Reviewerly/exHarmony huggingface dataset:
Application in Augmented Reality for Learning Mathematical Functions: A Study for the Development of Spatial Intelligence in Secondary Education Students

Spatial intelligence is an essential skill for understanding and solving real-world problems. These visuospatial skills are fundamental in the learning of different Science, Technology, Engineering and Mathematics (STEM) subjects, such as Technical Drawing, Physics, Robotics, etc., in order to build mental models of objects or graphic representations from algebraic expressions, two-dimensional designs, or oral descriptions. It must be taken into account that spatial intelligence is not an innate skill but a dynamic skill, which can be enhanced by interacting with real and/or virtual objects. This ability can be enhanced by applying new technologies such as augmented reality, capable of illustrating mathematical procedures through images and graphics, which help students considerably to visualize, understand, and master concepts related to mathematical functions. The aim of this study is to find out whether the integration of the Geogebra AR (Augmented Reality) within a contextualized methodological environment affects the academic performance and spatial skills of fourth year compulsory secondary education mathematics students.
Increasing Speaking Skill through YouTube Video as English Learning Material during Online Learning in Pandemic Covid-19

YouTube video is one alternative solution in teaching speaking during pandemic. This study attempts to find the use of YouTube videos to improve speaking skill of students and how teaching and learning process using YouTube videos are implemented in the class. A classroom action research was conducted to first semester college students of Muhammadiyah University of Kudus in 2020. The population of this study was all non-English program and the samples were 85 students in redundant class chosen using purposive sampling. The data were obtained from speaking assessment and interview and later analyzed by using constant comparative method and descriptive statistic. This study showed that YouTube video as English learning material improved speaking skill of students including fluency, vocabulary, pronunciation, grammar, and content. Therefore, it can be concluded that the use of YouTube Video is able to improve the students’ speaking skill during online learning in pandemic Covid-19. Further study may concern on the use of YouTube Video to teach other English skills like reading and writing.
A Novel Hierarchical Soft Actor-Critic Algorithm for Multi-Logistics Robots Task Allocation

In intelligent unmanned warehouse goods-to-man systems, the allocation of tasks has an important influence on the efficiency because of the dynamic performance of AGV robots and orders. The paper presents a hierarchical Soft Actor-Critic algorithm to solve the dynamic scheduling problem of orders picking. The method proposed is based on the classic Soft Actor-Critic and hierarchical reinforcement learning algorithm. In this paper, the model is trained at different time scales by introducing sub-goals, with the top-level learning a policy and the bottom level learning a policy to achieve the sub-goals. The actor of the controller aims to maximize expected intrinsic reward while also maximizing entropy. That is, to succeed at the sub-goals while moving as randomly as possible. Finally, experimental results for simulation experiments in different scenes show that the method can make multi-logistics AGV robots work together and improves the reward in sparse environments about 2.61 times compared to the SAC algorithm.
Public Sentiment Analysis on Twitter Data during COVID-19 Outbreak

The COVID-19 pandemic, is also known as the coronavirus pandemic, is an ongoing serious global problem all over the world.The outbreak first came to light in December 2019 in Wuhan, China.This was declared pandemic by the World Health Organization on 11th March 2020.COVID-19 virus infected on people and killed hundreds of thousands of people in the United States, Brazil, Russia, India and several other countries.Since this pandemic continues to affect millions of lives, and a number of countries have resorted to either partial or full lockdown.People took social media platforms to share their emotions, and opinions during this lockdown to find a way to relax and calm down.In this research work, sentiment analysis on the tweets of people from top ten infected countries has been conducted.The experiments have been conducted on the collected data related to the tweets of people from top ten infected countries with the addition of one more country chosen from Gulf region, i.e.Sultanate of Oman.A dataset of more than 50,000 tweets with hashtags like #covid-19, #COVID19, #CORONAVIRUS, #CORONA, #StayHomeStaySafe, #Stay Home, #Covid_19, #CovidPandemic, #covid19, #Corona Virus, #Lockdown, #Qurantine, #qurantine, #Coronavirus Outbreak, #COVID etc. posted between June 21, 2020 till July 20, 2020 was considered in this research.Based on the tweets posted in English a sentiment analysis was performed.This research was conducted to understand how people from different infected countries cope with the situation.The tweets were collected, preprocessed and then text mining algorithms used and finally sentiment analysis have been done and presented with the results.The purpose of this research paper to know about the sentiments of people from COVID-19 infected countries.
PPChain: A Privacy-Preserving Permissioned Blockchain Architecture for Cryptocurrency and Other Regulated Applications

Existing (popular) blockchain architectures, including the widely used Ethereum and Hyperledger, are generally not designed to achieve conflicting properties such as anonymity and regulation, and transparency and confidentiality. In this article, we propose a privacy-preserving permissioned blockchain architecture (PPChain) that permits one to also introduce regulation, where PPChain's architecture is modified from that of Ethereum. Specifically, we integrate the cryptographic primitives (group signature and broadcast encryption), and adopt practical byzantine fault tolerance consensus protocol with a validate-record separation mechanism, as well as removing the transaction fee and mining reward. To show the utility of PPChain, we provide qualitative security and privacy analysis, and performance analysis. We also explain how PPChain can be deployed in regulation applications, using cryptocurrency, food supply chain, and sealed-bid auctions as examples.
Real-Time Evolution for Ultracompact Hamiltonian Eigenstates on Quantum Hardware

In this work we present a detailed analysis of variational quantum phase estimation (VQPE), a method based on real-time evolution for ground- and excited-state estimation on near-term hardware. We derive the theoretical ground on which the approach stands, and demonstrate that it provides one of the most compact variational expansions to date for solving strongly correlated Hamiltonians, when starting from an appropriate reference state. At the center of VQPE lies a set of equations, with a simple geometrical interpretation, which provides conditions for the time evolution grid in order to decouple eigenstates out of the set of time-evolved expansion states, and connects the method to the classical filter-diagonalization algorithm. Furthermore, we introduce what we call the unitary formulation of VQPE, in which the number of matrix elements that need to be measured scales linearly with the number of expansion states, and we provide an analysis of the effects of noise that substantially improves previous considerations. The unitary formulation allows for a direct comparison to iterative phase estimation. Our results mark VQPE as both a natural and highly efficient quantum algorithm for ground- and excited-state calculations of general many-body systems. We demonstrate a hardware implementation of VQPE for the transverse field Ising model. Furthermore, we illustrate its power on a paradigmatic example of strong correlation (Cr2 in the def2-SVP basis set), and show that it is possible to reach chemical accuracy with as few as approximately 50 time steps.4 MoreReceived 5 April 2021Revised 17 February 2022Accepted 1 March 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020323Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum simulationCondensed Matter, Materials & Applied PhysicsQuantum Information
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries

Pretrained language models have been suggested as a possible alternative or complement to structured knowledge bases.However, this emerging LM-as-KB paradigm has so far only been considered in a very limited setting, which only allows handling 21k entities whose name is found in common LM vocabularies.Furthermore, a major benefit of this paradigm, i.e., querying the KB using natural language paraphrases, is underexplored.Here we formulate two basic requirements for treating LMs as KBs: (i) the ability to store a large number facts involving a large number of entities and (ii) the ability to query stored facts.We explore three entity representations that allow LMs to handle millions of entities and present a detailed case study on paraphrased querying of facts stored in LMs, thereby providing a proof-of-concept that language models can indeed serve as knowledge bases.
Monitoring and Securing the Healthcare Data Harnessing IOT and Blockchain Technology

Blockchain and Internet of Things (IoT) technologies are used in many domains, predominantly for electronic-healthcare. Here, IoT devices has the ability to provide real-time sensor data from patients to get processed and analyzed. As a single point of failure, mistrust, data manipulation and tampering, and privacy avoidance may all occur as a result of such a method. Through offering shared computing and storage for IoT data, blockchain can help solve such issues.Maintaining and sharing Medical data is necessary here.If there occurs loss of confidence means it threatens the medical data and loss of integrity creates impact on the life of patient. So, the first objective is to protect the medical records. Also, a central server to the records will pretend the hackers to attack and continuous fetching is difficult.Therefore, combining Blockchain and IoT will be a threat breaker for computerized medical records.
SGUARD: Towards Fixing Vulnerable Smart Contracts Automatically

Smart contracts are distributed, self-enforcing programs executing on top of blockchain networks. They have the potential to revolutionize many industries such as financial institutes and supply chains. However, smart contracts are subject to code-based vulnerabilities, which casts a shadow on its applications. As smart contracts are unpatchable (due to the immutability of blockchain), it is essential that smart contracts are guaranteed to be free of vulnerabilities. Unfortunately, smart contract languages such as Solidity are Turing-complete, which implies that verifying them statically is infeasible. Thus, alternative approaches must be developed to provide the guarantee. In this work, we develop an approach which automatically transforms smart contracts so that they are provably free of 4 common kinds of vulnerabilities. The key idea is to apply run-time verification in an efficient and provably correct manner. Experiment results with 5000 smart contracts show that our approach incurs minor run-time overhead in terms of time (i.e., 14.79%) and gas (i.e., 0.79%).
Debiasing Pre-trained Contextualised Embeddings

In comparison to the numerous debiasing methods proposed for the static noncontextualised word embeddings, the discriminative biases in contextualised embeddings have received relatively little attention.We propose a fine-tuning method that can be applied at token-or sentence-levels to debias pre-trained contextualised embeddings.Our proposed method can be applied to any pretrained contextualised embedding model, without requiring to retrain those models.Using gender bias as an illustrative example, we then conduct a systematic study using several state-of-the-art (SoTA) contextualised representations on multiple benchmark datasets to evaluate the level of biases encoded in different contextualised embeddings before and after debiasing using the proposed method.We find that applying token-level debiasing for all tokens and across all layers of a contextualised embedding model produces the best performance.Interestingly, we observe that there is a trade-off between creating an accurate vs. unbiased contextualised embedding model, and different contextualised embedding models respond differently to this trade-off.
Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.
Dual-state purification for practical quantum error mitigation

Quantum error mitigation is essential for many computing tasks on the noisy quantum computer with a limited number of qubits. In this paper, we propose a practical protocol of error mitigation by virtually purifying the quantum state without qubit overhead or requiring only one ancilla qubit. In dual-state purification, we effectively generate a purified state with increased fidelity using the erroneous state and its dual state, respectively, prepared with the noisy quantum circuit and the dual map of its inverse circuit. Combined with tomography purification, we can make sure that the final estimate of an observable is obtained from a pure state. While the absolute error increases, the numerical result, based on a depolarizing model, suggests that our protocol reduces the error compared to the case without the protocol by a rescaling factor decreasing with the qubit number and circuit depth; i.e., the relative performance of purification is better for larger circuits. On a cloud quantum computer, we successfully demonstrate the reduced error with a quantum variational eigensolver circuit.
Gradient-based Adversarial Attacks against Text Transformers

We propose the first general-purpose gradientbased adversarial attack against transformer models.Instead of searching for a single adversarial example, we search for a distribution of adversarial examples parameterized by a continuous-valued matrix, hence enabling gradient-based optimization.We empirically demonstrate that our white-box attack attains state-of-the-art attack performance on a variety of natural language tasks, outperforming prior work in terms of adversarial success rate with matching imperceptibility as per automated and human evaluation.Furthermore, we show that a powerful black-box transfer attack, enabled by sampling from the adversarial distribution, matches or exceeds existing methods, while only requiring hard-label outputs.
Mutual Contrastive Learning for Visual Representation Learning

We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks. A crucial component of MCL is Interactive Contrastive Learning (ICL). Compared with vanilla contrastive learning, ICL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations for visual recognition tasks. We emphasize that the resulting MCL is conceptually simple yet empirically powerful. It is a generic framework that can be applied to both supervised and self-supervised representation learning. Experimental results on image classification and transfer learning to object detection show that MCL can lead to consistent performance gains, demonstrating that MCL can guide the network to generate better feature representations. Code is available at https://github.com/winycg/MCL.
Chaotic Lightweight Cryptosystem for Image Encryption

Data transmission over the Internet and the personal network has been risen day by day due to the advancement of multimedia technology. Hence, it is today’s prime concern to protect the data from unauthorized access and encrypt the multimedia element as they are stored on the web servers and transmitted over the networks. Therefore, multimedia data encryption is essential. But, the multimedia encryption algorithm is complex to implement as it requires more time and memory space. For this reason, the lightweight image encryption algorithm gains popularity that requires less memory and less time along with low power or energy and provides supreme security for limited devices. In this study, we have studied the chaotic-based lightweight image encryption method. At first, we have presented a standard framework and algorithm based on two chaotic maps such as Arnold and logistic for lightweight image encryption and performed some experiments. We have analyzed different groups of images such as miscellaneous, medical, underwater, and texture. Experimentations have provided the largest entropy 7.9920 for medical image (chest X-ray), large key space 2m×m×8, and average encryption and decryption times are 3.9771 s and 3.1447 s, respectively. Besides, we have found an equal distribution of pixels and less correlation coefficients among adjacent pixels of the encrypted image. These criteria indicate an efficient image encryption method. Also, our method is efficient and less complex than the existing state-of-the-art methods.
A Novel Imbalanced Data Classification Method Based on Weakly Supervised Learning for Fault Diagnosis

The class imbalance problem has a huge impact on the performance of diagnostic models. When it occurs, the minority samples are easily ignored by classification models. Besides, the distribution of class imbalanced data differs from the actual data distribution, which makes it difficult for classifiers to learn an accurate decision boundary. To tackle the above issues, this article proposes a novel imbalanced data classification method based on weakly supervised learning. First, Bagging algorithm is employed to sample majority data randomly to generate several relatively balanced subsets, which are then used to train several support vector machine (SVM) classifiers. Next, these trained SVM classifiers are adopted to predict the labels of those unlabeled data, and samples that are predicted as minority class are added to the original dataset to reduce the imbalance ratio. The critical idea of this article is to introduce real-world samples into the imbalanced dataset by virtue of weakly supervised learning. In addition, bidirectional gated recurrent units are used to construct a diagnostic model for fault diagnosis, and a new weighted cross-entropy function is proposed as the loss function to reduce the impact of noise. Besides, it also increases the model's attention to the original minority samples. Furthermore, experimental evaluations of the proposed method are conducted on two datasets, i.e., Prognostics and Health Management challenge 2008 and 2010 datasets, and the experimental results demonstrate the effectiveness and superiority of the proposed method.
Cluster-Guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in an unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the result of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by colors in images. In this paper, we attempt to suppress the negative dominating influence of colors to learn more effective features for unsupervised person Re-ID. Specifically, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which clustering result is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. In CACL, both instance-level and cluster-level contrastive learning are employed to help the siamese network learn discriminant features with respect to the clustering result within and between different data augmentation views, respectively. In addition, we also present a cluster refinement method, and validate that the cluster refinement step helps CACL significantly. Extensive experiments conducted on three benchmark datasets demonstrate the superior performance of our proposal.
Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering

Video Question Answering is a task which requires an AI agent to answer questions grounded in video.This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between language and vision information.We propose Motion-Appearance Synergistic Networks (MASN), which embed two crossmodal features grounded on motion and appearance information and selectively utilize them depending on the question's intentions.MASN consists of a motion module, an appearance module, and a motion-appearance fusion module.The motion module computes the action-oriented cross-modal joint representations, while the appearance module focuses on the appearance aspect of the input video.Finally, the motion-appearance fusion module takes each output of the motion module and the appearance module as input, and performs question-guided fusion.As a result, MASN achieves new state-of-the-art performance on the TGIF-QA and MSVD-QA datasets.We also conduct qualitative analysis by visualizing the inference results of MASN.