Reviewerly/exHarmony huggingface dataset:
A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.
Reward-Reinforced Generative Adversarial Networks for Multi-Agent Systems

Multi-agent systems deliver highly resilient and adaptable solutions for common problems in telecommunications, aerospace, and industrial robotics. However, achieving an optimal global goal remains a persistent obstacle for collaborative multi-agent systems, where learning affects the behaviour of more than one agent. A number of nonlinear function approximation methods have been proposed for solving the Bellman equation, which describe a recursive format of an optimal policy. However, how to leverage the value distribution based on reinforcement learning, and how to improve the efficiency and efficacy of such systems remain a challenge. In this work, we developed a reward-reinforced generative adversarial network to represent the distribution of the value function, replacing the approximation of Bellman updates. We demonstrated our method is resilient and outperforms other conventional reinforcement learning methods. This method is also applied to a practical case study: maximising the number of user connections to autonomous airborne base stations in a mobile communication network. Our method maximises the data likelihood using a cost function under which agents have optimal learned behaviours. This reward-reinforced generative adversarial network can be used as a generic framework for multi-agent learning at the system level.
Duplicate Frame Detection in Forged Videos Using Sequence Matching

This paper presents a method that can detect multiple frame duplication in forged videos. In this kind of forgery, a number of frames are copied and placed somewhere in a video for hiding or highlighting some incidents and/or objects. It becomes very challenging to identify the duplicated frames in such digital videos, as it is difficult to detect by the human eyes as well as by computer program. In past works, various methods have been proposed so far to detect the same. Despite that, there are very a few methods which can determine frame duplication in the video with both static and non-static background. To this end, this work proposes a simple yet effective method to detect the frame duplication forgery found in the videos. In this method, the structural similarity index measure (SSIM) is used to assess the resemblance between the consecutive frames. Finally, to detect and localize tampered frames, a searching algorithm is used. For experimental need, we have collected original videos from Urban Tracker, derf’s collection and REWIND databases, and then prepared the forged videos using frame duplication. Experimental results on these videos confirm that the present method detects such forgery with an impressive average frame duplication detection accuracy of 98.90%.
Pycheron: A Python-Based Seismic Waveform Data Quality Control Software Package

Abstract Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology’s Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.
V-Lattice: A Lightweight Blockchain Architecture Based on DAG-Lattice Structure for Vehicular Ad Hoc Networks

With the development of wireless communication technology and the automobile industry, the Vehicular Ad Hoc Networks bring many conveniences to humans in terms of safety and entertainment. In the process of communication between the nodes, security problems are the main concerns. Blockchain is a decentralized distributed technology used in nonsecure environments. Using blockchain technology in the VANETs can solve the security problems. However, the characteristics of highly dynamic and resource-constrained VANETs make the traditional chain blockchain system not suitable for actual VANETs scenarios. Therefore, this paper proposes a lightweight blockchain architecture using DAG-lattice structure for VANETs, called V-Lattice. In V-Lattice, each node (vehicle or roadside unit) has its own account chain. The transactions they generated can be added to the blockchain asynchronously and parallelly, and resource-constrained vehicles can store the pruned blockchain and execute blockchain related operations normally. At the same time, in order to encourage more nodes to participate in the blockchain, a reputation-based incentive mechanism is introduced in V-Lattice. This paper uses Colored Petri Nets to verify the security of the architecture and verifies the feasibility of PoW anti-spam through experiment. The validation results show that the architecture proposed in this paper is security, and it is feasible to prevent nodes from generating malicious behaviors by using PoW anti-spam.
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots

Multilingual models have demonstrated impressive cross-lingual transfer performance.However, test sets like XNLI are monolingual at the example level.In multilingual communities, it is common for polyglots to code-mix when conversing with each other.Inspired by this phenomenon, we present two strong blackbox adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit.The former uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation.The latter directly aligns the clean example with its translations before extracting phrases as perturbations.Our phrase-level attack has a success rate of 89.75% against XLM-R large , bringing its average accuracy of 79.85 down to 8.18 on XNLI.Finally, we propose an efficient adversarial training scheme that trains in the same number of steps as the original model and show that it improves model accuracy. 1 Original P: The girl that can help me is all the way across town.H: There is no one who can help me.Adversary P: olan girl that can help me is all the way across town.
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning

We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020).A recent work, named PROVER (Saha et al., 2020), performs such reasoning by answering a question and also generating a proof graph that explains the answer.However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer.Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases.Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems.In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph.We propose two variants of a proof-set generation model, MULTIPROVER.Our first model, Multilabel-MULTIPROVER, generates a set of proofs via multi-label classification and implicit conditioning between the proofs; while the second model, Iterative-MULTIPROVER, generates proofs iteratively by explicitly conditioning on the previously generated proofs.Experiments on multiple synthetic, zero-shot, and human-paraphrased datasets reveal that both MULTIPROVER models significantly outperform PROVER on datasets containing multiple gold proofs.Iterative-MULTIPROVER obtains state-of-the-art proof F1 in zero-shot scenarios where all examples have single correct proofs.It also generalizes better to questions requiring higher depths of reasoning where multiple proofs are more frequent.
Self-Supervised Test-Time Learning for Reading Comprehension

Recent work on unsupervised question answering has shown that models can be trained with procedurally generated question-answer pairs and can achieve performance competitive with supervised methods.In this work, we consider the task of unsupervised reading comprehension and present a method that performs "test-time learning" (TTL) on a given context (text passage), without requiring training on large-scale human-authored datasets containing context-question-answer triplets.This method operates directly on a single test context, uses self-supervision to train models on synthetically generated question-answer pairs, and then infers answers to unseen humanauthored questions for this context.Our method achieves accuracies competitive with fully supervised methods and significantly outperforms current unsupervised methods.TTL methods with a smaller model are also competitive with the current state-of-the-art in unsupervised reading comprehension.
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning

Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem.Conventional methods aim to learn a matching model between text and labels, using a graph encoder to incorporate label hierarchies to obtain effective label representations (Rios and Kavuluru, 2018).More recently, pretrained models like BERT (Devlin et al., 2018) have been used to convert classification tasks into a textual entailment task (Yin et al., 2019).This approach is naturally suitable for the ZS-MTC task.However, pretrained models are underexplored in the existing work because they do not generate individual vector representations for text or labels, making it unintuitive to combine them with conventional graph encoding methods.In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task.We propose a Reinforced Label Hierarchy Reasoning (RLHR) approach to encourage interdependence among labels in the hierarchies during training.Meanwhile, to overcome the weakness of flat predictions, we design a rollback algorithm that can remove logical errors from predictions during inference.Experimental results on three reallife datasets show that our approach achieves better performance and outperforms previous non-pretrained methods on the ZS-MTC task.
Copula-based conformal prediction for multi-target regression

There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions for inductive conformal prediction, and illustrate our proposal by applying it to deep neural networks and random forests. We show that the proposed method ensures efficiency and validity for multi-target regression problems on various data sets.
AVA: an Automatic eValuation Approach for Question Answering Systems

We introduce AVA, an automatic evaluation approach for Question Answering, which given a set of questions associated with Gold Standard answers (references), can estimate system Accuracy.AVA uses Transformer-based language models to encode question, answer, and reference texts.This allows for effectively assessing answer correctness using similarity between the reference and an automatic answer, biased towards the question semantics.To design, train, and test AVA, we built multiple large training, development, and test sets on public and industrial benchmarks.Our innovative solutions achieve up to 74.7% F1 score in predicting human judgment for single answers.Additionally, AVA can be used to evaluate the overall system Accuracy with an error lower than 7% at 95% of confidence when measured on several QA systems.
Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.
Chaos in metaheuristic based artificial intelligence algorithms: a short review

Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area -besides engineering systems- where chaos theory is widely employed is optimization problems. Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already in process. In this article, scientific studies that cover the most popular metaheuristic algorithms in the literature in recent years and chaos theory subtopics together are examined. Great number of studies on metaheuristic algorithms and chaos issues exist in scientific literature. This article, hitherto, had to be limited to the most common meta-heuristic algorithms. In chaos-based metaheuristic algorithms, some advantages such as easy implementation, short application time and search acceleration have been addressed. This article is believed to contribute to two groups of researchers: The first group includes researchers already using meta-heuristic algorithms and who will come to understand that they can even improve their current techniques with chaos theory subarguments. The second group includes those who currently utilize chaotic analysis and methods in areas like nonlinear prediction modeling design and who will realize that they can make their existing methods even smarter with metaheuristic methods. Some metaheuristic algorithms use chaotic maps to solve problems such as trapping in local optimal solutions and premature convergence. In this study, such algorithms are examined through the benchmark functions they use. In addition, PSO, FA, ABC, WO algorithms are compared in terms of their common features, and algorithms with the best success rate are presented.
Profiling of Intertextuality in Latin Literature Using Word Embeddings

Identifying intertextual relationships between authors is of central importance to the study of literature.We report an empirical analysis of intertextuality in classical Latin literature using word embedding models.To enable quantitative evaluation of intertextual search methods, we curate a new dataset of 945 known parallels drawn from traditional scholarship on Latin epic poetry.We train an optimized word2vec model on a large corpus of lemmatized Latin, which achieves state-of-the-art performance for synonym detection and outperforms a widely used lexical method for intertextual search.We then demonstrate that training embeddings on very small corpora can capture salient aspects of literary style and apply this approach to replicate a previous intertextual study of the Roman historian Livy, which relied on hand-crafted stylometric features.Our results advance the development of core computational resources for a major premodern language and highlight a productive avenue for cross-disciplinary collaboration between the study of literature and NLP. 1
Fine-Grained and Controllably Redactable Blockchain with Harmful Data Forced Removal

Notoriously, immutability is one of the most striking properties of blockchains. As the data contained in blockchains may be compelled to redact for personal and legal reasons, immutability needs to be skillfully broken. In most existing redactable blockchains, fine-grained redaction and effective deletion of harmful data are mutually exclusive. To close the gap, we propose a fine-grained and controllably redactable blockchain with harmful data forced removal. In the scheme, the originator of the transaction has fine-grained control over who can perform the redaction and which portions of the transaction can be redacted. The redaction transaction is performed after collecting enough votes from miners. All users can provide the index of the block containing the harmful data to receive rewards, which are borne by the malicious user who initially posted the data. Miners can forcibly remove the harmful data based on the index. The malicious user will be blacklisted if the reward is not paid within a period of time, and any transaction about such user will not be performed later. In addition, the scheme supports the redaction of additional data and unexpended transaction output (UTXO) simultaneously. We demonstrate that the scheme is secure and feasible via formal security analysis and proof-of-concept implementation.
Detecting the Phishing Website with the Highest Accuracy

Phishing attacks are increasing and it becomes necessary to use appropriate response methods and to respond effectively to phishing attacks. This paper aims to uncover phishing attack sites by analyzing a three-module set to prevent damage and reconsider the awareness of phishing attacks. Based on the analyzed content, a countermeasure was proposed for each type of phishing attack by using website features. These features will be classified in order to determine the effectiveness of the countermeasure. Finally, the proposed method enhanced the site security as anti-phishing technology. The phishing detection used three classification algorithms, which are the decision tree; the supporting vector machine and the random forest were combined into one system that was proposed in this paper for the purpose of obtaining the highest accuracy in detecting phishing sites. The results of the proposed algorithm showed 98.52% higher accuracy than others.
Towards Low-Resource Real-Time Assessment of Empathy in Counselling

Gauging therapist empathy in counselling is an important component of understanding counselling quality.While session-level empathy assessment based on machine learning has been investigated extensively, it relies on relatively large amounts of well-annotated dialogue data, and real-time evaluation has been overlooked in the past.In this paper, we focus on the task of low-resource utterance-level binary empathy assessment.We train deep learning models on heuristically constructed empathy vs. non-empathy contrast in general conversations, and apply the models directly to therapeutic dialogues, assuming correlation between empathy manifested in those two domains.We show that such training yields poor performance in general, probe its causes, and examine the actual effect of learning from empathy contrast in general conversation.r/offmychest ... Speaker: being married to a depressed person is so lonely .that is all .thanks for listening .Listener: sorry to say this but it 's not worth being in a relationship if both of you are n't happy .r/CasualConversation ... Speaker: can you recommend me some good music ?i 've decided to expand my taste in music and i need some advice .what are your favourite songs , redditors ?Listener: something corporate , jimmy eat world , and fall out boy are my top three recommendations .RolePlayMI ... Client: well I guess if I don't want to take more pills I have to give up some of my sweets my cookies and my potato chips Therapist: so those types of sweets