Reviewerly/exHarmony huggingface dataset:
Horizontal Max Pooling a Novel Approach for Noise Reduction in Max Pooling for Better Feature Detect

As of now CNN(Convolutional Neural Network) in DNN(Deep Neural Network) has become a main focus for better and faster feature detections. More and more deep neural networks have been proposed to improve the efficiency and accuracy of models. While all the models are based on the same building pillars like convolution subsampling, pooling, activations functions etc. used in the model. Till now many researchers have shown interest in using different activation functions like relu, leaky relu and as of now using softmax activation function but yet they all are using similar pooling approach “Max Pooling”. Using this we can detect the feature more prominently but also highlighting some unwanted features too referred to as noise. So in this paper we have introduced a new pooling approach “Horizontal Max Pooling” to reduce this noise for better feature detection and extraction. Although this paper contains a single image example we have tested this approach on many different images and matrices each result in noise reduction and visible and noticeable change.
3D Capsule Networks for Brain Image Segmentation

<h3>BACKGROUND AND PURPOSE:</h3> Current autosegmentation models such as UNets and nnUNets have limitations, including the inability to segment images that are not represented during training and lack of computational efficiency. 3D capsule networks have the potential to address these limitations. <h3>MATERIALS AND METHODS:</h3> We used 3430 brain MRIs, acquired in a multi-institutional study, to train and validate our models. We compared our capsule network with standard alternatives, UNets and nnUNets, on the basis of segmentation efficacy (Dice scores), segmentation performance when the image is not well-represented in the training data, performance when the training data are limited, and computational efficiency including required memory and computational speed. <h3>RESULTS:</h3> The capsule network segmented the third ventricle, thalamus, and hippocampus with Dice scores of 95%, 94%, and 92%, respectively, which were within 1% of the Dice scores of UNets and nnUNets. The capsule network significantly outperformed UNets in segmenting images that were not well-represented in the training data, with Dice scores 30% higher. The computational memory required for the capsule network is less than one-tenth of the memory required for UNets or nnUNets. The capsule network is also &gt;25% faster to train compared with UNet and nnUNet. <h3>CONCLUSIONS:</h3> We developed and validated a capsule network that is effective in segmenting brain images, can segment images that are not well-represented in the training data, and is computationally efficient compared with alternatives.
What is in a game? The impact of advergame design and reward elements on gamers' brand patronage

Purpose This research investigates how advergame design elements (fantasy vs realistic advergame experiences) may impact players' brand patronage (BP), under the influence of mediators (hot and cold brand relationship quality [BRQ]) and moderators (advergame reward system and brand personality). Design/methodology/approach This research comprises of a survey (study 1) and a lab experiment (study 2). Findings Study 1 shows that fantasy (reality) based advergame experience leads to brand patronage through strong mediation of hot (cold) BRQ and weak mediation of cold (hot) BRQ. Introducing a utilitarian (hedonic) advergame reward system positively moderates the effect of fantasy (reality) based advergame experience on cold (hot) BRQ. Study 2 shows that an advergame that elicits fantasy (realistic) experience and offers hedonic (utilitarian) rewards for a brand having affective (vs cognitive) brand personality strongly impacts hot (cold) BRQ. Originality/value This research is an effort to understand how gamification as leisure information systems may be used to create gamers' advergame experiences that elicit BP by strategically designing advergame reward systems specific to brand personality types.
A field test of computer-vision-based gaze estimation in psychology

Abstract Computer-vision-based gaze estimation refers to techniques that estimate gaze direction directly from video recordings of the eyes or face without the need for an eye tracker. Although many such methods exist, their validation is often found in the technical literature (e.g., computer science conference papers). We aimed to (1) identify which computer-vision-based gaze estimation methods are usable by the average researcher in fields such as psychology or education, and (2) evaluate these methods. We searched for methods that do not require calibration and have clear documentation. Two toolkits, OpenFace and OpenGaze, were found to fulfill these criteria. First, we present an experiment where adult participants fixated on nine stimulus points on a computer screen. We filmed their face with a camera and processed the recorded videos with OpenFace and OpenGaze. We conclude that OpenGaze is accurate and precise enough to be used in screen-based experiments with stimuli separated by at least 11 degrees of gaze angle. OpenFace was not sufficiently accurate for such situations but can potentially be used in sparser environments. We then examined whether OpenFace could be used with horizontally separated stimuli in a sparse environment with infant participants. We compared dwell measures based on OpenFace estimates to the same measures based on manual coding. We conclude that OpenFace gaze estimates may potentially be used with measures such as relative total dwell time to sparse, horizontally separated areas of interest, but should not be used to draw conclusions about measures such as dwell duration.
Investigation of Intelligent Service Mode of Digital Stadiums and Gymnasiums in the Context of Smart Cities

With the innovation of information technology, digital stadiums and gymnasiums are based on independent innovation, information management, and intelligent cultivation and development, which promotes the construction of a new ecology of intelligent sports and also determines the development situation and development mode of traditional sports. In the construction of intelligent service mode of digital stadiums and gymnasiums in the context of smart cities, the management information system, as the application support of stadium information technology, is the key channel to improve the intelligent level of stadiums and gymnasiums. The establishment and promotion of a smart management information system is an inevitable trend for smart stadiums and gymnasiums to complete key technologies, consumer experience, and efficient functional innovation of management methods, which is also an important content of science and technology-enabling stadiums and gymnasiums. This paper proposed the research on intelligent service mode of digital stadiums and gymnasiums under the background of smart cities.
A family of bipartite separability criteria based on Bloch representation of density matrices

We study the separability of bipartite quantum systems in arbitrary dimensions based on the Bloch representation of density matrices. We present two separability criteria for quantum states in terms of the matrices $T_{\alpha\beta}(\rho)$ and $W_{ab,\alpha\beta}(\rho)$ constructed from the correlation tensors in the Bloch representation. These separability criteria can be simplified and detect more entanglement than the previous separability criteria. Detailed examples are given to illustrate the advantages of results.
Automated Code Extraction from Discussion Board Text Dataset

This study introduces and investigates the capabilities of three different text mining approaches, namely Latent Semantic Analysis, Latent Dirichlet Analysis, and Clustering Word Vectors, for automating code extraction from a relatively small discussion board dataset. We compare the outputs of each algorithm with a previous dataset that was manually coded by two human raters. The results show that even with a relatively small dataset, automated approaches can be an asset to course instructors by extracting some of the discussion codes, which can be used in Epistemic Network Analysis.
Sub-Nyquist Bandpass Sampling

The work is devoted to an in-depth study of the aliasing process in sub-Nyquist bandpass sampling. The cluster nature of aliasing is revealed. Designations for clusters of aliases are proposed. Intracluster and intercluster aliasings are defined. The results obtained in this work will be used in the restoration of signals distorted by aliasing using the multi-channel multi-rate sampling developed by the authors.
Object identification in cerebral visual impairment characterized by gaze behavior and image saliency analysis

<h2>Abstract</h2> Individuals with cerebral visual impairment (CVI) have difficulties identifying common objects, especially when presented as cartoons or abstract images. In this study, participants were shown a series of images of ten common objects, each from five possible categories ranging from abstract black & white line drawings to color photographs. Fifty individuals with CVI and 50 neurotypical controls verbally identified each object and success rates and reaction times were collected. Visual gaze behavior was recorded using an eye tracker to quantify the extent of visual search area explored and number of fixations. A receiver operating characteristic (ROC) analysis was also carried out to compare the degree of alignment between the distribution of individual eye gaze patterns and image saliency features computed by the graph-based visual saliency (GBVS) model. Compared to controls, CVI participants showed significantly lower success rates and longer reaction times when identifying objects. In the CVI group, success rate improved moving from abstract black & white images to color photographs, suggesting that object form (as defined by outlines and contours) and color are important cues for correct identification. Eye tracking data revealed that the CVI group showed significantly greater visual search areas and number of fixations per image, and the distribution of eye gaze patterns in the CVI group was less aligned with the high saliency features of the image compared to controls. These results have important implications in helping to understand the complex profile of visual perceptual difficulties associated with CVI.
Smart Traffic Light Switching and Traffic Density Calculation Model using Computer Vision

Different Traffic control systems have played a crucial part in traffic management around the globe, especially in densely populated major cities, but they are still not as efficient as they could be. Perhaps some changes can be made to better deal with the traffic in this ever-changing traffic density environment. Traffic congestion has consistently been a rage issue in numerous urban cities. The traditional way was to give each lane a specific predefined time with the green light and had to stop for the rest of the time. Even the lanes with no traffic got the same amount of time as the lane with huge traffic jams. These were promoting traffic congestion rather than solving the issue. Thus, the need for a better system has emerged for changing the current traffic handling setup to be smarter enough to meet this ever-changing demand. In this paper, the idea of traffic lights controlled by live video feed is explored with an enhanced traffic flow system to optimally benefit from the computer vision technology used.
Polarization Entanglement from Parametric Down-conversion with an LED Pump

Spontaneous parametric down-conversion (SPDC) is a reliable platform for entanglement generation. Routinely, a coherent laser beam is an essential prerequisite for pumping the nonlinear crystal. Here we break this barrier to generate polarization-entangled photon pairs by using a commercial light-emitting diode (LED) source to serve as the pump beam. This effect is counterintuitive, as the LED source is of extremely low spatial coherence, which is transferred during the down-conversion process to the biphoton wave function. However, the type-II phase matching condition naturally filters the specific frequency and wavelength of LED light exclusively to participate in SPDC such that localized polarization Bell states can be generated, regardless of the global incoherence over the full transverse plane. In our experiment, we characterize the degree of LED light-induced polarization entanglement in the standard framework of the violation of Bell's inequality. We achieve the Bell value $S=2.33\ifmmode\pm\else\textpm\fi{}0.097$, obviously surpassing the classical bound $S=2$ and thus witnessing the quantum entanglement. Our work can be extended to prepare polarization entanglement by using other natural light sources, such as sunlight and biolight, which holds promise for electricity-free quantum communications in outer space.
Conversion Operation: From Semi-structured Collection of Documents to Column-Oriented Structure

Over the last few years, NoSQL databases have been a key for several problems for storing Big Data sources as well as implementing data warehouses (DW). With decisional systems, NoSQL column-oriented structure can provide relevant results for storing a multidimensional structure, where the relational databases cannot be able to handle the semi-structured data. In this research paper, we attempt to model a conversion operation in the ETL process, which is responsible for Extracting, Transforming and Loading data into the DW. Our proposed operation is handled in the ETL extraction phase, which is responsible for converting a series of semi-structured data to column-oriented structure. In the implementation phase, we propose a new component using Talend open studio for Big Data (TBD), which helps ETL designer to convert semi-structured data into column-oriented structure.
Thinking space generation using context-enhanced knowledge fusion for systematic brain computing

The convergence of systems neuroscience and open science arouses great interest in the current brain big data era, highlighting the thinking capability of intelligent agents in handling multi-source knowledge, information and data across various levels of granularity. To realize such thinking-inspired brain computing during a brain investigation process, one of the major challenges is to find a holistic brain map that can model multi-dimensional variables of brain investigations across brain functions, experimental tasks, brain data and analytical methods synthetically. In this paper, we propose a context-enhanced graph learning method to fuse open knowledge from different sources, including: contextual information enrichment, structural knowledge fusion, and holistic graph learning. Such a method can enhance contextual learning of abstract concepts and relational learning between two concepts that have large gap from different dimensions. As a result, an extensible space, namely Thinking Space, is generated to represent holistic variables and their relations in a map, which currently contributes to the field of brain research for systematic brain computing. In the future, the Thinking Space coupled with the rapid development and spread of artificial intelligence generated content will be developed in more scenarios so as to promote global interactions of intelligence in the connected world.
Economical Solution to Automatic Evaluation of an OMR Sheet Using Image Processing

Optical Mark Recognition (OMR) Sheet, also called as Bubble sheet is a special type of form used to answer graded multiple choice question examinations, where students have to mark or darken the bubbles to answer the questions. OMR sheets are used by various school, college, university and competitive examinations. Larger institutions like universities have specialized machines to optically detect the marked answers and grade the student. However, these scanners are significantly heavy on the pocket and cannot be afforded by small tuitions or individual teachers. With the help of mobile phones, it is easily possible to scan multiple sheets within seconds. The user is expected to upload a single file of all responses and view the results in a tabular format. Using various image processing techniques, the algorithm evaluates the responses of the student and displays the grade and percentage on the input image along with the correctly, wrongly marked and actual answers with green, red and yellow colors respectively. The user also has access to an interactive dashboard which presents various analytics regarding the students’ performance. A unique feature in this implementation, is that users can choose the number of questions, number of choices, marks per question etc. after which he/she would be able to download a customized OMR response sheet. We experimented on 300 sample OMR sheets with 12,800 questions and obtained an average accuracy of 99.12%. This paper aims to present a cost-effective solution to accurately scan OMR sheets without the need of scanners.
A partially block randomized extended Kaczmarz method for solving large overdetermined inconsistent linear systems

&lt;abstract&gt;&lt;p&gt;This paper presents a partial block randomized extended Kaczmarz (PBREK) method for solving large overdetermined inconsistent linear system of equations $ Ax = b $. The convergence theorem of the PBREK method is derived. Several examples are given to illustrate the effectiveness of the proposed PBREK method compared with the prevuious PREK method and the randomized extended Kaczmarz (REK) method.&lt;/p&gt;&lt;/abstract&gt;
Entanglement-assisted detection of fading targets via correlation-to-displacement conversion

Quantum illumination utilizes an entanglement-enhanced sensing system to outperform classical illumination in detecting a suspected target, despite the entanglement-breaking loss and noise. However, practical and optimal receiver design to fulfill the quantum advantage has been a long open problem. Recently, Shi et al. [arXiv:2207.06609 (2022)] proposed the correlation-to-displacement $(`\mathrm{C}\phantom{\rule{0.16em}{0ex}}{\phantom{\rule{0.16em}{0ex}}}_{\stackrel{P\vec}{}}\phantom{\rule{0.16em}{0ex}}\mathrm{D}\text{'})$ conversion module to enable an optimal receiver design that greatly reduces the complexity of the previous known optimal receiver [Q. Zhuang, Z. Zhang, and J. H. Shapiro, Phys. Rev. Lett. 118, 040801 (2017)]. There, the analyses of the conversion module assume an ideal target with a known reflectivity and a fixed return phase. In practical applications, however, targets often induce a random return phase; moreover, their reflectivities can have fluctuations obeying a Rayleigh distribution. In this paper, we extend the analyses of the $\mathrm{C}\phantom{\rule{0.16em}{0ex}}{\phantom{\rule{0.16em}{0ex}}}_{\stackrel{P\vec}{}}\phantom{\rule{0.16em}{0ex}}\mathrm{D}$ module to realistic targets and show that the entanglement advantage is maintained albeit reduced. In particular, the conversion module allows exact and efficient performance evaluation despite the non-Gaussian nature of the quantum channel involved.
Blockchain-Based Architecture for a Sustainable Supply Chain in Cloud Architecture

Blockchain technology, as a well-known technology in financial spaces, has many advantages in non-financial industries and supply chains. Two of the main benefits of blockchain technology are smart contracts and distributed decision-making processes. These features can be especially useful in implementing Industry 4.0. Moreover, this technology can increase productivity in supply chains by enhancing transparency, reducing operational costs, and improving monitoring and supervision throughout the lifecycle of products. In this paper, we introduce a blockchain-based architecture for a supply chain in cloud architecture. This approach leads to the more efficient implementation of Industry 4.0 and increases sustainability in the supply chain. In this study, we aim to investigate whether the proposed blockchain-based platform affects sustainability in the supply chain. From a sustainability perspective, we solve the large-scale problem of a cloud-based production–distribution system in centralized and distributed states. The results of the solution indicate a significant improvement in the decentralized state compared to the centralized state and this improvement enhances sustainability in the supply chain. We verify the proposed model by considering an axiomatic design algorithm. In the distributed model, the system cost is reduced by up to 45%, and the solving time is decreased by approximately 51% in pessimistic conditions and by about 87% in optimistic conditions. These improvements directly enhance economic and environmental sustainability, resulting in reduced energy consumption.
A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction

In this article, we consider the problem of inferring the sign of a link based on known sign data in signed networks. Regarding this link sign prediction problem, signed directed graph neural networks (SDGNNs) provides the best prediction performance currently to the best of our knowledge. In this article, we propose a different link sign prediction architecture called subgraph encoding via linear optimization (SELO), which obtains overall leading prediction performances compared to the state-of-the-art algorithm SDGNN. The proposed model utilizes a subgraph encoding approach to learn edge embeddings for signed directed networks. In particular, a signed subgraph encoding approach is introduced to embed each subgraph into a likelihood matrix instead of the adjacency matrix through a linear optimization (LO) method. Comprehensive experiments are conducted on five real-world signed networks with area under curve (AUC), F1, micro-F1, and macro-F1 as the evaluation metrics. The experiment results show that the proposed SELO model outperforms existing baseline feature-based methods and embedding-based methods on all the five real-world networks and in all the four evaluation metrics.
Boundary-Guided and Region-Aware Network With Global Scale-Adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images

Breast ultrasound (BUS) image segmentation is a critical procedure in the diagnosis and quantitative analysis of breast cancer. Most existing methods for BUS image segmentation do not effectively utilize the prior information extracted from the images. In addition, breast tumors have very blurred boundaries, various sizes and irregular shapes, and the images have a lot of noise. Thus, tumor segmentation remains a challenge. In this article, we propose a BUS image segmentation method using a boundary-guided and region-aware network with global scale-adaptive (BGRA-GSA). Specifically, we first design a global scale-adaptive module (GSAM) to extract features of tumors of different sizes from multiple perspectives. GSAM encodes the features at the top of the network in both channel and spatial dimensions, which can effectively extract multi-scale context and provide global prior information. Moreover, we develop a boundary-guided module (BGM) for fully mining boundary information. BGM guides the decoder to learn the boundary context by explicitly enhancing the extracted boundary features. Simultaneously, we design a region-aware module (RAM) for realizing the cross-fusion of diverse layers of breast tumor diversity features, which can facilitate the network to improve the learning ability of contextual features of tumor regions. These modules enable our BGRA-GSA to capture and integrate rich global multi-scale context, multi-level fine-grained details, and semantic information to facilitate accurate breast tumor segmentation. Finally, the experimental results on three publicly available datasets show that our model achieves highly effective segmentation of breast tumors even with blurred boundaries, various sizes and shapes, and low contrast.