Reviewerly/exHarmony huggingface dataset:
Extensible Chatbot Architecture Using Metamodels of Natural Language Understanding

In recent years, gradual improvements in communication and connectivity technologies have enabled new technical possibilities for the adoption of chatbots across diverse sectors such as customer services, trade, and marketing. The chatbot is a platform that uses natural language processing, a subset of artificial intelligence, to find the right answer to all users’ questions and solve their problems. Advanced chatbot architecture that is extensible, scalable, and supports different services for natural language understanding (NLU) and communication channels for interactions of users has been proposed. The paper describes overall chatbot architecture and provides corresponding metamodels as well as rules for mapping between the proposed and two commonly used NLU metamodels. The proposed architecture could be easily extended with new NLU services and communication channels. Finally, two implementations of the proposed chatbot architecture are briefly demonstrated in the case study of “ADA” and “COVID-19 Info Serbia”.
Flexible Mechanisms for Remote Attestation

Remote attestation consists of generating evidence of a system’s integrity via measurements and reporting the evidence to a remote party for appraisal in a form that can be trusted. The parties that exchange information must agree on formats and protocols. We assert there is a large variety of patterns of interactions among appraisers and attesters of interest. Therefore, it is important to standardize on flexible mechanisms for remote attestation. We make our case by describing scenarios that require the exchange of evidence among multiple parties using a variety of message passing patterns. We show cases in which changes in the order of evidence collection result in important differences to what can be inferred by an appraiser. We argue that adding the ability to negotiate the appropriate kind of attestation allows for remote attestations that better adapt to a dynamically changing environment. Finally, we suggest a language-based solution to taming the complexity of specifying and negotiating attestation procedures.
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models

We use a dataset of U.S. first names with labels based on predominant gender and racial group to examine the effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias in BERT, GPT-2, T5, and XLNet.We show that predominantly female and non-white names are less frequent in the training corpora of these four language models.We find that infrequent names are more self-similar across contexts, with Spearman's ρ between frequency and self-similarity as low as -.763.Infrequent names are also less similar to initial representation, with Spearman's ρ between frequency and linear centered kernel alignment (CKA) similarity to initial representation as high as .702.Moreover, we find Spearman's ρ between racial bias and name frequency in BERT of .492,indicating that lower-frequency minority group names are more associated with unpleasantness.Representations of infrequent names undergo more processing, but are more self-similar, indicating that models rely on less context-informed representations of uncommon and minority names which are overfit to a lower number of observed contexts.
InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation

Multi-person pose estimation is an attractive and challenging task. Existing methods are mostly based on two-stage frameworks, which include top-down and bottom-up methods. Two-stage methods either suffer from high computational redundancy for additional person detectors or they need to group keypoints heuristically after predicting all the instance-agnostic keypoints. The single-stage paradigm aims to simplify the multi-person pose estimation pipeline and receives a lot of attention. However, recent single-stage methods have the limitation of low performance due to the difficulty of regressing various full-body poses from a single feature vector. Different from previous solutions that involve complex heuristic designs, we present a simple yet effective solution by employing instance-aware dynamic networks. Specifically, we propose an instance-aware module to adaptively adjust (part of) the network parameters for each instance. Our solution can significantly increase the capacity and adaptive-ability of the network for recognizing various poses, while maintaining a compact end-to-end trainable pipeline. Extensive experiments on the MS-COCO dataset demonstrate that our method achieves significant improvement over existing single-stage methods, and makes a better balance of accuracy and efficiency compared to the state-of-the-art two-stage approaches.
Social Robot Co-Design Canvases: A Participatory Design Framework

Design teams of social robots are often multidisciplinary, due to the broad knowledge from different scientific domains needed to develop such complex technology. However, tools to facilitate multidisciplinary collaboration are scarce. We introduce a framework for the participatory design of social robots and corresponding canvas tool for participatory design. The canvases can be applied in different parts of the design process to facilitate collaboration between experts of different fields, as well as to incorporate prospective users of the robot into the design process. We investigate the usability of the proposed canvases with two social robot design case studies: a robot that played games online with teenage users and a librarian robot that guided users at a public library. We observe through participants’ feedback that the canvases have the advantages of (1) providing structure, clarity, and a clear process to the design; (2) encouraging designers and users to share their viewpoints to progress toward a shared one; and (3) providing an educational and enjoyable design experience for the teams.
Optimal Training of Feedforward Neural Networks Using Teaching-Learning-Based Optimization with Modified Learning Phases

Feedforward neural network (FNN) emerges as a promising machine learning algorithm widely used for crucial tasks such as prediction, classification, clustering, regression, and etc. due to its excellent performance in training, learning and organizing data. Conventional approaches such as gradient-based algorithm is commonly used to train FNNs, but it has drawbacks of being trapped into local optima easily, slow convergence rate and high sensitivity to initial solutions generated. Metaheuristic search algorithm such as teaching-learning-based optimization (TLBO) is envisioned as a potential solution to train FNNs due to their stochastic nature and excellent global search ability. In this paper, a new TLBO variant named as teaching-learning-based optimization with modified learning phases (TLBO-MLPs) is designed as the training algorithm of FNN, aiming to optimize its neuron weights, biases and selection of activation functions by referring to the datasets of given classification problems. The classification performances of FNN trained by TLBO-MLPs are evaluated by using the real-life classification datasets from UCI Machine Learning Repository and compared with those optimized by the other well-established TLBO variants. Rigorous simulation studies show that the FNN trained by TLBO-MLPs outperforms most of its peer algorithms significantly in solving different classification problems. Particularly, the FNN classifiers optimized by proposed TLBO-MLPs are reported to outperform its competitors up to 15.72% and 29.07% in terms of classification accuracy for both training and testing datasets, respectively.
Pay Attention to MLPs

Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years. Here we propose a simple network architecture, gMLP, based on MLPs with gating, and show that it can perform as well as Transformers in key language and vision applications. Our comparisons show that self-attention is not critical for Vision Transformers, as gMLP can achieve the same accuracy. For BERT, our model achieves parity with Transformers on pretraining perplexity and is better on some downstream NLP tasks. On finetuning tasks where gMLP performs worse, making the gMLP model substantially larger can close the gap with Transformers. In general, our experiments show that gMLP can scale as well as Transformers over increased data and compute.
A General Framework for Inference on Algorithm-Agnostic Variable Importance

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response—in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection. Supplementary materials for this article are available online.
The WRF-Solar Ensemble Prediction System to Provide Solar Irradiance Probabilistic Forecasts

In this study, we introduce the recently developed WRF-solar ensemble prediction system and a calibration method. The performances of forecast models are evaluated using the National Solar Radiation Database observational analysis for day-ahead solar irradiance predictions. The results demonstrate that the ensemble forecast improves the quality of the forecasts by considering the uncertainty of each ensemble member. The analog ensemble calibration contributed to the reduction of positive bias and an overall improvement in the probabilistic attributes, such as reliability and statistical consistency.
Solving string constraints with Regex-dependent functions through transducers with priorities and variables

Regular expressions are a classical concept in formal language theory. Regular expressions in programming languages (RegEx) such as JavaScript, feature non-standard semantics of operators (e.g. greedy/lazy Kleene star), as well as additional features such as capturing groups and references. While symbolic execution of programs containing RegExes appeals to string solvers natively supporting important features of RegEx, such a string solver is hitherto missing. In this paper, we propose the first string theory and string solver that natively provides such support. The key idea of our string solver is to introduce a new automata model, called prioritized streaming string transducers (PSST), to formalize the semantics of RegEx-dependent string functions. PSSTs combine priorities, which have previously been introduced in prioritized finite-state automata to capture greedy/lazy semantics, with string variables as in streaming string transducers to model capturing groups. We validate the consistency of the formal semantics with the actual JavaScript semantics by extensive experiments. Furthermore, to solve the string constraints, we show that PSSTs enjoy nice closure and algorithmic properties, in particular, the regularity-preserving property (i.e., pre-images of regular constraints under PSSTs are regular), and introduce a sound sequent calculus that exploits these properties and performs propagation of regular constraints by means of taking post-images or pre-images. Although the satisfiability of the string constraint language is generally undecidable, we show that our approach is complete for the so-called straight-line fragment. We evaluate the performance of our string solver on over 195000 string constraints generated from an open-source RegEx library. The experimental results show the efficacy of our approach, drastically improving the existing methods (via symbolic execution) in both precision and efficiency.
Solving the class imbalance problem using a counterfactual method for data augmentation

Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having a majority class that naturally has many more instances than its minority class (e.g., genuine bank transactions occur much more often than fraudulent ones). Many methods have been proposed to solve the class imbalance problem, among the most popular being oversampling techniques (such as SMOTE). These methods generate synthetic instances in the minority class, to balance the dataset, performing data augmentations that improve the performance of predictive machine learning (ML). In this paper, we advance a novel, data augmentation method (adapted from eXplainable AI), that generates synthetic, counterfactual instances in the minority class. Unlike other oversampling techniques, this method adaptively combines existing instances from the dataset, using actual feature-values rather than interpolating values between instances. Several experiments using four different classifiers and 25 datasets involving binary classes are reported, which show that this Counterfactual Augmentation (CFA) method generates useful synthetic datapoints in the minority class. The experiments also show that CFA is competitive with many other oversampling methods, many of which are variants of SMOTE. The basis for CFA’s performance is discussed, along with the conditions under which it is likely to perform better or worse in future tests.
Attention-Based Octave Network for Hyperspectral Image Denoising

Inevitable corruption and degeneration make the performance of subsequent high-level semantic tasks in hyperspectral images (HSIs) unsatisfactory. Despite that many denoising methods have been proposed, significant room for improvement still remains. To better suppress noise and preserve the HSI spatial&#x2013;spectral structure, we propose an attention-based Octave dense network. A separable spectral feature extraction module is introduced to extract the spatial&#x2013;spectral features consistent with the structure prior. The extracted features are fine-tuned by the attention module in both channel and spatial domains; then, several dense denoising blocks are elaborately employed to focus on noise feature learning; in order to focus on high-frequency features, which usually have more noise information, we introduce the Octave kernel to implement these blocks. Experiments based on simulated and real-world noisy images demonstrate that the proposed method outperforms the existing traditional and learning-based methods in both quantitative evaluations and visual effects, benefiting the subsequent classification task. In addition, the effectiveness of each module is proven by ablation experiments. Our source code is made available at: <uri>https://github.com/LbzSteven/AODN</uri>.
Examining K-12 Teachers’ Digital Competency and Technology Self-Efficacy During COVID-19 Pandemic

This study used an online survey to collect data from K-12 teachers (N=109) across twelve states in the U.S. The goal was to examine teachers' experiences, digital competency, and instructional technology self￾efficacy as they transitioned from in-person to virtual learning environments. We found that a) many teachers had the digital competency for classroom instruction, they could not engage effectively with their students due to inadequate training for online instruction and limited digital tools and resources, b) teachers' level of digital competency corresponded with their self-efficacy, and c) the COVID-19 Pandemic further exposed the existing digital divide and inequitable distribution of resources.
RiFeGAN2: Rich Feature Generation for Text-to-Image Synthesis From Constrained Prior Knowledge

Text-to-image synthesis is a challenging task that generates realistic images from a textual description. The description contains limited information compared with the corresponding image and is ambiguous and abstract, which will complicate the generation and lead to low-quality images. To address this problem, we propose a novel generation text-to-image synthesis method, called RiFeGAN2, to enrich the given description. To improve the enrichment quality while accelerating the enrichment process, RiFeGAN2 exploits a domain-specific constrained model to limit the search scope and then uses an attention-based caption matching model to refine the compatible candidate captions based on constrained prior knowledge. To improve the semantic consistency between the given description and the synthesized results, RiFeGAN2 employs improved SAEMs, SAEM2s, to compact better features of the retrieved captions and effectively emphasize the descriptions via incorporating centre-attention layers. Finally, multi-caption attentional GANs are exploited to synthesize images from those features. Experiments performed on widely-used datasets show that the models can generate vivid images from enriched captions and effectually improve the semantic consistency.
Working Together Apart through Embodiment

Computer-mediated collaboration has long been a core research interest in CSCW and HCI. As online social spaces continue to evolve towards more immersive and higher fidelity experiences, more research is still needed to investigate how emerging novel technology may foster and support new and more nuanced forms and experiences of collaboration in virtual environments. Using 30 interviews, this paper focuses on what people may collaborate on and how they collaborate in social Virtual Reality (VR). We broaden current studies on computer-mediated collaboration by highlighting the importance of embodiment for co-presence and communication, replicating offline collaborative activities, and supporting the seamless interplay of work, play, and mundane experiences in everyday lives for experiencing and conceptualizing collaboration in emerging virtual environments. We also propose potential design implications that could further support everyday collaborative activities in social VR
Multi-Level Second-Order Few-Shot Learning

We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order base learner streams combined with features that express multiple levels of visual abstraction, and we use self-supervised discriminating mechanisms. As Second-order Pooling (SoP) is popular in image recognition, we employ its basic element-wise variant in our pipeline. The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning. As SoP can handle convolutional feature maps of varying spatial sizes, we also introduce image inputs at multiple spatial scales into MlSo. To exploit the discriminative information from multi-level and multi-scale features, we develop a Feature Matching (FM) module that reweights their respective branches. We also introduce a self-supervised step, which is a discriminator of the spatial level and the scale of abstraction. Our pipeline is trained in an end-to-end manner. With a simple architecture, we demonstrate respectable results on standard datasets such as Omniglot, mini-ImageNet, tiered-ImageNet, Open MIC, fine-grained datasets such as CUB Birds, Stanford Dogs and Cars, and action recognition datasets such as HMDB51, UCF101, and mini-MIT.
Observational equality: now for good

Building on the recent extension of dependent type theory with a universe of definitionally proof-irrelevant types, we introduce TTobs, a new type theory based on the setoidal interpretation of dependent type theory. TTobs equips every type with an identity relation that satisfies function extensionality, propositional extensionality, and definitional uniqueness of identity proofs (UIP). Compared to other existing proposals to enrich dependent type theory with these principles, our theory features a notion of reduction that is normalizing and provides an algorithmic canonicity result, which we formally prove in Agda using the logical relation framework of Abel et al. Our paper thoroughly develops the meta-theoretical properties of TTobs, such as the decidability of the conversion and of the type checking, as well as consistency. We also explain how to extend our theory with quotient types, and we introduce a setoidal version of Swan's Id types that turn it into a proper extension of MLTT with inductive equality.
Simuliris: a separation logic framework for verifying concurrent program optimizations

Today’s compilers employ a variety of non-trivial optimizations to achieve good performance. One key trick compilers use to justify transformations of concurrent programs is to assume that the source program has no data races : if it does, they cause the program to have undefined behavior (UB) and give the compiler free rein. However, verifying correctness of optimizations that exploit this assumption is a non-trivial problem. In particular, prior work either has not proven that such optimizations preserve program termination (particularly non-obvious when considering optimizations that move instructions out of loop bodies), or has treated all synchronization operations as external functions (losing the ability to reorder instructions around them). In this work we present Simuliris , the first simulation technique to establish termination preservation (under a fair scheduler) for a range of concurrent program transformations that exploit UB in the source language. Simuliris is based on the idea of using ownership to reason modularly about the assumptions the compiler makes about programs with well-defined behavior. This brings the benefits of concurrent separation logics to the space of verifying program transformations: we can combine powerful reasoning techniques such as framing and coinduction to perform thread-local proofs of non-trivial concurrent program optimizations. Simuliris is built on a (non-step-indexed) variant of the Coq-based Iris framework, and is thus not tied to a particular language. In addition to demonstrating the effectiveness of Simuliris on standard compiler optimizations involving data race UB, we also instantiate it with Jung et al.’s Stacked Borrows semantics for Rust and generalize their proofs of interesting type-based aliasing optimizations to account for concurrency.