Reviewerly/exHarmony huggingface dataset:
Object Detection Using Machine Learning for Visually Impaired People

In this challenging evolution, the primary task in detecting the objects requires a computer vision that deals over indoor and outdoor classes.Over the past decades, this zeal requires more attentiveness.Previous implementation techniques involve in object detection with a strategy of single labelling. Aim and Objectives:In this regard, a multi-label approach using machine learning and vision technologies, and accurate response can be acknowledged based on its accuracy and effectiveness.In the proposed work, we solve the existing system problem by using classification/clustering techniques that are used to reduce the recognize time of multi objects in less time with best time complexities. Model:The model used to assist the visually impaired people can independently recognize objects which are near to them.The reverence, combined with the study, confounded the inception of these machine learning algorithms for visually impaired persons in assisting the accurate navigation, including indoor and outdoor circumstances. Conclusion:In this connection, an indoor and outdoor architecture on Retina Net is implemented for its detection techniques, and also neural network technologies support this framework.Based on the effectiveness and implementation time, ResNet and FPN act as a crucial module for its accuracy.
Bidirectional Quantum Teleportation by Using Two GHZ-States as the Quantum Channel

Bidirectional quantum teleportation protocol enables two way quantum communication between two quantum nodes in a quantum communication network. In quantum network, the quantum nodes are linked to each other through a quantum channel. In this letter, by using the quantum channel composed of two three-qubit GHZ states, we propose an improved protocol for asymmetric bidirectional quantum teleportation which is based on local CNOT gate operations, Bell state measurements, GHZ-state measurements and unitary operations. Compared with previous protocols for bidirectional quantum teleportation, the proposed protocol requires less consumption of quantum and classical resources, possesses higher intrinsic efficiency and less operation complexity.
Fusion of whole and part features for the classification of histopathological image of breast tissue

Nowadays Computer-Aided Diagnosis (CAD) models, particularly those based on deep learning, have been widely used to analyze histopathological images in breast cancer diagnosis. However, due to the limited availability of such images, it is always tedious to train deep learning models that require a huge amount of training data. In this paper, we propose a new deep learning-based CAD framework that can work with less amount of training data. We use pre-trained models to extract image features that can then be used with any classifier. Our proposed features are extracted by the fusion of two different types of features (foreground and background) at two levels (whole-level and part-level). Foreground and background feature to capture information about different structures and their layout in microscopic images of breast tissues. Similarly, part-level and whole-level features capture are useful in detecting interesting regions scattered in high-resolution histopathological images at local and whole image levels. At each level, we use VGG16 models pre-trained on ImageNet and Places datasets to extract foreground and background features, respectively. All features are extracted from mid-level pooling layers of such models. We show that proposed fused features with a Support Vector Classifier (SVM) produce better classification accuracy than recent methods on BACH dataset and our approach is orders of magnitude faster than the best performing recent method (EMS-Net). We believe that our method would be another alternative in the diagnosis of breast cancer because of performance and prediction time.
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding

Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics

This paper focuses on learning a model of system dynamics online while satisfying safety constraints.Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation.Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics. In turn, the distribution is used to optimize the system behavior andensure safety with high probability, by specifying a chance constraint over a control barrier function.
kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes.
Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model

Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights. Interpreting and making sense of such visualizations can be challenging for some people, such as those who are visually impaired or have low visualization literacy. In this work, we introduce a new dataset and present a neural model for automatically generating natural language summaries for charts. The generated summaries provide an interpretation of the chart and convey the key insights found within that chart. Our neural model is developed by extending the state-of-the-art model for the data-to-text generation task, which utilizes a transformer-based encoder-decoder architecture. We found that our approach outperforms the base model on a content selection metric by a wide margin (55.42% vs. 8.49%) and generates more informative, concise, and coherent summaries.
Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

Existing end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager. The dialog memory manager dynamically expands the dialog memory turn by turn and keeps track of dialog history with an updating mechanism, which encourages the model to filter irrelevant dialog history and memorize important newly coming information. The KB memory manager shares the structural KB triples throughout the whole conversation, and dynamically extracts KB information with a memory pointer at each turn. Experimental results on three benchmark datasets demonstrate that DDMN significantly outperforms the strong baselines in terms of both automatic evaluation and human evaluation. Our code is available at https://github.com/siat-nlp/DDMN.
System FR: formalized foundations for the stainless verifier

We present the design, implementation, and foundation of a verifier for higher-order functional programs with generics and recursive data types. Our system supports proving safety and termination using preconditions, postconditions and assertions. It supports writing proof hints using assertions and recursive calls. To formalize the soundness of the system we introduce System FR, a calculus supporting System F polymorphism, dependent refinement types, and recursive types (including recursion through contravariant positions of function types). Through the use of sized types, System FR supports reasoning about termination of lazy data structures such as streams. We formalize a reducibility argument using the Coq proof assistant and prove the soundness of a type-checker with respect to call-by-value semantics, ensuring type safety and normalization for typeable programs. Our program verifier is implemented as an alternative verification-condition generator for the Stainless tool, which relies on the Inox SMT-based solver backend for automation. We demonstrate the efficiency of our approach by verifying a collection of higher-order functional programs comprising around 14000 lines of polymorphic higher-order Scala code, including graph search algorithms, basic number theory, monad laws, functional data structures, and assignments from popular Functional Programming MOOCs.
Social Sensemaking with AI: Designing an Open-ended AI Experience with a Blind Child

AI technologies are often used to aid people in performing discrete tasks with well-defined goals (e.g., recognising faces in images). Emerging technologies that provide continuous, real-time information enable more open-ended AI experiences. In partnership with a blind child, we explore the challenges and opportunities of designing human-AI interaction for a system intended to support social sensemaking. Adopting a research-through-design perspective, we reflect upon working with the uncertain capabilities of AI systems in the design of this experience. We contribute: (i) a concrete example of an open-ended AI system that enabled a blind child to extend his own capabilities; (ii) an illustration of the delta between imagined and actual use, highlighting how capabilities derive from the human-AI interaction and not the AI system alone; and (iii) a discussion of design choices to craft an ongoing human-AI interaction that addresses the challenge of uncertain outputs of AI systems.
Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real-life solutions using cutting-edge tools and techniques to generate insights into the data. The data-driven machine learning models are now offering more or less worthy results when they are certainly balanced in the input data sets. Imbalanced data occurs when an unequal distribution of classes occurs in the input datasets. Building a predictive model on the imbalanced data set would cause a model that appears to yield high accuracy but does not generalize well to the new data in the minority class. Now the time has come to look into the datasets which are not so-called ‘balanced’ in nature but such datasets are generally encountered frequently in a workspace. To prevent creating models with false levels of accuracy, the imbalanced data should be rearranged before creating a predictive model. Those data are, sometimes, voluminous, heterogeneous and complex in nature and generate from different autonomous sources with distributed and decentralized control. The driving force is to efficiently handle these data sets using latest tools and techniques for research and commercial insights. The present article provides different such tools and techniques, in different computing frameworks, to handle such Internet of Things and other related datasets to review common techniques for handling imbalanced data in data ecosystems and offers a comparative data modelling framework in Keras for balanced and imbalanced datasets.
Query Optimization in a Blockchain-Based Land Registry Management System

Digitization of land records is not sufficient for preventing fraud cases, time delay, and brokers' involvement. Distributed Ledger Technology (DLT) is used for making this digitized record more secure and process it in a decentralized way, and reduces paperwork in selling and buying of land. Blockchain technology has come to the fore in recent years and is the center of technical discussion, with intuitive applications driven by its network architecture. It has been firmly established as one of the most important emerging technologies. This article aims to implement a land registry mechanism using blockchain technology and optimize searching of land records in blockchain. Interplanetary File System (IPFS) provides an infrastructure that offers a precise portrayal of all the members' roles. The application interacts with the blockchain network, which is built using IPFS. This paper will help in providing a secure and decentralized system for the land registry process. The proposed consensus algorithm MRRCM achieves less time required to generated a block on an average by 3.06% round-robin and 96.48% PoW approach. The proposed modified hash table search approach requires less time to search the blockchain's land record block than the extensive liner searches and hash table search approach. A search for a land record in the blockchain reduces the search time on an average by 59.5% compared to the traditional extensive liner search approach and by 18.68% as compared with the hash table search approach.
Boundary-Adaptive Encoder With Attention Method for Chinese Sign Language Recognition

The sign language signal has hierarchically related information over short and long distances. Due to the intricate temporal correlation of input sequences, Chinese sign language recognition (SLR) has a modeling challenge. The conventional encoders based on recurrent networks cannot discover and leverage the hierarchical structure of sign language well. In this paper, we propose a novel encoder-decoder method based on boundary adaptive learning for Chinese SLR. The hierarchical structure of sign language signal can be encoded by the boundary-adaptive encoder (BAE) in the proposed method. In order to improve efficiency in modeling long sign language sequences, the window attention model based on location is utilized in the decoding phase, which can generate more effective weight coefficients. Besides, we use sign language subword units to realize both isolated and continuous Chinese SLR in the same sequence learning framework in our method. Theoretical analysis and experimental results demonstrate the effectiveness and superiority of the proposed method.
Context-Aware Image Inpainting with Learned Semantic Priors

Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still challenging to restore reasonable contents as the contextual information within the missing regions tends to be ambiguous. To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. In particular, we perform knowledge distillation on pretext models and adapt the features to image inpainting. The learned semantic priors ought to be partially invariant between the high-level pretext task and low-level image inpainting, which not only help to understand the global context but also provide structural guidance for the restoration of local textures. Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator. The semantic learner and the image generator are trained in an end-to-end manner. We name the model SPL to highlight its ability to learn and leverage semantic priors. It achieves the state of the art on Places2, CelebA, and Paris StreetView datasets
Authorship attribution of source code: a language-agnostic approach and applicability in software engineering

Authorship attribution (i.e., determining who is the author of a piece of source code) is an established research topic. State-of-the-art results for the authorship attribution problem look promising for the software engineering field, where they could be applied to detect plagiarized code and prevent legal issues. With this article, we first introduce a new language-agnostic approach to authorship attribution of source code. Then, we discuss limitations of existing synthetic datasets for authorship attribution, and propose a data collection approach that delivers datasets that better reflect aspects important for potential practical use in software engineering. Finally, we demonstrate that high accuracy of authorship attribution models on existing datasets drastically drops when they are evaluated on more realistic data. We outline next steps for the design and evaluation of authorship attribution models that could bring the research efforts closer to practical use for software engineering.
Machine Learning on Cloud With Blockchain: A Secure, Verifiable and Fair Approach to Outsource the Linear Regression

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work, and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.
Evaluation of Scopio Labs X100 Full Field PBS: The first high‐resolution full field viewing of peripheral blood specimens combined with artificial intelligence‐based morphological analysis

Abstract Background Current digital cell imaging systems perform peripheral blood smear (PBS) analysis in limited regions of the PBS and require the support of manual microscopy without achieving full digital microscopy. We report a multicenter study that validated the Scopio Labs X100 Full Field PBS, a novel digital imaging system that utilizes a full field view approach for cell recognition and classification, in a decision support system mode. Methods We analyzed 335 normal and 310 abnormal PBS from patients with various clinical conditions and compared the performance of Scopio's Full Field PBS as the test method, with manual PBS analysis as the reference method. Deming regression analysis was utilized for comparisons of WBC and platelet estimates. Measurements of WBC and platelet estimation accuracy along with the agreement on RBC morphology evaluation were performed. Reproducibility and repeatability (R&amp;R) of the system were also evaluated. Results Scopio's Full Field PBS WBC accuracy was evaluated with an efficiency of 96.29%, sensitivity of 87.86%, and specificity of 97.62%. The agreement between the test and reference method for RBC morphology reached 99.77%, and the accuracy for platelet estimation resulted in an efficiency of 94.89%, sensitivity of 90.00%, and specificity of 96.28%, with successful R&amp;R tests. The system enabled a comprehensive review of full field PBS as shown in representative samples. Conclusions Scopio's Full Field PBS showed a high degree of correlation of all tested parameters with manual microscopy. The novel full field view of specimens facilitates the long‐expected disengagement between the digital application and the manual microscope.
Nerfies: Deformable Neural Radiance Fields

We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones. Our approach augments neural radiance fields (NeRF) by optimizing an additional continuous volumetric deformation field that warps each observed point into a canonical 5D NeRF. We observe that these NeRF-like deformation fields are prone to local minima, and propose a coarse-to-fine optimization method for coordinate-based models that allows for more robust optimization. By adapting principles from geometry processing and physical simulation to NeRF-like models, we propose an elastic regularization of the deformation field that further improves robustness. We show that our method can turn casually captured selfie photos/videos into deformable NeRF models that allow for photorealistic renderings of the subject from arbitrary viewpoints, which we dub nerfies. We evaluate our method by collecting time-synchronized data using a rig with two mobile phones, yielding train/validation images of the same pose at different viewpoints. We show that our method faithfully reconstructs non-rigidly deforming scenes and reproduces unseen views with high fidelity.