Reviewerly/exHarmony huggingface dataset:
Mobile-D Approach-based Persona for Designing User Interface

Nowadays, many people use social media in different aspects of their life.One of the most important fields is education, where both faculty members and students communicate frequently to facilitate collaboration between them.In this paper, we aimed to design and develop a new interactive academic, social application called Shake Hand, to reduce the gap that exists between faculty members and students and to improve the users' experiences with one another.The proposed design is based on Mobile-D approach by dividing the application's functionalities into three releases.Persona was used to collect the requirements and help analyze and visualize the target user to design interface that meet and fulfill their needs.The proposed application offers two main functions: hosting discussion forums and exchange services, and addresses many academic and social needs.The usability testing approach was applied to measure the performance, productivity, and satisfaction of the user.Our contribution demonstrated in this paper is to measure the user's productivity by mapping the design implication of cognitive process to the user interface of the application.The output of the mapping is a set of guidelines which guide the designer through the designing of a mobile application in a distracting environment.
Session-based item recommendation with pairwise features

The RecSys Challenge 2019 seeks a better solution for item recommendation on short session-based data with limited user history. This paper describes the team PVZ's approach to this challenge, which won the 3rd place in the contest. Our solution consists of the following components. Firstly, we cast the hotel recommendation task as a binary classification problem. Secondly, we spend most of the time doing feature engineering and mining a series of useful features in various aspects. Then we train individual models with a different set of features and blend them with some important features using stacking method. At last, we create other new pair-wise features based on the existing model predictions and train a stacking model again which generates our final result. Our final solution achieved a public score of 0.685929 and a private score of 0.684071, ranking the third place on both sides.
Uncertainty-aware workflow migration among edge nodes based on blockchain

Abstract Workflow is one of the most typical applications in distributed computing, which makes a variety of complex computing work orderly. However, assigning workflow tasks to nodes in the process of multi-node collaboration is still a challenge, because there are some unpredictable emergencies, i.e., uncertainty, in the process of workflow scheduling. The paper proposes a blockchain-powered resource provisioning (BPRP) method to solve the above problems. Technically, we use the directed acyclic graph in the graph theory to represent the workflow task and optimize the workflow scheduling strategy in the presence of uncertainty. The processing time and energy consumption of workflow tasks are also optimized by using non-dominated sorting genetic algorithm III (NSGA-III). Finally, we carry out experimental simulations to verify the effectiveness of the proposed method.
Reduce & Attribute: Two-Step Authorship Attribution for Large-Scale Problems

Authorship attribution is an active research area which has been actively studied for many decades.Nevertheless, the majority of approaches consider problem sizes of a few candidate authors only, making them difficult to apply to recent scenarios incorporating thousands of authors emerging due to the manifold means to digitally share text.In this study, we focus on such large scale problems and propose to effectively reduce the number of candidate authors before applying common attribution techniques.By utilizing document embeddings, we show on a novel, comprehensive dataset collection that the set of candidate authors can be reduced with high accuracy.Moreover, we show that common authorship attribution methods substantially benefit from a preliminary reduction if thousands of authors are involved.
Performance Analysis on Federated Learning with Differential Privacy

In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises. Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i.e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$-random scheduling strategy, where $K$ ($1<K<N$) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop the corresponding convergence bound of the loss function in this case and the $K$-random scheduling strategy can also retain the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the designs on various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.
Suppression of the negative effect of abnormal data based on the Hough transform and application to the magnetic compensation of airborne optically-pumped magnetometer data

A magnetic sensor of the optically pumped magnetometer may enter a dead zone during an aeromagnetic survey, resulting in unavoidable abnormal data and seriously affecting the effect of aeromagnetic compensation. We propose a fast discrimination and culling method based on the Hough transform to prevent abnormal data from participating in the coefficient estimation. In the proposed method, the parameter space is partitioned into small buckets and the most frequently passed region of sinusoidal curves is detected to eliminate abnormal data. Although the conventional method performs similarly to the proposed method when there is only 1% abnormal data, it is theoretically shown that the proposed method has a better goodness of fit of 0.9518, compared with a value of 0.1956 for the conventional method, in the presence of 45% abnormal data. Furthermore, we construct an experimental platform and conduct a flight test in which the proposed method has an improvement ratio of 4.11 compared with a value of 0.34 for the conventional method.
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning

Many architectures for multi-task learning (MTL) have been proposed to take advantage of transfer among tasks, often involving complex models and training procedures.In this paper, we ask if the sentence-level representations learned in previous approaches provide significant benefit beyond that provided by simply improving word-based representations.To investigate this question, we consider three techniques that ignore sequence information: a syntactically-oblivious pooling encoder, pre-trained non-contextual word embeddings, and unigram generative regularization.Compared to a state-of-the-art MTL approach to textual inference, the simple techniques we use yield similar performance on a universe of task combinations while reducing training time and model size.
Decs-Net: Convolutional Self-Encoding Network for Hyperspectral Image Denoising

Noises in hyperspectral image (HSI) degrades both spatial and spectral features of ground objects, and greately defects the following processing, such as classification, target detection and recognition. In this paper, a convolutional self-encoding network (DeCS-Net) is designed for HSI denoising, which integrates the superiority of convolutional neural network (CNN) and auto-encoder (AE) to learn multi-scale features. The noise in the observed HSI is estimated by residual learning strategy, and is removed from the observed HSI to obtain an estimation of the ideal HSI without noise. Experimental results on benchmark HSI data set illustrate that the proposed DeCS-Net is effective for HSI denoising and outperforms the state-of-the-art CNN based HSI denoising methods.
JsonToOnto: Building Owl2 Ontologies from Json Documents

The amount of data circulating through the web has grown rapidly recently.This data is available as semistructured or unstructured documents, especially JSON documents.However, these documents lack semantic description.In this paper, we present a method to automatically extract an OWL2 ontology from a JSON document.We propose a set of transformation rules to transform JSON elements to ontology components.Our approach also allows analyzing the content of JSON documents to discover categorization in order to generate class hierarchy.Finally, we evaluate our approach by conducting experiments on several JSON documents.The results show that the obtained ontologies are rich in terms of taxonomic relationships.
Stochastic Momentum Method With Double Acceleration for Regularized Empirical Risk Minimization

Momentum acceleration technique is famously known for building gradient-based algorithms with fast convergence in large-scale optimization. Recently, Nesterov 's momentum and Katyusha momentum have significantly improved the convergence for stochastic optimization problems. However, the practical gain of acceleration with Nesterov's momentum is mainly a by-product of mini-batching, while acceleration merely with Katyusha momentum in stochastic steps would make the optimization unstable. In this paper, we build a stochastic and doubly accelerated momentum method (SDAMM) which incorporates the Nesterov's momentum and Katyusha momentum in the framework of variance reduction, to stabilize the accelerated algorithm and reduce the dependence on the mini-batching. Theoretically, SDAMM achieves the best-known convergence rates for convex objectives. The experimental results demonstrate that our SDAMM is competitive with state-of-the-art methods for the optimization problems in machine learning.
VORTEX ENTERPRISE: DECENTRALIZED VIRTUAL REALITY BLOCKCHAIN-BASED PLATFORM

Virtual Reality (VR) has been used in industries since the 1960s. However, the recent development of hardware and software has made it possible for VR technologies to become widely adopted by the mass market with the highest level of immersion and affordable price. However, building platforms for VR technologies will require many active users and their engagement in project development. Those activities will include platform activities, content creation, social media marketing, processing power, and storage. For that purpose, we introduce VoRtex enterprise high-level software architecture and decentralized blockchain solution. Introduced VoRtex enterprise high-level software architecture will be used to develop blockchain-based virtual world (VW) platform with VR support. The goal of the VW platform is to support the development of education, certification and massive open online courses (MOOC). A blockchain component inside the VW platform will be used for engaging users in project development through collaboration and content sharing. Active users that promote platform content will get rewards for contributions using cryptocurrency. Also, we will showcase this feature on the VoRtex platform prototype where the user earns cryptocurrencies for activity inside the VW. In the end, we will evaluate the proposed solution
Performance of grid-connected solar photovoltaic power plants in the Middle East and North Africa

A conceptual design Study of a solar electrical power system using PV array for a 5.3MW as nominal power required is presented. A Bird model has been used to estimate hourly, daily, monthly and yearly solar radiation amounts. f-f-chart is a design method was chosen to simulate the fraction of the solar energy required for the load given the PV array areas and climatic conditions. Four cities in the Middle East and North Africa representing different locations at southern Mediterranean region are selected Tripoli, Alexandria, Tunisia and Gaza city. Tripoli City has the best performance for 73% of nominal Power followed by Alexandria about 66% and then Gaza around 63%, Tunisia City has lowest solar fraction about 59% according to the Monthly and annual solar fraction Data.
A Shared Task for the Digital Humanities Chapter 2: Evaluating Annotation Guidelines

In this section, we will discuss our idea of guideline evaluation and the underlying considerations. Evaluating annotation guidelines in this way is a fairly new endeavor, and we have developed the evaluation setup from the ground up. Although we do not claim our choices to be universally valid or applicable, we believe that this approach to guideline evaluation is relevant for similar settings and can be adapted to projects that might have other preferences and priorities.
Implementasi Algoritma Long Short-Term Memory (LSTM) Untuk Mendeteksi Ujaran Kebencian (Hate Speech) Pada Kasus Pilpres 2019

Researches involving Artificial Neural Network (ANN) or its derivative have been published all around the world, spesifically to solve data mining problem, classification, clusterinf, or detection problems. Recurrent Neural Network is a class of ANN with Long Short Term Memory (LSTM) as its one of the architecture that commonly used in deep learning problems. On this paper, we use LSTM to detect hate speech on social media related with Indonesia President Election on 2019. There are several steps on this research, we start with literature study, data collection, data preprocessing, training step, and testing step. The dataset consist of 950 sentences, while the testing data consist of 190 comments on Facebook. The best model performance was reached with recall value 0.7021, which menas that from the whole relevant instances on the testing data, 70.21% were categorized as relevant, on this case as hate speech (HS). The other performance parameter value as in accuracy and precision still quite low due to the testing data that comes directly from social media which highly possible consist of inconsistent choises of words, informal words, or contains grammatically error sentences.
The ASVspoof 2019 database.

Automatic speaker verification (ASV) is one of the most natural andconvenient means of biometric person recognition. Unfortunately, just like allother biometric systems, ASV is vulnerable to spoofing, also referred to as''presentation attacks.'' These vulnerabilities are generally unacceptable andcall for spoofing countermeasures or presentation attack systems.In addition to impersonation, ASV systems are vulnerable to replay, speechsynthesis, and voice conversion attacks. The ASVspoof 2019 edition is the firstto consider all three spoofing attack types within a single challenge. Whilethey originate from the same source database and same underlying protocol, theyare explored in two specific use case scenarios. Spoofing attacks within alogical access (LA) scenario are generated with the latest speech synthesis andvoice conversion technologies, including state-of-the-art neural acoustic andwaveform model techniques. Replay spoofing attacks within a physical access(PA) scenario are generated through carefully controlled simulations thatsupport much more revealing analysis than possible previously. Also new to the2019 edition is the use of the tandem detection cost function metric, whichreflects the impact of spoofing and countermeasures on the reliability of afixed ASV system. This paper describes the database design, protocol, spoofingattack implementations, and baseline ASV and countermeasure results. It alsodescribes a human assessment on spoofed data in logical access. It wasdemonstrated that the spoofing data in the ASVspoof 2019 database have varieddegrees of perceived quality and similarity to the target speakers, includingspoofed data that cannot be differentiated from bona-fide utterances even byhuman subjects.
Influence of the player on the dynamics of the electric guitar

The sound of the electric guitar is strongly dependent on the string vibration. Where a mode of the structure coincides with a mode of the fretted string, coupling between the string and structure occurs at that "deadspot." The coupling significantly lowers decay time, leading to the name [Paté, Le Carrou, and Fabre (2014). J. Acoust. Soc. Am. 135(5), 3045-3055]. But how the guitarist affects the dynamic behavior of the structure by grasping the neck, holding the instrument with the strap, or laying the instrument on his/her thigh remains to be investigated. This is the aim of the paper. Two methods are proposed to identify the modal parameters of the electric guitar structure, either by a classical modal analysis in simulated playing configuration, or by an operational modal analysis in real playing configuration. For this latter method, modal parameters are identified from dynamic measurements performed when each string is plucked. Both methods are compared and allow one to quantify the modal frequency modification and the added modal damping, which depend on the player's body-part in contact with the structure and on the modal shape considered. Consequences of these modal parameters on the modeled sound show that the player can increase the decay time close to a deadspot.
Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks

As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years. Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold-start and one-class collaborative filtering problems. Many existing studies have focused on how to overcome these challenges by exploiting different types of contexts (e.g., social and geographical information). However, most of these methods only model these contexts as regularization terms, and the deep information hidden in the network structure has not been fully exploited. On the other hand, neural network-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data. According to the above observations, to well utilize the network information, a neural network-based embedding method (node2vec) is first exploited to learn the user and POI representations from a social network and a predefined location network, respectively. To deal with the implicit feedback, a pair-wise ranking-based method is then introduced. Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based POI recommendation method is proposed. As this method consists of an embedding model and a collaborative filtering model, when the training data are absent, the predictions will mainly be generated by the extracted embeddings. In other cases, this method will learn the user and POI factors from these two components. Experiments on two real-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.
Light field planar homography and its application

Light field planar homography is essential for light field camera calibration and light field raw data rectification. However, most previous researches assume light field camera as pinhole camera array and deduce the planar homography matrix of sub-aperture image, which is analogous to traditional method. In this paper, we regard light field as a whole and present a novel light field planar homography matrix based on multi-projection-center (MPC) model. The projections of point, line and conic are exploited based on light field planar homography. In addition, a camera calibration method and a homography estimation are proposed to verify the light field planar homography. Experimental results on light field datasets have verified the performance of the proposed methods.
A Margin-based Loss with Synthetic Negative Samples for Continuous-output Machine Translation

Neural models that eliminate the softmax bottleneck by generating word embeddings (rather than multinomial distributions over a vocabulary) attain faster training with fewer learnable parameters.These models are currently trained by maximizing densities of pretrained target embeddings under von Mises-Fisher distributions parameterized by corresponding model-predicted embeddings.This work explores the utility of margin-based loss functions in optimizing such models.We present syn-margin loss, a novel marginbased loss that uses a synthetic negative sample constructed from only the predicted and target embeddings at every step.The loss is efficient to compute, and we use a geometric analysis to argue that it is more consistent and interpretable than other marginbased losses.Empirically, we find that synmargin provides small but significant improvements over both vMF and standard marginbased losses in continuous-output neural machine translation.
An automatic lexicon generation for indonesian news sentiment analysis: a case on governor elections in Indonesia

Sentiment analysis has been popularly used in analyzing data from the internet. One of the techniques used is lexicon based sentiment analysis. Generating lexicon is not an easy process, and lexicon in Bahasa Indonesia is rarely available. This paper proposes an automatic lexicon generation in Bahasa Indonesia for sentiment analysis purpose. Experiments were performed using the generated lexicon for doing sentiment analysis on Indonesian political news about the 2018 governor election in three provinces in Indonesia. The conducted experiments show promising results where it can predict the candidate’s rank, the election winner, and the percentage of votes for each candidate with better accuracy than the previous work which used manually generated lexicon.
Probabilistic Teleportation of a Single Qubit: Unearthing New W-Class of States

In this work, we propose a probabilistic teleportation protocol to teleport a single qubit via three-qubit W-states using two-qubit measurement basis. We show that for the proper choice of the state parameter of the resource state, it is possible to make success probability of the protocol very high. We deduce the condition for the successful execution of our teleportation protocol and this gives us new class of three-qubit W-states which act as a resource state. We have constructed operators that can be used to verify the condition of teleportation in experiment. This verification is necessary for the detection of whether the given three-qubit state is useful in our teleportation protocol or not. Further we quantify the amount of entanglement contained in the newly identified shared W-class of states. Moreover, we show that the W-class of shared state used in the teleportation protocol can be prepared using NMR set up.