Reviewerly/exHarmony huggingface dataset:
On uniform belief revision

Abstract Rational belief-change policies are encoded in the so-called AGM revision functions, defined in the prominent work of Alchourrón, Gärdenfors and Makinson. The present article studies an interesting class of well-behaved AGM revision functions, called herein uniform-revision operators (or UR operators, for short). Each UR operator is uniquely defined by means of a single total preorder over all possible worlds, a fact that in turn entails a significantly lower representational cost, relative to an arbitrary AGM revision function, and an embedded solution to the iterated-revision problem, at no extra representational cost. Herein, we first demonstrate how weaker, more expressive—yet, more representationally expensive—types of uniform revision can be defined. Furthermore, we prove that UR operators, essentially, generalize a significant type of belief change, namely, parametrized-difference revision. Lastly, we show that they are (to some extent) relevance-sensitive, as well as that they respect the so-called principle of kinetic consistency.
Deep Reinforcement Learning-based ROS-Controlled RC Car for Autonomous Path Exploration in the Unknown Environment

Nowadays, Deep reinforcement learning has become the front runner to solve problems in the field of robot navigation and avoidance. This paper presents a LiDAR-equipped RC car trained in the GAZEBO environment using the deep reinforcement learning method. This paper uses reshaped LiDAR data as the data input of the neural architecture of the training network. This paper also presents a unique way to convert the LiDAR data into a 2D grid map for the input of training neural architecture. It also presents the test result from the training network in different GAZEBO environment. It also shows the development of hardware and software systems of embedded RC car. The hardware system includes-Jetson AGX Xavier, teensyduino and Hokuyo LiDAR; the software system includes-ROS and Arduino C. Finally, this paper presents the test result in the real world using the model generated from training simulation.
iOrthoPredictor

In this paper, we present iOrthoPredictor, a novel system to visually predict teeth alignment in photographs. Our system takes a frontal face image of a patient with visible malpositioned teeth along with a corresponding 3D teeth model as input, and generates a facial image with aligned teeth, simulating a real orthodontic treatment effect. The key enabler of our method is an effective disentanglement of an explicit representation of the teeth geometry from the in-mouth appearance, where the accuracy of teeth geometry transformation is ensured by the 3D teeth model while the in-mouth appearance is modeled as a latent variable. The disentanglement enables us to achieve fine-scale geometry control over the alignment while retaining the original teeth appearance attributes and lighting conditions. The whole pipeline consists of three deep neural networks: a U-Net architecture to explicitly extract the 2D teeth silhouette maps representing the teeth geometry in the input photo, a novel multilayer perceptron (MLP) based network to predict the aligned 3D teeth model, and an encoder-decoder based generative model to synthesize the in-mouth appearance conditional on the original teeth appearance and the aligned teeth geometry. Extensive experimental results and a user study demonstrate that iOrthoPredictor is effective in qualitatively predicting teeth alignment, and applicable to the orthodontic industry.
Conditional Activation GAN: Improved Auxiliary Classifier GAN

A conditional generative adversarial network (cGAN) is a generative adversarial network (GAN) that generates data with a desired condition from a latent vector.Among the different types of cGAN, the auxiliary classifier GAN (ACGAN) is the most frequently used.In this study, we describe the problems of an AC-GAN and propose replacing it with a conditional activation GAN (CAGAN) to reduce the number of hyperparameters and improve the training speed.The loss function of a CAGAN is defined as the sum of the loss of each GAN created for each condition.The proposed CAGAN is an integration of multiple GANs, where each GAN shares all hidden layers, and their integration can be considered as a single GAN.Therefore, the structure of the integrated GANs does not significantly increase the number of computations.Additionally, to prevent the conditions given in the discriminator of a cGAN from being ignored with batch normalization, we propose mixed batch training, in which every batch for the discriminator keeps the ratio of the real and generated data consistent.
Stop

In Non-Intrusive Load Monitoring (NILM), as in many other machine learning problems, significant computational resources and time are spent training models using as much data as possible. This is perhaps driven by the preconception that more data leads to more accurate models and, eventually, better performing algorithms. When has enough prior training been done? When has a NILM algorithm encountered new, unseen data? This work applies the notion of Bayesian surprise to answer these important questions for both, supervised and unsupervised algorithms. We compare the performance of several NILM algorithms to establish a suggested threshold on two combined measures of surprise: postdictive surprise and transitional surprise. We validate the use of transitional surprise by exploring the performance of a particular Hidden Markov Model as a function of surprise threshold. Finally, we explore the use of a surprise threshold as a regularization technique to avoid overfitting in cross-house performance. We provide preliminary insights and clear evidence showing a point of diminishing returns for model performance with respect to dataset size, which can have implications for future model development, dataset acquisition, as well as aiding in model flexibility during deployment.
Fair-VQA: Fairness-Aware Visual Question Answering Through Sensitive Attribute Prediction

Visual Question Answering (VQA) is a task that answers questions on given images. Although previous works achieve a great improvement in VQA performance, they do not consider the fairness of answers in terms of ethically sensitive attributes, such as gender. Therefore, we propose a Fair-VQA model that contains two modules: VQA module and SAP (Sensitive Attribute Prediction) module. On top of VQA module, which predicts various kinds of answers, SAP module predicts only sensitive attributes using the same inputs. The predictions of SAP module are utilized to rectify answers from VQA module to be fairer in terms of the sensitive attributes with graceful performance degradation. To validate the proposed method, we conduct extensive experiments on VQA, GQA, and our proposing VQA-Gender datasets. In all the experiments, our method shows the fairest results in various metrics for fairness. Moreover, we demonstrate that our method works interpretably through the analysis of visualized attention maps.
Transforming Multi-concept Attention into Video Summarization

Video summarization is among the challenging tasks in computer vision, which aims at identifying highlight frames or shots over a lengthy video input. In this paper, we propose a novel attention-based framework for video summarization with complex video data. Unlike previous works which only apply attention mechanism on the correspondence between frames, our multi-concept video self-attention (MC-VSA) model is presented to identify informative regions across temporal and concept video features, which jointly exploit context diversity over time and space for summarization purposes. Together with consistency between video and summary enforced in our framework, our model can be applied to both labeled and unlabeled data, making our method preferable to real-world applications. Extensive and complete experiments on two benchmarks demonstrate the effectiveness of our model both quantitatively and qualitatively, and confirms its superiority over the state-of-the-arts.
Describing Unseen Videos via Multi-modal Cooperative Dialog Agents

With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information sources. To this end, in this paper, we introduce a new task called video description via two multi-modal cooperative dialog agents, whose ultimate goal is for one conversational agent to describe an unseen video based on the dialog and two static frames. Specifically, one of the intelligent agents - Q-BOT - is given two static frames from the beginning and the end of the video, as well as a finite number of opportunities to ask relevant natural language questions before describing the unseen video. A-BOT, the other agent who has already seen the entire video, assists Q-BOT to accomplish the goal by providing answers to those questions. We propose a QA-Cooperative Network with a dynamic dialog history update learning mechanism to transfer knowledge from A-BOT to Q-BOT, thus helping Q-BOT to better describe the video. Extensive experiments demonstrate that Q-BOT can effectively learn to describe an unseen video by the proposed model and the cooperative learning method, achieving the promising performance where Q-BOT is given the full ground truth history dialog. Codes and models are available at https://github.com/L-YeZhu/Video-Description-via-Dialog-Agents-ECCV2020 .
From abstract specifications to application generation

We present an approach for platform-agnostic application modelling, complemented by an execution and lifecycle management protocol. It enables a systematic way of generating native applications capable of leveraging specific features and sensors for any target device while contextualizing them to the needs of the application logic. In addition, it provides a way to maintain and update all the deployed instances simultaneously across the variety of target devices, without requiring client-side intervention.
Mind control attack: Undermining deep learning with GPU memory exploitation

Modern deep learning frameworks rely heavily on GPUs to accelerate the computation. However, the security implication of GPU device memory exploitation on deep learning frameworks has been largely neglected. In this paper, we argue that GPU device memory manipulation is a novel attack vector against deep learning systems. We present a novel attack method leveraging the attack vector, which makes deep learning predictions no longer different from random guessing by degrading the accuracy of the predictions. To the best of our knowledge, we are the first to show a practical attack that directly exploits deep learning frameworks through GPU memory manipulation. We confirmed that our attack works on three popular deep learning frameworks, TensorFlow, CNTK, and Caffe, running on CUDA. Finally, we propose potential defense mechanisms against our attack, and discuss concerns of GPU memory safety.
Facial Expression Recognition on Video Data with Various Face Poses Using Deep Learning

Facial expressions in humans produce non-verbal communication to convey emotional states in humans; hence, they play an essential role in social interactions between humans. Along with the times, research on facial expression analysis has expanded to automatic facial expression recognition by computers. The facial expression recognition plays a vital role in human-computer interactions, monitoring human behavior, educational techniques, psychological, to sociable robots. In this study, the development of human facial expression recognition was carried out using a deep learning method called You Only Look Once (YOLO) based on Convolutional Neural Network (CNN). There are seven classes of facial expressions that can be recognized, namely angry, disgust, fear, happy, sadness, surprise, and neutral. The datasets used are video-based facial expression datasets such as CK+, IMED, and video data from 8 students of the Informatics Department, Institut Teknologi Sepuluh Nopember (ITS), with various face poses. Based on the experimental results, the best accuracy of the still image dataset is 94% on the CK+ dataset with channel three and learning rate 0.01. Moreover, the accuracy of video data with various face poses achieves 73%.
They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning

Semi-Supervised Learning (SSL) with mismatched classes deals with the problem that the classes-of-interests in the limited labeled data are only a subset of the classes in massive unlabeled data. As a result, classical SSL methods would be misled by the classes which are only possessed by the unlabeled data. To solve this problem, some recent methods divide unlabeled data to useful in-distribution (ID) data and harmful out-of-distribution (OOD) data, among which the latter should particularly be weakened. As a result, the potential value contained by OOD data is largely overlooked. To remedy this defect, this paper proposes a “Transferable OOD data Recycling” (TOOR) method which properly utilizes ID data as well as the “recyclable” OOD data to enrich the information for conducting class-mismatched SSL. Specifically, TOOR treats the OOD data that have a close relationship with ID data and labeled data as recyclable, and employs adversarial domain adaptation to project them to the space of ID data and labeled data. In other words, the recyclability of an OOD datum is evaluated by its transferability, and the recyclable OOD data are transferred so that they are compatible with the distribution of known classes-of-interests. Consequently, our TOOR extracts more information from unlabeled data than existing methods, so it achieves an improved performance which is demonstrated by the experiments on typical benchmark datasets.
Exploring Coreference Features in Heterogeneous Data

The present paper focuses on variation phenomena in coreference chains.We address the hypothesis that the degree of structural variation between chain elements depends on language-specific constraints and preferences and, even more, on the communicative situation of language production.We define coreference features that also include reference to abstract entities and events.These features are inspired through several sources -cognitive parameters, pragmatic factors and typological status.We pay attention to the distributions of these features in a dataset containing English and German texts of spoken and written discourse mode, which can be classified into seven different registers.We apply text classification and feature selection to find out how these variational dimensions (language, mode and register) impact on coreference features.Knowledge on the variation under analysis is valuable for contrastive linguistics, translation studies and multilingual natural language processing (NLP), e.g. machine translation or cross-lingual coreference resolution.
Recognizing Japanese Historical Cursive with Pseudo-Labeling-aided CRNN as an Application of Semi-Supervised Learning to Sequence Labeling

Pseudo-labeling is semi-supervised learning to learn unlabeled data as well as labeled data by predicting labels of unlabeled data. As far as we know, pseudo-labeling is applied to the task to predict a category label. In this paper, we apply pseudo-labeling to sequence labeling which is a task to predict a sequence of labels for sequential data such as texts. To predict the pseudo-label of unlabeled data, we first initialize a representative sequence with one of the sequences inferred by multiple instances of a deep neural network by measuring edit distances among them. Then, to make the sequence more natural, the representative is refined by a local search that aims to minimize a metric defined as a linear combination of perplexity and average edit distance. To show the effectiveness of our method, we focus on recognizing Japanese historical cursive. Experimental results on ALCON2017 and Kaggle competition show that our method outperformed most of the prior works. Our method reduced the character error rate (CER) at most around 10%.
Tutorbot: A Chatbot for Higher Education Practice

In this paper, we present the design of Tutorbot – a chatbot software to support learning and teaching in higher education. We account for the implementation of the design as a proof-of-concept and share reflections from experiences in the design and implementation process expressed as design considerations for the design of chatbots in a higher education setting.
A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places

With the emergence of the COVID19 virus in late 2019 and the declaration that the virus is a worldwide pandemic, health organizations and governments have begun to implement severe health precautions to reduce the spread of the virus and preserve human lives. The enforcement of social distancing at work environments and public areas is one of these obligatory precautions. Crowd management is one of the effective measures for social distancing. By reducing the social contacts of individuals, the spread of the disease will be immensely reduced. In this paper, a model for crowd counting in public places of high and low densities is proposed. The model works under various scene conditions and with no prior knowledge. A Deep CNN model (DCNN) is built based on convolutional neural network (CNN) structure with small kernel size and two fronts. To increase the efficiency of the model, a convolutional neural network (CNN) as the front-end and a multi-column layer with Dilated Convolution as the back-end were chosen. Also, the proposed method accepts images of arbitrary sizes/scales as inputs from different cameras. To evaluate the proposed model, a dataset was created from images of Saudi people with traditional and non-traditional Saudi outfits. The model was also trained and tested on some existing datasets. Compared to current counting methods, the results show that the proposed model has significantly improved efficiency and reduced the error rate. We achieve the lowest MAE by 67%, 32% .and 15.63% and lowest MSE by around 47%, 15% and 8.1% than M-CNN, Cascaded-MTL, and CSRNet respectively.
Impact of mixed reality implementation on early-stage interactive product design process

This article proposes a novel approach towards quick concept generation and validation of interactive-product behaviours. When designing for user-product interactions, designers have to consider spatial and behavioural elements besides form/tangible aspects and perform quick-validation of the generated concepts often done through functional prototyping at later design stages. As a result, the designed-outcomes often depend on parameters like designer's familiarity with the design tools used, the level of fidelity achieved while prototyping and the frequency of design-iterations, limiting a thorough-exploration of concept-space and outcomes' creativity at the early design stages. This research targets such dependencies and non-creative hindrances at concept generation stage through a Mixed Reality implementation. This work establishes requirements for creating a suitable design-tool and presents a proof-of-concept use-case. A design task to ideate, create and revise concepts of playful product-behaviours swiftly was performed to assess the impact of the implemented method. In an empirical study, a broader exploration of solution-space and an overall improvement in creative output-flow was observed when compared to the design-outcomes in the traditional storyboard design approach. Though the implemented design-tool's unfamiliarity and capability presented a challenge, a significant increase in usage of iterative concept-design behaviour was observed throughout the study.
MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network

We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to guide the fine-grained image editing process. With the introduction of a mask-guided reconstruction loss, MagGAN learns to only edit the facial parts that are relevant to the desired attribute changes, while preserving the attribute-irrelevant regions (e.g., hat, scarf for modification 'To Bald'). Further, a novel mask-guided conditioning strategy is introduced to incorporate the influence region of each attribute change into the generator. In addition, a multi-level patch-wise discriminator structure is proposed to scale our model for high-resolution (\(1024 \times 1024\)) face editing. Experiments on the CelebA benchmark show that the proposed method significantly outperforms prior state-of-the-art approaches in terms of both image quality and editing performance.
Electro-optic Reversible Toffoli Gate with Optimal Count of LiNbO<sub>3</sub> Mach-Zehnder Interferometers

In this epoch, the study of reversible gates have received a huge impulse from research fraternity as a potential alternative to the limitation like heat/power dissipation of CMOS integrated chips and found application in quantum computing, nanotechnology, arithmetic/logical circuits for biomedical science and so on. This scheme retrieves the inputs from outputs (due to unique one-to-one mapping) thus offering no loss of information. Implementing reversible logic in optical domain is the best practice when dealing with ultra-high speed circuits as light has inherent characteristics of parallelism. This paper, to the best of our knowledge, configures the first ever electrooptic (EO) arrangement of Toffoli gate (TG) using minimal count of Titanium diffused Lithium Niobate based Mach-Zehnder interferometers. We have also discussed the use of TG to produce several logic operations. The design is carried out using Beam Propagation Method in OptiBPM tool and the computed normalized power model at the output ports of the gate is validated through the MATLAB simulations.
Digital Education in Times of COVID-19: The Experience of Medical Educators.

Medical and health education is characterized by its situated teaching, where students develop their competencies in the clinical field and where the interactions between the teacher, the student, and the patient play a vital role in academic success. The outbreak of the new coronavirus (COVID-19) has forced many schools worldwide to change their teaching model, moving from face-to-face to virtual instruction (online education), which for the educator in medicine and health sciences has resulted in breaking the teaching paradigm towards the use of technology. The objective of this research is to describe the perception of the professors in the School of Medicine and Health Sciences in Tecnologico de Monterrey about their transition to distance education during the period of the health contingency. The research presented uses a quantitative method with a non-experimental design and descriptive analysis. In this study participated 141 medical and health educators. The results indicate that the participants have experienced a paradigm shift in the integration and appropriation of technology, being able to recognize advantages and disadvantages and break with pre-established beliefs. This study concludes that the model established by the university accomplished its purpose, leading to the adoption of technology by professors and the establishment of a favorable environment for academic continuity. Moreover, this study points to better acquisition of knowledge for Faculty Development by identifying strengths, opportunities, weaknesses, and threats.