Reviewerly/exHarmony huggingface dataset:
Association Analysis

In the retail industry, market basket analysis explores the relationship between products by considering the co-occurrence of purchases in previous transactions. Association analysis is a generalization of applications, like market basket analysis, and is now commonly applied in clickstream analysis, cross-selling recommendation engines, and information security. Association analysis is an unsupervised data science technique where there is no target variable to predict. Instead, the algorithm reviews each transaction containing a number of items (products) and extracts useful relationship patterns among the items in the form of rules. The challenge in association analysis is to differentiate a significant observation against unscrupulous rules. The Apriori and Frequent Pattern Growth algorithms offer efficient approaches to extract these rules from large datasets in the transaction logs.
Incorporation mechanism of structurally bound gold in pyrite: Insights from an integrated chemical and atomic-scale microstructural study

Abstract Pyrite is one of the most important carriers for Au in hydrothermal deposits, but the incorporation mechanism of structurally bound Au has long been a matter of debate. This is due to a poor understanding of the chemical state and coordination environments of Au in the pyrite structure. Arsenian pyrite is the dominant Au-hosting mineral from the Shanggong Au deposit (130 t Au) in the southern margin of the North China Craton. Our EPMA and LA-ICP-MS results show that the arsenian pyrite has remarkably high Au (0.05–0.78 wt%) and As (0.39–4.60 wt%) contents. Au is negatively correlated with Fe but positively correlated with As. Z-contrast HAADF-STEM imaging reveals that Au atoms sit at the Fe atom sites in the arsenian pyrite structure. Our μ-XANES results and previously reported data suggest that Au and As in arsenian pyrite are predominant of chemically bound Au+ and As–, respectively. We thus propose that Au+ and As– are both structurally bound and sit at the Fe and S atom sites of pyrite, respectively. As may not be necessary but favorable for the incorporation of structurally bound Au in pyrite. These results have significant implications for understanding the enrichment mechanism of Au from ore-forming fluids with low-Au and low-As concentrations into arsenian pyrite and for revealing the role of As in Au mineralization of hydrothermal deposits.
Full-Field Modal Analysis by Using Digital Image Correlation Technique

Digital Image Correlation (DIC) is a non-contact full-field image analysis technique which allows to retrieve strains and displacements in three dimensions at the surface of any type of material and under arbitrary loading. In recent years, high-speed and high-resolution cameras have been developed for static as well as for dynamic applications. As consequence, the application fields for DIC have broadened and it has proven to be a flexible and very accurate measurement solution for deformation analysis and material characterization. In this work DIC technique is used to get the full-field displacement of the structure under test. This information could then be used to derive the modal characteristic of the structure (e.g. natural frequencies, damping ratios and full-field mode shapes). These results can be validated by using classical sensors (e.g. strain gauges, accelerometers) or other optical methods (e.g. laser doppler vibrometers). Several test cases are discussed, and two different approaches are used for combining the data obtained during the vibration tests. The most obvious approach would be the alignment of the time histories of input (shaker) and output based on reference signals for Frequency Response Functions (FRFs) calculation prior to perform any further processing. An alternative in case of broadband excitation, requires processing time data into auto and crosspowers and identify the modal parameters by using Operational Modal Analysis (OMA).
Subordinating Speech and Speaking Up

Abstract When we encounter speech that denigrates others unjustly, we sometimes feel a desire and an obligation to respond with more speech—a response that this paper calls ‘speaking up’. Even so, it is also common to have doubts about the value of speaking up. The paper exploits Stalnaker’s notion of a Context Set and Lewis’s work on Accommodation to further develop a model of subordinating speech that has been taking shape in the literature on pornography and hate speech. It then uses that model to suggest five ways in which speaking up can make a difference—while also noting that the model suggests that the effectiveness of speaking up can be limited by certain aspects of the context.
Research on robot path planning based on whale optimization algorithm

The working environment of robot and the working functions entrusted by people are becoming more and more complex. For mobile robot, intelligent path planning can be realized based on sensor technology and intelligent system. In order to realize intelligent mobile robot path planning, whale algorithm is introduced and optimized by genetic algorithm. Combined with computer perception technology, an innovative path planning algorithm is proposed. In order to verify the availability of the proposed method, the simulation analysis shows that the improved algorithm shows better path optimization performance, and can quickly analyze and calculate an intended optimization path.
Review of the progress of communication-based multi-agent reinforcement learning

Reinforcement learning (RL) technology has been successfully applied to various continuous decision environments in decades of development. Nowadays, RL is attracting more attention, even being touted as one of the closest approaches to general artificial intelligence. However, real-world problems often involve multiple intelligent agents interacting with each other. Thus, we focus on multi-agent reinforcement learning (MARL) to deal with such multi-agent systems in practice. In the past decade, the combination of multi-agent system and RL has become increasingly close, gradually forming and enriching the research field of MARL. Reviewing the studies on MARL, we found that researchers mainly solve MARL problems from three perspectives: learning framework, joint action learning, and communication-based MARL. In this paper, we focus from the studies on the communication perspective. We first state the reasons for choosing communication-based MARL and then list the president studies falling into the MARL category but different in nature. We hope that this article can provide a reference for developing MARL methods that can solve practical problems for the national welfare.
Bias in Knowledge Graph Embeddings

In this paper, we study bias in knowledge graph embeddings. We focus on gender bias in occupations, but our approach is applicable to other types of bias. We start by proposing measures for identifying bias in the dataset (i.e., in the KG) and then present two methods for testing whether any bias in the dataset is amplified by the embeddings. First, we look for gender-specific occupation analogies in the embeddings. Second, we test whether link prediction (i.e., occupation prediction in our case) aggregates gender bias by proposing gender-dominated occupations to people of the corresponding gender more often than expected. Then, we use a debiasing approach based on projections on the gender subspace. We present experimental results using the Wikidata dataset and pretrained TransE embeddings. Our results show that there exists gender bias in the dataset and that such bias is amplified by the embeddings. Our debiasing approach removes bias with a small penalty on accuracy.
Duplicate Frame Video Forgery Detection Using Siamese-based RNN

Video and image data is the most important and widely used format of communication today.It is used as evidence and authenticated proof in different domains such as law enforcement, forensic studies, journalism, and others.With the increase of video applications and data, the problem of forgery in video and images has also originated.Although a lot of work has been done on image forgery, video forensic is still a challenging area.Videos are manipulated in many ways.Frame insertion, deletion, and frame duplication are a few of the major challenges.Moreover, in the perspective of duplicated frames, frame rate variation and loop detection are also key issues.Identification of forged duplication frames for large videos with variant frame rates in real-time is not applicable due to computational limitations, lack of generalization, and low-performance accuracy.This research has investigated the problem of frame duplication with varied frame rates using a deep learning approach.A novel deep learning framework consisting of Inflated 3D (I3D) and Siamese-based Recurrent Neural Network (RNN) is proposed to resolve the aforementioned issues.The first step in the proposed framework is to extract the features and convert videos into frames.I3D network receives an original and a forged video to detect frame-to-frame duplication.Then multiple frames are merged to create a sequence.This sequence is passed to Siamese-based RNN which is used for the sequence to sequence forgery detection in video.Media Forensic Challenge (MFC) is a relatively new dataset with various frame rates, and a huge volume of videos.MFC and Image Retrieval and Analysis Tool (VIRAT) datasets are used for training and validation of the proposed model.The accuracy of the proposed method with the VIRAT dataset is 86.6% and with the MFC dataset 93%.The comparative analysis with stateof-the-art approaches has shown the robustness of the proposed approach.
Recommendation Engines

The advent of the digital economy has exponentially increased the choices of available items to customers, which can be overwhelming. Personalized recommendation lists help by narrowing the choices to a few items relevant to a particular user and aid users in making the final consumption decision. Recommendation lists, created by Recommendation engines, are one of the most prolific utilities of machine learning in everyday experience. Recommendation engines are a class of machine learning techniques that predict a user preference for an item. There are a wide range of techniques available to build a recommendation engine. This chapter discusses the most common methods starting with Collaborative Filtering and Content-based Filtering and their implementation using a practical dataset.
Behavior-Driven Development as an Approach to Improve Software Quality and Communication Across Remote Business Stakeholders, Developers and QA: two Case Studies

In this work, we describe two of our experiences applying BDD: one at a large publisher of financial information and business news, and the other in a secure collaboration platform vendor. The team composition involves both a local team and an external service provider. We analyze two adoption processes, one driven by the product team, and the other driven by the development team. We cover the communication benefits and impacts on areas such as stakeholder alignment, software design, code refactoring, and bug fixing. We also discuss lessons learned, caveats and how we plan to expand this practice in the future.
Constant-size CCA-secure multi-hop unidirectional proxy re-encryption from indistinguishability obfuscation

In ACM CCS 2007, Canetti and Hohenberger left an open problem of how to construct a multi-hop unidirectional proxy re-encryption scheme secure against chosen-ciphertext attacks (CCA). To resolve this problem, we utilize the recent advances in indistinguishability obfuscation, overcome several obstacles and propose a multi-hop unidirectional proxy re-encryption scheme. The proposed scheme is proved to be CCA-secure in the standard model (i.e., without using the random oracle heuristic), and its ciphertext remains constant-size regardless of how many times it has been transformed. It is worth noting that, our techniques proposed in this paper can also be used to construct an identity-based multi-hop unidirectional PRE scheme with constant-size ciphertexts and CCA-security in the standard model.
The 10th IJCAR automated theorem proving system competition – CASC-J10

The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic Automated Theorem Proving (ATP) systems. CASC-J10 was the twenty-fifth competition in the CASC series. Twenty-four ATP systems and system variants competed in the various competition divisions. This paper presents an outline of the competition design, and a commentated summary of the results.
Improving Generalization of Deepfake Detection by Training for Attribution

Recent advances in automated video and audio editing tools, generative adversarial networks (GANs), and social media allow the creation and fast dissemination of high-quality tampered videos, which are commonly called deepfakes. Typically, in these videos, a face is automatically swapped with the face of another person. The simplicity and accessibility of tools for generating deepfakes pose a significant technical challenge for their detection and filtering. In response to the threat, several large datasets of deepfake videos and various methods to detect them were proposed recently. However, the proposed methods suffer from the problem of over-fitting on the training data and the lack of generalization across different databases and generative approaches. In this paper, we approach deepfake detection by solving the related problem of attribution, where the goal is to distinguish each separate type of a deepfake attack. Using publicly available datasets from Google and Jigsaw, FaceForensics++, Celeb-DF, DeepfakeTIMIT, and our own large database DF-Mobio, we demonstrate that an XceptionNet and EfficientNet based models trained for an attribution task generalize better to unseen deepfakes and different datasets, compared to the same models trained for a typical binary classification task. We also demonstrate that by training for attribution with a triplet-loss, the generalization in cross-database scenario improves even more, compared to the binary system, while the performance on the same database degrades only marginally.
Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms.We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer.The Deep-CNN is utilized to extract features from mammograms.A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases.The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier.Two texture features are included: texture features of spatial dependency and gradient-based histograms.Both are employed to locate breast masses in mammograms.Next we apply transfer learning to the classifier of the SVM.Four techniques are devised for the experimental evaluation of the proposed system.The fourth technique combines the Deep-CNN with texture features and local features extracted by the scale-invariant feature transform (SIFT) algorithm.Experiments are designed to measure the performance of the various techniques.The results demonstrate that the proposed CNN coupled with the texture features and the SIFT outperforms the other models and performs best with transfer learning embedded.The accuracy of this model is 97.8%, with a true positive rate of 98.45% and a true negative rate of 96%.
Exploring the nature, motives, and implications of foreign capital in Africa

Africa, like many developing regions, has long suffered from savings and foreign exchange gaps. This has contributed to its low rate of capital accumulation, and lagging development. In search of ways to boost the domestic capital stock, a number of scholars have recommended increasing financial flow from external sources. For example, some scholars have advocated for a reasonable and sustained flow of development aid in order to help these countries meet and surpass that threshold necessary for the takeoff into self-sustained growth. Others have embraced the debt relief programs as an additional policy tool for tackling the poverty problem in these countries and consequently, increasing the domestic savings rate. Moreover, proponents of the free market system have strongly pushed for integrating Africa into the global financial system in order to attract foreign direct investment (FDI) and other portfolio-type investments. In some countries in the region, such efforts have only resulted into greater volatility, with consequences for exchange rate instability and capital outflows. It is against the aforementioned that this paper evaluates the influence of foreign capital in Africa. Its novel contribution lies in its multifaceted analysis and exploring some of the nuances that are often ignored in the traditional econometric analysis.
How to issue a privacy-preserving central bank digital currency

Many central banks are currently investigating Central Bank Digital Currency (CBDC) and possible designs. A recent survey conducted by the European Central Bank has found that both citizens and professionals consider privacy the most important feature of a CBDC. We show how a central bank could issue a CBDC that would be easily scalable and allow the preservation of a key feature of physical cash: transaction privacy. At the same time the proposed design would meet regulatory requirements and thus offer an appropriate balance between privacy and legal compliance.
Emotion Mining Using Semantic Similarity

Social networks are considered as the most abundant sources of affective information for sentiment and emotion classification. Emotion classification is the challenging task of classifying emotions into different types. Emotions being universal, the automatic exploration of emotion is considered as a difficult task to perform. A lot of the research is being conducted in the field of automatic emotion detection in textual data streams. However, very little attention is paid towards capturing semantic features of the text. In this article, the authors present the technique of semantic relatedness for automatic classification of emotion in the text using distributional semantic models. This approach uses semantic similarity for measuring the coherence between the two emotionally related entities. Before classification, data is pre-processed to remove the irrelevant fields and inconsistencies and to improve the performance. The proposed approach achieved the accuracy of 71.795%, which is competitive considering as no training or annotation of data is done.
Introduction to machine learning and Python

This chapter covers basic definitions of Artificial Intelligence, machine learning, and data mining. It then provides step-by-step instructions on how to set up Python Anaconda and Jupyter Notebook and all useful shortcuts. Afterward, an introduction to the following Python concepts is given; including data structures, e.g., lists, dictionary, tuples, sets, and control flows, e.g., if statements, for loops, nested loops, while loops, list comprehension, and functions. These concepts are explained using step-by-step examples. Next, pandas and numpy libraries are discussed in depth with multiple oil and gas examples. Various pandas' functions and concepts such as column selection, basic statistics, column renaming/manipulation, loc/iloc, column calculations, column dropping, conditional selection, grouping by, joining, merging, concatenating, pandas operations, and dealing with missing values are discussed with examples. Finally, various numpy library concepts such as creating numpy array, n by n matrix, identity function, random numbers (both real and integer), etc., are discussed with examples. Numpy indexing and selections are also discussed at the end of this chapter.
Kernel density estimation and correntropy based background modeling and camera model parameter estimation for underwater video object detection

Underwater video object detection is challenging because of the complex background and the movement of the camera. In order to address this, we propose a novel scheme for simultaneously estimating the camera model parameters and detecting the object. The object detection phase includes background modeling and its learning. Background is modeled by the proposed spatial kernel density estimation (SKDE) model and the model learning happens in the SKDE feature space. Background modeling and its learning is pixel-based approach. The model histograms learn the new pixel through its histogram representation. Our learning and classification strategy is different from that of the algorithm proposed by Heikkila et al. in the year 2006 in the context of similarity measure. We have developed the correntropy-based similarity measure strategy that is used for model learning and pixel classification. The camera model parameters are estimated by 2D optimization method where we have used the corner features of an object at subpixel accuracy level. These subpixel level features are used in the pipelining framework for model parameter estimation. The estimated model parameters are used to transform the input frame, which in turn is used for model learning and classification. The proposed scheme has been tested with underwater video frames from six datasets. The efficacy of the proposed scheme is compared with seven existing schemes and it is found that the proposed scheme exhibits improved performance as compared to the existing methods.
Students' Perspectives on Using Online Sources and Apps for EFL Learning in the Mobile-Assisted Language Learning Context

Mobile-assisted language learning (MALL) has been developed rapidly and integrated into language learning in various levels in recent years, particularly for the young generation. Many previous studies reveal that young learners have autonomous learning in using online sources or apps in the MALL context. However, not many studies in China have examined Chinese students' use of MALL. Therefore, this chapter investigates how Chinese college students perceived their use of mobile devices for English as a foreign language (EFL) learning. It was based on two small studies and focused on using online sources and mobile apps for EFL learning on learners' mobile devices. Participants were 166 students from 21 universities in China. Research tools consisted of questionnaires and interviews. The findings demonstrate a high motivation among the participants in using online sources and apps for EFL learning on their mobile devices. The results also showed the impact of different regions on students' attitudes toward MALL. Mobile apps that are related to EFL class context could enhance students' EFL learning.