Reviewerly/exHarmony huggingface dataset:
A Canonicalization of Distinctive Phonetic Features to Improve Arabic Speech Recognition

The robustness of speech classification and recognition systems can be improved by the adoption of language distinctive phonetic feature (DPF) elements that can increase the effective characterization of a speech signal. This paper presents the results of applying Hidden Markov Models (HMMs) that perform Arabic phoneme recognition in conjunction with the inclusion and classification of their DPF element classes. The research focuses on classifying Modern Standard Arabic (MSA) phonemes within isolated words without a language context. HMM-based phoneme recognition is tested using 8, 16, and 32 HMM Gaussian mixture models. The monophone configuration is designed with consideration of 2-gram language model to evaluate the inherent performance of the system. The overall correct rates for classifying DPF element classes for the three versions of HMM systems are 83.29% 88.96%, and 92.70% for 8, 16, and 32 HMM Gaussian mixture model systems, respectively.
Gender recognition using motion data from multiple smart devices

Using multiple smart devices, such as smartphone and smartwatch simultaneously, is becoming a popular life style with the popularity of wearables. This multiple-sensor setting provides new opportunities for enhanced user trait analysis via multiple data fusion. In this study, we explore the task of gender recognition by using motion data collected from multiple smart devices. Specifically, motion data are collected from smartphone and smart band simultaneously. Motion features are extracted from the collected motion data according to three aspects: time, frequency, and wavelet domains. We present a feature selection method considering the redundancies between motion features. Gender recognition is performed using four supervised learning methods. Experimental results demonstrate that using motion data collected from multiple smart devices can significantly improve the accuracy of gender recognition. Evaluation of our method on a dataset of 56 subjects shows that it can reach an accuracy of 98.7% compared with the accuracies of 93.7% and 88.2% when using smartphone and smart band individually.
Leveraging augmented reality for training transfer: a case of healthcare service providers in ophthalmology

Purpose The purpose of this case study is to highlight the efficacy of Augmented Reality (AR) as an effective tool to facilitate training transfer of behavioral skills and knowledge imparted during the training program, back on the job. Design/methodology/approach Insights were gathered through qualitative assessments in the form of post-training depth interviews with participants, with the objective of investigating the success of AR as an effective training transfer tool. Findings The findings of the study revealed encouraging results related to the application of AR towards training transfer at the workplace. The learners revealed positive impact of AR on training transfer and found the experience to be highly enriching. Practical implications The study offers insights in the domain of technology enabled tools such as AR to drive transfer of training through an immersive and engaging learning environment. Social implications AR as a training transfer tool can bridge the gap between training delivery and training implementation in behavioral trainings for several key industries, thus eliminating geographic and language barriers for learners. Originality/value The study is first of its kind and promises further inquiry in the domain of organizational learning and development. Insights revealed in this case are gathered through personal experience and offer a new perspective towards training transfer at the workplace.
Dissipative generation of steady-state squeezing of superconducting resonators via parametric driving

A dissipative method is proposed for generating the squeezed state of a microwave field in a superconducting transmission line resonator, which is coupled to a superconducting qubit. When the resonator is subject to the parametric driving, the qubit-resonator coupling can be exponentially enhanced. We show that the dissipation of the qubit as a resource can be used to produce and stabilize the single-mode squeezed state of the superconducting resonator at steady state. Compared with previous works, our scheme has the advantage that only a single drive is used for the quantum state preparation, which can simplify the experimental operations. Combined with the quantum interference effect, we also generalize our idea to the dissipative preparation of a two-mode squeezed state in two directly coupled superconducting resonators. The present work may have many potential applications in the field of quantum information processing with superconducting circuits.
Digital library science as a new discipline? Setting up the problem

Considering the changes in technologies, the library scientists admit that information technologies impact significantly the library sphere and call for changing paradigms. Today, the scientific library community cannot but agree that that the issues related to computer-aided technologies application require specially focused examination and research within the library studies as well as specific methodology, tools, and a new terminology system. The library scientific community today is focused on the issues of e-libraries typology, theory of user services within the digital environment, theory of library information retrieval instruments integrated into the modern information space. The problem of the library economics in the circumstances of the digital economy is no less significant. The authors analyze the demand for theoretical substantiation and specific studies within the new discipline, namely “Electronic (or digital) library science”, review papers on the problem, and propose several subdivisions for the discipline of digital library science; they also attempt to reveal the new discipline’s interconnection with other disciplines.
Light field reconstruction from scattered light using plenoptic data

We investigate the utility of plenoptic data for extracting information from a scene where the light from objects in the scene is viewable only after scattering from a surface such as a wall. We derive the rigorous relationship between the object and the scattered light, which is cast in terms of a system of Fredholm integral equations of the first kind with the bidirectional reflectance distribution function (BRDF) of the scattering surface; further, the object information is reconstructed by solving the equations. Based on the Fourier transformation, we propose a simple BRDF model and analyze the reconstruction errors by introducing newly defined parameters reflecting the BRDF’s characteristics, i.e., the degree of specularity and the effective SNR, and obtain optimal regularized solutions under a variety of surface BRDFs. Moreover, we provide a fundamental limit of retrievable information content from the scattered light. A comparison with experimental results is reported.
Penetrating the influence of regularizations on neural network based on information bottleneck theory

Regularization is a very effective algorithm to solve overfitting problem in neural network, which improves the generalization ability of the model. However, their working mechanisms and the impact on the model performance have not been fully explored. In this paper, we study and analyze them using information bottleneck theory and one theory from human brain sensory system. We propose a metric to characterise the encoding length of hidden layers, named as AEntry value. Then, we implement extensive experiments on MNIST and FashionMNIST datasets with several commonly used regularization algorithms, and calculate the corresponding AEntry values. We analyze these results and obtain three conclusions. (1) The introduction of regularization influences the encoding of relative features with prediction task in neural network. The early stopping technique avoids introducing unrelated information with the task into the model by stopping the training process as an appropriate iterations. Laplace, Gaussian and Sparse Response regularizations compress the related representation and improve the performance of neural network by introducing the prior information into the model. In contrast, Dropout, Batch Normalization, and Layer Normalization increase the encoding length of features by adopting redundant representation to improve the performance. (2) The encoding of neural network does not satisfy the data processing inequality of information theory, which is mainly caused by redundant coding of extracted features. (3) The overfitting is caused by introducing irrelative information with the target. These results can give us insight into building more efficient regularization algorithm to improve the performance of neural network model.
Computational linguistic prosody rule-based unified technique for automatic metadata generation for Hindi poetry

Metadata generation for the poems based on the unified rules is very complex from the viewpoint of computational linguistics. This is more tedious when it comes to Hindi poems. Prosody or `Chhand' as it is called in the Hindi language consists of several sets of rules and which are used while construction of a Hindi poem. Currently, no such metadata generator or technique is in existence which can generate the metadata of Hindi poetry based on the prosody or any other Hindi grammatical rule. In this research paper, we are trying to wrap up all the difficulties with respect to the metadata generator based on `Chhands', Types of `Chhands', Parts of `Chhands', Classification of `Chhands'. Along with this paper majorly focuses on the unified-rule based technique for the generation of metadata based on the different set of rules of prosody. On 3026 different UTF-8 based inputs of poems, part of poems, stories, etc., We were able to achieve 98.09% accuracy with the implementation of this unified-rule based technique of metadata generation, rest 1.91% failure was mostly due to input data related issue.
A Survey on Accelerating the Classifier Training Using Various Boosting Schemes Within Cascades of Boosted Ensembles

In this paper, we study how to handle current issues encountered once training classifiers at intervals within the defined framework, i.e., the cascades of boosted ensembles (CoBEs). This CoBEs framework became more popular ones they are more successful in the face detector. From there on researchers started improvising the present procedure by implementing new procedures through various directions such as another possible boosting methods and attributes sets. In the present scenario, a big challenge faced by this framework is unreasonable classifier training runtimes. The lengthy runtimes are an obstacle for this framework, for the broader usage. In addition to this they deter the method of creating effective object detection applications. They also considered as verifications of other analyses when creating the testing of the latest theories and algorithms, which is an extreme challenge. One more limitation of this CoBEs framework is its reduced capability to train existing classifiers gradually. At present, the highest guarantee technique of combining latest data set into a live data set classifier is, to train a classifier from starting point using the combination of old and new data sets. It is unworthy and it is short of a defined modularity and removes important data obtained in earlier training. To work with the existing issues, the current paper studies and compares alternative CoBEs framework for training classifiers. The alternative framework reduces runtimes of training by a degree of enormity than regular CoBEs framework and introduces other accountability to the existing procedure. They obtain this, while keeping alive the generalization capability of existing classifiers. This paper also studies a new process for raising training of CoBEs classifiers and presents, how existing classifiers executed without retraining them from the start, i.e., boosting chain learning. Even though it is capable to increase the successful detection rates of existing classifiers (AdaBoost, FloatBoost), presently it is not capable to lower the negative detection rates.
Classical simulation of noncontextual Pauli Hamiltonians

Noncontextual Pauli Hamiltonians decompose into sets of Pauli terms to which joint values may be assigned without contradiction. We construct a quasiquantized model for noncontextual Pauli Hamiltonians. Using this model, we give an algorithm to classically simulate the noncontextual variational quantum eigensolver. We also use the model to show that the noncontextual Hamiltonian problem is NP-complete. Finally, we explore the applicability of our quasiquantized model as an approximate simulation tool for contextual Hamiltonians. These results support the notion of noncontextuality as classicality in near-term quantum algorithms.
The Worth of Words: How Technical White Papers Influence ICO Blockchain Funding

Blockchain initiatives face a number of challenges, among the most eminent is the need to quickly and clearly communicate the strategic position and technological credibility of the platform. Presented is a discussion of blockchain startups through the lens of initial coin offerings (ICO) and their accompanying strategic positioning documents, white papers. Lessons learned from the ICO market include the need to emphasize robust technological write-ups rather than those that focus on a single key dimension. Further discussion explores the practices that have emerged since the collapse of the ICO market and the key takeaways for the CXO.
Integrating Local Vertex/Edge Embedding via Deep Matrix Fusion and Siamese Multi-label Classification

Network embedding techniques aim to encode each vertex/edge as a low-dimensional vector, enabling easy integration with existing graph mining algorithms. This paper presents a novel network embedding framework, VEEMBEDCLASS, that combines local vertex/edge embedding with deep matrix fusion and Siamese multi-label classification for facilitating classification-based local network embedding. First, we propose to perform the embeddings of each vertex/edge on K local vertex/edge embedding models respectively, with the joint optimization by considering both intra-class and inter-class correlations, to learn their latent local features on each class. The deep matrix fusion technique is developed to preserve the first-order and second-order proximity of vertices and edges on each of K classes simultaneously. Second, a Student t-distribution based Siamese multi-label classification method is designed to train associated vertices and edges with similar local characteristics together and learn their class membership probabilities, in response to the power-law vertex degree distribution widespread in real graphs. A principle of vertex-edge homophily is introduced to guarantee that the common edge/vertex shared by two associated vertices/edges and themselves are similar in terms of both structural correlations and class memberships. Finally, we integrate local vertex/edge embedding and Siamese multi-label classification into a unified model by mutually enhancing each other.
Input Processing in Adult L2 Acquisition

Input processing (IP) is concerned with the situations, the reason being that acquisition is, to a certain degree, a by-product of comprehension. IP makes a number of claims about what guides learners’ processing of linguistic data in the input as they are engaged in comprehension. The underlying features of lo prohibit it from being taken as a subject in Italian, and presumably this would happen in Spanish as second language for these learners. The learner might incorrectly reparse the sentence to mean “the parent scolded the child” and send information to the internal processors that the language has OVS structures. Learners are then asked to indicate whether the action occurred in the past is happening or happens everyday or is going to/will happen in the future. Learners are more likely to process meaningful grammatical markers before nonmeaningful grammatical markers.
The Fast Modification of Evolutionary Bioinspired Cat Swarm Optimization Method

This paper discusses the optimization problem based on cat swarm optimization by introducing elements of a random search in a stochastic modification of the basic procedure into the seeking and tracing modes, that improves the speed and accuracy of determining the direction of movement in the seeking mode and improves the global properties of the procedure in the tracing mode. The proposed optimization method, being a representative of evolutionary algorithms, is intended for using hybrid systems of computational intelligence and, above all, in learning tasks of artificial neural networks, neuro-fuzzy systems, as well as in clustering and classification problems.
Robust (Deep) Learning Framework Against Dirty Labels and Beyond

Data is generated with unprecedented speed, due to the flourishing of social media and open platforms. However, due to the lack of scrutinizing, both clean and dirty data are widely spreaded. For instance, there is a significant portion of images tagged with corrupted dirty class labels. Such dirty data sets are not only detrimental to the learning outcomes, e.g., misclassified images into the wrong classes, but also costly. It is pointed out that bad data can cost the U.S. up to a daunting 3 trillion dollars per year. In this paper, we address the following question: how prevailing (deep) machine learning models can be robustly trained given a non-negligible presence of corrupted labeled data. Dirty labels significantly increase the complexity of existing learning problems, as the ground truth of label's quality are not easily assessed. Here, we advocate to rigorously incorporate human experts into one learning framework where both artificial and human intelligence collaborate. To such an end, we combine three strategies to enhance the robustness for deep and regular machine learning algorithms, namely, (i) data filtering through additional quality model, (ii) data selection via actively learning from expert, and (iii) imitating expert's correction process. We demonstrate three strategies sequentially with examples and apply them on widely used benchmarks, such as CIFAR10 and CIFAR100. Our initial results show the effectiveness of the proposed strategies in combating dirty labels, e.g., the resulting classification can be up to 50% higher than the state-of-the-art AI-only solutions. Finally, we extend the discussion of robust learning from the trusted data to the trusted execution environment.
Generalized Zero-Shot Learning With Multi-Channel Gaussian Mixture VAE

Generalized Zero-shot learning (GZSL) for object recognition is to solve the problem of recognizing samples in both seen and unseen classes, while only having seen classes in training. As labeling abundant examples for all kinds of classes in realistic scenarios is costly and impractical, GZSL has become a novel and hot field of research in recent years. Most previous methods tried to learn a fixed one-directional mapping, either from visual to semantic features, or from semantic to visual features. However, recently, cross mapping between visual and semantic features has achieved good results and many methods come up based on this idea. In this paper, we propose a novel model. It is Multi-channel Gaussian Mixture VAE(MCGM-VAE), which introduces Gaussian mixture model to our multi-modal VAE with multiple channels. These channels are of different weight coefficients following with channel-weight layers, so as to produce a Gaussian mixture distribution. Then the latent space could be generated from it. We evaluate our method on several benchmark databases, i.e. CUB, SUN, AWA1, AWA2, aPY and prove our approach outperforms state-of-the-art methods. Through the experimental data analysis, the impact of some hyperparameters on the experimental performance is further analyzed.
Opinion Mining of Bengali Review Written with English Character Using Machine Learning Approaches

In this paper, we have done sentiment analysis for English written Bengali words given in different online shops in Bangladesh. For this work, we have chosen four latest mobile phones popular in Bangladesh. Here, the user reviews were in Bengali words written by English characters. The data was taken from online shopping sites from Bangladesh. Here, we have assumed six different features of mobiles written in the Result section. The main objective of the study was to find out the sentiment of Bengali words written with English alphabets. As it is a trend to write such reviews in Bangladesh, the data was taken and preprocessed to fit in algorithm, and they were compared whether it is positive or negative. Python was used as simulation tool, and Pursehub was used to extract the data set, and the system successfully finds out the positivity and negativity of the reviews. This result was achieved by using confusion matrix and that is making the overall performance of those mobile handsets. Out of 1201 reviews, 599 were found to be negative and 826 were found to be positive. The F1 score was 85.25%, accuracy was achieved 85.31%, and recall rate was 84.95%.
Toward Intelligence in Photonic Systems

In the not-too-distant future, advances in machine learning will spur a new, transformative generation of optical communication and measurement systems.
Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Abstract Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.