Reviewerly/exHarmony huggingface dataset:
Complete vectorlike fourth family and new U(1)$^\prime$ for muon anomalies

I introduce a model to explain the recent anomalies in the muon anomalous magnetic moment and the rare semi-leptonic $B$ meson decays. The Standard Model is extended by a $U(1)^\prime$ gauge symmetry and a complete fourth family fermions which are vector-like under the gauge symmetry. We found parameter points which can explain the anomalies consistently with the other observables. We then propose an interesting possibility to have signals with four muons or more from vector-like lepton pair production.
Spatial-Temporal Graph Convolutional Networks for Action Recognition with Adjacency Matrix Generation Network

The proposal of Graph Convolutional Networks (GCN) gives the neural network a better performance to understand graph structure data. Therefore, GCN has a natural advantage in the action recognition task. The traditional adjacency matrix was generated depending on the physical connection of human joints. It limits the ability of GCN to understand skeleton data. In this paper, we propose a new adaptive method to generate the graph adjacency matrix and a new GCN unit to process the adjacency matrix. A series of experiments have been implemented to verify the practicability and potential of the proposed method.
Precondition and Effect Reasoning for Action Recognition

Human action recognition has drawn a lot of attention in the recent years due to the research and application significance. Most existing works on action recognition focus on learning effective spatial-temporal features from videos, but neglect the strong causal relationship among the precondition, action and effect. Such relationships are also crucial to the accuracy of action recognition. In this paper, we propose to model the causal relationships based on the precondition and effect to improve the performance of action recognition. Specifically, a Cycle-Reasoning model is proposed to capture the causal relationships for action recognition. To this end, we annotate precondition and effect for a large-scale action dataset. Experimental results show that the proposed Cycle-Reasoning model can effectively reason about the precondition and effect and can enhance action recognition performance.
Object Detection as Probabilistic Set Prediction

Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems. The development and evaluation of probabilistic object detectors have been hindered by shortcomings in existing performance measures, which tend to involve arbitrary thresholds or limit the detector’s choice of distributions. In this work, we propose to view object detection as a set prediction task where detectors predict the distribution over the set of objects. Using the negative log-likelihood for random finite sets, we present a proper scoring rule for evaluating and training probabilistic object detectors. The proposed method can be applied to existing probabilistic detectors, is free from thresholds, and enables fair comparison between architectures. Three different types of detectors are evaluated on the COCO dataset. Our results indicate that the training of existing detectors is optimized toward non-probabilistic metrics. We hope to encourage the development of new object detectors that can accurately estimate their own uncertainty.
Discriminant laplacian eigenmaps by the approximation of discriminant analysis using similarity

The dimension of data influences the clustering method used for pattern recognition. Dimension reduction method, therefore, has a significant impact on clustering performance. This study compares discriminant analysis (DA) and Laplacian eigenmaps (LE), two supervised and unsupervised dimension reduction methods, from the standpoint of the degree of separation. This comparison revealed that LE suffers from a loss of accuracy due to the lack of an averaging operation. Therefore, we propose a new dimensionality reduction method, discriminant LE (DLE), which eliminates the shortcomings of LE. DLE is a straightforward approximation of DA using the similarity. We also propose recursive similarity processing method to reduce pseudoclusters. Finally, we also conclude that DLE is more useful than LE for clustering and that recursive similarity processing improves the performance of DLE.
Data Analysis for Implementing an Efficient Testing Model in Software Testing Using Machine Learning

In modern days, testing models are used extensively over most of the fields of technology and automation. The cruciality of testing is so high that any misuse or out-of-sight incidents will result in inefficient modeling of the system. Therefore, it requires a very highly maintained and highly automated testing strategy to reduce/minimize any human-involved errors. The testing stations have the collaboration of numerous equipment that are required to undergo testing, as well as, involvement of financial concerns. To cut back on the resources invested in testing, the proposed model makes use of an amalgamation of RFM analysis and regression techniques which can be used on the previously available dataset. These methodologies are used to automate the model and understand the behavior of the test cases. The automation in terms of the execution time of the test case can be projected using regression problem-solving.
Rough analysis of computation trees

This paper deals with computation trees over an arbitrary structure consisting of a set along with collections of functions and predicates that are defined on it. It is devoted to the comparative analysis of three parameters of problems with $n$ input variables over this structure: the complexity of a problem description, the minimum complexity of a computation tree solving this problem deterministically, and the minimum complexity of a computation tree solving this problem nondeterministically. Rough classification of relationships among these parameters is considered and all possible seven types of these relations are enumerated. The changes of relation types with the growth of the number $n$ of input variables are studied.
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping

We investigate the "Visual Pushing for Grasping" (VPG) system by Zeng et al. and the "Hourglass" system by Ewerton et al., an evolution of the former. The focus of our work is the investigation of the capabilities of both systems to learn long-term rewards and policies. Zeng et al. original task only needs a limited amount of foresight. Ewerton et al. attain their best performance using an agent which only takes the most immediate action under consideration. We are interested in the ability of their models and training algorithms to accurately predict long-term Q-Values. To evaluate this ability, we design a new bin sorting task and reward function. Our task requires agents to accurately estimate future rewards and therefore use high discount factors in their Q-Value calculation. We investigate the behaviour of an adaptation of the VPG training algorithm on our task. We show that this adaptation can not accurately predict the required long-term action sequences. In addition to the limitations identified by Ewerton et al., it suffers from the known Deep Q-Learning problem of overestimated Q-Values. In an effort to solve our task, we turn to the Hourglass models and combine them with the Double Q-Learning approach. We show that this approach enables the models to accurately predict long-term action sequences when trained with large discount factors. Our results show that the Double Q-Learning technique is essential for training with very high discount factors, as the models Q-Value predictions diverge otherwise. We also experiment with different approaches for discount factor scheduling, loss calculation and exploration procedures. Our results show that the latter factors do not visibly influence the model's performance for our task.
Introduction

The fourth industrial revolution, or Industry 4.0, brings together digital and physical technologies to create responsive and interconnected operations. Companies use AI, robotics, edge computing, and the cloud to make informed and timely decisions from the supply chain to the smart factory [1]. Solutions designed for the Industrial Internet of Things (IIoT) use connected sensors and edge devices to help improve product quality and operational efficiency in factories in real time. From workshops and worksites to decision-making stations, tools and equipment are now networked to become interconnected. Through sensors, detectors, and other monitoring devices, machines and operators are collecting increasingly huge amounts of data, the management, analysis, and exploitation of which constitute enormous challenges for companies [2]. The Internet of Things brings a wealth of promise in productivity, quality, performance, and security [3].
Provable Adversarial Robustness in the Quantum Model

Modern machine learning systems have been applied successfully to a variety of tasks in recent years but making such systems robust against adversarially chosen modifications of input instances seems to be a much harder problem. It is probably fair to say that no fully satisfying solution has been found up to date and it is not clear if the standard formulation even allows for a principled solution. Hence, rather than following the classical path of bounded perturbations, we consider a model similar to the quantum PAC-learning model introduced by Bshouty and Jackson [1995]. Our first key contribution shows that in this model we can reduce adversarial robustness to the conjunction of two classical learning theory problems, namely (Problem 1) the problem of finding generative models and (Problem 2) the problem of devising classifiers that are robust with respect to distributional shifts. Our second key contribution is that the considered framework does not rely on specific (and hence also somewhat arbitrary) threat models like $\ell_p$ bounded perturbations. Instead, our reduction guarantees that in order to solve the adversarial robustness problem in our model it suffices to consider a single distance notion, i.e. the Hellinger distance. From the technical perspective our protocols are heavily based on the recent advances on delegation of quantum computation, e.g. Mahadev [2018]. Although the considered model is quantum and therefore not immediately applicable to ``real-world'' situations, one might hope that in the future either one can find a way to embed ``real-world'' problems into a quantum framework or that classical algorithms can be found that are capable of mimicking their powerful quantum counterparts.
Toeplitz Least Squares Problems, Fast Algorithms and Big Data

In time series analysis, when fitting an autoregressive model, one must solve a Toeplitz ordinary least squares problem numerous times to find an appropriate model, which can severely affect computational times with large data sets. Two recent algorithms (LSAR and Repeated Halving) have applied randomized numerical linear algebra (RandNLA) techniques to fitting an autoregressive model to big time-series data. We investigate and compare the quality of these two approximation algorithms on large-scale synthetic and real-world data. While both algorithms display comparable results for synthetic datasets, the LSAR algorithm appears to be more robust when applied to real-world time series data. We conclude that RandNLA is effective in the context of big-data time series.
Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of  Rigid Motion Artifact in Brain MRI

In this paper, we develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans.
Explanation as Question Answering based on Design Knowledge

Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access and use this design knowledge for generating explanations. Many AI agents used in practice, such as intelligent tutoring systems fielded in educational contexts, typically come with a User Guide that explains what the agent does, how it works and how to use the agent. However, few humans actually read the User Guide in detail. Instead, most users seek answers to their questions on demand. In this paper, we describe a question answering agent (AskJill) that uses the User Guide for an interactive learning environment (VERA) to automatically answer questions and thereby explains the domain, functioning, and operation of VERA. We present a preliminary assessment of AskJill in VERA.
Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning

The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine learning (ML) algorithms. However, the conventional device design wisdom is disconnected with system optimization. We report a device-system co-design methodology to optimize a free-space optical general matrix multiplication (GEMM) hardware accelerator by engineering a spatially reconfigurable array made from chalcogenide phase change materials. With a highly-parallelized hardware emulator constructed based on experimental information, we demonstrate the design of unit device by optimizing GEMM calculation accuracy via reinforcement learning, including deep Q-learning neural network, Bayesian optimization, and their cascaded approach, which show a clear correlation between system performance metrics and physical device specifications. Furthermore, we employ physics-aware training approaches to deploy optimized hardware to the tasks of image classification, materials discovery, and a closed-loop design of optical ML accelerators. The demonstrated framework offers insights into the co-design of optoelectronic devices and systems with reduced human-supervision and domain-knowledge barriers.
Enhanced Performance and Data Security using Elliptic Curve Cryptography in Cloud Environment

Cloud computing provides access to resources of computing, network, storage on a demand basis over the internet for use by the customers. Cloud computing faces many challenges, which makes the users to be wary of using it to store their huge data and applications in the cloud. One of the prime challenges of cloud is security. Data is secured on the cloud by encrypting the data using a cryptosystem. From the literature survey of works carried out by researchers in the area of encryption algorithms, it is seen that Elliptic curve cryptography algorithm is very beneficial for performing computations on encrypted data on the cloud. The proposed work uses an enhanced ECC for securing cloud data. The experimentations have shown that the proposed mechanism provides better security and improved performance compared to the existing cryptographic mechanisms in practice.
COMM-G: A Communication Glove for Smart Communication

This paper proposes a smart communication process using google assistant for sign language identification with the help of the components majorly comprising of flex sensors, MPU6050 sensors, and microcontrollers. The basic idea evolved overtime to make the process simpler and more efficient. The conversion from gesture to speech is carried in two sub-processes, firstly gesture is converted into text with the help of flex sensors and the text is converted into speech with the help of default functionality of google assistant. The test results are prudently evaluated with the respective output of the sensors. This proposed idea can assist people with impaired learning and people working in industries.
Aspect-level Sentiment Analysis based on BERT Fusion Multi-module

Aspect-level sentiment analysis is a fine-grained sentiment analysis task. Its task is to calculate people's opinions, evaluations, attitudes and emotions expressed by entities. In recent years, many deep language models have made great progress in this regard, including BERT. The initial layer to the middle layer of BERT can extract grammatical information, but the semantic information of higher layers is often easily ignored. Because extracting sentence sentiment is based on semantics, this article adds two modules of parallel aggregation and hierarchical aggregation on the basis of BERT. Parallel aggregation is used for aspect extraction, and hierarchical aggregation is used for aspect sentiment classification tasks, and the conditional random field is used as Sequence marking tasks to extract more semantic information. From the experimental results on the SemEval 2014 and SemEval 2016 data sets, it can be seen that the accuracy and F1 value of the model proposed in this paper are better than the comparison model, confirming the effectiveness of the model.
An Exploration of Students’ Foreign Language Anxiety in English Classroom

An Exploration of Students’ Foreign Language Anxiety in English Classroom. Objectives: This study aims to investigate students’ foreign language anxiety in English class: students’ anxiety levels, students’ anxiety kinds, and students’ attitudes toward English class. Methods: This study used a quantitative approach and a survey method with a descriptive research design. Findings: The findings of the study showed 62.5% of the students felt anxious in foreign language level sequenced from mildly anxious, anxious, and very anxious. Students’ dominant kind of foreign language anxiety was fear of negative evaluation. Finally, more than half of the students had negative attitudes toward English class. Meanwhile, the rest of the students showed positive attitude. Conclusion: In conclusion, most of the students feel anxious in English class. They mostly experience anxiety due to the negative evaluation, and their attitudes toward English class could not completely determine their foreign language anxiety level. Keywords: foreign language anxiety, English classroom, students’ attitudes Abstrak: Eksplorasi Kecemasan Bahasa Asing Siswa di Kelas Bahasa Inggris. Tujuan: Penelitian ini bertujuan untuk menginvestigasi kecemasan bahasa asing siswa di kelas bahasa Inggris. Metode: Penelitian ini menggunakan pendekatan kuantitatif dan metode survei dengan desain penelitian deskriptif. Temuan: Temuan penelitian menunjukkan bahwa 62,5% siswa merasa cemas dalam tingkat bahasa asing yang diurutkan dari agak cemas, cemas, dan sangat cemas. Jenis kecemasan belajar bahasa asing siswa yang dominan adalah ketakutan akan evaluasi negatif. Akhirnya, lebih dari separuh siswa memiliki sikap negatif terhadap kelas bahasa Inggris. Sementara itu, siswa lainnya menunjukkan sikap positif. Kesimpulan: Kesimpulannya, sebagian besar siswa merasa cemas di kelas bahasa Inggris. Mereka sebagian besar mengalami kecemasan karena takut akan penilaian negatif, dan sikap mereka terhadap kelas bahasa Inggris tidak dapat sepenuhnya menentukan tingkat kecemasan bahasa asing mereka. Kata kunci: kecemasan bahasa asing, kelas bahasa Inggris, sikap siswa. DOI: http://dx.doi.org/10.23960/jpp.v12.i1.202213
Adaptive Activation-based Structured Pruning

Pruning is a promising approach to compress complex deep learning models in order to deploy them on resource-constrained edge devices. However, many existing pruning solutions are based on unstructured pruning, which yields models that cannot efficiently run on commodity hardware and require users to manually explore and tune the pruning process, which is time-consuming and often leads to sub-optimal results. To address these limitations, this paper presents an adaptive, activation-based, structured pruning approach to automatically and efficiently generate small, accurate, and hardware-efficient models that meet user requirements. First, it proposes iterative structured pruning using activation-based attention feature maps to effectively identify and prune unimportant filters. Then, it proposes adaptive pruning policies for automatically meeting the pruning objectives of accuracy-critical, memory-constrained, and latency-sensitive tasks. A comprehensive evaluation shows that the proposed method can substantially outperform the state-of-the-art structured pruning works on CIFAR-10 and ImageNet datasets. For example, on ResNet-56 with CIFAR-10, without any accuracy drop, our method achieves the largest parameter reduction (79.11%), outperforming the related works by 22.81% to 66.07%, and the largest FLOPs reduction (70.13%), outperforming the related works by 14.13% to 26.53%.
Feature visualization for convolutional neural network models trained on neuroimaging data

A major prerequisite for the application of machine learning models in clinical decision making is trust and interpretability. Current explainability studies in the neuroimaging community have mostly focused on explaining individual decisions of trained models, e.g. obtained by a convolutional neural network (CNN). Using attribution methods such as layer-wise relevance propagation or SHAP heatmaps can be created that highlight which regions of an input are more relevant for the decision than others. While this allows the detection of potential data set biases and can be used as a guide for a human expert, it does not allow an understanding of the underlying principles the model has learned. In this study, we instead show, to the best of our knowledge, for the first time results using feature visualization of neuroimaging CNNs. Particularly, we have trained CNNs for different tasks including sex classification and artificial lesion classification based on structural magnetic resonance imaging (MRI) data. We have then iteratively generated images that maximally activate specific neurons, in order to visualize the patterns they respond to. To improve the visualizations we compared several regularization strategies. The resulting images reveal the learned concepts of the artificial lesions, including their shapes, but remain hard to interpret for abstract features in the sex classification task.
Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel Model

This paper summarizes our submission to Task 2 of the second track of the 10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations". Similar to the previous year's iteration, the task consists of three subtasks: detecting whether a turn is knowledge seeking, selecting the relevant knowledge document and finally generating a grounded response. This year, the focus lies on adapting the system to noisy ASR transcripts. We explore different approaches to make the models more robust to this type of input and to adapt the generated responses to the style of spoken conversations. For the latter, we get the best results with a noisy channel model that additionally reduces the number of short and generic responses. Our best system achieved the 1st rank in the automatic and the 3rd rank in the human evaluation of the challenge.