Reviewerly/exHarmony huggingface dataset:
Object Classification using SVM and KD-Tree

In this proposed work, we presented a system to classify the object. Firstly, the given images are segmented using Region merging Segmentation method. Later the background eliminated images are divided into number of blocks viz., 4, 16, 32. The features like Scale Invariant Feature Transform (SIFT) and Histogram of Gradients (HOG) are extracted from divided blocks of size 4, 16, 32. To measure the strength of proposed method we compare the Classification vs Retrieval using Support Vector Machine and KD Tree. We conducted the experimentation on Caltech 101 data set. To study the effect of accuracy in classification we pick images from database randomly. The Performance revels that the SVM achieves good performance.
On the Analysis of the Outsourced Revocable Identity-Based Encryption from Lattices

For identity-based encryption (IBE) with identity revocation, or simply revocable IBE (R-IBE), an indirect revocation method in which a trusted center (i.e., private key generator, PKG) initially generates all users’ long-term private keys and periodically issues time update keys for non-revoked users seems to be a flexible choice, because it invites a sender to generate ciphertexts without caring about revoked (and non-revocked) users. However, these computation and communication overheads in frequent time keys update operations remain as a daunting task for PKG. In order to alleviate the offload of PKG and improve its scalability in the quantum computers attack environment, Dong et al. recently extended the concept of R-IBE to support outsourcing computation with a semi-trusted key update cloud service provider (KU-CSP), and proposed an outsourced revocable lattice-based IBE (OR-IBE) scheme.In this work, we show that the OR-IBE scheme of Dong et al. does not satisfy the correctness property of OR-IBE, meanwhile, it is not decryption key exposure resistance (DKER), a default security requirement for R-IBE. In addition, we provide a modification of their construction to be a correct and secure OR-IBE scheme. In particular, the first lattice-based OR-IBE scheme with DKER is introduced.
Phase-Retrieval Coherent Detection with On-Chip Photonic Linear Processing

We propose phase-retrieval coherent detection with hybrid optical and digital signal processing. By using an on-chip photonic convolutional processor and digital postprocessing, 20-GBaud QPSK signals were recovered from intensity-only measurement.
Photovoltaic Power Prediction of BP Neural Network Based on Singular Spectrum Analysis

Aiming at the intermittent and fluctuating characteristics of photovoltaic power output, a BP neural network photovoltaic prediction model based on singular spectrum analysis is proposed. This method combines SSA and correlation analysis techniques, which can effectively improve the prediction accuracy of the model. First, SSA technology is used to decompose the photovoltaic output time series into trend series, oscillation series and noise series, and then the noise series with less influence are removed, and the trend series and oscillation series are predicted by the BP neural network model that takes into account similar days. Finally, the actual data in a certain area is taken as an example, and the prediction error analysis proves that the proposed method has higher prediction accuracy.
Testing matrix product states

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Testing matrix product statesMehdi Soleimanifar and John WrightMehdi Soleimanifar and John Wrightpp.1679 - 1701Chapter DOI:https://doi.org/10.1137/1.9781611977073.68PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Matrix product states (MPS) are a class of physically-relevant quantum states which arise in the study of quantum many-body systems. A quantum state comprised of n qudits is said to be an MPS of bond dimension r if the reduced density matrix ψ1, …, k has rank r for each k ∊ {1, …, n}. When r = 1, this corresponds to the set of product states, i.e. states of the form |ψ1〉 ⊗ ⃛ ⊗ |ψn), which possess no entanglement. For larger values of r, this yields a more expressive class of quantum states, which are allowed to possess limited amounts of entanglement. Devising schemes for testing the amount of entanglement in quantum systems has played a crucial role in quantum computing and information theory. In this work, we study the problem of testing whether an unknown state |ψ〉 is an MPS in the property testing model. In this model, one is given m identical copies of |ψ〉, and the goal is to determine whether |ψ〉 is an MPS of bond dimension r or whether |ψ〉 is far from all such states. For the case of product states, we study the product test, a simple two-copy test previously analyzed by Harrow and Montanaro [17], and a key ingredient in their proof that QMA(2) = QMA(k) for k ≥ 2. We give a new and simpler analysis of the product test which achieves an optimal bound for a wide range of parameters, answering open problems in [17] and [23]. For the case of r ≥ 2, we give an efficient algorithm for testing whether |ψ〉 is an MPS of bond dimension r using m = O(nr2) copies, independent of the dimensions of the qudits, and we show that Ω(n1/2) copies are necessary for this task. This lower bound shows that a dependence on the number of qudits n is necessary, in sharp contrast to the case of product states where a constant number of copies suffices. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Application-Specific Photonic Integrated Circuit

We introduce an application-specific photonic integrated circuit that can solve partial differential equation using finite difference mesh. By using the solution from this photonic circuit as an initial condition, we can reduce the iteration number of finer solvers by 25%.
From Off-Screen and Online: Hybrid Programming for Any Situation

P rior to the pandemic, play was synonymous with our library. As Fred Rogers said, “It’s the things we play with and the people who help us play that make a great difference in our lives.” As a Family Place Library, we focus on the importance of building connections among caregivers, children, and our community, particularly through play-based experiences. Some programs adapted quickly to virtual (like storytimes), while others took more thought.
Global Contextual Dependency Network for Object Detection

Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpful to recognize objects with featureless appearances and restrain false detections. To tackle the problem, a simple framework, named Global Contextual Dependency Network (GCDN), is presented to enhance the classification ability of two-stage detectors. Our GCDN mainly consists of two components, Context Representation Module (CRM) and Context Dependency Module (CDM). Specifically, a CRM is proposed to construct multi-scale context representations. With CRM, contextual information can be fully explored at different scales. Moreover, the CDM is designed to capture global contextual dependencies. Our GCDN includes multiple CDMs. Each CDM utilizes local Region of Interest (RoI) features and single-scale context representation to generate single-scale contextual RoI features via the attention mechanism. Finally, the contextual RoI features generated by parallel CDMs independently are combined with the original RoI features to help classification. Experiments on MS-COCO 2017 benchmark dataset show that our approach brings continuous improvements for two-stage detectors.
The Benefits and Challenges of Group Discussion in Syntax Project

In the frame of qualitative research, this study aims to know the benefits and challenges of group discussion in Syntax project for students in EFL context in accomplishing Syntax project. The participants of this study consist of 25 students from the students of Syntax class in one of the higher educations in Kendari. The qualitative data were analyzed by questionnaire and students’ interview. The results of the research revealed that the students’ responded positively to the group discussion activity. It indicated this activity can improve students’ confidence and communication skills. Nevertheless, the students found it challenging in terms of the time allocation to finish the project as they cannot manage their time to finish the project well
Sentiment Analysis using Rapid Miner

Now a day the data grows day by day so data mining replaced by big data. Under data mining, Text mining is one of the processes of deriving structured or quality information or data from text document. It helps to business for finding valuable knowledge. Sentiment analysis is one of the applications in text mining. In sentiment analysis, determine the emotional tone under the text. It is the major task of natural language processing. The objective of this paper to categorize the document in sentence level and review level, and classification techniques applied on the dataset (electronic product data). There is an ensemble number of classification techniques applied on the dataset. Then compare each techniques, based on various parameters and find out which one is best. According to that give better suggestions to the company for improving the product.
KI-HABS: Key Information Guided Hierarchical Abstractive Summarization

With the unprecedented growth of textual information on the Internet, an efficient automatic summarization system has become an urgent need.Recently, the neural network models based on the encoder-decoder with an attention mechanism have demonstrated powerful capabilities in the sentence summarization task.However, for paragraphs or longer document summarization, these models fail to mine the core information in the input text, which leads to information loss and repetitions.In this paper, we propose an abstractive document summarization method by applying guidance signals of key sentences to the encoder based on the hierarchical encoder-decoder architecture, denoted as KI-HABS.Specifically, we first train an extractor to extract key sentences in the input document by the hierarchical bidirectional GRU.Then, we encode the key sentences to the key information representation in the sentence level.Finally, we adopt key information representation guided selective encoding strategies to filter source information, which establishes a connection between the key sentences and the document.We use the CNN/Daily Mail and Gigaword datasets to evaluate our model.The experimental results demonstrate that our method generates more informative and concise summaries, achieving better performance than the competitive models.
Composition of binary quadratic forms over number fields

Abstract In this article, the standard correspondence between the ideal class group of a quadratic number field and the equivalence classes of binary quadratic forms of given discriminant is generalized to any base number field of narrow class number one. The article contains an explicit description of the correspondence. In the case of totally negative discriminants, equivalent conditions are given for a binary quadratic form to be totally positive definite.
Cambridge Analytica

The Cambridge Analytica (CA) scandal is a case of large scale, transnational data misuse, deceptive practices, and privacy breaches. The scandal led to industry-shaking legal challenges resulting in an eventual liquidation of the company, following revelations of its data practices, combining addressable adtech, political campaigning, and psychological profiling, on individuals in relations to democratic processes. An exercise of data subject's rights under the UK Data Protection Act of 1998 (DPA 98) set important precedents related to cross-border jurisdiction with the UK data protection authority prosecuting actions on behalf of US voters. This chapter details findings from the UK Information Commissioners' Office (ICO) cumulative reports on the CA/SCL data forensics, among other primary sources, revealing how CA applied adtech to shape US electorate opinion leading up to the US presidential election in 2016.
Sound Can Help Us See More Clearly

In the field of video action classification, existing network frameworks often only use video frames as input. When the object involved in the action does not appear in a prominent position in the video frame, the network cannot accurately classify it. We introduce a new neural network structure that uses sound to assist in processing such tasks. The original sound wave is converted into sound texture as the input of the network. Furthermore, in order to use the rich modal information (images and sound) in the video, we designed and used a two-stream frame. In this work, we assume that sound data can be used to solve motion recognition tasks. To demonstrate this, we designed a neural network based on sound texture to perform video action classification tasks. Then, we fuse this network with a deep neural network that uses continuous video frames to construct a two-stream network, which is called A-IN. Finally, in the kinetics dataset, we use our proposed A-IN to compare with the image-only network. The experimental results show that the recognition accuracy of the two-stream neural network model with uesed sound data features is increased by 7.6% compared with the network using video frames. This proves that the rational use of the rich information in the video can improve the classification effect.
Joint projection learning and structured analysis-synthesis dictionary pair learning for pattern classification

Analysis-synthesis dictionary pair learning has attracted much attention in the field of pattern classification. To reduce the negative effect of trivial information contained in raw training samples and improve the computation efficiency, most existing dictionary pair learning methods first learn a projection matrix to project raw training samples into a low-dimensional subspace and then use the dimensionality-reduced samples for dictionary pair learning. However, the separation of projection learning (PL) and dictionary pair learning may make the dimensionality-reduced training samples not fit well for dictionary pair learning. To address this issue, we proposed a joint PL and structured analysis-synthesis dictionary pair learning (PLSDPL) method for pattern classification. Specifically, PLSDPL integrates PL and dictionary pair learning into a unified framework, so the dimensionality-reduced training samples are more suitable for the structured analysis-synthesis dictionary pair learning, thus making the learned analysis-synthesis dictionary pair have powerful discrimination capability. In addition, PLSDPL also imposes a low-rank constraint on each synthesis sub-dictionary to reduce the negative effect of noise contained in training samples. Experimental results on several image datasets show that PLSDPL is effective for pattern classification tasks.
Neural Network Based Filtering Method for Cancer Detection

Objective: The growth of anomalous cells in the human body in an uncontrolled manner is characterized as cancer. The detection of cancer is a multi-stage process in the clinical examination. Methods: It is mainly involved with the assistance of radiological imaging. The imaging technique is used to identify the spread of cancer in the human body. This imaging-based detection can be improved by incorporating the Image Processing methodologies. In image processing, the preprocessing is applied at the lower-level abstraction. It removes the unwanted noise pixel present in the image, which also distributes the pixel values based on the specific distribution method. Results: Neural Network is a learning and processing engine, which is mainly used to create cognitive intelligence in various domains. In this work, the Neural Network (NN) based filtering approach is developed to improve the preprocessing operation in the cancer detection process. Conclusion: The performance of the proposed filtering method is compared with the existing linear and non-linear filters in terms of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF).
PENGARUH PENDAMPINGAN BELAJAR ORANG TUA TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS V SD NEGERI SEKECAMATAN BUAYAN KABUPATEN KEBUMEN TAHUN AJARAN 2020/2021

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;The study aimed to determine the positive effect of parental assistance on mathematics learning outcomes to fifth grade students of public elementary schools in Buayan Sub-district in academic year of 2020/2021 and to measure the contribution of parental assistance on mathematics learning outcomes to fifth grade students of public elementary schools in Buayan Sub-district in academic year of 2020/2021. The research was a quantitative research with simple linear regression method. The samples were 306 fifth grade students in ten public elementary schools throughout Buayan Sub-district. Sampling method was probability sampling technique. The results showed that: (1) there was a positive and significant effect of parental assistance on mathematics learning outcomes to fifth grade students of public elementary schools in Buayan Sub-district and regression was 0.266; (2) the parental assistance contributed 26.6% on mathematics learning outcomes to fifth grade students of public elementary schools in Buayan Sub-district in academic year of 2020/2021 and the remaining 73.4% was influenced by other factors, such as: interests, talents, intelligence, attention, motivation, perseverance, school environment, and community environment. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: parental assistance, mathematics learning outcomes&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Abstrak&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Penelitian ini bertujuan untuk: (1) mengetahui adanya pengaruh positif pendampingan belajar orang tua terhadap hasil belajar matematika siswa kelas V SD Negeri se-Kecamatan Buayan pada Tahun Ajaran 2020/2021; (2) mengetahui besarnya sumbangan pendampingan belajar orang tua terhadap hasil belajar matematika siswa kelas V SD Negeri se-Kecamatan Buayan pada Tahun Ajaran 2020/2021. Penelitian ini merupakan penelitian kuantitatif dengan metode regresi linier sederhana dengan sampel penelitiannya yaitu 306 siswa kelas V SD di sepuluh SDN Se-Kecamatan Buayan yang dipilih secara acak dengan teknik &lt;em&gt;probability sampling&lt;/em&gt;. Hasil penelitian menunjukkan bahwa (1) terdapat pengaruh positif dan signifikan pendampingan belajar orang tua terhadap hasil belajar matematika siswa kelas V SDN se-Kecamatan Buayan dengan regresi sebesar 0,266; (2) sumbangan pendampingan belajar orang tua terhadap hasil belajar matematika siswa kelas V SDN se-Kecamatan Buayan tahun ajaran 2020/2021 sebesar 26,6% dan sisanya 73,4% dipengaruhi oleh faktor lain, seperti: minat, bakat, kecerdasan, perhatian, motivasi, ketekunan, lingkungan sekolah, dan lingkungan masyarakat.&lt;/p&gt;&lt;strong&gt;Kata Kunci&lt;/strong&gt;: Pendampingan Belajar, Hasil Belajar Matematika
Correctness of Automatically Generated Choreography Specifications

The service choreography approach has been proposed for the declarative specification of multi-party conversations between participant services, in service-oriented applications and web transactions. Constraint solvers such as Alloy Analyzer can be used for the automated generation and verification of declarative choreography specifications. This presumes a mapping between the declarative specification of business rules in Semantics of Business Vocabulary and Rules (SBVR), an OMG standard for specifying business models in structured English, and the Alloy Analyzer which is a SAT based constraint solver. This paper is concerned with the correctness of such mapping between the generated instance (choreography) in Alloy and the global graph obtained as a direct visual representation of the SBVR model specification.
Modulation Recognition Algorithm Based On Partial Domain Adaptation

Aiming at the fact that the intelligent modulation recognition algorithm cannot adapt to the test data (target domain) that is inconsistent with the distribution of the train data (source domain), especially when the modulation signals types of the source domain and the target domain are different (the types of modulation signals in the target domain is a proper subset of the types of modulation signals in the source domain), the recognition performance deteriorates sharply, we propose a Class Weighted Domain adversarial Neural Network-based Partial Domain Adaptation Modulation Recognition (CWDA-MR) algorithm. We use the domain adversarial neural network as the backbone network, and applying class weights to the loss functions of the label predictor and the domain discriminator to improve the modulation recognition algorithm performance when the modulation signals source domain and target domain data distribution and modulation types are all different. Simulation experiments have verified its effectiveness and reliability. Compared with ordinary domain adversarial neural network-based modulation recognition algorithms, the accuracy of the modulation recognition algorithm is increased by 37.12%. Compared with training on the source domain and direct testing on the target domain, the accuracy is increased by 48.39%.
Push

tutorial Public Access Share on Push Author: Lee Spector UMass Amherst UMass AmherstView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 1119–1134https://doi.org/10.1145/3449726.3461401Published:08 July 2021Publication History 0citation44DownloadsMetricsTotal Citations0Total Downloads44Last 12 Months19Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Methods for attracting foreign students of regional universities of Russia in the context of internationalization of Higher education

Nowadays the study of the analysis of internationalization of higher education in Russian universities become relevant in context of the Federal project “Export of Education”. The purpose of this analysis is to determine the level of internationalization of universities in the Ural and Siberia regions and to identify the most effective methods for attracting foreign students, taking into account regional peculiarities. The empirical basis of the analysis is a sociological survey of the heads of international services of universities of the Ural and Siberia regions. According to the results of the analysis, it is concluded that the activity of the international office of the university is an important component of the international strategy of the university. The pandemic made an impact to the processes of recruiting and transformation of methods for attracting foreign students: there is a slight decrease in the number of foreign students, the main reason was the closure of state boarders. However, universities which are situated in regions were able to transfer part of the educational services and provide them online, which allowed them to save the contingent and not lose income from international activities. The analysis revealed the influence of the regional component on the level of internationalization of the university, as well as the most effective methods for attracting foreign students in regional universities of the Urals and Siberia.