Reviewerly/exHarmony huggingface dataset:
Hybrid fast unsupervised feature selection for high-dimensional data

The emergence of ``curse of dimensionality” issue as a result of high reduces datasets deteriorates the capability of learning algorithms, and also requires high memory and computational costs. Selection of features by discarding redundant and irrelevant features functions as a crucial machine learning technique aimed at reducing the dimensionality of these datasets, which improves the performance of the learning algorithm. Feature selection has been extensively applied in many application areas relevant to expert and intelligent systems, such as data mining and machine learning. Although many algorithms have been developed so far, they are still unsatisfying confronting high-dimensional data. This paper presented a new hybrid filter-based feature selection algorithm based on acombination of clustering and the modified Binary Ant System (BAS), called FSCBAS, to overcome the search space and high-dimensional data processing challenges efficiently. This model provided both global and local search capabilities between and within clusters. In the proposed method, inspired by genetic algorithm and simulated annealing, a damped mutation strategy was introduced that avoided falling into local optima, and a new redundancy reduction policy adopted to estimate the correlation between the selected features further improved the algorithm. The proposed method can be applied in many expert system applications such as microarray data processing, text classification and image processing in high-dimensional data to handle the high dimensionality of the feature space and improve classification performance simultaneously. The performance of the proposed algorithm was compared to that of state-of-the-art feature selection algorithms using different classifiers on real-world datasets. The experimental results confirmed that the proposed method reduced computational complexity significantly, and achieved better performance than the other feature selection methods.
A Novel Neural Network for Remote Sensing Image Matching

Rapid development of remote sensing (RS) imaging technology makes the acquired images have larger size, higher resolution, and more complex structure, which goes beyond the reach of classical hand-crafted feature-based matching. In this paper, we propose a feature learning approach based on two-branch networks to transform the image matching task into a two-class classification problem. To match two key points, two image patches centered at the key points are entered into the proposed network. The network aims to learn discriminative feature representations for patch matching, so that more matching pairs can be obtained on the premise of maintaining higher subpixel matching accuracy. The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images. An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point. Thus, each patch can preserve the texture structure around its key point rather than all patches have a predetermined size. In the matching prediction stage, two strategies, namely, superpixel-based sample graded strategy and superpixel-based ordered spatial matching, are designed to improve the matching efficiency and matching accuracy, respectively. The experimental results and theoretical analysis demonstrate the feasibility, robustness, and effectiveness of the proposed method.
Recent Progress of Anomaly Detection

Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection. Recently, a significant number of anomaly detection methods with a variety of types have been witnessed. This paper intends to provide a comprehensive overview of the existing work on anomaly detection, especially for the data with high dimensionalities and mixed types, where identifying anomalous patterns or behaviours is a nontrivial work. Specifically, we first present recent advances in anomaly detection, discussing the pros and cons of the detection methods. Then we conduct extensive experiments on public datasets to evaluate several typical and popular anomaly detection methods. The purpose of this paper is to offer a better understanding of the state-of-the-art techniques of anomaly detection for practitioners. Finally, we conclude by providing some directions for future research.
Fairness and Transparency in Ranking

Ranking in Information Retrieval (IR) has been traditionally evaluated from the perspective of the relevance of search engine results to people searching for information, i.e., the extent to which the system provides "the right information, to the right people, in the right way, at the right time." However, people in current IR systems are not only the ones issuing search queries, but increasingly they are also the ones being searched. This raises several new problems in IR that have been addressed in recent research, particularly with respect to fairness/non-discrimination, accountability, and transparency. This is a summary of some these initial developments.
Reading scene text with fully convolutional sequence modeling

Reading text in the wild is a challenging task in computer vision. Existing approaches mainly adopt connectionist temporal classification (CTC) or attention models based on recurrent neural network (RNN), and are computationally expensive and hard to train. In this paper, instead of the chain structure of RNN, we propose an end-to-end fully convolutional network with the stacked convolutional layers to effectively capture the long-term dependencies among elements of scene text image. The stacked convolutional layers are much more efficient than bidirectional long short-term memory (BLSTM) in modeling the contextual dependency. In addition, we design a discriminative feature encoder by incorporating the residual attention blocks into a small densely connected network to enhance the foreground text and suppress the background noise. Extensive experiments on seven standard benchmarks, the Street View Text, IIIT5K, ICDAR03, ICDAR13, ICDAR15, COCO-Text and Total-Text, validate that our method not only achieves state-of-the-art or highly competitive recognition performance, but significantly improves the efficiency and reduces the number of parameters as well.
Semi-supervised Aspect-level Sentiment Classification Model based on Variational Autoencoder

Aspect-level sentiment classification aims to predict the sentiment of a text in different aspects and it is a fine-grained sentiment analysis task. Recent work exploits an Attention-based Long Short-Term Memory Network to perform aspect-level sentiment classification. Most previous work are based on supervised learning that needs a large number of labeled samples, but the problem is that only a limited subset of data samples are labeled in practical applications. To solve this problem, we propose a novel Semi-supervised Aspect Level Sentiment Classification Model based on Variational Autoencoder (AL-SSVAE) for semi-supervised learning in the aspect-level sentiment classification. The AL-SSVAE model inputs a given aspect to an encoder a decoder based on a variational autoencoder (VAE), and it also has an aspect level sentiment classifier. It enables the attention mechanism to deal with different parts of a text when different aspects are taken as input as previous methods. Due to that the sentiment polarity of a word is usually sensitive to the given aspect, a single vector for a word is problematic. Therefore, we propose the aspect-specific word embedding learning from a topical word embeddings model to express a word and also append the corresponding sentiment vector into the word input vector. We compare our AL-SSVAE model with several recent aspect-level sentiment classification models on the SemEval 2016 dataset. The experimental results indicate that the proposed model is able to capture more accurate semantics and sentiment for the given aspect and obtain better performance on the task of the aspect level sentiment classification. Moreover, the AL-SSVAE model is able to learn with the semi-supervised mode in the aspect level sentiment classification, which enables it to learn efficiently using less labeled data.
A new image encryption algorithm with nonlinear-diffusion based on Multiple coupled map lattices

This paper proposes a new spatiotemporal chaos system named Multiple coupled map lattices (MCML). The proposed spatiotemporal chaos system has outstanding cryptographic features, which is very suitable for encryption algorithms. Based on this system, this paper proposes a new image encryption algorithm. The proposed algorithm employs the strategy of nonlinear-diffusion for the first time, and simultaneously performs shuffling and diffusion. The ciphertext value of each pixel in the diffusion phase depends on a chaotic interference value, a pixel value of the plain image, and two values of the ciphered image non-adjacent to it. This strategy reduces the correlation between adjacent pixels of the plain image as well as the correlation between the R, G and B components of color image. Theoretical analysis and experimental results prove the high efficiency and security of the proposed algorithm.
Memetic feature selection for multilabel text categorization using label frequency difference

Multilabel text categorization is an important task in modern text mining applications. Text datasets comprise an excessive number of terms, and this can degrade the accuracy. Therefore, conventional studies applied a feature selection method before text categorization. Recently, memetic feature selection methods that hybridize an evolutionary feature wrapper and a filter have gained popularity and showed promising results. However, conventional memetic text feature selection methods suffer from limited performance because the used feature filter requires problem transformation that degrades the search capability, resulting in unrefined feature subsets with poor accuracy. In this study, we propose an effective memetic feature selection method based on a novel feature filter that is highly specialized to multilabel text categorization. Our experiments demonstrate that the proposed method significantly outperforms several conventional methods.
Blockchain-Powered Value Creation in the 5G and Smart Grid Use Cases

Before the implementation of a solution, it is cost-efficient and practical to be able to evaluate and analyze the expected value of use cases. Especially, this is emphasized in blockchain (BC) use cases, which typically have a wide business ecosystem and possibly disruptive business models. This paper presents two BC use case value evaluations and results. The IoT use cases were selected from two different industry segments: telecommunications-oriented 5G network slice brokering and the energy industry-related internal electricity allocation in a housing society. The use case value was assessed by applying a resource configuration framework and 4C - commerce, context, content, and connection - business model typology against BC and smart contracts characteristics and capabilities. The results derived from the data collected from the expert workshops proved the expected value of the use cases, and in general, the feasibility of BC technology for facilitating various value-creating resource configuration processes was shown. Furthermore, the resource configuration framework proved to be a valuable theoretical approach for analyzing and developing also the BC-enabled novel use cases and business models. According to the findings, further development of the framework is proposed with an introduced novel decentralized resource configuration prototype that can replace predominant platform-based business models.
Towards human-guided machine learning

Automated Machine Learning (AutoML) systems are emerging that automatically search for possible solutions from a large space of possible kinds of models. Although fully automated machine learning is appropriate for many applications, users often have knowledge that supplements and constraints the available data and solutions. This paper proposes human-guided machine learning (HGML) as a hybrid approach where a user interacts with an AutoML system and tasks it to explore different problem settings that reflect the user's knowledge about the data available. We present: 1) a task analysis of HGML that shows the tasks that a user would want to carry out, 2) a characterization of two scientific publications, one in neuroscience and one in political science, in terms of how the authors would search for solutions using an AutoML system, 3) requirements for HGML based on those characterizations, and 4) an assessment of existing AutoML systems in terms of those requirements.
Exploring Users' Internal Influence from Reviews for Social Recommendation

In recent years, we have witnessed a flourish of social review websites. Internet users can easily share their experiences on some products and services with their friends. Therefore, measuring interpersonal influence becomes a popular method for recommender systems. However, traditional works are all based on external tangible activities, such as following, retweeting, mentioning, etc. In this paper, we explore user internal factors to measure his/her influence on a specific domain, namely, the social network on local businesses. The proposed user internal factors include user sentimental deviations and the review's reliability. The internal factors are not from explicit behavior but could help us to understand users. In addition, we utilize an attention mechanism that could auto-learn the weights of different factors. Through a case study on the Yelp dataset, we found that the proposed user internal factors on influence, that is, the proposed user sentimental deviations and the review's reliability, are effective in improving the accuracy of rating predictions.
Region-based scalable smart system for anomaly detection in pedestrian walkways

Different-sized anomalies and its occurrence in a shorter period have always been an open research issue. To resolve the issue of detecting anomalies of different sizes, especially in pedestrian pathways, within a shorter time period, the current research article introduced a Region based Scalable Convolution Neural Network (RS-CNN). The proposed method used region based proposals for faster identification and performed well with the scalability issues. The RS-CNN model was validated using different video sequences from the UCSD anomaly detection dataset. When compared with state-of-the-art detection techniques such as Fast R-CNN, Minimization of Drive Testing (MDT), Mixtures of Probabilistic Principal Component Analyzers (MPPCA) and Social Force (SF), the RS-CNN model was found to be faster and efficient even in the presence of anomalies of various sizes.
Unsupervised Anomaly Detection for Intricate KPIs via Adversarial Training of VAE

To ensure the reliability of the Internet-based application services, KPIs (Key Performance Monitors) are closely monitored in real time and the anomalies presented in the KPIs must be discovered in time. While anomaly detection for the seasonal smooth service-level KPIs (e.g., number of transactions per minute) have been solved reasonably well in the literature, the intricate KPIs at the machine level (e.g., the number of I/O requests on a server monitored per second) has been little studied. These intricate KPIs are prevalent and important, but exhibit non-Gaussian noises and complex data distribution that are hard to model. In this paper, we propose an adversarial training method in the Bayesian network based on partition analysis with solid theoretical proof. Based on it, we propose the first unsupervised anomaly detection algorithmBuzz for intricate KPIs with high performance. Its best F-scores on the data from a global Internet company range from 0.92 to 0.99, significantly outperforming a state-of-art VAE-based unsupervised approach without adversarial training and a state-of-art supervised approach.
Learning-Based Recursive Aggregation of Abstract Syntax Trees for Code Clone Detection

Code clone detection remains a crucial challenge in maintaining software projects. Many classic approaches rely on handcrafted aggregation schemes, while recent work uses supervised or unsupervised learning. In this work, we study several aspects of aggregation schemes for code clone detection based on supervised learning. To this aim, we implement an AST-based Recursive Neural Network. Firstly, our ablation study shows the influence of model choices and hyperparameters. We introduce error scaling as a way to effectively and efficiently address the class imbalance problem arising in code clone detection. Secondly, we study the influence of pretrained embeddings representing nodes in ASTs. We show that simply averaging all node vectors of a given AST yields strong baseline aggregation scheme. Further, learned AST aggregation schemes greatly benefit from pretrained node embeddings. Finally, we show the importance of carefully separating training and test data by clone clusters, to reliably measure generalization of models learned with supervision.
A Robust Framework for Abnormal Human Action Recognition Using $\boldsymbol{\mathcal{R}}$ -Transform and Zernike Moments in Depth Videos

The aim of the algorithm is to detect the abnormal actions that are more prone to elderly people in order to make them more independent and improve their quality of life. The framework is structured to construct a robust feature vector by computing R-transform and Zernike moments on average energy silhouette images (AESIs). The AESIs are generated by the integral sum of the segmented silhouettes obtained from the Microsoft's Kinect sensor v1. The proposed feature descriptor possesses scale-, translation-, and rotation-invariant properties that are also less sensitive to noise and minimizes data redundancy. It enhances the proposed algorithm's robustness and makes the classification process more efficient. The proposed work is validated on a novel abnormal human action (AbHA) dataset and three publically available 3D datasets-UR fall detection dataset, Kinect Activity Recognition dataset, and multiview NUCLA dataset. The proposed framework exhibits superior results from other state-of-the-art methods in terms of average recognition accuracy (ARA). The experimental results demonstrate 96.5%, 96.64%, 95.9%, and 86.4% ARA on the UR fall detection dataset, the KARD dataset, the AbHA dataset, and the multi-view NUCLA dataset, respectively.
TILAA: Tactile Internet-based Ambient Assistant Living in fog environment

Ambient assisted living provides an environment to aid regular activities (such as exercise, medical diagnosis etc.) for well being of users. In this direction, posture correcting mechanisms can be developed to have a real-time detection and correction system, which allow doctors and caregivers to monitor the patients' health without worrying about potential falls and backaches. Most of the data related to patient health resides on the cloud environment to be accessed from anywhere by different users. However, the existing cloud computing infrastructure does not support a quick feedback for posture correction by the patients located geographically separated locations.However, with the advent of 5G networks, a new networking paradigm of Tactile Internet emerges having low latency communication for ubiquitously connecting heterogeneous devices at the edge of the network. Keeping focus on these issues, we propose, TILAA, a framework for Tactile Internet-based Ambient Assistance Living in fog environment in this paper. It provides a collaborative communication feedback to the patients and doctors with reduced delay. TILAA consists of a micro controller, sensing devices such as-accelerometer and gyroscopes, and a battery holder. The straps on the chest wearable consist of actuators, which send vibrations as a Tactile feedback to the doctors or to the hospitals for taking proactive measures in case of emergency. Performance evaluation of TILAA shows that it has superior performance for the back posture of all patients as compared to pre-existing expensive detection and posture schemes.
A practical augmented reality-assisted maintenance system framework for adaptive user support

This paper describes an Augmented Reality Maintenance System (ARMS) that can provide adaptive support and feedback to a user during maintenance. An innovative wrist-based haptic tracking methodology is implemented to provide maintenance step awareness to the maintenance operator when he/she is working within the maintenance workspace. This is integrated with an image-based step detection module to facilitate the determination on whether a step has been conducted correctly. To further adapt to the user's visualisation requirements during maintenance, a novel adaptive human-machine interface module has been incorporated. The result is a seamless AR-assisted maintenance platform that can better cater to the needs of the operators during maintenance by providing useful feedback to the operators. The system has been demonstrated through a case study to highlight the viability of the methodology in actual industrial scenarios.
Software-Defined Far Memory in Warehouse-Scale Computers

Increasing memory demand and slowdown in technology scaling pose important challenges to total cost of ownership (TCO) of warehouse-scale computers (WSCs). One promising idea to reduce the memory TCO is to add a cheaper, but slower, "far memory" tier and use it to store infrequently accessed (or cold) data. However, introducing a far memory tier brings new challenges around dynamically responding to workload diversity and churn, minimizing stranding of capacity, and addressing brownfield (legacy) deployments. We present a novel software-defined approach to far memory that proactively compresses cold memory pages to effectively create a far memory tier in software. Our end-to-end system design encompasses new methods to define performance service-level objectives (SLOs), a mechanism to identify cold memory pages while meeting the SLO, and our implementation in the OS kernel and node agent. Additionally, we design learning-based autotuning to periodically adapt our design to fleet-wide changes without a human in the loop. Our system has been successfully deployed across Google's WSC since 2016, serving thousands of production services. Our software-defined far memory is significantly cheaper (67% or higher memory cost reduction) at relatively good access speeds (6us) and allows us to store a significant fraction of infrequently accessed data (on average, 20%), translating to significant TCO savings at warehouse scale.
Feature-guided Gaussian mixture model for image matching

This study proposes a novel feature-guided Gaussian mixture model (FG-GMM) for image matching, which generally requires matching two sets of feature points extracted from the provided images. The problem is formulated as the estimation of a feature-guided mixture of densities: a GMM is fitted to one point set, in which both the centers and local features of the Gaussian densities are constrained to coincide with another point set. The said problem is solved under a unified maximum-likelihood framework, in which an iterative semi-supervised expectation-maximization algorithm initialized by the confident feature correspondence is also implemented. This algorithm is flexible and has a general scope, which can handle both rigid and non-rigid image transformations. The transformation in the non-rigid case is specified in a reproducing kernel Hilbert space, and a sparse approximation is adopted to accomplish rapid implementation. Extensive experiments on different real images show that the proposed approach consistently outperforms other state-of-the-art methods, which validates its robustness.