Reviewerly/exHarmony huggingface dataset:
NLP2SQL Using Semi-supervised Learning

Human Computer interaction has been moving towards Natural language in the modern age. SQL (Structured Query Language) is the chief database query language used today. There are many flavors of SQL but all of them have the same basic underlying structure. This paper attempts to use the Natural Language inputs to query the databases, which is achieved by translating the natural language (which in our case is English) input into the SQL (specific to MySQL database) query language. Here we use a semi-supervised learning with Memory augmented policy optimization approach to solve this problem. This method uses the context of the natural language questions through database schema, and hence its not just generation of SQL code. We have used the WikiSQL dataset for all our experiments. The proposed method gives a 2.3% higher accuracy than the state of the art semi-supervised method on an average.
Wafer Container Monitoring Concerning Airborne Molecular Contaminations along a 300 mm Power Semiconductor Production Flow

The focus of the study was to understand the behavior of airborne molecular contaminations (AMC) within the 300 mm wafer containers called front-opening unified pods (FOUPs) in a high-volume fabrication facility for power semiconductors of Infineon Technologies Dresden. A main goal was to implement new concepts and strategies to prevent the different power semiconductors from any yield losses driven by AMC. It could be shown, that there is a strong dependency of the concentration and the type of the determined contaminations on the investigated process steps.
Algebraic Stability Analysis of Particle Swarm Optimization Using Stochastic Lyapunov Functions and Quantifier Elimination

Abstract This paper adds to the discussion about theoretical aspects of particle swarm stability by proposing to employ stochastic Lyapunov functions and to determine the convergence set by quantifier elimination. We present a computational procedure and show that this approach leads to a reevaluation and extension of previously known stability regions for PSO using a Lyapunov approach under stagnation assumptions.
The anomaly detection mechanism using deep learning in a limited amount of data for fog networking

The treatment of brain ischemia is the use of tissue plasminogen activator. But after treatment there may be a risk of Brain hemorrhage. Under the pressure of rescue time, medical personnel and their families must make decisions. There is currently no benchmark that can provide what can happen after treatment. This is because there are too many uncertainties. This study proposes that an adaptive deep autoencoder model is used to learn the features of a particular data and analyze the results that the data belongs to. For data preprocessing, the study also proposes methods such as K-means and connecting and labeling non-background areas to de-noise the image and preserve the desired areas to the maximum extent. In addition, we use Variational AutoEncoder Wasserstein Generative Adversarial Network with Gradient Penalty (VAE WGAN-GP) to generate 3D medical images to solve the problem of too few training data. We study key techniques such as pre-processing of real data and image generation with limited real data, so that the model can be trained smoothly. From our experiments, we found that the autoencoder model analyzes real data that is bleeding and non-bleeding and achieves 76% accuracy.
Algorithmic Trading Regulation: The Frameworks for Human Supervision and Direct Market Interventions

This paper identifies the regulatory gaps that currently exist in algorithmic trading and provides a framework for machine learning regulation in finance. It compares the regulation of algorithmic trading in the capital markets by both human supervision and direct market intervention in the UK, the EU and the US to identify techniques they have in common, as well as local differences. Section II sets out what algorithmic trading is, how it is defined, which of its functions have a positive effect and which are negative for risk and impact. Section III examines how trading risks can be managed by human supervision. Section IV looks at how direct market intervention can mitigate the risks of algorithmic trading, focusing on the circuit breaker requirement. Finally, the liability of the parties involved (traders, firms, and trading venues) are examined and the possible enforcement actions that regulators may take are set out.
Improved D*Lite algorithm path planning in complex environment

Aiming at the problem of slow planning speed of D*Lite algorithm in unmanned cruise ship path planning, an improved D*Lite algorithm is proposed and applied to complex environments with dynamic obstacles. This method reduces the unnecessary search area by improving the heuristic function, so as to shorten the path search time, and smooth the obtained path to reduce the path planning distance. Experiments show that the improved D*Lite algorithm effectively improves the performance of path planning in many aspects.
A Effective Method Using Wavelet Transform for Haze and Noisy Image

In order to enhance the visualization of low-quality images and adapt to the practical work of public security picture detection and visual detection in haze weather, we integrate the currently widely applicable processing methods and adopt a new processing method. This method uses the quad-tree method to solve the problem of the selection defect of the priori atmospheric light value of the dark channel. Aiming at the interference effect between the wavelet frequency division reconstruction coefficients, the adaptive correlation method under the gradient is applied. For noise that gains along with wavelet coefficients, we gives a noise reduction method by compromises the threshold. The experimental results show that the hierarchical contrast of the image is fine, the color fidelity is fine, and the indicators are generally better than other algorithms.
Target Tracking Algorithm Based on Kernel Correlation Filter with Anti-occlusion Mechanisms

To solve the problem of target occlusion in kernel correlation filter, the paper proposes a target tracking algorithm based on kernel correlation filter with anti-occlusion mechanism. It combines histogram of oriented gradient, histogram of local intensities and color names to improve the tracking accuracy. Moreover, two anti-occlusion mechanisms are combined to deal with the problem of target occlusion. Firstly, the online support vector machine classifier is employed to re-detect the target. Secondly, the image correlation matching combined with color histogram matching are applied to locate the target lost for a long time due to the occlusion. Through an experiment on the OTB100 dataset, the results indicate that the algorithm proposed in this paper are superior to the other mainstream tracking algorithms in overlap precision, distance precision and center location error. Therefore, it has relatively high precision and robustness in the anti-occlusion performance.
Unconventional Mechanisms for Biometric Data Acquisition via Side-Channels

In this paper, we discuss the proliferation of household smart devices and review the literature to explore whether the implementation characteristics of such systems may provide avenues of attack to obtain private biometric data. Examples include the use of mechanical hard drives as audio microphones and interception of soft-keyboard input through audio analysis of haptic feedback. As the use of biometric data increases in casual environments, the opportunity for it to be stolen in unexpected ways is also increasing. There are many examples of the technology being utilised by hackers to enable unexpected use such as spoofing. We examine the importance and sanctity of biometric data in the modern world and posit that manufacturers must avoid complacency and advocate secure design, to ensure security and privacy of users.
The decision tree classification with C4.5 and C5.0 algorithm based on R to detect case fatality rate of dengue hemorrhagic fever in Indonesia

Abstract Dengue hemorrhagic fever (DHF) is a deadly disease that is transmitted through mosquito bites from the genus Aedes especially Aedes aegypti, Aedes aegypti can occur every year and affect any age. DHF has a high case fatality rate (CFR). Therefore we need a method that can detect CFR of DHF in Indonesia, one of which is the decision tree classification based on C4.5 and C5.0 algorithm. C4.5 and C5.0 algorithm starting with forming a root node and ending with a leaf node by evaluating attributes using information gain to measure the effect of attributes in classifying a dataset. In this article, an applied research is carried out, namely applying the decision tree classification with C5.0 and C4.5 algorithm based on R software to detect CFR of DHF in Indonesia. The attributes used are the incidence rate (IR), population density, many hospitals, and many medical personnel. The results show that C5.0 algorithm has a big error than the C4.5 algorithm, while the C5.0 algorithm has a smaller tree than the C4.5 algorithm.
Exploring the Challenges and Potential Alternatives to Insulin Pump Technologies

This paper examines the current technology of diabetes management devices, primarily insulin pumps. Insulin pumps are effective tools for the precise control of glucose levels, for type 1 diabetes (T1D) patients. Many design and usability challenges still exist with insulin pump technology. In this study, we investigated current shortcomings and limitations of insulin pumps through survey (N=103) and interview (N=7) data collection methods. Our findings revealed issues with current insulin pumps including: 1) wear-ability and accessibility in public; 2) operating devices while performing demanding tasks; 3) interruptions with social activities and interactions; 4) continuity of maintenance, and 5) interface operations. Our study aspires to inform the future design of novel insulin pumps that enable people with T1D to maintain better control of their glucose levels through consistent and steady interactions with these tools during their everyday activities.
Towards Decentralized Trust Management Using Blockchain in Crowdsourcing Networks

Crowdsourcing networks solve difficult problems by utilizing large-scale human intelligence. The openness of crowdsourcing inevitably introduces malicious users, which affects the trustworthiness of crowdsourced results. To address the above issue, this paper proposes a decentralized trust management approach using blockchain for crowd computing scenarios. Through inserting known-result subtasks into crowdsourced computing tasks, the trustworthiness of the result of each task is calculated, as well as the trust value offset of crowdsourcing workers and the trustworthiness of the final result. Then, a novel consensus mechanism Proof of Capturing-Work (PoCW) is proposed, where miners get a smaller difficulty of mining if they capture more lazy actions in the crowd computing. Under the consensus mechanism, a trustworthiness blockchain is developed to conveniently manage trust for crowdsourcing networks. Finally, the effectiveness of the proposed solution is analyzed in terms of security and cost. Simulations are conducted to validate the correctness and theoretical results.
Fine-Tuning of Learning Rate for Improvement of Object Detection Accuracy

The ubiquitous applications like Autonomous vehicles, Biometric Recognition, Security in defence sector, Medical field for disease identification, Video surveillance and Scene understanding activated vast research in the realm of Deep Learning. Deep Learning techniques [1] have emerged as a powerful tool for Feature Extraction from images or videos and also led to remarkable breach in the field of Object Detection. The field of Deep Learning [2] is predominantly becoming popular due to the improvement of Convolution Neural Network (CNN) Architectures. The Deep Learning model's accuracy depends on various Hyper-Parameters such as 'Learning Rate', 'Batch Size', 'Epoch Rate', 'Optimization Function', 'Activation Function', 'Dropout Rate' etc. Identifying the best values of these Hyper-Parameters, improves the Object Detection accuracy. This paper mainly concentrates on the selection of a better value of 'Learning Rate', at which maximum Object Detection accuracy is obtained. Different Datasets are considered for analysis and the Learning Rate at which each dataset results better accuracy is identified. After rigorous experimentation, a relationship is formulated between 'Learning Rate' and 'Dataset Size' which holds good for any dataset.
Revisiting Feature Ranking Methods using Information-Centric and Evolutionary Approaches: Survey

Abstract: Feature ranking can have a severe impact on the feature selection problem. Feature ranking methods refer to the structure of features that can accept the designed data and have a positive effect on the quality of features. Moreover, accessing useful features helps in reducing cost and improving the performance of a feature ranking algorithm. There are numerous methods for ranking the features that are available in the literature. The developments of the past 20 years in the domain of knowledge research have been explored and presented in terms of relevance and various known concepts of feature ranking problems. The latest developments are mostly based on the evolutionary approaches which broadly include variations in ranking, mutual information, entropy, mutation, parent selection, genetic algorithm, etc. For a variety of algorithms based on differential evolution, it has been observed that although the suitability of the mutation operator is extremely important for feature selection yet other operators can also be considered. Therefore, the special emphasis of various algorithms is observing and reviewing the algorithms and finding new research directions: The general approach is to review a rigorous collection of articles first and then obtain the most accurate and relevant data followed by the narrow down of research questions. Research is based on the research questions. These are reviewed in four phases: designing the review, conducting the review, analyzing, and then writing the review. Threats to validity is also considered with research questions. In this paper, many feature ranking methods have been discussed to find further direction in feature ranking and differential evolution. A literature survey is performed on 93 papers to find out the performance in relevance, redundancy, correlation with differential evolution. Discussion is suitable for cascading the direction of differential evolution in integration with information-theoretic, entropy, and sparse learning. As differential evolution is multiobjective in nature so it can be incorporated with feature ranking problems. The survey is being conducted on many renowned journals and is verified with their research questions. Conclusions of the survey prove to be essential role models for multiple directions of a research entity. In this paper, a comprehensive view on the current-day understanding of the underlying mechanisms describing the impact of algorithms and review current and future research directions for use of evolutionary computations, mutual information, and entropy in the field of feature ranking is complemented by the list of promising research directions. However, there are no strict rules for the pros and cons of alternative algorithms.
Radar Plot Classification Method Based on Recurrent Neural Network

Radar will be affected by clutter in the process of detecting target inevitably, and clutter plot will be generated in signal processing and detection. In this paper, the binary classification of target and clutter is carried out by recurrent neural network. Firstly, the multidimensional feature parameters of radar plot are extracted, and the influence of single feature and different feature combination on radar plot classification is studied. We find that the classification method in this paper is better than the traditional support vector machine and fully connected neural network model under the given model effect evaluation index. All the data used in this paper are radar measured data, which verifies the effectiveness of the proposed algorithm.
The Virtual Tutor: Tasks for conversational agents in Online Collaborative Learning Environments

Online collaborative learning environments are becoming increasingly popular in higher education.Etutors need to supervise, guide students and look out for conflicts within the online environment to ensure a successful learning experience.Web-based platforms allow for interactive elements such as conversational agents to disencumber the e-tutor.Repeatable tasks, which do not require a human response, can be automatized by these systems.The aim of this study is to identify and synthesize the tasks an e-tutor has and to investigate the automatisation potential with conversational agents.Using a design science research approach a literature review is conducted, identifying 13 tasks.Subsequently, a matrix is established, contrasting the tasks with requirements for the use of conversational agents.Furthermore, a virtual tutor framework is developed, clarifying the agent type selection, the technical structure and components for a prototype development in an online collaborative learning environment.
Image Processing Using Quantum Computing

Image processing via the quantum platform is an emerging area for researchers. Researchers are more interested to move on towards quantum image processing instead of classical image processing. This chapter starts with the review of different quantum image computing-based research papers with a brief idea of the ethics which inspire quantum computing in the background and focus on the current scenario of recent trends of quantum image representation, pitfalls, and summarization of the pros and cons of it, with the limitations of the technologies used and focus on the recent work to be going on and application of it in a different field. In the next, it will focus on the different methods used by the researcher in the previous papers. The next section discussed the different methods based on quantum image representation used. Some different techniques of image storage, retrieval, and representation in a quantum system are discussed. Also, this chapter briefs the pros and cons of using different techniques in quantum systems in comparison to classical systems.
Underwater Scene Recovery Using Wavelength-Dependent Refraction of Light

This paper proposes a method of underwater depth estimation from an orthographic multispectral image. In accordance with Snell's law, incoming light is refracted when it enters the water surface, and its directions are determined by the refractive index and the normals of the water surface. The refractive index is wavelength-dependent, and this leads to some disparity between images taken at different wavelengths. Given the camera orientation and the refractive index of a medium such as water, our approach can reconstruct the underwater scene with unknown water surface from the disparity observed in images taken at different wavelengths. We verified the effectiveness of our method through simulations and real experiments on various scenes.
The research on binocular ranging technology for transmission lines based on two - dimensional line matching

Using binocular distance measurement technology to obtain the distance between the dangerous object and the transmission line, which provides powerful data information for the judgment of the risk degree of external damage and the assessment of the probability of the line suffering from external damage, so as to ensure the safety of the line. In order to solve the problem of large epipolar error caused by serious image distortion under wide-angle lens, local two-dimensional line matching scanning technology, which based on the object position information obtained by target detection, reduced the influence of epipolar error and increase the robustness of matching. At the same time, the dense calculation of global image distance is degraded to the local dense calculation of target, which improves the real-time performance of binocular ranging algorithm in high-resolution monitoring image. Finally, the image segmentation technology is used to improve the mismatching problem at the depth discontinuity to further improve the accuracy of binocular ranging.
Instance Segmentation of Visible Cloud Images Based on Mask R-CNN Applying Transfer Learning Approach

Checking cloud images conditions assume an urgent matter in climate wellbeing, mainly when a cataclysmic event occurs. Conventional systems regularly utilize manual investigation and costly instruments. By using a deep learning approach, it is simpler to perceive fast change. Picture examination dependent on a deep learning approach gives a prudent and productive answer for cloud picture observing. This paper presents a programmed arrangement model of cloud images conditions employing a deep learning approach dependent on four label information like flower, fish, gravel, sugar. A changed Mask R-CNN model has applied by coordinating cloud pictures information dependent on transfer learning. The test results are eventually split into four manual judgments. The labels of the marked conditions have shown remarkably high precision in evaluating the precise surface of the cloud data collection.
An Improved Divide-and-Conquer Algorithm for Finding Top-k Longest Common Subsequence

Finding the multiple longest common subsequence(MLCS) and other derived problems belong to NP-hard problems, which is especially used in comparing files, comparison of DNA and protein sequences. An efficient method applicable to solve top-k MLCS has been proposed in this paper. It implements to quickly find the LCS of all k sequences in a given n sequences with less space occupation. To reduce retrieval time, SF_MLCS algorithm was proposed firstly, which improve the hash storage structure of the direct successor nodes of matching points in irredundant common subsequence graph. On the basis of it, introducing the divide-and-conquer strategy, DC_MLCS algorithm was proposed, which divided the whole MLCS problems into several subproblems, to meet the requirement for longer or long-scale sequences. At the same time, in the process of solving the direct successor nodes, the number of sequences satisfying the conditions was limited to change the structure of matching points, and the DC_MLCS algorithm was extended to the DC_MLCS+ algorithm of finding top-k LCS. Experiments based on real datasets verify the efficiency of our algorithm.
Research on Distributed Energy Transaction Technology Based on Blockchain

The distributed power industry has the characteristics of multiple participants, long business processes, and wide distribution areas, which leads to serious data islands, difficulties in credit transmission, and outstanding transaction risks. Blockchain has technical characteristics such as openness, transparency, traceability, tamper resistance, and decentralization, making it an inevitable trend for its extensive and indepth application in the energy and power industry. The article first analyzes the generation and development of block technology, and then analyzes the current application status of block chain technology in distributed power transactions; then, based on the summary of existing research, it introduces in detail the blockchain-based technology The technical mechanism design of the distributed energy trading scheme analyzes the problems existing in the current application of blockchain in the field of distributed power trading, and proposes targeted development suggestions.