Reviewerly/exHarmony huggingface dataset:
ESP COURSE DESIGN: THE NEED ANALYSIS ON TOURISM DEPARTMENT IN INDONESIA VOCATIONAL HIGH SCHOOLS

Centered on the 2013 Curriculum (Indonesia's national curriculum), English subject matter is equated to the high school standard. Both high school and vocational school students receive the same English content, while English is to be differentiated for special purposes for high school and vocational high school students. It is difficult to find an English textbook in a vocational school which fits into the department of the student. Additionally, there is often not enough time for teachers to develop English materials for vocational students. This study aims to explore the content that should be included in SMKN 1 Pangsid Kabupaten Sidenreng Rappang to design English language materials based on needs analysis in the Tourism Program and to find out the response of teachers to the development of English material in the Tourism Programme. Fundamental analysis uses Qualitative Descriptive, and the findings are described in terms. The study results indicate that the content to be included in the English language material in the SMKN 1 Pangsid Kabupaten Sidenreng Rappang tourism program is speaking and pronunciation for language skills and components; media videos and images; and asking questions about teaching techniques. Creation of English language materials consists of several speaking practice presentations, many videos to practice listening, speaking and reading, reading sections and documents, and some information that the Tourism Program learners need to know.
Developing an Android-Based Game for Chemistry Learners and Its Usability Assessment

&lt;p class="0abstract"&gt;Nowadays, smartphones are not only used for communication purposes but also for gaming and studying. Gaming is trendy among adolescents in daily activity. The gamification of chemistry concepts, therefore, becomes very interesting and promising to enhance students’ intention of learning and promoting ICT literacy among university students. In this article, we reported the development of an Android-based game on the concept of colloids. Volunteered students (N=100) from the Faculty of Education and Teacher Training took part in the implementation phase. Need assessment suggested that the concept colloids should be a specific topic to be developed as chosen by a significant number of respondents. The validity of the app in terms of the instructional design, software design, and the visual aspects scored above average (between 90 and 99 %). The developed app was found to be very easy to use, practical, and straightforward, with an overall usability score of 85.1%.&lt;/p&gt;
Harnessing Green Cloud Computing- An Energy Efficient Methodology for Business Agility and Environmental Sustainability

The role of Information, Communication and Technology (ICT) is important now-a-days and its impact on the nature is huge which emits more heat and CO2.Cloud Computing is playing an impressive role to face the challenges in IT Modern Digital Era to process the growing large volume of data and storing that data in Data Centers (DC) and provides the resources and applications to the customers with more scalability, flexibility, cost-effective, pay-on-demand basis and anything as-a-service in cloud but most of the prevailing cloud datacenter architectures are not aligned to green standards.To minimize the impact on cloud computing environment energy-efficient methods are required.Green Cloud is still under research and at nascent stage in its growth and implementation.Green cloud computing is an eco-friendly methodology helps to make the cloud computing environment with more power efficient, maximum resource utilization not by over-provisioning, less carbon footprints, minimized Green House Gas (GHG) emissions and reduction in E-wastages.In order to save cost, large enterprises come up with cutting edge technologies like Green Cloud Computing (GCC) by bringing them into the cloud environment to reduce the operational expenditures (OPEX) and allow the organizations to make less use of physical components and resources with virtualization techniques that requires only less cooling, less water consumption and minimized power consumption to keep the nature calm.The Cloud Service Providers (CSP) should upgrade their cloud DC infrastructure by eliminating the negative impacts which causes harm to the nature.Green Cloud Computing focuses on bringing the necessary characteristics and approaches in cloud environment like containerization, Designing Green Data center facility infrastructure, VM placement, Server Consolidation, Thin Clients, Green metrics, Green Network and Protocols, Green Data Management, Sustainable software development and safe disposable methods with 3 R's (Reuse, Reduce and Recycle).In this paper the discussions are about various elements and green cloud methodologies which are most needed to bring positive impact in our environment and to reduce metered bill for business profit margin and agility.
Image Editing via Segmentation Guided Self-Attention Network

Image editing is one of the most popular directions in computer vision. Recently, many methods have benefited from the advances in deep learning, showing promising performance in the image editing task by inpainting the editing areas. These methods take advantage of edge information as user guidance to generate the desired content. However, they are suffering from generating color discrepancy and inconsistent boundaries. In this letter, we propose a deep image editing method based on a self-attention network which copies information for each of the small patches from distant spatial locations. The proposed method smooths the image, computes segmentation maps, and utilizes the segmentation information for guiding the self-attention layers to explicitly leverage image features from surrounding areas with similar appearances. Experimental results show that the proposed method achieves better performance, is flexible for different purposes, and is fast for implementation.
A probabilistic spin annihilation method for quantum chemical calculations on quantum computers

A probabilistic spin annihilation method based on the quantum phase estimation algorithm is presented for quantum chemical calculations on quantum computers.
Survei IoT Healthcare Device

The Internet of Things (IoT) has enormous potential in creating the value of life related to technology. IoT has various application domains, including in the health sector. IoT-based healthcare services are expected to reduce costs, improve quality of life, and enrich user experience. The presence of IoT devices for healthcare services can also avoid unnecessary hospitalization and ensure that patients who need health services get it quickly. This paper surveys advances in IoT-based health care technology and reviews the latest architectures / platforms, platforms, applications and industry trends in IoT-based healthcare solutions. Some IoT devices and prototypes in the healthcare field are also discussed in this paper. Through this paper, it is expected that readers can be known and discuss IoT devices in the healthcare sector.
StarNet: towards Weakly Supervised Few-Shot Object Detection

Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and for adaptation to novel classes, and classification approaches rarely provide localization of objects in the scene. In this paper, we introduce StarNet - a few-shot model featuring an end-to-end differentiable non-parametric star-model detection and classification head. Through this head, the backbone is meta-trained using only image-level labels to produce good features for jointly localizing and classifying previously unseen categories of few-shot test tasks using a star-model that geometrically matches between the query and support images (to find corresponding object instances). Being a few-shot detector, StarNet does not require any bounding box annotations, neither during pre-training nor for novel classes adaptation. It can thus be applied to the previously unexplored and challenging task of Weakly Supervised Few-Shot Object Detection (WS-FSOD), where it attains significant improvements over the baselines. In addition, StarNet shows significant gains on few-shot classification benchmarks that are less cropped around the objects (where object localization is key).
Fast high-fidelity multiqubit state transfer with long-range interactions

We describe an efficient protocol to perform quantum state transfer using Hamiltonian dynamics with long-range interactions. The time to transfer $n$ qubits a sufficiently large distance is proportional to $\sqrt{n}$. Even without error correction, the fidelity of this multiqubit state transfer process remains finite for arbitrarily-well-separated qubits in the presence of uncorrelated random errors in coupling constants.
Generalized coherence vector applied to coherence transformations and quantifiers

One of the main problems in any quantum resource theory is the characterization of the conversions between resources by means of the free operations of the theory. In this work, we advance on this characterization within the quantum coherence resource theory by introducing the generalized coherence vector of an arbitrary quantum state. The generalized coherence vector is a probability vector that can be interpreted as a concave roof extension of the pure states coherence vector. We show that it completely characterizes the notions of being incoherent, as well as being maximally coherent. Moreover, using this notion and the majorization relation, we obtain a necessary condition for the conversion of general quantum states by means of incoherent operations. These results generalize the necessary conditions of conversions for pure states given in the literature, and show that the tools of the majorization lattice are useful also in the general case. Finally, we introduce a family of coherence quantifiers by considering concave and symmetric functions applied to the generalized coherence vector. We compare this proposal with the convex roof measure of coherence and others quantifiers given in the literature.
TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa

Hate speech detection on social media platforms is crucial as it helps to avoid severe harm to marginalized people and groups.The application of Natural Language Processing (NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection.The expression of hate, however, is varied and ever-evolving.Thus better detection systems need to adapt to this variance.Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions.In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition.Our contribution can be perceived through our results, that was first an F1-score of 0.9087, and with further refinements described here climb up to 0.9166.It serves to give more support to our hypothesis that one of the variants of BERT, namely RoBERTa can successfully differentiate between offensive and non-offensive tweets, given the proper preprocessing steps.
The Development of an Intelligent Multilevel Item Bank Model for the National Evaluation of Undergraduates

The objectives of this research were 1) to develop an Intelligent Multilevel Item Bank (I-MIB) Model for the National Evaluation of Undergraduates and 2) to assess the appropriateness of the I-MIB Model for the National Evaluation of Undergraduates.The results of the research were as follows: 1.The I-MIB Model for the National Evaluation of Undergraduates consisted of four parts: Input, Process, Output, Feedback.The Input consisted of 1. Curriculum, 2. Subject, 3. Topic, 4. Objectives, 5. Cognitive Skills, 6. Teacher, 7. Student, and 8. Program Designated Lecturers.The Process consisted of 1.
LVBERT: Transformer-Based Model for Latvian Language Understanding

This paper presents LVBERT – the first publicly available monolingual language model pre-trained for Latvian. We show that LVBERT improves the state-of-the-art for three Latvian NLP tasks including Part-of-Speech tagging, Named Entity Recognition and Universal Dependency parsing. We release LVBERT to facilitate future research and downstream applications for Latvian NLP.
Diminuendo! Tactics in Support of FaaS Migrations

Abstract Function-as-a-Service (FaaS) receives close attention due to highly desirable characteristics, including pay-as-you-go pricing, high elasticity, and its fully managed nature. To leverage these benefits for existing applications, developers face the challenge of migrating legacy code to a FaaS platform (FaaSification). Unfortunately, however, actionable guidance on how to do so for real-world applications does not exist. In this paper, we report on our experience from FaaSifying a data-intensive application, and evaluating different options through extensive experimentation, using approaches such as regression tests and tracing. Based on the obtained results, we present five migration tactics in support of future FaaSification.
Diagrammatic technique for simulation of large-scale quantum repeater networks with dissipating quantum memories

We present a detailed description of the diagrammatic technique for a semianalytical description of large-scale quantum repeater networks devised recently by V. V. Kuzmin et al. [npj Quantum Inf. 5, 115 (2019)]. The technique takes into account all essential experimental imperfections, including continuous dissipative Liouville dynamics of the network quantum memories and the classical communication delays. The results obtained with the semianalytic method match the exact Monte Carlo simulations while the required computational resources scale only linearly with the network size, thus allowing for precise comparison and optimization of large-scale quantum networks subjected to relevant realistic imperfections. We illustrate the potential of the method by optimizing the secret key rate in one- and two-dimensional quantum networks for a range of network sizes and memory coherence times. The presented approach opens new possibilities for the development and efficient optimization of future quantum networks.
The drought-alert decision support system for water resources management

In this paper, we discuss the drought-alert decision support system (DA-DSS), a solution developed by the Small Medium Enterprise Amigo s.r.l., (Rome, Italy) and MaP Ltd., (Athens, Greece).within the Horizon 2020 cross-sectoral project "cross-climate".DA-DSS is a prototype of the WebGIS application aimed to support the water utilities (WUs) in the management of drinking water in a changing climate.DA-DSS combines climate data, infrastructures geospatial data, and visualization tools.Climate data exploit the seasonal forecasts provided by the EU Copernicus services in order to obtain short and mid-term DA, up to a six months' time range.This information is integrated with geospatial data with advanced geographic information system technology, and finally implemented into a WebGIS application.Two small and medium WUs, located in Greece and Italy respectively, served as test cases for the system development in a co-design approach, based on their existing decision chains.The prototype system was then released in the municipality of Zakynthos, Greece.The investigation of the user's needs suggested that both the WUs are interested in predictions on 3 months-cumulated precipitation, 3 months in advance, from November to March.The test of the prototype in November 2017 demonstrated that the uncertainty, assessed in terms of false rate, is low for the short-term prediction (3 months).
Correction to: MongoDB Vs PostgreSQL: a comparative study on performance aspects

The article “MongoDB Vs PostgreSQL: A comparative study on performance aspects”, written by Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dimitrios Zissis, Dimosthenis Anagnostopoulos, was originally published electronically on the publisher’s internet portal on 05 June 2020 without open access.
Evaluating Sentence Segmentation and Word Tokenization Systems on Estonian Web Texts

Texts obtained from web are noisy and do not necessarily follow the orthographic sentence and word boundary rules. Thus, sentence segmentation and word tokenization systems that have been developed on well-formed texts might not perform so well on unedited web texts. In this paper, we first describe the manual annotation of sentence boundaries of an Estonian web dataset and then present the evaluation results of three existing sentence segmentation and word tokenization systems on this corpus: EstNLTK, Stanza and UDPipe. While EstNLTK obtains the highest performance compared to other systems on sentence segmentation on this dataset, the sentence segmentation performance of Stanza and UDPipe remains well below the results obtained on the more well-formed Estonian UD test set.
Enhanced ion–cavity coupling through cavity cooling in the strong coupling regime

Incorporating optical cavities in ion traps is becoming increasingly important in the development of photonic quantum networks. However, the presence of the cavity can hamper efficient laser cooling of ions because of geometric constraints that the cavity imposes and an unfavourable Purcell effect that can modify the cooling dynamics substantially. On the other hand the coupling of the ion to the cavity can also be exploited to provide a mechanism to efficiently cool the ion. In this paper we demonstrate experimentally how cavity cooling can be implemented to improve the localisation of the ion and thus its coupling to the cavity. By using cavity cooling we obtain an enhanced ion-cavity coupling of $2\pi \times (16.7\pm 0.1)$ MHz, compared with $2\pi \times (15.2\pm 0.1)$ MHz when using only Doppler cooling.
Cross-Layer Protocol Fingerprint for Large-Scale Fine-Grain Devices Identification

Internet-connected Internet of Things (IoT) devices are exploding, which pose a significant threat for their management and security protection. IoT device identification is a prerequisite for discovering, monitoring, and protecting these devices. Although the existing proactive identification methods based on protocol fingerprint can discover and identify large-scale IoT devices, the fingerprint granularity is difficult to meet the requirements of security risk assessment for large-scale IoT devices. Since IoT devices usually support multiple network protocols for specific collection and control tasks, we propose a cross-layer protocol fingerprint to achieve large-scale fine-grained devices identification instead of traditional single protocol fingerprint. We first design a probing scheme for gathering HTTP and TCP cross-layer packets. Then we select the specific field of the HTTP and TCP protocols based on the diversity and consistence of field value. Finally, we utilize convolutional neural network (CNN) and long-term memory network (LSTM) to extract and construct feature fingerprint of these specific fields, and achieve a fine-grain IoT devices identification with high accuracy. The experimental results show that our identification accuracy of devices model reaches 96.6%, the recall rate reaches 97.4%.
k-Means+++: Outliers-Resistant Clustering

The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a provably small approximation error in time that is linear in n. We generalize k-means++ to support outliers in two sense (simultaneously): (i) nonmetric spaces, e.g., M-estimators, where the distance dist(p,x) between a point p and a center x is replaced by mindist(p,x),c for an appropriate constant c that may depend on the scale of the input. (ii) k-means clustering with m≥1 outliers, i.e., where the m farthest points from any given k centers are excluded from the total sum of distances. This is by using a simple reduction to the (k+m)-means clustering (with no outliers).
Contextualizing Support Vector Machine Predictions

Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to.Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context.A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization.
The Implication of Employing Magnetic Test Kit on Students' Interest, Teachers' Teaching Method and Students' Visualization Ability

Education is an ongoing process and is constantly accepting changes and improvement in line with the needs and wants of the community.The 21st -century world is seeing the use of multimedia and information technology monopolizing the lives of the general public with the motto of a borderless world.One of the most popular developments today is Augmented Reality (AR) technology that uses barcode scanners as a medium for learning.This study aims to find out students' interest in learning, teachers' teaching method, and students' visualization ability in welding technology subjects.The quantitative study through a survey method was used in this study.The study population involved 105 welding technology students of Batu Pahat Vocational College.The purposive sampling consists of 30 respondents of 3rd-year students who are taking the Non-Destructive Test (NDT) welding subject under the topic of magnetic particle test.The instrument used was a questionnaire.Data were collected and analyzed descriptively.The finding shows that the Cronbach Alpha value is 0.73 which indicates that the instrument's reliability is high.The results of this study found that by using magnetic particle test teaching kits can increase students' interest in learning (M=4.76),positively affect the teachers' teaching method (M=4.72), and helped to enhance students' visualization ability (M=4.80).Thus, the findings indicate that the use of this teaching kit improves the teaching and learning process either in the classroom or in the workshop.
SPECIAL FEATURES OF THE EDUCATIONAL COMPONENT DESIGN OF DEVICES ON MICROCONTROLLERS AND FPGA

Questions of features of the distribution and the implementation of the educational component "Design of devices on microcontrollers and field programmed logical gate arrays" with the support of all stakeholders' requirements to the latest technical knowledge are considered.Structural and technical description of the educational component is discussed.