Reviewerly/exHarmony huggingface dataset:
Binocular Rivalry Impact on Macroblock-Loss Error Concealment for Stereoscopic 3D Video Transmission

Three-dimensional video services delivered through wireless communication channels have to deal with numerous challenges due to the limitations of both the transmission channel's bandwidth and receiving devices. Adverse channel conditions, delays, or jitters can result in bit errors and packet losses, which can alter the appearance of stereoscopic 3D (S3D) video. Due to the perception of dissimilar patterns by the two human eyes, they can not be fused into a stable composite pattern in the brain and hence try to dominate by suppressing each other. Thus, a psychovisual sensation that is called binocular rivalry occurs. As a result, undetectable changes causing irritating flickering effects are seen, leading to visual discomforts such as eye strain, headache, nausea, and weariness. This study addresses the observer's quality of experience (QoE) by analyzing the binocular rivalry impact on the macroblock (MB) losses in a frame and its error propagation due to predictive frame encoding in stereoscopic video transmission systems. To simulate the processing of experimental videos, the Joint Test Model (JM) reference software has been used as it is recommended by the International Telecommunication Union (ITU). Existing error concealing techniques were then applied to the contiguous lost MBs for a variety of transmission impairments. In order to validate the authenticity of the simulated packet loss environment, several objective evaluations were carried out. Standard numbers of subjects were then engaged in the subjective testing of common 3D video sequences. The results were then statistically examined using a standard Student's t-test, allowing the impact of binocular rivalry to be compared to that of a non-rivalry error condition. The major goal is to assure error-free video communication by minimizing the negative impacts of binocular rivalry and boosting the ability to efficiently integrate 3D video material to improve viewers' overall QoE.
Visuo-Spatial Working Memory and Mathematical Skills in Children: A Network Analysis Study

Visuo-spatial working memory is one of the main domain-general cognitive mechanisms underlying mathematical abilities and their development in children. However, if visuo-spatial working memory involves different processes and components, then the term 'mathematics' refers to a broad concept that includes multiple domains and skills. The aim of this present study was to investigate the relationship between different visuo-spatial working memory components and several mathematical abilities in a sample of third- to fifth-grade Italian children. To assess the relationships between different visuo-spatial working memory components and different mathematical abilities, we relied on Network Analysis (NA). Results indicate that some but not all visuo-spatial working memory components are associated with some mathematical abilities.
Sentiment Analysis of Multilingual Dataset of Bahraini Dialects, Arabic, and English

Sentiment analysis is an application of natural language processing (NLP) that requires a machine learning algorithm and a dataset. In some cases, the dataset availability is scarce, particularly with Arabic dialects, precisely the Bahraini ones, which necessitates using an approach such as translation, where a rich source language is exploited to create the target language dataset. In this study, a dataset of Amazon product reviews in Bahraini dialects is presented. This dataset was generated using two cascading stages of translation—a machine translation followed by a manual one. Machine translation was applied using Google Translate to translate English Amazon product reviews into Standard Arabic. In contrast, the manual approach was applied to translate the resulting Arabic reviews into Bahraini ones by qualified native speakers utilizing constructed customized forms. The resulting parallel dataset of English, Standard Arabic, and Bahraini dialects is called English_Modern Standard Arabic_Bahraini Dialects product reviews for sentiment analysis “E_MSA_BDs-PR-SA”. The dataset is balanced, composed of 2500 positive and 2500 negative reviews. The sentiment analysis process was implemented using a stacked LSTM deep learning model. The Bahraini dialect product dataset can be utilized in the transfer learning process for sentimentally analyzing another dataset in Bahraini dialects.
Vector quantization using <i>k</i>‐means clustering neural network

Abstract Vector Quantization (VQ) is a clustering problem in the fields of signal processing, source coding, information theory etc. Taking advantage of recent advances in the field of deep neural networks, this paper investigates the performance between VQ clustering problems and deep neural networks. A k ‐means‐based deep network architecture for VQ is presented to solve clustering problems. By applying the deep learning implementation of convergence optimization, a clustering neural network (algorithm) for the purpose of VQ is proposed. In practice, the proposed network quantifies the vectors over a set of stacked neural layers, overcoming the exponential complexity problem of VQ methods by trainable parameters. Experiments show that the work can improve the results without human intervention, and outperforms traditional clustering methods modified for VQ.
Considerations for Developing Robot-Assisted Crisis De-Escalation Practices

Robots are increasingly entering the social sphere and taking on more sophisticated roles. One application for which robots are already being deployed is in civilian security tasks, in which robots augment security and police forces. In this domain, robots will encounter individuals in crisis who may pose a threat to themselves, others, or personal property. In such interactions with human police and security officers, a key goal is to de-escalate the situation to resolve the interaction. This paper considers the task of utilizing mobile robots in de-escalation tasks, using the mechanisms developed for de-escalation in human–human interactions. What strategies should a robot follow in order to leverage existing de-escalation approaches? Given these strategies, what sensing and interaction capabilities should a robot be capable of in order to engage in de-escalation tasks with humans? First, we discuss the current understanding of de-escalation with individuals in crisis and present a working model of the de-escalation process and strategies. Next, we review the capabilities that an autonomous agent should demonstrate to be able to apply such strategies in robot-mediated crisis de-escalation. Finally, we explore data-driven approaches to training robots in de-escalation and the next steps in moving the field forward.
Digital Image Identification and Verification Using Maximum and Preliminary Score Approach with Watermarking for Security and Validation Enhancement

Digital face approaches possess currently received awesome attention because of their huge wide variety of digital audio, and visual programs. Digitized snapshots are progressively more communicated using an un-relaxed medium together with cyberspace. Consequently, defence, clinical, medical, and exceptional supervised photographs are essentially blanketed towards trying to employ it; such controls ought to damage such choices constructed totally based on those pictures. So, to shield the originality of digital audio/visual snapshots, several approaches proposed. Such techniques incorporate traditional encoding, breakable and nominal breakable watermarking with virtual impressions which are based upon the material of image content. Over the last few decades, various holistic approaches are proposed for improving image identification and verification. In this paper, a combination of both the feature level and score level of different techniques were used. Image is one of the identities of a person which reflects its emotions, feeling, age etc. which also helps to gather an information about a person without knowing their name, caste, and age and this could be not of much importance when it is used for domestic or framing applications. To secure the originality of digital audio/visual impressions many methods come into pictures and are proposed which include digital signatures, watermarking, cryptography, and fragile depend upon face contents. The objective of this research article is to identify &amp; verify real-time video images using feature and score levels using watermarking that will help to judge the authenticity of any images at the initial stage by extracting the features which are evaluated by following an algorithm known as Viterbi and where input data is changed initially into an embedded treat or state then the matrix is evaluated of achieved transformation and on this basis preliminary score estimation will be generated after many iterations for each image that will help in validation. Finally, the tested image will be verified using several approaches to protect and provide security to the original image being verified. This approach may be useful for different surveillance applications for real-time image identification and verification. Also, measurement of accuracy was done by reconfiguring the HMM to identify the constant segmentation and feature removal of the image was settled by initializing parameters and teaching the image feature using the algorithm “Viterbi”.
Automatic Tumor Identification from Scans of Histopathological Tissues

Latest progress in development of artificial intelligence (AI), especially machine learning (ML), allows to develop automated technologies that can eliminate or at least reduce human errors in analyzing health data. Due to the ethics of usage of AI in pathology and laboratory medicine, to the present day, pathologists analyze slides of histopathologic tissues that are stained with hematoxylin and eosin under the microscope; by law it cannot be substituted and must go under visual observation, as pathologists are fully accountable for the result. However, a profuse number of automated systems could solve complex problems that require an extremely fast response, accuracy, or take place on tasks that require both a fast and accurate response at the same time. Such systems that are based on ML algorithms can be adapted to work with medical imaging data, for instance whole slide images (WSIs) that allow clinicians to review a much larger number of health cases in a shorter time and give the ability to identify the preliminary stages of cancer or other diseases improving health monitoring strategies. Moreover, the increased opportunity to forecast and take control of the spread of global diseases could help to create a preliminary analysis and viable solutions. Accurate identification of a tumor, especially at an early stage, requires extensive expert knowledge, so often the cancerous tissue is identified only after experiencing its side effects. The main goal of our study was to expand the ability to find more accurate ML methods and techniques that can lead to detecting tumor damaged tissues in histopathological WSIs. According to the experiments that we conducted, there was a 1% AUC difference between the training and test datasets. Over several training iterations, the U-Net model was able to reduce the model size by almost twice while also improving accuracy from 0.95491 to 0.95515 AUC. Convolutional models worked well on groups of different sizes when properly trained. With the TTA (test time augmentation) method the result improved to 0.96870, and with the addition of the multi-model ensemble, it improved to 0.96977. We found out that flaws in the models can be found and fixed by using specialized analysis techniques. A correction of the image processing parameters was sufficient to raise the AUC by almost 0.3%. The result of the individual model increased to 0.96664 AUC (a more than 1% better result than the previous best model) after additional training data preparation. This is an arduous task due to certain factors: using such systems’ applications globally needs to achieve maximum accuracy and improvement in the ethics of Al usage in medicine; furthermore if hospitals could give scientific inquiry validation, while retaining patient data anonymity with clinical information that could be systemically analyzed and improved by scientists, thereby proving Al benefits.
Provable Secure Attribute-Based Proxy Signature Over Lattice Small Integer Solution Problem in Random Oracle Model

Current proxy signature schemes are mostly identity-based proxy signatures that distinguish users by identity. This signature method faces some problems, such as identity information leakage and single access control. Attribute-based proxy signature (ABPS) divides the signer’s identity information into a collection of attributes; thus, users’ identity information can be protected and access control can become fine-grained. With the development of quantum computers, the security of signature schemes based on traditional number theory problems is under threat. Therefore, we construct a new attribute-based proxy signature scheme on a lattice that can resist quantum attacks. This scheme has the properties of both attribute-based signatures and proxy signatures, i.e., fine-grained access control and strong undeniability properties. Moreover, based on the small integer solution problem (SIS), our scheme is provably secure in the random oracle model and protects the proxy signer in the adaptive security model.
Assessing high school students' perceptions and preferences for aquaculture versus wild‐caught seafood: The case of Oʻahu, Hawaiʻi

Abstract An increasing population and demand for seafood have provoked a need to understand the factors affecting consumers' perceptions of aquaculture and wild‐caught seafood to ensure the sustainability of ocean resources. A survey was developed to assess Hawaiʻi's high school students' perceptions and preference for aquaculture and wild‐caught seafood using a cognitive mapping framework. The survey was administered to three high schools across urban, suburban, and rural areas of the island of Oʻahu, Hawaiʻi. Using descriptive statistics, analysis of variance (ANOVA) tests, and mental modeling techniques the following results from the survey indicated that most students had some knowledge of aquaculture. Residence and gender demographics of students make a difference in their preference for aquaculture and wild‐caught seafood. Overall, the students indicated a higher preference for wild‐caught seafood as compared to aquaculture in terms of taste and the reverse is true for environmental impacts. However, rural residents and male students showed a significant preference for aquaculture seafood. The implications of this study provide the information needed to improve public perceptions of aquaculture and promote sustainable seafood practices and consumption. Furthermore, education focused on aquaculture can be introduced into school curricula to enhance their knowledge of environmental, health, and nutrition impacts.
Continuous Semi-Supervised Nonnegative Matrix Factorization

Nonnegative matrix factorization can be used to automatically detect topics within a corpus in an unsupervised fashion. The technique amounts to an approximation of a nonnegative matrix as the product of two nonnegative matrices of lower rank. In certain applications it is desirable to extract topics and use them to predict quantitative outcomes. In this paper, we show Nonnegative Matrix Factorization can be combined with regression on a continuous response variable by minimizing a penalty function that adds a weighted regression error to a matrix factorization error. We show theoretically that as the weighting increases, the regression error in training decreases weakly. We test our method on synthetic data and real data coming from Rate My Professors reviews to predict an instructor’s rating from the text in their reviews. In practice, when used as a dimensionality reduction method (when the number of topics chosen in the model is fewer than the true number of topics), the method performs better than doing regression after topics are identified—both during training and testing—and it retrains interpretability.
Scaling Up Q-Learning via Exploiting State–Action Equivalence

Recent success stories in reinforcement learning have demonstrated that leveraging structural properties of the underlying environment is key in devising viable methods capable of solving complex tasks. We study off-policy learning in discounted reinforcement learning, where some equivalence relation in the environment exists. We introduce a new model-free algorithm, called QL-ES (Q-learning with equivalence structure), which is a variant of (asynchronous) Q-learning tailored to exploit the equivalence structure in the MDP. We report a non-asymptotic PAC-type sample complexity bound for QL-ES, thereby establishing its sample efficiency. This bound also allows us to quantify the superiority of QL-ES over Q-learning analytically, which shows that the theoretical gain in some domains can be massive. We report extensive numerical experiments demonstrating that QL-ES converges significantly faster than (structure-oblivious) Q-learning empirically. They imply that the empirical performance gain obtained by exploiting the equivalence structure could be massive, even in simple domains. To the best of our knowledge, QL-ES is the first provably efficient model-free algorithm to exploit the equivalence structure in finite MDPs.
Seq2Pat: Sequence‐to‐pattern generation to bridge pattern mining with machine learning

Abstract Pattern mining is an essential part of knowledge discovery and data analytics. It is a powerful paradigm, especially when combined with constraint reasoning. In this overview, we showcase Seq2Pat , a constraint‐based sequential pattern mining (SPM) tool with a high‐level declarative user interface. The library finds frequent patterns in large sequence databases subject to constraints. We highlight key benefits especially desirable in industrial settings where scalability, explainability, rapid experimentation, reusability, and reproducibility are of great practical interest. We then bridge SPM with supervised machine learning via dichotomic pattern mining (DPM). DPM exploits the dichotomy between outcomes correlated with patterns that uniquely distinguish them. Last, we present an automated feature extraction powered by Seq2Pat and DPM to discover high‐level insights and boost downstream machine learning models for intent prediction in digital behavior analysis.
Making a Grand Contribution: Simple Ways to Increase Innovation on Your Campus

Assessment UpdateVolume 35, Issue 2 p. 11-12 Grand Challenges for Assessment Making a Grand Contribution: Simple Ways to Increase Innovation on Your Campus Daniel Bureau, Daniel BureauSearch for more papers by this authorKaren Singer-Freeman, Karen Singer-FreemanSearch for more papers by this author Daniel Bureau, Daniel BureauSearch for more papers by this authorKaren Singer-Freeman, Karen Singer-FreemanSearch for more papers by this author First published: 30 March 2023 https://doi.org/10.1002/au.30342Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. References Horn, M. B., and A. Dunagan. 2018. “Innovation and Quality Assurance in Higher Education.” Clayton Christensen Institute for Disruptive Innovation. Kezar, A. 2015. Scaling and Sustaining Change and Innovation: Lessons Learned from the Teagle Foundation's “Faculty Work and Student Learning” Initiative. New York, NY: Teagle Foundation. Mintz, S. (2019, July 23). “Why innovations fail: Ten Hard-Won Lessons.” Inside Higher Ed. Retrieved from https://bit.ly/2SJLSfl. Singer-Freeman, K. E., and C. Robinson. 2020a. Grand Challenges in Assessment: Collective Issues in Need of Solutions. (Occasional Paper No. 47.) Urbana, IL: University of Illinois and Indiana University, National Institute for Learning Outcomes Assessment (NILOA). Retrieved from https://bit.ly/3zZl8hX. Singer-Freeman, K. E., and C. Robinson. 2020b. “Grand Challenges for Assessment in Higher Education.” Research and Practice in Assessment 15(2), 1–20. Retrieved from https://bit.ly/3dbpo51. Volume35, Issue2March/April 2023Pages 11-12 ReferencesRelatedInformation
On the 2-crossed polymodules, pullback and pushout 2-crossed polymodules

The concepts of 2-crossed polymodule of polygroups, and pullback and pushout 2-crossed polymodules are introduced and some of their basic properties are investigated. In addition, the construction of pullback and pushout 2-crossed polymodules are described.
A knee point driven Kriging-assisted multi-objective robust fuzzy clustering algorithm for image segmentation

To enhance the segmentation performance and optimization efficiency of multi-objective evolutionary clustering algorithms for noisy images, this study presents a knee point driven Kriging-assisted multi-objective robust fuzzy clustering algorithm for image segmentation (K2 MORFC). First, pixel and region-level fitness functions are developed to comprehensively consider the spatial constraints and region consistency in an image. In the pixel-level fitness function, a Kullback–Leibler (KL) divergence-based spatial constraint term is employed and combined with intra-class compactness to preserve the membership similarity between an arbitrary image pixel and its non-local neighbors. In the region-level fitness function, image edge information is utilized to evaluate the regional consistency of the partition. Moreover, an adaptive determination mechanism of the weight factor for the spatial constraint term is designed in the pixel-level fitness function to control the influence of the spatial constraint for each pixel. Furthermore, considering that the preference toward knee points can effectively improve the convergence performance, a knee point driven Kriging-assisted evolutionary algorithm (K2EA) is proposed to efficiently optimize these two fitness functions, including a dynamic subspace knee point searching strategy, a knee point guided environmental selection strategy, and a Kriging model management mechanism. Finally, we construct a fuzzy clustering validity index with a KL divergence-based non-local spatial constraint term to select the optimal solution from the nondominated solution set. Competitive experimental results on the DTLZ benchmark verify the effectiveness of K2EA. Segmentation results on color images from the Berkeley dataset and magnetic resonance images from the BrainWeb and IBSR datasets confirm the performance of K2MORFC.