Reviewerly/exHarmony huggingface dataset:
Development of a Need Based IVRI-Dairy Manager App and its Perceived Utility

Mobile is today's most popular device for information dissemination owing to the 275 million smart phone subscribers present in India and providing a hope for the improvement of the extension services through the recent advancement in this field, mobile applications and its use.For the development of IVRI-Dairy Manager App, study was conducted in Ludhiana and Chandigarh, the two high productivity regions of Punjab among the commercial dairy farmers.For assessing the perceived utility of the app, a total of 30 commercial dairy farmers were selected randomly as respondents.Semi structured interview schedule and observation method were selected for data collection.The app was developed on the basis of the perceived information needs and the unfelt needs assessed through the gap score in the scientific recommended practices of dairy farming.The app was placed in Google play store on 31 st May, 2018.The app has received a high viewers rating.Assessment of the app among the dairy farmers revealed a high average perceived utility score of 19.58.No major constraints were reported for the use of app by dairy farmers.The app can be used as a very good tool for quick referral to scientific management practices and refreshing the knowledge of not only the farmers but also the veterinarians, development organisations which indirectly will help the farmers to increase the productivity and to have enhanced income in the near future.
Detection of confusion behavior using a facial expression based on different classification algorithms

Confusion detection systems (CDSs) that need Noninvasive, mobile, and cost-effective methods use facial expressions as a technique to detect confusion. In previous works, the technology that the system used represents a major gap between this proposed CDS and other systems. This CDS depends on the Facial Action Coding System (FACS) that is used to extract facial features. The FACS shows the motion of the facial muscles represented by Action Units (AUs); the movement is represented with one facial muscle or more. Seven AUs are used as possible markers for detecting confusion that has been implemented in the form of a single vector of facial action; the AUs that have been used in this work are AUs 4, 5, 6, 7, 10, 12, and 23. The database used to calculate the performance of the proposed CDS is gathered from 120 participants (91males, 29 females), between the ages of 18-45. Four types of classification algorithms are used as individuals; these classifiers are (VG-RAM), (SVM), Logistic Regression and Quadratic Discriminant classifiers. The best success rate was found when using Logistic Regression and Quadratic Discriminant. This work introduces different classification techniques to detect confusion by collecting an actual database that can be used to evaluate the performance for every CDS employing facial expressions and selecting appropriate facial features.
Secured Data Storage and Retrieval using Elliptic Curve Cryptography in Cloud

Security of data stored in the cloud databases is a challenging and complex issue to be addressed due to the presence of malicious attacks, data breaches and unsecured access points. In the past, many researchers proposed security mechanisms including access control, intrusion detection and prevention models, Encryption based storage methods and key management schemes. However, the role based access control policies that were developed to provide security for the data stored in cloud databases based on the sensitivity of the information are compromised by the attackers through the misuse of privileges gained by them from multiple roles. Therefore, it is necessary to propose more efficient mechanisms for securing the sensitive information through attribute based encryption by analyzing the association between the various attributes. For handling the security issue related to the large volume of cloud data effectively, the association rule mining algorithm has been extended with temporal constraints in this work in order to find the association among the attributes so that it is possible to form groups among the attributes as public attributes with insensitive data, group attributes with medium sensitive data and owner with highly sensitive attributes and data for enhancing the strength of attribute based encryption scheme. Based on the associations among the attributes and temporal constraints, it is possible to encrypt the sensitive data with stronger keys and algorithms. Hence, a new key generation and encryption algorithm is proposed in this paper by combining the Greatest common divisor and the Least common multiple between the primary key value and the first numeric non key attribute that is medium sensitive attributes and data present in the cloud database for providing secured storage through effective attribute based encryption. Moreover, a new intelligent algorithm called Elliptic Curve Cryptography with Base100 Table algorithm is also proposed in this paper for performing encryption and decryption operations over the most sensitive data for the data owners. From the experiments conducted in this work, it is observed that the proposed model enhances the data security by more than 5% when it is compared with other existing secured storage models available for cloud
Multi-objective Route Planning of Museum Guide based on an Improved NSGA-II Algorithm

Abstract Museums have become a hot spot of tourism. However, the uneven distribution of passenger flow makes it difficult for tourists to find out the shortest and most comfortable tour route. The multi-objective route planning strategy is proposed for the design of museum tour route. This paper presents an improved version of the NSGA-II algorithm, named adaptive 2-opt_integerated non-dominated sorting genetic (AONSGA) algorithm. Based on NSGA-II algorithm, the adaptive probability and 2-opt local search strategy is introduced. Then the computation results on benchmark multi-objective problems show that the AONSGA algorithm has better convergence and diversity performance than the NSGA-II. Whereafter, taking the Palace Museum as an example, a map is established and AONSGA is applied to carry out multi-objective guide route planning. Finally, according to different requirements of tourists, three kinds of specific schemes of the guide route in the Palace Museum are recommended.
Successor Feature Sets: Generalizing Successor Representations Across Policies

Successor-style representations have many advantages for reinforcement learning: for example, they can help an agent generalize from past experience to new goals, and they have been proposed as explanations of behavioral and neural data from human and animal learners. They also form a natural bridge between model-based and model-free RL methods: like the former they make predictions about future experiences, and like the latter they allow efficient prediction of total discounted rewards. However, successor-style representations are not optimized to generalize across policies: typically, we maintain a limited-length list of policies, and share information among them by representation learning or GPI. Successor-style representations also typically make no provision for gathering information or reasoning about latent variables. To address these limitations, we bring together ideas from predictive state representations, belief space value iteration, successor features, and convex analysis: we develop a new, general successor-style representation, together with a Bellman equation that connects multiple sources of information within this representation, including different latent states, policies, and reward functions. The new representation is highly expressive: for example, it lets us efficiently read off an optimal policy for a new reward function, or a policy that imitates a new demonstration. For this paper, we focus on exact computation of the new representation in small, known environments, since even this restricted setting offers plenty of interesting questions. Our implementation does not scale to large, unknown environments --- nor would we expect it to, since it generalizes POMDP value iteration, which is difficult to scale. However, we believe that future work will allow us to extend our ideas to approximate reasoning in large, unknown environments. We conduct experiments to explore which of the potential barriers to scaling are most pressing.
High-purity pulsed squeezing generation with integrated photonics

Squeezed light has evolved into a powerful tool for quantum technology, ranging from quantum-enhanced sensing and quantum-state engineering based on partial postselection techniques. The pulsed generation of squeezed light is of particular interest, as it can provide accurate time stamps and a physically defined temporal mode, which are highly preferred in complex communication networks and large-scale information processing. However, the multimode feature of pulsed squeezing limits the purity of the output state, negatively impacting its application in quantum technology. Previous demonstrations and analysis of pulsed squeezing focus on single-pass configurations and synchronously pumped free-space cavities. In this paper, we propose a different approach to generate pulsed squeezing with high temporal purity, where a parametric down-conversion process in integrated photonic cavities is pumped by a single-pass pulse. We show that the effective mode number of the output pulsed squeezing approaches unity. Such a high-purity squeezed light can be realized with broad parameters and a low pump power, providing a robust approach to generating large-scale quantum resources.1 MoreReceived 30 July 2020Revised 5 August 2020Accepted 12 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013199Published 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 AreasPhotonicsQuantum cavitiesQuantum information processing with continuous variablesQuantum state engineeringQuantum states of lightSecond order nonlinear optical processesSqueezing of quantum noiseAtomic, Molecular & OpticalQuantum Information
A narrative inquiry of language teachers� perceptions and experiences in using WhatsApp during New Normal Post-Covid-19 era

The purpose of this inquiry is to explore and make sense of the stories of language teachers� perceptions and experiences in using WhatsApp during New Normal Post-Covid-19 era. More specifically, the study is intended to understand the popularity and the usage of WhatsApp in the process of teaching and learning. To achieve the purpose of the study, a narrative inquiry is employed. Four language teachers of SMA Maarif Yogyakarta participated in this study and shared their perceptions and experiences in using WhatsApp as learning medium during New Normal Post-Covid-19 era. The online interview was used to collect information from the teachers by using Google Form. The findings of this narrative inquiry indicate that WhatsApp is a popular learning medium and the use of WhatsApp in distance online learning during New Normal Post-Covid- 19 era in SMA Maarif Yogyakarta is really effective compared to other learning media such as Google Classroom or Zoom and the language teachers give positive reactions regarding the application.
A Methodology for Utilizing Vector Space to Improve the Performance of a Dog Face Identification Model

Recent improvements in the performance of the human face recognition model have led to the development of relevant products and services. However, research in the similar field of animal face identification has remained relatively limited due to the greater diversity and complexity in shape and the lack of relevant data for animal faces such as dogs. In the face identification model using triplet loss, the length of the embedding vector is normalized by adding an L2-normalization (L2-norm) layer for using cosine-similarity-based learning. As a result, object identification depends only on the angle, and the distribution of the embedding vector is limited to the surface of a sphere with a radius of 1. This study proposes training the model from which the L2-norm layer is removed by using the triplet loss to utilize a wide vector space beyond the surface of a sphere with a radius of 1, for which a novel loss function and its two-stage learning method. The proposed method classifies the embedding vector within a space rather than on the surface, and the model’s performance is also increased. The accuracy, one-shot identification performance, and distribution of the embedding vectors are compared between the existing learning method and the proposed learning method for verification. The verification was conducted using an open-set. The resulting accuracy of 97.33% for the proposed learning method is approximately 4% greater than that of the existing learning method.
Jordanian University Students’ Attitudes toward Online Learning during the COVID-19 Pandemic and Lockdowns: Obstacles and Solutions

This study aimed to investigate Jordanian university students’ attitudes toward online learning during the COVID-19 pandemic and lockdowns, shed light on the obstacles students encounter in online learning, and suggest possible solutions. A questionnaire designed by the researchers was used to elicit responses from a study sample consisting of 195 students from the Department of English Language and Literature at the University of Jordan – Aqaba. As this is an attitudinal study, the survey questionnaire was designed to elicit student responses on the following domains: gender, seniority level, socioeconomic status, training and orientation for using eLearning platforms, and their attitudes, whether positive or negative, toward online learning. Results of this quantitative research showed that student attitudes toward online learning are generally negative. The majority of the respondents reported that they prefer face-to-face classroom instruction over online learning because it gives them direct contact with the instructors. Furthermore, results revealed statistically significant diﬀerences amongst students attributed to their gender, whereas no differences were found with regards to their seniority of study, socioeconomic status, and the eLearning platform they prefer to use. The study concludes by proposing some pedagogical recommendations.
DRNet: A Depth-Based Regression Network for 6D Object Pose Estimation

This paper focuses on 6Dof object pose estimation from a single RGB image. We tackle this challenging problem with a two-stage optimization framework. More specifically, we first introduce a translation estimation module to provide an initial translation based on an estimated depth map. Then, a pose regression module combines the ROI (Region of Interest) and the original image to predict the rotation and refine the translation. Compared with previous end-to-end methods that directly predict rotations and translations, our method can utilize depth information as weak guidance and significantly reduce the searching space for the subsequent module. Furthermore, we design a new loss function function for symmetric objects, an approach that has handled such exceptionally difficult cases in prior works. Experiments show that our model achieves state-of-the-art object pose estimation for the YCB- video dataset (Yale-CMU-Berkeley).
HUMAN REMOTE MOBILE MEDICAL INFORMATION COLLECTION METHOD BASED ON INTERNET OF THINGS AND INTELLIGENT ALGORITHM

ABSTRACT In the environment of rapid social and economic development, the reform of medical informatization is constantly advancing, and the residents pay more and more attention to their own health status while improving their living standards. The traditional medical service system has some disadvantages in providing real-time, cross regional, long-term and easy-to-operate health services, which has become increasingly inadequate to meet the health needs of users. In order to solve the problem of difficulty in seeing a doctor caused by limited medical resources, and to carry out real-time health monitoring for a large number of groups suffering from chronic diseases and sub-health groups, this study conducted in-depth analysis and experimental exploration on the human remote mobile medical information collection method based on the Internet of things and intelligent algorithm. It established the information collection section by using KbaC clustering algorithm based on ant colony point system which, combined with a comparative study on the health indicators of related groups, has successfully proved that the Internet of things technology and intelligent algorithm for medical information collection and follow-up medical services are of certain positive significance, based on the Internet of things and other related technologies of human remote medical information collection system that can accurately and timely detect the patient's blood pressure, blood sugar and other health data, and then provide corresponding medical services.
SIP: An Efficient and Secure Information Propagation Scheme in E-Health Networks

Electronic healthcare (e-health) networks are increasingly popular, particular during pandemics such as COVID-19. This reinforces the importance of ensuring security and privacy for data-in-transit. One such solution is steganography-based schemes that utilize biological signals (e.g., ECG) as cover signals to preserve the privacy of patient personal information without affecting the diagnostic features. There are various limitations in existing steganography-based schemes, and in this study we present an effective privacy protection scheme leveraging both multidimensional steganography and shared keys. To enhance security and accelerate signal processing in our design, the Fast Walsh-Hadamard transform (FWHT) is employed to decompose ECG signals into a set of coefficients, of which the less-significant coefficients are used to construct the multidimensional space. The negotiated shared keys facilitate the embedding of encrypted data in the constructed space. We then evaluate the proposed scheme using different categories of ECG signals in the MIT-BIH database. It is observed that the signal distortion is minimal (i.e., less than 1%), even if the embedded data reaches the maximum embedding capacity. The security analysis also demonstrates that unauthorized retrieval of hidden information is not practical, within a short period of time.
A Group Authentication Protocol on Multilayer Structure for Privacy-Preserving IoT Environment

In the Internet of Things (IoT) systems, large amounts of data are accumulated from anywhere at any time, which may attack individuals' privacy, especially when systems are utilized in medical and everyday environments.With the promise of IoT's proactive systems, the integration of smart things into standard Internet creates several security challenges, because most Internet technologies, communication protocols and sensors are not designed to support IoT.Recent research studies have shown that launching security / privacy attacks against IoT active systems, in particular, Wearable Medical Sensor (WMS) systems, may lead to catastrophic situations and life-threatening conditions.Therefore, security threats and privacy concerns in the IoT area should be actively studied.This causes us in this paper to create a privacy authentication protocol for IoT end-devices on a four-layer structure that does not have the ability to accurately identify the device of request's sender so that some attacks can be minimized.We used the Blakley Sharing scheme to design a key generation and distribution system for secure communications between edge devices and end devices and examined the security properties of the protocol for the five common attacks in the IoT.The results of the experiments show that the proposed authentication protocol by the Blakley method is more efficient with increasing number of instructions in both fog structures and in a without fog structure, which shows a higher flexibility of the Blakley method than the Schemer because of the increasing number of instructions indicating increasing the number of nodes in the network.The proposed method has better performance.
Analysis of Twitter Users Sentiment against the Covid-19 Outbreak Using the Backpropagation Method with Adam Optimization

This research tries to take advantage of Twitter by analyzing Indonesian-language tweets that discuss the Covid-19 virus outbreak to find out what Twitter users think about the Covid-19 virus outbreak. This study tries to analyze sentiment to see opinions on Covid-19 tweets that contains Posittive, Negative or Neutral sentiments using Multi-layer Perceptron (MLP) using Backprogragation with Adam optimization. We collected 200 documents (tweets) in Indonesian about Covid-19 that were tweeted since November 2019 and then trained them to get our MLP Backpropagation model. Our model managed to get an accuracy of up to 70% with f1-scores for positive, negative, and neutral classes respectively 0.77, 0.75, and 0.5 from a maximum value of 1. This shows that our model is quite successful in carrying out the sentiment classification process for Indonesian tweets with the Covid-19 theme.
Comparison of K-Means Clustering &amp; Logistic Regression on University data to differentiate between Public and Private University

The development of advances in educational methods has developed in the last few decades. especially at the higher education level such as college. The rising interest of students in pursuing their higher education education has caused the sector to be split into two sectors, both private and public university. This difference raises several questions recently about how the two types differ in carrying out the educational process. whether there is a difference in terms of cost, service or quality, we really can't tell exactly. For this study, we will try to use the K-Means Clustering & Logistic Regression to group the University into two groups, Private and Public and then compare the two model accuracy. The results of this study show that the results obtained from the K-Means clustering model (22%) are much lower than the Logistic Regression model (91%).