Reviewerly/exHarmony huggingface dataset:
Chinese NER Using ALBERT and Multi-word Information

Recently, many Chinese Named Entity Recognition (NER) problems which utilize the character-based approach have a good performance, the character-based approach becomes the dominant current of the approaches, but there are two issues in the existing models. Firstly, the traditional character vector representation is too single to express the polysemia of characters. Secondly, words contain more information than characters, but the existing models cannot use the word information sufficiently. To address the above issues, the AM-BiLSTM model is provided in this work. With the introduction of ALBERT pre-training language model and Multi-word Information(MWI), the enhanced character embedding is composed. We perform experiments on two datasets and the results demonstrate that the capability of our method is improved outstandingly in comparison with the existing NER models.
Review: A Comparative Study based on Video Stitching Methods

Image/video stitching technology has been used in several fields like image and video processing, medical imaging applications, computer vision etc.., There are several image stitching methods and algorithms of which some are most popular and commonly used. Image stitching is the basis for video stitching. It can be easier to stitch images with a static background. Video stitching with static background is challenging but with a combination of various algorithms and techniques, it can be achieved. However, video stitching with dynamic elements is slightly harder as it results in errors like ghosting, blurring especially in the overlapping region between the images. This paper presents a deep survey based on the comparison of image stitching methods like feature based, position based and depth-based along with the algorithm in video stitching. It also presents some algorithms and concerns about the errors that arise during the image registering and stitching process. This paper also summarizes the advantages and limitations of various image and video stitching techniques and algorithms.
Learning Weakly Convex Sets in Metric Spaces

We introduce the notion of weak convexity in metric spaces, a generalization of ordinary convexity commonly used in machine learning. It is shown that weakly convex sets can be characterized by a closure operator and have a unique decomposition into a set of pairwise disjoint connected blocks. We give two generic efficient algorithms, an extensional and an intensional one for learning weakly convex concepts and study their formal properties. Our experimental results concerning vertex classification clearly demonstrate the excellent predictive performance of the extensional algorithm. Two non-trivial applications of the intensional algorithm to polynomial PAC-learnability are presented. The first one deals with learning k-convex Boolean functions, which are already known to be efficiently PAC-learnable. It is shown how to derive this positive result in a fairly easy way by the generic intensional algorithm. The second one is concerned with the Euclidean space equipped with the Manhattan distance. For this metric space, weakly convex sets form a union of pairwise disjoint axis-aligned hyperrectangles. We show that a weakly convex set that is consistent with a set of examples and contains a minimum number of hyperrectangles can be found in polynomial time. In contrast, this problem is known to be NP-complete if the hyperrectangles may be overlapping.
ABOUT THE PROBLEM OF PSYCHOLOGICAL REQUIREMENTS FOR THE PERSONALITY OF THE LEADER IN LEGAL PSYCHOLOGY

Today, the heads of the internal affairs bodies of the Russian Federation represent a fairly large share in the staffing of the Ministry of Internal Affairs of Russia. The article provides an overview of approaches to the requirements for the personality of a leader in foreign and domestic psychology, as well as a theoretical analysis of these approaches within the framework of legal psychology. Management researchers have always set themselves the task of establishing the optimal structure of the qualities that an effective leader should have. A scientific direction that has formed in the psychology of management, within the framework of which attempts were made to determine this set of qualities, diagnose their presence and outline a system of actions for their formation and development.
Spectral Norm Regularization for Blind Image Deblurring

Blind image deblurring is a well-known ill-posed inverse problem in the computer vision field. To make the problem well-posed, this paper puts forward a plain but effective regularization method, namely spectral norm regularization (SN), which can be regarded as the symmetrical form of the spectral norm. This work is inspired by the observation that the SN value increases after the image is blurred. Based on this observation, a blind deblurring algorithm (BDA-SN) is designed. BDA-SN builds a deblurring estimator for the image degradation process by investigating the inherent properties of SN and an image gradient. Compared with previous image regularization methods, SN shows more vital abilities to differentiate clear and degraded images. Therefore, the SN of an image can effectively help image deblurring in various scenes, such as text, face, natural, and saturated images. Qualitative and quantitative experimental evaluations demonstrate that BDA-SN can achieve favorable performances on actual and simulated images, with the average PSNR reaching 31.41, especially on the benchmark dataset of Levin et al.
A lossless compression method for logging data while drilling

Mud pulse transmission technology is widely used in logging while drilling (LWD), while its low data transmission efficiency has restricted the development of LWD technology for a long time. In order to improve the equivalent data transmission efficiency of LWD system, a data compression method, namely, Frame Prediction Huffman Coding (FPHC) with a group of frames, is proposed in this paper. Several factors including the data accuracy, the need for real-time transmission, and the existence of transmission errors are comprehensively considered in the proposed method. Through the probability statistical analysis of LWD data, it is proved that the data have a relatively stable probability distribution after predictive coding. In addition, the Huffman code table generated from predictive coding is universally applicable to other LWD data. Through data compression experiments on logging data, it is verified that the proposed method has higher compression efficiency compared with Huffman coding, Lempel–Ziv–Welch algorithm, adaptive Huffman coding and adaptive arithmetic coding. This lossless compression method will have a good application prospect because of its ability to prevent error diffusion, simple implementation and easy to be embedded in down-hole equipment.
Interpretability of deep neural networks used for the diagnosis of Alzheimer's disease

Abstract Alzheimer's disease (AD) is a chronic brain disorder and is the most common cause of dementia. Patients suffering from AD experience memory loss, confusion, and other cognitive and behavioral complications. As the disease progresses, these symptoms become severe enough to interfere with the patient's daily life. Since AD is an irreversible disease and existing treatments can only slow down its progress, early diagnosis of AD is a key moment in fighting this disease. In this article, we propose a novel approach for diagnosing AD via deep neural networks from magnetic resonance imaging images. Additionally, we propose three new propagation rules for the layer‐wise relevance propagation (LRP) method, which is a method used for visualizing evidence in deep neural networks to obtain a better understanding of the network's behavior. We also propose various rule configurations for the LRP to achieve better interpretability of the network. Our proposed classification method achieves a 92% accuracy when classifying AD versus healthy controls, which is comparable to state‐of‐the‐art approaches and could potentially aid doctors in AD diagnosis and reduce the occurrence of human error. Our proposed visualization approaches also show improvements in evidence visualization, which helps the spread of computer‐aided diagnosis in the medical domain by eliminating the “black‐box” nature of the neural networks.
Automatic Treatment Regimen Design

As a data-driven healthcare service, automatic treatment regimen design has great potential to improve healthcare efficiency and quality. However, it is a nontrivial endeavor to develop such a healthcare service due to two major challenges: 1) the treatment records are complex data objects consisting of various semantic and temporal information, and 2) the treatment outcome usually depends on a large number of internal and external factors. Because of these difficulties, automatic treatment regimen design is still an open research problem nowadays. To fill this research gap, this paper first formulates a treatment sequence as temporal sets, then provides a novel Extended Jaccard Similarity (EJS) measure to quantify the similarities between treatment sequences. We show that the proposed EJS is a general and effective measure to capture the similarity between two complex temporal sets. Further, we develop an efficient clustering algorithm which can achieve reasonable clustering results with only a portion of the pairwise similarities between treatment sequences and then extract a semantic prototype of the treatment regimen from each cluster of treatment sequences. Finally, we adopt a matrix factorization framework to predict the treatment outcomes by integrating multiple internal and external factors. We conduct comprehensive experiments on Electronic Medical Records (EMRs) of more than 28,000 patients from 14 hospitals. The results demonstrate the effectiveness of our approach and its superiority over the state-of-the-art ones.
Active Learning in Gaussian Process State Space Model

We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.
Chapter 6: The Supergiant, High-Grade, Paleoproterozoic Metasedimentary Rock- and Shear Vein-Hosted Obuasi (Ashanti) Gold Deposit, Ghana, West Africa

Abstract Obuasi, with a total mineral resource plus past production of 70 Moz, is the largest gold deposit in West Africa, and one of the largest in the world. It is hosted by ~2135 Ma siliciclastic rocks of the Eburnean Kumasi Basin, which were obliquely shortened along an inverted boundary with the older Eoeburnean Ashanti belt to the east. Greenschist facies metamorphism was coeval with mineralization and related alteration at ~2095 Ma. The steeply dipping, ENE-plunging lodes extend over an 8-km strike length and to depths of &amp;gt;2.5 km. They include paragenetically complex gold-rich quartz veins surrounded by refractory auriferous arsenopyrite and closely associated carbonate-muscovite alteration halos in deformed carbonaceous phyllites and subordinate metaigneous host rocks. Gold and arsenic were initially precipitated during deformation-assisted interaction with reduced host rocks at ~350°C and 100 to 200 MPa. The mineralizing fluids were derived primarily from deeper, As-rich metasedimentary sources by basinal fluid expulsion and metamorphic devolatilization triggered by inversion and shortening, followed by transpression. Continued fluid injection during and after the metamorphic peak produced changes in gold fineness, sulfide assemblages, repeated dissolution (stylolites) and reprecipitation of mineralized veins, and a change from early deformed shear-related, sulfide-rich lodes to later quartz-rich lodes that plunge down or across the axes of younger transpressional folds. Channelized fluid flow due to reactivation of basin-edge transfer structures, and/or irregularly distributed gold source rocks, may explain the variation in gold endowment along the former basin boundary.
Completeness Theorems for $$\exists \Box $$-Fragment of First-Order Modal Logic

The paper expands upon the work by Wang [], who proposes a new framework based on quantifier-free predicate language extended by a new modality \(\exists x\Box \) and axiomatizes the logic over S5 frames. This paper gives the logics over K, D, T, 4, S4 frames with increasing and constant domains. And we provide a general strategy for proving completeness theorems for logics w.r.t. the increasing domain and logics w.r.t. the constant domain respectively.
Automatic Classification of Vulnerabilities using Deep Learning and Machine Learning Algorithms

As the field of computer science has advanced over the years, there has been a tremendous increase in the software being created, and this increase has been accompanied by an increase in the number of software vulnerabilities. A software vulnerability is a security flaw found in software that can potentially be exploited by attackers to perform cyber attacks. Since automatic approaches for identifying and analyzing vulnerabilities have become a trending topic in research, community, the classification of vulnerability is still an open issue. Developers need to know more about characteristics and types of vulnerabilities in systems to adopt suitable countermeasures in current and next versions. With this paper, we investigate whether vulnerability descriptions alone can be used to identify the type of vulnerability, by comparing five shallow learning models and fourteen deep learning models. The model with the highest F1-score was the Stacking-DNN (98.8%). On performing comprehensive analysis, the experiments demonstrate that both shallow and deep classifiers show comparable performance when trained and tested using the dataset without duplicates, while shallow classifiers showed better performance when trained and tested using the dataset with duplicates.
Empirical Findings of Assessment of Critical Infrastructure Degree of Dependency on ICT

This paper presents an assessment of the degree of dependency of Critical Infrastructure (CI) on Information and Communications Technology (ICT). The assessment used the ICT Dependency Model, and a software tool based on the model to measure the degree of ICT dependency grounded on predefined metrics and indicators. The outcomes are the ICT Dependency Index (IDI) - a composite value of the quantitative summation of the metrics, the ICT Dependency Quadrant (IDQ) - a mechanism that comparatively groups the IDI into four bands represented as quadrants i.e. Q1, Q2, Q3, and Q4, which depicts the intersection of dependency and cyber risk. The Q4 quad demonstrates a high degree of ICT dependency, and consequently a potential high cyber risk, Q1 depicts low ICT dependency with a corresponding potential low cyber risk. The results show that increasing level of ICT dependency by CIs has the potential to exacerbate their cyber risk as depicted by the 20 organisations in Q3 and Q4 quad-bands. Consequently, the quantitative approach helps to comparatively assess, group, and visualize the degree of dependency of CI sectors and organisations in a more intuitive fashion using the IDI and IDQ. The IDI and IDQ establish the threshold of ICT dependency and potential cyber risk in a single view. Our solution applies scientific and empirical tools for continuously measuring and ranking ICT dependency of sectors and their organisations in a universal and repeatable fashion. This process can eliminate bias and facilitate proportionate national investment in critical information infrastructure protection (CIIP).
Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory

Visual Commonsense Reasoning (VCR) predicts an answer with corresponding rationale, given a question-image input. VCR is a recently introduced visual scene understanding task with a wide range of applications, including visual question answering, automated vehicle systems, and clinical decision support. Previous approaches to solving the VCR task generally rely on pre-training or exploiting memory with long dependency relationship encoded models. However, these approaches suffer from a lack of generalizability and prior knowledge. In this paper we propose a dynamic working memory based cognitive VCR network, which stores accumulated commonsense between sentences to provide prior knowledge for inference. Extensive experiments show that the proposed model yields significant improvements over existing methods on the benchmark VCR dataset. Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning. A Python implementation of our mechanism is publicly available at https://github.com/tanjatang/DMVCR
FPGA-Based Design Architecture for Fast LWE Fully Homomorphic Encryption

A high-speed field-programmable gate array (FPGA) implementation architecture is purposed to implement fast learning with error (LWE) fully homomorphic encryption. Currently, there are many security issues with conventional cryptosystems. In addition, encrypting and decrypting a large volume of data consume enormous computing time which makes the conventional cryptosystems ineffective. In this work, a novel fully homomorphic encryption algorithm, LWE, has been analyzed using linear algebraic equations. The same has been simulated in Python. In addition, to map the LWE scheme in the FPGA, digital circuits are conceptualized to implement mathematical operations such as modulo adder, multiplier, and noise generator.
Quantification of the transferability of features between deep neural networks

The computationally expensive nature of Deep Neural Networks, along with their significant hunger for labeled data, can impair the overall performance of these models. Among other techniques, this challenge can be tackled by Transfer Learning, which consists in re-using the knowledge previously learned by a model: this method is widely used and has proven effective in enhancing the performance of models in low resources contexts. However, there are relatively few contributions regarding the actual transferability of features in a deep learning model. This paper presents QUANTA (QUANtitative TrAnsferability), a method for quantifying the transferability of features of a given model. A QUANTA is a 2-parameters layer added in a target model at the level at which one wants to study the transferability of the corresponding layer in a source model. Data from the target domain being fed to both the source and the target models, the parameters of the QUANTA layer are trained in such a way that a mutually exclusive quantification occurs between the source model (trained and frozen) and the (trainable) target model. The proposed approach is evaluated on a set of experiments on a visual recognition task using Convolutional Neural Networks. The results show that QUANTA is a promising tool for quantifying the transferability of features of a source model, as well as a new way of assessing the quality of a transfer.
MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients

Adaptive gradient methods such as RMSprop and Adam use exponential moving estimate of the squared gradient to compute adaptive step sizes, achieving better convergence than SGD in face of noisy objectives. However, Adam can have undesirable convergence behaviors due to unstable or extreme adaptive learning rates. Methods such as AMSGrad and AdaBound have been proposed to stabilize the adaptive learning rates of Adam in the later stage of training, but they do not outperform Adam in some practical tasks such as training Transformers []. In this paper, we propose an adaptive learning rate principle, in which the running mean of squared gradient in Adam is replaced by a weighted mean, with weights chosen to maximize the estimated variance of each coordinate. This results in a faster adaptation to the local gradient variance, which leads to more desirable empirical convergence behaviors than Adam. We prove the proposed algorithm converges under mild assumptions for nonconvex stochastic optimization problems, and demonstrate the improved efficacy of our adaptive averaging approach on machine translation, natural language understanding and large-batch pretraining of BERT. The code is available at https://github.com/zhuchen03/MaxVA.
Acceleration and deceleration of quantum dynamics based on inter-trajectory travel with fast-forward scaling theory

Quantum information processing requires fast manipulations of quantum systems in order to overcome dissipative effects. We propose a method to accelerate quantum dynamics and obtain a target state in a shorter time relative to unmodified dynamics, and apply the theory to a system consisting of two linearly coupled qubits. We extend the technique to accelerate quantum adiabatic evolution in order to rapidly generate a desired target state, thereby realizing a shortcut to adiabaticity. Further, we address experimental limitations to the rate of change of control parameters for quantum devices which often limit one's ability to generate a desired target state with high fidelity. We show that an initial state following decelerated dynamics can reach a target state while varying control parameters more slowly, enabling more experimentally feasible driving schemes.
Scalable Optimal Classifiers for Adversarial Settings Under Uncertainty

We consider the problem of finding optimal classifiers in an adversarial setting where the class-1 data is generated by an attacker whose objective is not known to the defender -- an aspect that is key to realistic applications but has so far been overlooked in the literature. To model this situation, we propose a Bayesian game framework where the defender chooses a classifier with no a priori restriction on the set of possible classifiers. The key difficulty in the proposed framework is that the set of possible classifiers is exponential in the set of possible data, which is itself exponential in the number of features used for classification. To counter this, we first show that Bayesian Nash equilibria can be characterized completely via functional threshold classifiers with a small number of parameters. We then show that this low-dimensional characterization enables to develop a training method to compute provably approximately optimal classifiers in a scalable manner; and to develop a learning algorithm for the online setting with low regret (both independent of the dimension of the set of possible data). We illustrate our results through simulations.