Reviewerly/exHarmony huggingface dataset:
A Strategy of Smart Library Construction in the Future

Library is going to forward with the development of information technology nowadays, contains Artificial intelligence (AI), 5G in the future, big data, cloudy technology, etc. that are applied in library, a point of view that this is smart library.However, high technology is not able to delegate smart library.In this article, I propose my opinion about smart library from more overall situation, discuss the goal of smart library construction, smart services contain items, analyze the route of realizing smart services, clarify the relationships between services and devices, reader and big data, expound the data center which is the head of a smart library.Proposing the route of construct smart library, in a sense smart library will lead innovative studies.
An Analysis of Particle Swarm Optimization of Multi-objective Knapsack Problem

Based on the Particle Swarm Optimization (PSO), two methods are adopted to solve the multi-objective knapsack problem, which is a knapsack problem with multiple objective functions. One is the weighted method: each objective function is given a certain weight according to the actual demand, so the multi-objective problem is transformed into an one objective problem and then PSO is used to solve it. Another one is the method of non-inferior solution: PSO is combined with the non-inferior solution. Therefore, more feasible solutions are shown in the final result, and the decision-maker can select the best solution according to the performance of values. The experiment shows that to solve the multi-objective knapsack problem, the non-inferior method is better than the weighted method because the former is more convenient for decision-makers and does not depend on the weights, which is important for the latter and may confuse the decision-maker.
Aplikasi Web Pemetaan Wilayah Kelayakan Tanam Jagung Berdasarkan Hasil Panen di Kabupaten Lampung Selatan

South Lampung Regency is a district with the capital city of Kalianda which dominates agricultural areas, one of which is corn. With the corn harvest spread in each district in the South Lampung Regency, it will attract investors to invest or invest in corn farming in the South Lampung region. This application was built with the aim of making it easier for the community and potential investors to see the potential of the sub-district corn planting area based on the yield in the form of map visualization to make it easier to find the location of the area to be planted with corn. The result of this application is a web-based system in the form of map visualization to show potential areas for planting corn. Based on the results of tests conducted, this application has a percentage score of 85.96% meaning this application is very good to be implemented.
Single Image Dehazing: An Analysis on Generative Adversarial Network

Haze is a very common phenomenon that degrades or reduces visibility.It causes various problems where high-quality images are required such as traffic and security monitoring.So haze removal from scenes is an immediate demand for clear vision.Recently, in addition to the conventional dehazing mechanisms, different types of deep generative adversarial networks (GAN) are applied to suppress the noise and improve the dehazing performance.But it is unclear how these algorithms would perform on hazy images acquired "in the wild" and how we could gauge the progress in the field.To bridge this gap, this presents a comprehensive study on three single image dehazing state-of-the-art GAN models, such as AOD-Net, cGAN, and DHSGAN.We have experimented using benchmark dataset consisting of both synthetic and real-world hazy images.The obtained results are evaluated both quantitatively and qualitatively.Among these techniques, the DHSGAN gives the best performance.
Two single-state semi-quantum secure direct communication protocols based on single photons

Semi-quantum protocol is a hot topic in quantum cryptography. One of the original motivational reasons to study semi-quantum protocol is to better understand “how quantum” a protocol needs to be in order to gain an advantage over its classical counterpart. Semi-quantum secure direct communication (SQSDC) is an important branch of the semi-quantum protocol, which makes it possible to directly transfer large amounts of data between quantum participant and classical participant without need to share the secret key in advance. In this paper, we propose two new SQSDC protocols based on single photons to study how to reduce the quantum resource requirements of both quantum participant and classical participant. In both protocols, the classical participant requires neither quantum memory or quantum delay lines. We first propose in the first protocol that the quantum participant only need to prepare single-state qubits in the preparation phase, which is different from previous SQSDC protocols. Then we propose in the second protocol that quantum participant can accomplish the same work as the first one even without quantum memory. Efficiency analysis shows that the first protocol is more efficient than some protocol, and the second one can save more quantum resources at the expense of some efficiency. Compared with the existing SQSDC protocols based on single photons, both protocols can use fewer quantum states or quantum resource requirements than other protocols. Finally, we analyze the security of both protocols.
dK-Microaggregation: Anonymizing Graphs with Differential Privacy Guarantees

With the advances of graph analytics, preserving privacy in publishing graph data becomes an important task. However, graph data is highly sensitive to structural changes. Perturbing graph data for achieving differential privacy inevitably leads to inject a large amount of noise and the utility of anonymized graphs is severely limited. In this paper, we propose a microaggregation-based framework for graph anonymization which meets the following requirements: (1) The topological structures of an original graph can be preserved at different levels of granularity; (2) \(\varepsilon \)-differential privacy is guaranteed for an original graph through adding controlled perturbation to its edges (i.e., edge privacy); (3) The utility of graph data is enhanced by reducing the magnitude of noise needed to achieve \(\varepsilon \)-differential privacy. Within the proposed framework, we further develop a simple yet effective microaggregation algorithm under a distance constraint. We have empirically verified the noise reduction and privacy guarantee of our proposed algorithm on three real-world graph datasets. The experiments show that our proposed framework can significantly reduce noise added to achieve \(\varepsilon \)-differential privacy over graph data, and thus enhance the utility of anonymized graphs.
Testing, Verification and Validation of Distributed Ledger Systems

Improving the quality of software that implements distributed ledger systems based on blockchain technology is an urgent task for the transport industry. In this article, we will look at tools to improve quality such as testing, validation, and validation. The paper lists a number of existing tools for testing and verifying blockchain systems and smart contracts, and also identifies the problem of the lack of an appropriate normative base and standards in this area.
Levitation Simulator: Prototyping Ultrasonic Levitation Interfaces in Virtual Reality

We present the Levitation Simulator, a system that enables researchers and designers to iteratively develop and prototype levitation interface ideas in Virtual Reality. This includes user tests and formal experiments. We derive a model of the movement of a levitating particle in such an interface. Based on this, we develop an interactive simulation of the levitation interface in VR, which exhibits the dynamical properties of the real interface. The results of a Fitts' Law pointing study show that the Levitation Simulator enables performance, comparable to the real prototype. We developed the first two interactive games, dedicated for levitation interfaces: LeviShooter and BeadBounce, in the Levitation Simulator, and then implemented them on the real interface. Our results indicate that participants experienced similar levels of user engagement when playing the games, in the two environments. We share our Levitation Simulator as Open Source, thereby democratizing levitation research, without the need for a levitation apparatus.
COWS-L2H: A corpus of Spanish learner writing

This paper presents the Corpus of Written Spanish of L2 and Heritage Speakers (COWS-L2H), a large corpus of compositions written by North American university students learning Spanish. The goals of this work are to (1) build a large corpus of Spanish learner writing that provides samples of written data from Spanish learners in the context of a North American university, (2) to contribute corpus data collected not only from second language (L2) learners of Spanish but also from learners of Spanish as a heritage language (SHL), and (3) to develop one of the few Spanish learner corpora to provide longitudinal data.
Transfer learning by mapping and revising boosted relational dependency networks

Statistical machine learning algorithms usually assume the availability of data of considerable size to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data by relying on a model learned in a source domain where data is easy to obtain to be a starting point for the target domain. On the other hand, real-world data contains objects and their relations, usually gathered from noisy environments. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning (SRL) area. Thus, to address domains with scarce, relational, and uncertain data, in this paper, we propose TreeBoostler, an algorithm that transfers the SRL state-of-the-art Boosted Relational Dependency Networks learned in a source domain to the target domain. TreeBoostler first finds a mapping between pairs of predicates to accommodate the additive trees into the target vocabulary. After, it employs two theory revision operators devised to handle incorrect relational regression trees aiming at improving the performance of the mapped trees. In the experiments presented in this paper, TreeBoostler has successfully transferred knowledge between several distinct domains. Moreover, it performs comparably or better than learning from scratch methods in terms of accuracy and outperforms a transfer learning approach in terms of accuracy and runtime.
Handling Concept Drift for Predictions in Business Process Mining

Predictive services nowadays play an important role across all business sectors.However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift.Prediction quality of models can be largely influenced by this phenomenon.Therefore, concept drift is usually handled by retraining of the model.However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model.Therefore, we systematically analyze different data selection strategies in this work.Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift.We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling.Furthermore, we depict the effects of the different data selection strategies.
Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge

In this paper, we explore vector quantization for acoustic unit discovery. Leveraging unlabelled data, we aim to learn discrete representations of speech that separate phonetic content from speaker-specific details. We propose two neural models to tackle this challenge - both use vector quantization to map continuous features to a finite set of codes. The first model is a type of vector-quantized variational autoencoder (VQ-VAE). The VQ-VAE encodes speech into a sequence of discrete units before reconstructing the audio waveform. Our second model combines vector quantization with contrastive predictive coding (VQ-CPC). The idea is to learn a representation of speech by predicting future acoustic units. We evaluate the models on English and Indonesian data for the ZeroSpeech 2020 challenge. In ABX phone discrimination tests, both models outperform all submissions to the 2019 and 2020 challenges, with a relative improvement of more than 30%. The models also perform competitively on a downstream voice conversion task. Of the two, VQ-CPC performs slightly better in general and is simpler and faster to train. Finally, probing experiments show that vector quantization is an effective bottleneck, forcing the models to discard speaker information.
NAUTILUS: a Versatile Voice Cloning System

We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker. By using a multi-speaker speech corpus to train all requisite encoders and decoders in the initial training stage, our system can clone unseen voices using untranscribed speech of target speakers on the basis of the backpropagation algorithm. Moreover, depending on the data circumstance of the target speaker, the cloning strategy can be adjusted to take advantage of additional data and modify the behaviors of text-to-speech (TTS) and/or voice conversion (VC) systems to accommodate the situation. We test the performance of the proposed framework by using deep convolution layers to model the encoders, decoders and WaveNet vocoder. Evaluations show that it achieves comparable quality with state-of-the-art TTS and VC systems when cloning with just five minutes of untranscribed speech. Moreover, it is demonstrated that the proposed framework has the ability to switch between TTS and VC with high speaker consistency, which will be useful for many applications.
Robust metric learning based on the rescaled hinge loss

Distance/Similarity learning is a fundamental problem in machine learning. For example, kNN classifier or clustering methods are based on a distance/similarity measure. Metric learning algorithms enhance the efficiency of these methods by learning an optimal distance function from data. Most metric learning methods need training information in the form of pair or triplet sets. Nowadays, this training information often is obtained from the Internet via crowdsourcing methods. Therefore, this information may contain label noise or outliers leading to the poor performance of the learned metric. It is even possible that the learned metric functions perform worse than the general metrics such as Euclidean distance. To address this challenge, this paper presents a new robust metric learning method based on the Rescaled Hinge loss. This loss function is a general case of the popular Hinge loss and initially introduced in Xu et al. (Pattern Recogn 63:139–148, 2017) to develop a new robust SVM algorithm. In this paper, we formulate the metric learning problem using the Rescaled Hinge loss function and then develop an efficient algorithm based on HQ (Half-Quadratic) to solve the problem. Experimental results on a variety of both real and synthetic datasets confirm that our new robust algorithm considerably outperforms state-of-the-art metric learning methods in the presence of label noise and outliers.
USE OF THE LINK RANKING METHOD TO EVALUATE SCIENTIFIC ACTIVITIES OFSCIENTIFIC SPACE SUBJECTS

A modification of the PageRank method based on link ranking is proposed to evaluate the research results of subjects of the scientific space, taking into account self-citation. The method of reducing the influence of self-citation on the final evaluation of the results of research activity of subjects of the scientific space is described. The evaluation of the results of research is calculated using the modified PR-q method, taking into account self-citation as a solution of a system of linear algebraic equations, matrix of which consists of coefficients determined by the number of citations of publications of one scientist in the publications of another scientist. The described method can be used for the task of evaluating the activity of the components of the scientific space: scientists, higher education institutions and their structural units. For the task of evaluating the research activity of subjects of the scientific space, a method based on link ranking (PageRank method for web pages) and taking into account the self-citation of scientists is proposed. The latter allows for an adequate assessment, taking into account the abuses associated with the authors excessive self-citation. The essence of the constructed method lies in the construction of a system of linear algebraic equations, whose coefficients of the matrix reflect the citations of some scientists by others in the citation network of scientific publications. The value of the coefficients of the matrix of such a system of linear algebraic equations is subject to certain restrictions, which allow to reduce the influence of the factor of excessive self-citation of the author on his overall assessment of research activity. The described method can be used to calculate the complex evaluation of the components of the scientific space: the scientist, the institution of higher education and its separate structural units. Evaluating research results provides an opportunity to verify the relevance of the research process to the goals identified at the planning stage and, if necessary, to adjust the progress of those studies. Also, the calculation of research evaluations of the components (objects and entities) of the scientific space is a powerful tool for managing research projects.
Generalization of Machine Learning Approaches to Identify Notifiable Conditions from a Statewide Health Information Exchange.

Healthcare analytics is impeded by a lack of machine learning (ML) model generalizability, the ability of a model to predict accurately on varied data sources not included in the model's training dataset. We leveraged free-text laboratory data from a Health Information Exchange network to evaluate ML generalization using Notifiable Condition Detection (NCD) for public health surveillance as a use case. We 1) built ML models for detecting syphilis, salmonella, and histoplasmosis; 2) evaluated generalizability of these models across data from holdout lab systems, and; 3) explored factors that influence weak model generalizability. Models for predicting each disease reported considerable accuracy. However, they demonstrated poor generalizability across data from holdout lab systems being tested. Our evaluation determined that weak generalization was influenced by variant syntactic nature of free-text datasets across each lab system. Results highlight the need for actionable methodology to generalize ML solutions for healthcare analytics.
Designing quantum networks using preexisting infrastructure

Abstract We consider the problem of deploying a quantum network on an existing fiber infrastructure, where quantum repeaters and end nodes can only be housed at specific locations. We propose a method based on integer linear programming (ILP) to place the minimal number of repeaters on such an existing network topology, such that requirements on end-to-end entanglement-generation rate and fidelity between any pair of end-nodes are satisfied. While ILPs are generally difficult to solve, we show that our method performs well in practice for networks of up to 100 nodes. We illustrate the behavior of our method both on randomly-generated network topologies, as well as on a real-world fiber topology deployed in the Netherlands.
Agnostic Learning of a Single Neuron with Gradient Descent

We consider the problem of learning the best-fitting single neuron as measured by the expected square loss $\mathbb{E}_{(x,y)\sim \mathcal{D}}[(σ(w^\top x)-y)^2]$ over some unknown joint distribution $\mathcal{D}$ by using gradient descent to minimize the empirical risk induced by a set of i.i.d. samples $S\sim \mathcal{D}^n$. The activation function $σ$ is an arbitrary Lipschitz and non-decreasing function, making the optimization problem nonconvex and nonsmooth in general, and covers typical neural network activation functions and inverse link functions in the generalized linear model setting. In the agnostic PAC learning setting, where no assumption on the relationship between the labels $y$ and the input $x$ is made, if the optimal population risk is $\mathsf{OPT}$, we show that gradient descent achieves population risk $O(\mathsf{OPT})+ε$ in polynomial time and sample complexity when $σ$ is strictly increasing. For the ReLU activation, our population risk guarantee is $O(\mathsf{OPT}^{1/2})+ε$. When labels take the form $y = σ(v^\top x) + ξ$ for zero-mean sub-Gaussian noise $ξ$, we show that the population risk guarantees for gradient descent improve to $\mathsf{OPT} + ε$. Our sample complexity and runtime guarantees are (almost) dimension independent, and when $σ$ is strictly increasing, require no distributional assumptions beyond boundedness. For ReLU, we show the same results under a nondegeneracy assumption for the marginal distribution of the input.
Subject-Based Reading Behaviour Differences of Young Adults under Emerging Digital Paradigm

Abstract There appears a change in reading trends in the growing digital culture around the globe. The current research intended to identify the influence of this culture on the reading behaviour of young adults. A survey of final year Masters students from public and private sector universities in various knowledge domains (sciences and technology, social sciences, and arts and humanities) was conducted. The findings revealed a number of significant differences in both reading patterns and preferred reading formats among students of three knowledge domains. The findings suggest that all subjects should be treated separately when deciding on collection development and library services. This exploratory study proves that the digital environment creates a significant impact on individuals’ reading behaviour which needs to be considered by academics and library practitioners. It is a baseline study and opens many potential directions for future research.
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models

Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society. However, due to their inherent opaqueness, some recently raised concerns about using neural models are starting to be taken seriously. In fact, intentional or unintentional behaviors could lead to a dialogue system to generate inappropriate responses. Thus, in this paper, we investigate whether we can learn to craft input sentences that result in a black-box neural dialogue model being manipulated into having its outputs contain target words or match target sentences. We propose a reinforcement learning based model that can generate such desired inputs automatically. Extensive experiments on a popular well-trained state-of-the-art neural dialogue model show that our method can successfully seek out desired inputs that lead to the target outputs in a considerable portion of cases. Consequently, our work reveals the potential of neural dialogue models to be manipulated, which inspires and opens the door towards developing strategies to defend them.