Reviewerly/exHarmony huggingface dataset:
Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark

Twitter integrates with streaming data technologies and machine learning to add new value to healthcare. This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. The proposed system consists of two major components: developing an offline building model and an online prediction pipeline. For the first component, we made a correlation between the features to determine the correlation between features and reduce the number of features from the Breast Cancer Wisconsin Diagnostic dataset. Two feature selection algorithms are recursive feature elimination and univariate feature selection algorithms which are applied to features after correlation to select the essential features. Four decision trees, logistic regression, support vector machine, and random forest classifier have been used on features after correlation and feature selection. Also, hyperparameter tuning and cross-validation have been applied with machine learning to optimize models and enhance accuracy. Apache Spark, Apache Kafka, and Twitter Streaming API are used to develop the second component. The best model with the highest accuracy obtained from the first component predicts breast cancer in real time from tweets’ streaming. The results showed that the best model is the random forest classifier which achieved the best accuracy.
Application for Traffic Classification Using Machine Learning Algorithms

The timely detection of malicious traffic and the prevention of DDoS attacks is an important task in today's informational environment. There was considered a theoretical part of the classical machine learning algorithms (the principles of their work and the information about parameters). The Functional and non-functional requirements were identified then the action schemes and the use cases were presented. There was developed a traffic classification system appearing as a window application. There was presented the implementation of the classical machine learning algorithms and a user's interface. The developed network traffic identification and classification system uses the machine learning algorithms: k-NN, Naive Bayes, SVM, Ridge / Lasso, Decision Tree and k-Means. The accuracy of the system ranged from 46.69% to 99.81% depending on which algorithm was used.
The <scp>Data‐Driven</scp> Schrödinger Bridge

Abstract Erwin Schrödinger posed—and to a large extent solved—in 1931/32 the problem of finding the most likely random evolution between two continuous probability distributions. This article considers this problem in the case when only samples of the two distributions are available. A novel iterative procedure is proposed, inspired by Fortet‐IPF‐Sinkhorn type algorithms. Since only samples of the marginals are available, the new approach features constrained maximum likelihood estimation in place of the nonlinear boundary couplings, and importance sampling to propagate the functions ϕ and solving the Schrödinger system. This method mitigates the curse of dimensionality, compared to the introduction of grids, which in high dimensions lead to numerically unfeasible methods. The methodology is illustrated in two applications: entropic interpolation of two‐dimensional Gaussian mixtures, and the estimation of integrals through a variation of importance sampling. © 2020 Wiley Periodicals LLC.
Measuring the Accuracy of SVM with Varying Kernel Function for Classification of Indonesian Wayang on Images

Support vector machine (SVM) is a method that is often used in various studies to do pattern recognition of objects in the form of images. SVM is also a classification technique which is a characteristic of carrying out the training process and testing process. In classifying with data in the form of images, SVM is assisted by a feature extract technique where the process normalizes data so that a good training process can be carried out. however, SVM generally uses a linear kernel function in the testing phase. So that there is interest in this paper by designing a thought to make comparisons with several other kernel function techniques such as the cubic kernel function and the quadratic kernel function in classifying the Indonesian Wayang images which are the legacy of Indonesian ancestors. The results of this paper by varying the kernel function in SVM have an accuracy of the cubic kernel function of 83.4%.
DAVS: Dynamic-Chunk Quality Aware Adaptive Video Streaming using Apprenticeship Learning

To deliver video in a high quality across various network conditions, adaptive bitrate (ABR) algorithms dynamically select bitrate for each chunk according to perceived network rate and buffer occupancy. Unfortunately, though ameliorating the quality of chunks with dynamic scenes can obtain more QoE gain than the ones with static scenes, current ABR algorithms generally aim to maximize the average bitrate rather than perceptual quality, resulting in the QoE degradation. To address this issue, we propose a dynamic-chunk quality aware adaptive bitrate scheme via apprenticeship learning named DAVS, in which higher quality is chosen for the dynamic chunks without decreasing the quality of static chunks excessively. The experimental results show that DAVS enhances the quality of dynamic chunks and greatly improves the overall QoE compared with the state-of-the-art ABR algorithms.
Clustering of Experimental Seismo-Acoustic Events Using Self-Organizing Map (SOM)

The analogue experiments that produce seismo-acoustic events are relevant for understanding the degassing processes of a volcanic system. The aim of this work is to design an unsupervised neural network for clustering experimental seismo-acoustic events in order to investigate the possible cause-effect relationships between the obtained signals and the processes. We focused on two tasks: 1) identify an appropriate strategy for parameterizing experimental seismo-acoustic events recorded during analogue experiments devoted to the study of degassing behavior at basaltic volcanoes; 2) define the set up of the selected neural network, the Self-Organizing Map (SOM), suitable for clustering the features extracted from the experimental events. The seismo-acoustic events were generated using an ad hoc experimental setup under different physical conditions of the analogue magma (variable viscosity), injected gas flux (variable flux velocity) and conduit surface (variable surface roughness). We tested the SOMs ability to group the experimental seismo-acoustic events generated under controlled conditions and conduit geometry of the analogue volcanic system. We used 616 seismo-acoustic events characterized by different analogue magma viscosity (10, 100, 1000 Pa s), gas flux (5, 10, 30, 60, 90, 120, 150, 180 × 10 −3 l/s) and conduit roughness (i.e. different fractal dimension corresponding to 2, 2.18, 2.99). We parameterized the seismo-acoustic events in the frequency domain by applying the Linear Predictive Coding to both accelerometric and acoustic signals generated by the dynamics of various degassing regimes, and in the time domain, applying a waveform function. Then we applied the SOM algorithm to cluster the feature vectors extracted from the seismo-acoustic data through the parameterization phase, and identified four main clusters. The results were consistent with the experimental findings on the role of viscosity, flux velocity and conduit roughness on the degassing regime. The neural network is capable to separate events generated under different experimental conditions. This suggests that the SOM is appropriate for clustering natural events such as the seismo-acoustic transients accompanying Strombolian explosions and that the adopted parameterization strategy may be suitable to extract the significant features of the seismo-acoustic (and/or infrasound) signals linked to the physical conditions of the volcanic system.
Petrology, Geochemistry, and the Origin of Sulfide-Bearing and PGe-mineralized Troctolites from the Konnikov Zone in the Yoko-Dovyren Layered Intrusion

Abstract —The exposures, structure, mineralogy, and composition of unusual sulfide-bearing troctolites from the Yoko-Dovyren layered intrusion in the northern Baikal area (Russia) are described in detail for the first time. The troctolite succession (referred to as the Konnikov Zone) is characterized by the presence of pegmatoid poikilite sulfides and sulfide dissemination with diverse PGE mineralization. The former are dominated by pyrrhotite–troilite products of exsolution of monosulfide solid solution (mss), and the latter is composed mostly of cubanite–chalcopyrite assemblages produced from an intermediate Ni–Cu–Fe solid solution (iss). The positive covariations between the contents of sulfur and chalcogens (Se, Te) along with the sublinear dependence of the Pd, Pt, Au, and Cu contents on the Te contents indicate a sulfide control of the distribution of these elements in troctolite cumulates. According to the sulfide-normalized contents of these elements in rocks, the average “100% sulfides” in the samples are subdivided into two groups: (1) strongly depleted in PGE, Au, Cu, and Te and (2) with 10–50-fold enrichment in them. This division is consistent with the morphological and mineralogical differences between the groups. Of genetic significance is the fact that the mss assemblages are somewhat poorer in PGE and Te than the primitive sulfides from the Dovyren basal zone, whereas the assemblages with predominant copper sulfides are significantly richer in these elements. This fact is confirmed by LA-ICP-MS data on the trace-element composition of the sulfide phases. The established specific features indicate a limited scale of fractionation of immiscible sulfides during the solidification of the troctolite cumulates. The formation of PGE- and Te-rich assemblages can be related to the course of crystallization of a sulfide precursor similar to the most primitive sulfide liquid. This is consistent with the known laws of crystallization of sulfide systems and explains the abnormally high S/Te ratios in pegmatoid troctolites enriched in mss products. Thus, sulfide melts act as an agent that transports precious metals and chalcogens in the troctolite cumulate area. This conclusion requires specification of the physical mechanisms and parameters (rheology, permeability, wettability by sulfides of different phases, etc.) of the cumulus medium favoring the spatial separation of a monosulfide solution and Cu-containing PGE-rich fractions with their subsequent infiltration and deposition at the boundaries of critical low permeability.
A Short-Term Photovoltaic Power Generation Forecast Method Based on LSTM

The intermittence and fluctuation of photovoltaic power generation seriously affect output power reliability, efficiency, fault detection of photovoltaic power grid, etc. The precise forecasting of photovoltaic power generation is the critical method to solve the above limitations. Current photovoltaic power generation forecasting methods generally usually adopt meteorological data and historical continuous photovoltaic power generation as inputs, but they do not take into account historical periodic photovoltaic power generation as inputs, which makes the existing methods inadequate in learning time correlation. Therefore, to further study the time correlation for improving the prediction accuracy, an LSTM-FC deep learning algorithm composed of long-term short-term memory (LSTM) and fully connected (FC) layers is proposed. The double-branch input of the model enables it not only to consider the impact of meteorological data on power generation but also to consider time continuity and periodic dependence, thereby improving the prediction accuracy to a certain extent. In this paper, meteorological data, historical continuous data, and historical periodic data are used as experimental data, and these three types of data are combined into different input forms to evaluate and compare LSTM-FC with other baseline models, including support vector machines (SVM), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), feedforward neural network (FFNN), and LSTM. The simulation results show that the accuracy of the models with meteorological data, continuous data, and periodic data as input is higher than that of other input forms, and the accuracy of LSTM-FC is the highest among these models, and its root mean square error (RMSE) is 11.79% higher than that of SVM.
Clarification of the Concept of Progress Through the Semiotics of Technology

Purpose: to consider technical progress as a particular case of systemic evolution, fed by the processes of human creativity. We define creativity as the action, which changes a rule or a sum of rules that define the procedures for cognition and activity. Progress, in the proper sense, is understood as a qualitative change of human environment and human activity due to the increasing complexity of the rules implemented in technical action. Methodology: the semiotics of technology. We understand the technology in general as a projective semiosis, the sum of the rules of projective sign activity. We study semantic, syntactic and pragmatic rules of projective process at each stage of projection. Projection is understood as “triact” in terms of P.K. Engelmeyer or as “form building forces” in terms of F. Dessauer. Results: The vector of progress is defined by the sequential technical development of a person: starting from the sphere of the sensually perceived world, then entering the sphere of logical and grammatical schemes of the mind, and, further, the sphere of reflection in intellectual activity. The article analyzes a number of progress models in humanitarian knowledge and concludes that in the current digital transformation of the society, the invention of the environment is added to the previously mastered philosophical inventions (aphorism, dialogue, and system). The way of implementation of a new technical and philosophical invention determines the current and future state of society to the same extent as the “age of progress” was determined by the invention of the system.
Progressive principle component analysis for compressing deep convolutional neural networks

In this work, we propose a progressive principal component analysis (PPCA) method for compressing deep convolutional neural networks. The proposed method starts with a prespecified layer and progressively moves on to the final output layer. For each target layer, PPCA conducts kernel principal component analysis for the estimated kernel weights. This leads to a significant reduction in the number of kernels in the current layer. As a consequence, the channels used for the next layer are also reduced substantially. This is because the number of kernels used in the current layer determines the number of channels for the next layer. For convenience, we refer to this as a progressive effect. As a consequence, the entire model structure can be substantially compressed, and both the number of parameters and the inference costs can be substantially reduced. Meanwhile, the prediction accuracy remains very competitive with respect to that of the baseline model. The effectiveness of the proposed method is evaluated on a number of classical CNNs (AlexNet, VGGNet, ResNet and MobileNet) and benchmark datasets. The empirical findings are very encouraging. The code is available at https://github.com/zhoujing89/ppca.
WRGAN: Improvement of RelGAN with Wasserstein Loss for Text Generation

Generative adversarial networks (GANs) were first proposed in 2014, and have been widely used in computer vision, such as for image generation and other tasks. However, the GANs used for text generation have made slow progress. One of the reasons is that the discriminator’s guidance for the generator is too weak, which means that the generator can only get a “true or false” probability in return. Compared with the current loss function, the Wasserstein distance can provide more information to the generator, but RelGAN does not work well with Wasserstein distance in experiments. In this paper, we propose an improved neural network based on RelGAN and Wasserstein loss named WRGAN. Differently from RelGAN, we modified the discriminator network structure with 1D convolution of multiple different kernel sizes. Correspondingly, we also changed the loss function of the network with a gradient penalty Wasserstein loss. Our experiments on multiple public datasets show that WRGAN outperforms most of the existing state-of-the-art methods, and the Bilingual Evaluation Understudy(BLEU) scores are improved with our novel method.
Needs-driven Heterogeneous Multi-Robot Cooperation in Rescue Missions

This paper focuses on the teaming aspects and the role of heterogeneity in a multi-robot system applied to robot-aided urban search and rescue (USAR) missions. We propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the system's performance in terms of the group utility and energy cost to achieve the rescue mission in a limited time. From the theoretical analysis, we prove that the needs-driven cooperation in a heterogeneous robot system enables higher group utility than a homogeneous robot system. We also perform simulation experiments to verify the proposed needs-driven collaboration and show that the heterogeneous multi-robot cooperation can achieve better performance and increase system robustness by reducing uncertainty in task execution. Finally, we discuss the application to human-robot teaming.
Spatial Entropy Quartiles-Based Texture-Aware Fractional-Order Unsharp Masking for Visibility Enhancement of Remotely Sensed Images

A noniterative fractional-order (FO) two-dimensional (2-D) adaptive filtering mechanism is proposed in this article. Quartiles-based adaptive textural segmentation is employed for the calculation of texture-dependent FO. Statistically, the quartiles/quintiles/quantiles are cut points for dividing the range of a probability distribution into continuous intervals with equal probabilities. For the purpose of texture-based isolation of the spatial regions, a 2-D textural map is framed by evaluating spatial entropy by considering a pixel-wise local circular neighborhood. A novel end-to-end framework is proposed for FO texture-dependent image sharpening in an independent manner without influencing the other classes of textural regions. A novel inclusion of texture-wise adaptive gamma correction is also proposed in this article by drafting a mechanism where different kinds of textural regions can be separately processed in an independent manner. A novel quintiles-based multiscale Retinex (MSR) inspired approach for reflectance computation is coined in this article for suppressing environmental artifacts and unbalanced/nonuniform illumination. In this context, various scales required for MSR are themselves computed through quartiles-based intensity levels. The proposed model is highly modular. So, it can also be pipelined in a parallel manner, along with any well-established state-of-the-art contrast enhancement approach. Also, this approach is noniterative and highly robust. It can be proposed as an add-on for several possible (as well as pre-existing) image processing procedures. Rigorous comparative evaluations are performed so that the excellence of the proposed approach can be underlined.
Learning to rank implicit entities on Twitter

Linking textual content to entities from the knowledge graph has received increasing attention in the context of which surface form representations of entities, e.g., terms or phrases, are disambiguated and linked to appropriate entities. This allows textual content, e.g., social user-generated content, to be interpreted and reasoned on at a higher semantic level. However, recent research has shown that at least 15% of social user-generated content do not have explicit surface form representation of entities that they discuss. In other words, the subject of the content is only implied. For such cases, existing entity linking methods, known as explicit entity linking, cannot perform linking because entity surface form is missing. In this paper, we investigate how implicit entities within social content can be identified and linked. The contributions of our work include (1) modeling the problem of implicit entity linking as a learn to rank problem where knowledge graph entities are ranked based on their relevance to the input tweet, (2) the introduction and systematic classification of appropriate features for identifying implicit entities, (3) extensive evaluation of the proposed approach in comparison with existing state of the art as well as performing feature analysis over proposed features, and (4) the qualitative assessment of the root causes for mislabeled instances in our experiments and careful discussion on how mislabeled entity links can be addressed as a part of future work. In our experiments, we show that our proposed features are able to improve the state of the art over the standard Precision at 1 ([email protected]) metric.
The management and the usage of electronic resources in academic libraries: A bi-directional relationship

Electronic resources (ERs) in academic libraries have become a global phenomenon due to technological developments, and these resources are changing the expectations of library users as well as the nature of the work of librarians. Although there is an extensive body of literature on the management and usage of ERs in academic institutions, few researchers have taken a holistic view by studying both concepts in a single study to reveal how they are related. Previous studies have investigated only the management or usage of ERs as separate concepts. This study investigates the management and usage of ERs in academic libraries in Ghana to explore the connection between the two concepts. The aim is to provide better recommendations for practice. This research applied a mixed methods design. Twenty-four library staff and three library consortium executives were interviewed. Faculty members and postgraduate students from four institutions were surveyed. Our findings show the various ways in which the management and usage of ERs have an influence on each other. These were mainly in the areas of selection, access provision, publicity, training, evaluation, and usage of ERs. The importance of appropriate and adequate resourcing, the marketing and demonstration of the value of ERs, training and usage evaluation are discussed.
A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model

To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.