Reviewerly/exHarmony huggingface dataset:
SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs

Analytical queries over RDF data are becoming prominent as a result of the proliferation of knowledge graphs. Yet, RDF databases are not optimized to perform such queries efficiently, leading to long processing times. A well known technique to improve the performance of analytical queries is to exploit materialized views.Although popular in relational databases, view materialization for RDF and SPARQL has not yet transitioned into practice, due to the non-trivial application to the RDF graph model. Motivated by a lack of understanding of the impact of view materialization alternatives for RDF data, we demonstrate Sofos, a system that implements and compares several cost models for view materialization. Sofos is, to the best of our knowledge, the first attempt to adapt cost models, initially studied in relational data, to the generic RDF setting, and to propose new ones, analyzing their pitfalls and merits. Sofos takes an RDF dataset and an analytical query for some facet in the data, and compares and evaluates alternative cost models, displaying statistics and insights about time, memory consumption, and query characteristics.
Research on Chinese Entity Relation Extraction Method Based on Deep Learning

With the continuous progress of deep learning, “Entity relation extraction” is the most important subtask of information extraction. The theory of entity relation extraction model based on deep learning in Chinese is mature gradually. The purpose of this paper is to introduce the concept, classification and characteristics of Chinese entity relation extraction and explain the evaluation index. Under the framework of deep learning extraction, aiming at the use of deep learning method in entity extraction, the main methods are divided into supervised method and distant supervision method. Based on the two methods, the derived methods are explained and compared in detail. Chinese domain relation extraction is due to different language structures and other reasons, and the applicability of the relation extraction model based on English is not good. Although it started late, with the efforts of researchers, the current method is relatively perfect. Finally, this paper predicts and prospects the development direction of Chinese entity relation extraction in the future.
The Key of Application Software Service in Science Cloud Computing

Application software service is foundation in science cloud computing, but it's always related to limited software licenses and hardware resources. So, it is important to study the software and hardware co-allocation algorithm and strategy under the environment of science cloud computing. In this paper, a novel architecture of campus science cloud computing is proposed which can support policy-based software and hardware management and allocation. Then, a model is set up considering resources, users, jobs and applications. Furthermore, the software and hardware co-allocation algorithm SHA is proposed. The algorithm SHA has been used in the NPU campus cloud computing environment. The results show that it can improve the utilization rate of software, realize the difference of QoS between the users with different roles, and ensure the fairness between the users with the same role.
On Generating JPEG Adversarial Images

Adversarial attacks slightly perturb the original image to fool deep neural networks (DNN). Various schemes have been proposed to generate uncompressed adversarial images, which are usually ineffective after being compressed during the transmission. In this paper, we propose to generate JPEG adversarial images directly from the DNN. Two adversarial rounding schemes, including fast rounding and iterative rounding, are proposed to produce quantized DCT coefficients of JPEG adversarial images. Both schemes use the gradients of adversarial images in the DCT domain to guide the rounding. In fast rounding, we propose a novel indicator to evaluate the importance of the DCT coefficients for adversarial attacks, where only those with high importance are adversarially rounded to reduce the distortion. In iterative rounding, we additionally incorporate a loss function to mea-sure the distortion caused by adversarial rounding. The experiments show that our schemes can obtain effective JPEG adversarial images with low distortion.
Geochemical, geostatistical and time series analysis techniques as a tool to achieve the Water Framework Directive goals: An example from Piedmont region (NW Italy)

In this study, geochemical data processing and statistical/geostatistical techniques were coupled to achieve Water Framework Directive goals, collecting useful information from a complex database. Considering the Greater Turin metropolitan area, located in North-Western Italy, chemical data of 135 wells sampled and collected from 2000 to 2018 by the Environmental Protection Agency of Piedmont were analyzed. Both the processes that determine the groundwater chemical composition and the origin and evolution of groundwater were decoded and understood. The presence of two different components in the foothill aquifer system investigated was found. A bicarbonate alkaline earthy type component, linked with different degrees to phenomena of water-rock interaction, and a chlorinated type component related to nitrates, whose effects related to human activities can be recognized. This last component was mapped by using geostatistical techniques to define the most critical zones of the study area. Time series analysis on chlorides and nitrates displayed the presence of statistically significant increasing trends in some areas of the Turin plain. This study shows that the combining of geochemical approach, geostatistical techniques, and time series analysis can be used as reliable tools to understand geochemical processes acting on the system, to identify temporal and spatial hydrochemical variability, as well as to forecast water quality evolution. In this perspective, the multi-disciplinary approach developed in this study represents a reliable methodology for extracting useful information from a complex database and achieving the Water Framework Directive goals.
Logging Multi-Component Supply Chain Production in Blockchain

The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge.
Cyberbullying Detection on Social Media Using SVM

Social media has become a part of human day-to-day life. With the increasing use of social media, cyberbullying has become most common on social networking sites. This article focuses on a more extensive method for detecting cyberbullying comments which appear in online social media. Cyberbullying detection using support vector machine (SVM) along with TF-IDF for word vectorization is implemented in this work. The SVM performs classification on the target corpus into bully or non-bully comments, after training with text data extracted from the Wikipedia dataset. A set of English text has been collected from available social media platforms and labeled as either bullied or not bullied for training the machine learning based classification model. The support vector machine-based algorithm achieved an accuracy of 92%.
Implementation of Ethereum Request for Comment (ERC20) Token

Blockchain technology has been an essential aspect for research due to its, peer to peer nature, distributed nature, and decentralization, some of its applications are Supply chain, Healthcare System, Real state, etc. Ethereum is a distributed public blockchain network that focuses on running the code of any decentralized application. A smart contract is a computer program having self-verifying, self-executing, tamper-resistant properties. Initial Coin Offering (ICO) is a process similar to crowdfunding, in which companies raise funds from investors, who, in exchange receive tokens or digital assets. This paper deals by explaining the proposed model, different functions of ERC20 standard, the different tasks performed by each function and implementing various functionalities of ERC20 Token in Solidity.
Research on Sports Video Image Based on Clustering Extraction

Today, with the continuous sports events, the major sports events are also loved by the majority of the audience, so the analysis of the video data of the games has higher research value and application value. This paper takes the video of volleyball, tennis, baseball, and water polo as the research background and analyses the video images of these four sports events. Firstly, image graying, image denoising, and image binarization are used to preprocess the images of the four sports events. Secondly, feature points are used to detect the four sports events. According to the characteristics of these four sports events, SIFT algorithm is adopted to detect the good performance of SIFT feature points in feature matching. According to the simulation experiment, it can be seen that the SIFT algorithm can effectively detect football and have good anti-interference. For sports recognition, this document adopts the frame cross-sectional cumulative algorithm. Through simulation experiments, it can be seen that the grouping algorithm can achieve a recognition rate of more than 80% for sporting events, so it can be seen that the recognition algorithm is suitable for recognizing sports events videos.
Intelligent Security Monitoring System Based on RISC-V SoC

With the development of the economy and society, the demand for social security and stability increases. However, traditional security systems rely too much on human resources and are affected by uncontrollable community security factors. An intelligent security monitoring system can overcome the limitations of traditional systems and save human resources, contributing to public security. To build this system, a RISC-V SoC is first designed in this paper and implemented on the Nexys-Video Artix-7 FPGA. Then, the Linux operating system is transplanted and successfully run. Meanwhile, the driver of related hardware devices is designed independently. After that, three OpenCV-based object detection models including YOLO (You Only Look Once), Haar (Haar-like features), and LBP (Local Binary Pattern) are compared, and the LBP model is chosen to design applications. Finally, the processing speed of 1.25 s per frame is realized to detect and track moving objects. To sum up, we build an intelligent security monitoring system with real-time detection, tracking, and identification functions through hardware and software collaborative design. This paper also proposes a video downsampling technique. Based on this technique, the BRAM resource usage on the hardware side is reduced by 50% and the amount of pixel data that needs to be processed on the software side is reduced by 75%. A video downsampling technology is also proposed in this paper to achieve better video display effects under limited hardware resources. It provides conditions for future function expansion and improves the models’ processing speed. Additionally, it reduces the run time of the application and improves the system performance.
Bivariate Polynomial Codes for Secure Distributed Matrix Multiplication

We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we present a non-direct secure extension of the recently introduced bivariate polynomial codes. Bivariate polynomial codes have been shown to be able to further speed up distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them while reducing the upload communication cost and/or the workers&#x2019; storage&#x2019;s capacity needs. We show that, especially for upload communication or storage constrained settings, the proposed approach reduces the average computation time of SDMM compared to its competitors in the literature.
Dynamical pattern recognition for sampling sequences based on deterministic learning and structural stability

This paper focuses on the recognition problem of dynamical patterns consisting of sampling sequences. Specifically, based on the concept of structural stability, a novel similarity measure for dynamical patterns is first given. Then, a specific realization is provided, which consists of: (1) an approximation scheme for computation of partial derivative information by utilizing the knowledge learned through deterministic learning; (2) a similarity comparison scheme using the recognition errors generated from the discrete-time dynamical estimators; and (3) performance analysis of the recognition scheme with general recognition conditions. Compared with the existing methods, in which misrecognition may occur when the differences of dynamics between adjacent training patterns are very small, the proposed method is more appealing in the sense that, the partial derivatives of dynamics are introduced to complement the similarity measures, such that the recognition performance is much improved. Simulation studies are conducted to verify the proposed method in a relatively large data set.
Enki

Data-driven Learning-enabled Systems are limited by the quality of available training data, particularly when trained offline. For systems that must operate in real-world environments, the space of possible conditions that can occur is vast and difficult to comprehensively predict at design time. Environmental uncertainty arises when run-time conditions diverge from design-time training conditions. To address this problem, automated methods can generate synthetic data to fill in gaps for training and test data coverage. We propose an evolution-based technique to assist developers with uncovering limitations in existing data when previously unseen environmental phenomena are introduced. This technique explores unique contexts for a given environmental condition, with an emphasis on diversity. Synthetic data generated by this technique may be used for two purposes: (1) to assess the robustness of a system to uncertain environmental factors and (2) to improve the system’s robustness. This technique is demonstrated to outperform random and greedy methods for multiple adverse environmental conditions applied to image-processing Deep Neural Networks.
Gesture recognition of radar micro doppler signatures using separable convolutional neural networks

Gesture Recognition which is an inevitable part of human computer interaction is an ever evolving active research area. This has been in use under varied applications like smart home systems, sign language recognition, augment reality and in device controls. The acquisition of hand gestures are usually through optical sensors, in this work a radar based hand gesture data set is used for classifying the gestures. Micro doppler signatures were used as the input to the model. Radar dataset has fewer data samples compared to optical based data sets. The proposed work uses separable convolutional neural networks model which does depth wise convolution followed by point wise convolution to reduce overfitting effect of the training data. The proposed model was built in such a way that the model is capable of classifying any unseen data without exactly mimicking the training samples. The proposed model has achieved 94.56% as the testing accuracy which is certainly better than the previous work on this Dop Net data set. Moreover the model has also minimized the computational hours of the model using separable convolutions.
BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

Encrypted image retrieval may return incorrect or incomplete results due to threats from malicious cloud servers. Most of the existing solutions focus on the efficiency and accuracy of retrieval, lack of verification of the completeness of search results, to achieve the reliability of search results and the transparency of the search process, we explore characteristic such as the decentralization and tamper-proof of blockchain, proposed a blockchain-based encrypted image retrieval scheme. This scheme stores the encrypted index on the blockchain (Ethereum), through the blockchain consensus mechanism and the function of searching on the smart contract, ensures the integrity and correctness of search results, then outsources the corresponding encrypted images to the cloud server to reduce storage cost, and designs a double-layer index structure using the bag of visual word model and simhash in the process of image similarity index. Experiments show that the reliability, high retrieval efficiency, and precision of the scheme also have a good privacy protection effect.
A Deep Autoencoder Based Multi-Criteria Recommender System

Traditional recommender systems (RSs) try to quantify the user preferences about items using a numerical value, called rating. However, since employing of RSs has been increased, user expectations have been differentiated. Users may judge items according to different criteria. This gives birth to multicriteria recommender systems where users provide the rating on multiple aspects of an item in new dimensions, thereby increasing the rating matrix's size and opening up some challenges for researchers in the field, such as sparsity, scalability, and the aggregation of multicriteria rating problems. In this paper, we propose a multicriteria recommender system based on a deep autoencoder to learn the nonlinear relation between users on a multicriteria context in order to reconstruct the missing ratings, and on a Multi-Criteria Decision Making method, which proposes a Correlation Coefficient and Standard Deviation (CCSD) integrated approach to determine the weight of the criteria. We compare our results to some other single and multicriteria recommendation models. The results show that our proposed approach boosts the performance up and outperforms all other methods in terms of recommendation accuracy measures.
Energy-Efficient Scheduling of MapReduce Tasks Based on Load Balancing and Deadline Constraint in Heterogeneous Hadoop YARN Cluster

Hadoop YARN has become a dominant framework for big data analysis and processing. However, the inbuilt scheduler in Hadoop YARN framework is not designed for energy efficiency. To overcome this problem, this paper presents an energy-efficient extension on the existing Hadoop YARN framework. In addition, we formulate the MapReduce scheduling in the heterogeneous Hadoop YARN cluster as an energy consumption optimization problem, and propose a heuristic algorithm to solve this optimization problem. The proposed algorithm takes advantage of both load balancing and dynamic voltage/frequency scaling to improve performance and energy efficiency of the Hadoop YARN cluster. We evaluate the effectiveness of our method by carrying out extensive experiments on a real Hadoop YARN cluster consisting of five servers. The results show that our method can provide significant energy savings and achieve better performance compared with three alternative methods applied to similar problems.
A Deeper Dive into Pattern-Aware Subgraph Exploration with PEREGRINE

Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. PEREGRINE is a general-purpose graph mining system that provides a generic runtime to efficiently explore subgraph structures of interest and perform various graph mining analyses. It takes a 'pattern-aware' approach by incorporating a pattern-based programming model along with efficient pattern matching strategies. The programming model enables easier expression of complex graph mining use cases and enables PEREGRINE to extract the semantics of patterns. By analyzing the patterns, PEREGRINE generates efficient exploration plans which it uses to guide its subgraph exploration. In this paper, we present an in-depth view of the patternanalysis techniques powering the matching engine of PEREGRINE. Beyond the theoretical foundations from prior research, we expose opportunities based on how the exploration plans are evaluated, and develop key techniques for computation reuse, enumeration depth reduction, and branch elimination. Our experiments show the importance of patternawareness for scalable and performant graph mining where the presented new techniques speed up the performance by up to two orders of magnitude on top of the benefits achieved from the prior theoretical foundations that generate the initial exploration plans.
Review on Detection of Fake Currency using Image processing Techniques

Many countries are affected by the matter of fake notes. Indian is one among them. With the improved technology, anyone can print fake notes. These notes are produced without legal sanction of the state and continues production of such kinds of notes can degrade countries economy. When such counterfeited notes are produced and circulated, it becomes impossible for ordinary citizens to distinguish whether the money is real or fake because they differentiate according to physical appearance. The biggest challenge for many countries like India is the detection of fake currency. Even if banks and other big organizations have automatic machines designed to identify counterfeit currency notes, ordinary people can hardly differentiate between them. Nowadays recognition of fake currency has become challenging issue for many researchers. The identification involves many steps like edge detection, feature extraction, image segmentation, image acquisition, grayscale conversion, and comparison of images. This paper provides some related works of paper-currency recognition and has explained the spread of various currency recognition systems. Choosing the right feature would improve overall system performance. The goal of this work is to review previous papers and literature, identify the benefits and disadvantages of every method.