Reviewerly/exHarmony huggingface dataset:
Algorithms for Implementing Repeated Homomorphic Operations on Restricted Data Type Ranges

Security is the primary concern for cloud-based data storage. While the data is stored in the cloud, it is queried and retrieved regularly from various remote locations. Various cloud providers come with built-in security features for all the data stored in the cloud. Even though cloud providers offer different security methods, organizations can’t rely on them when it comes to sensitive data. In various enterprise applications, there can be a requirement of performing different operations on data stored in coded form. In this situation, homomorphic ciphering techniques are useful. Different homomorphic encryption methods enable execution of operations on encrypted data without first decrypting it. Typically, these calculations include performing arithmetic operations on encrypted cloud-based data. When the data to be stored is structured and its elements are to be stored in various data type fields, there is a restriction on the range of values stored in the respective data type. Using existing homomorphic techniques, direct operations between two or more encrypted values cause the result value’s size to increase by that many bits. The size of the result is proportional to the number of times the operation performed. It is therefore challenging to enable numerous additions or multiplications over encrypted numeric values stored in databases. In this paper, we propose a methodology to support an unlimited number of homomorphic operations on encrypted data without increase in size of the final result by incorporating few changes in implementation of various operations. Our proposed work guarantees that it supports unlimited additions or multiplications over encrypted data such that the final result does not exceed the storage size of the data by more than the number of bits. This article provides algorithms for additive homomorphic with Paillier and multiplicative homomorphic with RSA.
Oral Cancer Detection using Deep Learning Techniques

One of the most serious tumors that affects the oral cavity is oral cancer. Smoking cigarettes and increased tobacco use are the main risk factors for mouth cancer. When oral cancer is found in its early stages and treated successfully, many lives can be saved Histological analysis of an oral cavity tissue sample is the accepted method in medicine for identifying oral cancer. This method requires more time and is more invasive than obtaining a brush sample and then performing a cytological analysis. For a better prognosis, treatment plan, and chance of survival, early diagnosis is essential. Therefore, this paper suggests deep learning techniques to perform early detection of oral cancer and eventually leads to its prevention. Deep learning techniques enable early detection of disease to provide precision medicine. According to the recent research reports, this method has significantly advanced the extraction of data and interpretation of crucial information related to medical imaging. It has the potential to identify oral cancer with a cost-efficient, non-invasive, and effective method, having substantial clinical implications.
Different Models of Transliteration - A Comprehensive Review

Accessing information because of language barriers has grown more crucial as globalization accelerates. The field of study called machine transliteration transforms words from one language to another while preserving their phonetic properties. With the rise of the Internet and the rapid expansion of online data, Information Retrieval (IR) has become a more significant field. Foreign terms are increasingly being incorporated into languages due to the development of new technologies and the flood of information available online. This generally indudes changing the adopted word's orthographical form as well as its original pronunciation to match the phonological norms of the target language. Transliteration describes this phonetic “translation” of a foreign language. Machine translation and cross-language information retrieval both benefit from transliteration. Ambiguity, variability, and out-of-vocabulary (OOV) words are a few of the crucial challenges that transliteration systems must deal with. This paper represents the transliteration models of the last decade used for transliteration-related research carried out by researchers.
Cloud Analytics Benchmark

The cloud facilitates the transition to a service-oriented perspective. This affects cloud-native data management in general, and data analytics in particular. Instead of managing a multi-node database cluster on-premise, end users simply send queries to a managed cloud data warehouse and receive results. While this is obviously very attractive for end users, database system architects still have to engineer systems for this new service model. There are currently many competing architectures ranging from self-hosted (Presto, PostgreSQL), over managed (Snowflake, Amazon Redshift) to query-as-a-service (Amazon Athena, Google BigQuery) offerings. Benchmarking these architectural approaches is currently difficult, and it is not even clear what the metrics for a comparison should be. To overcome these challenges, we first analyze a real-world query trace from Snowflake and compare its properties to that of TPC-H and TPC-DS. Doing so, we identify important differences that distinguish traditional benchmarks from real-world cloud data warehouse workloads. Based on this analysis, we propose the Cloud Analytics Benchmark (CAB). By incorporating workload fluctuations and multi-tenancy, CAB allows evaluating different designs in terms of user-centered metrics such as cost and performance.
Efficient Distributed Transaction Processing in Heterogeneous Networks

Countrywide and worldwide business, like gaming and social networks, drives the popularity of inter-data-center transactions. To support inter-data-center transaction processing and data center fault tolerance simultaneously, existing protocols suffer from significant performance degradation due to high-latency and unstable networks. In this paper, we propose RedT, a novel distributed transaction processing protocol that works in heterogeneous networks. In detail, nodes within a data center are inter-connected via the RDMA-capable network and nodes across data centers are inter-connected via TCP/IP networks. RedT extends two-phase commit (2PC) by decomposing transactions into sub-transactions in terms of the data center granularity, and proposing a pre-write-log mechanism that is able to reduce the number of inter-data-center round-trips from a maximal of 6 to 2. Extensive evaluation against state-of-the-art protocols shows that RedT can achieve up to 1.57× higher throughputs and 0.56× lower latency.
Natural Language Specification of Reinforcement Learning Policies Through Differentiable Decision Trees

Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy. This procedure is comprised of two steps; (1) Policy Specification, i.e. humans specifying the behavior they would like their companion robot to accomplish, and (2) Policy Optimization, i.e. the robot applying reinforcement learning to improve the initial policy. Existing approaches to enabling collaborative policy specification are often unintelligible black-box methods, and are not catered towards making the autonomous system accessible to a novice end-user. In this paper, we develop a novel collaborative framework to allow humans to initialize and interpret an autonomous agent's behavior. Through our framework, we enable humans to specify an initial behavior model via unstructured, natural language (NL), which we convert to lexical decision trees. Next, we leverage these translated specifications, to warm-start reinforcement learning and allow the agent to further optimize these potentially suboptimal policies. Our approach warm-starts an RL agent by utilizing non-expert natural language specifications without incurring the additional domain exploration costs. We validate our approach by showing that our model is able to produce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 80% translation accuracy, and that policies initialized by a human can match the performance of relevant RL baselines in two domains.
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition

Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of instances, and formulate the task of multi-label image recognition as an instance-label matching selection problem. To model such problem, we propose an innovative Semantic-aware Graph Matching framework for Multi-Label image recognition (ML-SGM), in which Graph Matching mechanism is introduced owing to its good performance of excavating the instance and label relationship. The framework explicitly establishes category correlations and instance-label correspondences by modeling the relation among content-aware (instance) and semantic-aware (label) category representations, to facilitate multi-label image understanding and reduce the dependency of large amounts of training samples for each category. Specifically, we first construct an instance spatial graph and a label semantic graph respectively and then incorporate them into a constructed assignment graph by connecting each instance to all labels. Subsequently, the graph network block is adopted to aggregate and update all nodes and edges state on the assignment graph to form structured representations for each instance and label. Our network finally derives a prediction score for each instance-label correspondence and optimizes such correspondence with a weighted cross-entropy loss. Empirical results conducted on generic multi-label image recognition demonstrate the superiority of our proposed method. Moreover, the proposed method also shows advantages in multi-label recognition with partial labels and multi-label few-shot learning, as well as outperforms current state-of-the-art methods with a clear margin. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yananwu0510/ML-SGM</uri> .
Single-Level Differentiable Contact Simulation

We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions of convex primitives. Classical physics engines rely on non-differentiable collision detection modules. More recent optimization-based approaches simulating contact between convex primitives rely on a bilevel formulation that separates collision detection and contact simulation. These latest approaches are unreliable in realistic contact simulation scenarios because isolating the collision detection problem introduces contact location non-uniqueness. Our approach combines contact simulation and collision detection into a unified single-level optimization problem. This disambiguates the collision detection problem in a physics-informed manner. Our formulation features improved simulation robustness and a reduction in computational complexity when compared to a similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">differentiable</i> simulation baseline. We illustrate the contact and collision differentiability on a robotic manipulation task requiring optimization-through-contact. We provide a numerically efficient implementation of our formulation called
Constraints in Cognitive Architectures

Cognitive architectures are computational theories that attempt to cover as much of cognition as possible. Developers of cognitive architectures need to balance functionality of the theory against its explanatory and predictive power, which are often at odds with one another. This chapter discusses this balance with respect to working memory, cognitive performance, perceptual and motor systems, learning and neuroscience. It discusses which solutions six cognitive architectures offer to these areas, and illustrates this with a number of successful models.
Identifying Multimodal Context Awareness Requirements for Supporting User Interaction with Procedural Videos

Following along how-to videos requires alternating focus between understanding procedural video instructions and performing them. Examining how to support these continuous context switches for the user has been largely unexplored. In this paper, we describe a user study with thirty participants who performed an hour-long cooking task while interacting with a wizard-of-oz hands-free interactive system that is aware of both their cooking progress and environment contexts. Through analysis of the session scripts, we identify a dichotomy between participant query differences and workflow alignment similarities, under-studied interactions that require AI functionality beyond video navigation alone, and queries that call for multimodal sensing of a user's environment. By understanding the assistant experience through the participants' interactions, we identify design implications for a smart assistant that can discern a user's task completion flow and personal characteristics, accommodate requests within and external to the task domain, and support nonvoice-based queries.
DAmcqrnn: An approach to censored monotone composite quantile regression neural network estimation

Quantile regression neural network (QRNN) model has recently become popular in solving complex nonlinear problems, but the quantile curves achieved by QRNN model may cross each other, leading to unreliable results. To remedy this defect, monotone composite quantile regression neural network (MCQRNN) model is more attractive with non-crossing quantile predictions. However, few works have been involved in the literature to apply MCQRNN model for censoring issues. Based on the data augmentation algorithm, this study presents an iterative estimation method, which is a three-step alternating process, for censored MCQRNN model. Step 1 imputes the censored data through a data augmentation procedure. Step 2 updates the MCQRNN model with the imputed data. Step 3 makes predictions by the updated MCQRNN model. Simulation studies and real data application demonstrate that our proposed method is not only superior to the available censored methods with respect to quantile loss and C-index, but also yields very close prediction results to those obtained by full uncensored data. Avoiding the quantile crossing problem, the proposed method is more flexible to cope with different types of censoring, overcoming the limitation that the existing censored methods only work for single censoring type with unbelievable predictions caused by the crossing phenomenon.
An empirical analysis of the shift and scale parameters in BatchNorm

Batch Normalization (BatchNorm) is a technique that improves the training of deep neural networks, especially Convolutional Neural Networks (CNN). It has been empirically demonstrated that BatchNorm increases performance, stability, and accuracy, although the reasons for such improvements are unclear. BatchNorm includes a normalization step as well as trainable shift and scale parameters. In this paper, we empirically examine the relative contribution to the success of BatchNorm of the normalization step, as compared to the re-parameterization via shifting and scaling. To conduct our experiments, we implement two new optimizers in PyTorch, namely, a version of BatchNorm that we refer to as AffineLayer, which includes the re-parameterization step without normalization, and a version with just the normalization step, that we call BatchNorm-minus. We compare the performance of our AffineLayer and BatchNorm-minus implementations to standard BatchNorm, and we also compare these to the case where no batch normalization is used. We experiment with four ResNet architectures (ResNet18, ResNet34, ResNet50, and ResNet101) over a standard image dataset and multiple batch sizes. Among other findings, we provide empirical evidence that the success of BatchNorm may derive primarily from improved weight initialization.
Multi-object tracking for horse racing

Multi-object tracking has been extensively studied in pedestrian behavior analysis and vehicle analysis, but has yet to be well extended to the more challenging horse racing scenarios. This work makes the first attempt to systematically analyze challenges for horse racing tracking problems, including occlusion, trajectory staggered, often switching of cameras (i.e., angles of views), and blurred sprint caused by horse competition. An augmentation-based multi-object tracking method (GMOT) is proposed to solve the above-mentioned challenges. The auxiliary classifier generative adversarial network is adopted in GMOT to augment the horse racing data to enhance target detection and re-identification results. Horse racing videos recorded in day and night scenes are both tested in our experiments. Experimental results show that the proposed method yields good results.
Dual-core mutual learning between scoring systems and clinical features for ICU mortality prediction

Perpetually improving mortality prediction in intensive care units (ICUs) via the implementation of eHealth evaluation approaches has become a major research hotspot in the field of medical data mining for the purpose of saving lives. Recently, researchers have attempted to achieve improved prediction accuracy by using only deep learning-based techniques. However, some problems remain. (1) Most of the existing methods utilize independent clinical features to predict mortality by eliminating the correlations between the latent features, but this technique may fail to comprehensively capture and evaluate the statuses of patients. (2) Several clinical features are needed to ensure strong prediction accuracy, but most methods only use static features that are not extendable. (3) An effective practical framework that unifies traditional ICU scoring systems and state-of-the-art deep learning methods to predict mortality is also lacking. (4) Moreover, the interpretability of existing deep learning-based methods needs to be further improved. Therefore, we propose a novel dual-core mutual learning framework (DMLF) between ICU scoring systems and clinical features for mortality prediction. In particular, we mutually utilize sequential organ failure assessment (SOFA) scores and clinical measurement features to learn a unified model for enhancing the accuracy and interpretability of our DMLF. Experiments conducted on five real-world disease datasets show that the DMLF achieves significantly better prediction accuracy and area under the receiver operating characteristic curve (AUROC) values than six baselines and four state-of-the-art methods. Moreover, clinicians utilize a familiarized SOFA system to conduct mortality prediction and achieve increased interpretability, which benefits the adoption of the proposed framework in real clinical scenarios.
Theoretical bounds of generalization error for generalized extreme learning machine and random vector functional link network

Ensuring the prediction accuracy of a learning algorithm on a theoretical basis is crucial and necessary for building the reliability of the learning algorithm. This paper analyzes prediction error obtained through the least square estimation in the generalized extreme learning machine (GELM), which applies the limiting behavior of the Moore-Penrose generalized inverse (M-P GI) to the output matrix of ELM. ELM is the random vector functional link (RVFL) network without direct input to output links Specifically, we analyze tail probabilities associated with upper and lower bounds to the error expressed by norms. The analysis employs the concepts of the L2 norm, the Frobenius norm, the stable rank, and the M-P GI. The coverage of theoretical analysis extends to the RVFL network. In addition, a criterion for more precise bounds of prediction errors that may give stochastically better network environments is provided. The analysis is applied to simple examples and large-size datasets to illustrate the procedure and verify the analysis and execution speed with big data. Based on this study, we can immediately obtain the upper and lower bounds of prediction errors and their associated tail probabilities through matrices calculations appearing in the GELM and RVFL. This analysis provides criteria for the reliability of the learning performance of a network in real-time and for network structure that enables obtaining better performance reliability. This analysis can be applied in various areas where the ELM and RVFL are adopted. The proposed analytical method will guide the theoretical analysis of errors occurring in DNNs, which employ a gradient descent algorithm.
Real Time Object Recognition Using OpenCV and Numpy in Python

Object recognition is the process of recognizing objects based on their characteristics like color, shape, and with the particular occurrence of the object in digital videos and as well as normal daily life. This object detection is the already known and introduced technology that performs the program using computer technology as the platform and using some image processing units for the detection of the objects using some class of data sets given by the user. Dual priorities, an imbalance in the classes, a lack of data, etc. are a few significant challenges. As this object detection is working as a real-time application and is mostly used for tracking objects, counting crowds, and self-driving cars, and even more useful in traffic control and also for security surveillance purposes which is the major purpose of usage. Some techniques like deep learning-based object detection a relatively new approach that has blurred the lines between object localization and detection. Region-Based Convolutional Neural Network was used to deal with it. Another was a fast Region-Based Convolutional Neural Network, which was proposed in Python and C++ and raises the problem of Region-Based Convolutional Neural Network and Spatial Pyramid Pooling-net when dealing with speed and accuracy. It was resolved using a deep VGG16 network. This application eventually came up with some basic concepts that are likely OpenCV, an open-source library with some functions that can be used for object detection, and the combination of python 2.7, improving the accuracy and efficiency of object detection. Even though many applications are available initially, by using the simple functions in OpenCV and NumPy, the proposed work achieves accurate results in finding the objects easily.