Reviewerly/exHarmony huggingface dataset:
Tracial embeddable strategies: Lifting MIP* tricks to MIPco

We prove that any two-party correlation in the commuting operator model can be approximated using a tracial embeddable strategy, a class of strategy defined on a finite tracial von Neumann algebra, which we define in this paper. Using this characterization, we show that any approximately synchronous correlation can be approximated to the average of a collection of synchronous correlations in the commuting operator model. This generalizes the result from Vidick [JMP 2022] which only applies to finite-dimensional quantum correlations. As a corollary, we show that the quantum tensor code test from Ji et al. [FOCS 2022] follows the soundness property even under the general commuting operator model. Furthermore, we extend the state-dependent norm variant of the Gowers-Hatami theorem to finite von Neumann algebras. Combined with the aforementioned characterization, this enables us to lift many known results about robust self-testing for non-local games to the commuting operator model, including a sample efficient finite-dimensional EPR testing for the commuting operator strategies. We believe that, in addition to the contribution from this paper, this class of strategies can be helpful for further understanding non-local games in the infinite-dimensional setting.
The Rise of Disappearing Frameworks in Web Development

The evolution of the web can be characterized as an emergence of frameworks paving the way from static websites to dynamic web applications. As the scope of web applications has grown, new technical challenges have emerged, leading to the need for new solutions. The latest of these developments is the rise of so-called disappearing web frameworks that question the axioms of earlier generations of web frameworks, providing benefits of the early web and simple static sites.
Dialogue-Contextualized Re-ranking for Medical History-Taking

AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models require a significant amount of data to train. To overcome this challenge, existing systems are developed using indirect data or expert knowledge. This leads to a training-inference gap as models are trained on different kinds of data than what they observe at inference time. In this work, we present a two-stage re-ranking approach that helps close the training-inference gap by re-ranking the first-stage question candidates using a dialogue-contextualized model. For this, we propose a new model, global re-ranker, which cross-encodes the dialogue with all questions simultaneously, and compare it with several existing neural baselines. We test both transformer and S4-based language model backbones. We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone, resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77% higher mean average precision (mAP).
Risk-Aware Distributed Multi-Agent Reinforcement Learning

Autonomous cyber and cyber-physical systems need to perform decision-making, learning, and control in unknown environments. Such decision-making can be sensitive to multiple factors, including modeling errors, changes in costs, and impacts of events in the tails of probability distributions. Although multi-agent reinforcement learning (MARL) provides a framework for learning behaviors through repeated interactions with the environment by minimizing an average cost, it will not be adequate to overcome the above challenges. In this paper, we develop a distributed MARL approach to solve decision-making problems in unknown environments by learning risk-aware actions. We use the conditional value-at-risk (CVaR) to characterize the cost function that is being minimized, and define a Bellman operator to characterize the value function associated to a given state-action pair. We prove that this operator satisfies a contraction property, and that it converges to the optimal value function. We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reaches consensus. We identify several challenges that arise in the implementation of the CVaR QD-Learning algorithm, and present solutions to overcome these. We evaluate the CVaR QD-Learning algorithm through simulations, and demonstrate the effect of a risk parameter on value functions at consensus.
Unsupervised Domain Adaptation of Object Detectors: A Survey

Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images, termed as domain adaptation problem. There is a plethora of works to adapt classification and segmentation models to label-scarce target datasets through unsupervised domain adaptation. Considering that detection is a fundamental task in computer vision, many recent works have focused on developing novel domain adaptive detection techniques. Here, we describe in detail the domain adaptation problem for detection and present an extensive survey of the various methods. Furthermore, we highlight strategies proposed and the associated shortcomings. Subsequently, we identify multiple aspects of the problem that are most promising for future research. We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research.
Asservissement visuel 3D direct dans le domaine spectral

This paper presents a direct 3D visual servo scheme for the automatic alignment of point clouds (respectively, objects) using visual information in the spectral domain. Specifically, we propose an alignment method for 3D models/point clouds that works by estimating the global transformation between a reference point cloud and a target point cloud using harmonic domain data analysis. A 3D discrete Fourier transform (DFT) in $\mathbb{R}^3$ is used for translation estimation and real spherical harmonics in $SO(3)$ are used for rotation estimation. This approach allows us to derive a decoupled visual servo controller with 6 degrees of freedom. We then show how this approach can be used as a controller for a robotic arm to perform a positioning task. Unlike existing 3D visual servo methods, our method works well with partial point clouds and in cases of large initial transformations between the initial and desired position. Additionally, using spectral data (instead of spatial data) for the transformation estimation makes our method robust to sensor-induced noise and partial occlusions. Our method has been successfully validated experimentally on point clouds obtained with a depth camera mounted on a robotic arm.
Simple and Efficient Confidence Score for Grading Whole Slide Images

Grading precancerous lesions on whole slide images is a challenging task: the continuous space of morphological phenotypes makes clear-cut decisions between different grades often difficult, leading to low inter- and intra-rater agreements. More and more Artificial Intelligence (AI) algorithms are developed to help pathologists perform and standardize their diagnosis. However, those models can render their prediction without consideration of the ambiguity of the classes and can fail without notice which prevent their wider acceptance in a clinical context. In this paper, we propose a new score to measure the confidence of AI models in grading tasks. Our confidence score is specifically adapted to ordinal output variables, is versatile and does not require extra training or additional inferences nor particular architecture changes. Comparison to other popular techniques such as Monte Carlo Dropout and deep ensembles shows that our method provides state-of-the art results, while being simpler, more versatile and less computationally intensive. The score is also easily interpretable and consistent with real life hesitations of pathologists. We show that the score is capable of accurately identifying mispredicted slides and that accuracy for high confidence decisions is significantly higher than for low-confidence decisions (gap in AUC of 17.1% on the test set). We believe that the proposed confidence score could be leveraged by pathologists directly in their workflow and assist them on difficult tasks such as grading precancerous lesions.
The Environment Design on Intelligent Classroom of Online Learning and Teaching

The environment for training and recognition in Chinese speech recognition under computer-aided design may vary due to the difference of channel and background noise.When the trained model cannot well represent the test data, the recognition rate of the system will drop sharply.The computer-aided design method focuses on using a small amount of Chinese voice data to improve the performance of the system in the test environment.In this paper, we choose the BiLSTM CRF word separation model under deep learning as the improved benchmark model, and combine the Bert language pre-training module to enhance the performance of Chinese word separation.Combining the deep learning sample transfer learning theory and the improved sampling strategy, an adaptive translation model for intelligent Chinese domain is constructed.The experimental results show that Bert Chinese word segmentation model is superior to other word segmentation models in different data sets and has the best word segmentation performance, which can provide reliable support for the application experiment of this model.The test results show that this method can achieve high speech recognition accuracy and good application results.
Multi-criteria-based Evaluation Index System of Content Production of Sports Short Video

The environment for training and recognition in Chinese speech recognition under computer-aided design may vary due to the difference of channel and background noise.When the trained model cannot well represent the test data, the recognition rate of the system will drop sharply.The computer-aided design method focuses on using a small amount of Chinese voice data to improve the performance of the system in the test environment.In this paper, we choose the BiLSTM CRF word separation model under deep learning as the improved benchmark model, and combine the Bert language pre-training module to enhance the performance of Chinese word separation.Combining the deep learning sample transfer learning theory and the improved sampling strategy, an adaptive translation model for intelligent Chinese domain is constructed.The experimental results show that Bert Chinese word segmentation model is superior to other word segmentation models in different data sets and has the best word segmentation performance, which can provide reliable support for the application experiment of this model.The test results show that this method can achieve high speech recognition accuracy and good application results.
Image contrast enhancement for preserving entropy and image visual features

Histogram equalization is essential for low-contrast enhancement in image processing. Several methods have been proposed; however, one of the most critical problems encountered by existing methods is their ability to preserve information in the enhanced image as the original. This research proposes an image enhancement method based on a histogram equalization approach that preserves the entropy and fine details similar to those of the original image. This is achieved through proposed probability density functions (PDFs) that preserve the small gray values of the usual PDF. The method consists of several steps. First, occurrences and clipped histograms are extracted according to the proposed thresholding. Then, they are equalized and used by a proposed transferring function to calculate the new pixel values in the enhanced image. The proposed method is compared with widely used methods such as Clahe, CS, HE, and GTSHE. Experiments using benchmark datasets and entropy, contrast, PSNR, and SSIM measurements are conducted to evaluate the performance. The results show that the proposed method is the only one that preserves the entropy of the enhanced image of the original image. In addition, it is efficient and reliable in enhancing image quality. This method preserves fine details and improves image quality, supporting computer vision and pattern recognition fields.
Adversarial Risk Analysis

Adversarial Risk Analysis (ARA) is a decision-theoretic alternative to game theory, applicable to corporate competition, auctions, and counterterrorism. In ARA, one builds a model for the strategic decision making of one's opponent(s), and then places subjective Bayesian distributions over unknown quantities. This structure enables the analyst to compartmentalize distinct kinds of uncertainty. Within this framework one can use standard Bayesian techniques to develop a probability distribution over the actions of the opponent. Given this distribution, the decision theorist chooses the action that maximizes expected utility.
Curbing Ghost Worker Fraud In Developing Countries Using Consortium Blockchain

One of the major issues facing the governments of developing countries, such as Nigeria, is ghost worker fraud. Ghost worker fraud refers to all practices relating to employee impersonations that have salary cost effects on the government. More than one hundred billion Naira (220 million US dollars), is lost annually due to ghost workers fraud in Nigeria. Solutions such as the development and implementation of the Integrated Payroll and Personnel Information System (IPPIS) by the Nigerian government did not solve the problem. Blockchain technology offers the promise of improved transparency, auditability, autonomy, security, and efficiency. To address the problem of ghost worker fraud, in this paper, we investigate blockchain and its relevance to ghost workers fraud and other forms of corruption in Nigeria. We also examine the problems of the Integrated Payroll and Personnel Information System (IPPIS). We then propose the use of a consortium blockchain framework to tackle ghost worker fraud and detail the type of consensus algorithm and the blockchain structure required. This framework could substantially reduce cases of ghost worker fraud in Nigeria and elsewhere.
Integrated Learning-based Multimodal Text Analysis Algorithm for College English Textbooks

The environment for training and recognition in Chinese speech recognition under computer-aided design may vary due to the difference of channel and background noise.When the trained model cannot well represent the test data, the recognition rate of the system will drop sharply.The computer-aided design method focuses on using a small amount of Chinese voice data to improve the performance of the system in the test environment.In this paper, we choose the BiLSTM CRF word separation model under deep learning as the improved benchmark model, and combine the Bert language pre-training module to enhance the performance of Chinese word separation.Combining the deep learning sample transfer learning theory and the improved sampling strategy, an adaptive translation model for intelligent Chinese domain is constructed.The experimental results show that Bert Chinese word segmentation model is superior to other word segmentation models in different data sets and has the best word segmentation performance, which can provide reliable support for the application experiment of this model.The test results show that this method can achieve high speech recognition accuracy and good application results.
Machine reasoning for segmentation of the kidneys on CT images: improving CNN performance by incorporating anatomical knowledge in post-processing

Purpose: Convolutional Neural Networks (CNN) are frequently used for organ segmentation in medical imaging. Many CNNs, however, struggle for acceptance into clinical practice because of mis-segmentations that are obvious to radiologists or other experts. We propose using a Cognitive AI framework that applies anatomical knowledge and machine reasoning to check and improve CNN segmentations and avoid obvious mis-segmentations. Methods: We used the open-source SimpleMind framework that allows post-processing and machine reasoning to be applied to CNN segmentation results. Within this framework, a 3D-UNet CNN was trained on 212 contrast-enhanced kidney CT scans. From an anatomical knowledge base, SimpleMind derived the following Cognitive AI post-processing steps: (1) identification of the abdomen, (2) segmentation of the spine by searching for bone in the posterior region of the abdomen, (3) CNN output separation into right and left kidneys using the spine as reference, (4) refinement of individual kidney segmentations using thresholds of volume, HU, and voxel count, which are designed to eliminate disconnected false positives, (5) morphological opening to reduce connected false positives bordering the kidneys’ segmentations. On a test set of 53 scans, using reference annotations, Dice Coefficient (DC), Hausdorff Distance (HD) and Average Symmetric Surface Distances (ASSD) were computed and compared for the CNN and post-processed outputs. Results: Post-processing with Cognitive AI reduced the kidney segmentation HD from 46.4&plusmn;55.8 mm to 30.7&plusmn;17.6 mm, with the decrease in variance being statistically significant (p = 0.0296). DC and ASSD metrics were also improved. Conclusions: This initial work demonstrates that the incorporation of anatomical knowledge using Cognitive AI techniques can improve the segmentation accuracy of CNNs. The CNN provides very good overall kidney segmentation performance, and in cases where segmentation errors occur the post processing was able to improve performance.
Augmented Reality in Physics Education: Students with Intellectual Disabilities Inquire the Structure of Matter

Abstract Immersive technologies support educational activities and provide motivating contexts which are increasingly implemented in special education settings. Augmented Reality (AR) seems to improve the level of engagement in teaching and learning processes for all students, including students with Intellectual Disabilities (ID). However, there is a lack of research that investigates AR learning environments where students with ID can be involved in inquiry-based activities and acquire academic content linked to real situations. The purpose of this study was to implement a single-subject design and evaluate the effects of an AR system on students’ performance on the microscopic level of the structure of matter and especially the phase-states of water. A functional relationship was found between students’ correct responses during probe sessions and the AR inquiry-based intervention. In addition, a social validity assessment indicated that the AR glasses helped students with ID to acquire physics concepts, as well as inquiry skills in a vivid experience. The students also reported satisfaction from using the AR glasses. Suggestions for future research include the design of AR-based interventions for other science concepts for students with ID as well as other special educational needs.
Students’ Performance in Speaking Skill and Self-Efficacy : An Correlational Study

The objective of this study is to investigate the extent to which students' levels of self-efficacy are correlated with their levels of success in speaking skill assessments of the students of MAN 1 Madiun. The research method used quantitative research. The population in this study are all the eleventh grade students. The sample for this study are students from class XI MIA 2 consists of 22 students. The researcher uses using purposive sampling technique. The researcher uses questionnaires to students to determine the level of students’ self-efficacy and uses speaking test. Pearson Product Moment (r) formula using t-test is used in this research. The results show that the highest score is 95. Meanwhile, the lowest score is 82. The result of the Pearson product moment correlation is.052 with a negative sign. The conclusion that can be drawn from these findings is that there is a negative correlation between speaking ability and self-efficacy, as well as the results of significance levels above 0.05.This is what causes scores on students to be lacking. Besides self-efficacy, it has a very positive effect on each student. The implication of this research is to describe the correlation between students’ self-efficacy and their performance in speaking skill so the teacher and other researchers can get information related with that.
Evaluating of the Implementing of the 12-Years Compulsory Learning Program in Southeast Sulawesi

This research aims to analyze the results of implementing the 12-year compulsory education program using the CIPP model in Southeast Sulawesi study using a qualitative approach with a CIPP evaluation method (context, input, process, and product). The research was conducted in the Province of Southeast Sulawesi, specifically at the Department of Education and Culture of Southeast Sulawesi. The participants involved were the head of the education and culture office at the Southeast Sulawesi Province Education and Culture Office and the head of the section responsible for managing elementary to high school education. School principals and teachers from elementary school, junior high school and senior high school are 50 people. Data collection techniques used observation, interviews, and documentation studies. Data analysis in this study consisted of three stages, namely 1) data reduction, 2) data presentation, and 3) conclusion. The study results showed that the 12-year compulsory education program had been implemented correctly. The context aspect had been carried out by the laws and regulations set by the government. The input aspect showed that program planning was adjusted to legality and field needs. However, the resources for education personnel were still lacking, and the government in Southeast Sulawesi government had appointed honorary teachers and provided various training to improve their competence. Whereas in the process aspect, it was shown in the lack of learning facilities and infrastructure, which still needs to be completed by developments in global needs so that graduates can be qualified, and in the product aspect, it has been shown by the achievement of pure enrollment rates and gross enrollment rates that exceed the initial target. So, this research has implications for the awareness of all parties involved in the implementation of education that the responsibility for the success of this compulsory education program requires cooperation.