Reviewerly/exHarmony huggingface dataset:
Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics

Unsupervised spoken term discovery (UTD) aims at finding recurring segments of speech from a corpus of acoustic speech data.One potential approach to this problem is to use dynamic time warping (DTW) to find well-aligning patterns from the speech data.However, automatic selection of initial candidate segments for the DTW-alignment and detection of "sufficiently good" alignments among those require some type of predefined criteria, often operationalized as threshold parameters for pair-wise distance metrics between signal representations.In the existing UTD systems, the optimal hyperparameters may differ across datasets, limiting their applicability to new corpora and truly low-resource scenarios.In this paper, we propose a novel probabilistic approach to DTW-based UTD named as PDTW.In PDTW, distributional characteristics of the processed corpus are utilized for adaptive evaluation of alignment quality, thereby enabling systematic discovery of pattern pairs that have similarity what would be expected by coincidence.We test PDTW on Zero Resource Speech Challenge 2017 datasets as a part of 2020 implementation of the challenge.The results show that the system performs consistently on all five tested languages using fixed hyperparameters, clearly outperforming the earlier DTW-based system in terms of coverage of the detected patterns.
Exploring Students' Views in the Use of Quizizz as an Assessment Tool in English as a Foreign Language (EFL) Class

The existence of educational applications that have been widely used in various universities creates new learning styles.One of the most frequently used online-based learning applications is Quizizz.Nowadays, Quizizz is commonly used as an assessment tool in almost all levels of education.This study aimed to explore students' views in using Quizizz as an assessment tool in an English class.This article employed a case study as a method.A semi-structured interview was utilized to gather the data.The participants of this research were students in one of the private universities in Yogyakarta.The results revealed that some of the students' views regarding the use of Quizizz are: (1) an interesting tool, (2) encouraging students' confidence, (3) increasing students' motivation, and (4) improving reading ability.Furthermore, the students gave a positive response to the use of Quizizz in the classroom.These findings contributed to a better understanding regarding the positive response to the use of Quizizz application as an assessment tool, particularly for the Indonesian teachers.In conclusion, several findings were highlighted.
Biological interpretation of deep neural network for phenotype prediction based on gene expression

Abstract Background The use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field. Results We focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians. Conclusion We propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.
Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking

Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST).Existing approaches generally fall into two extremes: choosing models without ontology or embedding ontology in models leading to over-dependence.In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN.Moreover, we supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value.SA shares knowledge between slots and utterances and only needs a simple structure to predict the supporting span.VN is designed specifically for the use of ontology, which can convert supporting spans to the values.Empirical results demonstrate that SAVN achieves the state-of-the-art joint accuracy of 54.52% on MultiWOZ 2.0 and 54.86% on MultiWOZ 2.1.Besides, we evaluate VN with incomplete ontology.The results show that even if only 30% ontology is used, VN can also contribute to our model.
Wrangling messy CSV files by detecting row and type patterns

Data scientists spend the majority of their time on preparing data for analysis. One of the first steps in this preparation phase is to load the data from the raw storage format. Comma-separated value (CSV) files are a popular format for tabular data due to their simplicity and ostensible ease of use. However, formatting standards for CSV files are not followed consistently, so each file requires manual inspection and potentially repair before the data can be loaded, an enormous waste of human effort for a task that should be one of the simplest parts of data science. The first and most essential step in retrieving data from CSV files is deciding on the dialect of the file, such as the cell delimiter and quote character. Existing dialect detection approaches are few and non-robust. In this paper, we propose a dialect detection method based on a novel measure of data consistency of parsed data files. Our method achieves 97% overall accuracy on a large corpus of real-world CSV files and improves the accuracy on messy CSV files by almost 22% compared to existing approaches, including those in the Python standard library. Our measure of data consistency is not specific to the data parsing problem, and has potential for more general applicability.
Entanglement dynamics of moving qubits in a common environment

In this paper we provide an analytical investigation of the entanglement dynamics of moving qubits dissipating into a common and (in general) non-Markovian environment for both weak and strong coupling regimes. We first consider the case of two moving qubits in a common environment and then generalize it to an arbitrary number of moving qubits. We show that for an initially entangled state, the environment washes out the initial entanglement after a finite interval of time. We also show that the movement of the qubits can play a constructive role in protecting of the initial entanglement. In this case, we observe a Zeno-like effect due to the velocity of the qubits. On the other hand, by limiting the number of qubits initially in a superposition of single excitation, a stationary entanglement can be achieved between the qubits initially in the excited and ground states. Surprisingly, we illustrate that when the velocity of all qubits are the same, the stationary state of the qubits does not depend on this velocity as well as the environmental properties. This allows us to determine the stationary distribution of the entanglement versus the total number of qubits in the system.
Efficient Quantum Teleportation of Unknown Qubit Based on DV-CV Interaction Mechanism

We propose and develop the theory of quantum teleportation of an unknown qubit based on the interaction mechanism between discrete-variable (DV) and continuous-variable (CV) states on highly transmissive beam splitter (HTBS). This DV-CV interaction mechanism is based on the simultaneous displacement of the DV state on equal in absolute value, but opposite in sign displacement amplitudes by coherent components of the hybrid in such a way that all the information about the displacement amplitudes is lost with subsequent registration of photons in the auxiliary modes. The relative phase of the displaced unknown qubit in the measurement number state basis can vary on opposite, depending on the parity of the basis states in the case of the negative amplitude of displacement that is akin to action of nonlinear effect on the teleported qubit. All measurement outcomes of the quantum teleportation are distinguishable, but the teleported state at Bob's disposal may acquire a predetermined amplitude-distorting factor. Two methods of getting rid of the factors are considered. The quantum teleportation is considered in various interpretations. A method for increasing the efficiency of quantum teleportation of an unknown qubit is proposed.
Robust mode conversion in NV centers using exceptional points

This paper proposes a way to realize topological mode switches with NV centers by using exceptional points. This theory is applicable to open quantum systems whose quantum state is described by a density matrix. The authors provide guidelines for coping with the main challenges for the experimental realization of this protocol: decoherence and mixed-state preparation.
Learning Adaptive Language Interfaces through Decomposition

Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings.We introduce a neural semantic parsing system that learns new high-level abstractions through decomposition: users interactively teach the system by breaking down high-level utterances describing novel behavior into low-level steps that it can understand.Unfortunately, existing methods either rely on grammars which parse sentences with limited flexibility, or neural sequence-to-sequence models that do not learn efficiently or reliably from individual examples.Our approach bridges this gap, demonstrating the flexibility of modern neural systems, as well as the one-shot reliable generalization of grammar-based methods.Our crowdsourced interactive experiments suggest that over time, users complete complex tasks more efficiently while using our system by leveraging what they just taught.At the same time, getting users to trust the system enough to be incentivized to teach high-level utterances is still an ongoing challenge.We end with a discussion of some of the obstacles we need to overcome to fully realize the potential of the interactive paradigm.
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning

Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks.However, the limited weight storage and computational speed on hardware platforms have impeded the popularity of pre-trained models, especially in the era of edge computing.In this work, we propose an efficient transformer-based large-scale language representation using hardware-friendly block structure pruning.We incorporate the reweighted group Lasso into block-structured pruning for optimization.Besides the significantly reduced weight storage and computation, the proposed approach achieves high compression rates.Experimental results on different models (BERT, RoBERTa, and DistilBERT) on the General Language Understanding Evaluation (GLUE) benchmark tasks show that we achieve up to 5.0× with zero or minor accuracy degradation on certain task(s).Our proposed method is also orthogonal to existing compact pre-trained language models such as DistilBERT using knowledge distillation, since a further 1.79× average compression rate can be achieved on top of DistilBERT with zero or minor accuracy degradation.It is suitable to deploy the final compressed model on resource-constrained edge devices.We share the related codes and models at: https://bi t.ly/3cvs2N2
Adversarial Semantic Collisions

We study semantic collisions: texts that are semantically unrelated but judged as similar by NLP models.We develop gradient-based approaches for generating semantic collisions and demonstrate that state-of-the-art models for many tasks which rely on analyzing the meaning and similarity of texts-including paraphrase identification, document retrieval, response suggestion, and extractive summarization-are vulnerable to semantic collisions.For example, given a target query, inserting a crafted collision into an irrelevant document can shift its retrieval rank from 1000 to top 3.We show how to generate semantic collisions that evade perplexity-based filtering and discuss other potential mitigations.Our code is available at https://github.com/csong27/collision-bert.
Test of the unitary coupled-cluster variational quantum eigensolver for a simple strongly correlated condensed-matter system

The variational quantum eigensolver has been proposed as a low-depth quantum circuit that can be employed to examine strongly correlated systems on today’s noisy intermediate-scale quantum computers. We examine details associated with the factorized form of the unitary coupled-cluster variant of this algorithm. We apply it to a simple strongly correlated condensed-matter system with nontrivial behavior — the four-site Hubbard model at half-filling. This work show some of the subtle issues one needs to take into account when applying this algorithm in practice, especially to condensed-matter systems.
InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction

Identifying informative tweets is an important step when building information extraction systems based on social media.WNUT-2020 Task 2 was organised to recognise informative tweets from noise tweets.In this paper, we present our approach to tackle the task objective using transformers.Overall, our approach achieves 10 th place in the final rankings scoring 0.9004 F1 score for the test set.
Semantic Relational Object Tracking

This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on the rich continuous data from perceptual sensor data. A novel anchoring matching function method learns to maintain object entities in space and time and is validated using a large set of trained humanly annotated ground truth data of real-world objects. For more complex scenarios, a high-level probabilistic object tracker has been integrated with the anchoring framework and handles the tracking of occluded objects via reasoning about the state of unobserved objects. We demonstrate the performance of our integrated approach through scenarios such as the shell game scenario, where we illustrate how anchored objects are retained by preserving relations through probabilistic reasoning.
On the Reliability and Validity of Detecting Approval of Political Actors in Tweets

Social media sites like Twitter possess the potential to complement surveys that measure political opinions and, more specifically, political actors' approval.However, new challenges related to the reliability and validity of social-media-based estimates arise.Various sentiment analysis and stance detection methods have been developed and used in previous research to measure users' political opinions based on their content on social media.In this work, we attempt to gauge the efficacy of untargeted sentiment, targeted sentiment, and stance detection methods in labeling various political actors' approval by benchmarking them across several datasets.We also contrast the performance of these pretrained methods that can be used in an off-the-shelf (OTS) manner against a set of models trained on minimal custom data.We find that OTS methods have low generalizability on unseen and familiar targets, while low-resource custom models are more robust.Our work sheds light on the strengths and limitations of existing methods proposed for understanding politicians' approval from tweets.
Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data

The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior.We identify failure modes of SOTA relation extraction (RE) models trained on TACRED, which we attribute to limitations in the data annotation process.We collect and annotate a challengeset we call Challenging RE (CRE), based on naturally occurring corpus examples, to benchmark this behavior.Our experiments with four state-of-the-art RE models show that they have indeed adopted shallow heuristics that do not generalize to the challenge-set data.Further, we find that alternative question answering modeling performs significantly better than the SOTA models on the challenge-set, despite worse overall TACRED performance.By adding some of the challenge data as training examples, the performance of the model improves.Finally, we provide concrete suggestion on how to improve RE data collection to alleviate this behavior.
BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues

Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances which query different segments and/or different objects in videos over multiple dialogue turns. However, existing approaches to video-grounded dialogues often focus on superficial temporal-level visual cues, but neglect more fine-grained spatial signals from videos. To address this drawback, we proposed Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos based on textual cues. Specifically, our approach not only exploits both spatial and temporal-level information, but also learns dynamic information diffusion between the two feature spaces through spatial-to-temporal and temporal-to-spatial reasoning. The bidirectional strategy aims to tackle the evolving semantics of user queries in the dialogue setting. The retrieved visual cues are used as contextual information to construct relevant responses to the users. Our empirical results and comprehensive qualitative analysis show that BiST achieves competitive performance and generates reasonable responses on a large-scale AVSD benchmark. We also adapt our BiST models to the Video QA setting, and substantially outperform prior approaches on the TGIF-QA benchmark.
Accelerated variational algorithms for digital quantum simulation of many-body ground states

One of the key applications for the emerging quantum simulators is to emulate the ground state of many-body systems, as it is of great interest in various fields from condensed matter physics to material science. Traditionally, in an analog sense, adiabatic evolution has been proposed to slowly evolve a simple Hamiltonian, initialized in its ground state, to the Hamiltonian of interest such that the final state becomes the desired ground state. Recently, variational methods have also been proposed and realized in quantum simulators for emulating the ground state of many-body systems. Here, we first provide a quantitative comparison between the adiabatic and variational methods with respect to required quantum resources on digital quantum simulators, namely the depth of the circuit and the number of two-qubit quantum gates. Our results show that the variational methods are less demanding with respect to these resources. However, they need to be hybridized with a classical optimization which can converge slowly. Therefore, as the second result of the paper, we provide two different approaches for speeding the convergence of the classical optimizer by taking a good initial guess for the parameters of the variational circuit. We show that these approaches are applicable to a wide range of Hamiltonian and provide significant improvement in the optimization procedure.
From human mesenchymal stromal cells to osteosarcoma cells classification by deep learning

Early diagnosis of cancer often allows for a more vast choice of therapy opportunities. After a cancer diagnosis, staging provides essential information about the extent of disease in the body and the expected response to a particular treatment. The leading importance of classifying cancer patients at the early stage into high or low-risk groups has led many research teams, both from the biomedical and bioinformatics field, to study the application of Deep Learning (DL) methods. The ability of DL to detect critical features from complex datasets is a significant achievement in early diagnosis and cell cancer progression. In this paper, we focus the attention on osteosarcoma. Osteosarcoma is one of the primary malignant bone tumors which usually afflicts people in adolescence. Our contribution to the classification of osteosarcoma cells is made as follows: a DL approach is applied to discriminate human Mesenchymal Stromal Cells (MSCs) from osteosarcoma cells and to classify the different cell populations under investigation. Glass slides of differ-ent cell populations were cultured including MSCs, differentiated in healthy bone cells (osteoblasts) and osteosarcoma cells, both single cell populations or mixed. Images of such samples of isolated cells (single-type of mixed) are recorded with traditional optical microscopy. DL is then applied to identify and classify single cells. Proper data augmentation techniques and cross-fold validation are used to appreciate the capabilities of a convolutional neural network to address the cell detection and classification problem. Based on the results obtained on individual cells, and to the versatility and scalability of our DL approach, the next step will be its application to discriminate and classify healthy or cancer tissues to advance digital pathology.
A Spatial-Temporal Recurrent Neural Network for Video Saliency Prediction

In this paper, a recurrent neural network is designed for video saliency prediction considering spatial-temporal features. In our work, video frames are routed through the static network for spatial features and the dynamic network for temporal features. For the spatial-temporal feature integration, a novel select and re-weight fusion model is proposed which can learn and adjust the fusion weights based on the spatial and temporal features in different scenes automatically. Finally, an attention-aware convolutional long short term memory (ConvLSTM) network is developed to predict salient regions based on the features extracted from consecutive frames and generate the ultimate saliency map for each video frame. The proposed method is compared with state-of-the-art saliency models on five public video saliency benchmark datasets. The experimental results demonstrate that our model can achieve advanced performance on video saliency prediction.