Reviewerly/exHarmony huggingface dataset:
A Research on the Teaching Mode of “English Teaching Methodology” Based on SPOC Blended Teaching

SPOC blended teaching model is a new teaching model which combines online self-directed learning with offline classroom teaching.The author explored and reformed the existing teaching mode of "English Teaching Methodology" and tried to use blended teaching mode in this course.By encouraging teacher-student interaction and student-student interaction, the teaching effect was improved, and relatively good results were achieved.However, it is found in practice that due to the limitations of teachers' comprehensive ability and students' autonomous learning ability, the application of this model in the curriculum needs to be optimized and improved.
Well Being Assistance Chat Application

Nowadays chatbots are widely used by almost every ecommerce, commercial and public welfare website to provide an intellectually rapid solution to cus- tomers. It provides extensive range of solutions from customer service to sug- gesting sales options, providing better service and customer satisfaction. Ever since the introduction of first Chabot, technological developments in the field of Artificial Intelligence has lead to tremendous advancements in designing chatbots that can efficiently mimic human conversations. This paper presents implementation of a chatbot for providing wellbeing assistance to the users. Wellbeing assistance chatbot not only offers effortless assistance to frequent enquiries of the users but additionally indicates the gravity of medical situa- tion to the user. It can converse with people about their health condition and prescribe medications for common sickness. It can be deployed in hospitals for efficiently reducing overcrowding of the patients. Accuracy of the working model can be further increased by creating and using real time demographic data to train the model even after deployment. The proposed wellbeing assis- tance Chabot is based on Natural language processing (NLP), client server architecture, neural network and server to generate reports.
Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances

Human action recognition is a challenging problem, particularly when there is high variability in factors such as subject appearance, backgrounds and viewpoint. While deep neural networks (DNNs) have been shown to perform well on action recognition tasks, they typically require large amounts of high-quality labeled data to achieve robust performance across a variety of conditions. Synthetic data has shown promise as a way to avoid the substantial costs and potential ethical concerns associated with collecting and labeling enormous amounts of data in the real-world. However, synthetic data may differ from real data in important ways. This phenomenon, known as domain shift, can limit the utility of synthetic data in robotics applications. To mitigate the effects of domain shift, substantial effort is being dedicated to the development of domain adaptation (DA) techniques. Yet, much remains to be understood about how best to develop these techniques. In this paper, we introduce a new dataset called Robot Control Gestures (RoCoG-v2). The dataset is composed of both real and synthetic videos from seven gesture classes, and is intended to support the study of synthetic-to-real domain shift for video-based action recognition. Our work expands upon existing datasets by focusing the action classes on gestures for human-robot teaming, as well as by enabling investigation of domain shift in both ground and aerial views. We present baseline results using state-of-the-art action recognition and domain adaptation algorithms and offer initial insight on tackling the synthetic-to-real and ground-to-air domain shifts. Instructions on accessing the dataset can be found at https://github.com/reddyav1/RoCoG-v2.
Syncretic Space Learning Network for NIR-VIS Face Recognition

To overcome the technical bottleneck of face recognition in low-light scenarios, Near-InfraRed and VISible (NIR-VIS) heterogeneous face recognition is proposed for matching well-lit VIS faces with poorly lit NIR faces. Current cross-modal synthesis methods visually convert the NIR modality to the VIS modality and then perform face matching in the VIS modality. However, using a heavyweight GAN network on unpaired NIR-VIS faces may lead to high synthesis difficulty, low inference efficiency, and other problems. To alleviate the above problems, we simultaneously synthesize NIR and VIS images into modality-independent syncretic images and propose a novel syncretic space learning (SSL) model to eliminate the modal gap. First, Syncretic Modality Generator (SMG) synthesizes NIR and VIS images into syncretic images using channel-level convolution with a shallow CNN. In particular, the discriminative structural information is well preserved and the face quality can be further improved with small modal variations in a self-supervised learning manner. Second, Modality-adversarial Syncretic space Learning (MSL) projects NIR and VIS images into the syncretic space by a syncretic-modality adversarial learning strategy with syncretic pattern guided objective, so the modal gap of NIR-VIS faces can be effectively reduced. Finally, the Syncretic Distribution Consistency (SDC) constructed by NIR-syncretic, syncretic-syncretic, and VIS-syncretic consistency can enhance the intra-class compactness and learn discriminative representations. Extensive experiments on three challenging datasets demonstrate the effectiveness of the SSL method.
Robot Person Following Under Partial Occlusion

Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often as-sume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of hislher joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.
Participación de los padres en la educación de sus hijos durante la pandemia

&nbsp;The participation of parents in the education of their children is an important aspect for academic progress, however, during the time of confinement due to the COVID-19 pandemic, there has been little information on the involvement of the family in children’s education. Therefore, the study is carried out whose main objective is to systematize the information published to date in different databases: Scopus, Scielo, Dialnet and REDIB, etc. For the search for information, keywords were used, such as “participation of parents in education” “parents and their educational function” “role of parents in education” “parental participation in education” “education, family, and COVID-19”. The results show that there are few works that account for the participation of parents in their children’s education even though it is a very important aspect for social, cultural, emotional, and affective development, which is directly related to academic success. Therefore, based on the reviewed literature, it is concluded that the educational role of parents contributes to the integral development of the child and generates emotional, physical, and cognitive well-being in them, for which it is necessary to promote research that continues to deepen, from different perspectives, this involvement of the family to improve the academic achievements of schoolchildren. &nbsp
Formal Specification, Verification and Repair of Contiki’s Scheduler

This article presents an approach for model extraction, formal specification, verification, and repair of the scheduler of Contiki, which is an event-driven lightweight operating system for the Internet of Things (IoT). We first derive a state machine–based abstraction of the scheduler’s modes of operation along with the control flow abstractions of the scheduler’s most important functions. We then use a set of transformation rules to formally specify the scheduler and all its internal functions in Promela. Additional contributions with respect to the conference version of this article include (1) modeling nested function calls in the Promela model of the scheduler using a novel technique amenable to model checking in SPIN; (2) modeling protothreads in Promela; (3) specifying and formally verifying 12 critical requirements of the scheduler; (4) detecting new design flaws in Contiki’s scheduler for the first time (to the best of our knowledge); (5) repairing the model and the source code of Contiki’s scheduler towards fixing the flaws detected through verification, as well as regression verification of the entire model of the scheduler; and (6) experimentally analyzing the time and space costs of verification before and after repair. The proposed formal model of Contiki’s scheduler along with novel modeling techniques enhance our knowledge regarding the most critical components of Contiki and provide reusable methods for formal specification and verification of other event-driven operating systems used in Cyber Physical Systems (CPSs) and the IoT.
App Based Implementation of Modern Agriculture Utilties for Farmers

The modern agriculture applications have the potential to increase crop yields, improve food security, and provide farmers with more efficient and cost-effective ways of managing their farms. However, the high cost of new technologies and the need for specialized knowledge and training can lead to financial strain for smaller-scale farmers, while the increased use of fertilizers and pesticides can have negative environmental and health impacts. This research work examines the impacts of modern agriculture applications on farmers and implements a modern method for farmers to sell and buy their weeds/ crops/ manicures, etc, online without the help of any middleman. Further, iOS and android based app implementation and its structure has been discussed. This research has been carried out in React Native. This analysis gives an overview of the various modern technologies and techniques used in agriculture and evaluates their effects on farmers in terms of their financial, social, and environmental well-being.
3D Harmonic Loss: Towards Task-Consistent and Time-Friendly 3D Object Detection on Edge for V2X Orchestration

The use of edge computing for 3D perception has garnered interest in intelligent transportation systems (ITS) due to its potential to enhance Vehicle-to-Everything (V2X) orchestration through real-time traffic monitoring.The ability to accurately measure depth information in the environment using LiDAR has led to a growing emphasis on 3D detection based on this technology, which has significantly advanced the field of 3D perception.However, the computationally-intensive nature of these operations has made it challenging to meet the real-time deployment requirements using existing methods.The object detection task in the pointcloud domain is hindered by a substantial inconsistency problem caused by its high sparsity, which remains unaddressed.This paper conducts an in-depth analysis of the issue, which has been brought to light by recent research on detecting inconsistency problems in image specialization.To address this problem, we propose a solution in the form of a 3D harmonic loss function, which aims to alleviate the inconsistent predictions based on pointcloud data.In addition, we showcase the viability of optimizing 3D harmonic loss mathematically.Our simulations employ the KITTI dataset and DAIR-V2X-I dataset, and our proposed approach significantly surpasses the performance of benchmark models.Additionally, we validate the efficiency of our proposed model through its deployment on an edge device (Jetson Xavier TX) in a simulated environment.
A Knowledge-Enhanced and Topic-Guided Domain Adaptation Model for Aspect-based Sentiment Analysis

Cross-domain aspect-based sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for supervised learning based methods. Most of current methods mainly focus on extracting domain-shared syntactic features to conduct the domain adaptation. Due to the language and syntax are diverse between domains, these methods lack generalization and even lead to syntactic transfer errors. External knowledge graphs have rich domain commonsense and share the relational structures between source and target domains. The domain-shared relational structure can effectively bridge the gap across domains and solve the problem of syntactic transfer errors. Moreover, not all the introduced external knowledge is equally important for the cross-domain aspect-based sentiment analysis. Motivated by these, we propose a knowledge-enhanced and topic-guided cross domain aspect-based sentiment analysis model with the domain-shared commonsense relational structure learning module and the topic-guided knowledge attention module. Extensive experiments are conducted and the experimental results evaluate the effectiveness of our proposed model.
Quasi-pointwise two-step phase-shifting profilometry with the fringe parameters estimated statistically

Fringe projection profilometry is a popularly used three-dimensional measurement technique in which phase-measuring algorithms based on two-step phase shifting are usually used because of their best tradeoff between measurement resolution and speed. Most two-step phase-shifting algorithms involve neighboring or other spatial operations, thus having degraded accuracies at edges and discontinuities of the measured object surface. Pointwise two-step algorithms enable overcoming this issue. With them, however, the offsets of the dynamic ranges of the projector and camera are usually improperly overlooked or inaccurately estimated, thus inducing errors in their measurement results. For solving this problem, this paper suggests a quasi-pointwise two-step phase-shifting algorithm for fringe projection profilometry. This algorithm models the captured fringe patterns practically by taking the offsets of the dynamic ranges of the projector and camera into account, and estimates the fringe parameters from the statistics of fringe intensities. As a result, we can calculate fringe phases in a pointwise way from two fringe patterns having a phase difference of π/2 radians. The simulation and experimental results demonstrate that the proposed method has a relatively low level of errors in measuring object surfaces having isolated regions and discontinuities.
Sistem Pendukung Keputusan Seleksi Calon Siswa Pelatihan Menggunakan Metode Saw Pada Balai Latihan Kerja Muaro Bungo

BLK is a government institution that provides free training to the community to improve the quality of human resources. In the running system there are obstacles in the process of selecting prospective students where parties must be able to select participants who are deemed worthy of training with specified criteria such as last education, age, economic background, and basic abilities of participants regarding the basic abilities of students. vocational they take through the results of the vocational examinations that are held. Based on these problems, a decision support system for the selection of training participants is needed so that it can assist BLK Muaro Bungo in producing accurate information so that it can be used in making education and training student admission selection decisions. The purpose of this study was to design a prototype for a student selection training decision support system using the SAW method and the UML system modeling tool. The result of this research is a prototype design of a decision support system for the student training selection process using SAW calculations so that it can become information that can be used in decision making by BLK Muaro Bungo.
Multi-Tag: A Hardware-Software Co-Design for Memory Safety based on Multi-Granular Memory Tagging

Memory safety vulnerabilities are a severe threat to modern computer systems allowing adversaries to leak or modify security-critical data. To protect systems from this attack vector, full memory safety is required. As software-based countermeasures tend to induce significant runtime overheads, which is not acceptable for production code, hardware assistance is needed. Tagged memory architectures, e.g., already offered by the ARM MTE and SPARC ADI extensions, assign meta-information to memory objects, thus allowing to implement memory safety policies. However, due to the high tag collision probability caused by the small tag sizes, the protection guarantees of these schemes are limited.
Hand Gesture to Control Virtual Keyboard using Neural Network

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.
Interpretive structural modelling of factors affecting perceived risk in adoption of mobile payment system: a vendor's perspective

In a research on adoption of a technology, perceived risk becomes a centrepiece of the study. Similarly, research on adoption of mobile payment systems (MPSs) is supplemented by risk research. This study focuses on studying the inter-relationships among various types of perceived risk from vendor's point of view using interpretive structural modelling (ISM). It focuses on the risk perception of small vendors towards adoption of MPS in India. The developed ISM in Indian conditions reveals unidirectional and bi-directional structural relationships among seven factors of risk perception. The paper also presents a decision making tool when it comes to risk mitigation. Decision maker can use it to chalk out a strategy to prioritise and subsequently mitigate the risk arising in adoption of MPS. Additionally, it supports the small vendor base by creating more awareness among government bodies as to how to reduce the perceived risk associated with MPS.
Best Practices for Integrating 360 VR Videos into Psychology Teaching

This article explores the benefits and challenges of using 360 VR videos as a teaching tool in psychology courses. The technology-enhanced learning approach can be effectively incorporated into the classroom, and have a positive impact on student engagement, motivation, and learning outcomes. We also discuss the challenges and limitations associated with the use of 360 VR videos, including the need for specialized equipment, technical issues, and potential distractions. Furthermore, we suggest that 360 VR videos have the potential to transform teaching and learning in psychology courses and beyond, providing a dynamic and interactive learning environment. Finally, we also give practical suggestions for assessing student learning using this technology.
Efficient Lattice-Based Threshold Signatures With Functional Interchangeability

A threshold signature scheme distributes the ability to generate signatures through distributed key generation and signing protocols. A threshold signature scheme should be functionally interchangeable, meaning that a signature produced by a threshold scheme should be verifiable by the same algorithm used for non-threshold signatures. To resist future attacks from quantum adversaries, lattice-based threshold signatures are desirable. However, the performance of existing lattice-based threshold signing protocols is still far from practical. This paper presents the first lattice-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -out-of- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> threshold signature scheme with functional interchangeability that has been implemented. To build an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -out-of- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> access structure for arbitrary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> ≤ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> , we first present a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -out-of- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> version of the SPDZ MPC protocol. For high concrete efficiency, we avoid using the MPC protocol to evaluate hash operations. Moreover, we design an efficient distributed rejection sampling protocol. As a consequence, the online phase of our distributed signing protocol takes only 0.5 seconds in the two-party setting and 7.3 seconds in the 12-party setting according to our implementation. As a byproduct, our scheme also presents a periodic key refreshment mechanism and offers proactive security.
Codebase release 0.1 for infstat

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>φ</mml:mi></mml:math> . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.