Reviewerly/exHarmony huggingface dataset:
Audio Captcha Recognition Using RastaPLP Features by SVM

Nowadays, CAPTCHAs are computer generated tests that human can pass but current computer systems can not. They have common usage in various web services in order to be able to detect a human from computer programs autonomously. In this way, owners can protect their web services from bots. In addition to visual CAPTCHAs which consist of distorted images, mostly test images, that a user must write some description about that image, there are a significant amount of audio CAPTCHAs as well. Briefly, audio CAPTCHAs are sound files which consist of human sound under heavy noise where the speaker pronounces a bunch of digits consecutively. Generally, in those sound files, there are some periodic and non-periodic noises to get difficult to recognize them with a program but not for a human listener. We gathered numerous randomly collected audio file to train and then test them using our SVM algorithm to be able to extract digits out of each conversation.
Optimal Measurements for Quantum Fidelity between Gaussian States

Quantum fidelity is a measure to quantify the closeness of two quantum states. In an operational sense, it is defined as the minimal overlap between the probability distributions of measurement outcomes and the minimum is taken over all possible positive-operator valued measures (POVMs). Quantum fidelity has been investigated in various scientific fields, but the identification of associated optimal measurements has often been overlooked despite its great importance for practical purposes. We find here the optimal POVMs for quantum fidelity between multi-mode Gaussian states in a closed analytical form. Our general finding is specified for selected single-mode Gaussian states of particular interest and we identify three types of optimal measurements: a number-resolving detection, a projection on the eigenbasis of operator $\hat{x}\hat{p}+\hat{p}\hat{x}$, and a quadrature detection, each of which is applied to distinct types of single-mode Gaussian states. We also show the equivalence between optimal measurements for quantum fidelity and those for quantum parameter estimation, enabling one to easily find the optimal measurements for displacement, phase, squeezing, and loss parameter estimations using Gaussian states.
True Service Orientation with SORCER

Service-oriented Mogramming Language (SML) is designed for service-orientation as UML was considered for object-orientation. SML is also an executable language in the SORCER platform based on service abstraction (everything is a service) and three d of service-orientation: context awareness (contexting) , multifidelity , and multityping . Context awareness is related to parametric polymorphism, multifidelity is a form of ad hoc polymorphism, and multityping is a net-centric form of type polymorphism. SML allows for defining polymorphic service systems that can reconfigure and morph service collaborations at runtime to definite an emergent form with distinct constraints and heuristics. Here, emergence of service system refers to the appearance of higher-level properties and behaviors of collaborating service federations that come from the collective dynamics of that collaborating networked services and activity of service morphers that manage multifidelities at runtime. In this paper the basic concepts of SML with the three design patterns of service collaborations are presented. Its runtime environment is introduced with the focus on the three pillars of service-orientation.
Keyword Spotting in Scanned Images of Historical Handwritten Devanagri Documents

Huge quantity of information is lying quiescent in historical manuscripts.This information would go wasted if it is not stored digitally.In keyword spotting, all occurrences of a query keyword image are retrieved from scanned document images.The problem of spotting words from handwritten documents is difficult due to its huge changeability in writing styles and its large vocabulary.Existing keyword spotting approach is mainly based on statistical depiction of word image.This paper presents an efficient structural depiction of word image, where the handwritten words are represented using graph based method for historical handwritten devanagari manuscripts.Experimentation is conducted on historical handwritten Shankaracharya's documents written in Devanagari.The results were promising in terms of accuracy and efficiency.
Incremental Reading for Question Answering

Any system which performs goal-directed continual learning must not only learn incrementally but process and absorb information incrementally. Such a system also has to understand when its goals have been achieved. In this paper, we consider these issues in the context of question answering. Current state-of-the-art question answering models reason over an entire passage, not incrementally. As we will show, naive approaches to incremental reading, such as restriction to unidirectional language models in the model, perform poorly. We present extensions to the DocQA [2] model to allow incremental reading without loss of accuracy. The model also jointly learns to provide the best answer given the text that is seen so far and predict whether this best-so-far answer is sufficient.
Research on Voluntary Information Disclosure of Listed Companies in China

Voluntary disclosure of information is a matter for the company's management to decide on its own disclosure. However, because Chinese voluntary information disclosure system is not mature at present, there are some problems in voluntary information disclosure. This paper selects the top enterprises in hydropower, thermoelectricity, environmental protection engineering and services, and new energy power generation as samples to analyze the status quo and problems of voluntary information disclosure content and disclosure quality and proposes countermeasures.
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

The performance of the code generated by a compiler depends on the order in which the optimization passes are applied. In high-level synthesis, the quality of the generated circuit relates directly to the code generated by the front-end compiler. Choosing a good order--often referred to as the phase-ordering problem--is an NP-hard problem. In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. We implement a framework in the context of the LLVM compiler to optimize the ordering for HLS programs and compare the performance of deep reinforcement learning to state-of-the-art algorithms that address the phase-ordering problem. Overall, our framework runs one to two orders of magnitude faster than these algorithms, and achieves a 16% improvement in circuit performance over the -O3 compiler flag.
Professor Nikolay Kalenov, Director, Library for Natural Sciences of Russian Academy of Sciences

Director of the Library for Natural Sciences of Russian Academy of Sciences Prof. Nikolay Kalenov answers questions about himself, features of the library, and the function of its director.
Pengembangan Sistem Informasi Geografi Bencana Alam Pada Kabupaten Madiun Berbasis Web Menggunakan Metode Build and Fix

Development Natural Disaster Information Geografis System is a website service utilizing digital map service. To facilitate the Regional Disaster Management Agency (BPBD) and the Community of Madiun Regency in providing and receiving information about the occurrence of Natural Disasters in Madiun District, the need for the application of information systems. The information system can display the points where the Natural Disasters occur. With the Information System, everyone can see the location of the map of the disaster occurrence area along with the description of the area. in this study developed a "Natural Disaster Information System at Madiun district". System development using Build and Fix method, because it is considered fast in making the system. The built system can be expected to assist BPBD officers in performing their duties. So that the system built must have the ability to form information accurately the occurrence of natural disasters. Therefore, the disaster information system in Madiun district has been tested using blackbox method, with 100% system success rate. Keywords : Web, Madiun District, Natural Disaster, Information Geografis System, Build and Fix, Blackbox
A Comprehensive Performance Evaluation for 3D Transformation Estimation Techniques

3D local feature extraction and matching is the basis for solving many tasks in the area of computer vision, such as 3D registration, modeling, recognition and retrieval. However, this process commonly draws into false correspondences, due to noise, limited features, occlusion, incomplete surface and etc. In order to estimate accurate transformation based on these corrupted correspondences, numerous transformation estimation techniques have been proposed. However, the merits, demerits and appropriate application for these methods are unclear owing to that no comprehensive evaluation for the performance of these methods has been conducted. This paper evaluates eleven state-of-the-art transformation estimation proposals on both descriptor based and synthetic correspondences. On descriptor based correspondences, several evaluation items (including the performance on different datasets, robustness to different overlap ratios and the performance of these technique combined with Iterative Closest Point (ICP), different local features and LRF/A techniques) of these methods are tested on four popular datasets acquired with different devices. On synthetic correspondences, the robustness of these methods to varying percentages of correct correspondences (PCC) is evaluated. In addition, we also evaluate the efficiencies of these methods. Finally, the merits, demerits and application guidance of these tested transformation estimation methods are summarized.
Attributed Network Embedding via Subspace Discovery

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec.
Deep Learning Assisted Antenna Selection in Untrusted Relay Networks

This letter mainly studies the transmit antenna selection(TAS) based on deep learning (DL) scheme in untrusted relay networks. In previous work, we discover that machine learning (ML)-based antenna selection schemes have small performance degradation caused by complicated coupling relationship between achievable secrecy rate and the channel gains. To solve the issue, we here introduce deep neural network (DNN) to decouple the complicated relationship. The simulation results show the DNN scheme can achieve better decoupling and thus perform almost the same performance with conventional exhausted searching scheme.
Design of Informational Teaching Methods and Processes of Higher Vocational Curriculum—A Case Study of “Hotel Marketing” Course Unit

Teaching informationization is continuously promoted in the development of higher vocational education. Taking the â€œMarketing of Hotel WeChat Official Account â€œunit in the â€œHotel Marketingâ€ course as a case study, the design ideas, teaching contents, teaching methods and information technology are analyzed. Finally, the effect and enlightenment of information teaching are reflected.
Learning with Collaborative Neural Network Group by Reflection

For the present engineering of neural systems, the preparing of extensive scale learning undertakings generally not just requires a huge neural system with a mind boggling preparing process yet additionally troublesome discover a clarification for genuine applications. In this paper, we might want to present the Collaborative Neural Network Group (CNNG). CNNG is a progression of neural systems that work cooperatively to deal with various errands independently in a similar learning framework. It is advanced from a solitary neural system by reflection. Along these lines, in light of various circumstances removed by the calculation, the CNNG can perform diverse techniques when handling the information. The examples of chose methodology can be seen by human to make profound adapting more reasonable. In our execution, the CNNG is joined by a few moderately little neural systems. We give a progression of examinations to assess the execution of CNNG contrasted with other learning strategies. The CNNG is able to get a higher accuracy with a much lower training cost. We can reduce the error rate by 74.5% and reached the accuracy of 99.45% in MNIST with three feedforward networks (4 layers) in one training epoch.
Increasing Student Speaking Capability by Using Substitution and Convert Drills at State Senior High School Number 5 Kota Jambi

The aim of this research is to investigate and to know about the strategy of learning speaking in English teaching, because there are many problems on teaching English, namely facility, method, media, and competency of teacher, capability of teacher, willingness of teacher, management of head Master, commitment of teacher, and discipline of teacher and student’s competency. All the problems that previous above are very influence of English teaching quality, on particularly Learning Speaking, that is why the writer ( researcher ) eager to express of sharing idea, what happen on teaching and learning English, especially on Learning Speaking. Various strategies is very important on each teaching in order student does not feel bore to follow on learning English, Specially on Speaking, therefore in this opportunity the researcher introduce the strategy that used which is entitled “ Substitution and Convert Drills“.
ARCHAIN: A Novel Blockchain Based Archival System

In this paper, we propose a novel archival system called ARCHAIN, developed for the State archive-keeping committee of the Republic of Tatarstan (Russia). The blockchain is the primary part of the system, which stores transactions (facts of transfer of documents to the archive) in a protected form.
Sistem Pendukung Keputusan Pemilihan Atlet Panahan Menggunakan Metode Analitycal Hierarchy Process Berbasis Web

Decision Support System is a system was used to help getting making decisions on a problem, such as selecting students in the archery sports that will be face to a championship event such as SEA GAMES (South East Games). In Decision Support System there are many methods that can be used, one of them is Analitycal Hierarchy Process method. Analitycal Hierarchy Process method is one of the more efficient decision-making methods because the data is very detailed, all the criteria and alternatives will be compared one by one. The calculation used to solve a problem uses multiplication to relate the preference value, where the value for each criteria and alternative must be searched for eigen / priority vector first, after it is proved consistency using RI table (ratio index), if the result of verification is less than 10% (0.1) then it is consistent and the calculation can be continued, but if the verification result is more than 10% (0.1) then the calculation is considered inconsistent and needs to be repeated from the beginning. This support system uses a web base, where the data entered will be saved into the MySQL database. The test results of the Student Selection Selection Decision Support System Using Analitycal Hierarchy Process Method Based Web get results as expected on the Black Box. So it can be concluded an accurate percentage of system success of 100%.Keywords : Decision Support System, Black Box, Analitycal Hierarchy Process, The Selection Of Athlete.
Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like Alexa, Cortana, Hi Alexa!, Whatsup Octavia? etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot and in I know a developer named Anna who can look into this github issue. Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks' loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.
Effect of the Diamagnetic term in the ultra-strong coupling regime

We consider a set of two level atoms interacting with a single quantized bosonic mode governed by the Dicke model. In this model it is well known that under a critical value of the light-matter coupling a spontaneous radiation process takes place. In the present work, we investigate the dynamics of the system and we study the Wigner distribution function to visualize the effect of the minimal coupling on the ground state wavefunction from the normal phase to the superradiant one. We show also that the entanglement of Bi-partite model is limited by the presence of the diamagnetic term.
Data Querying and Access Control for Secure Multiparty Computation

In the Internet of Things and smart environments data, collected from distributed sensors, is typically stored and processed by a central middleware. This allows applications to query the data they need for providing further services. However, centralization of data causes several privacy threats: The middleware becomes a third party which has to be trusted, linkage and correlation of data from different context becomes possible and data subject lose control over their data. Hence, other approaches than centralized processing should be considered. Here, Secure Multiparty Computation is a promising candidate for secure and privacy-preserving computation happening close to the sources of the data. In order to make SMC fit for application in these contexts, we extend SMC to act as a service: We provide elements which allow third parties to query computed data from a group of peers performing SMC. Furthermore, we establish fine-granular access control on the level of individual data queries, yielding data protection of the computed results. By adding measures to inform data sources about requests and the usage of their data, we show how a fully privacy-preserving service can be built on the foundation of SMC.
Dimension reduction and classification of hyperspectral images based on neural network sensitivity analysis and multi-instance learning

Hyperspectral remote image sensing is a rapidly developing integrated technology used widely in numerous areas. The rich spectral information from hyperspectral images aids in recognition and classification of many types of objects, but the high dimensionality of these images leads to information redundancy. In this paper, we used sensitivity analysis for dimension reduction. However, another challenge is that hyperspectral images identify objects as either a "different body with the same spectrum" or "same body with a different spectrum." Therefore, it is difficult to maintain the correct correspondence between ground objects and samples, which hinders classification of the images. This issue can be addressed using multi-instance learning for classification. In our proposed method, we combined neural network sensitivity analysis with a multiinstance learning algorithm based on a support vector machine to achieve dimension reduction and accurate classification for hyperspectral images. Experimental results demonstrated that our method provided strong overall classification effectiveness when compared with prior methods.
Using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions

We consider fuzzy, or continuous, bits, which take values in [0;1] and (-1;1] instead of {0;1}, and operations on them (NOT, XOR etc.) and on their sequences (ADD), to obtain the generalization of cryptographic hash functions, CHFs, for the messages consisting of fuzzy bits, so that CHFs become smooth and non-constant functions of each bit of the message. We then train the neural networks to predict the message that has a given hash, where the loss function for the hash of predicted message and given true hash is backpropagatable. The results of the trainings for the standard CHFs - MD5, SHA1, SHA2-256, and SHA3/Keccak - with small number of (optionally weakened) rounds are presented and compared.
Metaflow: A DAG-Based Network Abstraction for Distributed Applications

In the past decade, increasingly network scheduling techniques have been proposed to boost the distributed application performance. Flow-level metrics, such as flow completion time (FCT), are based on the abstraction of flows yet they cannot capture the semantics of communication in a cluster application. Being aware of this problem, coflow is proposed as a new network abstraction. However, it is insufficient to reveal the dependencies between computation and communication. As a result, the real application performance can be hurt, especially in the absence of hard barriers. Based on the computation DAG of the application, we propose an expressive abstraction namely metaflow that resides in the middle of the two extreme points of flows and coflows. Evaluation results show that metaflow-based scheduling can outperform the coflow-based algorithm by 1.78x.