Reviewerly/exHarmony huggingface dataset:
Informing a Statewide Investment: The NYS Voter Registration Data Pattern Detection Prototype Project

This management case study presents the results of a prototype project conducted with the New York State Board of Elections (NYSBOE) to investigate and make recommendations on pattern detection analytical models for the purposes of informing their future investment of a statewide detection and visualization system for voter registration data. NYSBOE, a bipartisan organization with a mission to protect the integrity of elections, recognized that a critical element of protecting elections includes a systematic and intelligence driven approach to monitoring voter registration data as part of an overall cybersecurity program. Using over thirteen years of data from the NYS voter registration database (NYSVoter), the prototypes yielded valuable insights on the analytical models and visualizations most appropriate for the purpose of pattern detection in voter registration data so that state election leaders can better inform their investment. This management paper presents a short background on voter registration data, elections, and the importance of pattern detection as a part of a cyber security program, prototype project background, insights generating in identifying most appropriate analytical models and visualizations for voter registration data, and a short conclusion.
Teaching Practice and Reforms of the Course of Mathematical Analysis

Mathematics is known as the Queen of Science. Mathematical Analysis is the most important and basic core course of mathematics. This article discusses some of the problems faced by the teaching of the mathematical analysis course in local colleges and universities and gives some practices and thoughts on the reform in the teaching of the Mathematical Analysis course. It is proposed that the teacher should strengthen the teaching of primary mathematics, incorporate the idea of mathematical modeling, promote the usage of mathematical software, pay attention to the counter-example teaching, carry out the reform of new teaching mode based on CAI and mobile internet and integrate mathematical culture into the teaching. Practice shows that these teaching reform measures have effectively improved the teaching effect.
Research on Calibration Technology of Monocular Camera Based on Improved BAS Algorithm

Aiming at the problem of low calibration accuracy and too slow speed of traditional cameras, a monocular camera calibration method based on improved BAS (Beetle Antennae Search) algorithm is proposed. In order to prevent falling into the local optimum, the basic BAS algorithm is generally set to a relatively large initial step length, and the variable step length coefficient of each iteration is a fixed value and close to 1, which causes the local minimum to be continuously jumped out in the early stage. The search efficiency is low and the convergence is slow. Inspired by the fact that SGD (stochastic gradient descent) can be used to iteratively solve the minimum value of the objective function in machine learning, the basic BAS algorithm is improved, and the variable step coefficient δ in each iteration adopts standard exponential decay. The experimental results show that the improved BAS algorithm converges significantly faster than the basic BAS algorithm, and the search results are better, more stable and reliable.
ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs

Deep neural networks often lack the safety and robustness guarantees needed to be deployed in safety critical systems. Formal verification techniques can be used to prove input-output safety properties of networks, but when properties are difficult to specify, we rely on the solution to various optimization problems. In this work, we present an algorithm called ZoPE that solves optimization problems over the output of feedforward ReLU networks with low-dimensional inputs. The algorithm eagerly splits the input space, bounding the objective using zonotope propagation at each step, and improves computational efficiency compared to existing mixed-integer programming approaches. We demonstrate how to formulate and solve three types of optimization problems: (i) minimization of any convex function over the output space, (ii) minimization of a convex function over the output of two networks in series with an adversarial perturbation in the layer between them, and (iii) maximization of the difference in output between two networks. Using ZoPE, we observe a $$25\times $$ speedup on property 1 of the ACAS Xu neural network verification benchmark compared to several state-of-the-art verifiers, and an $$85\times $$ speedup on a set of linear optimization problems compared to a mixed-integer programming baseline. We demonstrate the versatility of the optimizer in analyzing networks by projecting onto the range of a generative adversarial network and visualizing the differences between a compressed and uncompressed network.
Breast Cancer Prediction Through Multilayer Artificial Neural Network

In the current era, neural networks are playing a significant role in many sectors, especially in health care. In this work, the neural networks will be used for the diagnosis of disease, i.e., cancer. The proposed work designs a multilayer deep neural network to predict whether the person is a cancer patient (malign) or a healthy person (benign). This work evaluates the performance of the two-layer neural network and multilayer neural network with diverse activation functions like rectified linear unit (ReLU), softmax, and sigmoid. The proposed model uses ReLU activation function in the hidden layer and sigmoid activation function in the output layer. After evaluating the result, it can be stated that a multilayer neural network is performing better as compared to the two-layer neural network and achieved an accuracy of 97.7%.
CoConv: Learning Dynamic Cooperative Convolution for Image Recognition

In this paper, we present a conceptually simple, yet powerful method for image recognition. The method, called Cooperative Dynamic Convolution (CoConv), introduces a cooperative learning of dynamic convolution from multiple convolutional experts. CoConv can be used as a substitute for the traditional static convolution, and can be seamlessly integrated in various visual models. Moreover, CoConv is easy to train with only a minimal computational overhead introduced in the inference phase. CoConv is trained by using multiple convolutional experts simultaneously, and the convolutional weights are merged by a weighted summation before convolutional operations for efficiency during inference. Results from extensive experiments show that CoConv leads to consistent improvement for image classification on various datasets, independent of the choice of the base convolutional network. Remarkably, CoConv improves the top-1 classification accuracy of ResNet18 by 3.06% on ImageNet. The code is available at: https://github.com/Nyquixt/CoConv.
Development and Proposal System for the Formulation of Solar paint

The ideology of solar paint has created much attention among people because of its portability, usability, and compatibility when compared with regular solar panels. Solar paint is nothing but paint that is capable of changing any surface into a solar panel when it is applied. The key features of solar paint is its potential because of its portability, cost efficiency, and flexibility. This study aims to define the feasible approach for the development of solar paint to produce electricity from the photocatalysis process using solar energy. Solar paint has many advantages eg. When applied on the roof of a building it generates electricity and can be operated forever.
Part of Speech Tagging Using Bi-LSTM-CRF and Performance Evaluation Based on Tagging Accuracy

Part of speech tagging (POS) refers to the computational task of identifying related parts of speech for specific words in text documents. A research challenge is to use various techniques to identify and utilize these tags to improve several natural language processing applications. In this paper, a bidirectional long short-term with conditional random field (denoted as Bi-LSTM-CRF) model has been proposed for POS tagging. This novel model trained on the named entity resolution dataset is compared with other recurrent neural network models such as bidirectional long short-term model networks and long short-term model with conditional random field. Bi-LSTM-CRF model is applied to the annotated NER dataset and its tagging accuracy and F1-score are compared with the other pre-existing models. Experimental results show that Bi-LSTM-CRF provides better results for POS tagging. The Bi-LSTM-CRF model is competitive on the annotated NER dataset for English to produce greater accuracy and F1-score and outperform the rest of the models.
The slicing method: determining insensitivity regions of probability weighting functions

A popular rule of thumb, usually called ``heuristic technique'' in Behavioral Economics, for determining the likelihood insensitivity regions of probability weighting functions (pwf's) is based on searching for points at which the pwf's are twice their values at half the points. Although this technique works remarkably well for many commonly used pwf's, it sometimes fails to provide the correct answer. In order to cover the class of pwf's for which the heuristic technique does not work, in this paper we propose, discuss, and illustrate an extension of the technique into what we call the ``slicing method,'' which is capable of finding the subadditivity and insensitivity regions of any continuous pwf.
Using Machine Learning to Process Filters and Mimic Instant Camera Effect

In this paper we use Machine Learning to convert images taken with an iPhone camera and visually alter them to appear as if taken with a Leica Sofort Instant Camera, more commonly known as the Polaroid look. While such image filters already exist and are highly effective, they function using ad-hoc techniques. Our goal is to achieve similar results by having a model learn what the Polaroid look is on its own and how many image pairs are required to train it. We found that using linear regression we need, on average, 800 images before the model began displaying good consistent results while using Pix2Pix (Isola et al., Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1125–1134, 2017) (Conditional Adversarial Networks) and CycleGAN (Goodfellow et al., Generative adversarial nets. In Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems, vol 27, pp 2672–2680. Curran Associates, Inc., Red Hook, NY, 2014 [Online]. Available: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf) (Generative Adversarial Networks) only required 500 images.
Reform and Practice of Traditional Chinese Medicine in Information Teaching Method Promoted by ID3 Algorithm

Under the promotion of ID3 algorithm, the continuous development of information technology and the penetration of micro class and MOOC concept are increasingly affecting the reform of traditional classroom teaching mode of traditional Chinese medicine course. The application of information network teaching platform of Chinese pharmacy course plays an active role in classroom teaching. Under the promotion of ID3 algorithm, the reform and practice of traditional Chinese medicine in the information-based teaching method is of great significance to further explore the deep integration of classroom teaching and network teaching platform in the information environment.
Towards Open-domain Vision and Language Understanding with Wikimedia

Current state-of-the-art task-agnostic visio-linguistic approaches, such as ViLBERT [2], are limited to domains in which texts have a visual materialization (e.g. a person running). This work describes a project towards achieving the next generation of models, that can deal with open-domain media, and learn visio-linguistic representations that reflect data’s context, by jointly reasoning over media, a domain knowledge-graph and temporal context. This ambition will be leveraged by a Wikimedia data framework, comprised by comprehensive and high-quality data, covering a wide range of social, cultural, political and other type of events. Towards this goal, we propose a research setup comprised by an open-domain data framework and a set of novel independent research tasks.
Low-cost design of a PCB imaging system based on laser triangulation

Abstract With the increasing miniaturization of electronic products, fanless design is required. The volume of electronic components, especially the height, is critical to the structural design of electronic devices. This paper proposes the design of on-line height measurement equipment based on commercial charge-coupled device (CCD), analyses the principle of height measurement of components, and explains the system composition and working principle of the measurement system. Moreover, it presents a comparative analysis of common methods for determining the center position, which include circle fitting, the gray centroid and extended method or Gaussian fitting, which generally lead to diverse results. The experimental results show that Gaussian fitting is better than other methods under realistic conditions, a three-dimensional model of the components is provided, and the error factors that affect measurement are analyzed.
User Preference Excitation Network for Sequential Recommendation

Predicting users' preferences based on historical interactions plays a critical role in recommender systems. Conventionally, users' recent sequence behaviors are fed into a model to predict the next item that a user is most interested in. However, most of the existing methods simply utilize the user's recent behavior sequences to model short-term preferences. This is problematic, because users may pay more attention to partial features in the sequences due to their long-term preferences. To this end, we propose a user Preference Excitation network to more accurately model user sequence information. Specifically, a personalized gating layer is utilized to model the user's specific preferences and a preference excitation self-attention layer learns the complicated sequential patterns. Furthermore, the gate mechanism is used to adaptively fuse the information from these two aspects. Empirically, we evaluate our model on several real-world datasets and the comprehensive experimental results show that our model consistently outperforms the selective state-of-the-art methods. Source code of the paper is available at https://github.com/strange-jiong/PERec.
Practical application using the hexapod robot for interior inspection of a Boeing 737-300 aircraft fuel tank

This paper focuses on the dangers assoicated with the entry into aircraft fuel tank. The analysis will help to demonstrate that in the future a mobil robot will make this work easier for the human factor. There are number of advantages from this automation to make the job of the human factor easier and to work in a safe environment. The risk of contamination with harmful substances is reduced because we can put the robot to work. After detailed analysis of the working environment to which a mechanic is exposed in the aviation world, it has come to light that the introduction of a robot makes it minimal. I tried to analyze several types of mobile robots, and after this analysis I decided that the most suitable mobile robot is the hexapod.
Existing Approaches and Development Perspectives for Inferences

Statistical hypotheses testing is one of the basic direction of mathematical statistics the methods of which are widely used in theoretical research and practical applications. These methods are widely used in medical researches too. Scientists of different fields, among them of medical too, that are not experts in statistics, are often faced with the dilemma of which method to use for solving the problem they are interested. The article is devoted to helping the specialists in solving this problem and in finding the optimal resolution. For this purpose, here are very simple and clearly explained the essences of the existed approaches and are shown their positive and negative sides and are given the recommendations about their use depending on existed information and the aim that must be reached as a result of an investigation.
Personal Assistant for Career Coaching

There has been an increasing hype and usage of chat bots nowadays. If chat bots are used as career assistants who can guide an individual to the right track, then this will motivate people to learn and acquire new skills. Currently, there is no standard and proper resource which can prepare an individual pertaining to industry requirements. In this paper, we propose a model to develop a standard platform for assisting people in career coaching. The users of this system can get their career-related queries solved and can also obtain information needed for making them industry ready. The proposed system is developed in order to develop a tool for communicating with job seekers to help them find jobs according to their skills. This system will also recommend courses that will enhance their skills in their field of interest for professionals. The proposed system is developed in order to develop a tool for communicating with job seekers to help them find jobs according to their skills. This system will also recommend courses that will enhance their skills in their field of interest for professionals.
Approaches for Handling Immunopathological and Clinical Data Using Deep Learning Methodology: Multiplex IHC/IF Data as a Paradigm

Recent advancements in deep learning based artificial intelligence have enabled us to analyse complex data in order to provide patients with improved cancer prognosis, which is an important goal in precision health medicine. In this chapter, we would be discussing how deep learning could be applied to clinical data and immunopathological images to accurately determine survival rate prediction for patients. Multiplex immunohistochemistry/immunofluorescence (mIHC/IF) is a relatively new technology for simultaneous detection of multiple specific proteins from a single tissue section. To adopt deep learning, we collected and pre-processed the clinical and mIHC/IF data from a group of patients into three branches of data. These data were subsequently used to train and validate a neural network. The specific process and our recommendations will be further discussed in this chapter. We believe that our work will help the community to better handle their data for AI implementation while improving its performance and accuracy.
Editorial for <i>New Review of Academic Librarianship</i> (1) 2021

This is my last Editorial for the New Review of Academic Librarianship (NRAL) as I have decided to move on from being Editor-in-Chief at the end of 2020. In 2009, I took on this role and I am delig...
GAN Cocktail: Mixing GANs Without Dataset Access

Today’s generative models are capable of synthesizing high-fidelity images, but each model specializes on a specific target domain. This raises the need for model merging: combining two or more pretrained generative models into a single unified one. In this work we tackle the problem of model merging, given two constraints that often come up in the real world: (1) no access to the original training data, and (2) without increasing the network size. To the best of our knowledge, model merging under these constraints has not been studied thus far. We propose a novel, two-stage solution. In the first stage, we transform the weights of all the models to the same parameter space by a technique we term model rooting. In the second stage, we merge the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models. We demonstrate that our approach is superior to baseline methods and to existing transfer learning techniques, and investigate several applications. (Code is available at: https://omriavrahami.com/GAN-cocktail-page/ ).
FallbackWalk: A Random Walk Based Fallback for Heterogeneous Information Network

Graph Embedding, as a network representation learning technology, can analyze information networks and represent nodes as low dimensional dense vectors, which shows good performance and attracts a lot of research interests. Heterogeneous information networks contain rich information. However, the current models still have some limitations in capturing semantic information in information networks. Most models mainly focus on how to aggregate the semantic information in the information network but do not consider the differences of nodes in the feature extraction process. In order to solve this problem, a graph embedding model based on fallback strategy (FallbackWalk) is proposed. FallbackWalk takes into account the differences of nodes in the information network, and make more use of neighborhood nodes by random walk strategy based on fallback, and the Skip-gram model is used to train and get the vector representation of nodes. To validate our proposed network representation learning model, we present experiments on two real-world datasets and two representation learning tasks: node classification and node clustering. The node representation of FallbackWalk can capture the node characteristics more accurately. The experimental results show that the node representation of FallbackWalk outperforms state-of-the-art models on different downstream tasks.
Research status of optical fiber voltage transformer

Abstract Optical fiber voltage transformer (OVT) has the advantages of good insulation, bandwidth, and small size, and has good development prospects and application requirements. Through combing and analysis, the article summarizes the development and changes in the past ten years from the four parts of sensing probe, sensing fiber, optical information extraction and light source according to the basic principles of the OVT measurement system, analyzes the potential development direction, and finally discusses The main problems encountered in the current OVT research, and some suggestions have been put forward.
Mapping of the descriptive logic into RDF using binary relational data model

This paper is dedicated to the data integration problem. To establish relationships between data models is one of the key tasks in this solution. The descriptive logic and the relational data model are at the heart of a study. They have been used to create a mapping method on the theoretical level. The binary relational data model has been developed as a part of a mapping method. The previous studies are continued in this paper to prove on practice a mapping creation method between the descriptive logic and the binary relational data model. The method uses the binary relational data model as an integrating model. This paper continues the previous research of practical implementation of the mapping creation between the descriptive logic and the binary relational data model. The task to prove the theoretical mapping method on practice was formulated. A question how to map the binary relational data model into RDF-triples was considered. A brief overview of the R2R ML conversion tool was given. Triple maps were created to convert a conceptual information model of descriptive logic into RDF triplets with the help of R2R ML. Also, triples maps are described to convert basic mapping mechanisms into RDF with the help of R2R ML.