Reviewerly/exHarmony huggingface dataset:
Slope and generalization properties of neural networks

Neural networks are very successful tools in for example advanced classification. From a statistical point of view, fitting a neural network may be seen as a kind of regression, where we seek a function from the input space to a space of classification probabilities that follows the "general" shape of the data, but avoids overfitting by avoiding memorization of individual data points. In statistics, this can be done by controlling the geometric complexity of the regression function. We propose to do something similar when fitting neural networks by controlling the slope of the network. After defining the slope and discussing some of its theoretical properties, we go on to show empirically in examples, using ReLU networks, that the distribution of the slope of a well-trained neural network classifier is generally independent of the width of the layers in a fully connected network, and that the mean of the distribution only has a weak dependence on the model architecture in general. The slope is of similar size throughout the relevant volume, and varies smoothly. It also behaves as predicted in rescaling examples. We discuss possible applications of the slope concept, such as using it as a part of the loss function or stopping criterion during network training, or ranking data sets in terms of their complexity.
A COMPARISON OF SENTIMENTAL ANALYSIS ALGORITHMS ON TWITTER DATA USING MACHINE LEARNING

&lt;p&gt;Twitter sentiment analysis has come a long way in recent years, and many experiments have been conducted in this domain. Many machine learning techniques have been used for sentiment analysis in the past and will be utilized in the future to achieve more improved results. People nowadays express their sentimental emotions more openly since they have access to forums where they can freely express themselves, hence this gives us an opportunity where sentimental analysis could be performed and its results can be used for various different objectives as it could give information about an individual's emotional state. This work contributes to the sentiment analysis for customer review classification, which is useful for analyzing information in the form of a large number of tweets with highly unstructured thoughts that are either good or bad. Sentiment analysis is usually done in three ways: machine learning-based, sentiment lexicon-based, or a hybrid approach. The proposed approach for sentiment analysis consists of first pre-processing tweets and using a feature extraction method. Further, Logistic regression, SVM, Decision Trees (DTs), Random Forest (RF), and XBG Classifier are used for sentiment analysis classification in the proposed framework and then all the results are compared to find out the best techniques. Out of all the techniques used, SVM is working best with an accuracy of 95.21%.&lt;/p&gt;
Law Enforcement of Cyber Crime Jurisdiction in Transnasional Law

The rapid development of information technology has made the world borderless and lead to social change which is significantly fast.These advances are accompanied by negative impacts which threaten and endanger the social and economic development of the Ummah involving more than one country.Law enforcement against cybercrimes in Indonesia is regulated in constitution number 11 of 2008 about Electronic Information and Transaction as amended by constitution number 19 of 2016 about top change constitution number 11 of 2008 about Electronic Information and Transaction. The problem who the researchers do about Application of Law Enforcement Of Cyber Crime Jurisdiction Reviewed From International Law and Obstacles That Occur As Well As Solutions In The Law Enforcement Of Cyber Crime Jurisdiction In Terms OfInternational Law.The method approach who the writer use in this research is juridical normative, who do the approach with considering the focus problem is revolve to the rules who talk the relation between the rule and other rule and the connection with the application.The specification of this research is descriptive analysis who describe the used rule and connect to the law's theory and the application of the law enforcement about the problem.To analyze the data in this research, the writer used the qualitative analysis according to the legals norm who be found in the rules of law and the court decision and the society's norms.The result of the analysis is make a deductive conclusion, and describe it in narration explanation without using a formula or statistics so that the writer attracted the specific conclusion.The result of this research is showing a conclusion about application of law enforcement jurisdiction in cybercrimes it is not enough to just use national law, but the need for ratification of international agreements in the field of cyber involving between countries.The government has made various efforts to combat cybercrime, but there are still obstacles in its implementation, for this reason there is a need for harmony between laws and regulations, law enforcement, infrastructure, public, and cultural synergy, and implement international conventions and international cooperation in the form of applying international principles that can be recognized as international custom.
Solving Some Affine Equations over Finite Fields

Let $l$ and $k$ be two integers such that $l|k$. Define $T_l^k(X):=X+X^{p^l}+\cdots+X^{p^{l(k/l-2)}}+X^{p^{l(k/l-1)}}$ and $S_l^k(X):=X-X^{p^l}+\cdots+(-1)^{(k/l-1)}X^{p^{l(k/l-1)}}$, where $p$ is any prime. This paper gives explicit representations of all solutions in $\GF{p^n}$ to the affine equations $T_l^{k}(X)=a$ and $S_l^{k}(X)=a$, $a\in \GF{p^n}$. For the case $p=2$ that was solved very recently in \cite{MKCL2019}, the result of this paper reveals another solution.
The Future of Patient-Centric Data-Driven Healthcare

In the final chapter of the book “Real World Evidence in a Patient Centric Digital Era,” we present overall visions for the future of patient-centric and data-driven healthcare. Specifically, we emphasize harnessing real-world data (RWD) to gain real-world evidence (RWE) in present-day medical research, particularly in addressing noncommunicable diseases (NCDs) and offer ideas on a future vision for healthcare in which the community can follow the emerging trends in RWE to make an impact on individual patients and public health. As final remarks, we reiterate the importance of the role of the patient in evidence-based research.
Maximum Likelihood Recursive State Estimation using the Expectation Maximization Algorithm

A Maximum Likelihood recursive state estimator is derived for non-linear and non-Gaussian state-space models. The estimator combines a particle filter to generate the conditional density and the Expectation Maximization algorithm to compute the maximum likelihood state estimate iteratively. Algorithms for maximum likelihood state filtering, prediction and smoothing are presented. The convergence properties of these algorithms, which are inherited from the Expectation Maximization algorithm, are proven and examined in two examples. It is shown that, with randomized reinitialization, which is feasible because of the algorithm simplicity, these methods are able to converge to the Maximum Likelihood Estimate (MLE) of multimodal, truncated and skewed densities, as well as those of disjoint support.
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy

InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, this package allows to define, learn and evaluate general hierarchical probabilistic models containing deep neural networks in a compact and simple way. InferPy is built on top of Tensorflow Probability and Keras.
Research on Intelligent Embedded Fan System Based on YOLOv2 Lightweight Algorithm

With the development of artificial intelligence, the application of intelligent algorithms to low-power embedded chips has become a new research topic today. Based on this, this study optimizes the YOLOv2 algorithm by tailoring and successfully deploys it on the K210 chip to train the face object detection algorithm model separately. The intelligent fan with YOLOv2 model deployed in K210 chip can detect the target of the character and obtain the position and size of the character in the machine coordinates. Based on the obtained information of character coordinate position and size, the fan's turning Angle and the size of air supply are intelligently perceived. The experimental results show that the intelligent fan design method proposed here is a new embedded chip intelligent method of cutting and improving the YOLOv2 algorithm. It innovatively designed solo tracking, crowd tracking, and intelligent ranging algorithms, which perform well in human perception of solo tracking and crowd tracking and automatic air volume adjustment, improve the accuracy of air delivery and user comfort, and also provide good theoretical and practical support for the combination of AI and embedded in other fields.
Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping

Photovoltaic (PV) energy is rapidly growing and key to mitigating the energy crisis. However, distributed PV generation, which amounts to half of the PV installed capacity, is typically unavailable to transmission system operators (TSOs), making it increasingly difficult to balance the load and supply and avoid grid congestions. To assess distributed PV generation, TSOs need precise knowledge regarding the metadata of distributed PV installations. Many remote sensing-based approaches have been proposed to map these installations in recent years. However, to use these methods in industrial processes, assessing their accuracy over the mapping area, i.e., the area covered by the model during deployment, is necessary. We define the downstream task accuracy (DTA) as the accuracy over the mapping area, automatically computed using publicly available data sources and the model's outputs and expressed in an interpretable way for operators. We benchmark existing models for distributed PV mapping and show how they perform in terms of DTA. We show that the accuracy computed on the test set overestimates by about 30 percentage points the accuracy on the mapping area. Our approach paves the way for safer integration of deep-learning-based pipelines for remote PV mapping. Code is available at https://github.com/gabrielkasmi/deeppvmapper.
Reversible Information Hiding in Images Based on Histogram Shift Method

With the increasing popularity of wireless sensor networks in the IoT and Industry 4.0 era, the security of networks is critical in transmitting data and information.To address this need, we propose an optimized method for hiding and extracting information from image data.For this method, we created algorithms for hiding and extracting information based on the histogram shift method.The algorithms were developed using chessboard-and column-type prediction methods.Five different prediction methods were tested in the development of the algorithms, and the test results showed that the chessboard-type methods yielded better results with images.Then, the optimized prediction method was tested for various images along with previous methods.The results show that the proposed method has better results in terms of bits per pixel and peak signal-to-noise ratio.The method can be applied to image transmission through a wireless sensor network and provides a basis for the development of further applications.
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks

Anomaly detection plays in many fields of research, along with the strongly related task of outlier detection, a very important role. Especially within the context of the automated analysis of video material recorded by surveillance cameras, abnormal situations can be of very different nature. For this purpose this work investigates Generative-Adversarial-Network-based methods (GAN) for anomaly detection related to surveillance applications. The focus is on the usage of static camera setups, since this kind of camera is one of the most often used and belongs to the lower price segment. In order to address this task, multiple subtasks are evaluated, including the influence of existing optical flow methods for the incorporation of short-term temporal information, different forms of network setups and losses for GANs, and the use of morphological operations for further performance improvement. With these extension we achieved up to 2.4% better results. Furthermore, the final method reduced the anomaly detection error for GAN-based methods by about 42.8%.
Legitimation through Optimal Distinctiveness: Evidence from Emerging Technology-based Start-ups

How can emerging technology-based start-ups attain legitimacy from external stakeholders? This question is critical as emerging technology-based start-ups face dual liabilities of newness arising from both a short history and the radical novelty of emerging technologies themselves. Drawing on optimal distinctiveness perspective, we propose that emerging technology-based start-ups can obtain legitimacy through optimal business proximity to their counterparts. We further construct a multi-level contingency framework at venture (i.e. venture age), third-party (i.e. media attention), and country (i.e. information and communication technology (ICT) development) levels. With a sample of 3670 blockchain start-ups, we discover an inverted U-shaped influence of business proximity on legitimacy attainment of emerging technology-based start-ups. We also reveal that venture age, media attention and country-level ICT development will attenuate the inverted U-shaped relationship. Overall, this study contributes to the literature on legitimation of emerging technology-based start-ups by extending optimal distinctiveness perspective.
Effective Framework of Underwater Image Enhancement

Abstract: Underwater photographs are an excellent resource for learning about marine life and analysing the hydrothermal winds that blow across the seafloor. Low contrast, colour distortion, and poor visual appeal are the main obstacles that an underwater image faces. Light disperses and refracts as it progresses from rarer to denser, resulting in a plethora of complexities. The dispersion of light reduces the colour contrast. Water, in addition to dispersion, modifies subsea images due to the presence of subsea animals. We provide a new method for improving underwater fusion photos that accurately restores images underwater. Because the input and a set of processes, such as white balance, gamma correction, sharpening, and weight map manipulation, result in a single image. This research suggests a novel method for recovering and improving underwater photos. We recommended a light canal in front of the underwater environment as the initial step. A clear channel image was analysed and adjusted, environmental light was approximated, and the transmission image was refined in order to reconstruct underwater photos. The simulation was run using the MATLAB software.
Information System for Honda Motorcycle Spare Parts Palembang

In practice, the industrial world that looks the same as the service industry requires the players to do a lot of quality improvement in order to compete. Its development is so rapid that many business actors use special tricks to run their business. Likewise with business actors from motorcycle service and service workshops, namely AHASS SPS Motor Palembang. The supply of spare parts that must always be there when customers need them is the main attraction for every workshop. But in reality, there is no special inventory management technique that can help partman to order spare parts in the following month. Therefore, this study will create a web-based spare part inventory information system that can control the amount of spare part stock and help predict the need for spare parts every month with the forecasting technique used, namely the Weighted Moving Average method. The results given from this system are suggestions of spare part stock forecast results with a user-friendly web-based display so that it can improve partman performance, maintain credibility and optimize workshop cashflow
Analysis of English Translation Model Based on Artificial Intelligence Attention Mechanism

The particularity and quantity of English translation terms have a great impact on the quality and effect of machine translation and can not meet the requirements of English translation of terms. At the same time, technical exchange and communication in different fields need the expression of professional terms. In addition, although the neural machine translation model has good translation performance, it is not ideal for target languages with small translation needs and limited corpus resources. In order to solve the problems existing in the English translation model, this paper constructs the transformer model by replacing the cyclic neural network variables and introducing the attention mechanism. The term information is integrated into two pretraining models to improve the learning ability of model language sentence relationship. Maintain the integrity of terminology information by fully completing the training. The experimental results show that, compared with the other three term translation models, the translation model in this paper has the advantage of term information. At the same time, the deep neural network English term translation model can obtain more fine-grained word relevance. In different corpora, the Bleu score of the model is good, showing obvious translation performance advantages. This study provides a good professional reference value for the translation of English terms.
Using Participatory GPS Methods to Develop Rich Understandings of People's Diverse and Complex Livelihoods in the Global South

This chapter explores the use of participatory Global Positioning System (GPS) in relation to livelihoods and their associated mobilities in Global South contexts. Recent advancements in GPS technology have presented exciting opportunities for social scientists to expand their ‘methodological toolkits’. These methods have been innovatively adapted in diverse ways to promote participation. A critical overview is provided of the literature on livelihoods and mobilities which have used various GPS-based approaches. This includes an assessment of their opportunities and challenges, as well as discussions on the ethical considerations that should be made prior to employing GPS techniques in research. Previous studies which have utilised participatory GPS have gained detailed insights into people’s spatial knowledges and strategies which they draw on in relation to their complex livelihoods. These understandings have been crucial in advancing both theoretical and practical interpretations of the multifaceted nature of income generation. As contemporary challenges such as high rates of urbanisation and climatic changes continue to impact populations across the Global South, it will be crucial to draw on these knowledges whilst also continuing to develop and employ participatory GPS methods for future research into how livelihoods are sustained and adapted in response to these vicissitudes.
An Investigation of Several Models for Machine Learning Based Automated Essay Grading System

Abstract Automated Essay Grading (AEG), formerly known as scoring systems, is no longer a myth. These AI systems are a boon to the educational sector, as teachers often have difficulty evaluating students' essays. It's a real hurdle to examine a large number of student articles in a short period, even while providing feedback. However, AEG systems can now effortlessly evaluate human-written (not handwritten) essays. One of the best instances of its application is the TOEFL exam. Student’s essays are graded by both a human and an automatic essay grading system. Many of the characteristics that distinguish good and bad writing, as well as many of the linguistic, cognitive, and other skills that underpin human potential for both reading and writing, might be explained using AEG. Teachers frequently use free-text questions to assess students' overall competence, which has led to an increased focus on clever auto-grading systems to reduce the grading load on examiners. A revolutionary automatic essay scoring system based on Machine Learning and Python is presented in this work. The suggested method, in particular, encodes an essay as sequential embedding’s and uses a bi-directional LSTM to capture semantic information. Meanwhile, the method builds attention for each essay so that the network can learn to correctly focus on genuine material in an article, which can also provide reasonable evidence of the prediction outcome. The public essay set provided in Kaggle's Automated Student Assessment Prize was used for training and testing.
Teaching Evaluation Method Based on Fuzzy Support Vector Machine Algorithm

For schools, the quality of teaching and learning is an important guarantee for achieving educational goals. There are very many factors affecting teaching quality, including hard and soft factors. Among them, the soft factors mainly refer to the teaching quality of teachers and the teaching management mechanism of schools. The teaching quality of teachers is the most critical among all factors. Traditional teaching quality evaluation (TQE) mainly adopts the way of manual scoring. This way of TQE lacks data support, and there is no evaluation mechanism based on multiple data sources. Therefore, the TQE results obtained in this way are subjective and inaccurate. Considering that the main subjects of the whole teaching session are teachers, students, and schools, each subject generates massive information in the whole teaching process. In our study, a novel evaluation method based on fuzzy classification algorithm is proposed to be applied in teaching evaluation. Firstly, this paper collects the relevant data generated by the three subjects in the teaching process, respectively. Secondly, after removing the unsuitable data, principal component analysis is used to extract the main features of the applicable data. Finally, a fuzzy support vector machine (FSVM) is used to classify and analyze the feature data in order to derive each teacher’s teaching evaluation results. The final evaluation results in this paper are divided into five grades, i.e., five categories: excellent, good, moderate, passing, and failing. The comparison with other algorithms demonstrates that the teaching evaluation model based on fuzzy comprehensive evaluation algorithm used in this paper has the advantages of high evaluation accuracy and objectivity, and we hope that this study will be useful in the field of teaching evaluation.
Content-Based Image Retrieval Using Multilevel Robust Mechanism

Abstract Content-Based Image Retrieval only concentrates on the information about the image like its texture, color, shape, homogeneity, and contrast rather than the image tags, description, and keywords. Traditionally many machine learning algorithms are used to retrieve the information but fail to acquire accuracy and created overfitting problems. Not only that large image database handling becomes quite hard to achieve quicker results without human intervention. Hence, we are opting for a robust mechanism for every step of retrieval. Image pre-processing inhibited AlexNet architecture with latent feature extraction and feature selection wear out Mutual Information neural estimation, which is used to handle high dimensional images with fixed resolution and removes the redundancy of images in the database. The similarity measure using Euclidean distance is employed to retrieve the images. The accuracy and performance efficiency is considerably high and pertinent.