Unsupervised Named Entity Recognition for Hi-Tech Domain

This paper presents named entity recognition as a multi-answer QA task combined with contextual natural-language-inference based noise reduction. This method allows us to use pre-trained models that have been trained for certain downstream tasks to generate unsupervised data, reducing the need for manual annotation to create named entity tags with tokens. For each entity, we provide a unique context, such as entity types, definitions, questions and a few empirical rules along with the target text to train a named entity model for the domain of our interest. This formulation (a) allows the system to jointly learn NER-specific features from the datasets provided, and (b) can extract multiple NER-specific features, thereby boosting the performance of existing NER models (c) provides business-contextualized definitions to reduce ambiguity among similar entities. We conducted numerous tests to determine the quality of the created data, and we find that this method of data generation allows us to obtain clean, noise-free data with minimal effort and time. This approach has been demonstrated to be successful in extracting named entities, which are then used in subsequent components.

Designing a User-Centric P2P Energy Trading Platform: A Case Study—Higashi-Fuji Demonstration

Peer-to-peer (P2P) energy trading is gaining attention as a technology to effectively handle already existing distributed energy resources (DER). In order to manage a large number of DER, it is necessary to increase the number of P2P energy trading participants. For that, designing incentives for participants to engage in P2P energy trading is important. This paper describes a user-centric cooperative mechanism that enhances user participation in P2P energy trading. The key components of this incentive for participants to engage in P2P energy trading are described and evaluated in this study. The goal of the proposal is to make it possible to conduct economic transactions while reflecting the preferences of the traders in the ordering process, making it possible to conduct transactions with minimal effort. As a case study, the Higashi-Fuji demonstration experiment conducted in Japan verified the proposed mechanism. In this experiment, 19 households and 9 plugin hybrid vehicles (PHV) were evaluated. As a result, the study confirmed that prosumers were able to sell their surplus electricity, and consumers were able to preferentially purchase renewable energy when it was available. In addition, those trades were made economically. All trades were made automatically, and this efficiency allowed the users to continue using the P2P energy trading.

Rent-A-Cycle (Smart Bicycle Sharing Service-IOT Based)

Bicycle Renting Service or sharing service is about renting a cycle. Bicycle sharing schemes offer a minimal effort and ecologically helpful mean of transportation for short journeys. It can likewise be utilized as a shared mode to other open travel, for example, transports, neighborhood trains. Bicycle sharing systems unite the benefits of open and private transportation to all the more likely adventure the given transportation foundation. This paper high point smart bicycle sharing service which is totally accessed by android application. This research paper also states facts and figures that bicycle sharing services are environmentally friendly and also great for an individual health. The main scope of this bicycle renting schemes is to implement it to reduce environmental pollution and for health purposes. This scheme is smart bicycle renting scheme in which GPS tracking, QR scanning, online payment, automatic locking/unlocking through android application, and all features on just one android application. This scheme also has admin website where all the information of user and bikes are stored also history of bikes, payment and rides for security purpose. From a distinctive individual's perspective, bicycle share systems take out the burden of bicycle proprietorship, the need to look for parking places, and the dread of burglary. It helps in decreasing rush hour jam clog as number of vehicles on street can be fundamentally reduced.

Teaching Multiple Inverse Reinforcement Learners

In this paper, we propose the first machine teaching algorithm for multiple inverse reinforcement learners. As our initial contribution, we formalize the problem of optimally teaching a sequential task to a heterogeneous class of learners. We then contribute a theoretical analysis of such problem, identifying conditions under which it is possible to conduct such teaching using the same demonstration for all learners. Our analysis shows that, contrary to other teaching problems, teaching a sequential task to a heterogeneous class of learners with a single demonstration may not be possible, as the differences between individual agents increase. We then contribute two algorithms that address the main difficulties identified by our theoretical analysis. The first algorithm, which we dub SplitTeach, starts by teaching the class as a whole until all students have learned all that they can learn as a group; it then teaches each student individually, ensuring that all students are able to perfectly acquire the target task. The second approach, which we dub JointTeach, selects a single demonstration to be provided to the whole class so that all students learn the target task as well as a single demonstration allows. While SplitTeach ensures optimal teaching at the cost of a bigger teaching effort, JointTeach ensures minimal effort, although the learners are not guaranteed to perfectly recover the target task. We conclude by illustrating our methods in several simulation domains. The simulation results agree with our theoretical findings, showcasing that indeed class teaching is not possible in the presence of heterogeneous students. At the same time, they also illustrate the main properties of our proposed algorithms: in all domains, SplitTeach guarantees perfect teaching and, in terms of teaching effort, is always at least as good as individualized teaching (often better); on the other hand, JointTeach attains minimal teaching effort in all domains, even if sometimes it compromises the teaching performance.

QLREDActive Queue Management Algorithm

The Random Early Detection (RED) algorithm has not been successful in keeping the average queue size low. In this paper, we an improved RED-based algorithm called QLRED which divides the dropping probability function of the RED algorithm into two equal segments. The first segment utilises a quadratic packet dropping function while the second segment deploys a linear packet dropping function respectively so as to distinguish between light and high traffic loads. The ns-3 simulation performance evaluations clearly showed that QLRED algorithm effectively controls the average queue size under various network conditions resulting in a low delay. Replacing/upgrading the RED algorithm in Internet routers requires minimal effort since only the packet dropping probability profile needs to be adjusted.

Software Platform for Supercomputer Modeling of Aerothermodynamics Problems

Numerical solution of aerothermodynamics problems requires a lot of processor time, and for this reason, the numerical codes for such simulations must be efficiently parallelized. This paper presents a software platform based on a technological approach that greatly simplifies the parallelization of problems with unstructured grids. The paper formulates the principle of a unified mathematical address space of the problem for all used cluster nodes. The formalization of the presented technological approach and the implementation of its software basis in the form of data structures, exchange routines and work rules form a software platform on the basis of which parallel computational programs can be built. This approach is implemented and tested for the problem of modeling the characteristics of aircrafts, but it can be applied to other problems using unstructured grids with one-dimensional cell numbering. In this case, the physical and mathematical specifics of the problem are not important. Parallelization of the code with the new approach is carried out with minimal effort, without changing the main parts of the program. As a result, a single computational code can be created for all regimes — sequential, multi-threaded, and cluster. The performance results obtained with the new code confirm the good scalability of the parallelization method.

Perencanaan Manajemen Stakeholder di Pondok Pesantren Menggunakan Project Management Body of Knowledge (PMBOK)

Stakeholder adalah hal yang sangat penting untuk mencapai tujuan proyek. Tujuan dari penelitian ini adalah merancang strategi yang tepat agar Pemimpin Pondok Pesantren dapat melakukan manajemen stakeholder. Data Primer didapatkan dari data standar sekolah asrama (SSA) yang dibuat oleh Welsh Assembly Government kementerian kesehatan dan pelayanan sosial Inggris untuk boarding school pada tahun 2003 yang terdiri dari 52 standar dan Data Skunder sebagai pendukung yang didapatkan dari Project Management Body of Knowledge (PMBOK). Pada penelitian ini menggunakan analisis Management Stakeholder dalam Project Management Body of Knowledge (PMBOK). Dan hasil penelitian strategi stakeholder tergolong menjadi stakeholder key player ada mudir/kyai dan kepala pengurus, Stakeholder Keep Informed ada Kurikulum dan Pengajaran, Staf Pesantren, Stakeholder Keep Satisfied Santri dan Orang Tua Santri, Stakeholder Minimal Effort tidak ada.

Exploratory Only: A Tool for Large-Scale Exploratory Analyses

This article presents Exploratory Only: an intuitive tool for conducting large-scale exploratory analyses easily and quickly. Available in three forms (as a web application, standalone program, and R Package) and launched as a point-and-click interface, Exploratory Only allows researchers to conduct all possible correlation, moderation, and mediation analyses among selected variables in their data set with minimal effort and time. Compared to a popular alternative, SPSS, Exploratory Only is shown to be orders of magnitude easier and faster at conducting exploratory analyses. The article demonstrates how to use Exploratory Only and discusses the caveat to using it. As long as researchers use Exploratory Only as intended—to discover novel hypotheses to investigate in follow-up studies, rather than to confirm nonexistent a priori hypotheses (i.e., p-hacking)—Exploratory Only can promote progress in behavioral science by encouraging more exploratory analyses and therefore more discoveries.

Pressure-induced high-temperature superconductivity retained without pressure in FeSe single crystals

To raise the superconducting-transition temperature (Tc) has been the driving force for the long-sustained effort in superconductivity research. Recent progress in hydrides with Tcs up to 287 K under pressure of 267 GPa has heralded a new era of room temperature superconductivity (RTS) with immense technological promise. Indeed, RTS will lift the temperature barrier for the ubiquitous application of superconductivity. Unfortunately, formidable pressure is required to attain such high Tcs. The most effective relief to this impasse is to remove the pressure needed while retaining the pressure-induced Tc without pressure. Here, we show such a possibility in the pure and doped high-temperature superconductor (HTS) FeSe by retaining, at ambient pressure via pressure quenching (PQ), its Tc up to 37 K (quadrupling that of a pristine FeSe at ambient) and other pressure-induced phases. We have also observed that some phases remain stable without pressure at up to 300 K and for at least 7 d. The observations are in qualitative agreement with our ab initio simulations using the solid-state nudged elastic band (SSNEB) method. We strongly believe that the PQ technique developed here can be adapted to the RTS hydrides and other materials of value with minimal effort.

Determinants of Behavioral Intention of the Use of Self-Order Kiosks in Fast-Food Restaurants: Focus on the Moderating Effect of Difference Age

The aim of this study was to derive determinants that affect the behavior associated with using self-order kiosks among fast-food restaurant consumers through the Unified Theory of Acceptance and Use of Technology model, and to analyze the moderating effect of consumers’ difference age (difference between individuals’ cognitive age and chronological age) among the variables. From December 1 to 30, 2019, a survey was conducted on 316 customers using four different fast-food restaurants in the Seoul Station. The results showed that the higher the price value, social influence, performance expectancy, and hedonic motivation, the higher the behavioral intention of ordering through the kiosk; furthermore, the higher the difference age, the higher the behavioral intention of using a kiosk. Therefore, fast-food restaurant operators need to ensure that customers who are unfamiliar with using kiosks can order and make payments through kiosks with minimal effort and reasonable price value.