Reviewerly/exHarmony huggingface dataset:
TAN-NTM: Topic Attention Networks for Neural Topic Modeling

Topic models have been widely used to learn text representations and gain insight into document corpora.To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distribution.However, leveraging topic-word distribution for learning better features during document encoding has not been explored much.To this end, we develop a framework TAN-NTM, which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner.We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues.The output of topic attention module is then used to carry out variational inference.We perform extensive ablations and experiments resulting in ∼ 9 -15 percentage improvement over score of existing SOTA topic models in NPMI coherence on several benchmark datasets -20Newsgroups, Yelp Review Polarity and AGNews.Further, we show that our method learns better latent document-topic features compared to existing topic models through improvement on two downstream tasks: document classification and topic guided keyphrase generation.
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption

We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter. In this work, we propose an indicator to measure the robustness of neural network parameters by exploiting their vulnerability via parameter corruption. The proposed indicator describes the maximum loss variation in the non-trivial worst-case scenario under parameter corruption. For practical purposes, we give a gradient-based estimation, which is far more effective than random corruption trials that can hardly induce the worst accuracy degradation. Equipped with theoretical support and empirical validation, we are able to systematically investigate the robustness of different model parameters and reveal vulnerability of deep neural networks that has been rarely paid attention to before. Moreover, we can enhance the models accordingly with the proposed adversarial corruption-resistant training, which not only improves the parameter robustness but also translates into accuracy elevation.
ANALISIS KEPUASAN PENGGUNA SISTEM INFORMASI E-CAMPUS DI IAIN BUKITTINGGI MENGGUNAKAN METODE EUCS

This study aims to find out and analyze how the level of user satisfaction with an information system is being used, and to find out what factors influence the level of user satisfaction of the Information System. The object of this research is the E-Campus information system at the Islamic Institution of Bukittinggi state. Research is based on the importance of evaluation for the development of a system. The method used in this study is End User Computing Satisfaction (EUCS). The EUCS method consists of five independent variables, namely content, accuracy, shape, ease of use and timeliness and one dependent variable is satisfaction. Population data is N = 99 with the analysis results obtained by the correlation coefficient number of 73.3%. That is, there is a statistically positive relationship between content, accuracy, form, ease of use, timeliness, and satisfaction. if each variable has a higher correlation, it will increase the user satisfaction of the E-Campus Information System with the meaning of the relationship in the same direction.Keywords: End User Computing Satisfaction (EUCS), satisfaction, information system, E-Campus.
Tissue microarrays – brief history, techniques and clinical future

Introduction and Aim: There is a growing need for better, cheaper and faster histopathological diagnostic.The authors reviewed the main steps of the efforts towards the improvement of the pre-analytical phase of tissue processing for histological examination.Results: Since their introduction decades ago tissue microarrays (TMAs) proved their value by increasing efficiency, standardization and accuracy of many histological techniques, such as histochemistry, histoenzymology, immunohistochemistry, in situ hybridization, etc.By allowing the simultaneous analysis and comparison of multiple different tissues on a single histology slide (up to 1000 individual samples), TMAs are also having a significant economic advantage (consumables and labor).From its first description until recent years, the TMA techniques have evolved steadily but slowly despite many attempts to adapt it for clinical diagnostics.In this paper, we are reviewing the main techniques of obtaining TMA blocks from the beginning to the present day, as well as recent developments that are expanding their scope into high accuracy/efficiency clinical diagnostics.Conclusions: Considering recent developments, we believe that the prospect of high-throughput histology might be achievable in the not-so-distant future.
Boosting energy-time entanglement using coherent time-delayed feedback

The visibility of the two-photon interference in the Franson interferometer serves as a measure of the energy-time entanglement of the photons. We propose to control the visibility of the interference in the second-order coherence function by implementing a coherent time-delayed feedback mechanism. Simulating the non-Markovian dynamics within the matrix product state framework, we find that the visibility for two photons emitted from a three-level system (3LS) in ladder configuration can be enhanced significantly for a wide range of parameters by slowing down the decay of the upper level of the 3LS.
Developing Students’ Business Communication Competence In The Context Of Implementing FSES 3++

One of the main ideas of modern educational policy is the introduction of the federal standard (FSES HE 3++), which imposes special requirements for the organization and content of the entire educational system from general education to university levels.This approach ensures the unity of the all-Russian educational space from the continuity of the acquired knowledge to the guarantee of its quality.The introduction of FSES HE 3 ++ allows universities to quickly reflect the requirements and demands of the modern labor market, as well as to form effective competition on the Russian and foreign platform for the provision of educational services.The process of transition from one educational standard to another is observed, while the problem of ambiguous interpretation of some concepts of the same phenomena in the education system arises.One of the leading directions of modern higher education, fully revealing the content, effectiveness and quality of education, is the competence-based approach.The application of this approach in the context of the global modernization of the educational system forms a set of key competencies for a university graduate, which will be successfully implemented in the professional sphere in the future.With the advancement of the Federal State Educational Standard of Higher Education 3 ++ to universities, a new name for competencies arose -universal competencies.They detailed the previous formulations of general cultural and professional competences of previous versions of educational standards.
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation

Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequence.Such training objective is sub-optimal when the target sequence is not perfect, e.g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.To address the challenge, we propose a novel Edit-Invariant Sequence Loss (EISL), which computes the matching loss of a target n-gram with all n-grams in the generated sequence.EISL is designed to be robust to various noises and edits in the target sequences.Moreover, the EISL computation is essentially an approximate convolution operation with target n-grams as kernels, which is easy to implement and efficient to compute with existing libraries.To demonstrate the effectiveness of EISL, we conduct experiments on a wide range of tasks, including machine translation with noisy target sequences, unsupervised text style transfer with only weak training signals, and non-autoregressive generation with non-predefined generation order.Experimental results show our method significantly outperforms the common CE loss and other strong baselines on all the tasks.EISL has a simple API that can be used as a drop-in replacement of the CE loss. 1
TWAG: A Topic-Guided Wikipedia Abstract Generator

Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multidocument summarization techniques.However, previous works generally view the abstract as plain text, ignoring the fact that it is a description of a certain entity and can be decomposed into different topics.In this paper, we propose a two-stage model TWAG that guides the abstract generation with topical information.First, we detect the topic of each input paragraph with a classifier trained on existing Wikipedia articles to divide input documents into different topics.Then, we predict the topic distribution of each abstract sentence, and decode the sentence from topic-aware representations with a Pointer-Generator network.We evaluate our model on the Wi-kiCatSum dataset, and the results show that TWAG outperforms various existing baselines and is capable of generating comprehensive abstracts.
Reimagining Professional Development for Digital Literacies: Old, New and Pandemic

During the COVID-19 pandemic teachers have been expected to learn new digital literacy skills, often applying them immediately. While professional development structures within school districts and professional associations are organized to offer supports, teachers may be challenged to gain digital skills within existing professional development models. Within our study, teachers explored technologies with the aim of rethinking frames for teaching and learning literacy. Following the start of the COVID-19 pandemic they shared their experiences, insights and challenges. In our article, we address implications for digital literacy teaching and learning and the need for new ways of approaching teacher development.
Empowering Adaptive Early-Exit Inference with Latency Awareness

With the capability of trading accuracy for latency on-the-fly, the technique of adaptive early-exit inference has emerged as a promising line of research to accelerate the deep learning inference. However, studies in this line of research commonly use a group of thresholds to control the accuracy-latency trade-off, where a thorough and general methodology on how to determine these thresholds has not been conducted yet, especially with regard to the common requirements of average inference latency. To address this issue and enable latency-aware adaptive early-exit inference, in the present paper, we approximately formulate the threshold determination problem of finding the accuracy-maximum threshold setting that meets a given average latency requirement, and then propose a threshold determination method to tackle our formulated non-convex problem. Theoretically, we prove that, for certain parameter settings, our method finds an approximate stationary point of the formulated problem. Empirically, on top of various models across multiple datasets (CIFAR-10, CIFAR-100, ImageNet and two time-series datasets), we show that our method can well handle the average latency requirements, and consistently finds good threshold settings in negligible time.
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation

While synthetic bilingual corpora have demonstrated their effectiveness in low-resource neural machine translation (NMT), adding more synthetic data often deteriorates translation performance.In this work, we propose alternated training with synthetic and authentic data for NMT.The basic idea is to alternate synthetic and authentic corpora iteratively during training.Compared with previous work, we introduce authentic data as guidance to prevent the training of NMT models from being disturbed by noisy synthetic data.Experiments on Chinese-English and German-English translation tasks show that our approach improves the performance over several strong baselines.We visualize the BLEU landscape to further investigate the role of authentic and synthetic data during alternated training.From the visualization, we find that authentic data helps to direct the NMT model parameters towards points with higher BLEU scores and leads to consistent translation performance improvement.
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images

In multi-modal dialogue systems, it is important to allow the use of images as part of a multi-turn conversation.Training such dialogue systems generally requires a large-scale dataset consisting of multi-turn dialogues that involve images, but such datasets rarely exist.In response, this paper proposes a 45k multimodal dialogue dataset created with minimal human intervention.Our method to create such a dataset consists of (1) preparing and pre-processing text dialogue datasets, (2) creating image-mixed dialogues by using a textto-image replacement technique, and (3) employing a contextual-similarity-based filtering step to ensure the contextual coherence of the dataset.To evaluate the validity of our dataset, we devise a simple retrieval model for dialogue sentence prediction tasks.Automatic metrics and human evaluation results on such tasks show that our dataset can be effectively used as training data for multi-modal dialogue systems which require an understanding of images and text in a context-aware manner.Our dataset and generation code is available at https://github.com/shh1574/multi-modal-dialogue-dataset.
Exploring Auxiliary Reasoning Tasks for Task-oriented Dialog Systems with Meta Cooperative Learning

In this paper, we propose a Meta Cooperative Learning (MCL) framework for task-oriented dialog systems (TDSs). Our model consists of an auxiliary KB reasoning task for learning meta KB knowledge, an auxiliary dialogue reasoning task for learning dialogue patterns, and a TDS task (primary task) that aims at not only retrieving accurate entities from KB but also generating natural responses, which are coordinated to achieve collective success in both retrieving accurate KB entities and generating human-like responses via meta learning. Concretely, the dialog generation model amalgamates complementary meta KB and dialog knowledge from two novel auxiliary reasoning tasks that together provide integrated guidance to build a high-quality TDS by adding regularization terms to force primary network to produce similar results to auxiliary networks. While MCL automatically learns appropriate labels for the two auxiliary reasoning tasks from the primary task, without requiring access to any further data. The key idea behind MCL is to use the performance of the primary task, which is trained alongside the auxiliary tasks in one iteration, to improve the auxiliary labels for the next iteration with meta learning. Experimental results on three benchmark datasets show that MCL can generate higher quality responses compared to several strong baselines in terms of both automatic and human evaluations. Code to reproduce the results in this paper is available at: https://github.com/siat-nlp/MCL.
Connecting supplier and DoD blockchains for transparent part tracking

Blockchains have been around for more than ten years, and since 2015, a plethora of systems have been launched to target more flexible use cases. More recently, several enterprise blockchain systems, such as Consensys Quorum and Hyperledger Fabric, have been launched to make blockchain simpler to apply in complex organizational configurations. In this paper, we identify a specific Department of Defense use case, extrapolate requirements, and perform a thorough assessment of the different layers of the blockchain stack to identify the existing state of the art and undertake a gap analysis of the technology for this context. We describe a platform that meets many of these challenges and show how we architected, designed, and implemented a solution for this use case for deployment at NAVAIR. This solution connects transactions from two separate blockchain systems, Consensys Quorum and Hyperledger Fabric, by using a graph-based approach that preserves privacy while enabling full transparency across the military and supplier networks.
Spherical Image Generation from a Single Image by Considering Scene Symmetry

Spherical images taken in all directions (360 degrees by 180 degrees) allow the full surroundings of a subject to be represented, providing an immersive experience to viewers. Generating a spherical image from a single normal-field-of-view (NFOV) image is convenient and expands the usage scenarios considerably without relying on a specific panoramic camera or images taken from multiple directions; however, achieving such images remains a challenging and unresolved problem. The primary challenge is controlling the high degree of freedom involved in generating a wide area that includes all directions of the desired spherical image. We focus on scene symmetry, which is a basic property of the global structure of spherical images, such as rotational symmetry, plane symmetry, and asymmetry. We propose a method for generating a spherical image from a single NFOV image and controlling the degree of freedom of the generated regions using the scene symmetry. To estimate and control the scene symmetry using both a circular shift and flip of the latent image features, we incorporate the intensity of the symmetry as a latent variable into conditional variational autoencoders. Our experiments show that the proposed method can generate various plausible spherical images controlled from symmetric to asymmetric, and can reduce the reconstruction errors of the generated images based on the estimated symmetry.
Learning to Perturb Word Embeddings for Out-of-distribution QA

QA models based on pretrained language models have achieved remarkable performance on various benchmark datasets.However, QA models do not generalize well to unseen data that falls outside the training distribution, due to distributional shifts.Data augmentation (DA) techniques which drop/replace words have shown to be effective in regularizing the model from overfitting to the training data.Yet, they may adversely affect the QA tasks since they incur semantic changes that may lead to wrong answers for the QA task.To tackle this problem, we propose a simple yet effective DA method based on a stochastic noise generator, which learns to perturb the word embedding of the input questions and context without changing their semantics.We validate the performance of the QA models trained with our word embedding perturbation on a single source dataset, on five different target domains.The results show that our method significantly outperforms the baseline DA methods.Notably, the model trained with ours outperforms the model trained with more than 240K artificially generated QA pairs. Q:In what year was the Theodore m.Hesburgh library at Notre Dame finished?C: (…) the main building is the 14 -story Theodore m.Hesburgh library, completed in 1963, (…) this mural is popularly known as "touchdown jesus" because of its proximity
Learning Latent Jet Structure

We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted tt¯ jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.
Evaluation of Augmented Reality Application Development for Cultural Artefact Education

This paper presents the evaluation of Augmented Reality Application development for the educational purpose of Palembang cultural artefacts in Sultan Mahmud Badaruddin II (SMB II) Museum. The method applied in this study effectively developed an AR application to visualize the 3D object of SMB II Museum collections and created new digital 3D objects of Palembang cultural artefact which no longer exist. The digitization of the collection is an innovation that provides new culture learning experiences utilizing AR technology and also contributes to the preservation of Palembang cultural artefacts. Users can interact directly with these artefacts virtually which cannot be performed in the real environment but AR enabling it. In term of effectiveness and efficiency, some testing scenarios were performed. The results showed that the application could run its functionality as designed with time responsiveness for detecting marker was 0.599 seconds on average. App usability was also evaluated using the SUS method. The SUS survey score of 78.3 showed that the developed AR of the SMB II museum was acceptable by two variations of the millennial user. The augmented reality (AR) application is proposed as a medium to support the services of SMB II museum for education that effective improving users knowledge by 28.5% after using the App. The use of AR in the SMB II Museum leads to an enhanced interactive learning experience that promotes cultural heritage preservation in Palembang Indonesia.
PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation has become an important research topic in these days. The user check-in history used as the input to POI recommendation is very imbalanced and noisy because of sparse and missing check-ins. Although sample reweighting is commonly adopted for addressing this challenge with the input data, its fixed weighting scheme is often inappropriate to deal with different characteristics of users or POIs. Thus, in this paper, we propose PREMERE, an adaptive weighting scheme based on meta-learning. Because meta-data is typically required by meta-learning but is inherently hard to obtain in POI recommendation, we self-generate the meta-data via self-ensembling. Furthermore, the meta-model architecture is extended to deal with the scarcity of check-ins. Thorough experiments show that replacing a weighting scheme with PREMERE boosts the performance of the state-of-the-art recommender algorithms by 2.36–26.9% on three benchmark datasets.
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training

Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years.Although the general language representation learned from large-scale corpora does benefit MRC, the poor support in evidence extraction which requires reasoning across multiple sentences hinders PLMs from further advancing MRC.To bridge the gap between general PLMs and MRC, we present REPT, a REtrieval-based Pre-Training approach.In particular, we introduce two self-supervised tasks to strengthen evidence extraction during pre-training, which is further inherited by downstream MRC tasks through the consistent retrieval operation and model architecture.To evaluate our proposed method, we conduct extensive experiments on five MRC datasets that require collecting evidence from and reasoning across multiple sentences.Experimental results demonstrate the effectiveness of our pre-training approach.Moreover, further analysis shows that our approach is able to enhance the capacity of evidence extraction without explicit supervision.