Reviewerly/exHarmony huggingface dataset:
Merging Statistical Feature via Adaptive Gate for Improved Text Classification

Currently, text classification studies mainly focus on training classifiers by using textual input only, or enhancing semantic features by introducing external knowledge (e.g., hand-craft lexicons and domain knowledge). In contrast, some intrinsic statistical features of the corpus, like word frequency and distribution over labels, are not well exploited. Compared with external knowledge, the statistical features are deterministic and naturally compatible with corresponding tasks. In this paper, we propose an Adaptive Gate Network (AGN) to consolidate semantic representation with statistical features selectively. In particular, AGN encodes statistical features through a variational component and merges information via a well-designed valve mechanism. The valve adapts the information flow into the classifier according to the confidence of semantic features in decision making, which can facilitate training a robust classifier and can address the overfitting caused by using statistical features. Extensive experiments on datasets of various scales show that, by incorporating statistical information, AGN can improve the classification performance of CNN, RNN, Transformer, and Bert based models effectively. The experiments also indicate the robustness of AGN against adversarial attacks of manipulating statistical information.
Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning

Pretrained language models (PLMs) perform poorly under adversarial attacks.To improve the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted to cover more search space of adversarial attacks by adding textual adversarial examples during training.However, the number of adversarial examples for text augmentation is still extremely insufficient due to the exponentially large attack search space.In this work, we propose a simple and effective method to cover a much larger proportion of the attack search space, called Adversarial and Mixup Data Augmentation (AMDA).Specifically, AMDA linearly interpolates the representations of pairs of training samples to form new virtual samples, which are more abundant and diverse than the discrete text adversarial examples in conventional ADA.Moreover, to fairly evaluate the robustness of different models, we adopt a challenging evaluation setup, which generates a new set of adversarial examples targeting each model.In text classification experiments of BERT and RoBERTa, AMDA achieves significant robustness gains under two strong adversarial attacks and alleviates the performance degradation of ADA on the clean data.Our code is available at:
Transaction Fee Mechanism Design

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. EIP-1559 is a proposal to make several tightly coupled changes to the Ethereum blockchain's transaction fee mechanism, including the introduction of variable-size blocks and a burned base fee that rises and falls with demand. These changes are slated for deployment in Ethereum's "London fork," scheduled for late summer 2021, at which point it will be the biggest economic change made to a major blockchain to date. The first goal of this paper is to formalize the problem of designing a transaction fee mechanism, taking into account the many idiosyncrasies of the blockchain setting (ranging from off-chain collusion between miners and users to the ease of money-burning). The second goal is to situate the specific mechanism proposed in EIP-1559 in this framework and rigorously interrogate its game-theoretic properties. The third goal is to suggest competing designs that offer alternative sets of trade-offs. The final goal is to highlight research opportunities for the EC community that could help shape the future of blockchain transaction fee mechanisms.
Variational quantum algorithm with information sharing

We introduce an optimisation method for variational quantum algorithms and experimentally demonstrate a 100-fold improvement in efficiency compared to naive implementations. The effectiveness of our approach is shown by obtaining multi-dimensional energy surfaces for small molecules and a spin model. Our method solves related variational problems in parallel by exploiting the global nature of Bayesian optimisation and sharing information between different optimisers. Parallelisation makes our method ideally suited to next generation of variational problems with many physical degrees of freedom. This addresses a key challenge in scaling-up quantum algorithms towards demonstrating quantum advantage for problems of real-world interest.
Mobile Robot Dynamic Path Planning Based on Self-Adaptive Harmony Search Algorithm and Morphin Algorithm

As a vital part of autonomous navigation of mobile robot, path planning is a hot research direction which aims at searching a shortest collision-free path from the starting position to the goal position in a complex environment. In this paper, a method for global dynamic path planning is designed based on improved self-adaptive harmony search algorithm (ISAHS) and Morphin algorithm. Firstly, to improve the quality of new solution vector, a neighbors and optimal learning strategy is introduced. Secondly, two key parameters are adjusted adaptively and a probability disturbance strategy is designed for renewing harmony memory, and then an improved self-adaptive harmony search algorithm is proposed to obtain an initial optimal path in the static environment. Finally, the Morphin algorithm is introduced to avoid the moving obstacles in real time. Simulation results indicate that the proposed method performs well in planning an initial static optimal path and it can avoid all preset moving obstacles effectively.
Preference-Adaptive Meta-Learning for Cold-Start Recommendation

In recommender systems, the cold-start problem is a critical issue. To alleviate this problem, an emerging direction adopts meta-learning frameworks and achieves success. Most existing works aim to learn globally shared prior knowledge across all users so that it can be quickly adapted to a new user with sparse interactions. However, globally shared prior knowledge may be inadequate to discern users’ complicated behaviors and causes poor generalization. Therefore, we argue that prior knowledge should be locally shared by users with similar preferences who can be recognized by social relations. To this end, in this paper, we propose a Preference-Adaptive Meta-Learning approach (PAML) to improve existing meta-learning frameworks with better generalization capacity. Specifically, to address two challenges imposed by social relations, we first identify reliable implicit friends to strengthen a user’s social relations based on our defined palindrome paths. Then, a coarse-fine preference modeling method is proposed to leverage social relations and capture the preference. Afterwards, a novel preference-specific adapter is designed to adapt the globally shared prior knowledge to the preference-specific knowledge so that users who have similar tastes share similar knowledge. We conduct extensive experiments on two publicly available datasets. Experimental results validate the power of social relations and the effectiveness of PAML.
Twenty-nine years of the Journal of International Review of Economics and Finance: A scientometric overview (1992–2020)

This paper aims to conduct a comprehensive retrospective analysis of the International Review of Economics and Finance (IREF). Using the bibliometric approach, the study analyses the journal's performance, citation structure, network connection, and mapping analysis through which a conceptual structure is developed. The Scopus database is used to retrieve data and a wide range of bibliometric tools are utilized for analysis such as RStudio "Biblioshiny" package, VOSviewer, and Microsoft Excel. Analysing IREF performance over the last twenty-nine-years (1992–2020), the findings reveal that the most prolific authors have USA and China affiliations; the journal's authors derive their information and build on research from sources that are ranked (Q1, A*, 4, 4* and SSCI), which indicates high connectedness between IREF content and published work in top sources in economics and finance. Moreover, the citation of the journal is surging in an unprecedented way during the last five years. Thus, IREF has positioned itself as one of the most influential sources, especially in finance. Its citation performance is close to the Journal of Finance, Journal of Banking and Finance, and Journal of Corporate Finance, while being above many esteemed finance journals, such as Review of Finance, Emerging Markets Finance and Trade, and Journal of Empirical Finance. The most prominent themes discussed in the journal are capital structure, economic growth, corporate governance, monetary policy, foreign direct investment, exchange rates, and emerging markets. This article presents the trending topics in IREF over the last three decades, which is helpful for the journal's board of editors in drawing a successful strategy based on a bibliometric study of the economics and finance field, where it is likely to garner interest.
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization

Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors.While prior work has proposed models that improve faithfulness, it is unclear whether the improvement comes from an increased level of extractiveness of the model outputs as one naive way to improve faithfulness is to make summarization models more extractive.In this work, we present a framework for evaluating the effective faithfulness of summarization systems, by generating a faithfulnessabstractiveness trade-off curve that serves as a control at different operating points on the abstractiveness spectrum.We then show that the baseline system as well as recently proposed methods for improving faithfulness, fail to consistently improve over the control at the same level of abstractiveness.Finally, we learn a selector to identify the most faithful and abstractive summary for a given document, and show that this system can attain higher faithfulness scores in human evaluations while being more abstractive than the baseline system on two datasets.Moreover, we show that our system is able to achieve a better faithfulnessabstractiveness trade-off than the control at the same level of abstractiveness.
Autonomous and Safe Navigation of Mobile Robots in Vineyard with Smooth Collision Avoidance

In recent years, autonomous robots have extensively been used to automate several vineyard tasks. Autonomous navigation is an indispensable component of such field robots. Autonomous and safe navigation has been well studied in indoor environments and many algorithms have been proposed. However, unlike structured indoor environments, vineyards pose special challenges for robot navigation. Particularly, safe robot navigation is crucial to avoid damaging the grapes. In this regard, we propose an algorithm that enables autonomous and safe robot navigation in vineyards. The proposed algorithm relies on data from a Lidar sensor and does not require a GPS. In addition, the proposed algorithm can avoid dynamic obstacles in the vineyard while smoothing the robot’s trajectories. The curvature of the trajectories can be controlled, keeping a safe distance from both the crop and the dynamic obstacles. We have tested the algorithm in both a simulation and with robots in an actual vineyard. The results show that the robot can safely navigate the lanes of the vineyard and smoothly avoid dynamic obstacles such as moving people without abruptly stopping or executing sharp turns. The algorithm performs in real-time and can easily be integrated into robots deployed in vineyards.
Designing a Practical Code-Based Signature Scheme from Zero-Knowledge Proofs with Trusted Setup

This paper defines a new practical construction for a code-based signature scheme. We introduce a new protocol that is designed to follow the recent paradigm known as “Sigma protocol with helper”, and prove that the protocol’s security reduces directly to the Syndrome Decoding Problem. The protocol is then converted to a full-fledged signature scheme via a sequence of generic steps that include: removing the role of the helper; incorporating a variety of protocol optimizations (using e.g., Merkle trees); applying the Fiat–Shamir transformation. The resulting signature scheme is EUF-CMA secure in the QROM, with the following advantages: (a) Security relies on only minimal assumptions and is backed by a long-studied NP-complete problem; (b) the trusted setup structure allows for obtaining an arbitrarily small soundness error. This minimizes the required number of repetitions, thus alleviating a major bottleneck associated with Fiat–Shamir schemes. We outline an initial performance estimation to confirm that our scheme is competitive with respect to existing solutions of similar type.
A Path-Following Controller for a UAV-UGV Formation Performing the Final Step of Last-Mile-Delivery

This work discusses the accomplishment of a path-following task by a formation of an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). The idea behind such an application is that the UAV is returning after delivering a package, and should land on the UGV, its reference base, which continues following its route towards another delivery. A high level controller based on the virtual structure paradigm is designed to guide the formation, which also includes an obstacle avoidance algorithm to handle the possibility that the UGV, the reference for the formation, faces an obstacle in its path, such as another vehicle, for instance. After describing in details the controller designed and the obstacle avoidance algorithm, simulated and experimental results are shown and discussed, which validate the proposed controller.
Outsourcing Data Processing Jobs With Lithops

Unexpectedly, the rise of serverless computing has also collaterally started the “democratization” of massive-scale data parallelism. This new trend heralded by PyWren pursues to enable untrained users to execute single-machine code in the cloud at massive scale through platforms like AWS Lambda. Driven by this vision, this article presents <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lithops</i> , which carries forward the pioneering work of PyWren to better exploit the innate parallelism of à la MapReduce tasks atop several Functions-as-a-Service platforms such as AWS Lambda, IBM Cloud Functions, Google Cloud Functions or Knative. Instead of waiting for a cluster to be up and running in the cloud, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lithops</i> makes easy the task of spawning hundreds and thousands of cloud functions to execute a large job in a few seconds from start. With Lithops, for instance, users can painlessly perform exploratory data analysis from within a Jupyter notebook, while it is the Lithops’s engine which takes care of launching the parallel cloud functions, loading dependencies, automatically partitioning the data, etc. In this article, we describe the design and innovative features of Lithops and evaluate it using several representative applications, including sentiment analysis, Monte Carlo simulations, and hyperparameter tuning. These applications manifest the Lithops’ ability to scale single-machine code computations to thousands of cores. And very importantly, without the need of booting a cold cluster or keeping a warm cluster for occasional tasks.
SPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKS

This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker’s presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases
Types of Out-of-Distribution Texts and How to Detect Them

Despite agreement on the importance of detecting out-of-distribution (OOD) examples, there is little consensus on the formal definition of OOD examples and how to best detect them.We categorize these examples by whether they exhibit a background shift or a semantic shift, and find that the two major approaches to OOD detection, model calibration and density estimation (language modeling for text), have distinct behavior on these types of OOD data.Across 14 pairs of in-distribution and OOD English natural language understanding datasets, we find that density estimation methods consistently beat calibration methods in background shift settings, while performing worse in semantic shift settings.In addition, we find that both methods generally fail to detect examples from challenge data, highlighting a weak spot for current methods.Since no single method works well across all settings, our results call for an explicit definition of OOD examples when evaluating different detection methods.
Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis

Rate of change, second derivative and volatility of gamma-ray (GR) well-log curves provide useful indicators with which to characterize lithofacies in clastic sedimentary sequences. Rolling averages of these variables, as they change with depth, are also able to distinguish certain lithofacies features. These attributes make it possible to accurately distinguish key facies by using only gamma-ray data, both with formulaic calculations and employing machine-learning (ML) algorithms. This is useful in the many wellbores for which only basic logging suites are available. As well as enhancing lithofacies classification more generally using well-log variables, these GR attributes can be used to forecast facies in real time based on logging-while-drilling data. The application is demonstrated with simple formula using synthetic GR logs featuring common clastic lithofacies and their transitions. Seven widely used ML methods are each trained and validated with a synthetic GR curve (1450 data points) displaying six distinct facies. The ability of the ML model to distinguish those facies using seven GR attributes is compared and further tested with an independent GR data set (800 data points). The random forest algorithm outperforms the other ML models in this facies prediction task, achieving a mean absolute error of 0.25 (on a facies class range of 1 to 6) for the independent testing dataset. The results highlight the benefit of this technique in providing reliable facies analysis based only on GR data. Random forest, support vector classification and eXtreme Gradient Boost are the ML models that provide the most reliable facies classification from the GR attributes defined. Annotated confusion matrices assist in revealing the details of facies class prediction accuracy and precision achieved by the ML and models and classification formulas. Cited as: Wood, D.A. Gamma-ray log derivative and volatility attributes assist facies characterization in clastic sedimentary sequences for formulaic and machine learning analysis. Advances in Geo-Energy Research, 2022, 6(1): 69-85. https://doi.org/10.46690/ager.2022.01.06
Visual Anomaly Detection for Images: A Systematic Survey

Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision. This problem has attracted a considerable amount of attention in relevant research communities. Especially in recent years, the development of deep learning has sparked an increasing interest in the visual anomaly detection problem and brought a great variety of novel methods. In this paper, we provide a comprehensive survey of the classical and deep learning-based approaches for visual anomaly detection in the literature. We group the relevant approaches in view of their underlying principles and discuss their assumptions, advantages, and disadvantages carefully. We aim to help the researchers to understand the common principles of visual anomaly detection approaches and identify promising research directions in this field.
Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

To improve the process of diagnosis and treatment of cancer disease, automatic segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology images is the first step in digital pathology. The proposed deep structured residual encoder-decoder network (DSREDN) focuses on two aspects: first, it effectively utilized residual connections throughout the network and provides a wide and deep encoder-decoder path, which results to capture relevant context and more localized features. Second, vanished boundary of detected nuclei is addressed by proposing an efficient loss function that better train our proposed model and reduces the false prediction which is undesirable especially in healthcare applications. The proposed architecture experimented on three different publicly available H&E stained histopathological datasets namely: (I) Kidney (RCC) (II) Triple Negative Breast Cancer (TNBC) (III) MoNuSeg-2018. We have considered F1-score, Aggregated Jaccard Index (AJI), the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation. The evaluated score of nuclei segmentation indicated that the proposed architecture achieved a considerable margin over five state-of-the-art deep learning models on three different histopathology datasets. Visual segmentation results show that the proposed DSREDN model accurately segment the nuclear regions than those of the state-of-the-art methods.