Reviewerly/exHarmony huggingface dataset:
Deep Multiple Instance Convolutional Neural Networks for Learning Robust Scene Representations

The accuracy and efficiency of scene classification have immensely improved with the extensive application of deep convolutional neural networks (CNNs). However, standard CNNs classify images mostly based on the global features from the last fully connected layer, which may cause the negligence of discriminative local information and the sensitivity to various spatial transformations. In this article, we consider the problem of scene classification from the perspective of multiple instance learning (MIL) and propose an end-to-end multiple instance CNN (MI-CNN) for learning more robust scene representations. In MI-CNN, a scene is represented as a bag of local patches (instances). An instance-level classifier is trained to obtain the label of each patch in an MIL fashion, which makes the classifier more sensitive to the discriminative local patches. The patch labels are then aggregated into an image label by an MIL pooling layer, which is invariant to the order of local patches and helps construct more robust representations. We present extensive experiments on UC Merced Land use (UCM), Aerial Image data set (AID), and NWPU-RESISC (NWPU) data sets. Experimental results show that the proposed method achieves 1.17%, 1.70%, and 3.61% accuracy improvements with 90% parameter reduction compared with the standard CNNs.
Design of an Unsupervised Machine Learning-Based Movie Recommender System

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies–Bouldin Index.
Logistic Model Tree for Human Activity Recognition Using Smartphone-Based Inertial Sensors

Human Activity Recognition (HAR) systems using sensor data have widespread use in many real-life applications, making it an important emerging area of research. As inertial sensors are readily available in many handheld devices, HAR systems are generally designed based on the data obtained from them. In this paper, the Logistic Model Trees (LMT) machine learning method for predicting the human motion from smartphone-based inertial sensors is considered. This study aims to demonstrate the capabilities of LMT in obtaining higher prediction rates even with short time segment of data (1 sec), in comparison with longer time segments (2.5 sec) used in the literature. The performance of HAR system designed with LMT is compared with those designed with Random Forest (RF) and Logistic Regression Tree (LR) for a set of dynamic and static activities. The system is trained and tested on two publically available datasets, namely WISDM and UCI HAR. The proposed LMT method outperforms RF and LR by achieving recognition accuracies 90.86% and 94.02% on WISDM and UCI HAR respectively, and achieves between 89.82% - 88.73% overall accuracy during cross-dataset evaluation.
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In [10], the existence of Fq-linear MRD-codes of Fq6×6, with dimension 12, minimum distance 5 and left idealiser isomorphic to Fq6, defined by a trinomial of Fq6[x], when q is odd and q≡0,±1(mod5), has been proved. In this paper we show that this family produces Fq-linear MRD-codes of Fq6×6, with the same properties, also in the remaining q odd cases, but not in the q even case. These MRD-codes are not equivalent to the previously known MRD-codes. We also prove that the corresponding maximum scattered Fq-linear sets of PG(1,q6) are not PΓL(2,q6)-equivalent to any previously known linear set.
Automatic Classification of Microseismic Records in Underground Mining: A Deep Learning Approach

The identification of suspicious microseismic events is the first crucial step in processing microseismic data. In this paper, we present an automatic classification method based on a deep learning approach for classifying microseismic records in underground mines. A total of 35 commonly used features in the time and frequency domains were extracted from waveforms. To examine the discriminative ability of these features, a genetic algorithm (GA)-optimized correlation-based feature selection (CFS) method was applied. As a result, 11 features were selected to represent microseismic records. By dividing each microseismic record into 50 frames, an 11 × 50 feature matrix was utilized as the input. A convolutional neural network (CNN) with 35 layers was trained on 20,000 samples recorded at the Huangtupo Copper and Zinc Mine. There are 5 types of events: microseismic events, blasting, ore extraction, mechanical noise, and electromagnetic interference. The event type was correctly determined by the trained CNN classifier 98.2% of the time, outperforming traditional machine learning methods.
Transparent Robot Behavior Using Augmented Reality in Close Human-Robot Interaction

Most robots consistently repeat their motion with- out changes in a precise and consistent manner. But nowadays there are also robots able to dynamically change their motion and plan according to the people and environment that surround them. Furthermore, they are able to interact with humans and cooperate with them. With no information about the robot targets and intentions, the user feels uncomfortable even with a safe robot. In close human-robot collaboration, it is very important to make the user able to understand the robot intentions in a quick and intuitive way. In this work we have developed a system to use augmented reality to project directly into the workspace useful information. The robot intuitively shows its planned motion and task state. The AR module interacts with a vision system in order to display the changes in the workspace in a dynamic way. The representation of information about possible collisions and changes of plan allows the human to have a more comfortable and efficient interaction with the robot. The system is evaluated in different setups.
Modeling and Management Big Data in Databases—A Systematic Literature Review

The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question is how the number of published papers about Big Data modeling and management has evolved over time. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 2010 and 2019, were deemed relevant. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. This SLR reveal very interesting facts. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data.
Recent advances in the application of mineral chemistry to exploration for porphyry copper–gold–molybdenum deposits: detecting the geochemical fingerprints and footprints of hypogene mineralization and alteration

Recent advances in the application of mineral chemistry to exploration for porphyry copper-gold-molybdenum deposits
Reputation transferability across contexts: Maintaining cooperation among anonymous cryptomarket actors when moving between markets

Buyers and sellers of illegal drugs in cryptomarkets have been found to overcome trust issues created by anonymity and the lack of legal protection with the help of reputation systems. Cryptomarkets rarely operate for longer than a year before closing or getting shut down due to external shocks, such as law enforcement operations. This results in large flows of users migrating between market platforms. An important question in order to better understand why cryptomarkets recover quickly after external shocks is: to what extent can reputation be carried over between different markets? This problem is non-trivial given the anonymity of cryptomarket users and the fact that reputation is tied to a user's online identity. Here we analyze conditions under which sellers choose to migrate with the same identity and whether reputation history from previous cryptomarkets yields benefits in new contexts. We analyze sellers’ migration in three cryptomarkets (Abraxas, Agora and AlphaBay) and follow their reputation history by linking user accounts between marketplaces using the Grams database. We use longitudinal multi-level regression models to compare market success of migrant and non-migrant sellers. In total, the data contains more than 7,500 seller account and 2.5 million buyers’ reputational feedback messages over a period of 3 years. It is predominantly the successful sellers with a large number of sales and high reputation who choose to migrate and maintain their identity using cryptographic methods after market closures. We find that reputation history from previous markets creates a competitive advantage to migrant sellers compared to market entrants. Reputation transferability embeds cryptomarket users beyond a single market platform, which incentivizes cooperative behavior. The results also suggest that reputation transferability might contribute to a quick recovery of online drug trade after shutdowns and accumulation of market share in the hands of a small fraction of successful sellers.
Exceptional Concentrations of Gold Nanoparticles in 1,7 Ga Fluid Inclusions From the Kola Superdeep Borehole, Northwest Russia

Abstract In the drill core of the Kola super-deep borehole (SG-3, 12,262 m depth) gold-bearing rocks of Archaean age have been located at depths of 9,500 to 11,000 m. In veins, between 9,052 and 10,744 m, within this gold zone, quartz contains fluid inclusions with gold nanoparticles. There are 4 types of fluid inclusions (1) gas inclusions of dense CO 2 , (2) liquid-vapor two-phase aqueous inclusions, (3) three-phase inclusions with NaCl daughter crystals, and (4) CO 2 -aqueous inclusions. In all inclusion types, there are extremely high concentrations of gold. The highest gold concentrations were found in the type 3 and 4 fluid inclusions with an average concentration of c. 750 ppm and may be as high as 6,000 ppm. The presence of gold as nanoparticles in the solutions of these fluid inclusions was determined by optical and spectroscopic methods. We suggest that these fluids could be a precursor of “orogenic gold fluids” which, at the gold concentrations determined, would reduce the requirements for large volumes of metamorphic fluids to form orogenic ore deposits. Further, as nanoparticles, gold could be transported in larger amounts than in true solution.
Generatively Inferential Co-Training for Unsupervised Domain Adaptation

Deep Neural Networks (DNNs) have greatly boosted the performance on a wide range of computer vision and machine learning tasks. Despite such achievements, DNN is hungry for enormous high-quality (HQ) training data, which are expensive and time-consuming to collect. To tackle this challenge, domain adaptation (DA) could help learning a model by leveraging the knowledge of low-quality (LQ) data (i.e., source domain), while generalizing well on label-scarce HQ data (i.e., target domain). However, existing methods have two problems. First, they mainly focus on the high-level feature alignment while neglecting low-level mismatch. Second, there exists a class-conditional distribution shift even features being well aligned. To solve these problems, we propose a novel Generatively Inferential Co-Training (GICT) framework for Unsupervised Domain Adaptation (UDA). GICT is based on cross-domain feature generation and a specifically designed co-training strategy. Feature generation adapts the representation at low level by translating images across domains. Co-training is employed to bridge conditional distribution shift by assigning high-confident pseudo labels on target domain inferred from two distinct classifiers. Extensive experiments on multiple tasks including image classification and semantic segmentation demonstrate the effectiveness of GICT approach.
Restricted-Boltzmann-Based Extreme Learning Machine for Gas Path Fault Diagnosis of Turbofan Engine

Extreme learning machine (ELM) owns the advantages of less computational efforts and simple topology with single-hidden layer structure. However, the performance of plain ELM is sensitive to the input weights, bias, and the number of hidden neurons; and the former two are randomly generated. This paper develops a restricted Boltzmann strategy combined with Moore-Penrose generalized inverse to learn topological parameters in both input and output layers. A novel extreme learning model based on the restricted Boltzmann ELM, constructs a feature mapping and recursively tune the weights between input neurons and hidden neurons. The contribution of this paper is to provide a simple ELM topological network to handle low dimensionality problem with the merit of better accuracy and stability. The proposed methodology is evaluated on University of California Irvine (UCI) benchmark datasets for classification issue, and then extended to gas path fault diagnosis for a turbofan engine. The experimental results confirm the superiority to plain ELM.
Formal Ontologies in Information Systems Development: A Systematic Review

Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.
MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning

Integrating human-provided location priors into video object segmentation has been shown to be an effective strategy to enhance performance, but their application at large scale is unfeasible. Gamification can help reduce the annotation burden, but it still requires user involvement. We propose a video object segmentation framework that leverages the combined advantages of user feedback for segmentation and gamification strategy by simulating multiple game players through a reinforcement learning (RL) model that reproduces human ability to pinpoint moving objects and using the simulated feedback to drive the decisions of a fully convolutional deep segmentation network. Experimental results on the DAVIS-17 benchmark show that: 1) including user-provided prior, even if not precise, yields high performance; 2) our RL agent replicates satisfactorily the same variability of humans in identifying spatiotemporal salient objects; and 3) employing artificially generated priors in an unsupervised video object segmentation model reaches state-of-the-art performance.
The Multilingual Student Translation corpus: a resource for translation teaching and research

The Multilingual Student Translation (MUST) corpus is a corpus of translations produced by foreign language learners or trainee translators collected collaboratively by a large number of partner teams internationally. The corpus represents a prime example of community sourcing, as the data are collected and shared by the members of the MUST network. Two key characteristics of the corpus are that it involves a large number of language pairs and that each text is accompanied by a rich set of standardized metadata related to the source texts, the translation tasks and the students. The web interface on which the corpus is stored allows the data to be aligned and annotated with a purpose-built translation annotation system. The resulting corpus data lend themselves to a range of applications (translator training, materials design, pedagogical lexicography) and can also be used to advance empirical research in corpus-based translation studies.
On Assuring Learning About Code Quality

Most of the lifetime cost of a software product is incurred after it is first delivered, i.e. during the maintenance phase. It is generally acknowledged that the quality of the code significantly impacts maintenance costs. This suggests that "code quality" should be a key competency of graduates of computing qualifications. We examined the learning outcomes of 141 University courses on introduction to programming and found that only about 30% mention any code quality-related topic. In contrast, the relevant national high school assessment framework used in New Zealand requires that, in order to be successful, a student must demonstrate some understanding of code quality. Our recommendation is that, if we are to be confident about producing code-quality-capable students, we must consider adopting agreed achievement standards for code quality.
Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression

Compared with face recognition, the performance of person re-identification (re-ID) is still far from practical application. Among various interferences, there are two factors seriously limiting the performance improvement, i.e., the feature discriminability determined by "external network effectiveness", and the image quality determined by "internal background clutters". Target at the "external network effectiveness" problem, feature pyramids are effective to learn discriminative features because they can learn both detailed features from high-resolution shallow layers and semantical features from low-resolution deep layers, however, it can only achieve slight improvement on re-ID tasks because of the error back propagation problem. To handle the problem and utilize the effectiveness of feature pyramids, we propose a strategy called Feature Pyramid Optimization (FPO). Instead of concatenating features directly, the selected layers are optimized independently in a top-bottom order. Target at the "internal background clutters" problem, background suppression is generally considered for removing the environmental interference and improving the image quality. Several mask-based methods are used attempting to totally remove background clutters but achieve limited promotion because of the mask sharpening effect. We propose a novel strategy, i.e., Gradual Background Suppression (GBS) to reduce the background clutters and keep the smoothness of images simultaneously. Extensive experiments have been conducted and the results demonstrate the effectiveness of both FPO and GBS.
Machine learning classification of entrepreneurs in British historical census data

This paper presents a binary classification of entrepreneurs in British historical data based on the recent availability of big data from the I-CeM dataset. The main task of the paper is to attribute an employment status to individuals that did not fully report entrepreneur status in earlier censuses (1851–1881). The paper assesses the accuracy of different classifiers and machine learning algorithms, including Deep Learning, for this classification problem. We first adopt a ground-truth dataset from the later censuses to train the computer with a Logistic Regression (which is standard in the literature for this kind of binary classification) to recognize entrepreneurs distinct from non-entrepreneurs (i.e. workers). Our initial accuracy for this base-line method is 0.74. We compare the Logistic Regression with ten optimized machine learning algorithms: Nearest Neighbors, Linear and Radial Support Vector Machine, Gaussian Process, Decision Tree, Random Forest, Neural Network, AdaBoost, Naive Bayes, and Quadratic Discriminant Analysis. The best results are boosting and ensemble methods. AdaBoost achieves an accuracy of 0.95. Deep-Learning, as a standalone category of algorithms, further improves accuracy to 0.96 without using the rich text-data that characterizes the OccString feature, a string of up to 500 characters with the full occupational statement of each individual collected in the earlier censuses. Finally, and now using this OccString feature, we implement both shallow (bag-of-words algorithm) learning and Deep Learning (Recurrent Neural Network with a Long Short-Term Memory layer) algorithms. These methods all achieve accuracies above 0.99 with Deep Learning Recurrent Neural Network as the best model with an accuracy of 0.9978. The results show that standard algorithms for classification can be outperformed by machine learning algorithms. This confirms the value of extending the techniques traditionally used in the literature for this type of classification problem.