Reviewerly/exHarmony huggingface dataset:
Speech Recognition with Gender Identification and Speaker Diarization

Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Capturing the voice using the mic from all direction. Direction of arrival is being recorded with an angle of 360 degree. The voice is being detected using Voice Activity Detection to avoid capturing the noise in the surrounding. Captured voice is being processed using Acoustic Echo Cancellation. The voice is being analysed using speech algorithm. Voice is being separated based on their decibels (db). In the proposed system the voice is being recorded and is being converted to transcripts by processing it with various speech models. The Automatic Speech Recognition (ASR) is the process of converting an unknown speech waveform into the corresponding orthographic transcription. The speech signal using the algorithms of analysis synchronized with the pitch frequency. The online recogniser is being used reads a list of wav files and outputs the result into a text format. Speaker Diarization identifies who speaks when during the conversation on the recorded wav. The model attains the accuracy of 92 % for speech recognition. This research can further used in transcription platform and other smart voice devices.
Identification of connected arguments based on reasoning schemes “from expert opinion”

Abstract The work presented describes a combined approach to the partial extraction of the argumentative structure of a text that can be employed in the absence of sufficient annotated data to apply efficiently the machine learning methods for the direct detection of arguments and their relations. In this case, argument identification is performed by using the patterns of argumentation indicators created by a linguist and automatically expanded. These patterns enable the recognition of specific argument types with fine precision. In this study, arguments “from expert opinion” serve as such a pivot type. Besides, potential relations between recognized arguments are analyzed by dividing the text into superphrasal units (fragments united by one topic). The criterion for connecting arguments in an argumentative structure is their inclusion in the same superphrasal unit. An experiment for identifying potentially related arguments is conducted on a set of popular science texts with a minimum size of 1000 words.
Hybrid Feature Fusion Learning Towards Chinese Chemical Literature Word Segmentation

The rapid increase in the number of chemical science literature has brought challenges to researchers in search and data analysis. For many chemical scientific literature, extracting information from text and using knowledge is the focus of research. However, the existing Chinese text word segmentation methods have low recognition rate for chemical terms. The reason is that the addition of many new vocabulary and mixed professional vocabulary of Chinese and English brings challenges to word segmentation. In this paper, we propose a word segmentation method of Chinese chemical literature based on hybrid feature fusion learning Model(HFFLM). HFFLM first establishes chemical science corpus (Chem-pku) to train Chinese word segmentation (CWS) tasks. In addition, HFFLM uses BiLSTM and CNN to extract document features and fuse them. Then, HFFLM combines boundary features to construct conditional random field to train the end-to-end CWS model. In the end, HFFLM makes visual analysis of the word segmentation results. The experimental results indicate that HFFLM has high accuracy and recall rate, and is suitable for chemical industry vocabulary extraction with mixed Chinese and English.
A Literature Review of Recommendation Systems

Recommendation Systems (RS) are tools that recommend items to be viewed by users. While these tools have gained popularity since the 1990s they can be found in most applications on the internet nowadays to perform recommendations to users in order to sustain and increase the interactivity within the applications. RS come in different forms that approach the task of making recommendations in different ways. These techniques have evolved over the years and can be found to be conducted using both complex and simple algorithms. While there are many algorithms that perform recommendations different algorithms can prove to be more sustainable for different tasks. This paper reviews a number of simple and complex algorithms that are used for recommendation and discuss their applications and their advantages and disadvantages.
CMAC: Clustering Class Association Rules to Form a Compact and Meaningful Associative Classifier

Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rules discovered on those big datasets can easily exceed thousands of rules. To produce compact and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. To solve this problem researchers have proposed several associative classification approaches that combine two important data mining techniques, namely, classification and association rule mining. In this paper, we propose a new method that is able to reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier – CMAC, that uses agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules. Experimental results performed on selected datasets from the UCI ML repository show that CMAC is able to learn classifiers containing significantly less rules than state-of-the-art rule learning algorithms on datasets with larger number of examples. On the other hand, classification accuracy of the CMAC classifier is not significantly different from state-of-the-art rule-learners on most of the datasets. We can thus conclude that CMAC is able to learn compact (and meaningful) classifiers from "bigger" datasets, retaining an accuracy comparable to state-of-the-art rule learning algorithms.
Experience vs Data: A Case for More Data-Informed Retrospective Activities

Effective Retrospective meetings are vital for ensuring productive development processes because they provide the means for Agile software development teams to discuss and decide on future improvements of their collaboration. Retrospective agendas often include activities that encourage sharing ideas and motivate participants to discuss possible improvements. The outcomes of these activities steer the future directions of team dynamics and influence team happiness. However, few empirical evaluations of Retrospective activities are currently available. Additionally, most activities rely on team members experiences and neglect to take existing project data into account. With this paper we want to make a case for data-driven decision-making principles, which have largely been adopted in other business areas. Towards this goal we review existing retrospective activities and highlight activities that already use project data as well as activities that could be augmented to take advantage of additional, more subjective data sources. We conclude that data-driven decision-making principles, are advantageous, and yet underused, in modern Agile software development. Making use of project data in retrospective activities would strengthen this principle and is a viable approach as such data can support the teams in making decisions on process improvement.
Blind identification of space–time block codes based on deep learning

Deep Learning (DL) has important applications to both commercial and military communications, such as software-defined radio, cognitive radio and spectrum surveillance. While DL has been intensively studied for modulation recognition, there are very few investigations for blind identification of Space-Time Block Codes (STBCs). This paper proposes a Residual Network (RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna, including the same length of coding matrix. In our work, we use the frequency-domain correlation function of a single time delay as the training data of DL model. Then, we explore the suitable RN structure for blind identification of STBCs. Finally, we compare the RN model with convolutional neural network and traditional method, and test the performance of RN model. Simulation results show that our RN-based model provides good performance with low sensitivity to decay of the dataset, such as sample length and data size. At the same time, better identification accuracy can be achieved under the condition of different modulation types and channel fading parameters at low Signal to Noise Ratio (SNR).
A Reconfiguration Approach for Open Adaptive Systems-of-Systems

The drive for digitalization in industry and transport results in an increasing application of cooperative systems that form adaptive system-of-systems. With reconfiguration these systems are able to change their behavior to react on internal changes and changes in their environment. In this paper, we present a novel service-oriented approach for decentralized reconfiguration within such systems-of-systems that specifically supports open adaptive systems-of-systems. We introduce the concept of strategy blueprints that define possible combinations of services provided by individual members of the system-of-systems. During reconfiguration, individual member systems evaluate all strategies that are instantiable under the given conditions, and select the one optimally fulfilling a set of predefined goals as the most appropriate target configuration. With this novel approach, we support flexible reconfiguration across the borders of individual member systems, and the inclusion of new member system types that provide service variants not yet known at design time of the reconfiguration algorithm.
Developing phraseological competence in L2 legal translator trainees: a proposal of a data mining technique applied in translation from an LLD into ELF

Evidence from the contemporary translation services market and many centuries of translation practice demonstrate that translation into a non-native language (L2 translation) can be performed effectively, despite the once-strong resistance to it on the grounds of it being perceived as unprofessional and inherently deficient. L2 translation is in fact unavoidable in the case of so-called languages of low diffusion, the command of which happens to be rather limited among native speakers of major languages. However, although the academic dispute about the validity of L2 translation seems decidedly milder now, there is still a lacuna within L2 translator training that needs to be addressed. This paper indicates that what usually betrays an L2 translation is its phraseological profile, often recognised as unnatural by native speakers of the target language. The aim of this paper is to propose a corpus-based data mining technique that may help L2 legal translator trainees become more observant with regards to phraseological patterning of foreign legal discourse, and more self-confident in taking well-informed translation decisions.
A Geometric Approach to On-road Motion Planning for Long and Multi-Body Heavy-Duty Vehicles

Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles, is a difficult task in comparison to passenger cars. Most research on motion planning for autonomous vehicles has focused on passenger vehicles, and many unique challenges associated with heavy-duty vehicles remain open. However, recent works have started to tackle the particular difficulties related to on-road motion planning for buses and tractor-trailer vehicles using numerical optimization approaches. In this work, we propose a framework to design an optimization objective to be used in motion planners. Based on geometric derivations, the method finds the optimal trade-off between the conflicting objectives of centering different axles of the vehicle in the lane. For the buses, we consider the front and rear axles trade-off, whereas for articulated vehicles, we consider the tractor and trailer rear axles trade-off. Our results show that the proposed design strategy produces planned paths that considerably improve the behavior of heavy-duty vehicles by keeping the whole vehicle body in the center of the lane.
Correlation-based Oversampling aided Cost Sensitive Ensemble learning technique for Treatment of Class Imbalance

The issue of class imbalance and its consequences over the conventional learning models is a well-investigated topic, as it highly influences performances of real-life classification tasks. Amongst the available solutions, Synthetic Minority Oversampling Technique (SMOTE) imprints efficacy in balancing the data through synthetic minority instance generation. However, SMOTE suffers from the drawback of redundant data generation owing to uniform oversampling rate in regard to which, SMOTE with a customised oversampling rate has been investigated recently. In parallel to this, ensemble learning approaches are quite effective in improving prediction abilities of a set of weak classifiers through adaptive-weighted training. However, it does not account the imbalanced nature of the data during training. Through this paper, Correlation-based Oversampling aided Cost Sensitive Ensemble learning (CorrOV-CSEn) is proposed by integrating correlation-based oversampling with the AdaBoost ensemble learning model. The correlation-based oversampling entails to define a customised oversampling rate and a suitable oversampling zone while a misclassification ratio-based cost-function is introduced in the AdaBoost model to administer adaptive learning of imbalanced cases. CorrOV-CSEn is evaluated against 13 state-of-the-art methods by using 8 simulation datasets. The experimental results establish CorrOV-CSEn to be effective than the state-of-the-art methods in resolving the concerned issues.
Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.
Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network

Flight trajectories expectedly differ from each other, yet they follow a set of patterns that are flown previously and optimized for different conditions. These patterns may or may not be trivial and can be influenced by a number of effects, including airspace utilization, controllers’ cognitive complexity, weather, NOTAMs. Due to the high variance, it is a rather compelling task to accurately classify trajectories into a desired set of categories based purely on its statistical properties. Therefore, we propose a hybrid methodology to generalize over the flight trajectories and decide whether they are abnormal or not: a) with a more statistical approach considering the time-based features of the trajectories, and b) with a more pattern-based approach. The statistical approach utilizing the LSTM autoencoders enables to train the model with the history and rapidly predict the class of the flight, inherently considering the time-based features of a flight trajectory. The pattern generalization is achieved via a Generative Adversarial Network (GAN), which generates samples that look like real trajectories. The abnormality detection in the patterns is done with an autoencoder part of GAN that learns how to reconstruct typical sequentially dependent trajectory data after applying dimension reduction. While LSTM autoencoders enable to capture the class of a flight with short time windows, GAN predicts pattern anomaly with larger window observations. Hence, this heterogeneous approach allows us to sweep the airspace with asynchronous frequencies. The obtained results show that the proposed architecture is quite capable of the abnormality classification of the trajectories as it accomplishes to accurately detect simulated fighter aircraft trajectories in commercial flight heavy airspaces, and the generative model provides good utility to capture the pattern-based distribution of flight trajectories.
Blockchain Solutions in Education

Variant blockchain-based solutions were proposed during the last 5 years for education. This paper is the first to discuss these solutions and their usage and benefits. However, there is not yet any solution in the literature that issues or validates a certificate which includes courses from different educational institutes. This paper proposes a novel framework for cross-education blockchain-based solution. It collects participating institutes in one blockchain network. Graduates of this solution can join courses in the institutes that provide the latest and strongest contents. The issued certificate is self-validating for all the courses that are obtained from multiple institutes. It is cryptographically coded and securely stored using the blockchain technology. It can be validated instantaneously through one validation portal independently without contacting its issuing institutes. The saved time and resources are oriented toward enhancing students' services. This framework can be extended to fields that require issuing or validating cross-acquired certificates.
Inductive semi-supervised learning with Graph Convolution based regression

This brief paper introduces a framework for supervised and semi-supervised learning by estimating a non-linear embedding that incorporates Spectral Graph Convolutions structure. The proposed algorithm exploits data-driven graphs in two ways. First, it integrates data smoothness over graphs. Second, its regression loss function jointly uses the data and its graph in the sense that the regressor model sees convolved data samples. The resulting framework can solve the problem of over-fitting on local neighborhood structures for image data having varied natures like outdoor scenes, faces and man-made objects. The proposed Graph Convolution based Semi-supervised Embedding (GCSE) not only provides a new perspective to non-linear embedding research but also induces the standpoint on Spectral Graph Convolutions methods. A series of experiments are conducted on four image datasets in order to compare the proposed method with some state-of-art semi-supervised methods. This evaluation demonstrates the effectiveness of the proposed embedding method.
Knowledge base of Cenozoic volcanoes in China

Abstract Knowledge management plays an important role in scientific research and provides a basis for technical development in the era of Big Data. Studies of Cenozoic volcanoes in China have been undertaken for more than half a century, generating plentiful relevant literature and data. However, these data have stayed scattered between different authors and libraries, and this hampers management and access. Based on theories, knowledge bases and related technologies, we developed the Knowledge Base of Cenozoic Volcanoes (KBCV) to collect such volcanic data in China. The directory tree of the KBCV is structured based on five levels according to the volcano distribution, magma origin, data type and file format. The KBCV supports querying, searching and browsing. It can provide well-managed Cenozoic volcanic data and technical support for scientific research and public communication. The KBCV is still in its early stages and is imperfect with respect to data completeness and universalization of the system, and efforts are being made to continuously develop and popularize the system.
Comparative Analysis of Catastrophic Forgetting in Metric Learning

Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated the catastrophic forgetting in fully connected networks with some earlier work exploring activation functions and learning algorithms. Applications of neural networks have been extended to include similarity and metric learning. It is of significant interest to understand how metric learning loss functions would be affected by catastrophic forgetting. Our research investigates catastrophic forgetting for four well-known metric-based loss functions during incremental class learning. The loss functions are angular, contrastive, center, and triplet loss. Our results show that the rate of forgetting is different across loss functions on multiple datasets. Triplet loss was least affected followed by contrastive, center, and angular loss. Center and angular loss produce better embeddings on difficult tasks when trained on all available training data, however, they are the least robust to forgetting during incremental class learning. We argue that triplet loss provides the ideal middle ground for future improvements.
Combining Deep Reinforcement Learning And Local Control For The Acrobot Swing-up And Balance Task

In this work we present a novel extension of soft actor critic, a state of the art deep reinforcement algorithm. Our method allows us to combine traditional controllers with learned neural network policies. This combination allows us to leverage both our own domain knowledge and some of the advantages of model free reinforcement learning. We demonstrate our algorithm by combining a hand designed linear quadratic regulator with a learned controller for the acrobot problem. We show that our technique outperforms other state of the art reinforcement learning algorithms in this setting.
Edge Detection Using Guided Image Filtering and Ant Colony Optimization

Edge detection is an important phenomenon in various classes of engineering problems. The classical methods which are based on the kernel designs are not very accurate and edges are falsely detected. Recent methods which are based on soft computing are adaptive in nature, and therefore using soft computing methods accuracy of detected edges can be improved. This paper presents an ant colony optimization-based edge detection process, and where edges are enhanced using guided filtering. The simulation results clearly show that the proposed scheme is much superior to recently proposed edge detection methods.