Reviewerly/exHarmony huggingface dataset:
Hybrid Adaptive Moth-Flame Optimizer and Opposition-Based Learning for Training Multilayer Perceptrons

This chapter is dedicated to improving the optimization capability of moth-flame optimizer (MFO), which works based on a spiral operation mimicking the moths’ navigation behavior and dynamic coefficients. However, such a basic version can be easily trapped in the local optima and is associated with an unstable balance between exploratory and exploitative cores. To mitigate the shortcomings of slow convergence and local stagnation, an adaptive moth flame optimization with opposition-based learning (AMFOOBL) is presented, employing a new adaptive structure and the opposition-based learning (OBL) strategy. This original adaptive tool is devised to reduce the number of flames around which agents update their positions for balancing the exploration and exploitation stages more effectively. The performance of AMFOOBL is evaluated in two experiments: First, the quantitative results of 23 benchmark function tests show that AMFOOBL outperforms AMFO, followed by MFO, validating the effectiveness of the proposed approach in terms of accuracy and convergence rate. Second, AMFOOBL is demonstrated on multilayer perceptron's (MLP) structural realization and training compared with nine advanced algorithms. The simulation on eight datasets for pattern classification and function approximation reveals outstanding performance in the AMFOOBL-based trainer concerning classification accuracy and test error. Our findings suggest that AMFOOBL is a superior algorithm, and the developed evolutionary-enhanced MLP can be considered a helpful tool.
Sentiment Analysis and Opinion Mining (Business Intelligence 1)

This chapter covers the first part of our business intelligence discussion and gives the reader insights into opinion mining and sentiment analysis. Social media are seen as big data in the sense that they provide a massive amount of online reviews and ratings that can be collected and analyzed in order to consider the impact these data may have on organizations. Particularly, several studies have shown that more positive reviews and higher rates for an organization (and its products or services) may lead to a significantly higher number of desired business actions (e.g., higher sales or more subscriptions to an online newsletter). This chapter explains characteristics such as subjectivity and tone in opinions and shows how a sentiment model can be built. The chapter concludes with challenges faced by this research field today.
Auditable, Available and Resilient Private Computation on the Blockchain via MPC

Simple but mission-critical internet-based applications that require extremely high reliability, availability, and verifiability (e.g., auditability) could benefit from running on robust public programmable blockchain platforms such as Ethereum. Unfortunately, program code running on such blockchains is normally publicly viewable, rendering these platforms unsuitable for applications requiring strict privacy of application code, data, and results. In this work, we investigate using MPC techniques to protect the privacy of a blockchain computation. While our main goal is to hide both the data and the computed function itself, we also consider the standard MPC setting where the function is public. We describe GABLE (Garbled Autonomous Bots Leveraging Ethereum), a blockchain MPC architecture and system. The GABLE architecture specifies the roles and capabilities of the players. GABLE includes two approaches for implementing MPC over blockchain: Garbled Circuits (GC), evaluating universal circuits, and Garbled Finite State Automata (GFSA). We formally model and prove the security of GABLE implemented over garbling schemes, a popular abstraction of GC and GFSA from (Bellare et al., CCS 2012). We analyze in detail the performance (including Ethereum gas costs) of both approaches and discuss the trade-offs. We implement a simple prototype of GABLE and report on the implementation issues and experience.
An Improved U-Net Model for Segmenting Wind Turbines From UAV-Taken Images

Unmanned aerial vehicle (UAV) based inspection of wind turbine surface conditions is a research hot spot in the wind energy industry. To tackle the fundamental problems in UAV-based inspection, this letter proposes an improved U-Net model for segmenting wind turbines from UAV-taken images. In the proposed segmentation model, ResNet is employed as the backbone for feature extraction while two types of attention mechanism, ECA-Net and PSA-Net, are used to capture important details in images. Therefore, multiscale features are effectively fused and cross-channel attention is obtained. The feasibility and efficiency of the proposed method are validated based on UAV-taken images collected from four commercial wind farms. Meanwhile, the proposed method is benchmarked with the generic U-Net, UNet++, Res-UNet, DenseUNet, and DeepLabv3. The segmentation results show the proposed model outperforms other methods, and, thus, the proposed model is applicable for real UAV-based inspection tasks.
Probing the Robustness of Trained Metrics for Conversational Dialogue Systems

This paper introduces an adversarial method to stress-test trained metrics to evaluate conversational dialogue systems.The method leverages Reinforcement Learning to find response strategies that elicit optimal scores from the trained metrics.We apply our method to test recently proposed trained metrics.We find that they all are susceptible to giving high scores to responses generated by relatively simple and obviously flawed strategies that our method converges on.For instance, simply copying parts of the conversation context to form a response yields competitive scores or even outperforms responses written by humans.
Delta Hedging Bitcoin Options with a Smile

We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data set of hourly historical bitcoin option prices reveals that the bitcoin implied volatility curve behaves very differently from that of equity index options. We show that about 80\% of trading activity is on bitcoin options with expiry less than four weeks, and hence we only focus on those options in this study. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black-Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30\% for out-of-the-money puts, and reach an average of 15\% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.
Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension

Argument pair extraction (APE) aims to automatically mine argument pairs from two interrelated argumentative documents.Existing studies typically identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the modeling of the holistic argument-level interactions.Towards this issue, we propose to address APE via a machine reading comprehension (MRC) framework with two phases.The first phase employs an argument mining (AM) query to identify all arguments in two documents.The second phase considers each identified argument as an APE query to extract its paired arguments from another document, allowing to better capture the argument-level interactions.Also, this framework enables these two phases to be jointly trained in a single MRC model, thereby maximizing the mutual benefits of them.Experimental results demonstrate that our approach achieves the best performance, outperforming the state-of-the-art method by 7.11% in F 1 score.
Classifying Sincerity Using Machine Learning

Quora is an online platform that empowers people to learn from each other. On Quora, users can post questions and connect with others who contribute unique insights and quality answers. But as with any other social media or online platform, there is the potential for misuse. A key challenge in maintaining the integrity of such an online platform is to classify and flag negative content. On Quora, the challenge is to identify insincere questions. Insincere questions could be those founded upon false premises, are disparaging, inflammatory, intended to arouse discrimination in any form, or intend to make a statement rather than look for helpful answers. We propose to develop a text classification model that correctly labels questions as sincere or insincere on the Quora platform. For this purpose, we used the Quora Insincere Questions Classification dataset, which is available on Kaggle. We first trained classical machine learning models such as Logistic regression and SVMs to establish a baseline on the performance. However, to leverage the large dataset, we used neural network-based models. We trained several models including standard neural networks, and LSTM based models. The best model that we obtained is a two-layer Bidirectional LSTM network that takes word embeddings as inputs. The classification accuracy and F1-score for this model were 96% and 0.64, respectively.
Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models

Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence.The context encoding is undertaken by contextual parameters, trained on document-level data.In this work, we discuss the difficulty of training these parameters effectively, due to the sparsity of the words in need of context (i.e., the training signal), and their relevant context.We propose to pre-train the contextual parameters over split sentence pairs, which makes an efficient use of the available data for two reasons.Firstly, it increases the contextual training signal by breaking intra-sentential syntactic relations, and thus pushing the model to search the context for disambiguating clues more frequently.Secondly, it eases the retrieval of relevant context, since context segments become shorter.We propose four different splitting methods, and evaluate our approach with BLEU and contrastive test sets.Results show that it consistently improves learning of contextual parameters, both in low and high resource settings.
E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning

The ability to recognize analogies is fundamental to human cognition.Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-itskind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR).Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve.More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer.Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area.
A Quantum Dynamical Map in the Creation of Optimized Chaotic S-Boxes

The substitution boxes are an open challenge due to not meeting the theoretical criteria of a good S-box. Recently, the use of chaos in the design of efficient S-boxes was proposed. In this article, after introducing a new quantum system, we examine its effect on the formation of chaotic S-boxes. We compare the proposed quantum chaotic map with previous results. Also, in the previous work, the PSO algorithm was improved with the help of the classical map and then used in the optimization of chaotic S-boxes. We are using and improving the performance of PSO in generating the S-box, by the introduced quantum chaotic map. Then, by changing the type of optimization, we examine its effects. For the first time, the harmony search algorithm is improved by the said quantum map, and then we use it to optimize the produced chaotic S-box. By examining the performance of generated S-boxes by common attacks such as nonlinearity, BIC, SAC, LP, and DP. The results for the improved harmony search algorithm is better.
Local-Global Graph Pooling via Mutual Information Maximization for Video-Paragraph Retrieval

As a task of cross-modal retrieval between long videos and paragraphs, video-paragraph retrieval is a non-trivial task. Unlike traditional video-text retrieval, the video in video-paragraph retrieval usually contains multiple clips. Each clip corresponds to a descriptive sentence; all the sentences constitute the corresponding paragraph of the video. Previous methods for video-paragraph retrieval usually encode videos and para-graphs from segment-level (clips and sentences) and overall-level (videos and paragraphs). However, there are also contents about actions and objects that exist in the segment. Hence, we propose a Local-Global Graph Pooling Network (LGGP) via Mutual Information Maximization for video-paragraph retrieval. Our model disentangles videos and paragraphs into four levels: overall-level, segment-level, motion-level, and object-level. We construct the Hierarchical Local Graph (segment-level, motion-level, and object-level) and the Hierarchical Global Graph (overall-level, segment-level, motion-level, and object-level), respectively, for semantic interaction among different levels. Meanwhile, to obtain hierarchical pooling features with fine-grained semantic information, we design hierarchical graph pooling methods to maximize the mutual information between pooling features and corresponding graph nodes. We evaluate our model on two video-paragraph retrieval datasets with three different video features. The experimental results show that our model establishes state-of-the-art results for video-paragraph retrieval. Our code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PengchengZhang1997/LGGP</uri> .
Probing for Labeled Dependency Trees

Probing has become an important tool for analyzing representations in Natural Language Processing (NLP).For graphical NLP tasks such as dependency parsing, linear probes are currently limited to extracting undirected or unlabeled parse trees which do not capture the full task.This work introduces DEPPROBE, a linear probe which can extract labeled and directed dependency parse trees from embeddings while using fewer parameters and compute than prior methods.Leveraging its full task coverage and lightweight parametrization, we investigate its predictive power for selecting the best transfer language for training a full biaffine attention parser.Across 13 languages, our proposed method identifies the best source treebank 94% of the time, outperforming competitive baselines and prior work.Finally, we analyze the informativeness of task-specific subspaces in contextual embeddings as well as which benefits a full parser's non-linear parametrization provides.
Efficient Implementation of Kyber on Mobile Devices

Kyber, an IND-CCA-secure key encapsulation mechanism (KEM) based on the MLWE problem, has been shortlisted for the third round evaluation of the NIST Post-Quantum Cryptography Standardization. In this paper, we explored the optimizations of Kyber in high-performance processors from the ARM Cortex-A series, which are widely used in mainstream mobile phones. To improve the performance of Kyber, we utilized the powerful SIMD instruction set NEON in an ARMv8-A to parallelize the core modules of Kyber, i.e., modular reduction and NTT. Specifically, we specially designed the optimized implementation based on the characteristic of the NEON instruction set for the Barrett and Montgomery reduction algorithms. To make full use of the computing power of NEON instructions, we proposed a novel strategy for computing the 16-bit Barrett reduction without handling the 32-bit intermediate result. Our Barrett and Montgomery reduction showed 8.52 and 8.89 times faster than the reference implementation. As for NTT/INTT, we adopted the 2+5 layer merging strategy on an ARMv8-A to implement NTT/INTT after carefully analyzing the register occupancy of various layer merging techniques. Thanks to the selected layer merging strategy, our NTT and INTT achieved 11.89 and 13.45 times speedups compared with the reference implementation. Our optimized software achieved 1.77×, 1.85×, and 2.16× speedups for key generation, encapsulation, and decapsulation compared with Kyber's reference implementation.
EXPANSE, A Continual Deep Learning System; Research Proposal

The final goal of the Artificial Intelligence community is to achieve Artificial General Intelligence, a human level of intelligence with precision, persistence, and processing speed of computers. As humans, we have the ability to progress via continual learning. We always add a skill on top of our past learned skills. Deep transfer learning, followed by progressive learning, is attempting to mimic human skill learning ability, a task that is endless for human beings. However, even the most advanced deep learning models need to be trained on a massive dataset and still fail to face the same task in a different context. Progressive Learning, a subcalegory of deep transfer learning, is the closest technique to human continual learning ability. The main goal of the following proposed system is to drive the current progressive learning method a step closer to the final destination of Artificial General Intelligence. The current progressive learning method is focused on adding a fresh layer(s) to the end of a pre-trained model for a slightly different task or dataset. The idea behind EXPANSE is that we can tackle a more distant task and/or dataset by expanding all layers of the model, also adding a fresh layer(s) al the end In other words, we expand the network vertically and horizontally. The whole, old part of the network will be used frozen, and only the freshly added part should be trained on target data. Moreover, EXPANSE is introducing two steps of training inspired by the human educational system.
Hybrid Scheduling Strategy in Cloud Computing based on Optimization Algorithms

A cloud service provider&#x0027;s primary goal is to efficiently use resources by reducing execution time, cost, and other factors while increasing profit. As a result, effective scheduling algorithms remain a key issue in cloud computing, and this topic is categorized as an NP-complete problem. Researchers previously proposed several optimization techniques to address the NP-complete problem, but more work is needed in this area. This paper provides an overview of strategy for successful task scheduling based on a hybrid heuristic approach for both regular and larger workloads. The proposed optimum scheduling method employs two distinct techniques to select the suitable VM for the specified job. To begin, it enhances the LJFP method by employing OSIG, an upgraded version of the Genetic Algorithm, to choose solutions with improved fitness factors, crossover, and mutation operatorsThe proposed algorithm&#x0027;s performance is assessed using two distinct cloud scenarios with various VMs and tasks, and overall execution time and energy usage are calculated. The proposed algorithm outperforms existing techniques in terms of energy and average execution time usage in both scenarios.
Face-Mask Detection to Control the COVID-19 Spread Employing Deep Learning Approach

The world is in distress because of the COVID-19 pandemic; the best remedy is isolation from this infection. Wearing a face mask is vital to protect ourselves from the virus. A person wearing a face mask can be detected by Computer Vision and notify the authorities if the protocol is not followed. Since it is very labor-intensive to check if a person is wearing a face mask in public places, Computer Vision makes it easy for real-time monitoring. This method uses MobileNet and OpenCV to classify people into two categories, wearing the face mask or not, with 99.41&#x0025; and 100&#x0025; testing accuracy for face mask detection and without face mask detection, respectively. It requires low computing power and is compatible with mobile devices, which can help the concerned authorities enforce the rule of wearing face masks in public places.
Alias-Chain: Improving Blockchain Scalability via Exploring Content Locality among Transactions

A Blockchain is a promising infrastructure but it has serious scalability problems, i.e., long block synchronization time and high storage cost. Conventional coarse-grained data deduplication schemes (block or file level) are proved to be ineffective on this problem. Based on comprehensive analysis on typical blockchain workloads, we are the first to propose two new locality concepts: economic and argument locality. To further explore these new localities, we propose a novel fine-grained data deduplication scheme (transaction level) named Alias-Chain to improve the scalability of blockchains. Specifically, Alias-Chain replaces frequently used data, e.g., smart contract arguments, with much shorter aliases to reduce the block size. During prop-agation and preservation of blocks, smaller blocks result in both shorter synchronization time and lower storage cost. Simulation results show the average transfer and SC-call transaction sizes can be reduced by up to 11.23% and 43.23% in native Ethereum, and up to 61.95 % and 77.54 % in Ethereum optimized by state-of-the-art techniques, respectively. Prototyping-based experiments are further conducted on a testbed consisting of up to 3200 miners. The results demonstrate the effectiveness and efficiency of Alias-Chain on reducing block synchronization time and storage cost under typical real-world workloads.
Learning Robust Models Using the Principle of Independent Causal Mechanisms

Standard supervised learning breaks down under data distribution shift. However, the principle of independent causal mechanisms (ICM, []) can turn this weakness into an opportunity: one can take advantage of distribution shift between different environments during training in order to obtain more robust models. We propose a new gradient-based learning framework whose objective function is derived from the ICM principle. We show theoretically and experimentally that neural networks trained in this framework focus on relations remaining invariant across environments and ignore unstable ones. Moreover, we prove that the recovered stable relations correspond to the true causal mechanisms under certain conditions, turning domain generalization into a causal discovery problem. In both regression and classification, the resulting models generalize well to unseen scenarios where traditionally trained models fail.