Reviewerly/exHarmony huggingface dataset:
The Correlation Between Students’ Personality (Extrovert and Introvert) and Their Reading Comprehension Achievement

The students struggled with reading due to a lack of vocabulary and boredom when attempting to comprehend a reading text. Besides, the Teacher's technique did not match the student's personalities, so they did not follow the instructions given by the Teacher in the reading learning process. As a result, many students at MAN 1 Pesisir Barat Lampung got underscore in reading. This study aimed to determine whether there was a relationship between the personality traits of students (extrovert and introvert) and their reading comprehension success in the tenth grade of MAN 1 Pesisir Barat Lampung in the academic year 2020/21. This research used a correlation research design. The population of this research was the tenth grade of MAN 1 Pesisir Barat Lampung. The sample of this research was two classes, X Science 1 and 2, consisting of 77 students from those two classes. The data is collected by using personality questionnaires and reading comprehension tests. There were 20 questions for the personality questionnaires exam and 40 questions for the reading comprehension test, which had been verified. After collecting data for both variables, SPSS version 21 with the Pearson Correlation Formula was used to analyze the data. After doing the hypothetical test, the result showed that the value of significant generated Sig (P-value) = 0.006 &lt; ɑ = 0.05. As a result, H0 was rejected, and H1 was accepted. Thus, it can be concluded that there was a correlation between students' personalities (extrovert and introvert) and their reading comprehension achievement in the tenth grade of MAN 1 Pesisir Barat Lampung in the academic year 2020/2021. Then, the coefficient correlation between students' personality and their reading comprehension achievement was 0.749, which means that the correlation of both variables was high and positive. Keywords: Correlation Research Design; Personality; Reading Comprehension Achievement.
Analysis of Causing Factors on Teachers’ Code Switching

Code switching is an intriguing phenomenon to investigate because it is a part of the development process and the use of multiple languages (bilingual). In the world of education, both at the middle and upper levels, the use of code switching can be done by teachers or students in the classroom. This research was conducted to find out the factors causing teachers to do code switching in the classroom. The research method applied in this study was qualitative research in the form of a case study. The writers selected the participants for this research by using a convenience sampling technique. There were two teachers of English at SMA Negeri 15 Palembang, and those teachers were selected as the participants to collect the data. In collecting data, the writers used interviews through open-ended questions. Then, the writers analyzed the data obtained from the interview using a theme code system in the form of table codes, synchronizing the data with the aspects and items that prevailed in the interview protocol. The result of this research based on the interviews showed that there are two factors causing teachers to do code switching. Two factors are linguistics and non-linguistics. Language linguistics factors are divided into language stagnation and a lack of students’ knowledge of vocabulary. Furthermore, non-linguistical factors are divided into the emotional condition of the teacher and the condition of the class situation. Keywords: Code Switching; Actors of Code Switching; Linguistics; Non-Linguistics.
Efektivitas Pembelajaran Daring Di Sekolah MTSN 1 Murung

The purpose of this study is to find out the effectiveness of online learning in MTsN 1 Murung. The questions you want to answer through this study are: 1) To find out the effectiveness of online learning in MTsN 1 Moody. 2) To find out the advantages and disadvantages in the implementation of online learning in MTsN 1 Murung. 3) To know what needs to be improved so that the implementation of online learning runs better and in accordance with the wishes. This research uses qualitative research methods with qualitative descriptive analysis techniques or interview data collection and documentation. The subjects of the study were the head of the madrassa, teachers and learners of MTsN 1 Murung. The data analysis technique in this study uses qualitative descriptive data analysis of interactive models from Milles and Michael Huberman consisting of three concurrent activity paths, namely; data reduction, presentation of data, and withdrawal of conclusions. Based on the results of research and interviews showed that online learning in MTsN 1 Murung is not effective considering that in its short implementation so that gutu can not know whether learners have understood the material or not, the advantages of online learning in MTsN 1 Murung Raya that learning can be accessed anywhere and anytime, learners can learn independently, the provision of tasks is more structured through applications. While the lack of online learning in MTsN 1 Murung is monitoring or monitoring the situation of students sulik done, signals / connections that are less stable in some areas. The field that needs to be improved so that online learning can run well is to increase the creativity of teachers in making learning media that are easy to understand, monitoring / supervision to learners is more tightened, and cooperation between parents and teachers must be well established
Unmanned Aerial Vehicle Path Planning Based on Disturbed Fluid and Sparrow Search Algorithm

An effective algorithm based on disturbed fluid dynamics is presented to address the obstacle avoidance problem of unmanned aerial vehicle (UAV) flying in three-dimensional space. Inspired by the interfered fluid dynamical system, the algorithm proposed a tangential component which is helpful to improve the flight quality of UAV by providing more direction selectivity for obstacle avoidance. Then, the virtual target (VT) method is used to solve the local minimum problem when the UAV flies to the overlapping area of obstacle. Finally, taking into account the great optimization ability and fast convergence speed, a novel search algorithm to discover the best obstacle avoidance settings in current flight environment, sparrow search algorithm (SSA) is utilized. The effectiveness and stability of this algorithm are verified by simulation results.
The Norm Establishment in WhatsApp Group Conversations

This linguistics study aims to observe the development of norms in WhatsApp Groups (WAGs). Over the years, linguists have devoted their time to theorizing norms of im/politeness evaluations and linguistic interactions in computer-mediated communication (CMC). However, the norms of virtual interactions have not gained adequate attention. This study systematically documented and examined the conversations of 539 members of three WAGs to describe the norms of virtual group communication. The data obtained were enhanced through anecdotal evidence of the experiences acquired in joining other WAGs. The data indicated four sources of WAGs norms: the aims of creating the group, framing unmarked behaviors, and framing positive or negative evaluations of members' utterances or actions. The members' alignments toward non-virtual individual and social norms also significantly affect their negotiating WAG norms. In the stages of the norming process, WAGs tend to experience conflicts due to interpersonal differences. These disputes are likely to disrupt the group's performance or predispose some members. Theoretically, group development is comprised of formation, conflicts, norming, and performance. Depending on the homogeneity or heterogeneity of the member's background, WAGs do not necessarily experience conflicts in the norming process. However, some of them tend to leap the stages from formation to performance.
Environment Optimization for Multi-Agent Navigation

Traditional approaches to the design of multi-agent navigation algorithms consider the environment as a fixed constraint, despite the obvious influence of spatial constraints on agents' performance. Yet hand-designing improved environment layouts and structures is inefficient and potentially expensive. The goal of this paper is to consider the environment as a decision variable in a system-level optimization problem, where both agent performance and environment cost can be accounted for. We begin by proposing a novel environment optimization problem. We show, through formal proofs, under which conditions the environment can change while guaranteeing completeness (i.e., all agents reach their navigation goals). Our solution leverages a model-free reinforcement learning approach. In order to accommodate a broad range of implementation scenarios, we include both online and offline optimization, and both discrete and continuous environment representations. Numerical results corroborate our theoretical findings and validate our approach.
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of bomb craters by using deep learning is a promising way to improve the UXO disposal process. However, these methods require a large amount of manually labeled training data. This work leverages domain adaptation with moon surface images to address the problem of automated bomb crater detection with deep learning under the constraint of limited training data. This paper contributes to both academia and practice (1) by providing a solution approach for automated bomb crater detection with limited training data and (2) by demonstrating the usability and associated challenges of using synthetic images for domain adaptation.
Introducing Non-Linear Activations into Quantum Generative Models

Due to the linearity of quantum mechanics, it remains a challenge to design quantum generative machine learning models that embed non-linear activations into the evolution of the statevector. However, some of the most successful classical generative models, such as those based on neural networks, involve highly non-linear dynamics for quality training. In this paper, we explore the effect of these dynamics in quantum generative modeling by introducing a model that adds non-linear activations via a neural network structure onto the standard Born Machine framework - the Quantum Neuron Born Machine (QNBM). To achieve this, we utilize a previously introduced Quantum Neuron subroutine, which is a repeat-until-success circuit with mid-circuit measurements and classical control. After introducing the QNBM, we investigate how its performance depends on network size, by training a 3-layer QNBM with 4 output neurons and various input and hidden layer sizes. We then compare our non-linear QNBM to the linear Quantum Circuit Born Machine (QCBM). We allocate similar time and memory resources to each model, such that the only major difference is the qubit overhead required by the QNBM. With gradient-based training, we show that while both models can easily learn a trivial uniform probability distribution, on a more challenging class of distributions, the QNBM achieves an almost 3x smaller error rate than a QCBM with a similar number of tunable parameters. We therefore provide evidence that suggests that non-linearity is a useful resource in quantum generative models, and we put forth the QNBM as a new model with good generative performance and potential for quantum advantage.
Lightweight Transformers for Human Activity Recognition on Mobile Devices

Human Activity Recognition (HAR) on mobile devices has shown to be achievable with lightweight neural models learned from data generated by the user's inertial measurement units (IMUs). Most approaches for instanced-based HAR have used Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), or a combination of the two to achieve state-of-the-art results with real-time performances. Recently, the Transformers architecture in the language processing domain and then in the vision domain has pushed further the state-of-the-art over classical architectures. However, such Transformers architecture is heavyweight in computing resources, which is not well suited for embedded applications of HAR that can be found in the pervasive computing domain. In this study, we present Human Activity Recognition Transformer (HART), a lightweight, sensor-wise transformer architecture that has been specifically adapted to the domain of the IMUs embedded on mobile devices. Our experiments on HAR tasks with several publicly available datasets show that HART uses fewer FLoating-point Operations Per Second (FLOPS) and parameters while outperforming current state-of-the-art results. Furthermore, we present evaluations across various architectures on their performances in heterogeneous environments and show that our models can better generalize on different sensing devices or on-body positions.
Photonic interface between subcarrier wave and dual rail encodings

Quantum key distribution with multimode subcarrier wave encoding is propitious for being robust against environmental disturbance. For application in long-distance quantum communications this encoding has to be compatible with entanglement-assisted quantum repeaters that are commonly designed to work with dual rail encodings. We propose and demonstrate an interface between subcarrier wave and dual rail encodings with a fidelity of more than 92\% using a linear optical circuit. The developed scheme may find an application in future heterogeneous quantum networks that combine quantum key distribution with trusted and non-trusted nodes.
Exact conservation laws for neural network integrators of dynamical systems

The solution of time dependent differential equations with neural networks has attracted a lot of attention recently. The central idea is to learn the laws that govern the evolution of the solution from data, which might be polluted with random noise. However, in contrast to other machine learning applications, usually a lot is known about the system at hand. For example, for many dynamical systems physical quantities such as energy or (angular) momentum are exactly conserved. Hence, the neural network has to learn these conservation laws from data and they will only be satisfied approximately due to finite training time and random noise. In this paper we present an alternative approach which uses Noether's Theorem to inherently incorporate conservation laws into the architecture of the neural network. We demonstrate that this leads to better predictions for three model systems: the motion of a non-relativistic particle in a three-dimensional Newtonian gravitational potential, the motion of a massive relativistic particle in the Schwarzschild metric and a system of two interacting particles in four dimensions.
funcX: Federated Function as a Service for Science

funcX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, funcX decouples the cloud-hosted management functionality from the edge-hosted execution functionality. funcX's endpoint software can be deployed, by users or administrators, on arbitrary laptops, clouds, clusters, and supercomputers, in effect turning them into function serving systems. funcX's cloud-hosted service provides a single location for registering, sharing, and managing both functions and endpoints. It allows for transparent, secure, and reliable function execution across the federated ecosystem of endpoints--enabling users to route functions to endpoints based on specific needs. funcX uses containers (e.g., Docker, Singularity, and Shifter) to provide common execution environments across endpoints. funcX implements various container management strategies to execute functions with high performance and efficiency on diverse funcX endpoints. funcX also integrates with an in-memory data store and Globus for managing data that may span endpoints. We motivate the need for funcX, present our prototype design and implementation, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than 130 000 concurrent workers. We show that funcX's container warming-aware routing algorithm can reduce the completion time for 3000 functions by up to 61% compared to a randomized algorithm and the in-memory data store can speed up data transfers by up to 3x compared to a shared file system.
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning

Skeleton-based human action recognition has been drawing more interest recently due to its low sensitivity to appearance changes and the accessibility of more skeleton data. However, even the 3D skeletons captured in practice are still sensitive to the viewpoint and direction gave the occlusion of different human-body joints and the errors in human joint localization. Such view variance of skeleton data may significantly affect the performance of action recognition. To address this issue, we propose in this paper a new view-invariant representation learning approach, without any manual action labeling, for skeleton-based human action recognition. Specifically, we leverage the multi-view skeleton data simultaneously taken for the same person in the network training, by maximizing the mutual information between the representations extracted from different views, and then propose a global-local contrastive loss to model the multi-scale co-occurrence relationships in both spatial and temporal domains. Extensive experimental results show that the proposed method is robust to the view difference of the input skeleton data and significantly boosts the performance of unsupervised skeleton-based human action methods, resulting in new state-of-the-art accuracies on two challenging multi-view benchmarks of PKUMMD and NTU RGB+D.
The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022

This paper describes speaker verification (SV) systems submitted by the SpeakIn team to the Task 1 and Task 2 of the Far-Field Speaker Verification Challenge 2022 (FFSVC2022). SV tasks of the challenge focus on the problem of fully supervised far-field speaker verification (Task 1) and semi-supervised far-field speaker verification (Task 2). In Task 1, we used the VoxCeleb and FFSVC2020 datasets as train datasets. And for Task 2, we only used the VoxCeleb dataset as train set. The ResNet-based and RepVGG-based architectures were developed for this challenge. Global statistic pooling structure and MQMHA pooling structure were used to aggregate the frame-level features across time to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax to classify the resulting embeddings. We innovatively propose a staged transfer learning method. In the pre-training stage we reserve the speaker weights, and there are no positive samples to train them in this stage. Then we fine-tune these weights with both positive and negative samples in the second stage. Compared with the traditional transfer learning strategy, this strategy can better improve the model performance. The Sub-Mean and AS-Norm backend methods were used to solve the problem of domain mismatch. In the fusion stage, three models were fused in Task1 and two models were fused in Task2. On the FFSVC2022 leaderboard, the EER of our submission is 3.0049% and the corresponding minDCF is 0.2938 in Task1. In Task2, EER and minDCF are 6.2060% and 0.5232 respectively. Our approach leads to excellent performance and ranks 1st in both challenge tasks.
Non-contractive logics, Paradoxes, and Multiplicative Quantifiers

The paper investigates from a proof-theoretic perspective various non-contractive logical systems circumventing logical and semantic paradoxes. Until recently, such systems only displayed additive quantifiers (Gri\v{s}in, Cantini). Systems with multiplicative quantifers have also been proposed in the 2010s (Zardini), but they turned out to be inconsistent with the naive rules for truth or comprehension. We start by presenting a first-order system for disquotational truth with additive quantifiers and we compare it with Gri\v{s}in set theory. We then analyze the reasons behind the inconsistency phenomenon affecting multiplicative quantifers: after interpreting the exponentials in affine logic as vacuous quantifiers, we show how such a logic can be simulated within a truth-free fragment of a system with multiplicative quantifiers. Finally, we prove that the logic of these multiplicative quantifiers (but without disquotational truth) is consistent, by showing that an infinitary version of the cut rule can be eliminated. This paves the way to a syntactic approach to the proof theory of infinitary logic with infinite sequents.
Multi-Granularity Graph Pooling for Video-based Person Re-Identification

The video-based person re-identification (ReID) aims to identify the given pedestrian video sequence across multiple non-overlapping cameras. To aggregate the temporal and spatial features of the video samples, the graph neural networks (GNNs) are introduced. However, existing graph-based models, like STGCN, perform the \textit{mean}/\textit{max pooling} on node features to obtain the graph representation, which neglect the graph topology and node importance. In this paper, we propose the graph pooling network (GPNet) to learn the multi-granularity graph representation for the video retrieval, where the \textit{graph pooling layer} is implemented to downsample the graph. We first construct a multi-granular graph, whose node features denote image embedding learned by backbone, and edges are established between the temporal and Euclidean neighborhood nodes. We then implement multiple graph convolutional layers to perform the neighborhood aggregation on the graphs. To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods. Specifically, MHFAPool takes the main eigenvector of full attention matrix as the aggregation coefficients to involve the global graph information in each pooled nodes. Extensive experiments demonstrate that our GPNet achieves the competitive results on four widely-used datasets, i.e., MARS, DukeMTMC-VideoReID, iLIDS-VID and PRID-2011.
Analysis on Blockchain Consensus Mechanism Based on Proof of Work and Proof of Stake

In the white book of Bitcion, Satoshi Nakamoto described a bitcoin system that can realize point-to-point online payment without a third-party organization. After supporting this magical application scenario and subverting the traditional centralized system, the blockchain technology has attracted worldwide attention, triggered a research upsurge of blockchain consensus algorithm, and produced a large number of innovative applications. Although various consensus algorithms continue to evolve with the iteration of blockchain products and applications, Proof of Work (POW) and Proof of Skake (POS) algorithms are still the core of consensus algorithms. This paper discusses two algorithms of POW and POS in blockchain consensus mechanism, and analyzes the advantages and the existing problems of the two consensus mechanisms. Since consensus mechanism is the main focus of blockchain technology and has many influencing factors, this paper discusses the current problems and some improved ideas, and selects some typical algorithms for a more systematic introduction. In addition, some important issues related to safety and performance are also discussed. This paper provides the researchers a great reference on blockchain consensus mechanism.