Reviewerly/exHarmony huggingface dataset:
A Tour of Various Knowledge Representation Techniques in Artificial Intelligence for Making Machines Intelligent

Two major things are required in order to make any computer or mechanical device or machine exhibit intelligence as a man’s brain and they are to develop exhibit intelligence as human, that are knowledge representation (KR) and inference mechanism. Development of an artificial intelligence (AI) system has been an important job since at certain times incomplete information is available which can be vague and dicey. Hence, the answer to these concerns is to create effective database and an impactive inference mechanism. KR is an attractive zone spread over varied areas of cognitive science as well as computer science. It is difficult to recognize the need of an amalgamation of various techniques and conclusion mechanism to acquire the accurate results to the said problem domain. The basic model of the KR system has been explained in this chapter. This chapter also elaborately discussed the KR technology and various KR approaches with respect to different application. As we know, AI applications are very vast and can be applied in various domains like vision, problem solving, decision-making, robotics, question answering system (QAS) and natural language processing (NLP). The deep survey of core area of AI and various KR techniques is the objective of this chapter.
Toward Trainability of Deep Quantum Neural Networks

Quantum Neural Networks (QNNs) with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase. This result leads to a general belief that a deep QNN will not be feasible. In this work, we provide the first viable solution to the vanishing gradient problem for deep QNNs with theoretical guarantees. Specifically, we prove that for circuits with controlled-layer architectures, the expectation of the gradient norm can be lower bounded by a value that is independent of the qubit number and the circuit depth. Our results follow from a careful analysis of the gradient behaviour on parameter space consisting of rotation angles, as employed in almost any QNNs, instead of relying on impractical 2-design assumptions. We explicitly construct examples where only our QNNs are trainable and converge, while others in comparison cannot.
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distribution of tumor infiltrating lymphocytes within whole slide images (WSIs). A deep learning classification model was trained using 50*50 square micron patches extracted from the WSIs. We simulated a FL environment in which a dataset, generated from WSIs of cancer from numerous anatomical sites available by The Cancer Genome Atlas repository, is partitioned in 8 different nodes. Our results show that the model trained with the federated training approach achieves similar performance, both quantitatively and qualitatively, to that of a model trained with all the training data pooled at a centralized location. Our study shows that FL has tremendous potential for enabling development of more robust and accurate models for histopathology image analysis without having to collect large and diverse training data at a single location.
OhioLINK’s Formative Milestones: Part I, 1986–2003

OhioLINK, one of the leading academic library consortia in the United States, marks its 30th anniversary in 2022. This paper, the first of two parts, reviews the organization’s history through selected formative milestones and provides observations about the evolution of the consortium from its current executive director, revealing the importance of expanding cost-effective and easy access to scholarly resources, economies of scale, pushing technology, and a focus on the user.
Personal Archive of Archaeologist V.I. Sarianidi: Study, Preservation and Prospects for Use

The article presents the main aspects of the study of materials from the personal archive of the world famous archaeologist Viktor Ivanovich Sarianidi. For a long time, the scientist’s personal fund was in a state of archival placers, which did not allow using it fully. To organize the entire array of documents, their analysis, description and systematization were carried out. Several key thematic sections have been formed, including materials related to the period 1960–2013. These are manuscripts and copies of scientific papers, field materials from excavations of archaeological sites, photographs, documents on the organizational activities of V.I. Sarianidi, his correspondence, and documents of other persons deposited in the archives of the researcher. For a number of documents of the fund (field diaries, photographs and films), work was carried out on electronic digital copying. Working with the archive made it possible to systematize the entire available array of materials, contributed to a more detailed study of all sources, as well as the introduction of previously unknown information into scientific circulation.
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection From a Few Samples

Recent advanced methods for fashion landmark detection are mainly driven by training convolutional neural networks on large-scale fashion datasets, which has a large number of annotated landmarks. However, such large-scale annotations are difficult and expensive to obtain in real-world applications, thus models that can generalize well from a small amount of labelled data are desired. We investigate this problem of few-shot fashion landmark detection, where only a few labelled samples are available for an unseen task. This work proposes a novel framework named MetaCloth via meta-learning, which is able to learn unseen tasks of dense fashion landmark detection with only a few annotated samples. Unlike previous meta-learning work that focus on solving " N -way K -shot" tasks, where each task predicts N number of classes by training with K annotated samples for each class ( N is fixed for all seen and unseen tasks), a task in MetaCloth detects N different landmarks for different clothing categories using K samples, where N varies across tasks, because different clothing categories usually have various number of landmarks. Therefore, numbers of parameters are various for different seen and unseen tasks in MetaCloth. MetaCloth is carefully designed to dynamically generate different numbers of parameters for different tasks, and learn a generalizable feature extraction network from a few annotated samples with a set of good initialization parameters. Extensive experiments show that MetaCloth outperforms its counterparts by a large margin.
Intelligent Document Processing -- Methods and Tools in the real world

The originality of this publication is to look at the subject of IDP (Intelligent Document Processing) from the perspective of an end-user and industrialist and not that of a Computer Science researcher. This domain is one part of the challenge of information digitalisation that constitutes the Industrial Revolution of the twenty first century (Industry 4.0) and this paper looks specifically at the difficult areas of classifying, extracting information and subsequent integration into business processes with respect to forms and invoices. Since the focus is on practical implementation a brief review is carried out of the market in commercial tools for OCR, document classification and data extraction in so far as this is publicly available together with pricing (if known). Brief definitions of the main terms encountered in Computer Science publications and commercial prospectuses are provided in order to de-mystify the language for the layman. A small number of practical tests are carried out on a few real documents in order to illustrate the capabilities of tools that are commonly available at a reasonable price. The unsolved (so far) issue of tables contained in invoices is raised. The case of a typical large industrial company is evoked where the requirement is to extract 100 per cent of the information with 100 per cent reliability in order to integrate into the back-end Enterprise Resource Planning system. Finally a brief description is given of the state-of-the-art research by the huge corporations who are pushing the boundaries of deep learning techniques further and further with massive computing and financial power - progress that will undoubtedly trickle down into the real world at some later date. The paper finishes by asking the question whether the objectives and timing of the commercial world and the progress of Computer Science are fully aligned.
Digitalization of Healthcare System in India—A Perspective and PESTLE Analysis

In the time of emergency, where there is an urgent need for healthcare services, the digitalization of health care ensures active delivery of the services to their consumers and citizens. Telecommunication, especially Internet-based technologies, plays a vital role in collecting health-based data to deliver and employ people. The healthcare sector is directly responsible for human well-being and health and needs continuous political, economic, social, technological, legal, and environmental up-gradation and development that only improves the system but also its effectiveness and efficacy. E-health services have a tremendous blow worldwide and have positively prepared us with more knowledge, healthcare accessibility and reduce the distance and divide in demand between patients and healthcare providers. This paper attempts to address the Indian perspective on healthcare system digitalization and the different components based on secondary sources.
Dual Cluster Contrastive learning for Object Re-Identification

Recently, cluster contrastive learning has been proven effective for object ReID by computing the contrastive loss between the individual features and the cluster memory. However, existing methods that use the individual features to momentum update the cluster memory will fluctuate over the training examples, especially for the outlier samples. Unlike the individual-based updating mechanism, the centroid-based updating mechanism that applies the mean feature of each cluster to update the cluster memory can reduce the impact of individual samples. Therefore, we formulate the individual-based updating and centroid-based updating mechanisms in a unified cluster contrastive framework, named Dual Cluster Contrastive framework (DCC), which maintains two types of memory banks: individual and centroid cluster memory banks. Significantly, the individual cluster memory considers just one individual at a time to take a single step for updating. The centroid cluster memory applies the mean feature of each cluster to update the corresponding cluster memory. During optimization, besides the vallina contrastive loss of each memory, a cross-view consistency constraint is applied to exchange the benefits of two memories for generating a discriminative description for the object ReID. Note that DCC can be easily applied for unsupervised or supervised object ReID by using ground-truth labels or the generated pseudo-labels. Extensive experiments on three benchmarks, \emph{e.g.,} Market-1501, MSMT17, and VeRi-776, under \textbf{supervised Object ReID} and \textbf{unsupervised Object ReID} demonstrate the superiority of the proposed DCC.
SpatioTemporal Focus for Skeleton-based Action Recognition

Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition due to their powerful ability to model data topology. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. First, the predefined graph structures are shared throughout the network, lacking the flexibility and capacity to model the multi-grain semantic information. Second, the relations among the global joints are not fully exploited by the graph local convolution, which may lose the implicit joint relevance. For instance, actions such as running and waving are performed by the co-movement of body parts and joints, e.g., legs and arms, however, they are located far away in physical connection. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information from the body joints and parts. As a result, more explainable representations for different skeleton action sequences can be obtained by MCF. In this study, we follow the common practice that the dense sample strategy of the input skeleton sequences is adopted and this brings much redundancy since number of instances has nothing to do with actions. To reduce the redundancy, a temporal discrimination focus module, termed TDF, is developed to capture the local sensitive points of the temporal dynamics. MCF and TDF are integrated into the standard GCN network to form a unified architecture, named STF-Net. It is noted that STF-Net provides the capability to capture robust movement patterns from these skeleton topology structures, based on multi-grain context aggregation and temporal dependency. Extensive experimental results show that our STF-Net significantly achieves state-of-the-art results on three challenging benchmarks NTU RGB+D 60, NTU RGB+D 120, and Kinetics-skeleton.
Quantum variational solving of the Wheeler-DeWitt equation

One of the central difficulties in the quantization of the gravitational interactions is that they are described by a set of constraints. The standard strategy for dealing with the problem is the Dirac quantization procedure, which leads to the Wheeler-DeWitt equation. However, solutions to the equation are known only for specific symmetry-reduced systems, including models of quantum cosmology. Novel methods, which enable solving the equation for complex gravitational configurations are, therefore, worth seeking. Here, we propose and investigate a new method of solving the Wheeler-DeWitt equation, which employs a variational quantum computing approach, and is possible to implement on quantum computers. For this purpose, the gravitational system is regularized, by performing spherical compactification of the phase space. This makes the system's Hilbert space finite-dimensional and allows to use $SU(2)$ variables, which are easy to handle in quantum computing. The validity of the method is examined in the case of the flat de Sitter universe. For the purpose of testing the method, both an emulator of a quantum computer and the IBM superconducting quantum computer have been used. The advantages and limitations of the approach are discussed.
Probing Linguistic Information For Logical Inference In Pre-trained Language Models

Progress in pre-trained language models has led to a surge of impressive results on downstream tasks for natural language understanding. Recent work on probing pre-trained language models uncovered a wide range of linguistic properties encoded in their contextualized representations. However, it is unclear whether they encode semantic knowledge that is crucial to symbolic inference methods. We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations. Our probing datasets cover a list of linguistic phenomena required by major symbolic inference systems. We find that (i) pre-trained language models do encode several types of linguistic information for inference, but there are also some types of information that are weakly encoded, (ii) language models can effectively learn missing linguistic information through fine-tuning. Overall, our findings provide insights into which aspects of linguistic information for logical inference do language models and their pre-training procedures capture. Moreover, we have demonstrated language models' potential as semantic and background knowledge bases for supporting symbolic inference methods.
Indian Language Wordnets and their Linkages with Princeton WordNet

Wordnets are rich lexico-semantic resources. Linked wordnets are extensions of wordnets, which link similar concepts in wordnets of different languages. Such resources are extremely useful in many Natural Language Processing (NLP) applications, primarily those based on knowledge-based approaches. In such approaches, these resources are considered as gold standard/oracle. Thus, it is crucial that these resources hold correct information. Thereby, they are created by human experts. However, human experts in multiple languages are hard to come by. Thus, the community would benefit from sharing of such manually created resources. In this paper, we release mappings of 18 Indian language wordnets linked with Princeton WordNet. We believe that availability of such resources will have a direct impact on the progress in NLP for these languages.
BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Monocular depth estimation is a fundamental task in computer vision and has drawn increasing attention. Recently, some methods reformulate it as a classification-regression task to boost the model performance, where continuous depth is estimated via a linear combination of predicted probability distributions and discrete bins. In this paper, we present a novel framework called BinsFormer, tailored for the classification-regression-based depth estimation. It mainly focuses on two crucial components in the specific task: 1) proper generation of adaptive bins and 2) sufficient interaction between probability distribution and bins predictions. To specify, we employ the Transformer decoder to generate bins, novelly viewing it as a direct set-to-set prediction problem. We further integrate a multi-scale decoder structure to achieve a comprehensive understanding of spatial geometry information and estimate depth maps in a coarse-to-fine manner. Moreover, an extra scene understanding query is proposed to improve the estimation accuracy, which turns out that models can implicitly learn useful information from an auxiliary environment classification task. Extensive experiments on the KITTI, NYU, and SUN RGB-D datasets demonstrate that BinsFormer surpasses state-of-the-art monocular depth estimation methods with prominent margins. Code and pretrained models will be made publicly available at \url{https://github.com/zhyever/Monocular-Depth-Estimation-Toolbox}.
Drowning Person Target Intelligent Recognition Method Based on Fusion of Visible Light and Infrared Thermal Imaging

Inland waterway, especially mountainous waterway channel, has the characteristics of rapid current, different width of river and large change of water level, which brings great risks to the navigation of ships. Once the persons fall into the water, it is difficult to search and rescue. This study aimed at developing a rapid drowning person recognition method, which establishes a deep learning architecture for infrared and visible image fusion. Compared with the traditional convolution network, the coding network is combined with convolution layer, fusion layer and dense block, in which the outputs of each layer are connected with each other, which can be used to obtain more useful features from the source image in the coding process. The target detection experiment of drowning personnel is carried out in the Lancang River. The results show that the method can accurately identify the target under the conditions of insufficient illumination and fast-moving speed, and the recognition rate is 90%.
Canvas: Isolated and Adaptive Swapping for Multi-Applications on Remote Memory

Remote memory techniques for datacenter applications have recently gained a great deal of popularity. Existing remote memory techniques focus on the efficiency of a single application setting only. However, when multiple applications co-run on a remote-memory system, significant interference could occur, resulting in unexpected slowdowns even if the same amounts of physical resources are granted to each application. This slowdown stems from massive sharing in applications' swap data paths. Canvas is a redesigned swap system that fully isolates swap paths for remote-memory applications. Canvas allows each application to possess its dedicated swap partition, swap cache, prefetcher, and RDMA bandwidth. Swap isolation lays a foundation for adaptive optimization techniques based on each application's own access patterns and needs. We develop three such techniques: (1) adaptive swap entry allocation, (2) semantics-aware prefetching, and (3) two-dimensional RDMA scheduling. A thorough evaluation with a set of widely-deployed applications demonstrates that Canvas minimizes performance variation and dramatically reduces performance degradation.
Traffic Sign Detection Algorithm Based On Improved YOLOv4

Abstract This paper proposes a traffic sign detection algorithm using improved lightweight YOLOv4 in order to solve the problems of large parameters quantity, poor real-time performance, and low accuracy for the unmanned driving system. Firstly, we use the MobileNetV2 network to replace the original feature extraction network in YOLOv4 to improve the detection speed by reducing the number of model parameters. Secondly, we add an attention mechanism to the backbone network to effectively extract feature information. Trim the network layer of the prediction part and add residual structure, so that feature reuse enhancement can avoid the gradient disappearing when the network layer is deeper. The size of the improved network model is reduced by 79.5%. The experimental results show that the average accuracy of the network model is 2.5% lower than that of the YOLOV4 network, and the detection speed is 58.8% higher than that of the YOLOv4 network.
Multi-tenant Blockchain-Based State Land Allocation and Usage System for Government Agencies

In many countries, vacant or underutilized state land would be allocated by the government to their citizens for specific usages. Digital records on these allocations might be managed by multiple agencies in different ministries, which may not be accessible across agencies. This could potentially cause data inconsistency and have a negative impact on the overall data quality. Blockchain, the technology that allows multiple entities to reliably manage a single dataset in a distributed manner together, coupled with Geographic Information System, can improve data consistency of the allocated state lands and their usages. A prototype of blockchain-based land allocation system is proposed to facilitate collaboration in state land allocation data management among multiple agencies.
Automatic Information Extraction in the Third-Generation Semiconductor Materials Domain Based on DKNet and MANet

The third-generation semiconductor materials (TGSMs) is a frontier scientific domain, where researchers need to consult extensive literature for the entity information on materials, devices, preparation methods, and experimental performances, and sort out the complex relations between them.However, the explosion of relevant papers has far exceeded researchers' reading ability.In this article, the TGSM-field automatic information extraction is conducted based on entity recognition (ER) and relation extraction (RE) techniques.First, the corpora used for ER and RE in this field are created.Second, aiming at the complexity of the entities, a neural network using domain knowledge (DKNet) is proposed to improve ER performance.It uses the keyword sequence of each entity type as prior knowledge, adds a dedicated embedding to encode entity categories, then combines prior knowledge and encoded vectors with the context through a gated information fusion module to assist recognition.As for the indicative word dependence problem of entity relations, a multi-aspect attention-based network model (MANet) is proposed to enhance the attention to relation-indicative words, thereby improving the RE performance.Finally, F1 scores of 74.5 and 85.9 were achieved on the created ER and RE test sets, outperforming other advanced models by 3.4 ∼ 10.1, which is the best performance of the TGSM-field automatic information extraction.