Reviewerly/exHarmony huggingface dataset:
Measurement-induced nonbilocal correlation based on Wigner-Yanase skew information

Measurement-induced nonlocality (MIN) was proposed for measure the maximum global effect caused by locally invariant measurements. Similarly, the Measurement-induced nonbilocal correlation is a generalization of MIN can be used to measure the maximal global influence caused by the local measurement in the bilocal scenario. In this paper, we propose a new nonbilocal correlation measure based on the Wigner-Yanase skew information. The relationship between the MIN based on Wigner-Yanase skew information and our measure is discussed. We present an analytical expression of our measure for pure input states and also provide upper bounds for input general mixed states.
Data Summarization in Internet of Things

&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;With recent advances in the field of IoT (Internet of Things), the quantity of data being generated by various sensors and Internet users has increased dramatically which in turn has skyrocketed the need for efficient data compression methods. Data summarization is an efficient and effective technique for data compression that can generate a brief and succinct summary from typically larger quantities of data in an intelligent and highly useful manner, which can be done at various levels of abstraction. The impact of using such a technique in large IoT networks can be significantly advantageous in terms of reduction in the processing time, overall computation, data storage-transmission requirements, energy consumption and possible workload on IoT users. In this work, a review of existing methods for summarization techniques at various levels of abstraction of typical IoT networks are discussed. The levels of categorization that are considered are Low-level and High-level. Under each abstraction level, various techniques are further classified while briefly describing their essential characters.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
De-biasing facial detection system using VAE

Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society. There are many reasons behind a system being biased. The bias can be due to the algorithm we are using for our problem or may be due to the dataset we are using, having some features over-represented in it. In the face detection system bias due to the dataset is majorly seen. Sometimes models learn only features that are over-represented in data and ignore rare features from data which results in being biased toward those over-represented features. In real life, these biased systems are dangerous to society. The proposed approach uses generative models which are best suited for learning underlying features(latent variables) from the dataset and by using these learned features models try to reduce the threats which are there due to bias in the system. With the help of an algorithm, the bias present in the dataset can be removed. And then we train models on two datasets and compare the results.
A novel approach for audible acoustic quick response codes

Compared to image-based quick response (QR) codes, acoustic QR codes have some advantages. However, an acoustic QR scanner cannot recognize an acoustic QR code at a distance of more than two meters from an acoustic QR announcer. To this end, we propose a new sort of acoustic QR code, called an audible acoustic QR code (AAQRC), which employs humanly audible sound to carry users' information directly. First, a user's string of characters is translated into a string of pitches. Then, the related algorithms convert the string of pitches into a playable audio file. As a result, an AAQRC is generated, consisting of the audio itself. AAQRC recognition is the opposite process of AAQRC generation. Compared with the existing approach for acoustic QR codes, the new method can recognize acoustic QR codes at a longer distance, even if there are obstacles between the AAQRC announcer and AAQRC scanner.
Learning Compositional Representations for Effective Low-Shot Generalization

We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small compact vocabulary of concepts to represent each instance with. RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept. We find that this type of learning inspired by human cognition can overcome hurdles faced by deep convolutional networks in low-shot generalization tasks, like zero-shot learning, few-shot learning and unsupervised domain adaptation. Furthermore, we find a classifier using an RPC image encoder is fairly robust to adversarial attacks, that deep neural networks are known to be prone to. Given that our image encoding principle is based on human cognition, one would expect the encodings to be interpretable by humans, which we find to be the case via crowd-sourcing experiments. Finally, we propose an application of these interpretable encodings in the form of generating synthetic attribute annotations for evaluating zero-shot learning methods on new datasets.
The Role of Artificial Intelligence and Information Technology in Promoting Knowledge Management in Business Firms: A Review

Knowledge Management appears to be more advanced and sophisticated in the twenty-first century, as a result of the advancement of information technology and artificial intelligence technologies. Several academic and commercial scholars have been studying, exploring, and discussing knowledge management for a long time, owing to the importance of this topic for the success of companies in both the public and private sectors. In addition, many prominent firms throughout the world have used a variety of knowledge management methods to ensure that they remain one step ahead of their competitors in today's highly competitive business environment, including businesses that are constantly looking for methods to improve their knowledge management strategies. There have been some studies undertaken to gain a more in-depth grasp of the most recent research trend in knowledge management procedures and best practices in businesses. This subject, on the other hand, demands deeper examination from a variety of angles. The impact of using Artificial Intelligence (AI) and Information Technology (IT) concepts and techniques on the efficiency of knowledge management in modern businesses, particularly has been overlooked by many prior studies. The current study reviews the role of artificial intelligence and information technology in promoting knowledge management in business firms.
Challenges and Opportunities of Online Learning in the Philippine Context: Thriving in the New Normal

The research focused on the challenges and opportunities given by the fast transition of academic institutions to remote online learning amid the country's Covid-19 pandemic. Data from online surveys and focus groups were analyzed using descriptive statistics and theme analysis. The consequences and complexity of limited internet connectivity, loss of human interaction, and low skill in digital platforms were highlighted as the primary problems and disadvantages of online learning. The adaptability of the children to the online platform and the teachers' originality, flexibility, and eagerness to learn and adapt in the digital environment transformed these issues into learning opportunities. The university's continued efforts and will to prosper in the new normal of education, eventually preparing itself for the future normal of education, were attributed to the success of distant synchronous and asynchronous online learning.
Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture

In the smart agriculture system based on green-based technology of artificial intelligence (AI), flooding can be predicted early by forecasting the water surface and good agricultural irrigation. The process of rising and falling of the water surface in a water basin area can be explained theoretically, but since there are many related variables and the complexity of dependencies between variables, the mathematical model is difficult to construct. Forecasting water surface in the field of irrigation needs too many variable parameters, such as cross-sectional area, depth, volume of rivers and so on. Based on patterns in each period, forecasting can be done using a statistical method and AI. This study uses the support vector regression (SVR) method, regression, multiple linear regression, and algorithm backpropagation, all compared to one another. The results of tests carried out between SVR and multiple linear regression show that SVR is superior. This can be seen from the result of the mean square error (MSE) obtained for each method. SVR 0.03 and for multiple linear regression, 0.05. The result is also supported by the best MSE result in the regression method, which is 0.338, and the best MSE value in artificial neural network (ANN), which is 0.428.
Empirical research of student leadership’s content parameters

The aim of this article was to perform a retrospective analysis and generalization of the content parameters of student leadership through an empirical study. Sociometric method and psychodiagnostic instruments were used to establish the values of student group leaders. Significant correlations between the studied leadership parameters were found. The sociometric status of student leaders was determined and their values were clarified. The stages and challenges of forming student leadership qualities were outlined. The model of the development of student leadership qualities was graphically constructed and substantiated. It was generalized that the empirical study of the content parameters of student leadership contains significant scientific facts that should be implemented in the educational process; the data can be useful to university administrations and student group curators.
On the Nonexistence of Some Griesmer Codes with Dimension Four

Abstract In coding theory, it is important to construct codes meeting certain types of bounds. The codes meeting the Griesmer bound are called Griesmer codes. In this paper, we prove the nonexistence of some Griesmer codes of dimension four over finite fields of cardinality eight and nine. Moreover, we improve 24 cases on code lengths for given minimal distance and dimension 4. Two cases on precise lower bound of code lengths have also been determined.
Optimization Method of Microwave Devices Based on Improved Extreme Learning Machine

Abstract With the rapid development of modern wireless technology, it is necessary to continuously optimize microwave devices to meet the higher requirements of communication systems. In this paper, we propose a microwave device optimization method based on extreme learning machine (ELM) and gray wolf optimizer (GWO). we adopt GWO to optimize the parameters of ELM and establish the mapping relationships between the microwave device design parameters and their responses. According to the inverse mapping of expected response, the expected design parameters of microwave devices are obtained. The numerical results show that the proposed method can improve the design efficiency of microwave devices and realize automatic optimization design compared with the existing method.
Hematological indicators of mooses by means of automated analysis and microscopic method

In recent years, automatic hematological analyzers have been introduced into veterinary practice for clinical blood analysis. In this regard, a number of reports have appeared in the literature reflecting the morphological parameters of elk blood obtained on automatic hemoanalyzers for veterinary and medical purposes. However, it should be noted that most of the veterinary equipment is programmed to conduct research on agricultural, small domestic, less often exotic animals, not the mooses. In addition, the semicytometric (impedancemetric) method of cell counting, which is the basis for the work of hemoanalyzers, unlike the classical (visual) method, allows to differentiate only three or five subpopulations of leukocytes. In this regard, the purpose of the research was to study the morphological parameters of blood using automatic analyzers in a comparative aspect with the visual method. In the first series of experiments on 17 moose calves of 1.5–2 months, a comparative assessment of hematological parameters was carried out by the microscopic method and on automatic analyzers (differentiating leukocytes into three subpopulations) for veterinary purposes RT-7600 (in the profiles "cow", "goat") and for medical purposes BC-3000 Plus. It has been found that the use of this equipment in the "cow" and "human" profiles makes it possible to evaluate the parameters of erythrocytes and platelets in moose. However, all indicators (except for hemoglobin) have significant discrepancies. Statistically significant discrepancies between the automatic determination and the visual method of leukocyte subpopulations were also found in four out of seven cases using the RT-7600 hemoanalyzer ("cow" profile) and in five out of seven cases using the BC-3000Plus hemoanalyzer. In the second series, seven adult animals were used to compare blood parameters obtained by the classical method and automatic – the Vet Scan HM5 hemoanalyzer in the profiles "cow", "sheep", "goat" (differentiating leukocytes into five subpopulations). It was revealed that there were no statistically significant differences in the characteristics of all parameters of erythrocytes in the profiles "cow", "sheep", except for the average content of hemoglobin in erythrocytes (pg). The "goat" profile did not allow us to evaluate the platelet parameters, while the erythrocyte parameters were close to the previous profiles. Determination of leukocytes and their subpopulations is possible only by a visual method and automated in the "cow" profile. In this regard, automatic hematology analyzers can be used to a certain extent in mass screening for red blood, and to a lesser extent – for white blood.
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines

A misspecified reward can degrade sample efficiency and induce undesired behaviors in reinforcement learning (RL) problems. We propose symbolic reward machines for incorporating high-level task knowledge when specifying the reward signals. Symbolic reward machines augment existing reward machine formalism by allowing transitions to carry predicates and symbolic reward outputs. This formalism lends itself well to inverse reinforcement learning, whereby the key challenge is determining appropriate assignments to the symbolic values from a few expert demonstrations. We propose a hierarchical Bayesian approach for inferring the most likely assignments such that the concretized reward machine can discriminate expert demonstrated trajectories from other trajectories with high accuracy. Experimental results show that learned reward machines can significantly improve training efficiency for complex RL tasks and generalize well across different task environment configurations.
Stress-Testing Point Cloud Registration on Automotive LiDAR

Rigid Point Cloud Registration (PCR) algorithms aim to estimate the 6-DOF relative motion between two point clouds, which is important in various fields, including autonomous driving. Recent years have seen a significant improvement in global PCR algorithms, i.e. algorithms that can handle a large relative motion. This has been demonstrated in various scenarios, including indoor scenes, but has only been minimally tested in the Automotive setting, where point clouds are produced by vehicle-mounted LiDAR sensors. In this work, we aim to answer questions that are important for automotive applications, including: which of the new algorithms is the most accurate, and which is fastest? How transferable are deep-learning approaches, e.g. what happens when you train a network with data from Boston, and run it in a vehicle in Singapore? How small can the overlap between point clouds be before the algorithms start to deteriorate? To what extent are the algorithms rotation invariant? Our results are at times surprising. When comparing robust parameter estimation methods for registration, we find that the fastest and most accurate is not one of the newest approaches. Instead, it is a modern variant of the well known RANSAC technique. We also suggest a new outlier filtering method, Grid-Prioritized Filtering (GPF), to further improve it. An additional contribution of this work is an algorithm for selecting challenging sets of frame-pairs from automotive LiDAR datasets. This enables meaningful benchmarking in the Automotive LiDAR setting, and can also improve training for learning algorithms.
A Preliminary Report on Augmented Reality Assisted Carpal Tunnel Release

Augmented reality (AR) refers to the use of technology to enhance a real-world environment by computer generated visual, auditory, haptic, somatosensory and/or olfactory stimuli. We developed an augmented reality-assisted surgery (ARAS) for carpal tunnel syndrome (CTS). It generates a virtual image of the operative field that the surgeon can view. This enhances the operative experience. We report the use of ARAS in performing limited open carpal tunnel release. Level of Evidence: Level V (Therapeutic).
Privacy-Preserving Image Classification Using Isotropic Network

In this paper, we propose a privacy-preserving image classification method that uses encrypted images and an isotropic network such as the vision transformer. The proposed method allows us not only to apply images without visual information to deep neural networks (DNNs) for both training and testing but also to maintain a high classification accuracy. In addition, compressible encrypted images, called encryption-then-compression (EtC) images, can be used for both training and testing without any adaptation network. Previously, to classify EtC images, an adaptation network was required before a classification network, so methods with an adaptation network have been only tested on small images. To the best of our knowledge, previous privacy-preserving image classification methods have never considered image compressibility and patch embedding-based isotropic networks. In an experiment, the proposed privacy-preserving image classification was demonstrated to outperform state-of-the-art methods even when EtC images were used in terms of classification accuracy and robustness against various attacks under the use of two isotropic networks: vision transformer and ConvMixer.
CrossRectify: Leveraging Disagreement for Semi-supervised Object Detection

Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and unlabeled data with pseudo labels predicted by the detector itself, but their performances are always limited. Through experimental analysis, we reveal the underlying reason is that the detector is misguided by the incorrect pseudo labels predicted by itself (dubbed self-errors). These self-errors can hurt performance even worse than random-errors, and can be neither discerned nor rectified during the self-labeling process. In this paper, we propose an effective detection framework named CrossRectify, to obtain accurate pseudo labels by simultaneously training two detectors with different initial parameters. Specifically, the proposed approach leverages the disagreements between detectors to discern the self-errors and refines the pseudo label quality by the proposed cross-rectifying mechanism. Extensive experiments show that CrossRectify achieves outperforming performances over various detector structures on 2D and 3D detection benchmarks.
Unsupervised Extractive Opinion Summarization Using Sparse Coding

Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from the review and learns a latent representation of each sentence over semantic units. A semantic unit is supposed to capture an abstract semantic concept. Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews. SemAE is also able to perform controllable summarization to generate aspect-specific summaries. We report strong performance on SPACE and AMAZON datasets, and perform experiments to investigate the functioning of our model. Our code is publicly available at https://github.com/brcsomnath/SemAE.
Hierarchical Optimal Transport for Comparing Histopathology Datasets

Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels. Transfer learning allows researchers to overcome the limitations of small datasets by pre-training machine learning models on larger datasets similar to the small target dataset. However, similarity between datasets is often determined heuristically. In this paper, we propose a principled notion of distance between histopathology datasets based on a hierarchical generalization of optimal transport distances. Our method does not require any training, is agnostic to model type, and preserves much of the hierarchical structure in histopathology datasets imposed by tiling. We apply our method to H&E stained slides from The Cancer Genome Atlas from six different cancer types. We show that our method outperforms a baseline distance in a cancer-type prediction task. Our results also show that our optimal transport distance predicts difficulty of transferability in a tumor vs.normal prediction setting.
Study on Fraud Transaction Detection System

Fraudulent transactions are very common problem in banking systems internationally, accounting for $5.1 trillion dollars every year. Many financial institutions are facing the common problem of being targeted by transactions of fraudulent nature and its becoming more and more obvious that advanced technology, such as Machine Learning (ML) is needed to counter such acts. Machine learning is the most effective technique against these complex bank frauds when approaches relying on fragmented and siloed data, rules-based approaches or traditional point-solutions are not only costly but also not as effective as needed. Complex algorithms powered by ML can be used to reduce manual investigations in Financial Institutions. Volume of these transactions is huge, lots of current solutions do not focus on big data the proposed model will work on big data with the help of ‘Apache Spark’ using latest machine learning technology. The proposed model will try to find pattern in given data set and flag the transaction as fraudulent or not with probability score and then banking system can decide further course of action. We are also going to include different machine learning algorithms used to detect fraudulent transactions and provide a comparative study between those algorithms to show which is more effective.
Toward Using Multi-Modal Machine Learning for User Behavior Prediction in Simulated Smart Home for Extended Reality

In this work, we propose a multi-modal approach to manipulate smart home devices in a smart home environment simulated in virtual reality (VR). We determine the user&#x0027;s target device and the desired action by their utterance, spatial information (gestures, positions, etc.), or a combination of the two. Since the information contained in the user&#x0027;s utterance and the spatial information can be disjoint or complementary to each other, we process the two sources of information in parallel using our array of machine learning models. We use ensemble modeling to aggregate the results of these models and enhance the quality of our final prediction results. We present our preliminary architecture, models, and findings.