Reviewerly/exHarmony huggingface dataset:
A Text-Generated Method to Joint Extraction of Entities and Relations

Entity-relation extraction is a basic task in natural language processing, and recently, the use of deep-learning methods, especially the Long Short-Term Memory (LSTM) network, has achieved remarkable performance. However, most of the existing entity-relation extraction methods cannot solve the overlapped multi-relation extraction problem, which means one or two entities are shared among multiple relational triples contained in a sentence. In this paper, we propose a text-generated method to solve the overlapped problem of entity-relation extraction. Based on this, (1) the entities and their corresponding relations are jointly generated as target texts without any additional feature engineering; (2) the model directly generates the relational triples using a unified decoding process, and entities can be repeatedly presented in multiple triples to solve the overlapped-relation problem. We conduct experiments on two public datasets—NYT10 and NYT11. The experimental results show that our proposed method outperforms the existing work, and achieves the best results.
A Study of Text Classification for Indonesian News Article

This paper aims to do a study of text classification for Indonesian News Article. This research will explore the use of some machine learning algorithms like support vector machine, multinomial naive Bayes, and decision tree to classify news article in the Indonesian Language. From the experiment, it showed that support vector machine algorithm achieves high performance with 93% in f1 score.
A Non-Iterative Neural-Like Framework for Missing Data Imputation

The paper presents developed by the authors a non-iterative, neural-like framework for filling missing data. This framework applies the high-speed linear neural-like structure using the successive geometrical transformations model. The use of this computing intelligence tool decreases the duration of the training process, compared to known algorithms of solving the defined task. Besides, the developed framework provides a high degree of accuracy in solving the problem of filling missing data. The simulation of the method had conducted on the monitoring data of environmental pollution, where the task of fill missing data is very relevant. The high accuracy of the developed supervised learning-based framework based on the definition of RMSE, MAPE, MAE errors in recovering missing data was confirmed. Also, experiments with models were conducted comparing the duration of the training procedures of all investigated methods. The high efficiency of the developed framework was confirmed. Modern Data-driven methods have most often faced with the problem of filling out missing data in various fields. Thus developed framework can be used in medicine, information security, economics, service sciences, materials science.
Product collection recommendation in online retail

Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form "users who bought product A also bought products B, C, and D". Although such recommendations can be useful, there is no guarantee that products A, B, C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction. We extend on traditional approaches that use mostly transactional data by incorporating both domain knowledge from product suppliers in the form of hierarchies, as well as textual attributes from the products. Our approach starts by combining product hierarchies together with transactional data or domain knowledge to identify candidate sets of product collections. Then, it generates the product collection recommendations from these candidate sets by learning a deep similarity model that leverages textual attributes. Experimental evaluation on real data from the Home Depot online retailer shows that the proposed solution can recommend collections of products with increased accuracy when compared to expert-crafted collections.
Adaptive Framework for Deep Learning Based Dynamic and Temporal Topic Modeling from Big Data

Background: The large amount of data emanated from social media platforms need scalable topic modeling in order to get current trends and themes of events discussed on such platforms. Topic modeling play crucial role in many natural language processing applications like sentiment analysis, recommendation systems, event tracking, summarization, etc. Objective: The aim of the proposed work is to adaptively extract the dynamically evolving topics over streaming data, and infer the current trends and get the notion of trend of topics over time. Because of various world level events, many uncorrelated streaming channels tend to start discussion on similar topics. We aim to find the effect of uncorrelated streaming channels on topic modeling when they tend to start discussion on similar topics. Methods: An adaptive framework for dynamic and temporal topic modeling using deep learning has been put forth in this paper. The framework approximates online latent semantic indexing constrained by regularization on streaming data using adaptive learning method. The framework is designed using deep layers of feedforward neural network. Results: This framework supports dynamic and temporal topic modeling. The proposed approach is scalable to large collection of data. We have performed exploratory data analysis and correspondence analysis on real world Twitter dataset. Results state that our approach works well to extract topic topics associated with a given hashtag. Given the query, the approach is able to extract both implicit and explicit topics associated with the terms mentioned in the query. Conclusion: The proposed approach is a suitable solution for performing topic modeling over Big Data. We are approximating the Latent Semantic Indexing model with regularization using deep learning with differentiable ℓ1 regularization, which makes the model work on streaming data adaptively at real-time. The model also supports the extraction of aspects from sentences based on interrelation of topics and thus, supports aspect modeling in aspect-based sentiment analysis.
Multi-agent trajectory planning: A decentralized iterative algorithm based on single-agent dynamic RRT*

This paper addresses trajectory planning in a multi-agent cooperative setting, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> agents are moving in the same region and need to coordinate so as to maintain a certain pairwise safety distance, while avoiding obstacles. We introduce a decentralized strategy that is based on an iterative (re)plan-compare-assign process. The key features of the proposed strategy are that coordination is obtained via the compare-assign phase in at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> iterations (including the initialization), and (re)planning is performed by the agents using a single-agent planner, considering the tentative trajectories of the others fixed, and without sharing with them their tracking capabilities and adopted cost criterion. In the proposed implementation, each agent uses a dynamic Rapidly exploring Random Tree star (RRT*) planner that integrates a new prune and graft feature to avoid rebuilding a new tree from its root each time replanning is needed. The resulting Multi-RRT* algorithm is tested in 2D scenarios and shows promising results.
A simple yet efficient Path Tracking Controller for Autonomous Trucks

This paper presents a path tracking controller implemented for the Robot Operating System (ROS). It is developed for the funded project AutoTruck in which a 18 t truck will be automated for yard applications. The goal was to design a simple yet sufficient controller, which could also be used on a standard in vehicle control unit. Therefore, no advanced non linear controller was designed. The controller is a combination of a PID controller and a point follow controller. Both controllers individually have their drawbacks for the application. However, the combination proves to provide a good tracking performance. The controllers are tested in simulation and with 1:32 scale trucks in the test environment called DriveLab.
A n-Gated Recurrent Unit with review for answer selection

Answer selection is one of the most important techniques in question answering applications since it can improve the user experience to a large extend. To achieve a better answer selection performance, a fundamental approach is to better understand the answers and questions. In this research, motivated by the Ebbinghaus Forgetting Curve which indicated that people should review the knowledge timely to prevent from forgetting, we proposed a new n-Gated Recurrent Unit with Review (nGRUR) model which applies the review mechanism on the gated recurrent unit (GRU). The nGRUR model recurrently reviews the past information after a fixed time distance n, and the process of review is controlled by the new gated units. Experimental results have proven the potential of the proposed model and the quantitative analysis has demonstrated that our model is able to acquire a better sentence level representation.
Forecasting a Storm: Divining Optimal Configurations using Genetic Algorithms and Supervised Learning

With the advent of Big Data platforms like Apache Storm, computations once deemed infeasible locally become possible at scale. However, doing so entails orchestrating powerful yet expensive clusters. With its focus on stream processing, Storm optimizes for low-latency and high throughput. However, to realize this goal and thereby maximize the utility of these clusters' resources, operators must execute these tasks under their optimal configurations. Yet, the search space for finding such configurations is so vast and time-consuming to explore so as to be effectively intractable due to issues like the temporal overhead of testing new candidate configurations, the sheer number of permutations of parameters within each configuration and their interdependence among each other. In order to efficiently cover the search space, we automate the process with genetic algorithms. Moreover, we fuse this technique not only with additional cluster information gleaned from JMX profiling and Storm performance data but also with classifiers constructed from training data from past executions of a plethora of Storm topologies. Utilizing a diverse set of Storm benchmark topologies as evaluation data, we show that the fully enhanced genetic algorithms can efficiently find configurations that perform on average 4.67× better than "rules of thumb"-derived manual baselines. Moreover, we demonstrate that our fully refined classifiers enhance the GA throughput on average across the topologies by 22% while reducing search time by a factor of 6.47×.
Finding needles in a haystack: Leveraging co-change dependencies to recommend refactorings

A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method, change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches for recommending refactorings that change software decomposition (such as a move method) do not explore the use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First we evaluate our approach using 49 open-source Java projects, finding 610 evolutionary smells. Our approach automatically computes 56 refactoring recommendations that remove these evolutionary smells, without introducing new static dependencies. We also evaluate our approach by submitting pull-requests with the recommendations of our technique, in the context of one large and two medium size proprietary Java systems. Quantitative results show that our approach outperforms existing approaches for recommending refactorings when dealing with co-change dependencies. Qualitative results show that our approach is promising, not only for recommending refactorings but also to reveal opportunities of design improvements.
FMT

Older adults sometimes forget about whether or not they have completed routine actions and the states of objects that they have interacted with (e.g., the kitchen stove is on or off). In this work, we explore whether video clips captured from a body-worn camera every time objects of interest are found within its field of view can help older adults determine if they have completed certain actions with these objects and what their states are. We designed FMT ("Fiducial Marker Tracker")---a real-time capture and access application that opportunistically captures video clips of objects the user interacts with. To do this, the user places fiducial markers close to objects which would be captured when the marker enters the user's body-worn camera's field of view. We examine and discuss what objects this system would be best suited to track, and the usefulness and usability of this approach. FMT successfully captured direct interactions with an object at an average rate of 75.6% across all participants (SD = 9.9%). Our results also reveal how, what, and why users would use such a system for help.
An improved join‐free snowflake schema for ETL and OLAP of data warehouse

Summary The emergence of big data makes more and more enterprise change data management strategy, from simple data storage to OLAP query analysis; meanwhile, NoSQL‐based data warehouse receive more increasing attention than traditional SQL‐based database. By improving the JFSS model for ETL, this paper proposes the uniform distribution code (UDC), model identification code (MIC), standard dimension code (SDC), and attribute dimensional code (ADC); defines the data storage format of &lt;Rowkey Compound Code, Sec (Value)&gt;; and identifies the extraction, transformation, and loading strategies of data warehouse. Several experiments are carried out to analyze single record and range record queries as typical OLAP based on Hadoop database (HBase). The results show the proposed scheme can provide lower overhead than the traditional SQL‐based database while facilitating the scope and flexibility of data warehouse services.
Transformation and Additivity in Gaussian Processes

Li-Hsiang Lina & V. Roshan Joseph*aa H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA
Public Key Authenticated Encryption With Designated Equality Test and its Applications in Diagnostic Related Groups

Due to the massive growth of data and security concerns, data of patients would be encrypted and outsourced to the cloud server for feature matching in various medical scenarios, such as personal health record systems, actuarial judgments and diagnostic related groups. Public key encryption with equality test (PKEET) is a useful utility for encrypted feature matching. Authorized tester could perform data matching on encrypted data without decrypting. Unfortunately, due to the limited terminology in medicine, people within institutions may illegally use data, trying to obtain information through traversal methods. In this paper we propose a new PKEET notion, called public-key authenticated encryption with designated equality test (PKAE-DET), which could resist this kind of attacks launched by an inside adversary, known as offline message recovery attacks (OMRA). We propose a concrete construction of PKAE-DET, which only requires one single server to perform the feature matching job securely, and does not require any group mechanism. We prove its security based on some simple mathematical assumptions. Experimental results show that our scheme has efficiency comparable with those PKEET schemes which do not resist OMRA attacks or require group mechanism. We further show how our scheme could be effectively used in diagnostic related groups in medicine, demonstrating its practicability.
Domain adaptation in display advertising

Digital advertisements connects partners (sellers) to potentially interested online users. Within the digital advertisement domain, there are multiple platforms, e.g., user re-targeting and prospecting. Partners usually start with re-targeting campaigns and later employ prospecting campaigns to reach out to untapped customer base. There are two major challenges involved with prospecting. The first challenge is successful on-boarding of a new partner on the prospecting platform, referred to as partner cold-start problem. The second challenge revolves around the ability to leverage large amounts of re-targeting data for partner cold-start problem.
Authenticated Encryption with Nonce Misuse and Physical Leakage: Definitions, Separation Results and First Construction

We propose definitions of authenticated encryption (AE) schemes that offer security guarantees even in the presence of nonce misuse and side-channel information leakage. This is part of an important ongoing effort to make AE more robust, while preserving appealing efficiency properties. Our definitions consider an adversary enhanced with the leakage of all the computations of an AE scheme, together with the possibility to misuse nonces, be it during all queries (in the spirit of misuse-resistance), or only during training queries (in the spirit of misuse-resilience recently introduced by Ashur et al.). These new definitions offer various insights on the effect of leakage in the security landscape. In particular, we show that, in contrast with the black-box setting, leaking variants of INT-CTXT and IND-CPA security do not imply a leaking variant IND-CCA security, and that leaking variants of INT-PTXT and IND-CCA do not imply a leaking variant of INT-CTXT. They also bring a useful scale to reason about and analyze the implementation properties of emerging modes of operation with different levels of leakage-resistance, such as proposed in the ongoing NIST lightweight cryptography competition. We finally propose the first instance of mode of operation that satisfies our most demanding definitions.
Pedestrian Detection Algorithm Based on the Improved SSD

In recent years, pedestrian detection is a hot research topic in the field of computer vision and artificial intelligence, It is widely used in the fields of automatic driving safety and security and pedestrian analysis. But pedestrians as transfigure change target, and because the pedestrian dress differ in thousands ways, lead to the traditional pedestrian detection technology detection accuracy is not high, it restricts the development of pedestrian detection technology. With the development of deep learning, the pedestrian detection method based on deep learning greatly improves the accuracy of pedestrian detection. For the problem of low detection accuracy of small targets in SSD, this paper proposes an improved SSD pedestrian detection method based on the context information, by extracting the shallow features in the SSD convolutional neural network, fuses the shallow semantic information in the convolutional layer with the deep semantic information, and then detects the size of the pedestrian in the image. Features Redesigned pre-selection boxes with different aspect ratios, improve the SSD model test result is superior to the standard model of the SSD.
Algorithm Research on Improving Activation Function of Convolutional Neural Networks

Aiming at the slow convergence of the activation function of Sigmoid, Tanh, ReLu and Softplus as the model and the non-convergence caused by gradient dispersion, this paper proposes an algorithm to improve the activation function of convolutional neural network. Using R-SReLU as the activation function of the neural network, the convergence speed of various excitation functions to the network and the accuracy of image recognition are analyzed. The experimental data shows that the improved activation function R-SReLU not only has a fast convergence speed, but also has a small error rate, and can improve the accuracy of classification more effectively. The maximum recognition accuracy reaches 88.03%.
Hyperspectral Image Restoration via Local Low-Rank Matrix Recovery and Moreau-Enhanced Total Variation

In this letter, we present a hyperspectral image (HSI) mixed-noise removal method named Moreau-enhanced total variation (TV) regularized local low-rank matrix recovery (LLRMTV). The rank-fixed matrix recovery is first adopted to separate the low-rank clean HSI patches from the sparse noise. Then, a Moreau-enhanced TV regularized image reconstruction strategy is utilized to ensure the piecewise smoothness of the reconstructed image from the low-rank patches. The proposed Moreau-enhanced TV restoration method involves a nonconvex penalty designed to maintain the convexity of the objective function. Moreover, the proposed model is integrated into an augmented Lagrange multiplier (ALM) algorithm to produce final results, leading to a complete HSI restoration framework. Examples of restoration illustrate the improvement over the typical TV regularization.
SARA: Stably and quickly find optimal cloud configurations for heterogeneous big data workloads

With the development of cloud platforms, more and more workloads are operated on the cloud. It is important to find the optimal cloud configurations for these workloads. But most of the previous optimizers concentrate on optimizing single workload. They have high overhead when faced with a large number of workloads to be optimized. To address this problem, we propose SARA, a system that uses Deep Reinforcement Learning to find the optimal cloud configuration for workloads. It is especially stable and quick for any amount of heterogeneous big data workloads. Our research results show that SARA finds the optimal or near-optimal configuration for single workload at a low cost, which is about 28% of Brute Force. For collective workloads, the average probability of SARA to find the optimal configuration is 1.8 times higher than the state-of-the-art collective optimizer Micky, and the cost of SARA for small number of workloads is within 12.5%–80% of Micky.
An Approach towards an Efficient Encryption-Decryption of Grayscale and Color Images

Protecting images from unauthorized access and use is an important task in various areas. The problem of unauthorized use of images is solved at different levels: from the provisions on copyright, to methods of cryptography and steganography, digital watermarking etc. The paper considers a new, authors-developed approach to encryption-decryption of grayscale and color images, which is based on the RSA algorithm. We describe the algorithmic implementation of encrypting and decrypting procedures by one row of image matrix with and without use of additional noise-adding. We developed the procedures of encrypting-decrypting of images by one row of image matrix by combining elements of the RSA algorithm and bitwise binary operations. The effectiveness of the proposed approach is confirmed by conducting a series of experiments with grayscale and color images. We also study the influence of additional noise-adding functions applied to the source image as well as the various values of simple numbers of the RSA system on the results of the method. It was found that these parameters do not result in appearance of contours in the encrypted image, and the resulting image differs from the source one by a small change in the brightness level only. In addition to the high cryptographic strength of the RSA algorithm, the overall method’s cryptostrength is provided by the additional use of bitwise binary operations.