Reviewerly/exHarmony huggingface dataset:
A Construction of Robust Representations for Small Data Sets Using Broad Learning System

Feature processing is an important step for modeling and can improve the accuracy of machine learning models. Feature extraction methods can effectively extract features from high-dimensional data sets and enhance the accuracy of tasks. However, the performance of feature extraction methods is not stable in low-dimensional data sets. This article extends the broad learning system (BLS) to a framework for constructing robust representations in low-dimensional and small data sets. First, the BLS changed from a supervised prediction method to an ensemble feature extraction method. Second, feature extraction methods instead of random mapping are used to generate mapped features. Third, deep representations, called enhancement features, are learned from the ensemble mapped features. Fourth, data for generating mapped features and enhancement features can be randomly selected. The ensemble of mapped features and enhancement features can provide robust representations to enhance the performance of downstream tasks. A label-based autoencoder (LA) is embedded in the BLS framework as an example to show the effectiveness of the framework. A random LA (RLA) is presented to generate more different features. The experimental results show that the BLS framework can construct robust representations and significantly promote the performance of machine learning models.
eXplainable Cooperative Machine Learning with NOVA

Abstract In the following article, we introduce a novel workflow, which we subsume under the term “explainable cooperative machine learning” and show its practical application in a data annotation and model training tool called NOVA . The main idea of our approach is to interactively incorporate the ‘human in the loop’ when training classification models from annotated data. In particular, NOVA offers a collaborative annotation backend where multiple annotators join their workforce. A main aspect is the possibility of applying semi-supervised active learning techniques already during the annotation process by giving the possibility to pre-label data automatically, resulting in a drastic acceleration of the annotation process. Furthermore, the user-interface implements recent eXplainable AI techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanation. We show in an use-case evaluation that our workflow is able to speed up the annotation process, and further argue that by providing additional visual explanations annotators get to understand the decision making process as well as the trustworthiness of their trained machine learning models.
Gated Fusion Network for Degraded Image Super Resolution

Single image super resolution aims to enhance image quality with respect to spatial content, which is a fundamental task in computer vision. In this work, we address the task of single frame super resolution with the presence of image degradation, e.g., blur, haze, or rain streaks. Due to the limitations of frame capturing and formation processes, image degradation is inevitable, and the artifacts would be exacerbated by super resolution methods. To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately. The base features contain local and global information of the input image. On the other hand, the recovered features focus on the degraded regions and are used to remove the degradation. Those features are then fused through a recursive gate module to obtain sharp features for super resolution. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. We evaluate the proposed method in three degradation scenarios. Experiments on these scenarios demonstrate that the proposed method performs more efficiently and favorably against the state-of-the-art approaches on benchmark datasets.
Developing a new mesh quality evaluation method based on convolutional neural network

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Conversational Agent Research Toolkit

Conversational agents in the form of chatbots available in messaging platforms are gaining increasing relevance in our communication environment. Based on natural language processing and generation techniques, they are built to automatically interact with users in several contexts. We present here a tool, the Conversational Agent Research Toolkit (CART), aimed at enabling researchers to create conversational agents for experimental studies. CART integrates existing APIs frequently used in practice and provides functionality that allows researchers to create and manage multiple versions of a chatbot to be used as stimuli in experimental studies. This paper provides an overview of the tool and provides a step-by-step tutorial of to design an experiment with a chatbot.
LSA &amp; LDA topic modeling classification: comparison study on e-books

&lt;p&gt;With the rapid growth of information technology, the amount of unstructured text data in digital libraries is rapidly increased and has become a big challenge in analyzing, organizing and how to classify text automatically in E-research repository to get the benefit from them is the cornerstone. The manual categorization of text documents requires a lot of financial, human resources for management. In order to get so, topic modeling are used to classify documents. This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification. Our dataset consists of (300) books contain about 23 million words based on full text. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. The result of this paper showed that LDA has better results than LSA and the best results obtained from the LDA method was (&lt;strong&gt;0.592179&lt;/strong&gt;) of coherence value when the number of topics was &lt;strong&gt;20 while&lt;/strong&gt; the LSA coherence value was &lt;strong&gt;(0.5773026)&lt;/strong&gt; when the number of topics was 10.&lt;/p&gt;
Learning Constraints From Locally-Optimal Demonstrations Under Cost Function Uncertainty

We present an algorithm for learning parametric constraints from locally-optimal demonstrations, where the cost function being optimized is uncertain to the learner. Our method uses the Karush-Kuhn-Tucker (KKT) optimality conditions of the demonstrations within a mixed integer linear program (MILP) to learn constraints which are consistent with the local optimality of the demonstrations, by either using a known constraint parameterization or by incrementally growing a parameterization that is consistent with the demonstrations. We provide theoretical guarantees on the conservativeness of the recovered safe/unsafe sets and analyze the limits of constraint learnability when using locally-optimal demonstrations. We evaluate our method on high-dimensional constraints and systems by learning constraints for 7-DOF arm and quadrotor examples, show that it outperforms competing constraint-learning approaches, and can be effectively used to plan new constraint-satisfying trajectories in the environment.
On the role of local blockchain network features in cryptocurrency price formation

Abstract Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. The Canadian Journal of Statistics 48: 561–581; 2020 © 2020 Statistical Society of Canada
Auto-Ensemble: An Adaptive Learning Rate Scheduling Based Deep Learning Model Ensembling

Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. However, it is difficult to collect sufficient accurate and diverse models through once training. This paper proposes Auto-Ensemble (AE) to collect checkpoints of deep learning model and ensemble them automatically by adaptive learning rate scheduling algorithm. The advantage of this method is to make the model converge to various local optima by scheduling the learning rate in once training. When the number of lo-cal optimal solutions tends to be saturated, all the collected checkpoints are used for ensemble. Our method is universal, it can be applied to various scenarios. Experiment results on multiple datasets and neural networks demonstrate it is effective and competitive, especially on few-shot learning. Besides, we proposed a method to measure the distance among models. Then we can ensure the accuracy and diversity of collected models.
Deep Video Prediction Network-Based Inter-Frame Coding in HEVC

In this paper, we propose a novel Convolutional Neural Network (CNN) based video coding technique using a video prediction network (VPN) to support enhanced motion prediction in High Efficiency Video Coding (HEVC). Specifically, we design a CNN VPN to generate a virtual reference frame (VRF), which is synthesized using previously coded frames, to improve coding efficiency. The proposed VPN uses two sub-VPN architectures in cascade to predict the current frame in the same time instance. The VRF is expected to have higher temporal correlation than a conventional reference frame, and, thus it is substituted for a conventional reference frame. The proposed technique is incorporated into the HEVC inter-coding framework. Particularly, the VRF is managed in a HEVC reference picture list, so that each prediction unit (PU) can choose a better prediction signal through Rate-Distortion optimization without any additional side information. Furthermore, we modify the HEVC inter-prediction mechanisms of Advanced Motion Vector Prediction and Merge modes adaptively when the current PU uses the VRF as a reference frame. In this manner, the proposed technique can exploit the PU-wise multi-hypothesis prediction techniques in HEVC. Since the proposed VPN can perform both the video interpolation and extrapolation, it can be used for Random Access (RA) and Low Delay B (LD) coding configurations. It is shown in experimental results that the proposed technique provides -2.9% and -5.7% coding gains, respectively, in RA and LD coding configurations as compared to the HEVC reference software, HM 16.6 version.
Turbine: Facebook’s Service Management Platform for Stream Processing

The demand for stream processing at Facebook has grown as services increasingly rely on real-time signals to speed up decisions and actions. Emerging real-time applications require strict Service Level Objectives (SLOs) with low downtime and processing lag-even in the presence of failures and load variability. Addressing this challenge at Facebook scale led to the development of Turbine, a management platform designed to bridge the gap between the capabilities of the existing general-purpose cluster management frameworks and Facebook's stream processing requirements. Specifically, Turbine features a fast and scalable task scheduler; an efficient predictive auto scaler; and an application update mechanism that provides fault-tolerance, atomicity, consistency, isolation and durability. Turbine has been in production for over three years, and one of the core technologies that enabled a booming growth of stream processing at Facebook. It is currently deployed on clusters spanning tens of thousands of machines, managing several thousands of streaming pipelines processing terabytes of data per second in real time. Our production experience has validated Turbine's effectiveness: its task scheduler evenly balances workload fluctuation across clusters; its auto scaler effectively and predictively handles unplanned load spikes; and the application update mechanism consistently and efficiently completes high scale updates within minutes. This paper describes the Turbine architecture, discusses the design choices behind it, and shares several case studies demonstrating Turbine capabilities in production.
Discriminant Geometrical and Statistical Alignment With Density Peaks for Domain Adaptation

Unsupervised domain adaptation (DA) aims to perform classification tasks on the target domain by leveraging rich labeled data in the existing source domain. The key insight of DA is to reduce domain divergence by learning domain-invariant features or transferable instances. Despite its rapid development, there still exist several challenges to explore. At the feature level, aligning both domains only in a single way (i.e., geometrical or statistical) has limited ability to reduce the domain divergence. At the instance level, interfering instances often obstruct learning a discriminant subspace when performing the geometrical alignment. At the classifier level, only minimizing the empirical risk on the source domain may result in a negative transfer. To tackle these challenges, this article proposes a novel DA method, called discriminant geometrical and statistical alignment (DGSA). DGSA first aligns the geometrical structure of both domains by projecting original space into a Grassmann manifold, then matches the statistical distributions of both domains by minimizing their maximum mean discrepancy on the manifold. In the former step, DGSA only selects the density peaks to learn the Grassmann manifold and so to reduce the influences of interfering instances. In addition, DGSA exploits the high-confidence soft labels of target landmarks to learn a more discriminant manifold. In the latter step, a structural risk minimization (SRM) classifier is learned to match the distributions (both marginal and conditional) and predict the target labels at the same time. Extensive experiments on objection recognition and human activity recognition tasks demonstrate that DGSA can achieve better performance than the comparison methods.
Ensemble-Based Online Machine Learning Algorithms for Network Intrusion Detection Systems Using Streaming Data

As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of-Things (IoT) devices enter the market. This has motivated research in applying machine learning algorithms that can operate on streams of data, trained online or “live” on only a small amount of data kept in memory at a time, as opposed to the more classical approaches that are trained solely offline on all of the data at once. In this context, one important concept from machine learning for improving detection performance is the idea of “ensembles”, where a collection of machine learning algorithms are combined to compensate for their individual limitations and produce an overall superior algorithm. Unfortunately, existing research lacks proper performance comparison between homogeneous and heterogeneous online ensembles. Hence, this paper investigates several homogeneous and heterogeneous ensembles, proposes three novel online heterogeneous ensembles for intrusion detection, and compares their performance accuracy, run-time complexity, and response to concept drifts. Out of the proposed novel online ensembles, the heterogeneous ensemble consisting of an adaptive random forest of Hoeffding Trees combined with a Hoeffding Adaptive Tree performed the best, by dealing with concept drift in the most effective way. While this scheme is less accurate than a larger size adaptive random forest, it offered a marginally better run-time, which is beneficial for online training.
Does Explainable Artificial Intelligence Improve Human Decision-Making?

Explainable AI provides insights to users into the why for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect. Prior research on human and explainable AI interactions has focused on measures such as interpretability, trust, and usability of the explanation. There are mixed findings whether explainable AI can improve actual human decision-making and the ability to identify the problems with the underlying model. Using real datasets, we compare objective human decision accuracy without AI (control), with an AI prediction (no explanation), and AI prediction with explanation. We find providing any kind of AI prediction tends to improve user decision accuracy, but no conclusive evidence that explainable AI has a meaningful impact. Moreover, we observed the strongest predictor for human decision accuracy was AI accuracy and that users were somewhat able to detect when the AI was correct vs. incorrect, but this was not significantly affected by including an explanation. Our results indicate that, at least in some situations, the why information provided in explainable AI may not enhance user decision-making, and further research may be needed to understand how to integrate explainable AI into real systems.
“I Don’t Know If It Is Fake or Real News” How Little Indonesian University Students Understand Social Media Literacy

Concerns over fake news have been raised in Indonesia since there has been a significant increase in the spreading of fake news via social media. University students, as the most active social media users, are susceptible to consume and distribute fake news. This study is motivated by the assumption that media literacy interventions help users resist fake news. However, limited studies have specifically addressed their level of media literacy in identifying fake news. Using the new media literacy framework, this study analysed the level of media literacy to process fake news on social media among university students in Aceh. A total of 500 undergraduate students from Syiah Kuala University and Ar-Raniry State Islamic University participated in the survey. Following the survey, 28 students were selected to take part in four focus group discussions. The findings showed that students' degree of media literacy concerning fake news is relatively low. Although almost all students demonstrated a good skill in using multiple social media platforms and understanding information they received, they were not confident in their abilities to distinguish between fake and real news. Surprisingly, some students deliberately share controversial posts on social media for no reason or to please themselves. The study suggests that program of media literacy should emphasise the identification of fake news and the consequences of its distribution. Keywords: Media literacy, social media, fake news, university students, Aceh.
Cross-Certification Towards Distributed Authentication Infrastructure: A Case of Hyperledger Fabric

In Internet of Things ecosystems, where various entities trade data and data analysis results, public key infrastructure plays an important role in establishing trust relationships between these entities to specify who trusts whose private keys. The owner of a private key is provided with a public key certificate issued by a certificate authority (CA) representing a trusted third party. Although this certificate ensures the reliability of the ecosystem by verifying the data source and preventing the denial of trading, it often causes an overconcentration of trust in a particular CA. Consequently, if that CA is infringed, all the related trust relationships become compromised. The paper proposes a distributed authentication infrastructure called Meta-PKI that decentralizes such overconcentration via a cross-certification procedure performed by multiple CAs. Although cross-certification is capable of establishing mutual trust relationships, it does not evaluate the trustworthiness of other CAs in a standardized manner. Therefore, this paper also proposes a new cross-certification method using a distributed ledger technology for building trust relationships based on unified criteria. It also describes the implementation of a Meta-PKI system for Hyperledger Fabric as a proof of concept. Once trust relationships have been established, it takes approximately 65.7 ms to validate them using the proposed system, which is secure against CA takeover and spoofing by outsider attackers.
SRD: A Tree Structure Based Decoder for Online Handwritten Mathematical Expression Recognition

Recently, recognition of online handwritten mathe- matical expression has been greatly improved by employing encoder-decoder based methods. Existing encoder-decoder models use string decoders to generate LaTeX strings for mathematical expression recognition. However, in this paper, we importantly argue that string representations might not be the most natural for mathematical expressions – mathematical expressions are inherently tree structures other than flat strings. For this purpose, we propose a novel sequential relation decoder (SRD) that aims to decode expressions into tree structures for online handwritten mathematical expression recognition. At each step of tree construction, a sub-tree structure composed of a relation node and two symbol nodes is computed based on previous sub-tree structures. This is the first work that builds a tree structure based decoder for encoder-decoder based mathematical expression recognition. Compared with string decoders, a decoder that better understands tree structures is crucial for mathematical expression recognition as it brings a more reasonable learning objective and improves overall generalization ability. We demonstrate how the proposed SRD outperforms state-of-the-art string decoders through a set of experiments on CROHME database, which is currently the largest benchmark for online handwritten mathematical expression recognition.