Reviewerly/exHarmony huggingface dataset:
Development and Validation of the Purdue Global Online Teaching Effectiveness Scale

The currently available measures of online teaching effectiveness (OTE) have several flaws, including a lack of psychometric rigor, high costs, and reliance on the construct of traditional on-the-ground teaching effectiveness as opposed to the unique features of OTE (Blackman, Pedersen, March, Reyes-Fournier, &amp; Cumella, 2019). Therefore, the present research sought to establish a psychometrically sound framework for OTE and develop and validate a measure based on this clearly-defined construct. The authors developed pilot questions for the new measure based on a comprehensive review of the OTE literature and their many years of experience as online instructors. Students enrolled in exclusively online coursework and programs at Purdue University Global, N = 213, completed the survey, rating the effectiveness of their instructors. Exploratory Factor Analysis produced four clear OTE factors: Presence, Expertise, Engagement, and Facilitation. The resulting measure demonstrated good internal consistency and high correlations with an established OTE measure; good test-retest reliability; and predictive validity in relation to student achievement. Confirmatory Factor Analysis revealed a good fit of the data and yielded a final 12-item OTE measure. Further refinement and validation of the measure are recommended, particularly with students in other universities, and future research options are discussed.Keywords: online teaching effectiveness, instructor effectiveness, distance learning, student evaluations, asynchronous learning.
Differentially private publication of streaming trajectory data

User-generated trajectories (e.g. during traveling) can be leveraged to offer value-added services (e.g. smart city policy formulation), but there are also privacy implications. For example, information about the routes or destinations obtained from such published trajectories can be used to profile and identify users, including during contact tracing in pandemics (e.g., COVID-19) or the monitoring of demonstrations (e.g., surveillance). However, existing trajectory publishing algorithms generally rely on batch processing platforms, and rarely pay attention to real-time privacy protection processing in streaming scenarios. Therefore, we propose a stream processing framework containing two modules for spatio-temporal data. This framework is designed to achieve high data utility, while effectively ensuring the preservation of privacy in the published results. The first module is TSP, which concurrently receives real-time queries from individuals and releases new sanitizing trajectories. The second module is VCR comprising three algorithms based on differential privacy to facilitate the publication of the distribution of position statistics. Our experiments on real-world datasets demonstrate that our framework can effectively guarantee privacy with high data utility, when the appropriate parameter configuration is chosen. In addition, compared with the baseline algorithm Ht-publication, our group-based algorithm AGn-publication achieves better data accuracy in terms of visitor counts at the same level of privacy protection.
Mining Text Patterns over Fake and Real Tweets

With the exponential growth of users and user-generated content present on online social networks, fake news and its detection have become a major problem. Through these, smear campaigns can be generated, aimed for example at trying to change the political orientation of some people. Twitter has become one of the main spreaders of fake news in the network. Therefore, in this paper, we present a solution based on Text Mining that tries to find which text patterns are related to tweets that refer to fake news and which patterns in the tweets are related to true news. To test and validate the results, the system faces a pre-labelled dataset of fake and real tweets during the U.S. presidential election in 2016. In terms of results interesting patterns are obtained that relate the size and subtle changes of the real news to create fake news. Finally, different ways to visualize the results are provided.
Hate Speech Detection of Arabic Shorttext

The aim of sentiment analysis is to automatically extract the opinions from a certain text and decide its sentiment.In this paper, we introduce the first publicly-available Twitter dataset on Sunnah and Shia (SSTD), as part of a religious hate speech which is a sub problem of the general hate speech.We, further, provide a detailed review of the data collection process and our annotation guidelines such that a reliable dataset annotation is guaranteed.We employed many stand-alone classification algorithms on the Twitter hate speech dataset, including Random Forest, Complement NB, DecisionTree, and SVM and two deep learning methods CNN and RNN.We further study the influence of word embedding dimensions FastText and word2vec.In all our experiments, all classification algorithms are trained using a random split of data (66% for training and 34% for testing).The two datasets were stratified sampling of the original dataset.The CNN-FastText achieves the highest F-Measure (52.0%) followed by the CNN-Word2vec (49.0%), showing that neural models with FastText word embedding outperform classical feature-based models.
Development of GIS for Buildings in the Customary Village of Minangkabau Koto Gadang, West Sumatra, Indonesia

Indonesia aims to strengthen its local regions and villages. This has led to the encouragement of smart village development through several forms of assistance, including Information Technology (IT) services from the government. Koto Gadang, one of the many Minangkabau customary villages in West Sumatra, has been used as a model for the development of an IT service that can support tourism known as a Web- and mobile-based geographic information system (GIS) for buildings (GB) in order to map and visualize buildings and their inhabitants. This paper reports the development of the GB. This study takes the form of a literature review, a survey, data collection, and software development. The results of the literature review and survey were used as the basis for software development. Aerial photographs of micro, small, and medium enterprises (MSMEs), mosques, offices, schools, and health service centers, as well as residential buildings, were taken using a drone, while attribute data were collected directly by visiting the buildings. The users of the GB were divided into two groups: visitors and village officers. Moreover, there confidentiality was maintained for all the data provided, so the visitors were only allowed to search for buildings (MSMEs, offices, mosques, schools, and health centers) based on certain criteria, view locations, found information, and survey routes, while village officers were allowed to collect data on residents, buildings, and houses, and also search people’s homes. Furthermore, in situations where a visitor needs to find a resident’s house, the village officers are required to provide this assistance. These provisions were applied in the GB and implemented using the PostgreSQL/PostGIS database, PHP, CSS Bootstrap, jQuery, and Basic4Android, created according to the needs of the Koto Gadang smart customary village. The GB allows the village officers to better understand and monitor all the buildings, houses, and residents in the village and could also encourage many travelers to visit and see historic buildings and shop for embroidery and silver crafts.
One-dimensional sensor learns to sense three-dimensional space

A sensor system with ultra-high sensitivity, high resolution, rapid response time, and a high signal-to-noise ratio can produce raw data that is exceedingly rich in information, including signals that have the appearances of “noise”. The “noise” feature directly correlates to measurands in orthogonal dimensions, and are simply manifestations of the off-diagonal elements of 2 nd -order tensors that describe the spatial anisotropy of matter in physical structures and spaces. The use of machine learning techniques to extract useful meanings from the rich information afforded by ultra-sensitive one-dimensional sensors may offer the potential for probing mundane events for novel embedded phenomena. Inspired by our very recent invention of ultra-sensitive optical-based inclinometers, this work aims to answer a transformative question for the first time: can a single-dimension point sensor with ultra-high sensitivity, fidelity, and signal-to-noise ratio identify an arbitrary mechanical impact event in three-dimensional space? This work is expected to inspire researchers in the fields of sensing and measurement to promote the development of a new generation of powerful sensors or sensor networks with expanded functionalities and enhanced intelligence, which may provide rich n -dimensional information, and subsequently, data-driven insights into significant problems.
Data science for digital culture improvement in higher education using K-means clustering and text analytics

This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education.
An Iterative Multi-Source Mutual Knowledge Transfer Framework for Machine Reading Comprehension

The lack of sufficient training data in many domains, poses a major challenge to the construction of domain-specific machine reading comprehension (MRC) models with satisfying performance. In this paper, we propose a novel iterative multi-source mutual knowledge transfer framework for MRC. As an extension of the conventional knowledge transfer with one-to-one correspondence, our framework focuses on the many-to-many mutual transfer, which involves synchronous executions of multiple many-to-one transfers in an iterative manner.Specifically, to update a target-domain MRC model, we first consider other domain-specific MRC models as individual teachers, and employ knowledge distillation to train a multi-domain MRC model, which is differentially required to fit the training data and match the outputs of these individual models according to their domain-level similarities to the target domain. After being initialized by the multi-domain MRC model, the target-domain MRC model is fine-tuned to match both its training data and the output of its previous best model simultaneously via knowledge distillation. Compared with previous approaches, our framework can continuously enhance all domain-specific MRC models by enabling each model to iteratively and differentially absorb the domain-shared knowledge from others. Experimental results and in-depth analyses on several benchmark datasets demonstrate the effectiveness of our framework.
Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness

We turn the definition of individual fairness on its head - rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness. This can facilitate the discussion on the fairness of a model, addressing the issue that it may be difficult to specify a priori a suitable metric. Our contributions are twofold: First, we introduce the definition of a minimal metric and characterize the behavior of models in terms of minimal metrics. Second, for more complicated models, we apply the mechanism of randomized smoothing from adversarial robustness to make them individually fair under a given weighted Lp metric. Our experiments show that adapting the minimal metrics of linear models to more complicated neural networks can lead to meaningful and interpretable fairness guarantees at little cost to utility.
3D Part Guided Image Editing for Fine-Grained Object Understanding

Holistically understanding an object with its 3D movable parts is essential for visual models of a robot to interact with the world. For example, only by understanding many possible part dynamics of other vehicles (e.g., door or trunk opening, taillight blinking for changing lane), a self-driving vehicle can be success in dealing with emergency cases. However, existing visual models tackle rarely on these situations, but focus on bounding box detection. In this paper, we fill this important missing piece in autonomous driving by solving two critical issues. First, for dealing with data scarcity, we propose an effective training data generation process by fitting a 3D car model with dynamic parts to cars in real images. This allows us to directly edit the real images using the aligned 3D parts, yielding effective training data for learning robust deep neural networks (DNNs). Secondly, to benchmark the quality of 3D part understanding, we collected a large dataset in real driving scenario with cars in uncommon states (CUS), i.e. with door or trunk opened etc., which demonstrates that our trained network with edited images largely outperforms other baselines in terms of 2D detection and instance segmentation accuracy.
NAT: Noise-Aware Training for Robust Neural Sequence Labeling

Sequence labeling systems should perform reliably not only under ideal conditions but also with corrupted inputs-as these systems often process user-generated text or follow an errorprone upstream component.To this end, we formulate the noisy sequence labeling problem, where the input may undergo an unknown noising process and propose two Noise-Aware Training (NAT) objectives that improve robustness of sequence labeling performed on perturbed input: Our data augmentation method trains a neural model using a mixture of clean and noisy samples, whereas our stability training algorithm encourages the model to create a noise-invariant latent representation.We employ a vanilla noise model at training time.For evaluation, we use both the original data and its variants perturbed with real OCR errors and misspellings.Extensive experiments on English and German named entity recognition benchmarks confirmed that NAT consistently improved robustness of popular sequence labeling models, preserving accuracy on the original input.We make our code and data publicly available for the research community.
Self-Guided Evolution Strategies with Historical Estimated Gradients

Evolution Strategies (ES) are a class of black-box optimization algorithms and have been widely applied to solve problems, e.g., in reinforcement learning (RL), where the true gradient is unavailable. ES estimate the gradient of an objective function with respect to the parameters by randomly sampling search directions and evaluating parameter perturbations in these directions. However, the gradient estimator of ES tends to have a high variance for high-dimensional optimization, thus requiring a large number of samples and making ES inefficient. In this paper, we propose a new ES algorithm SGES, which utilizes historical estimated gradients to construct a low-dimensional subspace for sampling search directions, and adjusts the importance of this subspace adaptively. We prove that the variance of the gradient estimator of SGES can be much smaller than that of Vanilla ES; meanwhile, its bias can be well bounded. Empirical results on benchmark black-box functions and a set of popular RL tasks exhibit the superior performance of SGES over state-of-the-art ES algorithms.
Language (Re)modelling: Towards Embodied Language Understanding

While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.This work proposes an approach to representation and learning based on the tenets of embodied cognitive linguistics (ECL).According to ECL, natural language is inherently executable (like programming languages), driven by mental simulation and metaphoric mappings over hierarchical compositions of structures and schemata learned through embodied interaction.This position paper argues that the use of grounding by metaphoric inference and simulation will greatly benefit NLU systems, and proposes a system architecture along with a roadmap towards realizing this vision.
Hype-HAN: Hyperbolic Hierarchical Attention Network for Semantic Embedding

Hyperbolic space is a well-defined space with constant negative curvature. Recent research demonstrates its odds of capturing complex hierarchical structures with its exceptional high capacity and continuous tree-like properties. This paper bridges hyperbolic space's superiority to the power-law structure of documents by introducing a hyperbolic neural network architecture named Hyperbolic Hierarchical Attention Network (Hype-HAN). Hype-HAN defines three levels of embeddings (word/sentence/document) and two layers of hyperbolic attention mechanism (word-to-sentence/sentence-to-document) on Riemannian geometries of the Lorentz model, Klein model and Poincaré model. Situated on the evolving embedding spaces, we utilize both conventional GRUs (Gated Recurrent Units) and hyperbolic GRUs with Möbius operations. Hype-HAN is applied to large scale datasets. The empirical experiments show the effectiveness of our method.
EmoElicitor: An Open Domain Response Generation Model with User Emotional Reaction Awareness

Generating emotional responses is crucial for building human-like dialogue systems. However, existing studies have focused only on generating responses by controlling the agents' emotions, while the feelings of the users, which are the ultimate concern of a dialogue system, have been neglected. In this paper, we propose a novel variational model named EmoElicitor to generate appropriate responses that can elicit user's specific emotion. We incorporate the next-round utterance after the response into the posterior network to enrich the context, and we decompose single latent variable into several sequential ones to guide response generation with the help of a pre-trained language model. Extensive experiments conducted on real-world dataset show that EmoElicitor not only performs better than the baselines in term of diversity and semantic similarity, but also can elicit emotion with higher accuracy.
RDF-to-Text Generation with Graph-augmented Structural Neural Encoders

The task of RDF-to-text generation is to generate a corresponding descriptive text given a set of RDF triples. Most of the previous approaches either cast this task as a sequence-to-sequence problem or employ graph-based encoder for modeling RDF triples and decode a text sequence. However, none of these methods can explicitly model both local and global structure information between and within the triples. To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional graph-based meta-paths encoder) for the input triples. Experimental results on two different WebNLG datasets show that our proposed model outperforms the state-of-the-art baselines. Furthermore, we perform a human evaluation that demonstrates the effectiveness of the proposed method by evaluating generated text quality using various subjective metrics.
Texts as Lines: Text Detection with Weak Supervision

Scene text detection methods based on deep learning have recently shown remarkable improvement. Most text detection methods train deep convolutional neural networks with full masks requiring pixel accuracy for good quality training. Normally, a skilled engineer needs to drag tens of points to create a full mask for the curved text. Therefore, data labelling based on full masks is time consuming and laborious, particularly for curved texts. To reduce the labelling cost, a weakly supervised method is first proposed in this paper. Unlike the other detectors (e.g ., PSENet or TextSnake) that use full masks, our method only needs coarse masks for training. More specifically, the coarse mask for one text instance is a line across the text region in our method. Compared with full mask labelling, data labelling using the proposed method could save labelling time while losing much annotation information. In this context, a network pretrained on synthetic data with full masks is used to enhance the coarse masks in a real image. Finally, the enhanced masks are fed back to train our network. Analysis of experiments performed using the model shows that the performance of our method is close to that of the fully supervised methods on ICDAR2015, CTW1500, Total-Text, and MSRA-TD5000.
Quaternion Product Units for Deep Learning on 3D Rotation Groups

We propose a novel quaternion product unit (QPU) to represent data on 3D rotation groups. The QPU leverages quaternion algebra and the law of 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We prove that the representations derived by the proposed QPU can be disentangled into "rotation-invariant" features and "rotation-equivariant" features, respectively, which supports the rationality and the efficiency of the QPU in theory. We design quaternion neural networks based on our QPUs and make our models compatible with existing deep learning models. Experiments on both synthetic and real-world data show that the proposed QPU is beneficial for the learning tasks requiring rotation robustness.
Graph Diffusion Wasserstein Distances

Optimal Transport (OT) for structured data has received much attention in the machine learning community, especially for addressing graph classification or graph transfer learning tasks. In this paper, we present the Diffusion Wasserstein (\(\mathtt {DW}\)) distance, as a generalization of the standard Wasserstein distance to undirected and connected graphs where nodes are described by feature vectors. \(\mathtt {DW}\) is based on the Laplacian exponential kernel and benefits from the heat diffusion to catch both structural and feature information from the graphs. We further derive lower/upper bounds on \(\mathtt {DW}\) and show that it can be directly plugged into the Fused Gromov Wasserstein (\(\mathtt {FGW}\)) distance that has been recently proposed, leading - for free - to a DifFused Gromov Wasserstein distance (\(\mathtt {DFGW}\)) that allows a significant performance boost when solving graph domain adaptation tasks.