Reviewerly/exHarmony huggingface dataset:
Learning Robust Representations with Graph Denoising Policy Network

Existing representation learning methods based on graph neural networks and their variants rely on the aggregation of neighborhood information, which makes it sensitive to noises in the graph, e.g. erroneous links between nodes, incorrect/missing node features. In this paper, we propose Graph Denoising Policy Network (short for GDPNet) to learn robust representations from noisy graph data through reinforcement learning. GDPNet first selects signal neighborhoods for each node, and then aggregates the information from the selected neighborhoods to learn node representations for the down-stream tasks. Specifically, in the signal neighborhood selection phase, GDPNet optimizes the neighborhood for each target node by formulating the process of removing noisy neighborhoods as a Markov decision process and learning a policy with task-specific rewards received from the representation learning phase. In the representation learning phase, GDPNet aggregates features from signal neighbors to generate node representations for down-stream tasks, and provides task-specific rewards to the signal neighbor selection phase. These two phases are jointly trained to select optimal sets of neighbors for target nodes with maximum cumulative task-specific rewards, and to learn robust representations for nodes. Experimental results on node classification task demonstrate the effectiveness of GDNet, outperforming the state-of-the-art graph representation learning methods on several well-studied datasets.
Chatbot in Bahasa Indonesia using NLP to Provide Banking Information

FAQs are mostly provided on the company's website to inform their service and product. It's just that the FAQ is usually less interactive and presents too much information that is less practical. Chatbot can be used as an alternative in providing FAQ. In this study, chatbots were developed for BTPN in providing information about their products, namely Jenius. Chatbot developed utilizes natural language processing so that the system can understand user queries in the form of natural language. The cosine similarity algorithm is used to find similarities between queries and patterns in the knowledge base. Patterns with the highest cosine values are considered to be most similar to user queries. It's just that, this algorithm does not pay attention to the structure of the sentence so that it adds checking the structure of the sentence with the parse tree to give weight to the pattern. This chatbot application has been tested by 10 users and it was found that the suitability of the answers with user input was 84%. Therefore the chatbot developed can be used by BTPN to provide Jenius product information to consumers more interactively and practically.
Exploring temporal consistency for human pose estimation in videos

In this paper, we introduce a method of exploring temporal information for estimating human poses in videos. The current state-of-the-art methods utilizing temporal information can be categorized into two major branches. The first category is a model-based method that captures the temporal information entirely by using a learnable function such as RNN or 3D convolution. However, these methods are limited in exploring temporal consistency, which is essential for estimating human joint positions in videos. The second category is the posterior enhancement method, where an independent post-processing step (e.g., using optical flow) is applied to enhance the prediction. However, operations such as optical flow estimation can be susceptible to the occlusion and motion blur problems, which will adversely affect the final performance. We propose a novel Temporal Consistency Exploration (TCE) module to address both shortcomings. Compared to previous approaches, the TCE module is more efficient as it captures the temporal consistency at the feature level without having to post-process and calculate extra optical flow. Further, to capture the rich spatial context in video data, we design a multi-scale TCE to explore the time consistency information at multi-scale spatial levels. Finally, a video-based pose estimation network is designed, which is based on the encoder-decoder architecture and extended with the powerful multi-scale TCE module. We comprehensively evaluate the proposed model on two video datasets, Sub-JHMDB and Penn, and our model achieves state-of-the-art performance on both datasets.
Computing Classical Modular Forms

We discuss practical and some theoretical aspects of computing a database of classical modular forms in the L-functions and modular forms database (LMFDB).
Using Granule to Search Privacy Preserving Voice in Home IoT Systems

The Home IoT Voice System (HIVS) such as Amazon Alexa or Apple Siri can provide voice-based interfaces for people to conduct the search tasks using their voice. However, how to protect privacy is a big challenge. This paper proposes a novel personalized search scheme of encrypting voice with privacy-preserving by the granule computing technique. Firstly, Mel-Frequency Cepstrum Coefficients (MFCC) are used to extract voice features. These features are obfuscated by obfuscation function to protect them from being disclosed the server. Secondly, a series of definitions are presented, including fuzzy granule, fuzzy granule vector, ciphertext granule, operators and metrics. Thirdly, the AES method is used to encrypt voices. A scheme of searchable encrypted voice is designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granule of the voice. The experiments are conducted on corpus including English, Chinese and Arabic. The results show the feasibility and good performance of the proposed scheme.
Soil spatial patterns analysis at the ancient city of Ibida (Dobrogea, SE Romania), via portable X-ray fluorescence spectrometry and multivariate statistical methods

In this study we investigated the potential usefulness of multi-elemental soil analysis, obtained by a portable XRF spectrometer, to recognize ancient anthropogenic features, compared to the natural context on the territory of Roman-Byzantine Ibida city (Dobrogea, SE Romania). Soil samples were collected from 16 soil profiles and 2 archaeological excavations, from each soil horizon and cultural layer. Pearson correlation matrix of topsoil elements showed significant correlations among the various geogenic elements such as Ti, Fe, Mn, Zr, Ba and Cr. In the case of P, a strong correlation was found with Ca, a lower correlation with K, and no significant correlation with the other elements. Also, Cu and Zn behave differently, showing no significant correlation with any of the elements. Principal components analysis (PCA) and agglomerative hierarchical clustering (AHC) were carried out in order to identify groups of inter-correlated variables and assess the degree of association among soil profiles. The correlation between soil elements and the principal components suggest the presence of three groups which were defined as the geogenic, archaeo-anthropogenic and agro-anthropogenic component. Spatial distribution of topsoil elements was mapped by geostatistical methods in order to see if their spatial patterns reveal the influence of human activities. Although the results show that certain elements such as Ca, P and K present higher concentrations in the Ibida fortress area, we found that the interpretation of multi-elemental composition of the topsoil only is not sufficient for an accurate identification of the ancient characteristics which can be linked to the Roman-Byzantine period. Hence, we wanted to know if the presence of archaeological sites could be revealed by higher variation degree of soil elemental data along the profile depth. Therefore, we tested the utility of a synthetic statistical index, namely the variation coefficient, which was computed for each soil profile using all the available data. The results showed that if the index was standardized according to profile depth, then it became a useful indicator for the presence of ancient human activities.
Implementing CCTV-Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College

Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed-circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real-world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.
A verifiable multiparty quantum key agreement based on bivariate polynomial

Based on a point-to-point quantum key distribution protocol and classical key distribution technologies, the concept of quantum-classical key and a new multi-party quantum-classical key agreement protocol is proposed in this paper. The distributor and any one of the participants use quantum key distribution protocol to negotiate subkey, then each of the participants shares his subkey with other participants using Shamir threshold secret sharing scheme, thus each participant will sort these subkeys and his own subkey in order, and eventually get this quantum-classical key. In this protocol, a pair of function values of binary polynomials are used to ensure the authentication of sub-shares and information among participants, and Lagrange interpolation formula of unary polynomials is used to ensure the recovery of subkeys. Security analysis shows that this protocol is secure, practical and effective.
Optical encryption of hyperspectral images using improved binary tree structure and phase-truncated discrete multiple-parameter fractional Fourier transform

We present an optical encryption scheme for hyperspectral images using an improved binary tree structure (IBTS) and phase-truncated discrete multiple-parameter fractional Fourier transform (PTdmpFrFT). In the IBTS, the encryption modules based on the phase-truncated fractional Fourier transform represented the branch nodes, while the hyperspectral bands of the plain images were considered as leaf nodes. All pairs of bands were encoded into intermediate data using the IBTS, with subsequent encryption into asymmetric ciphertexts using the PTdmpFrFT. The proposed approach generated different pairs of bands with different secret keys, and encryption and decryption paths. Compared with state-of-the-art optical hyperspectral encryption methods, our approach not only achieved superior encryption security but also reduced the computation and storage requirements of the encrypted data. The original hyperspectral image information could be successfully decrypted only when using all the correct keys. Numerical simulations were performed to verify the performance of the proposed method.
Building Robust Phishing Detection System: an Empirical Analysis

To tackle phishing attacks, recent research works have resorted to the application of machine learning (ML) algorithms, yielding promising results.Often, a binary classification model is trained on labeled datasets of benign and phishing URLs (and contents) obtained via crawling.While phishing classifiers have high accuracy (precision and recall), they, however, are also prone to adversarial attacks wherein an adversary tries to evade the ML-based classifier by mimicking (feature values of) benign web pages.Based on this observation, in our work, we propose a simple approach to build a robust phishing page detection system.Our detection system, based on voting, employs multiple models, such that each model is trained by inserting (controlled) noises in a subset of randomly selected features from the full feature set.We conduct comprehensive experiments using real datasets, and based on a number of evasive strategies, evaluate the robustness of, both, the traditional native ML model and our proposed detection system.The results demonstrate that our proposed system, on one hand, performs close to the native model when there is no adversarial attack, and on the other hand, is more robust against evasion attacks than the native model.
Melting Pot of Origins: Compromising the Intermediary Web Services that Rehost Websites

Intermediary web services such as web proxies, web translators, and web archives have become pervasive as a means to enhance the openness of the web.These services aim to remove the intrinsic obstacles to web access; i.e., access blocking, language barriers, and missing web pages.In this study, we refer to these services as web rehosting services and make the first exploration of their security flaws.The web rehosting services use a single domain name to rehost several websites that have distinct domain names; this characteristic makes web rehosting services intrinsically vulnerable to violating the same origin policy if not operated carefully.Based on the intrinsic vulnerability of web rehosting services, we demonstrate that an attacker can perform five different types of attacks that target users who make use of web rehosting services: persistent man-in-the-middle attack, abusing privileges to access various resources, stealing credentials, stealing browser history, and session hijacking/injection.Our extensive analysis of 21 popular web rehosting services, which have more than 200 million accesses per day, revealed that these attacks are feasible.In response to this observation, we provide effective countermeasures against each type of attack.
Deep learning-based forecasting of aggregated CSP production

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production.
A Survey towards Federated Semi-supervised Learning.

Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner. Yet, in most applications of FL, such as keyboard prediction, labeling data requires virtually no additional efforts, which is not generally the case. In reality, acquiring large-scale labeled datasets can be extremely costly, which motivates research works that exploit unlabeled data to help build machine learning models. However, to the best of our knowledge, few existing works aim to utilize unlabeled data to enhance federated learning, which leaves a potentially promising research topic. In this paper, we identify the need to exploit unlabeled data in FL, and survey possible research fields that can contribute to the goal.
A deep learning based technique for plagiarism detection: a comparative study

&lt;table width="0" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="593"&gt;&lt;p&gt;The ease of access to the various resources on the web-enabled the democratization of access to information but at the same time allowed the appearance of enormous plagiarism problems. Many techniques of plagiarism were identified in the literature, but the plagiarism of idea steels the foremost troublesome to detect, because it uses different text manipulation at the same time. Indeed, a few strategies have been proposed to perform semantic plagiarism detection, but they are still numerous challenges to overcome. Unlike the existing states of the art, the purpose of this study is to give an overview of different propositions for plagiarism detection based on the deep learning algorithms. The main goal of these approaches is to provide a high quality of worlds or sentences vector representation. In this paper, we propose a comparative study based on a set of criterions like: Vector representation method, Level Treatment, Similarity Method and Dataset. One result of this study is that most of researches are based on world granularity and use the word2vec method for word vector representation, which sometimes is not suitable to keep the meaning of the whole sentences. Each technique has strengths and weaknesses; however, none is quite mature for semantic plagiarism detection.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;
An analysis of data paper templates and guidelines: types of contextual information described by data journals

Purpose: Data papers are a promising genre of scholarly communication, in which research data are described, shared, and published. Rich documentation of data, including adequate contextual information, enhances the potential of data reuse. This study investigated the extent to which the components of data papers specified by journals represented the types of contextual information necessary for data reuse.Methods: A content analysis of 15 data paper templates/guidelines from 24 data journals indexed by the Web of Science was performed. A coding scheme was developed based on previous studies, consisting of four categories: general data set properties, data production information, repository information, and reuse information.Results: Only a few types of contextual information were commonly requested by the journals. Except data format information and file names, general data set properties were specified less often than other categories of contextual information. Researchers were frequently asked to provide data production information, such as information on the data collection, data producer, and related project. Repository information focused on data identifiers, while information about repository reputation and curation practices was rarely requested. Reuse information mostly involved advice on the reuse of data and terms of use.Conclusion: These findings imply that data journals should provide a more standardized set of data paper components to inform reusers of relevant contextual information in a consistent manner. Information about repository reputation and curation could also be provided by data journals to complement the repository information provided by the authors of data papers and to help researchers evaluate the reusability of data.
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems. Recently, a growing effort has been made to the development of automatic feature engineering methods, so that the substantial and tedious manual effort can be liberated. However, for industrial tasks, the efficiency and scalability of these methods are still far from satisfactory. In this paper, we proposed a staged method named SAFE (Scalable Automatic Feature Engineering), which can provide excellent efficiency and scalability, along with requisite interpretability and promising performance. Extensive experiments are conducted and the results show that the proposed method can provide prominent efficiency and competitive effectiveness when comparing with other methods. What's more, the adequate scalability of the proposed method ensures it to be deployed in large scale industrial tasks.
An Efficient Accelerator for Multiple Convolutions From the Sparsity Perspective

Convolutional neural networks (CNNs) have emerged as one of the most popular ways applied in many fields. These networks deliver better performance when going deeper and larger. However, the complicated computation and huge storage impede hardware implementation. To address the problem, quantized networks are proposed. Besides, various convolutional structures are designed to meet the requirements of different applications. For example, compared with the traditional convolutions (CONVs) for image classification, CONVs for image generation are usually composed of traditional CONVs, dilated CONVs, and transposed CONVs, leading to a difficult hardware mapping problem. In this brief, we translate the difficult mapping problem into the sparsity problem and propose an efficient hardware architecture for sparse binary and ternary CNNs by exploiting the sparsity and low bit-width characteristics. To this end, we propose an ineffectual data removing (IDR) mechanism to remove both the regular and irregular sparsity based on dual-channel processing elements (PEs). Besides, a flexible layered load balance (LLB) mechanism is introduced to alleviate the load imbalance. The accelerator is implemented with 65-nm technology with a core size of 2.56 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It can achieve 3.72-TOPS/W energy efficiency at 50.1 mW, which makes it a promising design for embedded devices.
Reducing the Computational Requirements in the Mamdani-type Fuzzy Control

Fuzzy-based control systems are popular in practical applications where imprecision, subjectivity, and uncertainty can arise in the data and in the evaluation process, and it needs to be addressed.One possible solution is the fuzzy approach.However, computational requirements of these models can be extremely high, mainly defuzzification part of the Mamdani-type control.Whatever great advantages the Mamdani model has, it is closer to the human way of thinking compared to the Sugeno model, which is another popular controller.In the case where a short reaction time is required, the computational needs should be reduced.Here, we propose modified Mamdani models for this purpose.This new model is much faster while all the advantageous properties of the original Mamdani model are retained, and in some ways, it is better.
Weighting the domain of probability densities in functional data analysis

In functional data analysis, some regions of the domain of the functions can be of more interest than others owing to the quality of measurement, relative scale of the domain, or simply some external reason (e.g. interest of stakeholders). Weighting the domain is of interest particularly with probability density functions (PDFs), as derived from distributional data, which often aggregate measurements of different quality or are affected by scale effects. A weighting scheme can be embedded into the underlying sample space of a PDF when it is considered as continuous compositions applying the theory of Bayes spaces. The origin of a Bayes space is determined by a given reference measure, and this can be easily changed through the well‐known chain rule. This work provides a formal framework for defining weights through a reference measure, and it is used to develop a weighting scheme on the bounded domain of distributional data. The impact on statistical analysis is illustrated through an application to functional principal component analysis of income distribution data. Moreover, a novel centred log‐ratio transformation is proposed to map a weighted Bayes space into an unweighted space, enabling to use most tools developed in functional data analysis (e.g. clustering and regression analysis) while accounting for the weighting scheme. The potential of our proposal is shown on a real case study using Italian income data.