Reviewerly/exHarmony huggingface dataset:
Learned Index Benefits

Index selection remains one of the most challenging problems in relational database management systems. To find an optimum index configuration for a workload, accurately and efficiently quantifying the benefits of each candidate index configuration is indispensable. As materializing each index configuration candidate and physically executing queries are infeasible, most of index tuners rely on the cost estimations from optimizer with "what-if" API. However, "what-if" based index benefit estimations have the following two limitations. Firstly, they generate significant errors, which compromise index recommendation quality. Secondly, generating query plans and benefit estimations for each candidate index configuration takes a considerable amount of time. To address the two challenges in index selection, we propose an effective end-to-end machine learning based index benefit estimator. In particular, we propose novel feature extraction and encoding techniques that do not rely on "what-if" call to generate query plan for each index configuration candidate. In addition, we design an attention mechanism to address index interaction issue and aggregate the impacts of different query operations. Finally, we leverage transfer learning technique to improve the estimator's learning ability for adaption to new database. Comprehensive experiments are conducted on different workloads, and extensive experimental results show that our proposed method outperforms "what-if" based index benefit estimations in terms of accuracy and efficiency. In addition, integrating our method into existing index selection algorithms can significantly improve index recommendation quality.
Comparison of Machine Learning Methods in Sentiment Analysis PeduliLindungi Applications

KOMINFO, in partnership with the Ministry of State-Owned Enterprises (BUMN), developed the “PeduliLindungi” smartphone application, which has been implemented in the community. This application is unquestionably a topic that is frequently debated by the community via social media, namely Twitter. This PeduliLindung program harvests the positives and negatives of Twitter public opinion. Through posting on Twitter and expressing opinions on this problem, data can be collected and evaluated into negative and favorable opinion sentiments. From this occurrence, study was undertaken to analyze Twitter users' perspectives on the application's security. A dataset including 1,922 tweets will be analyzed using five Machine Learning techniques: Naive Base, SVM, Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest. Then, it will be compared to the value of accuracy and AUC score for the performance of each of the five approaches. Collecting user tweet data with crawling techniques, data processing, word weighting with the TD-IDF approach, modeling with five machine learning methods, comparison, and algorithm selection are the stages of the suggested study method. The results indicated that the accuracy of the KNN method was 98.40%, Decision Tree 97.60%, Random Forest 97.60%, SVM 97.10%, and Naive Bayes 87.70%, while the AUC score for each method was 99.80% for KNN, 99.80% for Decision Tree, 99.80% for Random Forest, 95.80% for SVM, and 91.27% for Naive Bayes. With an accuracy value of 98.40% and an AUC Score of 98.80%, the KNN approach gives superior performance than the other methods.
TSSuBERT: How to Sum Up Multiple Years of Reading in a Few Tweets

The development of deep neural networks and the emergence of pre-trained language models such as BERT allow to increase performance on many NLP tasks. However, these models do not meet the same popularity for tweet stream summarization, which is probably because their computation limitation requires to drastically truncate the textual input. Our contribution in this article is threefold. First, we propose a neural model to automatically and incrementally summarize huge tweet streams. This extractive model combines in an original way pre-trained language models and vocabulary frequency based representations to predict tweet salience. An additional advantage of the model is that it automatically adapts the size of the output summary according to the input tweet stream. Second, we detail an original methodology to construct tweet stream summarization datasets requiring little human effort. Third, we release the TES 2012-2016 dataset constructed using the aforementioned methodology. Baselines, oracle summaries, gold standard, and qualitative assessments are made publicly available. To evaluate our approach, we conducted extensive quantitative experiments using three different tweet collections as well as an additional qualitative evaluation. Results show that our method outperforms state-of-the-art ones. We believe that this work opens avenues of research for incremental summarization, which has not received much attention yet.
A Deep Reinforcement Learning Real-Time Recommendation Model Based on Long and Short-Term Preference

Abstract With the development of Internet technology, the problem of information overload has increasingly attracted attention. Nowadays, the recommendation system with excellent performance in information retrieval and filtering would be widely used in the business field. However, most existing recommendation systems are considered a static process, during which recommendations for internet users are often based on pre-trained models. A major disadvantage of these static models is that they are incapable of simulating the interaction process between users and their systems. Moreover, most of these models only consider users’ real-time interests while ignoring their long-term preferences. This paper addresses the abovementioned issues and proposes a new recommendation model, DRR-Max, based on deep reinforcement learning (DRL). In the proposed framework, this paper adopted a state generation module specially designed to obtain users’ long-term and short-term preferences from user profiles and user history score item information. Next, Actor-Critical algorithm is used to simulate the real-time recommendation process.Finally, this paper uses offline and online methods to train the model. In the online mode, the network parameters were dynamically updated to simulate the interaction between the system and users in a real recommendation environment. Experimental results on the two publicly available data sets were used to demonstrate the effectiveness of our proposed model.
High-dimensional quantum key distribution implemented with biphotons

We present a high-dimensional measurement device-independent (MDI) quantum key distribution (QKD) protocol employing biphotons to encode information. We exploit the biphotons as qutrits to improve the tolerance to error rate. Qutrits have a larger quantum system; hence they carry more bits of classical information and have improved robustness against eavesdropping compared to qubits. Notably, our proposed protocol is independent of measurement devices, thus eliminating the possibility of side-channel attacks. Also, we employ the finite key analysis approach to study the performance of our proposed protocol under realistic conditions where finite resources are used. Furthermore, we simulated the secret key rate for the proposed protocol in terms of the transmission distance for different fixed amounts of signals. The results prove that this protocol achieves a considerable secret key rate for a moderate transmission distance of 90 km by using [Formula: see text] signals. Moreover, the expected secret key rate was simulated to examine our protocol's performance at various intrinsic error rate values, [Formula: see text] caused by misalignment and instability due to the optical system. These results show that reasonable key rates are achieved with a minimum data size of about [Formula: see text] signals which are realizable with the current technology. Thus, implementing MDI-QKD using finite resources while allowing intrinsic errors due to the optical system makes a giant step forward toward realizing practical QKD implementations.
Intrinsic factors for decreasing role of libraries in information activities

Digital transformation of document arrays has changed the nature of information infrastructure, as the digital documents are created, distributed, discovered and delivered in principally other way than in the epoch of printed matters as the main information media. The transition to digital information infrastructure has resulted in the loss of role of libraries as an essential intermediate on the document author-to-reader way, reasonably displacing libraries to the periphery of information activities and depreciating the majority of methods and technologies of document array organization developed through the centuries. The author substantiates the rationale for degrading information component of library mission and the necessity to transit to prevalent analytical services. The decreasing role of libraries is determined by the following factors: reduction of overall printed publications; their lower contextual value; capabilities to store, distribute, process digital information array formally and semantically, information deliver to users without the input of librarian. Therefore, not all the manual processing operations have become demanded any more. The print arrays grow scanty quantitively and contextually, and the increasing and dynamical digital flows are processed by ever-improving software. The author sees the solution in expanding analytical function of libraries which envisages inclusion of professional competences related to complex analytical processes, i. e. fact search and verification; acquisition and processing of statistical and experimental research data, preparation of analytical surveys, reviews and reports.
An efficient ACO-based algorithm for task scheduling in heterogeneous multiprocessing environments

In heterogeneous computing environments, finding optimized solutions continues to be one of the most challenging problems as we continuously seek better and improved performances. Task scheduling in such environments is NP-hard, so it is imperative that we tackle this critical issue with a desire of producing effective and efficient solutions. For several types of applications, the task scheduling problem is crucial, and throughout the literature, there are a plethora of different algorithms using several different techniques and varying approaches. Ant Colony Optimization (ACO) is one such technique used to address the problem. This popular optimization technique is based on the cooperative behavior of ants seeking to identify the shortest path between their nest and food sources. It is with this in mind that we propose an ACO-based algorithm, called ACO-RNK, as an efficient solution to the task scheduling problem. Our algorithm utilizes pheromone and a priority-based heuristic, known as the upward rank value, as well as an insertion-based policy, along with a pheromone aging mechanism which aims to avoid premature convergence to guide the ants to good quality solutions. To evaluate the performance of our algorithm, we compared our algorithm with the HEFT algorithm and the MGACO algorithm using randomly generated directed acyclic graphs (DAGs). The simulation results indicated that our algorithm experienced comparable or even better performance, than the selected algorithms.
Use of topical and temporal profiles and their hybridisation for content-based recommendation

Abstract In the context of content-based recommender systems, the aim of this paper is to determine how better profiles can be built and how these affect the recommendation process based on the incorporation of temporality, i.e. the inclusion of time in the recommendation process, and topicality, i.e. the representation of texts associated with users and items using topics and their combination. To that end, we build both topically and temporally homogeneous subprofiles to represent items. The main contribution of the paper is to present two different ways of hybridising these two dimensions and to evaluate and compare them with other alternatives. Our proposals and experiments are carried out in the specific context of publication venue recommendation.
Versatile recurrent neural network for wide types of video restoration

Video shooting of natural scenes often suffers from various serious degradation, such as motion blur, impact of atmospheric turbulence, random noise and resolution reduction, etc. Different from the maturity of image restoration research, video restoration is much more complicated so that it lacks effective general method. Here, we present a versatile recurrent neural network (VRNN) to handle wide types of video degradation and generate stable videos with ideal clarity. We complete the design of VRNN through deducing a general video restoration paradigm that reveals the importance of simultaneously utilizing past and future information for restoring current frame. Specifically, we propose a novel RNN cell in which hidden state flows in bidirections, enriching temporal information contained in the extracted features. Furthermore, a feature fusion module involves temporal and spatial attention processing is designed to refine features of neighbouring frames and help reconstruct current frame. Extensive experiments on well-known public datasets (including four different kinds of video restoration tasks, with a total of 35,666 videos and 515,774 frames) show that the proposed VRNN achieves 1–4 dB of PSNR increasing or several times less of computational complexity in all tasks against state-of-the-art methods, manifesting the versatile and efficient ability of proposed VRNN in wide types of video restoration.
Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

The object detection technologies of remote sensing are widely used in various fields, such as environmental monitoring, geological disaster investigation, urban planning, and military defense. However, the detection algorithms lack the robustness to detect tiny objects against complex backgrounds. In this paper, we propose a Multiple Attention Mechanism Enhanced YOLOX (MAME-YOLOX) algorithm to address the above problem. Firstly, the CBAM attention mechanism is introduced into the backbone of the YOLOX, so that the detection network can focus on the saliency information. Secondly, to identify the high-level semantic information and enhance the perception of local geometric feature information, the Swin Transformer is integrated into the YOLOX's neck module. Finally, instead of GIOU loss, CIoU loss is adopted to measure the bounding box regression loss, which can prevent the GIoU from degenerating into IoU. The experimental results of three publicly available remote sensing datasets, namely, AIBD, HRRSD, and DIOR, show that the algorithm proposed possesses better performance, both in relation to quantitative and qualitative aspects.
A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach

Wearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers.We aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace.We used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system.We designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study.A decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.
Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram

In signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amplifier, and image rejection filter, the intermediate frequency signal is obtained by down-conversion, and then the IQ signal is obtained in the baseband by using the intermediate frequency band-pass filter and down-conversion. In this process, noise and signal frequency offset are inevitably introduced. As the basis of subsequent analysis and interpretation, modulation recognition has important research value in this environment. The introduction of deep learning also brings new feature mining tools. Based on this, this paper proposes a signal modulation recognition method based on multi-feature fusion and constructs a deep learning network with a double-branch structure to extract the features of IQ signal and multi-channel constellation, respectively. It is found that through the complementary characteristics of different forms of signals, a more complete signal feature representation can be constructed. At the same time, it can better alleviate the influence of noise and frequency offset on recognition performance, and effectively improve the classification accuracy of modulation recognition.
An Extension Network of Dendritic Neurons

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch–Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.
I-VITAL: Information aided visual tracking with adversarial learning

With the advent of convolutional neural networks (CNN), MDNet and the Siamese trackers posed tracking as supervised learning. They model an object's presence using classification (foreground and background) and location using regression. For the first time, we have brought probability distribution into the CNN framework for tracking. We have selected "Information maximization Generative Adversarial Network (InfoGAN)" to couple the target and background classes with two unique Gaussian distributions. This paper highlights the use of InfoGAN in information extraction & feedback to improve the tracking framework. Specifically, the novel features proposed in this tracking framework are (i) Coupling of unique probability distributions to target and background classes and (ii) Unsupervised tracker status (success/ failure) identification and correction through information feedback. We demonstrated the efficacy of the proposed I-VITAL tracker in visual tracking with experimental comparisons on well-known data sets such as GOT10K, VOT2020, and OTB-2015. Compared with base works, the proposed tracker has improved performance in locating the object of interest.
Text augmentation using a graph-based approach and clonal selection algorithm

Annotated data is critical for machine learning models, but producing large amounts of data with high-quality labeling is a time-consuming and labor-intensive process. Natural language processing (NLP) and machine learning models have traditionally relied on the labels given by human annotators with varying degrees of competency, training, and experience. These kinds of labels are incredibly problematic because they are defined and enforced by arbitrary and ambiguous standards. In order to solve these issues of insufficient high-quality labels, researchers are now investigating automated methods for enhancing training and testing data sets. In this paper, we demonstrate how our proposed method improves the quality and quantity of data in two cybersecurity problems (fake news identification & sensitive data leak) by employing the clonal selection algorithm (CLONALG) and abstract meaning representation (AMR) graphs, and how it improves the performance of a classifier by at least 5% on two datasets.
Spade

Real-time fraud detection is a challenge for most financial and electronic commercial platforms. To identify fraudulent communities, Grab, one of the largest technology companies in Southeast Asia, forms a graph from a set of transactions and detects dense subgraphs arising from abnormally large numbers of connections among fraudsters. Existing dense subgraph detection approaches focus on static graphs without considering the fact that transaction graphs are highly dynamic. Moreover, detecting dense subgraphs from scratch with graph updates is time consuming and cannot meet the real-time requirement in industry. Therefore, we introduce an incremental real-time fraud detection framework called Spade. Spade can detect fraudulent communities in hundreds of microseconds on million-scale graphs by incrementally maintaining dense subgraphs. Furthermore, Spade supports batch updates and edge grouping to reduce response latency. Lastly, Spade provides simple but expressive APIs for the design of evolving fraud detection semantics. Developers plug their customized suspiciousness functions into Spade which incrementalizes their semantics without recasting their algorithms. Extensive experiments show that Spade detects fraudulent communities in real time on million-scale graphs. Peeling algorithms incrementalized by Spade are up to a million times faster than the static version.