Reviewerly/exHarmony huggingface dataset:
Entity-aware Transformers for Entity Search

Pre-trained language models such as BERT have been a key ingredient to achieve state-of-the-art results on a variety of tasks in natural language processing and, more recently, also in information retrieval.Recent research even claims that BERT is able to capture factual knowledge about entity relations and properties, the information that is commonly obtained from knowledge graphs. This paper investigates the following question: Do BERT-based entity retrieval models benefit from additional entity information stored in knowledge graphs? To address this research question, we map entity embeddings into the same input space as a pre-trained BERT model and inject these entity embeddings into the BERT model. This entity-enriched language model is then employed on the entity retrieval task. We show that the entity-enriched BERT model improves effectiveness on entity-oriented queries over a regular BERT model, establishing a new state-of-the-art result for the entity retrieval task, with substantial improvements for complex natural language queries and queries requesting a list of entities with a certain property. Additionally, we show that the entity information provided by our entity-enriched model particularly helps queries related to less popular entities. Last, we observe empirically that the entity-enriched BERT models enable fine-tuning on limited training data, which otherwise would not be feasible due to the known instabilities of BERT in few-sample fine-tuning, thereby contributing to data-efficient training of BERT for entity search.
Simultaneous Matrix Orderings for Graph Collections

Undirected graphs are frequently used to model phenomena that deal with interacting objects, such as social networks, brain activity and communication networks. The topology of an undirected graph G can be captured by an adjacency matrix; this matrix in turn can be visualized directly to give insight into the graph structure. Which visual patterns appear in such a matrix visualization crucially depends on the ordering of its rows and columns. Formally defining the quality of an ordering and then automatically computing a high-quality ordering are both challenging problems; however, effective heuristics exist and are used in practice. Often, graphs do not exist in isolation but as part of a collection of graphs on the same set of vertices, for example, brain scans over time or of different people. To visualize such graph collections, we need a single ordering that works well for all matrices simultaneously. The current state-of-the-art solves this problem by taking a (weighted) union over all graphs and applying existing heuristics. However, this union leads to a loss of information, specifically in those parts of the graphs which are different. We propose a collection-aware approach to avoid this loss of information and apply it to two popular heuristic methods: leaf order and barycenter.The de-facto standard computational quality metrics for matrix ordering capture only block-diagonal patterns (cliques). Instead, we propose to use Moran's I, a spatial auto-correlation metric, which captures the full range of established patterns. Moran's I refines previously proposed stress measures. Furthermore, the popular leaf order method heuristically optimizes a similar measure which further supports the use of Moran's I in this context. An ordering that maximizes Moran's I can be computed via solutions to the Traveling Salesperson Problem (TSP); orderings that approximate the optimal ordering can be computed more efficiently, using any of the approximation algorithms for metric TSP. We evaluated our methods for simultaneous orderings on real-world datasets using Moran's I as the quality metric. Our results show that our collection-aware approach matches or improves performance compared to the union approach, depending on the similarity of the graphs in the collection. Specifically, our Moran's I-based collection-aware leaf order implementation consistently outperforms other implementations. Our collection-aware implementations carry no significant additional computational costs.
Higher-Order Multicuts for Geometric Model Fitting and Motion Segmentation

The minimum cost lifted multicut problem is a generalization of the multicut problem (also known as correlation clustering) and is a means to optimizing a decomposition of a graph w.r.t. both positive and negative edge costs. It has been shown to be useful in a large variety of applications in computer vision thanks to the fact that multicut-based formulations do not require the number of components given a priori; instead, it is deduced from the solution. However, the standard multicut cost function is limited to pairwise relationships between nodes, while several important applications either require or can benefit from a higher-order cost function, i.e., hyper-edges. In this paper, we propose a pseudo-boolean formulation for a multiple model fitting problem. It is based on a formulation of any-order minimum cost lifted multicuts, which allows to partition an undirected graph with pairwise connectivity such as to minimize costs defined over any set of hyper-edges. As the proposed formulation is <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">np</small> -hard and the branch-and-bound algorithm (as well as obtaining lower bounds) is too slow in practice, we propose an efficient local search algorithm for inference into resulting problems. We demonstrate versatility and effectiveness of our approach in several applications: 1) We define a geometric multiple model fitting, more specifically, a line fitting problem on all triplets of points and group points, that belong to the same line, together. 2) We formulate homography and motion estimation as a geometric model fitting problem where the task is to find groups of points that can be explained by the same geometrical transformation. 3) In motion segmentation our model allows to go from modeling translational motion to euclidean or affine transformations, which improves the segmentation quality in terms of F-measure.
Video Joint Modelling Based on Hierarchical Transformer for Co-summarization

Video summarization aims to automatically generate a summary (storyboard or video skim) of a video, which can facilitate large-scale video retrieval and browsing. Most of the existing methods perform video summarization on individual videos, which neglects the correlations among similar videos. Such correlations, however, are also informative for video understanding and video summarization. To address this limitation, we propose Video Joint Modelling based on Hierarchical Transformer (VJMHT) for co-summarization, which takes into consideration the semantic dependencies across videos. Specifically, VJMHT consists of two layers of Transformer: the first layer extracts semantic representation from individual shots of similar videos, while the second layer performs shot-level video joint modelling to aggregate cross-video semantic information. By this means, complete cross-video high-level patterns are explicitly modelled and learned for the summarization of individual videos. Moreover, Transformer-based video representation reconstruction is introduced to maximize the high-level similarity between the summary and the original video. Extensive experiments are conducted to verify the effectiveness of the proposed modules and the superiority of VJMHT in terms of F-measure and rank-based evaluation.
ECOSYSTEM APPROACH IN EDUCATION: FROM METAPHOR TO METHODOLOGY AND PRACTICE

The authors present a theoretical and practical justification for the implementation of the ecosystem approach as a new direction in accordance with the ideas of the “lifelong learning” concept. The article presents an analysis of the “ecosystem” concept, highlights the basic characteristics and features of educational ecosystems. By the example of the activities at state autonomous non-typical educational institution of Sverdlovsk region “Palace of Youth”, the program for the development of the ecosystem in the supplementary education for children of Sverdlovsk region is substantiated.
Introduction to Igneous and Metamorphic Petrology

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
Scalability Issues of Blockchain Technology

In last decade crypto currencies become popular as there is no third party involvement while doing the transactions. Blockchain is the technology for using crypto-currencies. It attracts the attention of researchers and academicians , along with different features of Blockchain it is having the major issue of scalability which can be categorized into throughput , cost, capacity and networking . Improvement in Scalability affects the application of blockchain in business . Scalability affects due to some other factors like block interval time and block size which also may reduce the security . System may become vulnerable to different attacks if we blindly modify the scalability .In this paper we analyze the different ways to improve the scalability then we compare the features of blockchain with respect to different algorithms used to solve the scalability issue.
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection

Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer.Recent advances in DAOD strive to change the emphasis of the adaptation process from global to local in virtue of fine-grained feature alignment methods.However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.In this case, only seeking one-vs-one alignment does not necessarily ensure the precise knowledge transfer.Moreover, conventional alignment-based approaches may be vulnerable to catastrophic overfitting regarding those less transferable regions (e.g.backgrounds) due to the accumulation of inaccurate localization results in the target domain.To remedy these issues, we first formulate DAOD as an open-set domain adaptation problem, in which the foregrounds and backgrounds are seen as the "known classes" and "unknown class" respectively.Accordingly, we propose a new and general framework for DAOD, named Foreground-aware Graph-based Relational Reasoning (FGRR), which incorporates graph structures into the detection pipeline to explicitly model the intra-and inter-domain foreground object relations on both pixel and semantic spaces, thereby endowing the DAOD model with the capability of relational reasoning beyond the popular alignment-based paradigm.FGRR first identifies the foreground pixels and regions by searching reliable correspondence and cross-domain similarity regularization respectively.The inter-domain visual and semantic correlations are hierarchically modeled via bipartite graph structures, and the intra-domain relations are encoded via graph attention mechanisms.Through message-passing, each node aggregates semantic and contextual information from the same and opposite domain to substantially enhance its expressive power.Empirical results demonstrate that the proposed FGRR exceeds the state-of-the-art performance on four DAOD benchmarks.
Towards digital organized crime and digital sociology of organized crime

As technology has changed people's lives, criminal phenomena are also constantly evolving. Today's digital society is changing the activities of organized crime and organized crime groups. In the digital society, very different organized crime groups coexist with different organizational models: from online cybercrime to traditional organized crime groups to hybrid criminal groups in which humans and machines 'collaborate' in new and close ways in networks of human and non-human actors. These criminal groups commit very different organized crime activities, from the most technological to the most traditional, and move from online to offline. They use technology and interact with computers for a variety of purposes, and the distinction between the physical and virtual dimensions of organized crime is increasingly blurred. These radical developments do not seem to be accompanied by a new criminological theoretical interpretive framework, with a definition of organized crime that is able to account for the changes that digital society brings to organized crime and generate modern research hypotheses. This article proposes the concept of digital organized crime and the spectrum theory of digital organized crimes, to be embedded within a current, revised sociological theory of the organization of crime and deviance in digital society (a new theory of digital criminal organizing) and argues that the study of digital organized crime will increasingly require a digital sociology of organized crime. Criminologists are called upon to work in this direction.
Assessing the performance of quantum annealing with nonlinear driving

Current generation quantum annealers have already proven to be successful problem-solvers. Yet, quantum annealing is still very much in its infancy, with suboptimal applicability. For instance, to date it is still an open question which annealing protocol causes the fewest diabatic excitations for a given eigenspectrum, and even whether there is a universally optimal strategy. Therefore, in this paper, we report analytical and numerical studies of the diabatic excitations arising from nonlinear protocols applied to the transverse field Ising chain, the exactly solvable model that serves as a quantum annealing playground. Our analysis focuses on several driving schemes that inhibit or facilitate the dynamic phases discussed in a previous work. Rather remarkably, we find that the paradigmatic Kibble-Zurek behavior can be suppressed with ``pauses'' in the evolution, both for crossing and for stopping at the quantum critical point of the system.
Correcting projector lens distortion in real time with a scale-offset model for structured light illumination

In fringe projection profilometry, inevitable distortion of optical lenses decreases phase accuracy and decreases the quality of 3D point clouds. For camera lens distortion, existing compensation methods include real time look-up tables derived from the related parameters of camera calibration. However, for projector lens distortion, so far, post-undistortion methods iteratively correcting lens distortion are relatively time-consuming while, despite avoiding iteration, pre-distortion methods are not suitable for binary fringe patterns. In this paper, we aim to achieve real-time phase correction for the projector by means of a scale-offset model that characterizes projector distortion by four correction parameters within a small-enough area, and thus we can speed up the post-undistortion by looking up tables. Experiments show that the proposed method can suppress the distortion error by a factor of 20 ×, i.e., the error of root mean square is less than 45 µm/0.7‰, while also proposed improving the computation speed by a factor of 50× over traditional iterative post-undistortion.
Comparing the Effects of Boltzmann Machines as Associative Memory in Generative Adversarial Networks between Classical and Quantum Samplings

We investigate the quantum effect on machine learning (ML) models exemplified by the Generative Adversarial Network (GAN), which is a promising deep learning framework.In the general GAN framework the generator maps uniform noise to a fake image.In this study, we utilize the Associative Adversarial Network (AAN), which consists of a standard GAN and an associative memory.Further, we set a Boltzmann Machine (BM), which is an undirected graphical model that learns low-dimensional features extracted from a discriminator, as the memory.Owing to difficulty calculating the BM's log-likelihood gradient, it is necessary to approximate it by using the sample mean obtained from the BM, which has tentative parameters.To calculate the sample mean, a Markov Chain Monte Carlo (MCMC) is often used.In a previous study, this was performed using a quantum annealer device, and the performance of the "Quantum" AAN was compared to that of the standard GAN.However, its better performance than the standard GAN is not well understood.In this study, we introduce two methods to draw samples: classical sampling via MCMC and quantum sampling via quantum Monte Carlo (QMC) simulation, which is quantum simulation on the classical computer.Then, we compare these methods to investigate whether quantum sampling is advantageous.Specifically, calculating the discriminator loss, the generator loss, inception score and Fréchet inception distance, we discuss the possibility of AAN.We show that the AANs trained by both MCMC and QMC are more stable during training and produce more varied images than the standard GANs.However, the results indicate no difference in sampling by QMC simulation compared to that by MCMC.
Trusted Platform Module-Based Privacy in the Public Cloud: Challenges and Future Perspective

Public cloud providers offer ready-to-use, easily scalable servers on demand for a variety of applications. Storing and processing private and sensitive data in the cloud brings multiple security issues and indeed these concerns currently prevent many users from utilizing cloud resources. Improving both security and trust for users is increasingly important for cloud providers. In this context, we first investigate the classes of security threats encountered by cloud applications. We then analyze various software- and hardware-based solutions to handle these security challenges and provide the user with a chain of trust. Our analysis shows that pure software-based solutions do not sufficiently mitigate the challenges of the cloud environment. Hardware-based solutions utilizing the Trusted Platform Module (TPM) alleviate the issues; however, it is challenging to implement in the public cloud environment. Finally, we introduce our TPM-SGX-based approach that utilizes software TPM and Software Guard Extension (SGX) to provide similar security as the hardware-based approach.
INMO

Collaborative filtering is one of the most common scenarios and popular research topics in recommender systems. Among existing methods, latent factor models, i.e., learning a specific embedding for each user/item by reconstructing the observed interaction matrix, have shown excellent performances. However, such user-specific and item-specific embeddings are intrinsically transductive, making it difficult to deal with new users and new items unseen during training. Besides, the number of model parameters heavily depends on the number of all users and items, restricting its scalability to real-world applications. To solve the above challenges, in this paper, we propose a novel model-agnostic and scalable Inductive Embedding Module for collaborative filtering, namely INMO. INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table. Under the theoretical analysis, we further propose an effective indicator for the selection of template users/items. Our proposed INMO can be attached to existing latent factor models as a pre-module, inheriting the expressiveness of backbone models, while bringing the inductive ability and reducing model parameters. We validate the generality of INMO by attaching it to both Matrix Factorization (MF) and LightGCN, which are two representative latent factor models for collaborative filtering. Extensive experiments on three public benchmarks demonstrate the effectiveness and efficiency of INMO in both transductive and inductive recommendation scenarios.
Face Recognition System Based on Four State Hidden Markov Model

Computational complexity is a matter of great concern in real time face recognition systems. In this paper, four state hidden Markov model for face recognition has been presented whereby coefficients of feature vectors have been curtailed. Face images have been divided into a sequence of overlapping blocks. An observation sequence containing coefficients of eigen values and eigenvectors of these blocks have been used to train the model and each subject is associated with a separate hidden Markov model. The computational complexity of the proposed model has been minimized by employing discrete wavelet transform in the preprocessing stage. Furthermore, singular value decomposition has been employed on face images and a threshold singular value is determined empirically to reject or accept test images. Principal component analysis is used for feature extraction. Accepted test images are classified based on the majority vote criteria using different observation sequences of image features. Experimental findings on Yale and ORL databases in noisy such as Salt and Pepper and noise free environments reveal that the recognition accuracy of the proposed model is comparable to the existing techniques with reduced computational cost.
TrustBlkSys: A Trusted and Blockchained Cybersecure System for IIoT

Industrial Internet of Things (IIoT) has emerged as a new paradigm in the era of smart systems where interactions and transmission of messages among various entities in an industrial ecosystem are done autonomously. This includes manufacturing of products, shipment process, storage of records, and counting the data, to name a few. However, the involvement of multiple cyber threats that accompany the smart devices may lead the organizations on a heavy risk of profits. Even though the number of cybersecurity approaches have been proposed to ensure a secure and efficient communication mechanism in IIoT systems, the identification of cybersecurity issues in IIoT applications while exchanging the data is still at its early stage. The aim of this article is to propose an efficient cybersecurity communication mechanism using the trust evaluation system of each device to make a correct decision during communication. In addition, the evaluated trust of each device is further analyzed and monitored through a blockchain-based mechanism. The proposed approach is validated against an existing scheme in terms of various security metrics, such as sensitivity of devices while communicating, convergence time of transmitting information and percentage, and probability of malicious devices in terms of data delivery rate, false authentication, and trust value computation.