Reviewerly/exHarmony huggingface dataset:
Multimodal Dialogue State Tracking

Designed for tracking user goals in dialogues, a dialogue state tracker is an essential component in a dialogue system.However, the research of dialogue state tracking has largely been limited to unimodality, in which slots and slot values are limited by knowledge domains (e.g.restaurant domain with slots of restaurant name and price range) and are defined by specific database schema.In this paper, we propose to extend the definition of dialogue state tracking to multimodality.Specifically, we introduce a novel dialogue state tracking task to track the information of visual objects that are mentioned in video-grounded dialogues.Each new dialogue utterance may introduce a new video segment, new visual objects, or new object attributes and a state tracker is required to update these information slots accordingly.We created a new synthetic benchmark and designed a novel baseline, Video-Dialogue Transformer Network (VDTN), for this task.VDTN combines both object-level features and segment-level features and learns contextual dependencies between videos and dialogues to generate multimodal dialogue states.We optimized VDTN for a state generation task as well as a self-supervised video understanding task which recovers video segment or object representations.Finally, we trained VDTN to use the decoded states in a response prediction task.Together with comprehensive ablation and qualitative analysis, we discovered interesting insights towards building more capable multimodal dialogue systems.Dialogue HUMAN: I am looking for a cheap restaurant in the centre of the city.
How to undertake reviews of large collections of articles and establish main contributions: an ontology-based literature review approach

Identifying the main contributions of scientific works may demand time and previous depth knowledge, besides it can be a bottleneck for reviewing processes. To make it easier, this paper presents a standard representation based on the Knowledge Discovery in Databases process to characterize knowledge-discovery-in-databases scientific documents. This first attempt consists of a string composed of five elements, which provide: (i) the complexity of the problem; the tasks from the (ii) preprocessing, (iii) processing, and (iv) post-processing step, and (v) the auxiliary approaches such document used to achieve its objectives. As all documents can be classified using this approach, this proposal is scalable by text mining algorithms, making the review working easier for scientists since this methodology saves the time of the document's main classification.
Readers’ affect: predicting and understanding readers’ emotions with deep learning

Abstract Emotions are highly useful to model human behavior being at the core of what makes us human. Today, people abundantly express and share emotions through social media. Technological advancements in such platforms enable sharing opinions or expressing any specific emotions towards what others have shared, mainly in the form of textual data. This entails an interesting arena for analysis; as to whether there is a disconnect between the writer’s intended emotion and the reader’s perception of textual content. In this paper, we present experiments for Readers’ Emotion Detection through multi-target regression settings by exploring a Bi-LSTM-based Attention model, where our major intention is to analyze the interpretability and effectiveness of the deep learning model for the task. To conduct experiments, we procure two extensive datasets REN-10k and RENh-4k, apart from using a popular benchmark dataset from SemEval-2007. We perform a two-phase experimental evaluation, first being various coarse-grained and fine-grained evaluations of our model performance in comparison with several baselines belonging to different categories of emotion detection, viz., deep learning, lexicon based, and classical machine learning. Secondly, we evaluate model behavior towards readers’ emotion detection assessing attention maps generated by the model through devising a novel set of qualitative and quantitative metrics. The first phase of experiments shows that our Bi-LSTM + Attention model significantly outperforms all baselines. The second analysis reveals that emotions may be correlated to specific words as well as named entities.
REFLECTIONS ON PUBLIC HEALTH IN MARANHÃO AND THE SEASONALITY OF COVID-19 CASES

The Geography of Health is a field of study that is gaining more and more space among geographers. Given the emergence of epidemic outbreaks, and more recently pandemics (COVID-19 in 2019 and still in effect), the relevance of health studies from a geographic perspective is verified. The main objective of this research was to reflect on public health in the state of Maranhão in the face of the health crisis caused by the Covid-19 pandemic, a disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), identifying the rates of lethality in the state, noting that the capital São Luís and the municipalities of Imperatriz, Açailândia, Balsas, Caxias, Santa Inês and Timon concentrate the largest numbers of confirmed cases.&#x0D;
A multi camera unsupervised domain adaptation pipeline for object detection in cultural sites through adversarial learning and self-training

Object detection algorithms allow to enable many interesting applications which can be implemented in different devices, such as smartphones and wearable devices. In the context of a cultural site, implementing these algorithms in a wearable device, such as a pair of smart glasses, allow to enable the use of augmented reality (AR) to show extra information about the artworks and enrich the visitors' experience during their tour. However, object detection algorithms require to be trained on many well annotated examples to achieve reasonable results. This brings a major limitation since the annotation process requires human supervision which makes it expensive in terms of time and costs. A possible solution to reduce these costs consist in exploiting tools to automatically generate synthetic labeled images from a 3D model of the site. However, models trained with synthetic data do not generalize on real images acquired in the target scenario in which they are supposed to be used. Furthermore, object detectors should be able to work with different wearable devices or different mobile devices, which makes generalization even harder. In this paper, we present a new dataset collected in a cultural site to study the problem of domain adaptation for object detection in the presence of multiple unlabeled target domains corresponding to different cameras and a labeled source domain obtained considering synthetic images for training purposes. We present a new domain adaptation method which outperforms current state-of-the-art approaches combining the benefits of aligning the domains at the feature and pixel level with a self-training process. We release the dataset at the following link https://iplab.dmi.unict.it/OBJ-MDA/ and the code of the proposed architecture at https://github.com/fpv-iplab/STMDA-RetinaNet.
Imperfection-insensitivity quantum random number generator with untrusted daily illumination

Quantum random number generators (QRNGs) promise secure randomness generation based on the foundational unpredictability of quantum mechanics. However, the unavoidable gaps between theoretical models and practical devices could lead to security invalidation. Recently, a source-independent quantum random number generator (SI-QRNG) has been proposed to solve the issue of uncharacteristic sources. However, in most current analyses of SI-QRNG protocols, the security proofs with imperfect measurements are individual for different factors and very sensitive to small deviations from theoretical models. Here, we establish a unified model for imperfect measurements in the SI-QRNG and provide a tight rate bound based on the uncertainty relation for smooth entropies. Then the performance with large device imperfections is evaluated and the randomness rate in our model can approach a similar order of magnitude of the rate upper bound in common discrete variable QRNGs. In addition, by utilizing the daily illumination and measurement devices with large imperfections, we experimentally demonstrate our scheme at the rate of the order of magnitude of Mbps.
Shilling attack detection for collaborative recommender systems: a gradient boosting method

Organized malicious shilling attackers influence the output of the collaborative filtering recommendation systems by inserting fake users into the rating matrix within the database. The existence of shilling attack poses a serious risk to the stability of the system. To counter this specific security threat, many attack detection methods are proposed. Some of the past methods suffer from two disadvantages, the first being that they only analyze the rating matrix from a single perspective of user rating values and ignore other perspectives. Another is that some methods only use a single classifier to handle the classification of malicious attackers. Considering the above disadvantages, this paper proposes a gradient boosting method (named XGB-SAD) to achieve attack detection by combining double-view and gradient boosting. We first analyze the rating matrix with a double-view of time and item, which in turn defines the TPUS collection. Then our method uses eXtreme Gradient Boosting to perform heuristic iterative optimization of the model's objective function and uses the idea of ensemble learning to integrate multiple sets of base classifiers into strong classifier. The integrated strong classifiers are used to complete the detection of malicious attackers. Finally, we perform several experiments and the results demonstrate that XGB-SAD outperforms the comparison methods in terms of small-scale attack detection and overall detection, which proves the performance of our method.
Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling

This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. Even though PGL formulations result in improvements in the generalization performance and lead to machine learning (ML) predictions that are more physically consistent, simply adding the physics-based loss function in the learning objective does not overcome the black-box nature of neural network architectures, which often involve arbitrary design choices. The temperature of water in a lake is a fundamental driver of lake biogeochemical processes, and it controls the growth, survival, and reproduction of fishes in the lake.
Evaluating the Usability of Virtual Tour Application Using the System Usability Scale (SUS) Method

UPN "Veteran" Jawa Timur Virtual Tour is a campus virtual application that offers digital information about the university's buildings, rooms, and facilities. It is required to examine usability in order to determine whether the virtual campus tour of UPN "Veteran" Jawa Timur is useful to students. Numerous issues were discovered in the virtual campus tour of UPN "Veteran" Jawa Timur based on observational research by several students. users-friendly The System Usability Scale is a technique for evaluating information systems that involves end users and looks at three aspects: adjective rating, grade scale, and acceptability. Ten statement items are employed as an evaluation measure in the process of evaluating the usability scale system. The ultimate outcome of the evaluation was a 64 on a scale of one to ten. According to these criteria, the Virtual Tour of UPN "Veteran" Jawa Timur belongs to the high adjective rating group, group D on the grading scale, and group good on the acceptability scale. If the Virtual of UPN "Veteran" Jawa Timur Tour is still used, it can be stated that the usability component of the virtual tour has to be improved.
VideoCLIP: A Cross-Attention Model for Fast Video-Text Retrieval Task with Image CLIP

Video-text retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant videos from a large and unlabelled dataset given textual queries. Existing methods that simply pool the image features (e.g., based on the CLIP encoder [14]) from frames to build the video descriptor often result in sub-optimal video-text search accuracy since the information among different modalities is not fully exchanged and aligned. In this paper, we proposed a novel dual-encoder model to address the challenging video-text retrieval problem, which uses a highly efficient cross-attention module to facilitate the information exchange between multiple modalities (i.e., video and text). The proposed VideoCLIP is evaluated on two benchmark video-text datasets, MSRVTT and DiDeMo, and the results show that our model can outperform existing state-of-the-art methods while the retrieval speed is much faster than the traditional query-agnostic search model.
Quantum correlation between a qubit and a relativistic boson in an expanding spacetime

Abstract We use the quantum correlation of both logarithmic negativity and mutual information between a qubit and a relativistic boson to analyze the dynamics of Universe expansion. These dynamical quantum correlations can encode the information about underlying spacetime structure, which suggests a promising application in observational cosmology. We find that the dynamics of both logarithmic negativity and mutual information between the qubit and the boson are very similar. They decrease monotonically with the growth of the expansion volume and the expansion rate. Smaller momentum and medium-sized mass of boson are more favourable for extracting the information about history of Universe expansion. The quantum correlation between the qubit and the antiboson however has very different behavior: the logarithmic negativity is always zero and the mutual information can be generated through the expansion of Universe. Smaller momentum and medium-sized mass of antiboson are beneficial for the production of mutual information. Finally, the trigger phenomenon and conservation for mutual information are witnessed.
ER: Equivariance Regularizer for Knowledge Graph Completion

Tensor factorization and distanced based models play important roles in knowledge graph completion (KGC). However, the relational matrices in KGC methods often induce a high model complexity, bearing a high risk of overfitting. As a remedy, researchers propose a variety of different regularizers such as the tensor nuclear norm regularizer. Our motivation is based on the observation that the previous work only focuses on the “size” of the parametric space, while leaving the implicit semantic information widely untouched. To address this issue, we propose a new regularizer, namely, Equivariance Regularizer (ER), which can suppress overfitting by leveraging the implicit semantic information. Specifically, ER can enhance the generalization ability of the model by employing the semantic equivariance between the head and tail entities. Moreover, it is a generic solution for both distance based models and tensor factorization based models. Our experimental results indicate a clear and substantial improvement over the state-of-the-art relation prediction methods.
QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer

Current keyword spotting systems are typically trained with a large amount of pre-defined keywords. Recognizing keywords in an open-vocabulary setting is essential for personalizing smart device interaction. Towards this goal, we propose a pure MLP-based neural network that is based on MLPMixer - an MLP model architecture that effectively replaces the attention mechanism in Vision Transformers. We investigate different ways of adapting the MLPMixer architecture to the QbyE open-vocabulary keyword spotting task. Comparisons with the state-of-the-art RNN and CNN models show that our method achieves better performance in challenging situations (10dB and 6dB environments) on both the publicly available Hey-Snips dataset and a larger scale internal dataset with 400 speakers. Our proposed model also has a smaller number of parameters and MACs compared to the baseline models.
Maximal speed of propagation in open quantum systems

We prove a maximal velocity bound for the dynamics of Markovian open quantum systems. The dynamics are described by one-parameter semi-groups of quantum channels satisfying the von Neumann-Lindblad equation. Our result says that dynamically evolving states are contained inside a suitable light cone up to polynomial errors. We also give a bound on the slope of the light cone, i.e., the maximal propagation speed. The result implies an upper bound on the speed of propagation of local perturbations of stationary states in open quantum systems.
Examination of Digital Literacy Levels of Science Teachers in the Distance Education Process

Digital literacy skills could make important contributions to teachers’ learning and teaching process and professional skills. The purpose of this article is to examine the level of science teachers’ digital literacy skills in terms of gender and the duration of professional experience. The sample of the study consisted of 88 science teachers working in various districts of Turkey. Digital literacy scale and distance education evaluation interview form were used as data collection tools. The results showed that, no statistically significant difference was found in the digital literacy levels of female and male science teachers. However, digital literacy of teachers with less professional experience is more positive than teachers with more professional experience. Also, science teachers mostly prefer lecturing during distance education. The results show that distance education should be strengthened in terms of infrastructure, implementation, quality in Turkey. Within the scope of the research, it was suggested to organize various trainings to improve the digital literacy levels of teachers, to enrich the content of EBA used in the distance education, and to increase the in-service trainings to improve the distance education skills of teachers.
Episodic Policy Gradient Training

We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly. Unlike other hyperparameter searches, we formulate hyperparameter scheduling as a standard Markov Decision Process and use episodic memory to store the outcome of used hyperparameters and their training contexts. At any policy update step, the policy learner refers to the stored experiences, and adaptively reconfigures its learning algorithm with the new hyperparameters determined by the memory. This mechanism, dubbed as Episodic Policy Gradient Training (EPGT), enables an episodic learning process, and jointly learns the policy and the learning algorithm's hyperparameters within a single run. Experimental results on both continuous and discrete environments demonstrate the advantage of using the proposed method in boosting the performance of various policy gradient algorithms.
MINIMAL: Mining Models for Universal Adversarial Triggers

It is well known that natural language models are vulnerable to adversarial attacks, which are mostly input-specific in nature. Recently, it has been shown that there also exist input-agnostic attacks in NLP models, called universal adversarial triggers. However, existing methods to craft universal triggers are data intensive. They require large amounts of data samples to generate adversarial triggers, which are typically inaccessible by attackers. For instance, previous works take 3000 data samples per class for the SNLI dataset to generate adversarial triggers. In this paper, we present a novel data-free approach, MINIMAL, to mine input-agnostic adversarial triggers from models. Using the triggers produced with our data-free algorithm, we reduce the accuracy of Stanford Sentiment Treebank’s positive class from 93.6% to 9.6%. Similarly, for the Stanford Natural LanguageInference (SNLI), our single-word trigger reduces the accuracy of the entailment class from 90.95% to less than 0.6%. Despite being completely data-free, we get equivalent accuracy drops as data-dependent methods
Improving Transformer-based Conversational ASR by Inter-Sentential Attention Mechanism

Transformer-based models have demonstrated their effectiveness in automatic speech recognition (ASR) tasks and even shown superior performance over the conventional hybrid framework. The main idea of Transformers is to capture the long-range global context within an utterance by self-attention layers. However, for scenarios like conversational speech, such utterance-level modeling will neglect contextual dependencies that span across utterances. In this paper, we propose to explicitly model the inter-sentential information in a Transformer based end-to-end architecture for conversational speech recognition. Specifically, for the encoder network, we capture the contexts of previous speech and incorporate such historic information into current input by a context-aware residual attention mechanism. For the decoder, the prediction of current utterance is also conditioned on the historic linguistic information through a conditional decoder framework. We show the effectiveness of our proposed method on several open-source dialogue corpora and the proposed method consistently improved the performance from the utterance-level Transformer-based ASR models.
Inconsistent Ranking Assumptions in Medical Search and Their Downstream Consequences

Given a query, neural retrieval models predict point estimates of relevance for each document; however, a significant drawback of relying solely on point estimates is that they contain no indication of the model's confidence in its predictions. Despite this lack of information, downstream methods such as reranking, cutoff prediction, and none-of-the-above classification are still able to learn effective functions to accomplish their respective tasks. Unfortunately, these downstream methods can suffer poor performance when the initial ranking model loses confidence in its score predictions. This becomes increasingly important in high-stakes settings, such as medical searches that can influence health decision making.
Bayesian quantile regression for longitudinal count data

This work introduces Bayesian quantile regression modeling framework for the analysis of longitudinal count data. In this model, the response variable is not continuous and hence an artificial smoothing of counts is incorporated. The Bayesian implementation utilizes the normal-exponential mixture representation of the asymmetric Laplace distribution for the response variable. An efficient Gibbs sampling algorithm is derived for fitting the model to the data. The model is illustrated through simulation studies and implemented in an application drawn from neurology. Model comparison demonstrates the practical utility of the proposed model.