Reviewerly/exHarmony huggingface dataset:
Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models

Domain-specific pre-trained language models (PLMs) have achieved great success over various downstream tasks in different domains.However, existing domain-specific PLMs mostly rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain-specific knowledge, which can be essential in many domains.Moreover, in knowledge-sensitive areas such as the biomedical domain, knowledge is stored in multiple sources and formats, and existing biomedical PLMs either neglect them or utilize them in a limited manner.In this work, we introduce an architecture to integrate domain knowledge from diverse sources into PLMs in a parameter-efficient way.More specifically, we propose to encode domain knowledge via adapters, which are small bottleneck feed-forward networks inserted between intermediate transformer layers in PLMs.These knowledge adapters are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller to enrich PLMs.Taking the biomedical domain as a case study, we explore three knowledge-specific adapters for PLMs based on the UMLS Metathesaurus graph, the Wikipedia articles for diseases, and the semantic grouping information for biomedical concepts.Extensive experiments on different biomedical NLP tasks and datasets demonstrate the benefits of the proposed architecture and the knowledge-specific adapters across multiple PLMs.
Cone Bearing Estimation Utilizing a Hybrid HMM and IFM Smoother Filter Formulation

Cone penetration testing (CPT) is a widely used geotechnical engineering in-situ test for mapping soil profiles and assessing soil properties.In CPT, a cone on the end of a series of rods is pushed into the ground at a constant rate and resistance to the cone tip is measured (q m ).The q m values are utilized to characterize the soil profile.Unfortunately, the measured cone tip resistance is blurred and/or averaged which can result in the distortion of the soil profile characterization and the inability to identify thin layers.This paper outlines a novel and highly effective algorithm for obtaining cone bearing estimates q t from averaged or smoothed q m measurements.This q t optimal filter estimation technique is referred to as the q t HMM-IFM algorithm and it implements a hybrid hidden Markov model and iterative forward modelling technique.The mathematical details of the q t HMM-IFM algorithm are outlined in this paper along with the results from challenging test bed.The test bed simulations have demonstrated that the q t HMM-IFM algorithm can derive accurate q t values from challenging averaged q m profiles.This allows for greater soil resolution and the identification and quantification of thin layers in a soil profile.
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation

Multi-modal machine translation (MMT) aims at improving translation performance by incorporating visual information.Most of the studies leverage the visual information through integrating the global image features as auxiliary input or decoding by attending to relevant local regions of the image.However, this kind of usage of visual information makes it difficult to figure out how the visual modality helps and why it works.Inspired by the findings of (Caglayan et al., 2019) that entities are most informative in the image, we propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation.Specifically, the approach is framed as a reconstruction task that reconstructs the original textural input from multi-modal input in which entities are replaced with visual features.Then, a multi-task framework is employed to combine the translation task and the reconstruction task to make full use of crossmodal entity representation learning.The extensive experiments demonstrate that our approach can achieve comparable or even better performance than state-of-the-art models.Furthermore, our in-depth analysis shows how visual information improves translation.
Supervised Classification Problems–Taxonomy of Dimensions and Notation for Problems Identification

The paper proposes a taxonomy for categorizing the main features of the supervised learning classification problems and a notation for the identification of the supervised learning classification problem categories. The proposed taxonomy has been based on the review and analysis of the recent literature. It allowed the construction of the landscape of decision problem factors influencing the supervised learning processes. To enable a concise and coherent identification of supervised classification problems we have suggested a notation enabling description and identification of various supervised learning classification problem types and their critical features. The notation consists of 5 fields representing, in a sequence, a structure and properties of decision classes, structural model and properties of attributes, features of the data source, and the performance measure used for constructing and evaluating a classifier. The proposed notation is open and could be extended in the case of need new developments within the machine learning theory.
The landscape of Blockchain research: impacts and opportunities

Blockchain technology has emerged as an important research domain in recent years. It not only supports the secure and efficient storage and processing of information but may also transform the business principles and processes embedded in traditional centralized organizations and societies. This editorial first provides a framework that identifies the emerging areas of blockchain research. The key characteristics of this framework in Blockchain 1.0, 2.0, and 3.0 are defined and introduced. The impacts and opportunities associated with blockchain research are identified and discussed. At last, the six articles in this special issue are characterized using the proposed research framework of blockchain research.
Banks’ Digital Challenges

Digital currencies make transfers in digital markets, providing transaction participants with many advantages: easy access to markets, maintaining the identity of participants in transfer transactions, even their application is constantly expanding when buying new and innovative goods. Banks are an integral and significant part of this turnover, which gives them additional advantages and direct effects and exposes them to additional difficulties and dangers. The increased interest in them was noted mainly due to the continuous growth of their market rate and the additional growth of cryptocurrency extraction. Most transactions with them are based on the regulations of the applicable law. Still, the possibility of being the object of a crime has provoked a backlash from financial supervisors to protect the rights of other market participants and especially banks as the most accessible of all. Although it is a legal system in place to prevent banking institutions from being involved in money laundering operations, digital currencies are now a new opportunity with the specific advantages that ensure their smooth transfer to the network. The leading business companies such as TESLA have offered the opportunity to buy electric cars with digital currencies, with the growing demand for cryptocurrency services. Partly aided by the rising value of essential natural resources, important components for building information infrastructure, and the Covid-19 pandemic, significant financial institutions have permanently established themselves in digital markets such as JPMorgan, BNY Mellon, and Morgan Stanley, BlackRock and many others. Despite the targeted actions of state regulatory institutions, whose duty is to ensure the public good “cybersecurity”, the mass entry into these markets leaves consumers relatively unprotected. Money laundering or terrorist financing often provokes crises among regulatory institutions because they are usually accompanied by arms deals, drug trafficking, tax evasion, and others, as well as tax fraud, terrorism, and drug trafficking. A current application of digital currencies is their use to pay for services related to cyber attacks on financial institutions, objects of national security, etc. when the entire population suffers the damage. The new roles of financial institutions in the digital markets strengthen the notion of compliance as possible risk threats, realizing through compliance functions to automate and implement the integrated approach to all types of risk that accompanies the movement of digital financial assets. For some banking intermediaries, this has changed their cybersecurity strategy.
Stochastic Expectation Propagation Learning of Infinite Multivariate Beta Mixture Models for Human Tissue Analysis

Nowadays, there is considerable and growing interest in applying accurate analysis tools to obtain meaningful information and extract knowledge from a huge amount of data. In this sense, unsupervised algorithms and clustering techniques have gained an increasing interest. These methods are helpful specifically when data annotation is time-consuming and costly. In this paper, we propose a new clustering method based on a Dirichlet process mixture of multivariate Beta distributions. To learn this novel Bayesian nonparametric model, we applied stochastic expectation propagation inference framework. This framework is able to define the model complexity and estimate the model’s parameters simultaneously. To demonstrate the efficiency of our model, we perform an experimental analysis using three real applications, breast, lung and colon histopathological tissue analysis. Our goal is to show that our algorithm could be considered as a machine learning framework in computer-assisted diagnosis and play the role of a complementary opinion to help the pathologists in making decisions with more accuracy.
An RRT* Based Method for Dynamic Mission Balancing for Urban Air Mobility Under Uncertain Operational Conditions

Urban air mobility provides an enabling technology towards on-demand and flexible operations for passenger and cargo transportation in metropolitan areas. Electric vertical-takeoff and landing (eVTOL) concept is a potential candidate for urban air mobility platform because of its lower carbon emissions, lower noise generations and potentially lower operational costs. However, such a transportation model is subject to numerous complicated environmental and urban design factors including buildings, dynamic obstacles and micro-weather patterns. In addition, communication, navigation and surveillance quality-of-service and availability would be affected on the overall system performance and resilience. Some social factors such as privacy, noise and visual pollution should also be considered to provide a seamless integration of the urban air mobility applications into the daily life. This paper describes an integrated RRT* based approach for designing and executing flight trajectories for urban airspace subject to operating constraints, mission constraints, and environmental conditions. The generated path is energy-efficient and enables aerial vehicle to perform mid-flight landing for battery changing or emergency situations. Moreover, this paper proposes another approach that allows on-the-fly path re-planning under dynamic constraints such as geofences or micro-weather patterns. As such, the approach also provides a method toward contingency operations such as emergency landing on the fly.
<i>B</i>+-Tree Based Multi-Keyword Ranked Similarity Search Scheme Over Encrypted Cloud Data

With the sustained evolution and expeditious popularization of cloud computing, an ever-increasing number of individuals and enterprises are encouraged to outsource data to cloud servers for reducing management overhead and ease of access. Privacy requirements demand encryption of sensitive information before outsourcing, which, on the other hand, diminishes the usability of data and makes considerable efficient keyword search techniques used on plaintext inapplicable. In this paper, we propose a secure multi-keyword ranked search scheme based on document similarity to work out the problem. In order to achieve the goals of multi-keyword search and ranking search results, we adopt the vector space model and TF-IDF model to generate index and query vectors. By introducing the secure kNN computation, index and query vectors can be encrypted to prevent cloud servers from obtaining sensitive frequency information. For the need of efficiency advancement, we adopt the B+-tree as the basic structure to build the index and construct a similar document collection for each document. Due to the use of our unique index structure, compared to linear search, the search efficiency is more exceptional. Extensive experiments on the real-world document collection are conducted to demonstrate the feasibility and efficiency of the proposed solution.
Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting

Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference Δt between consecutive steps is so large that the extracted time series looks apparently random. Here, we introduce a new long-short-term-memory (LSTM)-based recurrent architecture by tensorizing the cell-state-to-state propagation therein, maintaining the long-term memory feature of LSTM, while simultaneously enhancing the learning of short-term nonlinear complexity. We stress that the global minima of training can be most efficiently reached by our tensor structure where all nonlinear terms, up to some polynomial order, are treated explicitly and weighted equally. The efficiency and generality of our architecture are systematically investigated and tested through theoretical analysis and experimental examinations. In our design, we have explicitly used two different many-body entanglement structures-matrix product states (MPS) and the multiscale entanglement renormalization ansatz (MERA)-as physics-inspired tensor decomposition techniques, from which we find that MERA generally performs better than MPS, hence conjecturing that the learnability of chaos is determined not only by the number of free parameters but also the tensor complexity-recognized as how entanglement entropy scales with varying matricization of the tensor.
Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.
AdjMix: simplifying and attending graph convolutional networks

Abstract Simple graph convolution (SGC) achieves competitive classification accuracy to graph convolutional networks (GCNs) in various tasks while being computationally more efficient and fitting fewer parameters. However, the width of SGC is narrow due to the over-smoothing of SGC with higher power, which limits the learning ability of graph representations. Here, we propose AdjMix, a simple and attentional graph convolutional model, that is scalable to wider structure and captures more nodes features information, by simultaneously mixing the adjacency matrices of different powers. We point out that the key factor of over-smoothing is the mismatched weights of adjacency matrices, and design AdjMix to address the over-smoothing of SGC and GCNs by adjusting the weights to matching values. Experiments on citation networks including Pubmed, Citeseer, and Cora show that our AdjMix improves over SGC by 2.4%, 2.2%, and 3.2%, respectively, while achieving same performance in terms of parameters and complexity, and obtains better performance in terms of classification accuracy, parameters, and complexity, compared to other baselines.
A Language Model-based Generative Classifier for Sentence-level Discourse Parsing

Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence.Despite recent achievements in both tasks, there is still room for improvement due to the scarcity of labeled data.To solve the problem, we propose a language model-based generative classifier (LMGC) for using more information from labels by treating the labels as an input while enhancing label representations by embedding descriptions for each label.Moreover, since this enables LMGC to make ready the representations for labels, unseen in the pre-training step, we can effectively use a pretrained language model in LMGC.Experimental results on the RST-DT dataset show that our LMGC achieved the state-of-the-art F 1 score of 96.72 in discourse segmentation.It further achieved the state-of-the-art relation F 1 scores of 84.69 with gold EDU boundaries and 81.18 with automatically segmented boundaries, respectively, in sentence-level discourse parsing.
Exploiting Relationship for Complex-scene Image Generation

The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generation (with various interactions among multiple objects) still suffers from messy layouts and object distortions, due to diverse configurations in layouts and appearances. Prior methods are mostly object-driven and ignore their inter-relations that play a significant role in complex-scene images. This work explores relationship-aware complex-scene image generation, where multiple objects are inter-related as a scene graph. With the help of relationships, we propose three major updates in the generation framework. First, reasonable spatial layouts are inferred by jointly considering the semantics and relationships among objects. Compared to standard location regression, we show relative scales and distances serve a more reliable target. Second, since the relations between objects have significantly influenced an object's appearance, we design a relation-guided generator to generate objects reflecting their relationships. Third, a novel scene graph discriminator is proposed to guarantee the consistency between the generated image and the input scene graph. Our method tends to synthesize plausible layouts and objects, respecting the interplay of multiple objects in an image. Experimental results on Visual Genome and HICO-DET datasets show that our proposed method significantly outperforms prior arts in terms of IS and FID metrics. Based on our user study and visual inspection, our method is more effective in generating logical layout and appearance for complex-scenes.
Localized Fourier analysis for graph signal processing

We propose a new point of view in the study of Fourier analysis on graphs, taking advantage of localization in the Fourier domain. For a signal f on vertices of a weighted graph G with Laplacian matrix L, standard Fourier analysis of f relies on the study of functions g(L)f for some filters g on IL, the smallest interval containing the Laplacian spectrum sp(L)⊂IL. We show that for carefully chosen partitions IL=⊔1≤k≤KIk (Ik⊂IL), there are many advantages in understanding the collection (g(LIk)f)1≤k≤K instead of g(L)f directly, where LI is the projected matrix PI(L)L. First, the partition provides a convenient modelling for the study of theoretical properties of Fourier analysis and allows for new results in graph signal analysis (e.g. noise level estimation, Fourier support approximation). We extend the study of spectral graph wavelets to wavelets localized in the Fourier domain, called LocLets, and we show that well-known frames can be written in terms of LocLets. From a practical perspective, we highlight the interest of the proposed localized Fourier analysis through many experiments that show significant improvements in two different tasks on large graphs, noise level estimation and signal denoising. Moreover, efficient strategies permit to compute sequence (g(LIk)f)1≤k≤K with the same time complexity as for the computation of g(L)f.
Online English Teaching Based on Artificial Intelligence Internet Technology Embedded System

With education and teaching reform, Internet teaching is increasing. The introduction analysis of short text messages generated by e-learning can optimise class, improving innovation capacity and cooperation. To study the way of instructional design under artificial intelligence, the current status of the development of instructional has to be mentioned. In order to study the impact of online teaching on the improvement of computer thinking and skills in higher vocational education, this article is based on online English teaching embedded in the Internet and on virtual simulation technology, to enable primary school students to cultivate computer education thinking from an early age. The personalities can be fully represented and developed.
Application of Simultaneous Location and Map Construction Algorithms Based on Lidar in the Intelligent Robot Food Runner

Abstract With the rapid development of artificial intelligence, there are more and more “shadows” of artificial intelligence in restaurants. In the market, the intelligent robot food runner is relatively expensive and not widely used. In this paper, an intelligent robot food runner with lower price but better performance is proposed, which is suitable for restaurants. Among them, this paper mainly analyzes how to use laser radar SLAM to build restaurant map, locate and navigate, and how to build obstacle avoidance and path planning. Through ROS platform, the whole process of intelligent robot food runner is simulated and verified, which can meet the needs of restaurants.
An informationally complete Wigner function for the Tavis–Cummings model

Abstract Here we consider an informationally complete Wigner function approach to look at multiple atoms (qubits) coupled to a field mode. We consider the Tavis–Cummings interaction between a single field mode with two qubits and then with five.