Reviewerly/exHarmony huggingface dataset:
Attentive Siamese Networks for Automatic Modulation Classification Based on Multitiming Constellation Diagrams

Automatic modulation classification (AMC) is an essential part in a cognitive radio receiver. Benefited from the discriminative constellation characteristics among most modulations, AMC methods based on constellation diagrams usually achieve pleasant performance. However, in noncooperation communication systems, constellation diagrams expressing modulations explicitly are difficult to obtain via blind symbol timing synchronization, especially in complicated wireless channels. Therefore, this article proposes a novel constellation diagram-based AMC architecture called attentive Siamese networks (ASNs) by considering multitiming constellation diagrams (MCDs) and selecting the proper symbol timings at the feature level, which is a more robust way than the conventional signal-level symbol timing synchronization. In detail, convolutional neural networks sharing the same parameters first extract deep feature vectors for MCDs. Then, an attention inference module weights all the deep feature vectors. Finally, AMC is realized based on the weighted feature vectors. Moreover, the ASN architecture can be trained end-to-end. Comparing with the state-of-the-art methods that take diverse representations of received baseband signals as input, experimental results based on the RadioML 2018.01A dataset and non-Gaussian noise dataset demonstrate that ASN achieves a remarkable improvement, whose classification accuracy goes over 99% when the signal-to-noise ratio (SNR) > 10 dB.
Decoupled R-CNN: Sensitivity-Specific Detector for Higher Accurate Localization

Object detection, as a fundamental problem in computer vision, has been widely used in many industrial applications, such as intelligent manufacturing and intelligent video surveillance. In this work, we find that classification and regression have different sensitivities to the object translation, from the investigation about the availability of highly overlapping proposals. More specifically, the regressor head has intrinsic characteristics of higher sensitivity to translation than the classifier. Based on it, we propose a decoupled sampling strategy for a deep detector, named Decoupled R-CNN, to decouple the proposals sampling for the two tasks, which induces two sensitivity-specific heads. Furthermore, we adopt the cascaded structure for the single regressor head of Decoupled R-CNN, which is an extremely simple but highly effective way of improving the performance of object detection. Extensive empirical analyses using real-world datasets demonstrate the value of the proposed method when compared with the state-of-the-art models. The reproducing code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shouwangzhe134/Decoupled-R-CNN</uri> .
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation

The recent large-scale vision-language pretraining (VLP) of dual-stream architectures (e.g., CLIP) with a tremendous amount of image-text pair data, has shown its superiority on various multimodal alignment tasks.Despite its success, the resulting models are not capable of multimodal generative tasks due to the weak text encoder.To tackle this problem, we propose to augment the dual-stream VLP model with a textual pre-trained language model (PLM) via vision-language knowledge distillation (VLKD), enabling the capability for multimodal generation.VLKD is pretty data-and computation-efficient compared to the pre-training from scratch.Experimental results show that the resulting model has strong zero-shot performance on multimodal generation tasks, such as open-ended visual question answering and image captioning.For example, it achieves 44.5% zero-shot accuracy on the VQAv2 dataset, surpassing the previous state-of-the-art zero-shot model with 7× fewer parameters.Furthermore, the original textual language understanding and generation ability of the PLM is maintained after VLKD, which makes our model versatile for both multimodal and unimodal tasks.
Nearly Optimal Quantum Algorithm for Estimating Multiple Expectation Values

Many quantum algorithms involve the evaluation of expectation values. Optimal strategies for estimating a single expectation value are known, requiring a number of state preparations that scales with the target error $\varepsilon$ as $\mathcal{O}(1/\varepsilon)$. In this paper, we address the task of estimating the expectation values of $M$ different observables, each to within additive error $\varepsilon$, with the same $1/\varepsilon$ dependence. We describe an approach that leverages Gily\'en et al.'s quantum gradient estimation algorithm to achieve $\mathcal{O}(\sqrt{M}/\varepsilon)$ scaling up to logarithmic factors, regardless of the commutation properties of the $M$ observables. We prove that this scaling is worst-case optimal in the high-precision regime if the state preparation is treated as a black box, even when the operators are mutually commuting. We highlight the flexibility of our approach by presenting several generalizations, including a strategy for accelerating the estimation of a collection of dynamic correlation functions.
ViDMASK dataset for face mask detection with social distance measurement

The COVID-19 outbreak has extenuated the need for a monitoring system that can monitor face mask adherence and social distancing with the use of AI. With the existing video surveillance systems as base, a deep learning model is proposed for mask detection and social distance measurement. State-of-the-art object detection and recognition models such as Mask RCNN, YOLOv4, YOLOv5, and YOLOR were trained for mask detection and evaluated on the existing datasets and on a newly proposed video mask detection dataset the ViDMASK. The obtained results achieved a comparatively high mean average precision of 92.4% for YOLOR. After mask detection, the distance between people's faces is measured for high risk and low risk distance. Furthermore, the new large-scale mask dataset from videos named ViDMASK diversifies the subjects in terms of pose, environment, quality of image, and versatile subject characteristics, producing a challenging dataset. The tested models succeed in detecting the face masks with high performance on the existing dataset, MOXA. However, with the VIDMASK dataset, the performance of most models are less accurate because of the complexity of the dataset and the number of people in each scene. The link to ViDMask dataset and the base codes are available at https://github.com/ViDMask/VidMask-code.git.
Genetic and static algorithm for task scheduling in cloud computing

Technological advancement has required ever more computing resources. In this context the cloud computing emerges as a new paradigm to meet this demand, though its resources are physically limited due to the growing data traffic that the system may be subject. The task scheduling aims to distribute tasks in order to make them more efficient in the use of computing resources. Thus, this paper aims to propose a solution to the task scheduling problem in cloud computing to reduce the processing time of the tasks and the number of virtual machines (VM). The metaheuristic genetic algorithm (GA) was used in the first stage of the algorithm, in order to reduce the processing time of the tasks. The static algorithm is designed to solve the set partitioning problem. Their performance was compared with two algorithms, classic and heuristic, along with realistic workloads.
Application of natural language processing algorithms for extracting information from news articles in event-based surveillance

The focus of this article is the application of natural language processing (NLP) for information extraction in event-based surveillance (EBS) systems.We describe common information extraction applications from open-source news articles and media sources in EBS systems, methods, value in public health, challenges and emerging developments.
Face Recognition Using HOG Feature Extraction and SVM Classifier

The intelligent interaction between the human and the computer is of vital importance to reach the primary and contemporary goals of artificial intelligence.Digital imaging in life and in common society and in science and technology has become completely digital and this transformation of visual imagination into mathematical algorithms and numerical developments is significant for researchers to use it in everyday digitized world we live today.Numerous algorithms are developed on face recognition techniques particularly in the last two to three decades.The LBP and HOG feature extraction algorithms are preferable for real time applications.HOG feature extraction technique has been used in this paper for person detection using image analysis.The PCA an important feature method in Eigen faces method is today an important brainwave for almost all the face recognition algorithms unfolded into new and better programming methods.
Determinants of cloud ERP adoption in Jordan: an exploratory study

Enterprise resource planning (ERP) systems are usually adopted through two major options: cloud computing or on-premise infrastructure. The issues related to hardware, servers, implementation costs, and facilities, which are necessary to run on-premise ERP systems, are rather great for small and mid-sized enterprises (SMEs). The purpose of this research is to highlight the factors influencing cloud ERP adoption among SMEs. The technological, organisational, and environmental (TOE) model will be used as a theoretical base. This qualitative, exploratory study gathers data from 13 different SMEs and cloud ERP service providers through semi-structured interviews, focusing on SMEs in Jordan. According to our results, support from both service providers and top management within the company play an important role in whether a firm adopts cloud ERP services, in addition to a number of other factors at varying rates. Research findings can be used by service providers and business owners to enhance their approach to cloud ERP by showing the reasons why some SMEs chose to adopt this technology and others did not. Cloud ERP providers need to intensify their efforts to build a progressive environment for their services that will eliminate any ambiguity regarding this type of technology.
Anew (Old?) Approach To Three Phase Circuits

Three phase circuits can be confusing unless we really look at them and see them in a different light.I have taken the laws of single phase and three phase circuits and developed a number of templates so that most three phase circuits can be looked at as single phase circuits.When the students learn to look at three phase circuits with the use of the templates, much of the confusion of solving for unknowns in three phase circuits goes away.It can be shown that almost every circuit can be seen as a group of single phase circuits.As a matter of fact, if the voltage sources are known quantities, most unknown voltages and currents can be found by the use of one or two equations.This paper shows examples of how these templates are utilized to solve for unknown quantities in four typical three phase circuits.Two other common circuits are also shown.Part of the reason for developing this approach is to help students understand how three phase circuits really work, and when and why to use the √3 factor.
Factors that Affect the implementation of Teaching Writing Skills: Focus on Second Cycle Primary Schools in North Shoa

The purpose of this study was to determine the variables affecting the implementation of writing skills instruction and learning in grades 7 and 8 in North Shoa Zone Second Cycle Primary schools. The study's primary purpose was to determine the difficulties encountered by English language instructors and students while teaching and learning to write in English classes. The research included the participation of 25 English language instructors and 1484 pupils from 14 Second Cycle Primary Schools. A questionnaire and an interview were used to obtain the required data. Questionnaires were completed by instructors and students, and English language teachers were interviewed. The questionnaire data was tallied and examined statistically in terms of frequency and percentage. Similarly, interview data were categorized and qualitatively characterized. The investigation revealed that the majority of instructors and students struggle with teaching and learning writing skills. Additionally, although both English instructors and students believe they are engaging in writing activities in the classroom, the students' writing performance is not as promising as shown by open-ended questions and comments from English language teachers interviewed. Several key suggestions are made in light of the results of this research in order to mitigate the issues that impede the efficient implementation of writing instruction and learning in second cycle primary schools. The primary recommendation is to offer enough training for English language instructors on how to teach writing skills, which will then be conveyed to students in order for them to enhance their writing performance.
Determining the Major Policy Issues Regarding the Smart Schooling System Using the Analytical Hierarchy Process Method

Korea is one of the leading countries across the world to have promoted education through ICT using smart school practices. Using the Analytical Hierarchy Process (AHP) method, this paper aims to identify the determinants affecting the success of the smart schooling system in Korea; prioritise the indicators necessary for the success of the smart schooling system in terms of weight; and provide policy implications for policymakers and practitioners involved in smart school and education policy. The indicator teacher dedication has the greatest weight score, followed by attitudes toward e-learning and IT, meaning that the individual involved in the smart schooling system's activities should be viewed as more significant than any other indication.
A Method of Experts’ Knowledge Approval for Industrial Safety Expertise Task

The paper discusses a conception of a method of experts ' knowledge (opinions) approval for multidisciplinary tasks of industrial safety expertise of critical petrochemical objects.The proposed method is based on ontological models of a problem domain, as well as group decision-making methods.The main results of method are a model of a multidisciplinary task and its solution which are agreed by experts of various subject areas.The task model describes the concepts, laws of a subject area and solving methods.It is proposed to use the ARAMIS (Aggregation and Ranking Alternatives nearby the Multi-Attribute Ideal Situation) method to agreed experts' opinions.Also the paper presents examples of a model description for an identification task of technical states, and approval of experts' opinions at formalization of knowledge for a rule-based expert system intended for this task.
Connecting Ohio Universities with Industry to Drive Innovation, A Digital Science Case Study

In this case study The Ohio Innovation Exchange (OIEx) demonstrate the interoperability of Digital Science products across their research cycle. Using Digital Science tools, members of the OIEx are able to find experts and collaborate across multiple universities in the state of Ohio. Products include Altmetric, Dimensions, Figshare, Symplectic, ReadCube and Digital Science Consultancy.
Two 58 Watt 18 Inch Architectural Gooseneck LED Light Fixtures on Double Arm

Highly versatile 58 Watt, 18 Inch diameter gooseneck LED light fixture with single arm features a full cutoff making it dark sky friendly.
Evaluation Model of Node Trust in Ship Wireless Sensor Networks

Niu, J., 2019. Evaluation model of node trust in ship wireless sensor networks. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 729–734. Coconut Creek (Florida), ISSN 0749-0208.In order to improve that management and location capability of the ship's wireless sensor network, a network node location algorithm and a trust evaluation model of the ship's wireless sensor network are proposed. The invention improves the accuracy of the node data packet forwarding of the wireless sensor network of the ship, and performs the self-adaptive distributed deployment design of the trust node in the ship wireless sensor network, and the random link forwarding protocol is adopted to construct a routing detection model of the ship wireless sensor network, the self-adaptive forwarding control of the network trust node and the characteristic detection of the output data packet are carried out by adopting a decentralized block chain reconstruction method, the association rule information of the output data stream of the ship wireless sensor network node is extracted. The trust evaluation and adaptive control of the wireless sensor network node of the ship are carried out in combination with the large-data information fusion method, and the trust evaluation of the wireless sensor network node of the ship is realized. The simulation results show that the accuracy of the network node trust evaluation of the ship wireless sensor is high, the adaptability is good, and the accuracy of the packet forwarding of the network trust node of the ship wireless sensor is improved.
UPAYA MENINGKATKAN HASIL BELAJAR PKN MELALUI MODEL PEMBELAJARAN KOOPERATIF TIPE THINK PAIR SHARE (TPS) PESERTA DIDIK KELAS VII.8 SMP NEGERI 16 PALEMBANG

The purpose to be achieved in this study is to improve student learning outcomes in PKn lessons in class VII.8 students of SMP Negeri 16 Palembang by using a cooperative model type Think Pair Share (TPS). So the problem in this research is how to increase the learning outcomes of students in class VII.8 of SMP Negeri 16 Palembang through a Think Pair Share learning model? Based on the results of the research stated in the previous chapter, the conclusions that can be taken in this study are the cooperative learning type Think Pair Share (TPS) model which has a positive impact on improving PKn learning outcomes of students in class VII.8 Palembang State Middle School 18 increased learning completeness of students in each cycle, namely pre-cycle, cycle I and cycle II respectively 46.15%, 69.23% and 88.46%. The application of the cooperative learning model of the Think Pair Share (TPS) type has a positive influence, which can improve student learning outcomes as indicated by the average answers of students who state that students are interested and interested in the cooperative learning model type Think Pair Share (TPS) so they become motivated to learn.
in tensions, for dancer, cello, and motion-sensitive live electronics

Author(s): Rubin, Scott | Advisor(s): Ueno, Ken | Abstract: This paper is an account of the creative process that resulted in the dissertation for a PhD in music composition, in tensions, for dancer, cello, and motion-sensitive live electronics. The commentary is provided as a supplement to the performance that forms the primary work carried out for the dissertation. in tensions, written in collaboration with cellist Polina Streltsova and dancer Marie Albert in Paris as part of the Cursus program at the Institute for Research and Collaboration in Acoustics/Music, is an audio-visual performance that explores interdisciplinary interaction in analog and digital realms. The work interrogates the relationships between the two performers, their environment, and their roles in the performance space. The work’s invention used a variety of tools crafted to facilitate the creation of performative material, as well as digital solutions for composing electroacoustic sounds that aim to communicate the fusion of these materials in real time. This dissertation will analyze the methods and performance practices that drove the collaboration, the structure of the work, and the materials that comprise the work’s electroacoustic elements. Audio-visual documentation of the performance can be found at: https://www.youtube.com/watch?v=6PVBuPrA0pA
Fast restoring of high dynamic range image appearance for multi-partial reset sensor

Improving image quality by capturing high dynamic range (HDR) in a scene remains a technological challenge for CMOS sensors. The multi partial reset is a simple technique that allows a sensor to capture HDR inexpensively with high frame rate, low noise floor, and high signal to noise ratio. However, it flattens images due to compression of highlights, rending it useful only for machine vision and automotive applications. In this work, we present an inverse function that restores HDR image appearance specifically for a multi partial reset sensor (MPRS). This function can be applied in software or firmware before demosaicing. Results show that the function automatically enliven images with more depth and saturation that suit general purpose photography. Moreover, latency results show that it can be applied for real-time videography of high frame rate. These results would be computationally much more expensive to achieve using general image enhancement techniques, i.e. not specific to MPRS, especially for high definition, high frame rate, real-time video.
Corrigendum: Effect of Probability Information on Bayesian Reasoning: A Study of Event-Related Potentials

[This corrects the article DOI: 10.3389/fpsyg.2019.01106.].
Penggunaan Model Pembelajaran Teams Games Tournament Dengan Media Ular Tangga Untuk Meningkatkan Keterampilan Berhitung Dan Sikap Kerja Sama Siswa Pada Materi Pecahan KELAS VA MIN 4 Sragen 2019/2020

Cornelia Ririn Agata. A510150010. USING TEAMS GAMES TOURNAMENT LEARNING MODELS WITH HOUSEHOLD MEDIA TO IMPROVE CALCULATION SKILLS AND ATTITUDE TO STUDENT COOPERATION AT CLASSES VA MIN 4 SRAGEN 2019/2020, Thesis. Surakarta: Faculty of Teacher Training and Education, University of Muhammadiyah Surakarta. 2019.This study aims to determine the improvement of teacher teaching skills, student activity, cooperative attitude and numeracy skills MIN 4 Sragen with Teams Games Tournament learning model with snake ladder media. The method applied is Classroom Action Research. This research was conducted in 2 cycles and in each cycle there were 4 stages namely: planning, action, savings, and reflection. The results showed an increase in teacher teaching skills seen from the average teaching skills of teachers in cycle I (2.95), and cycle II (3.41), and student activities (2.74), and (3.35), and work attitudes the same as students (58.35), and (79.14), while completing students' numeracy skills were 9 students (34.61%), and experienced an increase of 24 students (92.39%). While the average value of students' numeracy skills in the first cycle (53.85), and (70.10).
THE EFFECT OF USING CONTEXTUAL REDEFINITION STRATEGY ON STUDENTS� READING NARRATIVE TEXT OF ELEVENTH GRADE AT SMA N 8 MEDAN

This study aimed to see the result of students�s achievement in reading narrative text with applyaing Contextual Redefinition Strategy. The population of the study was the elevent grade students of SMA N 8 Medan on 2019/2020 with the total number is 180 students. Two classes were taken as the sample they were IX IPA 4 and IX IPA 5. The experimental group was taught by using Contextual Redefinition while the control group (IX IPA 5) was taught by using conventional method. The instrument of collecting data was multiple choice test. The data were analyzed by using t-test. Based on the data, it was found that the students� score improved from pre-test and post-test in experimental class. The result shoe that t-observed is higher that t-table value (2.9&gt; 1.671) at the level significance 0,05 and the degree of freedom is 58. Therefore, it can be concluded that the null hypothesis (Ho) is rejected and alternative hypothesis (Ha) ia accepted. Based on the research result, there is a significant effect of using Contextual redefinition strategy in eading Narrative test of Eleventh Grade Students� at SMA N 8 Medan.