Reviewerly/exHarmony huggingface dataset:
Complementarity between quantum coherence and mixedness: a majorization approach

Abstract Quantum coherence is a relevant resource for various quantum information processing tasks, but it is fragile since it is generally affected by environmental noise. This is reflected in the loss of purity of the system, which in turn limits the amount of quantum coherence of it. As a consequence, a complementarity relation between coherence and mixedness arises. Previous works characterize this complementarity through inequalities between the ℓ 1 -norm of coherence and linear entropy, and between the relative entropy of coherence and von Neumann entropy. However, coherence–mixedness complementarity is expected to be a general feature of quantum systems, regardless of the measures used. Here, an alternative approach to coherence–mixedness complementarity, based on majorization theory, is proposed. Vectorial quantifiers of coherence and mixedness, namely the coherence vector and the spectrum, respectively, are used, instead of scalar measures. A majorization relation for the tensor product of both vectorial quantifiers is obtained, capturing general aspects of the trade-off between coherence and mixedness. The optimal bound for qubit systems and numerical bounds for qutrit systems are analyzed. Finally, coherence–mixedness complementarity relations are derived for a family of symmetric, concave and additive functions. These results provide a deeper insight into the relation between quantum coherence and mixedness.
Multi-aperture imaging with Fermat spiral sub-aperture arrangement

Multi-aperture optical telescopes have been extensively studied owing to their high resolution, low cost, and light weight. The next generation of optical telescopes is predicted to be equipped with dozens or even hundreds of segmented lenses; therefore, it is necessary to optimize the arrangement of the lens array. This paper proposes a new structure called the Fermat spiral array (FSA) to replace the conventional hexagonal or ring array for the sub-aperture arrangement of a multi-aperture imaging system. The point spread function (PSF) and modulation transfer function (MTF) of the imaging system are compared in detail at single and multiple incident wavelengths. The FSA can effectively weaken the sidelobe intensity of the PSF, which is 12.8 dB lower on average than conventional ones with a single incident wavelength in the simulation and 4.45 dB lower in the experiment. A new MTF evaluation function is proposed to describe the mean level of MTF at mid-frequencies. The FSA can improve the MTF of the imaging system and weaken the ringing effect in the images. The imaging simulation indicates that FSA has superior imaging quality compared to conventional arrays, with a higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The imaging experiments also achieve a higher SSIM with the FSA, which agrees well with the simulation results. The proposed FSA multi-aperture will help improve the imaging performance of next-generation optical telescopes.
Optimizing Diagnostic Classification Models Application Considering Real-Life Constraints

This article provides a process to carefully evaluate the suitability of a content domain for which diagnostic classification models (DCMs) could be applicable and then optimized steps for constructing a test blueprint for applying DCMs and a real-life example illustrating this process. The content domains were carefully evaluated using a set of defined criteria, which are purposely defined to improve the success rate of DCM implementation. Given the domain, the Q-matrix is determined by a simulation-based approach using correct classification rates as criteria. Finally, a physics test on the final Q-matrix was developed, administered, and analyzed by the author and the subject-matter experts (SMEs).
A UWB/INS Trajectory Tracking System Application in a Cycling Safety Study

This paper focuses on the safety issue for cyclists and pedestrians at unsignalized intersections. The cycling speed needs to be calmed when approaching the intersection. This study proposes and deploys an integrated portable ultra-wideband/inertial navigation system (UWB/INS) to extract cycling trajectories for a cycling safety study. The system is based on open-source hardware and delivers an open-source code for an adaptive Kalman filter to enhance positioning precision for data quality assurance at an outdoor experimental site. The results demonstrate that the system can deliver reliable trajectories for low-mobility objects. To mitigate accident risk and severity, varied cycling speed calming measures are tested at an experimental site. Based on the trajectory data, the statistical features of cycling velocities are evaluated and compared. A new proposed geometric design is found to be most effective when compared with conventional traffic signs.
Multimodal digital extensive reading program: a reflection on literacy movement in madrasah and pesantren

Over the years, ample studies on diverse school literacy movements have been discussed. However, multimodal digital extensive reading (MDER) has not been fully incorporated into literacy programs in Islamic private school (madrasah) and Islamic boarding school (pesantren). This study attempts to investigate how digital literacy and reading literacy in the form of MDER program in madrasah and pesantren can be deployed to build strong reading habits. This study employed a qualitative case study design. It involved nine novice teacher participants from ten madrasahs and pesantrens, and one supervisor. The data were garnered from the participants' reflective journals, Focus Group Discussions (FGDs), documentation, and interview with the supervisor. The collected data were analyzed using thematic analysis. The results of this study showed three fundamental tenets of the MDER program in madrasah and pesantren: teachers’ efforts, the sustainable collaboration between teachers and students, and a long-term partnership among school members and stakeholders. The result sheds light on how to build a reading habit and digital literacy in madrasah and pesantren. It also implicates the role of leaders’ policy in achieving successful program implementation.
Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization

Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce EpiForecast, a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique based on empirical dynamic modelling. EpiForecast's primary feature is a four-plot interactive dashboard which displays a variety of information to help the user understand how the forecasts are generated. In addition to point forecasts, the tool produces distributional forecasts using a kernel density estimation method-these are visualized using color gradients to produce a quick, intuitive visual summary of the estimated future. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.
Powerful promotions: An investigation of the teen‐directed marketing power of outdoor food advertisements located near schools in Australia

Adolescents are heavily exposed to unhealthy outdoor food advertisements near schools, however, the marketing power of these advertisements among adolescents has not yet been explored. This study aimed to investigate the teen-directed marketing features present and quantify the overall marketing power of outdoor food advertisements located near schools to explore any differences by content (ie, alcohol, discretionary, core and miscellaneous foods) school type (ie, primary, secondary, K-12) and area-level socio-economic status (SES; ie, low vs high).This cross-sectional study audited every outdoor food advertisement (n = 1518) within 500m of 64 randomly selected schools in Perth, Western Australia, using a teen-informed coding tool to score the marketing power of each advertisement.Outdoor alcohol advertisements around schools had the highest average marketing power score and number of advertising features present. Outdoor advertisements for alcohol and discretionary foods scored significantly higher in marketing power than core food advertisements (P < .001). Outdoor alcohol advertisements around secondary schools scored significantly higher in marketing power than around primary and K-12 schools (P < .001); and outdoor advertisements for discretionary foods in low SES areas scored significantly higher in marketing power than those in high SES areas (P < .001).This study found outdoor advertisements for unhealthy products, such as alcohol and discretionary foods, were more powerful than advertisements for core foods around schools. SO WHAT?: These findings strengthen the need for policies which restrict outdoor advertisements for non-core foods near schools, to reduce adolescents' exposure to powerful alcohol and discretionary food advertisements.
‘So Help Me God’? Does oath swearing in courtroom scenarios impact trial outcomes?

In countries such as Britain and the US, court witnesses must declare they will provide truthful evidence and are often compelled to publicly choose between religious ("oath") and secular ("affirmation") versions of this declaration. Might defendants who opt to swear an oath enjoy more favourable outcomes than those who choose to affirm? Two preliminary, pre-registered survey studies using minimal vignettes (Study 1, N = 443; Study 2, N = 913) indicated that people associate choice of the oath with credible testimony; and that participants, especially religious participants, discriminate against defendants who affirm. In a third, Registered Report study (Study 3, N = 1821), we used a more elaborate audiovisual mock trial paradigm to better estimate the real-world influence of declaration choice. Participants were asked to render a verdict for a defendant who either swore or affirmed, and were themselves required to swear or affirm that they would try the defendant in good faith. Overall, the defendant was not considered guiltier when affirming rather than swearing, nor did mock-juror belief in God moderate this effect. However, jurors who themselves swore an oath did discriminate against the affirming defendant. Exploratory analyses suggest this effect may be driven by authoritarianism, perhaps because high-authoritarian jurors consider the oath the traditional (and therefore correct) declaration to choose. We discuss the real-world implications of these findings and conclude the religious oath is an antiquated legal ritual that needs reform.
Trend of Paradigm for integrating Blockchain, Artificial Intelligence, Quantum Computing, and Internet of Things

The combination of blockchain (BC), artificial Intelligence (AI), quantum computing (QC), and the Internet of Things (IoT) can potentially transform various industries and domains, including healthcare, logistics, and finance. In this paper, we look at the trends and developments in integrating these emerging technologies and the potential benefits and challenges that come with them. We present a conceptual framework for integrating BC, AI, QC, and IoT and discuss the framework's key characteristics and challenges. We also look at the most recent cutting-edge research and developments in integrating these technologies, as well as the key challenges and opportunities that come with them. Our analysis highlights the potential benefits of integrating the technologies and looks to increased security, privacy, and efficiency to provide insights into the future of these technologies.
Data Security Analysis in Cloud Computing Environment

Cloud computing is heading inexorably toward consolidation, which is a necessary instrument for the continued survival using the internet. In the cloud computing utilized for large data analytics, backup, and private hybrid clouds. These days, it's common practice to keep databases and programs in a cloud setting. The services are used as needed. Users may access the cloud, where software is already accessible, without having to install it on their computers, and utilize it in the same way for storing data as well. Security is now the most important factor for the sensitive data stored on the cloud. The technique is to gather resources and analyze the data by allowing user Internet Protocol access to the resource, storing the data in the cloud, check for hacker information, and respond as necessary by restricting the appropriate Internet Protocol (IP). The restricted IP user is no longer able to access the resources. Big data security in the cloud enables the tracking of unauthorized users (hackers) who attempt to access the resources. Administrators have the unique power to stop the hacker's workstation by disabling the IP address of the targeted system. This article discusses about the data security, which is the most common problem for everyone, because users don't have the perfect knowledge of how their data is stored on cloud. So, this study will discuss about data security in the context of cloud computing.
Sentimental Analysis on Twitter Data using Supervised Algorithms

Sentimental Analytics is the phenomenon used to show the feelings of different people, emotions, opinions. It is also referred as the opinion meaning, which identifies the emotional tone behind a body of text The Mechanism gives whether the result is positive, negative, or neutral. It gives finer results, to keep track of users, and tells whether the tweet is uplifted or down lifted by the user's content. Machine learning can be analyzed by using various analysis algorithms to provide a precise value. Machine learning algorithms play a major role in various domains, and one of them includes data analysis. In this work, machine learning techniques are used to predict the Twitter analysis. Machine Learning (ML) algorithms such as Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) algorithm are used for implementation. The performance of the implemented algorithm is analyzed in the terms of accuracy, precision, recall, F1-score on the tweets taken from the social media.
Navigating the Healthcare Landscape with Recommendation Systems: A Survey of Current Applications and Potential Impact

Recommendation systems in healthcare are designed to provide personalized medical treatment and advice to patients by analyzing large amounts of data. These systems use various algorithms, they are content-based filtering, collaborative filtering, and hybrid methods, to analyze patient data and make recommendations. The integration of these systems is done with electronic health records (EHRs) that improves the accuracy of the recommendations. The applications of recommendation systems in healthcare include personalized medicine, disease management, and telemedicine. However, the use of these systems also raises concerns about privacy and bias. This paper surveys the present state-of-the-art in recommendation systems in healthcare, and discusses the potential applications, challenges and future directions in this field.
Identification of an Animal Footprint with time prediction using Deep learning

This research work addresses issues related to developing an effective and efficient Animal Footprint, classification and tracking system. Successful attempts towards developing algorithmic models for animal segmentation, Animal Footprint, classification and tracking are made. To support the effective classification of animals, two methods are proposed to segment animal image from its background. The proposed animal segmentation algorithm is evaluated using region-based performance measures. A classification model is also proposed based on different features and classifiers. The different features like colour, Gabor and LBP are extracted from the segmented animal images. In addition, a system has been proposed to classify animals in images and videos by using a deep-learning approach. Initially, features are extracted from images/frames using AlexNet pre-trained probabilistic neural network. Later, the extracted features are fed into a multi-class probabilistic neural network classifier for classification.
Modular Adder Design based on Reversible Toffoli CLA

Since reversible computing can lower heat loss and power consumption, it is a more intriguing research area today. Processor power and speed are significantly impacted by adders. Reversible Toffoli gates are used in the suggested structure in place of conventional logic for the look-ahead logic. The suggested structure is given a hybrid variable latency extend, which minimises energy usage without dramatically slowing down speed. Reversible computing necessitates an equal amount of input and output lines. Backward deterministic system is a technique recycled for nanotechnology, inadequate capacity of VLSI design, Photonic computer, and nondeterministic computing. Indicated work shows how a carry look-ahead adder with a reversible Toffoli gate can decrease trash production, gate count, and quantum efficiency in comparison to the current design.
Study of Feasibility of Image Compression with Wavelets over Hexagonal Pixel Arrays using a Custom Photodetector Integrated Circuit

The use of hexagonal grids of pixels to represent digital images is a topic that has been studied for decades; however, despite its advantages there is relatively little work done on the subject. On the other hand, for the compression of images with rectangular grids, where there is far more research, the use of the wavelet transform is a strategy that allows high levels of performance to be achieved; two examples of image compression systems based on wavelets are the JPEG2000 standard and the IW44 algorithm used by DjVu, both currently still in use. In this work we are interested in evaluating the feasibility of a hexagonal grid image compression system using wavelets for implementation on a configurable sensor made of pixels on a hexagonal grid previously developed by our group. In order to do this, theoretical and implementation aspects of image compression with wavelets on hexagonal grids of pixels are studied first. A complete hexagonal image compression system is then implemented using Haar like wavelets and an adaptation of the coding algorithm for rectangular images SPIHT. To evaluate the performance of the proposed system, a comparison is made with its rectangular analogous version by testing with multiple images. Finally, a simple test is carried out on the configurable sensor that shows the it is possible to directly read the decomposition coefficients instead of reading pixel by pixel.
Communication of Confidential Documents Through Email using Hybrid and Key-Exchange Algorithm

One of the fastest and most low-cost means of communication is Email. Most of the people made Email as part of their lifestyle, it changes the way of working. Email can be sent to an individual or group. It is more and more important to protect the transmission of data in the communication network. The existing PGP protocol uses RSA algorithm, it is not user-friendly, more complex in nature, slow encryption and both users need to use the same version of software. The aim of this project is to develop a new hybrid algorithm combining the benefits of AES, RSA and Polybius square cipher algorithms to improve the execution speed, improve security and transfer the secret key in a secured manner. This algorithm uses CFB mode instead of CBC mode for encryption to produce a unique output. The key of AES algorithm is now encrypted using RSA algorithm, but RSA only accepts numeric values. Using Polybius square cipher algorithm, the key value gets converted into numeric value. The key exchange algorithm will help us to prevent the attacks made to disclose the key which is transferred between client and receiver. The combination of hybrid and key exchange algorithm is to ensure the confidential documents are securely transferred.
Measuring the Performance of Clustered Image Data Sets using Image Proprietary

Image extraction from complicated sources of information is currently a speedily developing domain. Storing and extracting the image data is a puzzling assignment for several investigators because of the complication of the static picture information groups. The related content in this image data set can be extracted using various existing techniques. In the proposed method, image data sets are grouped and create ranking among the group. Based on the user input, high-ranking groups are retrieved and sent to the user. Here, image retrieval is done based on the image's optical structures. The resulting technology can be deployed in information systems and many other areas depending on the needs of the applications. The proposed method works fine with all kinds of response and retrieval processing time compared to the current methods.
Intelligent Crypto Mining Vehicle using AI Processor with IOT Payment System

Globally., all companies have made large capital investments in their workstation computers; however., most of these large investments have not been used efficiently., resulting in a lower profit-to-investment ratio. To achieve a high profit-to-investment ratio., workstation computers' new time should be efficiently used by Crypto Currency Mining with the help of Artificial Intelligence., which will earn extra income to the owners. The biggest issues encountered when mining bitcoin on conventional and traditional PC systems are: 1. System Health Issues., such as heating 2. System Availability Algorithm Mining time is available. 3. Electricity usage versus cryptocurrency coin ratio With the Intelligent AI algorithm., the suggested AI (LSAI48266x) board may address the aforementioned PC mining difficulties. The proposed model includes an artificial intelligence board based on numerous characteristics that will determine crypto currency mining anytime the employee seems to be absent from the workstation computers. While it is feasible to load up decent hashes., set everything up., and leave it running for years., it is preferable to utilize an intelligent AI LSAI48266x hardware board to get the most out of your CPU while consuming the least amount of power. This study intends to benefit and reduce the consumption without compromising the performance. The cryptocurrencies that do not need the construction of a huge mining rig are the easiest to mine. Because to the growing mining difficulty and the arrival of technology specialized exclusively for bitcoin mining., it is now almost impossible to mine Bitcoin for profit at home using the processing power of a PC or laptop computer.