Reviewerly/exHarmony huggingface dataset:
Neural Predicting Higher-order Patterns in Temporal Networks

Dynamic systems that consist of a set of interacting elements can be abstracted as temporal networks. Recently, higher-order patterns that involve multiple interacting nodes have been found crucial to indicate domain-specific laws of different temporal networks. This posts us the challenge of designing more sophisticated hypergraph models for these higher-order patterns and the associated new learning algorithms. Here, we propose the first model, named HIT, for full-spectrum higher-order pattern prediction in temporal hypergraphs. Particularly, we focus on predicting three types of common but important interaction patterns involving three interacting elements in temporal networks, which could be extended to even higher-order patterns. HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet. HIT could achieve significant improvement (averaged 20% AUC gain to identify the interaction type, uniformly more accurate time estimation) compared to both heuristic and other neural-network-based baselines on 5 real-world large temporal hypergraphs. Moreover, HIT provides a certain degree of interpretability by identifying the most discriminatory structural features on the temporal hypergraphs for predicting different higher-order patterns.
Towards extreme learning machine framework for lane detection on unmanned mobile robot

Purpose This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge. Design/methodology/approach A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed. Findings Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability. Originality/value This research can provide a theoretical and engineering basis for lane detection on unmanned robots.
Protecting qubit coherence by spectrally engineered driving of the spin environment

Abstract Modern quantum technologies rely crucially on techniques to mitigate quantum decoherence; these techniques can be either passive, achieved for example via materials engineering, or active, typically achieved via pulsed monochromatic driving fields applied to the qubit. Using a solid-state defect spin coupled to a microwave-driven spin bath, we experimentally demonstrate a decoherence mitigation method based on spectral engineering of the environmental noise with a polychromatic drive waveform, and show that it outperforms monochromatic techniques. Results are in agreement with quantitative modeling, and open the path to active decoherence protection using custom-designed waveforms applied to the environment rather than the qubit.
How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?

The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifying new and unseen entity mentions. However, it is questionable whether existing models can effectively handle them. In this work, we systematically analyze the three types of recognition abilities of BioNER models: memorization, synonym generalization, and concept generalization. We find that although current best models achieve state-of-the-art performance on benchmarks based on overall performance, they have limitations in identifying synonyms and new biomedical concepts, indicating they are overestimated in terms of their generalization abilities. We also investigate failure cases of models and identify several difficulties in recognizing unseen mentions in biomedical literature as follows: (1) models tend to exploit dataset biases, which hinders the models' abilities to generalize, and (2) several biomedical names have novel morphological patterns with weak name regularity, and models fail to recognize them. We apply a statistics-based debiasing method to our problem as a simple remedy and show the improvement in generalization to unseen mentions. We hope that our analyses and findings would be able to facilitate further research into the generalization capabilities of NER models in a domain where their reliability is of utmost importance.
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models.Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead for feasible future paths.Drawing inspiration from the A* search algorithm, we propose NEUROLOGIC A esque, 1 a decoding algorithm that incorporates heuristic estimates of future cost.We develop lookahead heuristics that are efficient for large-scale language models, making our method a dropin replacement for common techniques such as beam search and top-k sampling.To enable constrained generation, we build on NEU-ROLOGIC decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A esque estimates of future constraint satisfaction.Our approach outperforms competitive baselines on five generation tasks, and achieves new state-of-the-art performance on table-totext generation, constrained machine translation, and keyword-constrained generation.The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings.NEU-ROLOGIC A esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models.
Machine Learning &amp; its Classification Techniques

RecentT developmentsT inT informationT systemsT asT wellT asT computerizationT ofT businessT processesT byT organizationsT haveT ledT toT aT faster,T easierT andT moreT accurateT dataT analysisT andT accuracy.T DataT miningT andT machineT learningT techniquesT haveT beenT usedT increasinglyT inT theT analysisT ofT dataT inT variousT fieldsT rangingT fromT medicineT toT organization,T educationT andT energyT applications.T MachineT learningT techniquesT makeT itT possibleT toT deductT meaningfulT furtherT informationT fromT thoseT dataT processedT byT dataT mining.T SuchT meaningfulT andT significantT informationT helpsT organizationsT toT establishT theirT futureT policies.T ThisT studyT appliesT classificationT machineT learningT techniquesT alsoT surveyT ofT activeT learningT regardingT selectionT methods,T queryT strategies,T applications.
Tree-Based Methods

In this chapter, we describe tree-based methods for regression and classification. These involve stratifying or segmenting the predictor space into a number of simple regions. In order to make a prediction for a given observation, we typically use the mean or the mode response value for the training observations in the region to which it belongs.
Deep Learning

Deep learning is one of the fastest growing areas of data science. Deep learning refers to a class of algorithms which are based on artificial neural networks optimized to work with unstructured data such as images, voice, videos and text. While the techniques of deep learning date back to the mid-80s, their true potential has been realized only in the last 5 years or so. This chapter covers some of the early history, develops a conceptual framework for introducing deep learning in general. It also describes a practical implementation of deep learning.
Solving Large‐Scale Linear Systems of Equations by a Quantum Hybrid Algorithm

Abstract Today's intermediate‐scale quantum computers, although imperfect, already perform computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, so far, quantum‐enabled large‐scale solutions have been realized only for limited set of problems. Here a hybrid algorithm based on phase estimation and classical optimization of the circuit width and depth is employed for solving a specific class of large linear systems of equations ubiquitous to many areas of science and engineering. A classification of linear systems based on the entanglement properties of the associated phase‐estimation unitary operation is introduced, enabling a highly efficient search for solutions that is facilitated by a straightforward matrix‐to‐circuit map. A 2 17 ‐dimensional problem is implemented on several IBM quantum computer superconducting quantum processors, a record‐breaking result for a linear system solved by a quantum computer. Demonstrated realisation sets a clear benchmark in the quest for the future quantum speedup in the linear systems of equations solution.
TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images

Liver tumor segmentation plays an essential role in diagnosis and treatment of hepatocellular carcinoma or metastasis. However, accurate and automatic tumor segmentation remains a challenging task, owing to vague boundaries and large variations in shapes, sizes, and locations of liver tumors. In this paper, we propose a novel hybrid end-to-end network, called TD-Net, which incorporates Transformer and direction information into convolution network to segment liver tumor from CT images automatically. The proposed TD-Net is composed of a shared encoder, two decoding branches, four skip connections, and a direction guidance block. The shared encoder is utilized to extract multi-level feature information, and the two decoding branches are respectively designed to produce initial segmentation map and direction information. To preserve spatial information, four skip connections are used to concatenate each encoder layer and its corresponding decoder layer, and in the fourth skip connection a Transformer module is constructed to extract global context. Furthermore, a direction guidance block is well-designed to rectify feature maps to further improve segmentation accuracy. Extensive experiments conducted on public LiTS and 3DIRCADb datasets validate that the proposed TD-Net can effectively segment liver tumor from CT images in an end-to-end manner and its segmentation accuracy surpasses those of many existing methods.
Entanglement in a two-qubit Heisenberg XXX model with x-components of Dzyaloshinskii–Moriya and Kaplan–Shekhtman–Entin-Wohlman–Aharony interactions

This article explores the concurrence, including entanglement, in a two-qubit Heisenberg XXX chain with Dzyaloshinskii-Moriya and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions. The concurrence expression was developed using the physical variables connected with the chosen system. Our results indicate that temperature, the spin coupling constant $J$, the x-components $D_x$, and $\Gamma_x$ may all play a role in determining the degree of intricacy between states. Additionally, these findings imply that the separability of states is possible for high-temperature domains or ferromagnetic chains. In contrast, the entanglement of states may be achieved by using high values for the x-components $D_x$ or $\Gamma_x$ parameters or by using an antiferromagnetic chain.
Data augmentation for fairness-aware machine learning

Researchers and practitioners in the fairness community have highlighted the ethical and legal challenges of using biased datasets in data-driven systems, with algorithmic bias being a major concern. Despite the rapidly growing body of literature on fairness in algorithmic decision-making, there remains a paucity of fairness scholarship on machine learning algorithms for the real-time detection of crime. This contribution presents an approach for fairness-aware machine learning to mitigate the algorithmic bias / discrimination issues posed by the reliance on biased data when building law enforcement technology. Our analysis is based on RWF-2000, which has served as the basis for violent activity recognition tasks in data-driven law enforcement projects. We reveal issues of overrepresentation of minority subjects in violence situations that limit the external validity of the dataset for real-time crime detection systems and propose data augmentation techniques to rebalance the dataset. The experiments on real world data show the potential to create more balanced datasets by synthetically generated samples, thus mitigating bias and discrimination concerns in law enforcement applications.
Improving Software Quality in Cryptography Standardization Projects

The NIST post-quantum cryptography (PQC) standardization project is probably the largest and most ambitious cryptography standardization effort to date, and as such it makes an excellent case study of cryptography standardization projects. It is expected that with the end of round 3 in early 2022, NIST will announce the first set of primitives to advance to standardization, so it seems like a good time to look back and see what lessons can be learned from this effort. In this paper, we take a look at one specific aspect of the NIST PQC project: software implementations. We observe that many implementations included as a mandatory part of the submission packages were of poor quality and ignored decades-old standard techniques from software engineering to guarantee a certain baseline quality level. As a consequence, it was not possible to readily use those implementations in experiments for post-quantum protocol migration and software optimization efforts without first spending a significant amount of time to clean up the submitted reference implementations. We do not mean to criticize cryptographers who submitted proposals, including software implementations, to NIST PQC: after all, it cannot reasonably be expected from every cryptographer to also have expertise in software engineering. Instead, we suggest how standardization bodies like NIST can improve the software-submission process in future efforts to avoid such issues with submitted software. More specifically, we present PQClean, an extensive (continuous-integration) testing framework for PQC software, which now also contains “clean” implementations of the NIST round 3 candidate schemes. We argue that the availability of such a framework-either in an online continuous-integration setup, or just as an offline testing system-long before the submission deadline would have resulted in much better implementations included in NIST PQC submissions and overall would have saved the community and probably also NIST a lot of time and effort.
Residual block Convolutional Auto Encoder in Content-Based Medical Image Retrieval

Approximately 12 percent of men will suffer from prostate cancer during their lifetime. While some types of this cancer grow slowly and need minimal treatment, others are aggressive and spread quickly. In order to be able to provide a fast and accurate diagnosis, computer vision techniques are widely used to help pathologists in the diagnosis and prognosis tasks. In this work, a new Content-Based Medical Image Retrieval (CBMIR) method is presented. The method, which is named ResCAE, presents a modified Convolutional Auto-Encoder (CAE) with a residual block and a skip layer to extract the relevant features of prostate cancer in Whole Slide Images (WSIs) in SICAPv2 data set. To the best of the author’s knowledge, this is the first study of using a Res-CAE to perform a search engine in prostate cancer images. After extracting features, Euclidean and Cosine have been applied to calculate the similarity of the query image and the gallery. The experimental results show that the proposed method achieves 0.85, 0.78 of accuracy by computing 7-top and 5-top accuracy, respectively.
ReACC: A Retrieval-Augmented Code Completion Framework

Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development.Recent work has proved that statistical language modeling with transformers can greatly improve the performance in the code completion task via learning from large-scale source code datasets.However, current approaches focus only on code context within the file or project, i.e. internal context.Our distinction is utilizing "external" context, inspired by human behaviors of copying from the related code snippets when writing code.Specifically, we propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval.We adopt a stagewise training approach that combines a source code retriever and an auto-regressive language model for programming language.We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.
Recognition of Dubious Tissue by Using Supervised Machine Learning Strategy

Bosom malignancy is the primary stage of disease detection. Classifiers are thus constantly wanted with higher accuracy. A highly accurate classifier gives fewer opportunities to misinterpret a malignant growth patient. This paper explores how the concept of strategic recession is portrayed in a modified, enhanced manner. For minimizing cost efficiency, both inclination plunge and propelled streamlining are used. The theory, which is a sigmoid capacity, involves a weighing dimension of β. The weighting variable depends on the number of highlights, dataset size, and the type of simplification method used. Through correctly estimating β, which is part of the quantity and type of enhancement systems used, the accuracy of the bosom disease position is fundamentally improving. By increasing precision, affectability, and specialty, the achieved results are promising.
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding

Contrastive learning is emerging as a powerful technique for extracting knowledge from unlabeled data.This technique requires a balanced mixture of two ingredients: positive (similar) and negative (dissimilar) samples.This is typically achieved by maintaining a queue of negative samples during training.Prior works in the area typically uses a fixed-length negative sample queue, but how the negative sample size affects the model performance remains unclear.The opaque impact of the number of negative samples on performance when employing contrastive learning aroused our in-depth exploration.This paper presents a momentum contrastive learning model with negative sample queue for sentence embedding, namely MoCoSE.We add the prediction layer to the online branch to make the model asymmetric and together with EMA update mechanism of the target branch to prevent the model from collapsing.We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples.Our experiments find that the best results are obtained when the maximum traceable distance is at a certain range, demonstrating that there is an optimal range of historical information for a negative sample queue.We evaluate the proposed unsupervised MoCoSE on the semantic text similarity (STS) task and obtain an average Spearman's correlation of 77.27%.
Multi-Granularity Structural Knowledge Distillation for Language Model Compression

Transferring the knowledge to a small model through distillation has raised great interest in recent years.Prevailing methods transfer the knowledge derived from mono-granularity language units (e.g., token-level or sample-level), which is not enough to represent the rich semantics of a text and may lose some vital knowledge.Besides, these methods form the knowledge as individual representations or their simple dependencies, neglecting abundant structural relations among intermediate representations.To overcome the problems, we present a novel knowledge distillation framework that gathers intermediate representations from multiple semantic granularities (e.g., tokens, spans and samples) and forms the knowledge as more sophisticated structural relations specified as the pair-wise interactions and the triplet-wise geometric angles based on multi-granularity representations.Moreover, we propose distilling the well-organized multi-granularity structural knowledge to the student hierarchically across layers.Experimental results on GLUE benchmark demonstrate that our method outperforms advanced distillation methods.
BLOCKCHAIN AND THE APPLICATION OF BLOCKCHAIN TECHNOLOGY

Thanks to cryptocurrencies, blockchain technology has recently become the focus of interest of both scientists and the public. The paper presents the principles on which blockchain is based and analyzes the possibilities of the application of blockchain technology in more detail. In a part of the analysis, some of the most common cryptocurrencies were considered, such as Bitcoin, Ethereum, USD Coin, Ripple, and ADA. Blockchains' applicability in supply chains, finance, real estate, health care, voting, and smart cities the authors discussed in the chapter about the application of blockchain technology. A separate chapter discusses the limitations of blockchain technology. Based on the analysis, at the end of the paper, the authors conclude that blockchain technology has great potential in automating and optimizing business processes and protecting the information and privacy of users. Given the demonstrated interest of companies in blockchain technology, investments, and demand for staff who understand it and can create applications, the authors concluded that the time of blockchain is yet to come.