Reviewerly/exHarmony huggingface dataset:
Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data

Background Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias towards the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterised by under-diagnosis. Methods Study 1: We created synthetic data, produced to mimic EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic. Results Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR. Conclusions The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult to find. Our approach made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrated the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missingness in EHR data.
Assessing Individual Contributions to Software Engineering Projects with Git Logs and User Stories

Software Engineering courses often incorporate large-scale projects with collaboration between students working in teams. However, it is difficult to objectively assess individual students when their projects are a product of collaborative efforts. This study explores measurements of individuals' contributions to their respective teams.
A Smart Contract-Based Mobile Ticketing System with Multi-Signature and Blockchain

We develop a blockchain-based mobile ticketing system which utilizes smart contracts and multi-signature to enforce and authorize the executions of transactions. The transactions of buying and using mobile tickets are verified by the blockchain, and the immutable ledger and multi-signature on the blockchain ensures the authenticity and security of the tickets. The experimental results show that the proposed system is very efficient and requires only a nominal cost.
Crime Prediction System

The work implemented here is crime prediction system (CPS). We first created hypothetical datasets samples of major city areas and different crimes taking place and then we used the algorithms to analyze it. We used HTML and CSS along with PHP, while wamp as a Web server to this application. The objective of the proposed work is to analyze and predict the chance of a crime happening using apriori algorithm. In addition, we used decision tree as a searching algorithm and naïve Bayesian classifier to predict about the crime in particular geographical location at a particular point of time. The result of this can be used to raise people’s awareness regarding the dangerous locations and to help agencies to predict future crime in a specific location within a particular time.
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm

In this paper, a new texture descriptor named “Fractional Local Neighborhood Intensity Pattern” (FLNIP) has been proposed for content-based image retrieval (CBIR). It is an extension of an earlier work involving adjacent neighbors (local neighborhood intensity pattern). However, instead of considering two separate patterns for representing sign and magnitude information, one single pattern is generated. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3 × 3 window by considering the relationship with adjacent neighbors. In this work, the fractional change in the local neighborhood involving the adjacent neighbors has been calculated first with respect to one of the eight neighbors of the center pixel of a 3 × 3 window. Next, the fractional change has been calculated with respect to the center itself. The two values of fractional change are next compared to generate a binary bit pattern. The descriptor is applied on four images- one being the raw image and the other three being filtered gaussian images obtained by applying gaussian filters of different standard deviations on the raw image to signify the importance of exploring texture information at different resolutions in an image. The four sets of distances obtained between the query and the target image are then combined with a genetic algorithm based approach to improve the retrieval performance by minimizing the distance between similar class images. The performance of the method has been tested for image retrieval on four databases and the proposed method has shown a significant improvement over many other existing methods.
Enhancement of spontaneous entanglement generation via coherent quantum feedback

We investigate the entanglement dynamics of two two-level emitters (qubits) mediated by a semiinfinite, one-dimensional (1D) photonic waveguide. The coupling of each qubit to the waveguide is chiral, which depends on the propagation direction of light. The finite end of the waveguide is terminated by a perfect mirror, such that coherent quantum feedback is introduced to the system. We show that the chirally generated entanglement between the qubits can be preserved by controlling the time delay of the feedback. Moreover, when the time delay is negligible, the qubit-qubit reduced system evolves within the strong-coupling regime and the qubits can be almost maximally entangled. We also analyze the robustness of the protocol against variations of some relevant parameters.
Energetic, economic, and environmental (3 E) performances of high concentrated photovoltaic large scale installations: Focus on spatial analysis of Morocco

Producing electricity from a renewable source is a major challenge in Morocco nowadays and high concentrated photovoltaic (HCPV) is becoming an attractive solution to ensure clean energy generation due to its high efficiency. The aim of this paper is to estimate the « Energetic, economic, and environmental » performances of large scale HCPV power plants in the major Moroccan climatic zones and to compare their feasibility to PV plants. The mathematical model for estimating the energetic, economic, and environmental performances of large scale HCPV plants is shown, sensitivity and comparison analysis are exhibited. Another goal of the paper is the optimization of the performances of the algorithm through the study of the effect of different parameters. Our major finding is that the large scale HCPV plants are more competitive than PV plants in Errachidia climatic zone where the (capacity factor, LCOE) take the values (26.07%,8.57c$/kWh). Moreover, the capacity factor of large scale HCPV power plants is the most sensitive to the temperature coefficient while the (LCOEnominal, LCOEreal, NPV) are the most sensitive to the analysis period. The quantity of CO2 mitigated reaches a maximal value in the Errachidia zone with 2.59 × 105 tons of carbon per year avoided, and the cost of carbon gained there is estimated to be 10.4 M$. Innovative GIS maps of energetic, economic, and environmental performances of large scale HCPV power plants for all Morocco are given.
GAR: Graph Assisted Reasoning for Object Detection

It is well believed that object-object relations and object-scene relations inherently improve the accuracy of object detection. However, the way to efficiently model relations remains a problem. Graph Convolutional Network (GCN), an effective method to handle structured data with relations, inspires us to leverage graphs in modeling relations for objection detection tasks. In this work, we propose a novel approach, Graph Assisted Reasoning (GAR), to utilize a heterogeneous graph in modeling object-object relations and object-scene relations. GAR fuses the features from neigh-boring object nodes as well as scene nodes and produces better recognition than that produced from individual object nodes. Moreover, compared to previous approaches using Recurrent Neural Network (RNN), the light-weight and low-coupling architecture of GAR further facilitates its integration into the object detection module. Comprehensive experiments on PASCAL VOC and MS COCO datasets demonstrate the efficacy of GAR.
A Scheme for Collective Encoding and Iterative Soft-Decision Decoding of Cyclic Codes of Prime Lengths: Applications to Reed–Solomon, BCH, and Quadratic Residue Codes

A novel scheme is presented for encoding and iterative soft-decision decoding of cyclic codes of prime lengths. The encoding of a cyclic code of a prime length is performed on a collection of codewords which are mapped through Galois Fourier transform into a codeword in a low-density parity-check code with a binary parity-check matrix for transmission. Using this matrix, binary iterative soft-decision decoding algorithm is applied to jointly decode a collection of codewords from the cyclic code. The joint-decoding allows for information sharing among the received vectors corresponding to the codewords in the collection during the iterative decoding process. For decoding Reed-Solomon and BCH codes of prime lengths, the proposed decoding scheme not only requires much lower decoding complexity than other soft-decision decoding algorithms for these codes, but also yields superior performance. The proposed decoding scheme can also achieve a joint-decoding gain over the maximum likelihood decoding of individual codewords. The decoding scheme is also applied to quadratic residue codes.
Robust Explanations for Visual Question Answering

In this paper, we propose a method to obtain robust explanations for visual question answering(VQA) that correlate well with the answers. Our model explains the answers obtained through a VQA model by providing visual and textual explanations. The main challenges that we address are i) Answers and textual explanations obtained by current methods are not well correlated and ii) Current methods for visual explanation do not focus on the right location for explaining the answer. We address both these challenges by using a collaborative correlated module which ensures that even if we do not train for noise based attacks, the enhanced correlation ensures that the right explanation and answer can be generated. We further show that this also aids in improving the generated visual and textual explanations. The use of the correlated module can be thought of as a robust method to verify if the answer and explanations are coherent. We evaluate this model using VQA-X dataset. We observe that the proposed method yields better textual and visual justification that supports the decision. We showcase the robustness of the model against a noise-based perturbation attack using corresponding visual and textual explanations. A detailed empirical analysis is shown.
Merging and synchronizing corporate and personal voice agents: Comparison of voice agents acting as a secretary and a housekeeper

People are currently facing a one-to-multiple mode of interaction with voice agents (VAs), which are widely used in their personal and working lives separately owing to the rapid development of artificial intelligence. To explore potential trends in the use of VAs, we designed a merged VA and two either familiar (interconnected) or strange (completely independent) VAs, of which one acted as a secretary for work-related issues and the other acted as a housekeeper for personal issues. Seventy-two people participated in a within-group experiment to experience simulated interactions and complete tasks using the designed VAs in a random order. Objective and subjective evaluation data were collected to analyse user experience. The results show that the merged VA performs best and that participants also preferred it over the others based on the collected data on mental workload, performance, satisfaction, and perceived usefulness. The familiar VAs were the second best-performing VAs. Though there were no significant differences in the trust in the three designed VAs, participants held positive and cautious attitudes towards VAs in general. Additionally, this study highlights the importance of efficiency, the value of entertainment, and the distinction between work and personal life with respect to the personal and corporate functions of VAs.
Semi-Device-Independent Certification of Independent Quantum State and Measurement Devices

Certifying that quantum devices behave as intended is crucial for quantum information science. Here, methods are developed for certification of both state preparation devices and measurement devices based on prepare-and-measure experiments with independent devices. The experimenter assumes the independence of the devices and knowledge of the Hilbert space dimension. Thus no precise characterization of any part of the experiment is required. The certification is based on a randomized version of unambiguous state discrimination and targets the class of state ensembles corresponding to quantum $t$ designs of any size and any dimension. These quantum designs are sets of states over which the average of any $t$-degree polynomial equals its average over all pure states, and they accommodate many of the most useful discrete structures in quantum information processing. Furthermore, it is shown that the same experiments also certify the detection efficiency of the measurement devices, as well as their nonprojective nature. The presented methods can readily be implemented in experiments.
Deception Approach to Track-to-Track Radar Fusion Using Noncoherent Dual-Source Jamming

Dual-source jamming is an effective way to prevent monopulse radar systems from performing accurate angle measurements.In this investigation, based on noncoherent dual-source jamming, we propose a cooperative deception approach, which has the benefit of causing persistent and large angular measurement errors to protect a true target and therefore can greatly reduce the discrimination capability of a distributed track-to-track radar fusion system.A scenario involving a single target accompanied by a digital radio frequency memory (DRFM)-based repeater jammer countering two radar systems is illustrated.By controlling the amplitude ratio and time delay in a statistical manner, the active decoy signal and target echo signal can form a noncoherent angular glint effect and result in the deviation of the angle tracking loop of the two radar systems from the true target in opposite directions.The track-to-track association distance related to the retransmission parameters is explicitly derived, and its statistical characteristics are analyzed in detail.Simulations verify the feasibility of the approach.The advantage of the approach lies in its ability to destroy the so-called ''common origin'' signature of the physical target by injecting false angular information.The direct result is that the true target might not successfully complete the process of track-to-track association, thereby enabling us to realize the desirable effect of ''disguise the true target and instead show false decoys''.
Semantic Segmentation of Fashion Images Using Feature Pyramid Networks

In this work, we approach the problem of semantically segmenting fashion images into different categories of clothing. This problem poses particular challenges because of the importance of both textural information and cues from shapes and context. To this end, we propose a fully convolutional neural network based on feature pyramid networks (FPN), together with a backbone consisting of the ResNeXt architecture. Our experimental evaluation shows that the proposed model achieves state-of-the-art results on two standard fashion benchmark datasets, and a qualitative study verifies its effectiveness when applied to typical fashion images. The approach has a modest memory footprint and can be used without a conditional random field (CRF) without much degradation of quality which makes our model preferable from a computational perspective. When comparing all methods without a CRF, our approach outperforms all state-of-the-art models on both datasets by a clear margin in all evaluated metrics. In fact, our approach achieves a higher accuracy without the CRF than the state-of-the-art models using CRFs.
Library cultures of data curation: Adventures in astronomy

Abstract University libraries are partnering with disciplinary data producers to provide long‐term digital curation of research data sets. Managing data set producer expectations and guiding future development of library services requires understanding the decisions libraries make about curatorial activities, why they make these decisions, and the effects on future data reuse. We present a study, comprising interviews ( n = 43) and ethnographic observation, of two university libraries who partnered with the Sloan Digital Sky Survey (SDSS) collaboration to curate a significant astronomy data set. The two libraries made different choices of the materials to curate and associated services, which resulted in different reuse possibilities. Each of the libraries offered partial solutions to the SDSS leaders' objectives. The libraries' approaches to curation diverged due to contextual factors, notably the extant infrastructure at their disposal (including technical infrastructure, staff expertise, values and internal culture, and organizational structure). The Data Transfer Process case offers lessons in understanding how libraries choose curation paths and how these choices influence possibilities for data reuse. Outcomes may not match data producers' initial expectations but may create opportunities for reusing data in unexpected and beneficial ways.
Subjective and Objective Evaluation of Local Dimming Algorithms for HDR Images

In order to enhance viewing experiences, a number of backlight local dimming (BLD) algorithms have been developed to improve the image contrast ratio and provide power efficiency for modern displays.In order to evaluate which BLD algorithm performs best for HDR images rendering on dual-panel displays, this paper presents a comprehensive subjective and objective evaluation conducted with five BLD algorithms across a number of scenes.The subjective evaluation (N = 24) required participants to rank each BLD generated image based on which they thought was the most natural looking.The objective evaluation was undertaken via the use of a novel methodology to generate the images per BLD for comparison against the ground truth High Dynamic Range (HDR) image.Resulting images were compared with the ground truth using qualitative methods namely: HDR-VDP, puPSNR, puSSIM and puVIFP.The power-saving rate of each method was also calculated.The results demonstrate a strong correlation between objective and subjective evaluation.Furthermore, results show that BLD algorithms that consider the luminance balance between backlight and LCD images perform better than straightforward BLD methods.
Cloud customers service selection scheme based on improved conventional cat swarm optimization

Abstract With growing demand on resources situated at the cloud datacenters, the need for customers’ resource selection techniques becomes paramount in dealing with the concerns of resource inefficiency. Techniques such as metaheuristics are promising than the heuristics, most especially when handling large scheduling request. However, addressing certain limitations attributed to the metaheuristic such as slow convergence speed and imbalance between its local and global search could enable it become even more promising for customers service selection. In this work, we propose a cloud customers service selection scheme called Dynamic Multi-Objective Orthogonal Taguchi-Cat (DMOOTC). In the proposed scheme, avoidance of local entrapment is achieved by not only increasing its convergence speed, but balancing between its local and global search through the incorporation of Taguchi orthogonal approach. To enable the scheme to meet customers’ expectations, Pareto dominant strategy is incorporated providing better options for customers in selecting their service preferences. The implementation of our proposed scheme with that of the benchmarked schemes is carried out on CloudSim simulator tool. With two scheduling scenarios under consideration, simulation results show for the first scenario, our proposed DMOOTC scheme provides better service choices with minimum total execution time and cost (with up to 42.87%, 35.47%, 25.49% and 38.62%, 35.32%, 25.56% reduction) and achieves 21.64%, 18.97% and 13.19% improvement for the second scenario in terms of execution time compared to that of the benchmarked schemes. Similarly, statistical results based on 95% confidence interval for the whole scheduling scheme also show that our proposed scheme can be much more reliable than the benchmarked scheme. This is an indication that the proposed DMOOTC can meet customers’ expectations while providing guaranteed performance of the whole cloud computing environment.