Reviewerly/exHarmony huggingface dataset:
Dissipative two-identical-particle systems: diffraction and interference

Interference and diffraction of two-identical-particles are considered in the context of open quantum systems. This theoretical study is carried out within two approaches, the effective time-dependent Hamiltonian due to Caldirola-Kanai (CK) and the Caldeira-Leggett (CL) one where a master equation for the reduced density matrix is used under the presence of dissipation and temperature of the environment. Two simple but very illustrative examples are considered, diffraction by a single and two Gaussian slits by analyzing the mean square separation between particles, single-particle probability density and the simultaneous detection probability or diffraction patterns. Concerning the single Gaussian slit case, in the CK approach, the mean square separation drastically reduces with friction, reaching a constant value due to the localization effect of friction. On the contrary, in the CL approach, temperature has an opposite effect to friction and this quantity increases. Furthermore, there is a time-interval for which the joint detection probability is greater for fermions than for bosons. As has already been reported for non-dissipative systems, fermion bunching and boson anti-bunching are also observed.
First Arrival Picking of Seismic Data Based on Trace Envelope

We introduce a new method for first arrival traveltime picking based on the maximum difference between adjacent points of the envelope (MDPE) of a seismic trace. The method starts by calculating the Hilbert transform of the seismic trace to generate the complex seismic trace, which consists of the real and imaginary parts of a seismic trace. After generating the complex seismic trace, the instantaneous amplitude or envelope is extracted and used to pick the first arrival traveltime. We test the proposed method on synthetic shot records. When we compared the automatically picked first arrival traveltimes using our proposed method versus those picked manually by an experienced processor, we found that our proposed method estimated the first arrival traveltimes with more than 88% picking accuracy (within 0.1 s of manual picks). Furthermore, we perform the same test on real shot records and the proposed method estimated the first arrival traveltimes with a picking accuracy of 85% (within 0.1 s of manual picks). Finally, we apply the proposed method on 58 traces extracted from a real 2D land seismic data set from southeast Texas on which first arrival traveltime was difficult to pick automatically. We compare our proposed method and the P-phase picker method against manual picks. Results show that the proposed method has a picking accuracy of 99%, while the P-phase picker method gives 83% picking accuracy (within 0.1 s of manual picks).
Norm-Based Behavior Regulating Technique for Multi-Agent in Complex Adaptive Systems

In a complex adaptive system, norms are widely used to regulate agents’ behavior within a society in which the agents are not explicitly given the norms of different host systems, but instead detect and adapt the norms autonomously. Thus, failing to adopt a host’s norms resulting in deprived of accessing resources and services that would severely affect their performance. Few attempts have been made to overcome this problem but proposed solutions lack accuracy in identifying the norms of a domain. Consequently, this paper demonstrates a new effective technique called Regulative Norms Detection Technique (RNDT) to detect norms by analyzing odd events that trigger reward or penalty. Several tests have been conducted on agents exploiting the technique to detect norms in a virtual domain under varying environmental settings. The tests’ results show that the RNDT achieve well although the rate of success relies on the environmental variables settings. Specifically, the result shows that the rate of adopting the domain’s norms using RNDT is 78.0%. The rate of rewarded agents in three testing cycles increased from 30% to 70%, the rate of neutral agents also increased from 35% to 45%. The most noticeable change is in the penalty case in which 35% of agents are penalized in cycle 1, while in cycle 2 and 3 the rate is maintained at 0.0%.
Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol for Extending the Lifetime of the Wireless Sensor Network

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Library-Authored Web Content and the Need for Content Strategy

Increasingly sophisticated content management systems (CMS) allow librarians to publish content via the web and within the private domain of institutional learning management systems. “Libraries as publishers” may bring to mind roles in scholarly communication and open scholarship, but the authors argue that libraries’ self-publishing dates to the first “pathfinder” handout and continues today via commonly used, feature-rich applications such as WordPress, Drupal, LibGuides, and Canvas. Although this technology can reduce costly development overhead, it also poses significant challenges. These tools can inadvertently be used to create more noise than signal, potentially alienating the very audiences we hope to reach. No CMS can, by itself, address the fact that authoring, editing, and publishing quality content is both a situated expertise and a significant, ongoing demand on staff time. This article will review library use of CMS applications, outline challenges inherent in their use, and discuss the advantages of embracing content strategy.
Observer Design for Structure from Motion using Concurrent Learning

In this paper, a concurrent learning based observer for a perspective dynamical system (PDS) is developed. The PDS is a widely used model for estimating the depth of the feature point from a sequence of camera images. Building on the current progress of concurrent learning (CL) for parameter estimation in adaptive control, a state observer is developed for a PDS model where the inverse depth appears as a time-varying parameter in the dynamics. Using the data-recorded over a sliding time window in the near past, information about the recent depth values is used in a CL term and an observer is developed. A Lyapunov-based stability analysis is carried out to prove the uniformly ultimately bounded (UUB) stability of the observer. Comparisons in simulations are presented with the existing observers in terms of convergence, and error statistics. Comparisons reveal that CL improves the convergence and accuracy of the presented observer.
Dementia Patient Segmentation Using EMR Data Visualization: A Design Study

(1) Background: The Electronic Medical Record system, which is a digital medical record management architecture, is critical for reliable medical research. It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis of multidimensional datasets via a combination of 3-dimensional radial coordinate visualization (3D RadVis) and many-objective optimization (e.g., Parallel Coordinates). Also, we propose a user-driven research design to facilitate visualization. We followed a design process to (1) understand the demands of domain experts, (2) define the problems based on relevant works, (3) design visualization, (4) implement visualization, and (5) enable qualitative evaluation by domain experts. (3) Results: This study provides clinical insight into dementia based on EMR data via visual analysis. Results of a case study based on questionnaires surveying daily living activities indicated that daily behaviors influenced the progression of dementia. (4) Conclusions: This study provides a visual analytical tool to support cluster segmentation. Using this tool, we segmented dementia patients into clusters and interpreted the behavioral patterns of each group. This study contributes to biomedical data interpretation based on a visual approach.
How to Program a Chatbot – An Introductory Project and Student Perceptions

Aim/Purpose: One of the most fascinating developments in computer user interfaces in recent years is the rise of “chatbots”. Yet extent information system (IS) curriculum lacks teaching resources on chatbots programming.. Background: To better prepare students for this new technological development and to enhance the IS curriculum, we introduce a project that teaches students how to program simple chatbots, including a transactional chatbot and a conversational chatbot. Methodology: We demonstrated a project that teaches students how to program two types of simple chatbots: a transactional chatbot and a conversational chatbot. We also conducted a survey to examine students’ perceptions on their learning experience. Findings: Our survey on students’ perception of the project finds that learning chatbots is deemed very useful because chatbot programming projects have enabled the students to understand the subject better. We also found that social influence has positively motivated the students to learn chatbot programming. Though most of the students have no prior experiences programming chatbots, their self-efficacy towards chatbot programming remained high after working through the programming project. Despite the difficult tasks, over 71% of respondents agree to various degrees that chatbot programming is fun. Though most students agree that chatbot programming is not easy to learn, more than 70% of respondents indicated that they will use or learn chatbots in the near future. The overwhelmingly positive responses are impressive given that this is the first time for the students to program and learn chatbots. Recommendations for Practitioners: In this article, we introduced a step by step project on teaching chatbot programming in an information systems class. Following the project instructions, students can get their first intelligent chatbots up and running in a few hours using Slack. This article describes the project in detail as well as students’ perceptions. Recommendations for Researchers: We used UTAUT model to measure students’ perception of the projects. This study could be of value to researchers studying students’ technology learning and adoption behaviors. Impact on Society: To our best knowledge, pedagogical resources that teach IS students how to program chatbots, especially the introductory level materials, are limited. We hope this teaching case could be of value for IS educators when introducing IS students to the wonderful field of chatbot programming. Future Research: For future work, we plan to expand the teaching resources to cover more advanced chatbot programming projects, such as on how to make chatbot more human-like.
Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity

Due to the fast speed of data generation and collection from advanced equipment, the amount of data obviously overflows the limit of available memory space and causes difficulties achieving high learning accuracy. Several methods based on discard-after-learn concept have been proposed. Some methods were designed to cope with a single incoming datum but some were designed for a chunk of incoming data. Although the results of these approaches are rather impressive, most of them are based on temporally adding more neurons to learn new incoming data without any neuron merging process which can obviously increase the computational time and space complexities. Only online versatile elliptic basis function (VEBF) introduced neuron merging to reduce the space-time complexity of learning only a single incoming datum. This paper proposed a method for further enhancing the capability of discard-after-learn concept for streaming data-chunk environment in terms of low computational time and neural space complexities. A set of recursive functions for computing the relevant parameters of a new neuron, based on statistical confidence interval, was introduced. The newly proposed method, named streaming chunk incremental learning (SCIL), increases the plasticity and the adaptabilty of the network structure according to the distribution of incoming data and their classes. When being compared to the others in incremental-like manner, based on 11 benchmarked data sets of 150 to 581,012 samples with attributes ranging from 4 to 1,558 formed as streaming data, the proposed SCIL gave better accuracy and time in most data sets.
Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution

The goal of continuous variable quantum key distribution (CVQKD) is to be diffusely used and adopted in diverse scenarios, so the adhibition of atmospheric channel will play a crucial part in constituting global secure quantum communications. Atmospheric channel transmittance is affected by many factors and does not vary linearly, leading to great changes in signal-to-noise ratio. It is crucial to choose the appropriate modulation variance under different turbulence intensities to acquire the optimal secret key rate. In this paper, the four-state protocol, back-propagation neural network (BPNN) algorithm was discussed in the proposed scheme. We employ BPNN to CVQKD, which could adjust the modulation variance to an optimum value for ensuring the system security and making the system performance optimal. The numerical results show that the proposed scheme is equipped to improve the secret key rate efficiently.
An artificial intelligent platform for live cell identification and the detection of cross-contamination

Background: About 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products.Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive.The purpose of this study was to construct a novel artificial intelligence (AI) technology for "cell face" recognition, in which can predict DNA-level identification labels only using cell images.Methods: Seven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells.The microscopy images were obtained and labeled of cell types by the result of short tandem repeat profiling.About 2 million patch images were used for model training and testing.AlexNet was used to demonstrate the effectiveness of convolutional neural network (CNN) in cell classification.To further improve the feasibility of detecting cross-contamination, the bilinear network for fine-grained identification was constructed.The specificity, sensitivity, and accuracy of the model were tested separately by external validation.Finally, the cell semantic segmentation was conducted by DilatedNet. Results:The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet.The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines).DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. Conclusions:The deep CNN model proposed in this study has the ability to recognize small differences in cell morphology, and achieved high classification accuracy.
Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Scheduling is a heart of cloud computing as without appropriate scheduling it is impossible to get the desired results.Primary focus of this article is to focus on minimization of makespan, minimum utilization of resources and make the cloud services economic for an independant task.Out of the various task scheduling strategies, in last few years meta-heuristic algorithms have gained recognition in successful operation of task scheduling algorithms.League Championship based Algorithm (LCA) is fascinated from sports leagues through which best team/task in this case can be find out for scheduling.Task scheduling using Adaptive League Championship Algorithm (ALCA) is employed in this article and thereby it shrinks makespan, cloud utilization and cost.ALCA is implemented with cloudsim simulator using java as a programming tool and scheduling has followed the non-premptive approach.Implementation of ALCA results reducation in makespan by 32.95 %, 20.99 % and 7.29 % against customary Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Global League Championship Algorithm (GBLCA) respectively.ALCA also reduces significantly cloud utilization value and improves economy of scale.ALCA may serve as preferred choice for cloud broker as it proved to be multipurpose in the area of makespan, resource utilization and economy.
Fast and Robust Vanishing Point Detection Using Contourlet Texture Detector for Unstructured Road

Fast and robust vision-based road detection in an unstructured environment is very challenging.In this paper, we focus on vanishing-point (VP) detection in unstructured roads and propose a responsemodulated line-voting method based on a contourlet transform, followed by a voter selection process for VP detection.We first adopt the contourlet transform to estimate the dominant vector for each pixel, including orientation and its relevant response.The estimated dominant vector is then selected by a novel select function to retrieve approximately 40% of the pixels with a reliable dominant vector in the image to vote.Unlike previous methods, this method takes into account the magnitudes of response of the pixels to improve the efficiency of the voting process by suppressing possible interference by extreme and strong textures.The pixels are given a moderate response to vote.Finally, for situations where the road texture is likely to be selected as a criterion for voting by the line-voting scheme, we use this simple and fast scheme to vote for the VP.We conduct experiments on a public dataset of 1,003 different types of natural road images as well as on our own dataset of 400 such images.The results demonstrate that in our dataset, the proposed method is comparable to and outperforms the state-of-the-art methods.
Hand Gestures Recognition Based on One-Channel Surface EMG Signal

This paper presents an experiment using OPENBCI to collect data of two hand gestures and decoding the signal to distinguish gestures.The signal was extracted with three electrodes on the subject's forearm and transferred in one channel.After utilizing a Butterworth bandpass filter, we chose a novel way to detect gesture action segment.Instead of using moving average algorithm, which is based on the calculation of energy, We developed an algorithm based on the Hilbert transform to find a dynamic threshold and identified the action segment.Four features have been extracted from each activity section, generating feature vectors for classification.During the process of classification, we made a comparison between K-nearest-neighbors (KNN) and support vector machine (SVM), based on a relatively small amount of samples.Most common experiments are based on a large quantity of data to pursue a highly fitted model.But there are certain circumstances where we cannot obtain enough training data, so it makes the exploration of best method to do classification under small sample data imperative.Though KNN is known for its simplicity and practicability, it is a relatively time-consuming method.On the other hand, SVM has a better performance in terms of time requirement and recognition accuracy, due to its application of different Risk Minimization Principle.Experimental results show an average recognition rate for the SVM algorithm that is 1.25% higher than for KNN while SVM is 2.031 s shorter than that KNN.
Myeloma and Hybridoma Cell Counts and Viability Checks

For most purposes, the number of hybridoma or myeloma cells can be estimated simply by observing the cells under the microscope. When an exact cell count is needed, the number can be determined by using a hemocytometer (improved Neubauer counting chambers are the most commonly used). This is a simple device in which a special coverslip rests on supports that hold it 0.1 mm above the base of the slide. The slide is engraved with a series of lines that form 1 × 1-mm squares. By counting the number of cells within the 0.1-mm 3 chamber formed by the 1 × 1-mm square and the height of the coverslip, an accurate quantitation of cells per milliliter can be calculated. To determine the percentage of viable cells within a population, the cell suspension is mixed with a vital dye and observed under the microscope. Vital dyes are excluded from living cells but stain dead cells. The most common dye used for these stains is Trypan Blue.
Hand gesture monitoring using fiber-optic curvature sensors

A hand gesture monitoring system based on fiber-optic curvature sensors is developed. A glove is used as a carrier to support the monitoring system. According to the freedom of the hand joint, the layout of the sensors is established. An untreated fiber is introduced as a reference signal. The division operation between the sensing signal and reference signal is regarded as the monitoring result, which can reduce the monitoring error produced by external unstable factors. The experimental results show that the measurement errors are within the range of ±π/36 and ±π/75, respectively, when measuring the joint bending and abduction angle, which indicates the feasibility of the proposed monitoring system.
Exact and Approximate Rule Extraction from Neural Networks with Boolean Features

Rule extraction from classifiers treated as black boxes is an important topic in explainable artificial intelligence (XAI).It is concerned with finding rules that describe classifiers and that are understandable to humans, having the form of (I f ...T hen...Else).Neural network classifiers are one type of classifier where it is difficult to know how the inputs map to the decision.This paper presents a technique to extract rules from a neural network where the feature space is Boolean, without looking at the inner structure of the network.For such a network with a small feature space, a Boolean function describing it can be directly calculated, whilst for a network with a larger feature space, a sampling method is described to produce rule-based approximations to the behaviour of the network with varying granularity, leading to XAI.The technique is experimentally assessed on a dataset of cross-site scripting (XSS) attacks, and proves to give very high accuracy and precision, comparable to that given by the neural network being approximated.