Reviewerly/exHarmony huggingface dataset:
The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. The needed transformation of the LTG data culture requires emphasis in student education on science and management of data.
Quantum Computation of Molecular Structure Using Data from Challenging-To-Classically-Simulate Nuclear Magnetic Resonance Experiments

We propose a quantum algorithm for inferring the molecular nuclear spin Hamiltonian from time-resolved measurements of spin-spin correlators, which can be obtained via nuclear magnetic resonance (NMR). We focus on learning the anisotropic dipolar term of the Hamiltonian, which generates dynamics that are challenging to classically simulate in some contexts. We demonstrate the ability to directly estimate the Jacobian and Hessian of the corresponding learning problem on a quantum computer, allowing us to learn the Hamiltonian parameters. We develop algorithms for performing this computation on both noisy near-term and future fault-tolerant quantum computers. We argue that the former is promising as an early beyond-classical quantum application since it only requires evolution of a local spin Hamiltonian. We investigate the example of a protein (ubiquitin) confined on a membrane as a benchmark of our method. We isolate small spin clusters, demonstrate the convergence of our learning algorithm on one such example, and then investigate the learnability of these clusters as we cross the ergodic to non-ergodic phase transition by suppressing the dipolar interaction. We see a clear correspondence between a drop in the multifractal dimension measured across many-body eigenstates of these clusters, and a transition in the structure of the Hessian of the learning cost function (from degenerate to learnable). Our hope is that such quantum computations might enable the interpretation and development of new NMR techniques for analyzing molecular structure.
Nonlocal activation of bound entanglement via local quantum Zeno dynamics

Bound entanglement was shown to be activated [P. Horodecki et al., Phys. Rev. Lett. 82, 1056 (1999)] in the sense that the entanglement of a spatially separated two-qutrit system can be increased with nonzero probability via a sufficiently large number of preshared bound-entangled states, local three-level controlled operations, and classical communications. Here we present a local quantum Zeno scheme for activating bound entanglement which is based only on single-particle rotations and threshold measurements. In our scheme, neither a large number of bound-entangled states nor controlled operations are required, and classical communication is required only once at the end of the protocol. We show that a single bound-entangled state is sufficient for increasing the negativity of the target entangled state from 0.11 to 0.17, and by using four more bound-entangled states, negativity can be made greater than 0.42 and the fidelity to the maximally entangled state increases from 0.3 to 0.41, 0.50, 0.59, and 0.61. We believe our results are important not only for quantum technologies but also for a better understanding of quantum entanglement.
Finite Sample Based Mutual Information

Mutual information is a popular metric in machine learning.In case of a discrete target variable and a continuous feature variable the mutual information can be calculated as a sum-integral of weighted log likelihood ratio of joint and marginal density distributions.However, in practice the true density distributions are unavailable and only a finite sample of the population is given.In this paper, we propose a novel method for calculating the mutual information for continuous variables using a finite sample of the population.The proposed method is based on approximating the underlying continuous density distribution using Kernel Density Estimation.Unlike previous kernel-based approaches for estimating mutual information, our method calculates directly the integral involved in the formula.Numerical experiments demonstrate that the proposed method produces more accurate results than the currently used feature selection approaches.In addition, our method demonstrates substantially faster computation times than the benchmark methods.
Sentiment Analysis of Bangla Text Using Gated Recurrent Neural Network

Sentiment analysis is a fundamental part of Natural Language Processing. There are numerous works on this topic in English and other languages. However, it is still a comparatively new practice in Bangla. The absence of a suitable Bangla corpus is the primary obstacle for sentiment analysis tasks in Bangla. Nonetheless, Long Short-Term Memory (LSTM) is a common technique for resolving sentiments from a dataset containing a large amount of text data. However, Gated Recurrent Unit (GRU) is very efficient for datasets with a low amount of text data. In this manuscript, we present a five-layered GRU neural network model, each layer comprising of 48 neurons, applied the model on an existing Bangla corpus. We have implemented the ten-fold cross-validation approach and repeated the same processes three times. Each time, we have considered the averages of the ten validation accuracy and losses and compared the results with the state-of-the-art published outcome (77.85% highest accuracy) for Bidirectional LSTM (BLSTM). The highest accuracy for our model is 78.41%, while the lowest accuracy is 76.34%.
Design of a Virtual Assistant to Improve Interaction Between the Audience and the Presenter

This article presents a novel design of a Virtual Assistant as part of a human-machine interaction system to improve communication between the presenter and the audience that can be used in education or general presentations for improving interaction during the presentations (e.g., auditoriums with 200 people).The main goal of the proposed model is the design of a framework of interaction to increase the level of attention of the public in key aspects of the presentation.In this manner, the collaboration between the presenter and Virtual Assistant could improve the level of learning among the public.The design of the Virtual Assistant relies on non-anthropomorphic forms with 'live' characteristics generating an intuitive and self-explainable interface.A set of intuitive and useful virtual interactions to support the presenter was designed.This design was validated from various types of the public with a psychological study based on a discrete emotions' questionnaire confirming the adequacy of the proposed solution.The human-machine interaction system supporting the Virtual Assistant should automatically recognize the attention level of the audience from audiovisual resources and synchronize the Virtual Assistant with the presentation.The system involves a complex artificial intelligence architecture embracing perception of high-level features from audio and video, knowledge representation, and reasoning for pervasive and affective computing and reinforcement learning to teach the intelligent agent to decide on the best strategy to increase the level of attention of the audience.
Real-time BISINDO Hand Gesture Detection and Recognition with Deep Learning CNN

BISINDO is one of Indonesian sign language, which do not have many facilities to implement. Because it can cause deaf people have difficulty to live their daily life. Therefore, this research tries to offer an recognition or translation system of the BISINDO alphabet into a text. The system is expected to help deaf people to communicate in two directions. In this study the problems encountered is small datasets. Therefore this research will do the testing of hand gesture recognition, by comparing two model CNN algorithms, that is LeNet-5 and Alexnet. This test will look for which classification technique is better if the dataset conditions in an amount that does not reach 1000 images in each class. After testing, the results found that the CNN technique on the Alexnet architectural model is better to used, this is because when doing the testing process by using still-image and Alexnet model data which has been released in training process, Alexnet model data gives greater prediction results that is equal to 76%. While the LeNet model is only able to predict with the percentage of 19%. When that Alexnet data model used on the system offered, only able to predict correcly by 60%.&#x0D; &#x0D; Keywords: Sign language, BISINDO, Computer Vision, Hand Gesture Recognition, Skin Segmentation, CIELab, Deep Learning, CNN.
Deep Learning Approach for Voice Pathology Detection and Classification

A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.
Deep Feature CycleGANs: Speaker Identity Preserving Non-Parallel Microphone-Telephone Domain Adaptation for Speaker Verification

With the increase in the availability of speech from varied domains, it is imperative to use such out-of-domain data to improve existing speech systems. Domain adaptation is a prominent pre-processing approach for this. We investigate it for adapt microphone speech to the telephone domain. Specifically, we explore CycleGAN-based unpaired translation of microphone data to improve the x-vector/speaker embedding network for Telephony Speaker Verification. We first demonstrate the efficacy of this on real challenging data and then, to improve further, we modify the CycleGAN formulation to make the adaptation task-specific. We modify CycleGAN's identity loss, cycle-consistency loss, and adversarial loss to operate in the deep feature space. Deep features of a signal are extracted from an auxiliary (speaker embedding) network and, hence, preserves speaker identity. Our 3D convolution-based Deep Feature Discriminators (DFD) show relative improvements of 5-10% in terms of equal error rate. To dive deeper, we study a challenging scenario of pooling (adapted) microphone and telephone data with data augmentations and telephone codecs. Finally, we highlight the sensitivity of CycleGAN hyper-parameters and introduce a parameter called probability of adaptation.
Development of security systems using DNN and i &amp; x-vector classifiers

The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.
AMDFNet: Adaptive multi-level deformable fusion network for RGB-D saliency detection

Effective exploration of useful contextual information in multi-modal images is an essential task in salient object detection. Nevertheless, the existing methods based on the early-fusion or the late-fusion schemes cannot address this problem as they are unable to effectively resolve the distribution gap and information loss. In this paper, we propose an adaptive multi-level deformable fusion network (AMDFNet) to exploit the cross-modality information. We use a cross-modality deformable convolution module to dynamically adjust the boundaries of salient objects by exploring the extra input from another modality. This enables incorporating the existing features and propagating more contexts so as to strengthen the model’s ability to perceiving scenes. To accurately refine the predicted maps, a multi-scaled feature refinement module is proposed to enhance the intermediate features with multi-level prediction in the decoder part. Furthermore, we introduce a selective cross-modality attention module in the fusion process to exploit the attention mechanism. This module captures dense long-range cross-modality dependencies from a multi-modal hierarchical feature’s perspective. This strategy enables the network to select more informative details and suppress the contamination caused by the negative depth maps. Experimental results on eight benchmark datasets demonstrate the effectiveness of the components in our proposed model, as well as the overall saliency model.
Self-Supervised End-to-End ASR for Low Resource L2 Swedish

Unlike traditional (hybrid) Automatic Speech Recognition (ASR), end-to-end ASR systems simplify the training procedure by directly mapping acoustic features to sequences of graphemes or characters, thereby eliminating the need for specialized acoustic, language, or pronunciation models.However, one drawback of end-to-end ASR systems is that they require more training data than conventional ASR systems to achieve similar word error rate (WER).This makes it difficult to develop ASR systems for tasks where transcribed target data is limited such as developing ASR for Second Language (L2) speakers of Swedish.Nonetheless, recent advancements in selfsupervised acoustic learning, manifested in wav2vec models [1, 2, 3], leverage the available untranscribed speech data to provide compact acoustic representation that can achieve low WER when incorporated in end-to-end systems.To this end, we experiment with several monolingual and cross-lingual selfsupervised acoustic models to develop end-to-end ASR system for L2 Swedish.Even though our test is very small, it indicates that these systems are competitive in performance with traditional ASR pipeline.Our best model seems to reduce the WER by 7% relative to our traditional ASR baseline trained on the same target data.
Wheelchair Control With Inductive Intra-Oral Tongue Interface for Individuals With Tetraplegia

High level spinal cord injury drastically reduces the quality of life of the injured person.Various systems attempted to interface the still intact or residual abilities following injury in various monomodal or multimodal designs to compensate for the highly affected mobility.An intra-oral inductive tongue computer interface (ITCI) has been designed to provide real-time discrete and proportional control for computers and assistive devices and to meet specific requirements for individuals with tetraplegia.Operation of the ITCI for wheelchair control was demonstrated with two participants with tetraplegia in a short-term training study.Additionally, two able-bodied individuals participated in the study.For each participant, the ability to drive a Permobil C500 with the ITCI was compared to that when driving the wheelchair with joystick (mouth-stick in one case) along two different lanes of 39 m, by reporting the speed along the lanes and the number of obstacles hit.The lanes consisted of 90 0 , 360 0 , and complex maneuver segments linked by linear segments.The ITCI featured a mouthpiece encapsulating two pads of inductive sensors, driving electronics, and battery.The mouthpiece was attached to the palate of the participant's oral cavity with dental retainers.A piercing-like activation unit was attached to the tongue.Data were transmitted wirelessly to a central unit that controlled the wheelchair through wired interface.Among all participants, mean speeds along lane A or B reached maximal values between 0.42 and 0.74 m/s when driving with the ITCI, representing 41 to 71% of that obtained when driving with the joystick.
Fast Target Tracking Based on Improved Deep Sort and YOLOv3 Fusion Algorithm

Aiming at fast moving targets, such as ships, high-speed vehicles and athletes, this paper discusses a series of target detection algorithms based on neural network, YOLOv3 and background modeling. Compared KCF tracking with SSD tracking, Gaussian filter was applied to remove noise from pictures, and edge preserving filter was used to preserve edge features. Moreover, the algorithm combining deepsort tracking algorithm with YOLOv3 detection algorithm can improve the accuracy of YOLOv3 target detection, solve the problem of target loss during target tracking, adjust the frame size in real time, and improve the fit with the target position. Experiments show that the proposed algorithm based on detection before tracking has strong learning ability and robustness to unknown environment.
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages

We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning.We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages.A first of its kind detailed study is presented to track performance change as languages are added to a base language in a graded and greedy (in the sense of best boost of performance) manner; which reveals that careful selection of subset of related languages can significantly improve performance than utilizing all related languages.The Indo-Aryan (IA) language family is chosen for the study, the exact languages being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu.The script barrier is crossed by simple rule-based transliteration of the text of all languages to Devanagari.Experiments are performed on mBERT, IndicBERT, MuRIL and two RoBERTa-based LMs, the last two being pre-trained by us.Low resource languages, such as Oriya and Punjabi, are found to be the largest beneficiaries of multilingual fine-tuning.Textual Entailment, Entity Classification, Section Title Prediction, tasks of IndicGLUE and POS tagging form our test bed.Compared to monolingual fine tuning we get relative performance improvement of up to 150% in the downstream tasks.The surprise take-away is that for any language there is a particular combination of other languages which yields the best performance, and any additional language is in fact detrimental.
The Mediating Role of Student Satisfaction in the Relationship between Determinants of Online Student Satisfaction and Student Commitment

The present study uses a proposed model to explore satisfaction and commitment of students in an online learning environment. First, it presents the key determinants of student satisfaction and their impact on student commitment. Second, it examines the significance impact of each of the three key determinants namely course structure, online tutorials flexibility and technology quality on both student satisfaction and commitment.
Who is more trustworthy, Alexa or mom?: Children’s selective trust in a digital age.

Children obtain much of their early knowledge informally through first-hand observations (e.g., children as little scientists).Though the "little scientist" approach has a strong history within cognitive development, a newer perspective suggests it is not only independent observation of the environment through which children learn but also by posing questions to trustworthy individuals around them.Now, in a digital age, children can inform their understanding of the world by asking questions not only of adults but also of digital agents who are able to scour the internet and provide quick responses to children's questions.As a result of voicesearch functionalities typically present in digital voice assistants (DVAs; e.g., Alexa), children can query the internet at an earlier age, sidestepping literacy-based prerequisite skills needed for traditional computer search engines.This review examines several child-level and technology-level factors that may influence children's trust in DVAs relative to human informants.No work has combined these lines of inquiry in a way that has extended social (i.e., confidence, benevolence) and epistemic characteristics (i.e., accuracy, expertise) used in human-to-human interaction to digital voice assistant-to-human interaction.Results suggest DVS do not yet meet children's expectations for a trustworthy informant, providing implications to improve DVA functionality for young users.