RAT Multicast 360° Video Delivery

360° video streaming is rapidly progressing towards the multimedia industry, offering an interactive user experience but also challenging telco operators due to the huge volume of the video data and the user channel dynamics. We propose a software defined transport layer proxy overlay architecture, that builds on top of existing standard technologies, to facilitate hybrid multicast/unicast 360° video delivery as a service-particularly combining LTE multicast and WiFi unicast transmissions. In addition we holistically define the emergent optimization problem, where we jointly consider network association, multicast/unicast scheduling, and spectrum/bandwidth management in both networks, taking also into consideration user specific preferences, e.g., the popularity of the video tiles. The proposed framework is evaluated through a series of simulation based experiments following 3GPP standard parameters and real 360° video traces. Our findings reveal significantly increase in the user utility, particularly when the wireless spectrum is limited.
Analysis on the Perfection Of My Country’s Juvenile Legal Education System Based on Computer

Abstract From the process of campus violence to juvenile delinquency, we can observe that contemporary juvenile legal education is very lacking. As an indispensable content in the basic literacy education system of all citizens of our country. The rule of law education seems to be no longer applicable in the field of young people. It is also because of the increasing probability of juvenile criminal accidents. And the lower limit of the current criminal’s age is getting smaller [1] . This phenomenon is absolutely not allowed for a country under the rule of law like our country. In order to strengthen our country’s youth legal education, we should use school resources to improve the basic youth legal education system. While the current computer skills are going deep into the realm of life, what we need to do is to use computer teaching methods for youth legal education.
A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization

The lack of spelling conventions and the natural evolution of human language create a linguistic barrier inherent in historical documents. This barrier has always been a concern for scholars in humanities. In order to tackle this problem, spelling normalization aims to adapt a document’s orthography to modern standards. In this work, we evaluate several character-based neural machine translation normalization approaches—using modern documents to enrich the neural models. We evaluated these approaches on several datasets from different languages and time periods, reaching the conclusion that each approach is better suited for a different set of documents.
Downtime Data Classification Using Naïve Bayes Algorithm on 2008 ESEC Engine

In operational production activities, the data logger of a machine produces piles of diagnostic history data that can be utilized to make it easier to determine maintenance planning. Data mining is the process of identifying patterns from data sets in a large data stack to be able to produce valuable data. Processing data mining has the potential to carry out the process of selecting the type of downtime according to predefined attributes. By using the Naïve Bayes Algorithm, it is expected that the data classification process can be performed appropriately. The data generated in the form of information contains a sequence of problem events that often appear in tables and graphs. This study will discuss how to apply data mining to diagnostic history data using the Naïve Bayes Algorithm. Testing the diagnostic history data using Rapid miner achieves up to 100% accuracy.
Transition based neural network dependency parsing of Tibetan

In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic analysis of Tibetan language dependence. Part of the corpus of Qinghai Normal University’s part-of-speech tag set is marked by the corresponding mapping relationship is transformed into the corpus annotated by the national standard part-of-speech tag set. At the same time, the CoNLL format Tibetan language dependency syntax tree library is constructed, and the method of shift-reduce plus neural network is adopted to systematically study and analyze the Tibetan language dependency syntax. Thereby improving the quality of Tibetan dependency syntactic analysis, and its accuracy rate reaches UAS:94.59%
Image Retrieval Systems: From Underlying Feature Extraction to High Level Intelligent Systems

In this digital era, the profound amounts of complex images are being produced due to the up gradation of image capturing devices. So there is a huge demand of an efficient retrieval system for indexing and retrieving these images. Content based image retrieval (CBIR) system has been an active and promising research field in the area of image retrieval and processing. This system aims at retrieving the most appropriate and visually similar images from the large databases with the extraction of low level features of the images like color, edge and texture by various extraction techniques. This paper analyzes the basic CBIR system and the various achievements obtained in these systems mainly in the areas of feature extraction, indexing and intelligent CBIR systems. The most of the research in this area is now being focussed in developing of an advanced and intelligent CBIR system by using various deep learning algorithms which includes Convolutional neural network, auto encoders; long short term neural networks etc. so that the accuracy of the system can be improved. Finally, in the paper our insights and challenges are also provided for future research.
Measuring Student Satisfaction Level Regarding Aesthetic and Instructor Dimensions of Web-Based Distance Learning Programs

Today, many educational institutions provide their content to their recipients through web-based distance learning applications. While providing this education method to the recipients through communication technologies, the aesthetic design and the instructor dimension of the content are of utter importance. In this framework, the study measured the level of student satisfaction regarding the aesthetic and instructor dimension in web-based distance learning programs within the case of the Spiritual Guidance program. Accordingly, the study of distance learning programs in terms of aesthetic and instructive dimensions will contribute to the research that will be conducted in this direction.
Emerging Role of Intelligent Techniques for Effective Detection and Prediction of Mental Disorders

It has been established and accepted that mental disorders pose one of the most prominent health challenges worldwide. Information retrieval from mental health data which may be explicit in electronic health records and clinical notes or may be implicit in social media postings has vast potential to detect, distinguish and diagnose the status of mental health of individuals and aid in managing this problem. This paper summarizes some recent studies that apply the state of art Artificial Intelligence (AI) techniques to mental health data. The paper summarizes that newly emerging AI technologies hold a decent promise and can be leveraged to predict, assist in the diagnosis and management of mental disorders. The role of AI in this area becomes particularly important in a scenario where there is a worldwide dearth of qualified professionals who can deal with mental health disorders, where the cost of these services is high and the people suffering from these problems often refrain from availing these services due to social stigma associated with it.
WITHDRAWN: Super-Resolution Reconstruction Of Moving Images And Home Aerobic Exercise Monitoring Based On Sparse Representation

Super-resolution algorithm image sequences range from low resolution to high-resolution images. Motion estimation (ME) requires an overall search for accuracy and consistency. In this article, the new super-resolution reconstruction algorithm develops a robust mesh approach that continuously fuses multiple approximate motion vectors. The new algorithm has made two major improvements in previous studies. Instead, only two frames are used to make a more accurate and consistent estimate of each frame's motion vector throughout the first array. Second, quantitatively introduced to measure my reliability and cost-effectiveness of the reconstruction algorithm. The simulated and realistic sequences of this algorithm are applied, and the results are presented in the method. Our system has two different temperaments. Motion pattern representation. The production network then integrates these images to achieve a gray part by building in-depth video sequence motion detection, stance knowledge, and material usage information. The second part uses a new method to estimate the number of repetitions during the exercise period. Purpose Tax Practice, which includes data tested on our system. Finally, achieved very promising results regarding the identification rate and the number of iterations.
PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning

Imitation learning is a natural way for a human to describe a task to an agent, and it can be combined with reinforcement learning to enable the agent to solve that task through exploration. However, traditional methods which combine imitation learning and reinforcement learning require a very large amount of interaction data to learn each new task, even when bootstrapping from a demonstration. One solution to this is to use meta reinforcement learning (meta-RL) to enable an agent to quickly adapt to new tasks at test time. In this work, we introduce a new method to combine imitation learning with meta reinforcement learning, Probabilistic Embeddings for hybrid meta-Reinforcement and Imitation Learning (PERIL). Dual inference strategies allow PERIL to precondition exploration policies on demonstrations, which greatly improves adaptation rates in unseen tasks. In contrast to pure imitation learning, our approach is capable of exploring beyond the demonstration, making it robust to task alterations and uncertainties. By exploiting the flexibility of meta-RL, we show how PERIL is capable of interpolating from within previously learnt dynamics to adapt to unseen tasks, as well as unseen task families, within a set of meta-RL benchmarks under sparse rewards.