Reviewerly/exHarmony huggingface dataset:
Fast optimal and bounded suboptimal Euclidean pathfinding

We consider optimal and suboptimal algorithms for the Euclidean Shortest Path Problem (ESPP) in two dimensions. For optimal path planning, Our approach leverages ideas from two recent works: Polyanya, a mesh-based ESPP planner which we use to represent and reason about the environment, and Compressed Path Databases (CPD), a speedup technique for pathfinding on grids and spatial networks, which we exploit to efficiently compute candidate paths, in order to construct a completely novel ESPP algorithm, End Point Search (EPS). In a range of experiments and empirical comparisons we show that: (i) the auxiliary data structures required by EPS are cheap to build and store; (ii) for optimal search, the new algorithm is faster than a range of recent ESPP planners, with speedups ranging from several factors to over one order of magnitude; (iii) for anytime search, where feasible solutions are needed fast, we report even better performance. For suboptimal path planning, we extend the CPD such that it computes and compresses first move data of a larger number of selected candidate nodes covering every point in the Euclidean space. Our approach is search-free, simultaneously fast, and returns a path within a fixed bound of the optimal solution. In a range of empirical results, we show that: (i) our approach outperforms both offline/online optimal and suboptimal ESPP algorithms proposed in the literature; (ii) our approach demonstrates excellent path quality, better than all existing suboptimal ESPP algorithms; and (iii) the approach offers flexibility by allowing a trade-off between the CPD construction cost (space and time) and the suboptimality bound.
The road from MLE to EM to VAE: A brief tutorial

Variational Auto-Encoders (VAEs) have emerged as one of the most popular genres of generative models, which are learned to characterize the data distribution. The classic Expectation Maximization (EM) algorithm aims to learn models with hidden variables. Essentially, both of them are iteratively optimizing the evidence lower bound (ELBO) to maximize to the likelihood of the observed data. This short tutorial joins them up into a line and offer a good way to thoroughly understand EM and VAE with minimal knowledge. It is especially helpful to beginners and readers with experiences in machine learning applications but no statistics background.
Distinguishing Between Natural and GAN-Generated Face Images by Combining Global and Local Features

With the development of face image synthesis and generation technology based on generative adversarial networks (GANs), it has become a research hotspot to determine whether a given face image is natural or generated. However, the generalization capability of the existing algorithms is still to be improved. Therefore, this paper proposes a general algorithm. To do so, firstly, the learning on important local areas, containing many face key-points, is strengthened by combining the global and local features. Secondly, metric learning based on the ArcFace loss is applied to extract common and discriminative features. Finally, the extracted features are fed into the classification module to detect GAN-generated faces. The experiments are conducted on two publicly available natural datasets (CelebA and FFHQ) and seven GAN-generated datasets. Experimental results demonstrate that the proposed algorithm achieves a better generalization performance with an average detection accuracy over 0.99 than the state-of-the-art algorithms. Moreover, the proposed algorithm is robust against additional attacks, such as Gaussian blur, and Gaussian noise addition.
Sign Language Translation Using Multi Context Transformer

Sign Language Translation (SLT) is an important sequence-to-sequence problem that has been challenging to solve, because of the various factors which influence the meaning of a sign. In this paper, we implement a Multi Context Transformer architecture that attempts to solve this problem by operating on batched video segment representations called context vectors, intending to capture various temporal dependencies present between the frames to accurately translate the input signs. This architecture, being end-to-end also eliminates the need for sign language intermediaries known as glosses. Our model produces results that are on par with the state-of-the-art (98.19% score retention in the ROUGE-L score and 86.65% in the BLEU-4 score) while simultaneously achieving a 30.88% reduction in model parameters, which makes the model suitable for real-world applications. Our implementation is available on GitHub.(\(^{1}\)https://github.com/MBadriNarayanan/MultiContextTransformer)
Concepts for Vestibular and Visual Stimulation to Mitigate Carsickness in Stop-and-Go-Driving

The problem of motion sickness in cars is receiving increased attention due to the development of automated driving functions. This evolution of driving provokes changes in the role of the driver and non-driving activities become more relevant for all passengers. The full control of the driving dynamics enables car manufacturers to influence stimulations that provoke motion sickness. In parallel, passenger activities like video watching increase the probability of symptoms for individuals that are susceptible to motion sickness. Based on those circumstances, technical solutions that allow user experiences without motion sickness need to be developed. The movement perception by the associated organs of balance and the eyes plays a key role in the conflict of motion sickness. This investigation evaluated two concepts that address the human vestibular and visual systems in order to reduce motion sickness. The effects of a prototypical decoupled seat and a virtual animation of the vehicle environment were compared to a reference condition. The experimental environment consisted of a naturalistic reproduction of stop-and-go-driving on the proving ground. The participant's task was to watch a video while being driven. Meanwhile, symptoms of carsickness were retrieved and objective parameters of head and eye movement recorded. The study results do not show significant improvements on carsickness due to the realized concepts. Furthermore, the selected parameters for monitoring behavioral patterns of the head and eye movement were unaffected as well. Consequently, a review of the concept principles as well as an advancement of the physical prototypes are needed.
Graph Tree Neural Networks

Graph neural networks (GNNs) have recently shown good performance in various fields. In this paper, we propose graph tree neural networks (GTNNs) designed to solve the problems of existing networks by analyzing the structure of human neural networks. In GTNNs, information units are related to the form of a graph and then they become a bigger unit of information again and have a relationship with other information units. At this point, the unit of information is a set of neurons, and we can express it as a vector with GTNN. Defining the starting and ending points in a single graph is difficult, and a tree cannot express the relationship among sibling nodes. However, a graph tree can be expressed using leaf and root nodes as its starting and ending points and the relationship among sibling nodes. Depth-first convolution (DFC) encodes the interaction result from leaf nodes to the root node in a bottom-up approach, and depth-first deconvolution (DFD) decodes the interaction result from the root node to the leaf nodes in a top-down approach. GTNN is data-driven learning in which the number of convolutions varies according to the depth of the tree. Moreover, learning features of different types together is possible. Supervised, unsupervised, and semi-supervised learning using graph tree recursive neural network (GTR) , graph tree recursive attention networks (GTRAs), and graph tree recursive autoencoders (GTRAEs) are introduced in this paper. We experimented with a simple toy test with source code dataset.
tCLD-Net: A Transfer Learning Internet Encrypted Traffic Classification Scheme Based on Convolution Neural Network and Long Short-Term Memory Network

The Internet is about to enter the era of full encryption. Traditional traffic classification methods only work well in non-encrypted environments. How to identify the specific types of network encrypted traffic in an encrypted environment without decryption is one of the foundations for maintaining cyberspace security. Traffic classification based on machine learning relies heavily on the prior knowledge of experts to construct feature sets. Although traffic classification based on deep learning can reduce human intervention, it requires a large amount of labeled data for parameter determination. This paper proposes a tCLD-Net model that combines transfer learning and deep learning. It can be trained on a small amount of labeled data to distinguish network encrypted traffic with a high accuracy. It pre-trains a CLD-Net model in the source domain data set, and fixes the parameters of the convolutional neural network module in it, and trains and tests it in the target domain data set. In order to verify the effectiveness of the tCLD-Net model, we use the ISCX public data set to conduct experiments. The results show that our proposed model can complete 100 epoches training in 208 seconds when the training set only occupies 20% of the target domain. And achieve a classification accuracy rate about 86%. This is 4% higher than the model without pre-training, and the training time is only one third of the model without pre-training.
Quantum Software

Quantum Software is a formidable challenge. This is a conse- quence of a rst encounter between software, usually perceived as an engineering discipline, and quantum computing, an outgrowth of two complex and fundamental sciences: physics and computer science. However, Quantum Software is a signi cant creativity op- portunity. This short paper characterizes the unique opportunity, while keeping in mind the great challenge. It also stands as a re- port of a workshop (Q-SE 2021) exactly created to promote under- standing and trigger motivation to exploit this opportunity, while overcoming the challenge. Q-SE 2021 | the 2nd International Workshop on Quantum Software Engineering | was co-located with the 43rd International Conference on Software Engineering (ICSE 2021). Q-SE 2021 was virtually held on June 1 and June 2 of 2021.
Task-Relevant Adversarial Imitation Learning

We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. When the discriminator focuses on task-irrelevant features, it does not provide an informative reward signal, leading to poor task performance. We analyze this problem in detail and propose a solution that outperforms standard Generative Adversarial Imitation Learning (GAIL). Our proposed method, Task-Relevant Adversarial Imitation Learning (TRAIL), uses constrained discriminator optimization to learn informative rewards. In comprehensive experiments, we show that TRAIL can solve challenging robotic manipulation tasks from pixels by imitating human operators without access to any task rewards, and clearly outperforms comparable baseline imitation agents, including those trained via behaviour cloning and conventional GAIL.
Towards Multimodal Data Retrieval in Remote Sensing

The world around us is multimodal in nature: seeing scenes, hearing voices, watching videos, and savoring flavors. Recently, multimodal applications, which deal with multiple modalities, especially image-text retrieval (matching) a topic of broad and current interest in the general literature of computer vision. Yet most of the existing remote sensing image retrieval approaches rely on the concept of image-image matching (unimodal). In this paper, we aim to draw the attention of researchers in the remote sensing community to a recent direction multimodal data retrieval (matching), particularly image-text matching, which is considered a recent research direction, due to its importance for human intelligence to grasp the relation between visual and textual content and to bridge the semantic gap between such different contents (modalities) in light of tremendous progress in deep learning techniques through highlighting the three main challenges (multimodal representation, similarity measurement, and dataset availability) that face researchers in this research line.
Classical and Quantum Algorithms for Assembling a Text from a Dictionary

We study algorithms for solving the problem of assembling a text (long string) from a dictionary (a sequence of small strings). The problem has an application in bioinformatics and has a connection with the sequence assembly method for reconstructing a long deoxyribonucleic-acid (DNA) sequence from small fragments. The problem is assembling a string t of length n from strings s1,...,sm. Firstly, we provide a classical (randomized) algorithm with running time Õ(nL0.5 + L) where L is the sum of lengths of s1,...,sm. Secondly, we provide a quantum algorithm with running time Õ(nL0.25 + √mL). Thirdly, we show the lower bound for a classical (randomized or deterministic) algorithm that is Ω(n+L). So, we obtain the quadratic quantum speed-up with respect to the parameter L; and our quantum algorithm have smaller running time comparing to any classical (randomized or deterministic) algorithm in the case of non-constant length of strings in the dictionary.
Comparison Performance of Long Short-Term Memory and Convolution Neural Network Variants on Online Learning Tweet Sentiment Analysis

Sentiment analysis can be act as an assisted tool in improving the quality of online teaching and learning between teachers and students. Twitter social media platform currently more than 500 million tweets sent each day which is equal to 5787 tweets per second. Therefore, it is hard to track users’ overall opinions on the topics contained in social media. To catch up with the feedback on online learning, it is crucial to detect the topic being discussed and classify users’ sentiments towards those topics. Even though there are many approaches in developing sentiment analysis models, DL models prove to provide the best performance in the sentiment analysis field. Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are two mainstream models in DL used for sentiment analysis classification. Therefore, we evaluate CNN, LSTM, and its hybrids to classify sentiment or an online learning tweet from 2020 until 2021 of 23168 tweets. CNN-LSTM, LSTM-CNN, Bidirectional LSTM, CNN-Bidirectional LSTM models were designed and evaluated based on random hyperparameter tuning. We explain the proposed methodology and model design illustration. The outcome assesses the superiority of all models with a remarkable improvement of accuracy and a reduction loss when applying the random oversampling technique. Specifically, the LSTM-CNN model with random oversampling technique outperformed the other six models with an accuracy of 87.40% and loss value of 0.3432. However, the computational time has resulted increased when with random oversampling technique. Thus, in the future, the performance can be improved on computational time and hyperparameter selection with the employment of nature-inspired computing for fast and optimal results.
A Custom Unsupervised Approach for Pipe-Freeze Online Anomaly Detection

Currently, many companies are leveraging the advancement in IoT technologies by incorporating more Wireless Sensor Networks (WSNs)-based solutions in their services. WSNs are becoming cheaper and can be easily deployed in multiple locations to cover very wide geographic areas. These sensors are often an essential part of monitoring systems. Several applications exist for these systems. One application is to monitor temperatures inside buildings that are susceptible to pipe-freeze hazard. Hartford Steam Boiler (HSB) is one such company that uses temperature sensor nodes to monitor temperatures for thousands of institutional buildings across the United States to prevent pipe-freeze losses to its customers. For this to happen, it is essential to automate the prediction/detection of anomalies in the temperature sensor readings. In this paper, we introduce two generic techniques, namely, data thresholding and distance based filtering, that can be used to improve the performance of any anomaly detection algorithm. To illustrate the effectiveness of these techniques, we present a customized hybrid approach based on isolation forest (IF) and these two techniques. A set of hyperparameters has been proposed and carefully tuned to condition the data and extract only relevant information in a way that helps HSB detect anomalies, predict pipe-freeze, notify its customers, and hence prevent pipe-freeze related losses. Experiments carried out on real temperature sensors’ data show the efficacy of our proposed techniques and algorithm in terms of achieving the business goals of HSB.
Fast and Accurate 3D Eye-to-hand Calibration for Large-Scale Scene based on HALCON

The premise of robot executing vision-based tasks is to establish the coordination between robot eye (structured light 3D sensor) and a hand (end effector). However, it is inefficient to perform the 3D eye-to-hand calibration for large-scale scene due to large amounts of point clouds data contained in the scene. This paper describes a fast and accurate 3D eye-to-hand calibration method for large-scale scene. A common tee pipe is used as calibration object. The method presented here comprises the following steps: Firstly, voxel grid filtering method is used for reducing the number of point clouds data. Secondly, employing euclidean cluster extraction method, the point clouds data of the calibration object can be extracted from the large-scale scene. Thirdly, the pose of the calibration object is acquired by surface-based 3D matching. Finally, the 3D eye-to-hand calibration is performed, and robot grasping experiments are carried out on ABB YuMi platform to validate the accuracy of calibration results. The experiments demonstrate that our method can achieve high calibration accuracy and significantly improve the efficiency of calibration.
True Few-Shot Learning with Language Models

Pretrained language models (LMs) perform well on many tasks even when learning from a few examples, but prior work uses many held-out examples to tune various aspects of learning, such as hyperparameters, training objectives, and natural language templates (prompts). Here, we evaluate the few-shot ability of LMs when such held-out examples are unavailable, a setting we call true few-shot learning. We test two model selection criteria, cross-validation and minimum description length, for choosing LM prompts and hyperparameters in the true few-shot setting. On average, both marginally outperform random selection and greatly underperform selection based on held-out examples. Moreover, selection criteria often prefer models that perform significantly worse than randomly-selected ones. We find similar results even when taking into account our uncertainty in a model's true performance during selection, as well as when varying the amount of computation and number of examples used for selection. Overall, our findings suggest that prior work significantly overestimated the true few-shot ability of LMs given the difficulty of few-shot model selection.
Tele-Nininbaori: Intentional Harmonization in Cybernetic Avatar with Simultaneous Operation by Two-persons

Advances in technology have made it possible for people who could not participate in work due to difficulties in social participation to work. It is becoming easier for people who have lost their abilities due to the decline in their abilities as a result of aging or due to some kind of disability to enter work remotely. However, the lack of capacity of these people hinders the implementation of the task. In order to compensate for the lack of human ability and promote remote participation in society, the idea of supplementing a part of remote human ability has been proposed as in Cybernetic Avatar. However, it is not easy to automate the capabilities required at work. In this study, the authors propose a remote-controlled avatar method for people who lack the ability to temporarily borrow the ability of others. The study looked at the models needed for these avatars, and created a prototype to see what problems could arise when working. As a result of the verification, three gestures were observed: symbol gesture, motion gesture, and shape gesture. In addition, as the intention arbitration between the two is performed smoothly, it was observed that the intention arbitration changes from the motion gesture to the symbol gesture.
Two Mechanisations of WebAssembly 1.0

WebAssembly (Wasm) is a new bytecode language supported by all major Web browsers, designed primarily to be an efficient compilation target for low-level languages such as C/C++ and Rust. It is unusual in that it is officially specified through a formal semantics. An initial draft specification was published in 2017 [], with an associated mechanised specification in Isabelle/HOL published by Watt that found bugs in the original specification, fixed before its publication [].The first official W3C standard, WebAssembly 1.0, was published in 2019 []. Building on Watt’s original mechanisation, we introduce two mechanised specifications of the WebAssembly 1.0 semantics, written in different theorem provers: WasmCert-Isabelle and WasmCert-Coq. Wasm’s compact design and official formal semantics enable our mechanisations to be particularly complete and close to the published language standard. We present a high-level description of the language’s updated type soundness result, referencing both mechanisations. We also describe the current state of the mechanisation of language features not previously supported: WasmCert-Isabelle includes a verified executable definition of the instantiation phase as part of an executable verified interpreter; WasmCert-Coq includes executable parsing and numeric definitions as on-going work towards a more ambitious end-to-end verified interpreter which does not require an OCaml harness like WasmCert-Isabelle.
Associational Deductive Networks.

we introduce associational deductive networks(ADNs), a network that performs deductive reasoning. To have high-dimensional thinking, combining various axioms and putting the results back into another axiom is necessary to produce new relationships and results. For example, it would be given two propositions: is a man. and All men are mortals. and two propositions could be used to infer the new proposition, Therefore Socrates is mortal.. To evaluate, we used MNIST Dataset, a handwritten numerical image dataset, to apply it to the group theory and show the results of performing deductive learning.