Reviewerly/exHarmony huggingface dataset:
Utilizing Biofilter & Activated Carbon Technology Process to Transform Wastewater into a New Source of Service Water in PT PJB UP Muara Karang

one of world environmental issues is industrial pollution. This issue was followed up in 2015-2019 Ministry of Environment and Forestry Strategic Plan (RENSTRA KLHK 2105-2019) to reduce pollution discharged into the environment by the industry that already treated from wastewater treatment plants (WWTP) directly into the sea. In 2019, the average wastewater production of PT PJB UP Muara Karang is 2950 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /month. PT PJB UP Muara Karang modified the WWTP technology which not only could reduce discharged wastewater, but also recycle them into water service. It was modified by adding Bee-hive- media biofilter and changing filter media on filtration units using activated carbon. This paper aims to show modification process by using biofilter and activated carbon, also to show the potential of service water which already treated as a new source and the quality of wastewater that will be utilized. The result shows 2950 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> of production water complied to 26 parameters of clean water according to the Ministry of Health Regulation No. 32 in 2017. This amount can reduce the use of service water from the existing consumption by 21% of the monthly average which automatically impacts to operational efficiency of Combine Cycle Power Plant.
Privacy-Preserving and Public Auditing for Regenerating-Code-Based Cloud Storage Using Finger Print Authentication

Cloud computing is one of the emerging technologies that push many association customers to the next level. Security is one of the big challenges of this invention. Biometric systems ensure only that a genuine customer or an authenticated user, and no one else, should be able to access the administration. Customers are interpreted by biometric systems based on their comfortability or physiological attributes. In fact, the key purpose of cloud computing is to provide accurate information. It includes testing the use of TPA for unauthorized access. This work performs ensuring information and information recovery on the off chance that someone is misusing it. This operation is assigned to a Proxy server. The customer information will be put a way in the cloud's public and private field. The aim is to get customer information from the public cloud and to protect the private cloud. After some unapproved change is made, the first in the private cloud information will be returned to the client via proxy server. This paper reproduces a security system in which several biometric fingerprints are brought to the table in the center of the registration for the administration. In addition to biometric photo-preparing procedures for fingerprint recognition, the way to combine traditional customer identifications and hidden key components is investigated entirely to enhance public cloud health. The possibility that “biometry measurements as a software” will pose another cloud advantage is also studied.
Improving Social Recommendations with Item Relationships

Social recommendations have witnessed rapid developments for improving the performance of recommender systems, due to the growing influence of social networks. However, existing social recommendations often ignore to facilitate the substitutable and complementary items to understand items and enhance the recommender systems. We propose a novel graph neural network framework to model the multi-graph data (user-item graph, user-user graph, item-item graph) in social recommendations. In particular, we introduce a viewpoint mechanism to model the relationship between users and items. We conduct an extensive experiment on two public benchmarks, demonstrating significant improvement over several state-of-the-art models.
Toward OWL Restriction Reconciliation in Merging Knowledge

Merging ontologies is the standard way to achieve interoperability of heterogeneous systems in the Semantic Web. Because of the possibility of different modeling, OWL restrictions from one ontology may not necessarily be compatible with those from other ontologies. Thus, the merged ontology can suffer from restriction conflicts. This problem so far has got little attention. We propose a workflow to detect and resolve the OWL restriction conflicts within the merged ontology. We reconcile “one type” conflicts by building a subsumption hierarchy. We tackle cardinality restriction conflicts with least upper and greatest lower bound methods. By utilizing the semantic relatedness between two classes, we overcome value restriction conflicts.
Study of classification techniques for human action recognition based on PCA and width vectors

The article presents the results of the research of classification techniques for human action recognition based on PCA and width vectors using a video recorded in the optical range. The method used in this paper consists of the detection of a moving person on a video sequence with size normalization, the formation of a set of subsequences, and feature vectors. To form the feature vectors, we use two different approaches. The first approach is based on the dimensionality reduction using principal component analysis, and the second one utilizes the width profile of a detected silhouette. The classification of human actions is carried out using a support vector machine with different kernels, k-nearest neighbors, and random forest classifier. The obtained results allowed us to find the most effective parameters for the considered technique.
Path Planning System for Smart Cars Used in Education

In this paper, we developed a path planning system for smart cars for teaching electronic engineering or computer science, which consists of the interactive platform for smart cars development and path planning. Designed by Visual C++, the interactive platform can call Matlab engine, allows users to choose path optimization algorithms such as genetic or A-star(A*) algorithm for different tasks and control smart cars through serial ports. The simulation and practice demonstrate that our interactive platform can help learners to plan paths and control intelligent vehicles without specially designing a user interface.
Application of Data Analytics in Emerging Fields

Data, which is available in abundance and in accessible forms, if analyzed in an efficient manner, unfolds many patterns and promising solutions. The present world is moving from the information age to the digital age, entering a new era of analytics. Whatever the end user does is recorded and stored. The purpose of data analytics is to make the “best out of waste.” Analytics often employs advanced statistical techniques (logistic regression, multivariate regression, time series analysis, etc.) to derive meaning from data. There are essentially two kinds of analytics: 1) descriptive analytics and 2) predictive analytics. Descriptive analytics describes what has happened in the past. Predictive analytics predicts what will happen in the future.
Face Recognition Based on LBP Algorithm

In this paper, face recognition technology is studied and explored, and the classical local binary pattern (LBP) algorithm is studied and improved. An improved LBP algorithm is proposed to solve the deficiency of LBP: a “double circle” LBP algorithm is proposed, which improves the rotation invariability of LBP to a greater extent and has a good recognition effect on small-pose face images. On the basis of good recognition effect of LBP based on partitioning, the idea of “multiple partitioning + intermediate partitioning” is put forward, which effectively makes up for the shortage of incomplete extraction of peripheral information of original segmentation line. Facial features are the key features of face recognition. Different organs contribute differently to face image recognition. The idea of “key block” based on the original block is proposed. Finally, the application of an LBP algorithm for face recognition is taken as an example, and the experimental results verify the reliability of the face recognition method based on LBP for weak light and expression change images.
Training Lightweight yet Competent Network via Transferring Complementary Features

Though deep neural networks have achieved quite impressive performance in various image detection and classification tasks, they are often constrained by requiring intensive computation and large storage space for deployment in different scenarios and devices. This paper presents an innovative network that aims to train a lightweight yet competent student network via transferring multifarious knowledge and features from a large yet powerful teacher network. Based on the observations that different vision tasks are often correlated and complementary, we first train a resourceful teacher network that captures both discriminative and generative features for the objective of image classification (the main task) and image reconstruction (an auxiliary task). A lightweight yet competent student network is then trained by mimicking both pixel-level and spatial-level feature distribution of the resourceful teacher network under the guidance of feature loss and adversarial loss, respectively. The proposed technique has been evaluated over a number of public datasets extensively and experiments show that our student network obtains superior image classification performance as compared with the state-of-the-art.
U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments

Optical Flow is a popular method of computer vision for motion estimation. In this paper, we present a refined optical flow estimation method. Central to our approach is exploiting contour information as most of the motion lies on the edges. Further, we have formulated it as sparse to dense motion estimation. Proposed method has been evaluated on challenging real life image sequences of KITTI and Fish4Knowledge database. Results demonstrate that method performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change.
Segment-Level Sentiment Classification for Online Comments of Legal Cases

Sentiment analysis (SA) is to predict the sentiment polarity towards a given document. Our work focuses on sentiment analysis towards comments of legal cases, which reflects people's concern about public safety and the impartiality of judgment result. The existing methods treat this problem as a coarse document-level classification and ignore topics information, which cannot obtain an accurate sentiment polarity towards a sentence. With topics information attended, we present a novel segment-level model, to boost the performance. Firstly, we divide a sentence into several segments delimited by some important conjunctions. We find that opinion in a segment is usually explicit. Secondly, we apply two-step attention to obtain accurate representation. To enhance the interactions between comments and topics, we apply attention mechanisms twice, once between topics and context in the segment level, and the other between segments. Thirdly, we concatenate the segment and context representation to receive final representation. It unifies two types of information into a classifier. Experimental results on real-world datasets demonstrate that our model outperforms existing methods.
NPDSLINKS: Nexus-PORTAL-DOORS-Scribe Learning Intelligence aNd Knowledge System

With the continuing growth in use of large complex data sets for artificial intelligence applications (AIA), unbiased methods should be established for assuring the validity and reliability of both input data and output results. Advancing such standards will help to reduce problems described with the aphorism `Garbage In, Garbage Out' (GIGO). This concern remains especially important for AIA tools that execute within the environment of interoperable systems which share, exchange, convert, and/or interchange data and metadata such as the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure and its associated Learning Intelligence aNd Knowledge System (LINKS) applications. The PORTAL-DOORS Project (PDP) has developed the NPDS cyberinfrastructure with lexical PORTAL registries, semantic DOORS directories, hybrid Nexus diristries, and Scribe registrars. As a self-referencing and self-describing system, the NPDS cyberinfrastructure has been designed to operate as a pervasive distributed network of data repositories compliant with the Hierarchically Distributed Mobile Metadata (HDMM) architectural style. Building on the foundation of the NPDS cyberinfrastructure with its focus on data, PDP has now introduced LINKS applications with their focus on algorithms and analysis of the data. In addition, PDP has launched a pair of new websites at NPDSLINKS.net and NPDSLINKS.org which will serve respectively as the root of the NPDS cyberinfrastructure and the home for definitions and standards on quality descriptors and quantitative measures to evaluate the data contained within NPDS records. Prototypes of these descriptors and measures for use with NPDS and LINKS are introduced in this report. PDP envisions building better AIA and preventing the unwanted phenomenon of GIGO by using the combination of metrics to detect and reduce bias from data, the NPDS cyberinfrastructure for the data, and LINKS applications for the algorithms.
Information Entropy-Based Leakage Profiling

An accurate leakage model is critical to side-channel attacks and evaluations. Leakage certification plays an important role to address the following question: “how good is my leakage model?” Moreover, most of the current leakage model profiling only exploit the information from lower orders of moments. They still need to tolerate assumption error and estimation error from unknown leakage models. There are many probability density functions (PDFs) satisfying given moment constraints. As such, finding an unbiased, objective, and reasonable model still remains an unresolved problem. In this article, we address a more fundamental question: “which model can approach the leakage infinitely and is the optimal in theory?” In particular, we extract information from higher order moments and propose maximum entropy distribution (MED) to estimate the leakage model as MED is an unbiased, objective, and theoretically the most reasonable PDF conditioned upon the available information. MED is a moment-based statistical PDF model in side-channel attacks. It can theoretically use information on arbitrary higher order moments to infinitely approximate the leakage distribution, and well compensates the theory vacancy of model profiling and evaluation. Experimental results demonstrate the superiority of our proposed method for approximating the leakage model using MED estimation.
Document Summarization Based on Coverage with Noise Injection and Word Association

Automatic document summarization is a field of natural language processing that is rapidly improving with the development of end-to-end deep learning models. In this paper, we propose a novel summarization model that consists of three methods. The first is a coverage method based on noise injection that makes the attention mechanism select only important words by defining previous context information as noise. This alleviates the problem that the summarization model generates the same word sequence repeatedly. The second is a word association method to update the information of each word by comparing the information of the current step with the information of all previous decoding steps. According to following words, this catches a change in the meaning of the word that has been already decoded. The third is a method using a suppression loss function that explicitly minimizes the probabilities of non-answer words. The proposed summarization model showed good performance on some recall-oriented understudy for gisting evaluation (ROUGE) metrics compared to the state-of-the-art models in the CNN/Daily Mail summarization task, and the results were achieved with very few learning steps compared to the state-of-the-art models.
Autocorrelation Convolution Networks Based on Deep Learning for Automatic Modulation Classification

Automatic modulation classification (AMC) is challenging but significant in the field of cognitive radio. Despite recent deep learning methods have dominated as the best performers for AMC, they are challenged by the practical problem in low signal-to-noise ratios (SNRs). In this paper, we propose novel autocorrelation convolution networks (ACNs) to capture periodic representation for communication signals. In ACNs, modulation modes are classified with the periodic local features under an autocorrelation convolution criterion. The experimental results demonstrate that ACNs achieve a great improvement that outperforms recent deep learning methods in low SNRs.
Theoretical Studies on Quantum Walks with a Time-varying Coin

Quantum walks can reconstruct quantum algorithms for quantum computation, where the precise controls of quantum state transfers between arbitrary distant sites are required.Here, we investigate quantum walks using a periodically time-varying coin both numerically and analytically, in order to explore the controllability of quantum walks while preserving its random nature. Quantum walk with a time-varying coinLet us consider a quantum walk with a time-varying coin on a line.In random walks, the walker moves one step to right or left depending on the outcome of the coin toss.A quantum walk is a quantummechanical counterpart of the classical random walk.Then, a quantum walk lives in a bipartite system comprising positions and coin degrees of freedom represented as an entire Hilbert space H = H p ⊗ H c ,
Recovering Causal Structures from Low-Order Conditional Independencies

One of the common obstacles for learning causal models from data is that high-order conditional independence (CI) relationships between random variables are difficult to estimate. Since CI tests with conditioning sets of low order can be performed accurately even for a small number of observations, a reasonable approach to determine casual structures is to base merely on the low-order CIs. Recent research has confirmed that, e.g. in the case of sparse true causal models, structures learned even from zero- and first-order conditional independencies yield good approximations of the models. However, a challenging task here is to provide methods that faithfully explain a given set of low-order CIs. In this paper, we propose an algorithm which, for a given set of conditional independencies of order less or equal to k, where k is a small fixed number, computes a faithful graphical representation of the given set. Our results complete and generalize the previous work on learning from pairwise marginal independencies. Moreover, they enable to improve upon the 0-1 graph model which, e.g. is heavily used in the estimation of genome networks.
Pixel-Semantic Revising of Position: One-Stage Object Detector with Shared Encoder-Decoder

Recently, many methods have been proposed for object detection. However, they cannot detect objects by semantic features, adaptively. According to channel and spatial attention mechanisms, we mainly analyze that different methods detect objects adaptively. Some state-of-the-art detectors combine different feature pyramids with many mechanisms. However, they require more cost. This work addresses that by an anchor-free detector with shared encoder-decoder with attention mechanism, extracting shared features. We consider features of different levels from backbone (e.g., ResNet-50) as the basis features. Then, we feed the features into a simple module, followed by a detector header to detect objects. Meantime, we use the semantic features to revise geometric locations, and the detector is a pixel-semantic revising of position. More importantly, this work analyzes the impact of different pooling strategies (e.g., mean, maximum or minimum) on multi-scale objects, and finds the minimum pooling can improve detection performance on small objects better. Compared with state-of-the-art MNC based on ResNet-101 for the standard MSCOCO 2014 baseline, our method improves detection AP of 3.8%.
Concept drift detection on stream data for revising DBSCAN

Abstract Data stream mining of IoT data can support operator to immediately isolate causes of equipment alarms. The challenge, however, is to keep their classifiers high purity (the data ratio with same proper class in a cluster) with concept drifting ascribed to differences between alarm models and entities. We propose to continuously update data class according to their distribution changes. Through evaluation, no purity deterioration was verified for oscillation condition data with a drifting rate of 1%. The result suggested that the method improves operator decision making.
The Measurement of Chinese Sentence Semantic Complexity

The complexity of language is usually reflected in the complexity of sentences. At present, the research of sentence complexity mainly focuses on the analysis of syntactic complexity. In this paper, from the perspective of Leech's theory of sentence semantic structure, the predication structure is taken as the semantic unit to explore the sentence semantic complexity. The predication structures are extracted based on the result of sentence-based syntactic analysis, and then the linear expression sequence of a sentence is converted into a semantic hierarchy based on predicate semantic frameworks; the universality of predicate semantic frameworks is obtained by using the spectral clustering algorithm; and the sentence semantic complexity depends on the universality of predicate semantic frameworks at various layers. The experimental results show that the measurement method of sentence sematic complexity based on predicate semantic frameworks is more effective by comparing with the method that only considers the semantic categories of words in the sentence.
Detection of acoustic signals from Distributed Acoustic Sensor data with Random Matrix Theory and their classification using Machine Learning

This work aims at the detection and classification of Distributed Acoustic Sensor (DAS) acquired acoustic signals. We obtained the data by probing an optical fiber with light pulses and gauging the Rayleigh backscatter. Said data contains four different classes; Walking, Shovel and Pick digging as well as Hammer hitting. We first proceed by detecting the event and its location along the fiber and extracting it from the random noise using Spiked Random Matrix Theory (RMT) models, namely Marchenko-Pastur (MP) and Tracy-Widom (TW) distributions. We then label the datasets accordingly and proceed with the classification process using machine learning algorithms. For this, we test and evaluate Convolutional Neural Networks (CNN), which has been proven to provide high accuracies in similar studies, taking the spectrograms of the signals as our network’s input. We conclude by providing the performance of our CNN architecture and propose a few options to further improve the performance of the model.
Public key authentication scheme over quaternions

Authentication is one of the cryptographic primitives that allows one entity to convince to another of the truth of an assertion without revealing any information other than that the assertion is true. In this paper, public key authentication scheme over quaternions modulo an RSA composite n = pq, where p and q are primes of equal size is proposed. Security of the scheme relies on the root extraction problem together with the conjugacy search problem over quaternions modulo n. Furthermore, issues related to the security, space complexity and computational cost of the scheme are discussed.