Reviewerly/exHarmony huggingface dataset:
Determinant study for improvement priorities of irrigation assets (a case study of Kedungrejo irrigation network in Pilangkenceng sub-district, Madiun regency – Indonesia)

Abstract Irrigation plays an important role for increasing agricultural production in order to achieve national food sovereignty. However, in reality, requests of funding for the purpose of irrigation network management from year to year are not always fulfilled according to needs. As such, to resolve this issue, it is necessary to determine improvement priorities for repairs of irrigation assets so that management may be optimally carried out. The aim of this research is to determine and analyse the improvement priorities of irrigation assets in one of the irrigation areas of Madiun Regency. This study utilized the modified Irrigation Network Performance Evaluation Criteria and Weightings for the year 2018. In addition, statistical tests as the Kruskal-Wallis and Mann-Whitney tests were utilized, as the obtained data were in the form of rankings. Google Earth and ArcGIS software were utilized to facilitate the understanding of the conditions and management of all the assets. The required data was the inventory of the irrigation network. The results of the study showed that the Kedungrejo Irrigation Network has 10% of irrigation assets in very good condition, 37% in good condition, 49%, in fair condition, and 4% in poor condition. The highest ranking (ranking 1) was for the spillway, while the lowest ranking (ranking 82) was for a bridge.
Eliminating Data Collection Bottleneck for Wake Word Engine Training Using Found and Synthetic Data

Voice interfaces are fast becoming an important human-machine interfaces, and Wake Word Engines (WWEs) are a critical part of modern voice interfaces. There are recent advancements in applying Deep Learning (DL) or Deep Neural Network architectures for WWE construction. Similar to other applications of DL however, achieving good accuracy strongly depends on training using the right type of dataset - a task that traditionally requires significant time and human effort. In this paper, we present novel techniques for curating WWE datasets that significantly minimizes the need for data collection from humans. More specifically, we investigated two techniques: (1) Using “Found Data”: we have created automated data curation pipelines for locating and processing data from public sources (e.g., YouTube), and (2) Using Synthetic Data: we explored the use of Synthesized Speech (using Text to Speech and Voice Conversion) to synthetically generate WWE datasets. Using our techniques, we are able to train WWEs that demonstrate a level of performance that is on-par with the WWEs trained with data from expensive human collection (e.g., Mechanical Turk). For example, in our experiments using wake words `Computer' and `Shannon', for a given False Alarm rate of 1 per hour, WWEs trained with our novel methods exhibit a False Reject Rate, averaged across four different test environments, of 0.9% and 1.6% respectively compared to the 1.0% and 0.8% for the baseline trained using data from human collection. Cycle time savings of more than an order of magnitude is possible by utilizing these methods.
Few-shot Learning with Weakly-supervised Object Localization.

Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many data generation methods have improved the performance of FSL models, but require lots of annotated images to train a specialized network (e.g., GAN) dedicated to hallucinate new samples. We argue that localization is a more efficient approach because it provides the most discriminative regions without using extra samples. In this paper, we propose a novel method to address the FSL task by achieving weakly-supervised object localization within performing few-shot classification. To this end, we design (i) a triplet-input module to obtain the initial object seeds and (ii) an Image-To-Class-Distance (ITCD) based localizer to activate the deep descriptors of the key objects, thus obtaining the more discriminative representations used to perform few-shot classification. Extensive experiments show our method outperforms the state-of-the-art methods on benchmark datasets under various settings. Besides, our method achieves superior performance over previous methods when training the model on miniImageNet and evaluating it on the different datasets (e.g., Stanford Dogs), demonstrating its superior generalization capacity. Extra visualization shows the proposed method can localize the key objects accurately.
A Secure Certificateless Identity Authentication Scheme Based on Blockchain

Centralized systems based on the trusted third-party are widely used in identity authentication. However, there is a single point of failure inherent in the centralized systems. As a natural decentralized architecture, blockchain can bring the advantages of decentralization, trustworthiness and immutability to the identity authentication systems. The existing blockchain-based identity authentication systems can solve the problem of single point of failure, but there are still problems such as certificate management. In this paper, we propose an identity authentication scheme based on blockchain and certificateless public key cryptography. The scheme implements a decentralized database by deploying smart contracts in the Ethereum blockchain, and uses the certificateless public key signature algorithm during the authentication process. Compared with other blockchain-based identity authentication systems, our scheme not only prevents the single point of failure, but also avoids the deficiency of certificate management, and resists impersonation attacks and man-in-the-middle attacks. The security analysis and performance analysis show the security and stability of our scheme.
<i>Reflections</i>

This essay explores the methods of computational analysis for authorship attribution (stylometry), using The Woman of Colour as a case study. It compares the novel against a corpus of British women writers and a corpus of texts with Caribbean authors or themes. It examines the biases and limits of these methods from the perspective of both statistics and literature, by exploring the concepts of authorship, writing style, prevalence, sample size, and machine learning algorithms. This exploration of stylometric methods is relevant because many eighteenth-century novels were published anonymously and because of the increasing availability of and scholarly interest in digital analysis of texts and authorship attribution, especially for texts by women and minority writers.
An Encapsulation of Vital Non-Linear Frequency Features for Various Speech Applications

Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual linear prediction coefficients (PLPCs) are widely casted nonlinear vocal parameters in majority of the speaker identification, speaker and speech recognition techniques as well in the field of emotion recognition. Post 1980s, significant exertions are put forth on for the progress of these features. Considerations like the usage of appropriate frequency estimation approaches, proposal of appropriate filter banks, and selection of preferred features perform a vital part for the strength of models employing these features. This article projects an overview of MFCC and PLPC features for different speech applications. The insights such as performance metrics of accuracy, background environment, type of data, and size of features are inspected and concise with the corresponding key references. Adding more to this, the advantages and shortcomings of these features have been discussed. This background work will hopefully contribute to floating a heading step in the direction of the enhancement of MFCC and PLPC with respect to novelty, raised levels of accuracy, and lesser complexity.
Digital Self-Coherent Continuous Variable Quantum Key Distribution System

We investigate a continuous variable quantum key distribution system with digital tracking of both polarization and phase. Stable operation over 25km for 36 hours with secret key rates between 1.9 and 2.8 Mbit/s is demonstrated.
Graphs, Convolutions, and Neural Networks.

Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this work, we leverage graph signal processing to characterize the representation space of graph neural networks (GNNs). We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. These two properties offer insight about the workings of GNNs and help explain their scalability and transferability properties which, coupled with their local and distributed nature, make GNNs powerful tools for learning in physical networks. We also introduce GNN extensions using edge-varying and autoregressive moving average graph filters and discuss their properties. Finally, we study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.
A study of continuous space word and sentence representations applied to ASR error detection

This paper presents a study of continuous word representations applied to automatic detection of speech recognition errors. A neural network architecture is proposed, which is well suited to handle continuous word representations, like word embeddings. We explore the use of several types of word representations: simple and combined linguistic embeddings, and acoustic ones associated to prosodic features, extracted from the audio signal. To compensate certain phenomena highlighted by the analysis of the error average span, we propose to model the errors at the sentence level through the use of sentence embeddings. An approach to build continuous sentence representations dedicated to ASR error detection is also proposed and compared to the Doc2vec approach. Experiments are performed on automatic transcriptions generated by the LIUM ASR system applied to the French ETAPE corpus. They show that the combination of linguistic embeddings, acoustic embeddings, prosodic features, and sentence embeddings in addition to more classical features yields very competitive results. Particularly, these results show the complementarity of acoustic embeddings and prosodic information, and show that the proposed sentence embeddings dedicated to ASR error detection achieve better results than generic sentence embeddings.
Transferring and Optimizing a Laptop-based Learning Environment for the Use on iPads

With the growing presence of laptops, smartphones and tablets, daily life as well as professional needs are changing [1]. However, this means that mobile devices are playing an increasingly important role in schools and classrooms [2,3]. In particular, tablets are seen as a highly promising possibility to implement digital media in schools [4]. In this context, we would like to take a closer look at the use of tablets in initial chemistry classes. Therefore, we transferred a digital learning software, that had been developed for laptops, to iPads in order to optimize the learning environment through the possibilities offered by this latest innovation. This further development especially aims at the integration of experiments and a higher universal accessibility for learners in heterogeneous or inclusive learning groups. The aim of this project is to investigate the effect of the use of tablets in the following two teaching phases: During the input phase, the students work on the iPad-based learning environment. In the internalization phase, working with tablet-based tasks is compared with creating student explanatory videos regarding their impact on knowledge growth and attractiveness. In this paper, we focus the transfer of the laptop-based learning environment to iPads.
Weight Calculation Method for Consumer Goods Risk Assessment Indexes Based on Analytic Hierarchy Process

Abstract This Paper, by comprehensively analyzing the factors influencing consumer goods safety risk assessment and following the principles of completeness, maneuverability, rationality and comparability, constructs a consumer goods safety risk assessment index system covering human-product-environment, and proposes the weight calculation method for consumer goods safety risk assessment index system based on Analytic Hierarchy Process (AHP), laying the foundation for the related work of consumer goods safety risk assessment.
Prospects for the Development of Digital Educational Environment in North-Eastern Federal University in the Context of Adult Education

The article discusses the need for the digitalization of education in the context of developing a system of continuing professional education in North-Eastern Federal University and explores associated risks and prospects for the future, including the customizable online learning tool, tailored to the individual needs of a person.
Movie genre classification using TF-IDF and SVM

This paper studies the classification principle and process of SVM algorithm, and classifies the text containing movie information, so as to achieve the research purpose of movie classification. It focuses on the various steps that need to be completed in the process of classification, such as text word segmentation, feature engineering, text representation, etc., and designs and implements a movie classification system based on SVM algorithm. In addition, the following work has been done: i) In the process of sample selection and processing, all the sample data used in the experiment come from the film profile information of Douban Film Network. In order to facilitate data acquisition and formatting, the function of automatically crawling the film profile web page is also implemented in the experiment. ii) By combining document frequency with word frequency, text feature selection can effectively avoid the disadvantage of "low word frequency first" caused by using document frequency alone. The experimental results show that the size of word frequency also has a certain influence on the classification results when feature selection is carried out. iii) In the parameter optimization stage of SVM, the principle of using kernel function and the influence of the classification model trained by observing different parameter values on the classification results are analyzed. The crosschecking method based on grid search is used to find the relatively optimal parameters. The results proves that SVM is capable for movie classification with relatively high F-Score. The performance evaluation indicates that non-linear kernel such as RBF cannot outperform linear kernel when dealing with large amount of features.
Exploring the Use of Biometric Smart Cards for Voters’ Accreditation: A Case Study of Nigeria Electoral Process

<p class='IJASEITAbtract'>Voting remains an integral component of every democratic electoral process. it is an avenue for citizens to exercise their rights in order to elect those who will lead them in various vacant political offices. However, enhancing voters’ trust and confidence in electoral processes are significant factors that could encourage the active participation of citizens in elections. Eligible voters tend to decline to participate in an election when they have a feeling that their votes may not eventually count. Furthermore, electoral processes that lead to the emergence of candidates must be adjudged to be free, fair and credible to a high degree for the result to be widely acceptable. Unacceptable election results could lead to protests and total cancelation of the election thereby resulting in loss of time and resources invested in it. To ensure that only registered voters cast their votes on election days, measures must be put in place to accredit voters on election days effectively. Therefore, this article explores the use of biometric smart cards for voters’ verification and identification. With the Nigerian electoral process in view, the existing Nigerian voting procedure was reviewed, lapses were identified and solutions based on the use of the biometric smart card were proffered. If adopted, the proposed adoption of biometric smart cards for voters’ accreditation will enhance the country’s electoral process thereby ensuring that only registered voters cast their votes. The approach presented could also reduce the number of electoral processes and personnel required during election days, thus reducing voting time and cost.
Improving Telecom Industry Processes Using Ordered Transactions in Hyperledger Fabric

Despite the enormous potential of distributed ledgers such as blockchain to increase revenue and lower costs, telecom firms have been slower than other regulated industries to integrate blockchain into its operations. We examine how three backend tasks currently performed by telecoms (managing roaming agreements, mobile number portability, and inter-carrier settlement) can be accomplished with greater efficiency using ordered transactions in Hyperledger Fabric - a permissioned blockchain framework. We also explore the role of peer types in the ordering process, how these integrate with Raft ordering service, as well as how several peculiarities in telecom can be addressed using chaincode - Fabricâ€™s version of smart contracts.
Ultrasound image simulation with generative adversarial network

PURPOSE: It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to a real ultrasound image. We propose to evaluate the realism of ultrasound images simulated using a generative adversarial network. METHODS: To achieve our goal, kidney ultrasounds were collected, and relevant anatomy was segmented to create anatomical label-maps using 3D Slicer. Adversarial networks were trained to generate ultrasound images from these labelmaps. Finally, a two-part survey of 4 participants with sonography experience was conducted to assess the realism of the generated images. The first part of the survey consisted of 50 kidney ultrasound images; half of which were real while the other half were simulated. Participants were asked to label each of the 50 ultrasound images as either real or simulated. In the second part of the survey, the participants were presented with ten simulated images not included in the first part of the survey and asked to evaluate the realism of the images. RESULTS: The average number of correctly identified images was 28 of 50 (56%). On a scale of 1-5, where 5 is indistinguishable from real US, the generated images received an average score of 3.75 for realistic anatomy and 4.0 for realistic ultrasound effects. CONCLUSIONS: We evaluated the realism of kidney ultrasound images generated using adversarial networks. Generative adversarial networks appear to be a promising method of simulating realistic ultrasound images from crosssectional anatomical label-maps.
Assessment of Hierarchical Multi-Sensor Multi-Target Track Fusion in the Presence of Large Sensor Biases

The evaluation and assessment of a hierarchical sensor fusion system employing joint track fusion and bias estimation under the presence of large sensor biases is presented along with simulation results that illustrate the performance. In the proposed track fusion system, the first level of the system performs track to track correlation and joint fusion and bias estimation with tracks from a subset of sensors. The debiased fused tracks are in turn handed off to a second-level for track to track correlation and joint fusion and bias estimation with tracks from the remaining sensors. Track to track correlation is achieved using Murty&#x0027;s K-best Hypotheses algorithm with joint track fusion and bias estimation performed for each correlation hypotheses. Established and well known metrics of correlation correctness, root mean squared error, and normalized estimation error squared are used to characterize estimator and correlator performance versus track density. The hand-over of tracks from level 1 to level 2 fusion (i.e., AB to ABC) is assessed with pattern metrics computed over a cluster of tracks, which include pattern accuracy, pattern consistency, and pattern complex containment. In previously published results, [8], a degradation of system performance occurred with large sensor biases. In this paper, we specifically address the assessment of the performance of joint fusion and bias estimation in the presence of large sensor biases with cross correlations between the estimated tracks and biases from one level to the next. Monte Carlo simulations were performed to demonstrate and compare the tracking metrics, pattern metrics, and correlation correctness after hand-over in the presence of a large sensor bias (i.e., a bias covariance that is as large as the sensor&#x0027;s random error covariance).
A classification for the performance of online SGD for high-dimensional inference.

Stochastic gradient descent (SGD) is a popular algorithm for optimization problems arising in high-dimensional inference tasks. Here one produces an estimator of an unknown parameter from a large number of independent samples of data by iteratively optimizing a loss function. This loss function is high-dimensional, random, and often complex. We study here the performance of the simplest version of SGD, namely online SGD, in the initial search phase, where the algorithm is far from a trust region and the loss landscape is highly non-convex. To this end, we investigate the performance of online SGD at attaining a better than random correlation with the unknown parameter, i.e, achieving weak recovery. Our contribution is a classification of the difficulty of typical instances of this task for online SGD in terms of the number of samples required as the dimension diverges. This classification depends only on an intrinsic property of the population loss, which we call the information exponent. Using the information exponent, we find that there are three distinct regimes---the easy, critical, and difficult regimes---where one requires linear, quasilinear, and polynomially many samples (in the dimension) respectively to achieve weak recovery. We illustrate our approach by applying it to a wide variety of estimation tasks such as parameter estimation for generalized linear models, two-component Gaussian mixture models, phase retrieval, and spiked matrix and tensor models, as well as supervised learning for single-layer networks with general activation functions. In this latter case, our results translate into a classification of the difficulty of this task in terms of the Hermite decomposition of the activation function.
Privacy Preserving in Collaborative Filtering Based Recommender System: A Systematic Literature Review

Recommender systems solve the information overload problem by filtering data on the basis of the user’s preferences, interest, or previous behavior regarding an item. Data filtering techniques employed are content-based (based on the user’s past behavior), collaborative (based on the behavior of users that are alike to the active one), or hybrid (a combination of filtering techniques). Due to its versatility, the most popular technique used in the recommender systems is collaborative filtering. However, the privacy of the user is at risk because malicious users can attack the targeted user or the recommendation server may reveal the personal data of users’ to other parties or misuse the data for targeted advertising. The existing works mostly employ encryption or randomizations based methodologies, but often sacrifice privacy for accuracy and accuracy for privacy.