Reviewerly/exHarmony huggingface dataset:
Alternate methods for anomaly detection in high-energy physics via semi-supervised learning

Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.
Augmented Reality in Teaching and Learning English Reading: Realities, Possibilities, and Limitations

Mobile technology that has recently taken its flight into emergence is called Augmented Reality.Advancements of AR have largely incorporated its uses through mobile technology, which makes it possible for just about anyone who has a mobile device to use AR such as can be seen in the widespread popularity of the online mobile games.Although augmented reality is still in the beginning stages of making its way into the educational realm, it does offer the capabilities of greatly enhancing our educational system.In fact, based on previous scholarly studies, Augmented Reality has been shown to be effective in learning the English language and could improve student motivation.This conceptual paper thus reports on past studies on the use and usefulness of Augmented Reality in teaching and learning in English language reading.In this paper, the concept of Augmented Reality, its usage in language learning, the benefits of using AR in language learning, the limitations in using AR in language learning, and teacher's level of readiness and acceptance in using AR in classroom reading instruction will be discussed.The authors will also provide some recommendations for stakeholders, especially in regards to the use of Augmented Reality in teaching of English language reading, and also suggestions for future research
Manipulator Control Method Based on Deep Reinforcement Learning

Robotic arm have transformed the manufacturing industry and have been used for scientific exploration in human inaccessible environments. The existing manipulator control methods based on deep reinforcement learning usually discretize the action space or consider the planar manipulator, which results in great limitations of the tasks that the manipulator can accomplish complete. In this paper, we propose a control method based on the Deep Deterministic Policy Gradient (DDPG) algorithm for the 6 degree-of-freedom manipulator that reach the object position in three-dimensional space. This paper designs two types of reward functions, and introduces the manipulability index into the algorithm. The manipulability index evaluates the flexibility of the robotic arm in the work space, which is referenced by the algorithm to optimize the joint pose of the robotic arm to reach the object position. By building a simulation platform to compare the algorithms based on two reward functions, the effectiveness of the DDPG algorithm is verified, and the 6 degree-of-freedom manipulator can reach the object position with more flexible posture based on the DDPG algorithm with manipulability index.
Deep <i>Q</i>-Network-Based Feature Selection for Multisourced Data Cleaning

The Internet of Things (IoT) integrates information collected from multisources and is able to support various intelligent smart city applications, such as industrial manufacturing, power systems, and mobile healthcare. In the big data era, multisourced data are collected on a daily basis, whereas a large part of the data may be irrelevant, redundant, noisy, or even malicious from a machine learning perspective. Feature selection has been a powerful data cleaning technique to reduce data redundancy and improve system performance in machine learning. Inspired by reinforcement learning that learns from its experience, in this article, we propose a novel efficient deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DQN)-based feature selection method for multisourced data cleaning. In particular, we model the feature selection problem as a competition between an agent and the environment in dynamic states, which is solved by a DQN. Traditional DQN suffers from high computational complexity and requires a significant amount of time in order to converge in the training process. To tackle these challenges, we develop a space searching algorithm called SS to speed up the training process of the DQN agent. To validate the efficacy and efficiency of the proposed method, we conduct extensive experiments on various types of IoT data. Simulation results show that the proposed DQN-based feature selection algorithms achieve much better performance compared with state-of-the-art methods, and are robust under data poisoning attacks.
Visual Tracking With Multiview Trajectory Prediction

Recent progresses in visual tracking have greatly improved the tracking performance. However, challenges such as occlusion and view change remain obstacles in real world deployment. A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets (e.g. human), static cameras, and/or require camera calibration. To break through these limitations, we propose a generic multiview tracking (GMT) framework that allows camera movement, while requiring neither specific object model nor camera calibration. A key innovation in our framework is a cross-camera trajectory prediction network (TPN), which implicitly and dynamically encodes camera geometric relations, and hence addresses missing target issues such as occlusion. Moreover, during tracking, we assemble information across different cameras to dynamically update a novel collaborative correlation filter (CCF), which is shared among cameras to achieve robustness against view change. The two components are integrated into a correlation filter tracking framework, where features are trained offline using existing single view tracking datasets. For evaluation, we first contribute a new generic multiview tracking dataset (GMTD) with careful annotations, and then run experiments on the GMTD and CAMPUS datasets. The proposed GMT algorithm shows clear advantages in terms of robustness over state-of-the-art ones.
Quality Evaluation for Image Retargeting With Instance Semantics

To meet the ever-increasing demand for devices with diversified displays, image retargeting has become a prevalent technique for adaptive image resizing. In practice, the retargeting operation inevitably causes impairments in the images; thus, image retargeting quality assessment (IRQA) is urgently needed and, can be used to guide algorithm optimization, selection and design. Unlike traditional image quality assessment, image retargeting introduces geometric distortions, which typically affect high-level image semantics. With this motivation, this paper presents a quality evaluation model for image retargeting based on INstance SEMantics (INSEM). Considering that the human visual system (HVS) perceives images highly dependent on apprehensible areas and that impairments in image retargeting mainly degrade the salient instances, an image instance is utilized as the basic semantic unit, and a top-down method is devised to extract instance-level semantic features for IRQA. In addition, taking into account the influence of semantic categories on the perception of retargeting quality, we further propose Semantic-based self-adaptive pooling (SSAP) to integrate instance-based semantic features. Finally, global features are incorporated to generate quality scores that are more consistent with people's perceptions. Extensive experiments and comparisons of three public databases, in terms of both intradatabase and cross-database settings, demonstrate the superiority of the proposed metric over state-of-the-art methods.
An End-To-End Framework For Low-Resolution Remote Sensing Semantic Segmentation

High-resolution images for remote sensing applications are often not affordable or accessible, especially when in need of a wide temporal span of recordings. Given the easy access to low-resolution (LR) images from satellites, many remote sensing works rely on this type of data. The problem is that LR images are not appropriate for semantic segmentation, due to the need for high-quality data for accurate pixel prediction for this task. In this paper, we propose an end-to-end framework that unites a super-resolution and a semantic segmentation module in order to produce accurate thematic maps from LR inputs. It allows the semantic segmentation network to conduct the reconstruction process, modifying the input image with helpful textures. We evaluate the framework with three remote sensing datasets. The results show that the framework is capable of achieving a semantic segmentation performance close to native high-resolution data, while also surpassing the performance of a network trained with LR inputs.
Fast computation of spherical phase-space functions of quantum many-body states

Quantum devices are preparing increasingly more complex entangled quantum states. How can one effectively study these states in light of their increasing dimensions? Phase spaces such as Wigner functions provide a suitable framework. We focus on phase spaces for finite-dimensional quantum states of single qudits or permutationally symmetric states of multiple qubits. We present methods to efficiently compute the corresponding phase-space functions which are at least an order of magnitude faster than traditional methods. Quantum many-body states in much larger dimensions can now be effectively studied by experimentalists and theorists using these phase-space techniques.
Constrained Cross-Entropy Method for Safe Reinforcement Learning

We study a safe reinforcement learning problem, in which the constraints are defined as the expected cost over finite-length trajectories. We propose a constrained cross-entropy-based method to solve this problem. The key idea is to transform the original constrained optimization problem into an unconstrained one with a surrogate objective. The method explicitly tracks its performance with respect to constraint satisfaction and thus is well suited for safety-critical applications. We show that the asymptotic behavior of the proposed algorithm can be almost-surely described by that of an ordinary differential equation. Then, we give sufficient conditions on the properties of this differential equation for the convergence of the proposed algorithm. At last, we show the performance of the proposed algorithm in two simulation examples. In a constrained linear-quadratic regulator example, we observe that the algorithm converges to the global optimum with high probability. In a 2-D navigation example, we find that the algorithm effectively learns feasible policies without assumptions on the feasibility of initial policies, even with non-Markovian objective functions and constraint functions.
An improved password-authenticated key exchange protocol for VANET

With a securely pre-shared short password, Password-Authenticated Key Exchange (PAKE) protocol is able to produce a secure session key to protect the communication. However, in a practical self-organized network, e.g. Vehicular Ad-hoc Network (VANET), the pre-shared passwords are often insecure, because the mass-produced devices are frequently with similar default passwords, such as 0000 or 1234, which always never be changed at all. Therefore, in this paper, we proposed a more efficient, secure, and practical PAKE protocol for VANET by fully applying the real physical randomness, the fading phase of wireless slow fading channels that is random, independent but stable in different coherence timeslots. The proposed protocol first generates a high-entropy secret shared information (HSSI) based on the physical randomness and the pre-shared short password, then establishes session keys based on the HSSI other than the only pre-shared password. With the physical randomness, the proposed scheme replaces exponential operations with XOR evaluations to improve efficiency. The security of the proposed protocol has been proved in the real-or-random model.
Local ranking and global fusion for personalized recommendation

Most of top-n personalized recommendation algorithms perform a kind of global learning-to-rank based on the original user–item rating matrix, yet with an implicit presumption of a unified feature space for all users (all items). However, we argue that such a presumption is a limiting factor for better recommendation, because features learned in a global space could be too coarse-grained. As such, ranking a recommendation list against a unified space might be dominated by only a few significant aspects of users’ interests. In this paper, we propose a novel local ranking and global fusion (LRGF) framework for personalized recommendation, which allows using multiple fine-grained feature spaces for learning-to-rank. In LRGF, we first adopt two-stage random walk on the established user graph and item graph for selecting those mostly related users and items, so as to form multiple local user–item groups. Next, we propose a pairwise gap-aware Bayesian personalized ranking to obtain local ranking lists for each local user–item group. Finally, a global decision fusion is proposed to obtain the final top-n recommendation list for each user only from her local ranking lists. Experiments on the three real-world datasets show that the proposed LRGF outperforms the state-of-the-art schemes in terms of higher mean average precision and normalized discounted cumulative gain.
Deep Analysis for Smartphone-based Human Activity Recognition

Wearable-based approach and vision-based approach are two of the most common approaches in human activity recognition. However, the concern of privacy issues may limit the application of the vision-based approach. Besides, some individuals are reluctant to wear sensor devices. Hence, smartphone-based human physical activity recognition is a popular alternative. In this paper, we propose a deep analysis to interpret and predict accelerometer data captured using a smartphone for activity recognition. The proposed deep model is able to extract deep features from both spatial and temporal domains of the inertial data. The recognition accuracy of the proposed model is assessed using UCI and WISDM accelerometer data. Empirical results exhibit a promising performance, attaining accuracy score of 90% in UCI and 87% in WISDM database.
Kripke-Style Models for Logics of Evidence and Truth

In this paper, we propose Kripke-style models for the logics of evidence and truth LETJ and LETF. These logics extend, respectively, Nelson’s logic N4 and the logic of first-degree entailment (FDE) with a classicality operator ∘ that recovers classical logic for formulas in its scope. According to the intended interpretation here proposed, these models represent a database that receives information as time passes, and such information can be positive, negative, non-reliable, or reliable, while a formula ∘A means that the information about A, either positive or negative, is reliable. This proposal is in line with the interpretation of N4 and FDE as information-based logics, but adds to the four scenarios expressed by them two new scenarios: reliable (or conclusive) information (i) for the truth and (ii) for the falsity of a given proposition.
Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative information

The performance of a classifier is often limited by the amount of labeled data (absolute information) available. In order to overcome this limitation, the incorporation of side information into the classification process has become a popular research topic in the field of machine learning. In this work, we propose a new method for ordinal classification that combines absolute information and a specific type of side information: relative information. In particular, this method exploits both types of information to learn an appropriate distance metric and subsequently incorporates the learned distance metric into the classical method of k nearest neighbors. The experimental results show that the proposed method attains a good performance in terms of some of the most popular (ordinal) classification performance measures.
Improved Adaboost Algorithm for Classification Based on Noise Confidence Degree and Weighted Feature Selection

Adaboost is a typical ensemble learning algorithm and has been studied and widely used in classification tasks. In order to improve the classification performance of existing Adaboost algorithms effectively, a noise confidence degree and weighted feature selection based Adaboost algorithm (called NW_Ada) is proposed. Firstly, in order to decrease the impact of sample set density on noise detection results, the conceptions of clustering degree and deviated degree are introduced, and a new method of evaluating the noise confidence is proposed. Then, based on the traditional feature selections of filters, a weighted feature selection method is proposed to select the features which can effectively distinguish the samples those are misclassified. Finally, based on the traditional error rate calculation method, the category recall based classifier error rate calculation method is proposed to solve the problem that the traditional methods ignore the distribution of misclassified samples when dealing with the unbalanced datasets. The experimental results show that the proposed method comprehensively considers the influences of sample density, sample weight and dataset size on classification results, and obtains significant improvement on classification performance compared to traditional Adaboost algorithms when different datasets especially the unbalanced datasets are used.
DeepView Immersive Light Field Video

This Immersive Pavilion installation introduces our new system for capturing, reconstructing, compressing, and rendering light field video content. By leveraging DeepView, a recently introduced view synthesis algorithm, our system can reconstruct challenging scenes with view-dependent reflections, semi-transparent surfaces, and near-field objects as close as 34 cm to the surface of our 46 camera capture rig. Improving upon past light field video systems that required specialized storage and graphics hardware for playback, our compressed videos can be rendered in a web browser or on mobile VR headsets while being streamed over a gigabit network connection. This makes ours the first system to encode high quality light field video at sufficiently low bandwidth for internet streaming.
EchoBay

The increase in computational power of embedded devices and the latency demands of novel applications brought a paradigm shift on how and where the computation is performed. Although AI inference is slowly moving from the cloud to end-devices with limited resources, time-centric recurrent networks like Long-Short Term Memory remain too complex to be transferred on embedded devices without extreme simplifications and limiting the performance of many notable applications. To solve this issue, the Reservoir Computing paradigm proposes sparse, untrained non-linear networks, the Reservoir, that can embed temporal relations without some of the hindrances of Recurrent Neural Networks training, and with a lower memory occupation. Echo State Networks (ESN) and Liquid State Machines are the most notable examples. In this scenario, we propose EchoBay , a comprehensive C++ library for ESN design and training. EchoBay is architecture-agnostic to guarantee maximum performance on different devices (whether embedded or not), and it offers the possibility to optimize and tailor an ESN on a particular case study, reducing at the minimum the effort required on the user side. This can be done thanks to the Bayesian Optimization (BO) process, which efficiently and automatically searches hyper-parameters that maximize a fitness function. Additionally, we designed different optimization techniques that take in consideration resource constraints of the device to minimize memory footprint and inference time. Our results in different scenarios show an average speed-up in training time of 119x compared to Grid and Random search of hyper-parameters, a decrease of 94% of trained models size and 95% in inference time, maintaining comparable performance for the given task. The EchoBay library is Open Source and publicly available at https://github.com/necst/Echobay.
Remote Interview Methods in Chemical Education Research

Student interviews have found an increasingly important place in the educational research landscape. They are used to create and validate assessments and concept inventories, as well as to more clearly elucidate student thinking. One-on-one interviews are most often completed in person at the researcher's or student's institution. There are significant advantages, however, of conducting interviews remotely including access to more varied populations, travel-related time and cost savings, and the ability of a single researcher to efficiently collect data at multiple institutions. This paper presents a description of a remote interview process along with details of ways to maintain commonality with its in-person analog. Our research provides evidence that the efficacy of results is maintained and that remote interviews provide a viable alternative to more costly and time-intensive on-site interviews. These results are particularly timely as ways to enable graduate education and research to continue during the COVID-19 pandemic response.