Reviewerly/exHarmony huggingface dataset:
NoiseScope: Detecting Deepfake Images in a Blind Setting

Recent advances in Generative Adversarial Networks (GANs) have significantly improved the quality of synthetic images or deepfakes. Photorealistic images generated by GANs start to challenge the boundary of human perception of reality, and brings new threats to many critical domains, e.g., journalism, and online media. Detecting whether an image is generated by GAN or a real camera has become an important yet under-investigated area. In this work, we propose a blind detection approach called NoiseScope for discovering GAN images among other real images. A blind approach requires no a priori access to GAN images for training, and demonstrably generalizes better than supervised detection schemes. Our key insight is that, similar to images from cameras, GAN images also carry unique patterns in the noise space. We extract such patterns in an unsupervised manner to identify GAN images. We evaluate NoiseScope on 11 diverse datasets containing GAN images, and achieve up to 99.68% F1 score in detecting GAN images. We test the limitations of NoiseScope against a variety of countermeasures, observing that NoiseScope holds robust or is easily adaptable.
A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity

Named Entity Recognition (NER) is important in the cybersecurity domain.It helps researchers extract cyber threat information from unstructured text sources.The extracted cyberentities or key expressions can be used to model a cyber-attack described in an open-source text.A large number of generalpurpose NER algorithms have been published that work well in text analysis.These algorithms do not perform well when applied to the cybersecurity domain.In the field of cybersecurity, the open-source text available varies greatly in complexity and underlying structure of the sentences.General-purpose NER algorithms can misrepresent domain-specific words, such as "malicious" and "javascript".In this paper, we compare the recent deep learningbased NER algorithms on a cybersecurity dataset.We created a cybersecurity dataset collected from various sources, including "Microsoft Security Bulletin" and "Adobe Security Updates".Some of these approaches proposed in literature were not used for Cybersecurity.Others are innovations proposed by us.This comparative study helps us identify the NER algorithms that are robust and can work well in sentences taken from a large number of cybersecurity sources.We tabulate their performance on the test set and identify the best NER algorithm for a cybersecurity corpus.We also discuss the different embedding strategies that aid in the process of NER for the chosen deep learning algorithms.
Towards Knowledge Enhanced Language Model for Machine Reading Comprehension

Machine reading comprehension is a crucial and challenging task in natural language processing (NLP).Recently, knowledge graph (KG) embedding has gained massive attention as it can effectively provide side information for downstream tasks.However, most previous knowledge-based models do not take into account the structural characteristics of the triples in KGs, and only convert them into vector representations for direct accumulation, leading to deficiencies in knowledge extraction and knowledge fusion.In order to alleviate this problem, we propose a novel deep model KCF-NET, which incorporates knowledge graph representations with context as the basis for predicting answers by leveraging capsule network to encode the intrinsic spatial relationship in triples of KG.In KCF-NET, we fine-tune BERT, a highly performance contextual language representation model, to capture complex linguistic phenomena.Besides, a novel fusion structure based on multi-head attention mechanism is designed to balance the weight of knowledge and context.To evaluate the knowledge expression and reading comprehension ability of our model, we conducted extensive experiments on multiple public datasets such as WN11, FB13, SemEval-2010 Task 8 and SQuAD.Experimental results show that KCF-NET achieves state-of-the-art results in both link prediction and MRC tasks with negligible parameter increase compared to BERT-Base, and gets competitive results in triple classification task with significantly reduced model size.
Detection of multivariate geochemical anomalies associated with gold deposits by using distance anomaly factors

The outliers of the statistical population are usually far from most samples in the observation space. Therefore, the total distance from a sample to all remaining samples in the population can be used to quantitatively represent the anomaly level of the sample. The greater the total distance between the sample and all the remaining samples, the more likely the sample is to be an outlier. Accordingly, the total distance of each sample in the population can be used to define a distance anomaly factor for each sample for outlier detection. The spatial distance between each pair of samples in the population can be measured by different distance measures, such as the Mahalanobis distance, Manhattan distance, Euclidean distance, and kernel Euclidean distance. According to different distances, different distance anomaly factors can be defined for each sample in outlier detection. These distance anomaly factors are potentially useful in geoscientific data procession. For demonstration purposes, the distance anomaly factors were used to detect multivariate geochemical anomalies associated with gold deposits from the stream sediment survey data in the Jinchanggouliang district, Inner Mongolia, China. Receiver operating characteristic curve and area under the curve were used to evaluate the performance of distance anomaly factors for geochemical anomaly detection. The results show that the distance anomaly factors perform better than continuous restricted Boltzmann machine and one-class support vector machine in the detection of multivariate geochemical anomalies associated with gold deposits. The Youden index was used to determine the optimal threshold to separate geochemical anomalies from geochemical background. The geochemical anomalies detected by the distance anomaly factors occupy 6.2–14.4% of the study area while contain 70–91% of the known gold deposits. The geochemical anomalies detected by the continuous restricted Boltzmann machine occupy respectively 3.1% of the study area while contain 52% of the known gold deposits. The geochemical anomalies detected by the one-class support vector machine occupy respectively 8.5% of the study area while contain 70% of the known gold deposits. Therefore, the distance anomaly factors are potentially useful techniques for geochemical anomaly detection.
Belief rule mining using the evidential reasoning rule for medical diagnosis

A belief rule mining approach is proposed to generate belief rules with a customized set of criteria by mining from multiple belief rules that are trained using data with varied sets of criteria. As the theoretical basis of the belief rule mining approach, the key concepts are defined, including the weights and reliabilities of cases, criteria, models, and belief rules. Based on the key concepts, multiple sub-models composed of belief rules with varied sets of criteria are initialized and optimized. Then, the optimized sub-models are integrated using the evidential reasoning rule into belief rules with a customized set of criteria. In the belief rule mining process, the weights and reliabilities of the sub-models are considered according to the weight and reliability calculation procedures of models proposed in this study. The proposed approach is used to help diagnose thyroid nodules with 527 medical cases, in which its applicability is demonstrated. By comparative experiments, the diagnostic correctness of the proposed approach is verified to be higher than those of the directly-optimized model and the approach without the consideration of reliability.
BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

BayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example.
Mexican intercultural education in times of COVID-19 pandemic

The COVID-19 pandemic that since January 2020 has been battering different regions, countries, and continents has not only paralysed much of the world's economic activities, but it has also made an impact on the educational work of schools and families, teachers, and communities. In this brief contribution, we will sketch the effects of the pandemic on indigenous children and teenagers in Veracruz, Mexico, who are being affected by the closure of their schools – schools that are part of a public network of intercultural and bilingual education for indigenous students, conceived by the Mexican nation-state with the aim of including an intercultural approach and of taking advantage of the diversity of diversities as a learning resource. After describing the negative impacts the COVID-19 pandemic is having on these children and youngsters, we will briefly outline some positive long-term effects the pandemic and school closure crisis may have on the future of intercultural education in Veracruz and Mexico.
Compound Poisson approximation for regularly varying fields with application to sequence alignment

The article determines the asymptotic shape of the extremal clusters in stationary regularly varying random fields. To deduce this result, we present a general framework for the Poisson approximation of point processes on Polish spaces which appears to be of independent interest. We further introduce a novel and convenient concept of anchoring of the extremal clusters for regularly varying sequences and fields. Together with the Poissonian approximation theory, this allows for a concise description of the limiting behavior of random fields in this setting. We apply this theory to shed entirely new light on the classical problem of evaluating local alignments of biological sequences.
Know-Your-Customer (KYC) Requirements for Initial Coin Offerings

Abstract Blockchain technology is often proposed as an infrastructure for decentralized Know-Your-Customer (KYC) verification, i.e., a process determining whether a customer is eligible for a given transaction. The benefit of using blockchain technology lies in the expected compliance costs reduction for companies by automatically enforcing KYC-requirements, whose results are accessible by multiple financial institutions. While information systems researchers have proposed conceptual models and prototypes of blockchain-based KYC-systems, they do not yet consider severe penalties that are applicable to companies if KYC-requirements are not met. Hence, if the legal requirements for KYC-processes cannot be met, these systems are not applicable. The paper uses an objective-centered design science research approach to develop a blockchain-based KYC-system for the conduct of ICOs that is compliant-by-design. To this end, the authors first identify existing KYC-requirements and define corresponding system design objectives that are used to develop a KYC-system that automatically enforces KYC-regulations, thereby preventing money laundering and other forms of identity fraud. Second, the authors contribute to the literature by providing a blueprint for compliant-by-design blockchain-based KYC-systems, in the paper, integrated into the investment flow of an ICO. Third, the authors propose a KYC-system that is applicable in the real world, by making – due to legal certainty – KYC-processes cost-effective, i.e., the proposed blockchain-based KYC-system expectably reduces compliance costs for customers and financial organizations.
RETRACTED: RNN based prediction of spatiotemporal data mining

Abstract The Spatiotemporal pattern is considered by most of the researchers to be a rehashed arrangement or relationship of specific occasions or highlights of spatiotemporal and to distinguish these groupings or affiliations are related to the spatiotemporal patterns of wrongdoing events and proper separation are clearly based on length based estimations that are expected to oblige the size or state of the pattern and ST patterns comprises of various sizes and shapes after some time are non-consistently disseminate over space by performing analytical learning of spatiotemporal successions as it is capable of creating future pictures by knowledge from the authentic edges. Spatial advents and temporal varieties are two pivotal structures which are considered in this paper which proposes the predictive methodology which utilizes recurrent neural network where the approach of persistent neural networks stands apart as a suitable worldview for without model as the data is based on the prediction of nonlinear dynamical frameworks by applying the methodology in Spatiotemporal pattern which predicts the limited mistake.
Blockchain and off-chain: A Solution for Audit Issues in Supply Chain Systems

Even though there are researchers focused on implementing blockchain in auditing aspects, some data is cumbersome, and storing it on a blockchain requires too much computational cost. That's where a hybrid solution would be better. We aim to provide a blockchain-off-chain mechanism in the supply chain area. This paper presents an audit mechanism that, in the first instance, saves all the events (records) of a supply chain within a blockchain in a hashed form. In a second instance, users of a supply chain system can query integrity, data provenance, and traceability to blockchain through an intermediate server that establishes communication between blockchain and the supply chain. Our proposal has been validated employing a proof of concept where, some avocado supply chain services have been implemented using a microservices architecture.
Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication

A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.
A Secure and Effective Construction Scheme for Blockchain Networks

Blockchain technology has emerged as a novel distributed ledger technology, facilitating data sharing and system management securely and efficiently without interventions from a central authority. However, blockchain technology alone is not suitable for enterprise-class applications, mainly due to the limitations in capacity expansion and veriﬁcation speed of blockchain systems. This paper proposes a secure and effective construction scheme for blockchain networks to improve performance and address the effective management concerns of blockchain data based on transaction categories. We designed a network link protocol to construct a directed acyclic graph (DAG) blockchain network and used a sharding protocol to divide the DAG blockchain into multiple category shards to process transactions in parallel. We then extensively evaluated our proposed design on local clusters. The experimental results show that our link and shard protocols achieved high throughput and the category-based sharded DAG blockchain demonstrated high scalability.
Structural Evolutionary Learning for Composite Classification Models

In this paper, we propose an evolutionary learning approach for flexible identification of custom composite models for classification problems. To solve this problem in an efficient way, the problem-specific evolutionary operators are proposed and the effectiveness of different modifications of the common genetic programming algorithm is investigated. Also, several implementations of caching for the fitted models were compared from the performance point of view. To verify the proposed algorithm, both synthetic and real-world classification cases are examined. The implemented solution can identify the structure of the composite models from scratch, as well as be used as a part of automated machine learning solutions.
An Analysis Method for Interpretability of CNN Text Classification Model

With continuous development of artificial intelligence, text classification has gradually changed from a knowledge-based method to a method based on statistics and machine learning. Among them, it is a very important and efficient way to classify text based on the convolutional neural network (CNN) model. Text data are a kind of sequence data, while time sequentiality of the general text data is relatively weak, so text classification is usually less relevant to the sequential structure of the full text. Therefore, CNN-based text classification has gradually become a research hotspot when dealing with issues of text classification. For machine learning, especially deep learning, model interpretability has increasingly become the focus of academic research and industrial applications, and also become a key issue for further development and application of deep learning technology. Therefore, we recommend using the backtracking analysis method to conduct in-depth research on deep learning models. This paper proposes an analysis method for interpretability of a CNN text classification model. The method proposed by us can perform multi-angle analysis on the discriminant results of multi-classified text and multi-label classification tasks through backtracking analysis on model prediction results. Finally, the analysis results of the model can be displayed using visualization technology from multiple dimensions based on interpretability. The representative data set IMDB (Internet Movie Database) in text classification is verified by examples, and the results show that the model can be effectively analyzed when using our method.
Causal BERT: Language Models for Causality Detection Between Events Expressed in Text

Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management, and finance. On close examination, one can find a huge amount of textual content both in the form of formal documents or in content arising from social media like Twitter, dedicated to communicating and exploring various types of causality in the real world. Recognizing these "Cause-effect" relationships between natural language events continues to remain a challenge simply because it is often expressed implicitly. Implicit causality is hard to detect through most of the techniques employed in literature and can also, at times be perceived as ambiguous or vague. Also, although well-known datasets do exist for this problem, the examples in them are limited in the range and complexity of the causal relationships they depict especially when related to implicit relationships. Most of the contemporary methods are either based on lexico-semantic pattern matching or are feature-driven supervised algorithms. Therefore, these methods are more geared towards handling explicit causal relationships leading to limited coverage for implicit relationships, and are hard to generalize. In this paper, we investigate the language model's capabilities for causal association among events expressed in natural language text using sentence context combined with event information, and by leveraging masked event context with in-domain and out-of-domain data distribution. Our proposed methods achieve the state-of-art performance in three different data distributions and can be leveraged for extraction of a causal diagram and/or building a chain of events from unstructured text.
A Risk Management Tool for Agile Software Development

Lack of risk management or its inadequate application is one reason that software development projects fail. Agile methods, which have become increasingly popular, do not offer specific activities to manage risks. We develop and evaluate a tool for managing risks in software development projects that use agile methods. The proposed tool provides a collection of risk management practices and an iterative lifecycle. To analyze the effectiveness of the proposed tool we conducted an experiment with the participation of experts in agile methods. Our tool increased the effectiveness of risk response planning without increasing the time invested in this process.
An Intelligent Model for the Detection of White Blood Cells using Artificial Intelligence

Background and Objective: The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images of blood is designed. Methods: The proposed algorithm uses the ellipse detection approach to approximate the presence of WBCs in the Blood. A newly designed artificial electric field algorithm with novel velocity and position bound (AEFA-C) is employed for this purpose. The problem of detection of WBCs is transformed into an optimization problem where the random candidate solutions (ellipses) are efficiently mapped. These candidate ellipses are mapped onto the edge map of the smear image, and a complete mapping is obtained using the AEFA-C algorithm. Results: The effectiveness of the AEFA-C based detector is tested over the 60 smear images of the blood, having all the five types of WBCs or leukocytes. The developed algorithm obtained an overall detection accuracy of 96.90%. Further, the robustness test is performed on the same dataset which justifies that the technique can handle the different noises with the detection accuracy of 90.33%. Also, the comparative study of the proposed detection algorithm with the state-of-art detection algorithms is carried out. Conclusions: The experimental results demonstrate the efficiency of the proposed scheme for the detection of the WBCs in terms of detection accuracy, stability, and robustness and its outperformance over the state-of-art algorithms.