Reviewerly/exHarmony huggingface dataset:
SISTEM INFORMASI PELAYANAN PUBLIK PADA SATUAN POLISI PAMONG PRAJA PROVINSI JAMBI BERBASIS WEB

One of the technologies currently developing is computer technology. This has made computerized systems increasingly implemented by agencies in all fields, both private and government agencies. This is related to jobs that are usually done manually by humans, which will be faster and more efficient when done with a computerized system. The Civil Service Police Unit (SatPolPP) is a government agency that has a duty as a public servant in the field of city / provincial security and control. However, for public service facilities, public reporting using electronic media does not yet exist so that people report directly to the service office. There are several services at the Jambi Provincial Civil Service Police Work Unit, namely the service of peace and public order, regional legislation, community protection, community control. In 2019 the civil service police unit of the province of Jambi already has a website issued by the Ministry of Home Affairs (Kemendagri) but the website is still internal in nature used by the civil service police unit so that the website does not yet have public services in the field of reporting and information about Police Units Pamong Praja Jambi Province. This causes frequent errors in the data processing system that is still manual, such as recording in the agenda book, filling in the form provided manually by coming directly to the office so that public reports often experience delays because making community reports must come directly to the Sat Pol service office. PP and filling out the report data, the process is still very slow, the book data is not controlled. Therefore, the Civil Service Police Unit is in dire need of an information system that processes public report data which is expected to facilitate the public reporting process to simplify reporting and work, be accurate, and efficient, to reduce and even eliminate errors that may occur in the data processing process. With the existence of problems in the old system based on the purpose of this study, it is proposed to be able to use a computer tool using Web programming with PHP and MySql programming languages ​​as a database that has public data input, complaints and complaint categories that will be processed into command data. and the action of complaints by the civil service police unit of the province of Jambi with the outpot of the complaint report.
DIAGNOSIS OF PARENT INVOLVEMENT IN CHILDREN'S EDUCATION DURING THE PANDEMIC

The objective of the research is to diagnose parental involvement in the education of children during the health emergency generated by COVID 19, where they were forced to stay at home with their children, sharing most of the educational process, and adapting to a new modality of virtual studies with the use of technological tools and resources. The research presents a qualitative-quantitative, descriptive approach through surveys of parents, through the Google Drive application, which allowed to analyze by statistical data the involvement of the education of their children, in which most parents were not prepared for this challenge.
Development of Cholesterol Calculator Websites: Investigation on Food Cholesterol in Malaysia Using Web-Based Calculator:

In this research, we are interested in creating a cholesterol calculator website to help people calculate their cholesterol from dietary intake. We study and apply the programming tools required in website development such as Firebase, HTML, CSS, and JS. We collect the cholesterol data from particular food in Malaysia, and we use collected data to construct a website database by using JSON afterward. We ensure the cholesterol calculator website's performance and function are well before publishing it to the public on the internet. Lastly, we will discuss the result based on the website performance and observe a user's eating pattern through their food cholesterol data save on the website.
Machine Learning-Based Security Authentication for Wireless Multimedia Network

For supporting various services in wireless multimedia networks, security is one of the major concerns. The traditional upper-layer authentication system and conventional security solution lack efficiency and does not solve the emerging security problems and ignores physical layer protection. To overcome these challenges, this paper presents a lightweight physical layer authentication system developed by understanding certain physical layer attributes in the wireless multimedia network through supervised machine learning algorithm. Support vector classifier (SVC) is used for classification. Classification can also be performed by other supervised algorithm and select based on each detection rate. Experimental analysis proved that the proposed system could protect wireless multimedia networks.
Leveraging on Machine Moderation to Improve Content Organization

The growing pressure from the government on operators of different platforms on the needs to manage content in order to eliminate misinformation and ‘hate speech, this study examines the introduction of machine moderation mechanisms; surveys approximately the prevailing automated tools employed by key players in social media to manage rights violation, sabotage and hate speech; and recognizes major structures as the requirement for the implementation. We attempt to address the purpose of this paper by reviewing some selected pieces of literature. The article provides automated ‘hash matching and projecting artificial intelligence or machine learning devices. We also define machine moderation as a technological approach set up to demeanor content moderation at balance by foremost platforms for user-created content like YouTube, Facebook, and Twitter.
Detecting anomalous behaviors in multivariate systems using machine learning

Aircrafts are complex systems that are generating more and more data. An Airbus A320 equippedwith FOMAX (Flight Operations and MAintenance eXchanger) records 24000 parameters and aPratt & Whitney PW1000G GTF engine incorporates 5000 sensors, leading to terabytes of databeing recorded for each flight. Modernization of military aircrafts and usage of Unmanned AerialVehicle (UAV) fleets also lead to large quantity of data. Detecting anomalies in those systems isvaluable as a warning system to detect unexpected behaviors, faulty systems to be replaced or evendiscard readings of faulty sensors.
Database Management System (DBMS): A Cloud-Enabled Service for Small to Medium Enterprise

Cloud storage is a new IT technology that has created tremendous interest in IT academics and customers. Through linking a vast range of servers across the world through the internet and pooling the services, they generate, cloud storage is a capable invention that is intended to offer tremendous cost savings and exceptional versatility in how companies access the services. In particular, the small and medium-sized companies (SMEs) view cloud infrastructure as a persuasive threat to themselves. Nevertheless, previous studies which examine cloud computing from a business perspective are minimal. Moreover, despite the exponential growth of cloud infrastructure in small and medium-sized businesses, there is no prior study concentrating on SMEs. This research explores the factors that lead specifically for their data to the rapid growth of cloud computing in SMEs. It explores the roles cloud computing played within the Database Management System (DBMS). Owing to the strong prevalence of cloud storage within it, the SMEs are selected, and the homogeneous design of DBMS services. This study allows the SMEs’ IT expertise and performs a sample using a systematic questionnaire. The findings showed the term cloud infrastructure applies to several kinds of services. Cloud storage for SMEs is focused on an external (client-oriented) role and an internal (DBMS) function. On the other side, the drawbacks of cloud-based information management systems are restricted mainly to small businesses. This study formulates an important contribution to cloud device science in the field. It should be understood that cloud computing’s position is expanding ahead of its technical merits and differs based on market characteristics and company approach. Future experiments are proposed to consider how companies integrate cloud-enabled technologies into their business operation and policy.
PROFESSIONAL DEVELOPMENT OF TEACHERS AND SCHOOL MENTORING

The modern dynamic reality poses many challenges to professional realization and career development in the field of education - such are competition and job insecurity, striving for expression and self-affirmation, the concept of loyalty, rapid change of policies and technologies, changed values in society. Decisive condition for quality education are the training of teachers, their training and qualification.The goals of the training and qualification of the teachers / educators in the educational institutions are to increase their competence, quality, motivation, satisfaction, career opportunities and achieving personal success for the individual.Organized and independent training contribute to professional and professional growth and career development. Modern teacher training is centered on the learner himself. It is based on the experience, initiative and empathy of the learner and the learner. Based on the Lifelong Learning Program, the content of training and qualification in educational institutions should help teachers to know and be able to do more; to have an attitude, to expect and to be ready for the responsible social role they have to play in education.The construction of a system for raising the qualification of the pedagogical staff in the educational institutions includes the following main stages:• Research and establishment of the qualification needs of the pedagogues;• Defining the general and specific objectives of the qualification;• Planning of activities, resources, participants and trainers;• Selection of the content and adequate methods and means of training;• Organization and implementation of the qualification process;• Monitoring of implementation and evaluation of results.With regard to young teachers and given the critical nature of the first years of the teaching profession, it is extremely important to introduce mentoring programs and joint lesson planning with more experienced teachers. The aim is to attract and retain young people in the education system, who are less and less, and parents. More opportunities for interaction between the teaching staff are needed in order to better support the new teachers.
Bayes Approach in Applied Probability

The following sections are included:IntroductionThe Bayesian ParadigmThe Prior DistributionThe normal as a prior: Its elicitationSpecial priorsSampling PhaseThe sampling phaseConjugacyCase of the normal: N(μ, τ)Case of the multinormal: X ∼ Nk(M, r = Σ−1)The Posterior DistributionComputation of the posteriorDominanceThe highest posterior density regionGeneralization of the hpd intervalThe Predictive DistributionBayesian Sub-domainsSequential BayesEmpirical BayesBayes operatorBayesian decision theoryCommon loss functions in Bayesian decision theoryConjugate Bayesian Analysis TableConclusionBibliography
Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation

We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension. Velocidapter augments datasets by simulating synthetic conversations for a task-oriented dialogue domain, requiring a small amount of bootstrapping work for each new domain. We evaluate the efficacy of our framework on a task-oriented dialogue comprehension dataset, MRCWOZ, which we curate by annotating questions for slots in the restaurant, taxi, and hotel domains of the MultiWOZ 2.2 dataset (Zang et al., 2020). We run experiments within a low-resource setting, where we pretrain a model on SQuAD, fine-tuning it on either a small original data or on the synthetic data generated by our framework. Velocidapter shows significant improvements using both the transformer-based BERTBase and BiDAF as base models. We further show that the framework is easy to use by novice users and conclude that Velocidapter can greatly help training over task-oriented dialogues, especially for low-resourced emerging domains.
Improving Named Entity Recognition in Spoken Dialog Systems by Context and Speech Pattern Modeling

While named entity recognition (NER) from speech has been around as long as NER from written text has, the accuracy of NER from speech has generally been much lower than that of NER from text. The rise in popularity of spoken dialog systems such as Siri or Alexa highlights the need for more accurate NER from speech because NER is a core component for understanding what users said in dialogs. Deployed spoken dialog systems receive user input in the form of automatic speech recognition (ASR) transcripts, and simply applying NER model trained on written text to ASR transcripts often leads to low accuracy because compared to written text, ASR transcripts lack important cues such as punctuation and capitalization. Besides, errors in ASR transcripts also make NER from speech challenging. We propose two models that exploit dialog context and speech pattern clues to extract named entities more accurately from open-domain dialogs in spoken dialog systems. Our results show the benefit of modeling dialog context and speech patterns in two settings: a standard setting with random partition of data and a more realistic but also more difficult setting where many named entities encountered during deployment are unseen during training.
Directions of development of information and library services in the XXI century

This article examines the main trends in the development of library and information services in the rapidly developing XXI century. Changes are noted in the ways of creating, distributing, organizing and using information technologies and the use of digital repositories is expanding, which significantly reduces paper consumption. Means are created by which the reader’s request is quickly satisfied in this, the automation of the reader’s service plays a significant role. And also the article provides methodological advice for attracting the reader to the library. It is indicated that the use of the repository requires special knowledge, skills and skill. It is necessary to thoroughly develop processes for processing and preserving electronic resources, today both traditional and modern methods of serving in the library are used.
ONLINE LEARNING: THE NEED FOR CRITICAL REFLECTION AS A RESULT OF THE COVID-19 PANDEMIC

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 1522-1527ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0367Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Variational Reward Estimator Bottleneck: Towards Robust Reward Estimator for Multidomain Task-Oriented Dialogue

Despite its significant effectiveness in adversarial training approaches to multidomain task-oriented dialogue systems, adversarial inverse reinforcement learning of the dialogue policy frequently fails to balance the performance of the reward estimator and policy generator. During the optimization process, the reward estimator frequently overwhelms the policy generator, resulting in excessively uninformative gradients. We propose the variational reward estimator bottleneck (VRB), which is a novel and effective regularization strategy that aims to constrain unproductive information flows between inputs and the reward estimator. The VRB focuses on capturing discriminative features by exploiting information bottleneck on mutual information. Quantitative analysis on a multidomain task-oriented dialogue dataset demonstrates that the VRB significantly outperforms previous studies.
Detection and Classification of Malicious Websites

Phishing is an attractive methodology that is used by the attackers to steal personal data from people. Phishing websites are fraudulent web pages used for cyber thefts. They use domain names, similar to that of an original website, send mail and direct messages to lure someone in sharing personal information. Although many new anti-phishing methods are out ,still these attackers innovate new methods to target these. Therefore there is an alarming need to develop new ways to protect people from phishing. The Project uses different models of comparison in classification of phishing websites for detection and prediction using different machine learning models. The main goal of the project is to make Machine Learning Algorithm for data input and prediction and classify URL’s that are malicious . Using python packages(Fastai,Scikit,pandas,Seaborn etc.) the dataset is converted into Distribution Plots and Confusion Matrix.
Towards Stronger Functional Signatures

Functional digital Signatures (FS) schemes introduced by Boyle, Goldwasser and Ivan (PKC 2014) provide a method to generate fine-grained digital signatures in which a master key-pair $(\msk,\mvk)$ is used to generate a signing secret-key $\sk_\function$ for a function $f$ that allows to sign any message $\msg$ into the message $f(\msg)$ and signature $\sigma$. The verification algorithm takes the master verification-key $\mvk$ and checks that the signature $\sigma$ corresponding to $f(\msg)$ is valid. In this paper, we enhance the FS primitive by introducing a function public-key $\pk_f$ that acts as a commitment for the specific signing key $\sk_f$. This public-key is used during the verification phase and guarantees that the message-signature pair is indeed the result generated by employing the specific key $\sk_f$ in the signature phase, a property not achieved by the original FS scheme. This enhanced FS scheme is defined as Strong Functional Signatures (SFS) for which we define the properties of unforgeability as well as the function hiding property. Finally, we provide an unforgeable, function hiding SFS instance in the random oracle model based on Boneh-Lynn-Shacham signature scheme (ASIACRYPT 2001) and Fiore-Gennaro's publicly verifiable computation scheme (CCS 2012).
Smart Secured Seamless Online Payment Against Counterfeit Transaction Through Aadhaar

To develop a system for achieving digitalization by enabling Aadhaar-linked payment gateways ensuring feasible and authorized access via fingerprint and One-Time Password. The authentication provides greater access control and user identity. Thus, the privacy is maintained throughout the transaction.
iNaturalist and Citizen Science Images [slides]

Slides from a lightning round presentation for the ACRL Image Resources Interest Group (IRIG) Summer Virtual Meeting - June 17, 2021.
Efficient Online Inference for Nonparametric Mixture Models

Natural data are often well-described as belonging to latent clusters. When the number of clusters is unknown, Bayesian nonparametric (BNP) models can provide a flexible and powerful technique to model the data. However, algorithms for inference in nonparametric mixture models fail to meet two critical requirements for practical use: (1) that inference can be performed online, and (2) that inference is efficient in the large time/sample limit. In this work, we propose a novel Bayesian recursion to efficiently infer a posterior distribution over discrete latent variables from a sequence of observations in an online manner, assuming a Chinese Restaurant Process prior on the sequence of latent variables. Our recursive filter, which we call the Recursive Chinese Restaurant Process (R-CRP), has quasilinear average time complexity and logarithmic average space complexity in the total number of observations. We experimentally compare our filtering method against both online and offline inference algorithms including Markov chain Monte Carlo, variational approximations and DP-Means, and demonstrate that our inference algorithm achieves comparable or better performance for a fraction of the runtime.
IMPLEMENTASI ALGORITMA FUZZY C-MEANS PADA PENGELOMPOKAN SISWA PKL: (Studi Kasus : SMK MUTIARA SALAWU)

Mutiara Salawu Vocational School also participates in government programs to carry out PKL. However, PKL at SMK Mutiara Salawu is different as it does with other SMKs, it does not mean that there is nothing but rarely SMKs that direct their students to PKL in a place that has been determined based on the specified report cards. Because Salawu Mutiara Vocational School places PKL students who have been determined by the school, it is necessary to have a supportive system for classifying students based on the specified report cards. Implementation of Fuzzy C-Means algorithm to classify street vendors students based on their respective report cards. The results of this implementation are in the form of software that can be used as a tool to help schools classify PKI class XI students based on report cards to help prepare PKL placement as desired by the street vendors chosen by the school using the Fuzzy C- algorithm Means Based on the test results, the grouping of street vendors students can be concluded that this grouping can produce output in accordance with what users expect. Keywords: Algorithms, Fuzzy C-Means, Grouping students
Channel discord and distortion

Abstract Discord, originally notable as a signature of bipartite quantum correlation, in fact can be nonzero classically, i.e. arising from noisy measurements by one of the two parties. Here we redefine classical discord to quantify channel distortion, in contrast to the previous restriction of classical discord to a state, and we then show a monotonic relationship between classical (channel) discord and channel distortion. We show that classical discord is equivalent to (doubly stochastic) channel distortion by numerically discovering a monotonic relation between discord and total-variation distance for a bipartite protocol with one party having a noiseless channel and the other party having a noisy channel. Our numerical method includes randomly generating doubly stochastic matrices for noisy channels and averaging over a uniform measure of input messages. Connecting discord with distortion establishes discord as a signature of classical, not quantum, channel distortion.
SOCIAL DISTANCING DETECTOR USING DEEP LEARNING

This paper presents a methodology for detecting social distance using deep learning and computer vision between people to control the spread of covid-19. This application is developed to give alerts to people for maintaining social distance in crowded places. By using pre-recorded video as input and the open-source object detection pretrained model using the YOLOv3 algorithm We can tell if people are following social distancing or not and based on that we are creating red or green bounding boxes over it. It is also working on web cameras, CCTV, etc, and can detect people in real-time. This may help authorities to redesign the layout of public places or to take precautionary actions to mitigate high-risk zones.it can be used in other fields also like autonomous vehicles, human action recognition, crowd analysis.