Reviewerly/exHarmony huggingface dataset:
Emulation and modelling of semiconductor optical amplifier-based all-optical photonic integrated deep neural network with arbitrary depth

Abstract We experimentally demonstrate the emulation of scaling of the semiconductor optical amplifier (SOA) based integrated all-optical neural network in terms of number of input channels and layer cascade, with chromatic input at the neuron and monochromatic output conversion, obtained by exploiting cross-gain-modulation effect. We propose a noise model for investigating the signal degradation on the signal processing after cascades of SOAs, and we validate it via experimental results. Both experiments and simulations claim that the all-optical neuron (AON), with wavelength conversion as non-linear function, is able to compress noise for noisy optical inputs. This suggests that the use of SOA-based AON with wavelength conversion may allow for building neural networks with arbitrary depth. In fact, an arbitrarily deep neural network, built out of seven-channel input AONs, is shown to guarantee an error minor than 0.1 when operating at input power levels of −20 dBm/channel and with a 6 dB input dynamic range. Then the simulations results, extended to an arbitrary number of input channels and layers, suggest that by cascading and interconnecting multiple of these monolithically integrated AONs, it is possible to build a neural network with 12-inputs/neuron 12 neurons/layer and arbitrary depth scaling, or an 18-inputs/neuron 18-neurons/layer for single layer implementation, to maintain an output error &lt;0.1. Further improvement in height scalability can be obtained by optimizing the input power.
Review: Challenges and Obstacles of Trusted Elements for Mobile Health Records Management

This study aims to identify the significant difficulties concerned with trusted elements of mobile health records management (MHRM) systems. For instance, data migration and lack of staff training have created huge challenges for organizations using such technologies. As such, this study presents potential improvement methods for electronic medical records (EMRs) by promoting the application of the latest technologies and relevant data security techniques. Secondary sources published between 2017 and 2020 were qualitatively reviewed to collect relevant information about EMR advancement. These data allowed reviewers to identify EMRs&rsquo; reliability and challenges efficiently. In short, this paper identified four major challenges in records management systems: data migration, culture shock among staff, insufficient training, and data privacy. To counter these challenges, this review demonstrated that these difficulties can be effectively resolved through strategic inclusion and proper initiative of the latest innovative technologies.
Construction and Practice of English Writing Model Based on Random Matrix

Writing is an indispensable part of English comprehensive ability. It can test learners’ ability in viewpoint content, text organization, language use, and so on. In view of the many problems existing in the English writing of contemporary primary and middle school students, high school students and even college students, this paper proposes an English writing model based on random matrix. This method collects the necessary English vocabulary information through the English word database and combines the M-P law and the density spectrum value of the hybrid matrix to accurately locate students’ writing needs, clarify their writing needs, identify, and construct dynamic English compositions. The random matrix is used to judge and test the English level threshold of different students, so as to get the abnormal use of words when students write. By comparing the predicted data of students’ abnormal vocabulary with the accurate vocabulary data, different students’ writing ability is judged, and corresponding suggestions are given, and qualified writing formats are provided. The experimental results show that the English writing model based on random matrix has strong learning ability and judgment, and can better achieve one-to-one targeted teaching.
Visualizing Generic Reaction Patterns

Reaction schemes for organic molecules play a crucial role in modern in silico drug design processes. In contrast to the classical drawn reaction diagrams, computational chemists prefer SMARTS based line notations due to a substantially increased expressiveness and precision. They are used to search databases, calculate synthesizability, generate new molecules, or simulate novel reactions. Working with computer-readable representations of reaction schemes can be challenging due to the complexity of the features to be represented. Line representations of reaction schemes can often be cryptic, even to experienced users. To simplify the work with Reaction SMARTS for synthetic, computational, and medicinal chemists, we introduce a visualization technique for reaction schemes and provide a respective tool, called ReactionViewer. ReactionViewer is able to convert reaction schemes encoded as Reaction SMILES, Reaction SMARTS, or SMIRKS into a visual representation. The visualization technique is based on the concept of structure diagrams and follows IUPAC's "Compendium of Chemical Terminology" definition of chemical reaction equations for the reaction symbols. We demonstrate the applicability of the method using two data sets of organic synthesis reaction schemes taken from recent publications. We discuss various properties of the visualization and highlight its readability and interpretability.
Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution

We introduce relaxed dot plots as an improvement of nonlinear dot plots for unit visualization. Our plots produce more faithful data representations and reduce moiré effects. Their contour is based on a customized kernel frequency estimation to match the shape of the distribution of underlying data values. Previous nonlinear layouts introduce column-centric nonlinear scaling of dot diameters for visualization of high-dynamic-range data with high peaks. We provide a mathematical approach to convert that column-centric scaling to our smooth envelope shape. This formalism allows us to use linear, root, and logarithmic scaling to find ideal dot sizes. Our method iteratively relaxes the dot layout for more correct and aesthetically pleasing results. To achieve this, we modified Lloyd's algorithm with additional constraints and heuristics. We evaluate the layouts of relaxed dot plots against a previously existing nonlinear variant and show that our algorithm produces less error regarding the underlying data while establishing the blue noise property that works against moiré effects. Further, we analyze the readability of our relaxed plots in three crowd-sourced experiments. The results indicate that our proposed technique surpasses traditional dot plots.
PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel Coordinate Displays

Parallel coordinate plots (PCPs) have been widely used for high-dimensional (HD) data storytelling because they allow for presenting a large number of dimensions without distortions. The axes ordering in PCP presents a particular story from the data based on the user perception of PCP polylines. Existing works focus on directly optimizing for PCP axes ordering based on some common analysis tasks like clustering, neighborhood, and correlation. However, direct optimization for PCP axes based on these common properties is restrictive because it does not account for multiple properties occurring between the axes, and for local properties that occur in small regions in the data. Also, many of these techniques do not support the human-in-the-loop (HIL) paradigm, which is crucial (i) for explainability and (ii) in cases where no single reordering scheme fits the users' goals. To alleviate these problems, we present PC-Expo, a real-time visual analytics framework for all-in-one PCP line pattern detection and axes reordering. We studied the connection of line patterns in PCPs with different data analysis tasks and datasets. PC-Expo expands prior work on PCP axes reordering by developing real-time, local detection schemes for the 12 most common analysis tasks (properties). Users can choose the story they want to present with PCPs by optimizing directly over their choice of properties. These properties can be ranked, or combined using individual weights, creating a custom optimization scheme for axes reordering. Users can control the granularity at which they want to work with their detection scheme in the data, allowing exploration of local regions. PC-Expo also supports HIL axes reordering via local-property visualization, which shows the regions of granular activity for every axis pair. Local-property visualization is helpful for PCP axes reordering based on multiple properties, when no single reordering scheme fits the user goals. A comprehensive evaluation was done with real users and diverse datasets confirm the efficacy of PC-Expo in data storytelling with PCPs.
NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis

The success of DL can be attributed to hours of parameter and architecture tuning by human experts. Neural Architecture Search (NAS) techniques aim to solve this problem by automating the search procedure for DNN architectures making it possible for non-experts to work with DNNs. Specifically, One-shot NAS techniques have recently gained popularity as they are known to reduce the search time for NAS techniques. One-Shot NAS works by training a large template network through parameter sharing which includes all the candidate NNs. This is followed by applying a procedure to rank its components through evaluating the possible candidate architectures chosen randomly. However, as these search models become increasingly powerful and diverse, they become harder to understand. Consequently, even though the search results work well, it is hard to identify search biases and control the search progression, hence a need for explainability and human-in-the-loop (HIL) One-Shot NAS. To alleviate these problems, we present NAS-Navigator, a visual analytics (VA) system aiming to solve three problems with One-Shot NAS; explainability, HIL design, and performance improvements compared to existing state-of-the-art (SOTA) techniques. NAS-Navigator gives full control of NAS back in the hands of the users while still keeping the perks of automated search, thus assisting non-expert users. Analysts can use their domain knowledge aided by cues from the interface to guide the search. Evaluation results confirm the performance of our improved One-Shot NAS algorithm is comparable to other SOTA techniques. While adding Visual Analytics (VA) using NAS-Navigator shows further improvements in search time and performance. We designed our interface in collaboration with several deep learning researchers and evaluated NAS-Navigator through a control experiment and expert interviews.
Full-text chemical identification with improved generalizability and tagging consistency

Chemical identification involves finding chemical entities in text (i.e. named entity recognition) and assigning unique identifiers to the entities (i.e. named entity normalization). While current models are developed and evaluated based on article titles and abstracts, their effectiveness has not been thoroughly verified in full text. In this paper, we identify two limitations of models in tagging full-text articles: (1) low generalizability to unseen mentions and (2) tagging inconsistency. We use simple training and post-processing methods to address the limitations such as transfer learning and mention-wise majority voting. We also present a hybrid model for the normalization task that utilizes the high recall of a neural model while maintaining the high precision of a dictionary model. In the BioCreative VII NLM-Chem track challenge, our best model achieves 86.72 and 78.31 F1 scores in named entity recognition and normalization, significantly outperforming the median (83.73 and 77.49 F1 scores) and taking first place in named entity recognition. In a post-challenge evaluation, we re-implement our model and obtain 84.70 F1 score in the normalization task, outperforming the best score in the challenge by 3.34 F1 score. Database URL: https://github.com/dmis-lab/bc7-chem-id.
Research on cloud computing data sharing system based on blockchain (Withdrawal Notice)

When sharing data on the cloud platform, cloud computing users will face high-risk risks such as data leakage. The traditional way of going to the cloud through data encryption has disadvantages in flexibility and execution efficiency. With the help of the decentralized consensus and tamper proof characteristics of blockchain, a model and implementation scheme based on blockchain are proposed. The double-layer structure of private chain and public chain is adopted. The data is queried and analyzed according to the specification through metadata, and the query results can be verified. The data is stored in the private chain, and the private chain block is stored in the public chain. The calculation is carried out through the smart contract, and only the data results are returned. The data will not be leaked to the third party, let alone sensitive problems. The data encryption adopts the identity based public key encryption algorithm without public key certificate authentication to ensure the encryption performance. Simulation results show that the proposed scheme is operable. This paper will take the typical cloud data sharing scenario of "financial supermarket" as an application case to prove it.
A Binary Pattern Based Secure Data Hiding in Digital Images

Information security is one of the most challenging tasks in the recent era of advanced technologies and communication. It is important to ensure the secure exchange of confidential data. Data hiding techniques have a key role in ensuring security. The proposed technique is one such technique to enhance the security of secret information by many folds. The proposed technique detects a binary pattern to classify the cover image pixels into two groups of pixels, i.e., the group of pixels used for information hiding and the group of pixels that remains unaffected. The secret information is hidden in the selected pixels using LSB substitution without creating any detectable modifications. The results show that the technique creates a high-quality stego image with a PSNR larger than the threshold level of 30dB and SSIM higher than 0.9999. Along with the good quality of the stego images, the binary pattern enhances the security of the hidden information twofold.
A Needs Learning Algorithm Applied to Stable Gait Generation of Quadruped Robot

Based on Maslow's hierarchy of needs theory, we have proposed a novel machine learning algorithm that combines factors of the environment and its own needs to make decisions for different states of an agent. This means it can be applied to the gait generation of a quadruped robot, which needs to make demand decisions. To evaluate the design, we created an experimental task in order to compare the needs learning algorithm with a reinforcement learning algorithm, which was also derived from psychological motivation theory. It was found that the needs learning algorithm outperformed the reinforcement learning in tasks that involved making decisions between different levels of needs. Finally, we applied the needs learning algorithm to the problem of stable gait generation of quadruped robot, and it had achieved good results in simulation and real robot.
Research on task scheduling algorithm of PSO-BAS in cloud environment

In the cloud computing environment, how to ensure that tasks are efficient and reasonable in scheduling and execution, is a problem that researchers and developers need attention. This paper combines particle swarm algorithm (PSO) and beetle antennae search algorithm (BAS) to comprehensively consider overall consumption and longest completion time, including scheduling time and processing time. The simulation experiments are carried out by Cloudsim, corresponding comparisons with FCFS, Round Robin, SJF, and PSO algorithms are shown: by comparing single PSO, the convergence effect is better; by comparing with other algorithms, it can not only reduce the average completion time, but also save costs.
A flexible framework for multiple-role discovery in real networks

Abstract In complex networks, the role of a node is based on the aggregation of structural features and functions. However, in real networks, it has been observed that a single node can have multiple roles. Here, the roles of a node can be defined in a case-by-case manner, depending on the graph data mining task. Consequently, a significant obstacle to achieving multiple-role discovery in real networks is finding the best way to select datasets for pre-labeling. To meet this challenge, this study proposes a flexible framework that extends a single-role discovery method by using domain adversarial learning to discover multiple roles for nodes. Furthermore, we propose a method to assign sub-networks, derived through community extraction methods, to a source network and a validation network as training datasets. Experiments to evaluate accuracy conducted on real networks demonstrate that the proposed method can achieve higher accuracy and more stable results.
Application Design of Linguistics in Computer Technology under Artificial Intelligence Background

Computer technology is called the fourth industrial revolution, indicating the importance of computer technology. At the same time, as the most important way for our daily exchanges, it is also connected to computer technology. This article is intended to study the application design of linguistics in computer technology in controversy. This paper proposes a documentary conversion algorithm based on a BP neural network and has made corresponding analysis for linguistics. It designed a documentary conversion system for the application of language technology. The experimental results show that the language has more in-depth applications in computer technology, and the documentary conversion system designed herein can reach more than 97% of the accuracy of 20 people in the system.
Gamma Radiation Image Noise Prediction Method Based on Statistical Analysis and Random Walk

The gamma radiation environment is one of the harshest operating environments for image acquisition systems, and the captured images are heavily noisy. In this paper, we improve the multi-frame difference method for the characteristics of noise and add an edge detection algorithm to segment the noise region and extract the noise quantization information. A Gaussian mixture model of the gamma radiation noise is then established by performing a specific statistical analysis of the amplitude and quantity information of the noise. The established model is combined with the random walk algorithm to generate noise and achieve the prediction of image noise under different accumulated doses. Evaluated by objective similarity matching, there is no significant difference between the predicted image noise and the actual noise in subjective perception. The ratio of similarity-matched images in the sample from the predicted noise to the actual noise reaches 0.908. To further illustrate the spillover effect of this research, in the discussion session, we used the predicted image noise as the training set input to a deep residual network for denoising. The network model was able to achieve a good denoising effect. The results show that the prediction method proposed in this paper can accomplish the prediction of gamma radiation image noise, which is beneficial to the elimination of image noise in this environment.
Monthly Average Solar Radiation and Angstrom-Prescott Model for Islamabad, Pakistan

For any solar energy utilization, it is essential to know the value of radiation on an hourly basis and monthly basis. Generally, for Islamabad, Pakistan, available data are limited, and it is difficult to correctly evaluate the harnessed solar energy. In this paper, the monthly average solar radiation of Islamabad on an hourly basis has been presented, as compared to the available yearly average data in the literature. Moreover, the Angstrom&ndash;Prescott constant for Islamabad, Pakistan, has been calculated. The proposed relation has the error of radiation less than 1.5%.
Web-Based Parametric Effort Estimation for Mobile Application Development

Estimation methods are continuously being adapted to obtain better and clearer estimations needed to achieve development goals. Some estimation methods were invented before the modern mobile application technology that is currently available. Thus, these methods are unable to cater to the requirements for estimating modern mobile application features. The objective of this paper is to propose a web-based system as a method to estimate the effort and cost of developing a mobile application. The key idea behind this study is to identify cost drivers that can be applied in mobile application development through literature review. From the analysis, 19 cost drivers are found to fit the vision of this study. In addition, this study also seeks to investigate the price range of cost drivers acquired from existing similar systems. The total price range is accumulated, and the mean value of each cost driver is obtained, which is then inserted further into the new estimation metric. The proposed system is then evaluated by comparing the obtained results with six similar systems according to basic user needs requirements in an application. The results demonstrate that the proposed system is a more enhanced cost estimation software that contains more cost driver options, which users can utilize to estimate mobile application development costs.
Automated Crowd Parameter Estimation and Crowd Movement Analysis in Kumbh Mela

Understanding crowd behavior is essential in mass religious gatherings for crowd managers. Surveillance devices such as CCTV provide data in real time in the form of raw video, while the crowd manager estimates the crowd state from video based on their experience. In this study, we propose a methodology to automate the crowd parameter estimation process using an object detection model and tracking algorithm, which will assist crowd managers in estimating the state of the crowd. There are two key contributions to this study. First, the study proposes a methodology to automate crowd parameter estimation from video in a mass religious gathering. Second, the existing state-of-the-art object detection model has been improved to adapt to the challenging situation of mass religious gatherings with high density, high diversity crowd videos. CCTV videos from Kumbh Mela 2016 are used for this study.