Reviewerly/exHarmony huggingface dataset:
Shutter, the Robot Photographer: Leveraging Behavior Trees for Public, In-the-Wild Human-Robot Interactions

Deploying interactive systems in-the-wild requires adaptability to situations not encountered in lab environments. Our work details our experience about the impact of architecture choice on behavior reusability and reactivity while deploying a public interactive system. In particular, we introduce Shutter, a robot photographer and a platform for public interaction. In designing Shutter's architecture, we focused on adaptability for in-the-wild deployment, while developing a reusable platform to facilitate future research in public human-robot interaction. We find that behavior trees allow reactivity, especially in group settings, and encourage designing reusable behaviors.
Retraction: Impact of Feature Selection for Data Classification Using Naive Bayes Classifier (J. Phys.: Conf. Ser. 1879 022088)

This article has been retracted by IOP Publishing following an allegation that this article may contain questionable references [1]. IOP Publishing has investigated in line with the COPE guidelines and have found the article contains unreliable references that have no significant relevance to the article and agree this article should be retracted [2]. The authors neither agree nor disagree to this retraction. [1] https://pubpeer.com/publications/2AE026290395620A7918F33163C7B0 [2] Eman Hato 2021 J. Phys.: Conf. Ser. 1879 022088 Retraction published: 13 February 2023
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos

Extensive research has demonstrated that deep neural networks (DNNs) are prone to adversarial attacks. Although various defense mechanisms have been proposed for image classification networks, fewer approaches exist for video-based models that are used in security-sensitive applications like surveillance. In this paper, we propose a novel yet simple algorithm called Pseudo-Adversarial Training (PAT), to detect the adversarial frames in a video without requiring knowledge of the attack. Our approach generates `transition frames' that capture critical deviation from the original frames and eliminate the components insignificant to the detection task. To avoid the necessity of knowing the attack model, we produce `pseudo perturbations' to train our detection network. Adversarial detection is then achieved through the use of the detected frames. Experimental results on UCF-101 and 20BN-Jester datasets show that PAT can detect the adversarial video frames and videos with a high detection rate. We also unveil the potential reasons for the effectiveness of the transition frames and pseudo perturbations through extensive experiments.
On Matrix Multiplication and Polynomial Identity Testing

We show that lower bounds on the border rank of matrix multiplication can be used to non-trivially derandomize polynomial identity testing for small algebraic circuits. Letting $\underline{R}(n)$ denote the border rank of $n \times n \times n$ matrix multiplication, we construct a hitting set generator with seed length $O(\sqrt{n} \cdot \underline{R}^{-1}(s))$ that hits $n$-variate circuits of multiplicative complexity $s$. If the matrix multiplication exponent $ω$ is not 2, our generator has seed length $O(n^{1 - \varepsilon})$ and hits circuits of size $O(n^{1 + δ})$ for sufficiently small $\varepsilon, δ&gt; 0$. Surprisingly, the fact that $\underline{R}(n) \ge n^2$ already yields new, non-trivial hitting set generators for circuits of sublinear multiplicative complexity.
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization

Training deep neural networks is a very demanding task, especially challenging is how to adapt architectures to improve the performance of trained models. We can find that sometimes, shallow networks generalize better than deep networks, and the addition of more layers results in higher training and test errors. The deep residual learning framework addresses this degradation problem by adding skip connections to several neural network layers. It would at first seem counter-intuitive that such skip connections are needed to train deep networks successfully as the expressivity of a network would grow exponentially with depth. In this paper, we first analyze the flow of information through neural networks. We introduce and evaluate the batch-entropy which quantifies the flow of information through each layer of a neural network. We prove empirically and theoretically that a positive batch-entropy is required for gradient descent-based training approaches to optimize a given loss function successfully. Based on those insights, we introduce batch-entropy regularization to enable gradient descent-based training algorithms to optimize the flow of information through each hidden layer individually. With batch-entropy regularization, gradient descent optimizers can transform untrainable networks into trainable networks. We show empirically that we can therefore train a "vanilla" fully connected network and convolutional neural network -- no skip connections, batch normalization, dropout, or any other architectural tweak -- with 500 layers by simply adding the batch-entropy regularization term to the loss function. The effect of batch-entropy regularization is not only evaluated on vanilla neural networks, but also on residual networks, autoencoders, and also transformer models over a wide range of computer vision as well as natural language processing tasks.
Energy transport and optimal design of noisy Platonic quantum networks

Optimal transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissipative Markovian noise. The network and the environmental characteristics corresponding the optimal design are obtained and investigated for five Platonic networks with 4, 6, 8, 12, and 20 number of sites that one of the sites is connected to a sink site through a dissipative process. Such optimal designs could have various applications like switching and multiplexing in quantum circuits.
Towards Interoperability of Open and Permissionless Blockchains: A Cross-Chain Query Language

The rise of open and permissionless blockchains has introduced novel platforms for applications based on distributed data storage. At the application and business levels, long-established query languages such as SQL provide interoperability that can be complemented by blockchain-based data storage today, enabling permissionless and verifiable data storage along with decentralized execution across tens of thousands of nodes. However, when accessing one or more blockchains, interoperability is not provided today, posing challenges such as inhomogeneous data access in addition to different features and trade-offs, e.g. in data and distribution, scalability, and security. Towards interoperability in data access among the increasing number of blockchain platforms, this paper introduces a cross-chain query language for data access across blockchains. Similar to SQL, the language abstracts from implementation based on a data model compatible with the largest open and permissionless blockchains (OPB) today. The language, data model, and processing architecture are demonstrated and evaluated with an implemented prototype, aiming to contribute to the discussion on blockchain interoperability among OPB.
The Relationship between Children's Learning Motivation and Early Childhood Learning Outcomes

This study aims to determine (1) parental support of children. (2) children's learning motivation (3) the relationship between parental support and motivation to children's learning outcomes. This research is included in quantitative research. The population of this study were all parents in the school, totaling 33 people. Determination of the sample using the saturated sample method, where all the population is sampled. Data collection techniques used in this study were observation, questionnaires or questionnaires and documentation. The data analysis technique in this study used statistical test data with SPSS ver. 23.0 for windows.
SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration

Next Best View computation (NBV) is a long-standing problem in robotics, and consists in identifying the next most informative sensor position(s) for reconstructing a 3D object or scene efficiently and accurately. Like most current methods, we consider NBV prediction from a depth sensor like Lidar systems. Learning-based methods relying on a volumetric representation of the scene are suitable for path planning, but have lower accuracy than methods using a surface-based representation. However, the latter do not scale well with the size of the scene and constrain the camera to a small number of poses. To obtain the advantages of both representations, we show that we can maximize surface metrics by Monte Carlo integration over a volumetric representation. In particular, we propose an approach, SCONE, that relies on two neural modules: The first module predicts occupancy probability in the entire volume of the scene. Given any new camera pose, the second module samples points in the scene based on their occupancy probability and leverages a self-attention mechanism to predict the visibility of the samples. Finally, we integrate the visibility to evaluate the gain in surface coverage for the new camera pose. NBV is selected as the pose that maximizes the gain in total surface coverage. Our method scales to large scenes and handles free camera motion: It takes as input an arbitrarily large point cloud gathered by a depth sensor as well as camera poses to predict NBV. We demonstrate our approach on a novel dataset made of large and complex 3D scenes.
Evaluating Cardiovascular Surgical Planning in Mobile Augmented Reality

Advanced surgical procedures for congenital heart diseases (CHDs) require precise planning before the surgeries. The conventional approach utilizes 3D-printing and cutting physical heart models, which is a time and resource intensive process. While rapid advances in augmented reality (AR) technologies have the potential to streamline surgical planning, there is limited research that evaluates such AR approaches with medical experts. This paper presents an evaluation with 6 experts, 4 cardiothoracic surgeons, and 2 cardiologists, from Children's Healthcare of Atlanta (CHOA) Heart Center to validate the usability and technical innovations of CardiacAR, a prototype mobile AR surgical planning application. Potential future improvements based on user feedback are also proposed to further improve the design of CardiacAR and broaden its access.
Hands-On 3D Printed Electronics

The parallel improvements in multi-material 3D printers and the quality of conductive filament open new possibilities for the fabrication of tangible and functional objects. In this studio, we discuss best practices for 3D printed electronics, talk about encountered problems, and derive design recommendations. We will guide the participants through a fabrication process by practically designing and printing objects. Consequently, we contemplate individual functional fabricated components, including small printed circuits and multi-material prints. We aim to spark a discussion about individually experienced challenges participants encountered during their design and fabrication process. This discussion includes problem-solving strategies, whose insights benefit other participants. Finally, we show the potential of printed electronics and discuss encouraging new opportunities in this field.
ShiftTouch: Extending Touchscreens with Passive Interfaces using Small Occluded Area for Discrete Touch Input

We present ShiftTouch, an attachment-type passive interface that provides multiple inputs for capacitive touchscreens with minimal screen occlusion. ShiftTouch utilizes multiple linear electrodes to control the fine displacement of the touch position. The touch input is activated under the electrodes when several adjacent electrodes are grounded simultaneously. Each input area shares several electrodes with neighboring input areas, and the touchscreen identifies each one by detecting the fine displacement of the touch position. ShiftTouch can effectively reduce the occlusion area while inheriting the advantages of existing touch extension interfaces, which are battery-free, freely detachable, and easy to construct. Depending on the number of inputs to implement, ShiftTouch can alleviate screen occlusion by up to 80.5% compared to existing approaches using finger-sized electrodes.
Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks

As a fundamental computer vision task, crowd counting plays an important role in public safety. Currently, deep learning based head detection is a promising method for crowd counting. However, the highly concerned object detection networks cannot be well applied to this problem for three reasons: (1) Existing loss functions fail to address sample imbalance in highly dense and complex scenes; (2) Canonical object detectors lack spatial coherence in loss calculation, disregarding the relationship between object location and background region; (3) Most of the head detection datasets are only annotated with the center points, i.e. without bounding boxes. To overcome these issues, we propose a novel Mask Focal Loss (MFL) based on heatmap via the Gaussian kernel. MFL provides a unifying framework for the loss functions based on both heatmap and binary feature map ground truths. Additionally, we introduce GTA_Head, a synthetic dataset with comprehensive annotations, for evaluation and comparison. Extensive experimental results demonstrate the superior performance of our MFL across various detectors and datasets, and it can reduce MAE and RMSE by up to 47.03% and 61.99%, respectively. Therefore, our work presents a strong foundation for advancing crowd counting methods based on density estimation.
PAMI: partition input and aggregate outputs for model interpretation

There is an increasing demand for interpretation of model predictions especially in high-risk applications. Various visualization approaches have been proposed to estimate the part of input which is relevant to a specific model prediction. However, most approaches require model structure and parameter details in order to obtain the visualization results, and in general much effort is required to adapt each approach to multiple types of tasks particularly when model backbone and input format change over tasks. In this study, a simple yet effective visualization framework called PAMI is proposed based on the observation that deep learning models often aggregate features from local regions for model predictions. The basic idea is to mask majority of the input and use the corresponding model output as the relative contribution of the preserved input part to the original model prediction. For each input, since only a set of model outputs are collected and aggregated, PAMI does not require any model detail and can be applied to various prediction tasks with different model backbones and input formats. Extensive experiments on multiple tasks confirm the proposed method performs better than existing visualization approaches in more precisely finding class-specific input regions, and when applied to different model backbones and input formats. The source code will be released publicly.
Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition

Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many datasets for SER are substantially imbalanced, where data utterances of one class (most often Neutral) are much more frequent than those of other classes. Furthermore, only a few data resources are available for many existing spoken languages. To address these problems, we exploit a GAN-based augmentation model guided by a triplet network, to improve SER performance given imbalanced and insufficient training data. We conduct experiments and demonstrate: 1) With a highly imbalanced dataset, our augmentation strategy significantly improves the SER performance (+8% recall score compared with the baseline). 2) Moreover, in a cross-lingual benchmark, where we train a model with enough source language utterances but very few target language utterances (around 50 in our experiments), our augmentation strategy brings benefits for the SER performance of all three target languages.
Privacy Preserving Secure Deduplication in Cloud Storage

Abstract: Among the main parts of cloud computing is distributed storage, which makes it much easier for cloud clients to store and share their information in the cloud with authorized users. Secure deduplication has received a lot of attention in distributed storage since it can lessen correspondence overhead and storage space by eliminating redundancy in encrypted data.In terms of privacy and security, numerous current secure deduplication plans typically concentrate on accomplishing the accompanying properties:confidentialityof the data, consistency of the tags, command over access, and protection from animal power assaults. However, to the extent that we are aware, not a single one of them can all the while satisfy these four necessities.To resolve this issue, we present in this paper a client-definable, secure, and effective deduplication strategy.Particularly, our plan maximizes the elimination of duplicates without compromising cloud users' privacy or security by permitting just the cloud specialist co-op to approve information access for proprietors. Our authorized secure deduplication plot, according to a comprehensive security analysis, prevents brute-force attacks while maintaining data confidentiality and tag consistency.In addition, broad reproductions exhibit that our strategy outflanks the contending ones as far as the efficiency of deduplication furthermore, the overheads related with calculation, correspondence,and storage.
Federated Automatic Differentiation

Federated learning (FL) is a general framework for learning across heterogeneous clients while preserving data privacy, under the orchestration of a central server. FL methods often compute gradients of loss functions purely locally (ie. entirely at each client, or entirely at the server), typically using automatic differentiation (AD) techniques. We propose a federated automatic differentiation (FAD) framework that 1) enables computing derivatives of functions involving client and server computation as well as communication between them and 2) operates in a manner compatible with existing federated technology. In other words, FAD computes derivatives across communication boundaries. We show, in analogy with traditional AD, that FAD may be implemented using various accumulation modes, which introduce distinct computation-communication trade-offs and systems requirements. Further, we show that a broad class of federated computations is closed under these various modes of FAD, implying in particular that if the original computation can be implemented using privacy-preserving primitives, its derivative may be computed using only these same primitives. We then show how FAD can be used to create algorithms that dynamically learn components of the algorithm itself. In particular, we show that FedAvg-style algorithms can exhibit significantly improved performance by using FAD to adjust the server optimization step automatically, or by using FAD to learn weighting schemes for computing weighted averages across clients.
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic

COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data was also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.
Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric

(Conditional) Generative Adversarial Networks (GANs) have found great success in recent years, due to their ability to approximate (conditional) distributions over extremely high dimensional spaces. However, they are highly unstable and computationally expensive to train, especially in the time series setting. Recently, it has been proposed the use of a key object in rough path theory, called the signature of a path, which is able to convert the min-max formulation given by the (conditional) GAN framework into a classical minimization problem. However, this method is extremely expensive in terms of memory cost, sometimes even becoming prohibitive. To overcome this, we propose the use of \textit{Conditional Neural Stochastic Differential Equations}, which have a constant memory cost as a function of depth, being more memory efficient than traditional deep learning architectures. We empirically test that this proposed model is more efficient than other classical approaches, both in terms of memory cost and computational time, and that it usually outperforms them in terms of performance.
Feynman checkers: number-theoretic properties

We study Feynman checkers, an elementary model of electron motion introduced by R. Feynman. In this model, a checker moves on a checkerboard, and we count the turns. Feynman checkers are also known as a one-dimensional quantum walk. We prove some new number-theoretic results in this model, for example, sign alternation of the real and imaginary parts of the electron wave function in a specific area. All our results can be stated in terms of Young diagrams, namely, we compare the number of Young diagrams with an odd and an even number of steps.
A Variational Perspective on Generative Flow Networks

Generative flow networks (GFNs) are a class of models for sequential sampling of composite objects, which approximate a target distribution that is defined in terms of an energy function or a reward. GFNs are typically trained using a flow matching or trajectory balance objective, which matches forward and backward transition models over trajectories. In this work, we define variational objectives for GFNs in terms of the Kullback-Leibler (KL) divergences between the forward and backward distribution. We show that variational inference in GFNs is equivalent to minimizing the trajectory balance objective when sampling trajectories from the forward model. We generalize this approach by optimizing a convex combination of the reverse- and forward KL divergence. This insight suggests variational inference methods can serve as a means to define a more general family of objectives for training generative flow networks, for example by incorporating control variates, which are commonly used in variational inference, to reduce the variance of the gradients of the trajectory balance objective. We evaluate our findings and the performance of the proposed variational objective numerically by comparing it to the trajectory balance objective on two synthetic tasks.