Reviewerly/exHarmony huggingface dataset:
Optimal Path Finding using Iterative SARSA

This paper presents a novel and state-of-the-art algorithm named Iterative SARSA to effectively determine the optimal trajectory for an autonomous mobile robot present in an unknown environment. Additionally, a detailed comparative analysis of the proposed algorithm is provided along with other traditional reinforcement learning algorithms using apropos parameters such as path length, computational time, and execution risk. The traditional algorithms used here are Q-learning and SARSA (State-Action-Reward-State-Action). Based on the calculation of next step, these algorithms use either of two reinforcement learning methods - the on-policy or off-policy. While SARSA and Iterative SARSA use the on-policy method, Q-learning utilizes the off-policy method. Optimized trajectory planning along with obstacle avoidance has always been a challenging yet foundational component of various principal applications. Being one of the most primary algorithms of machine learning, any development using Iterative SARSA should render a greater applicative scope.
Blockchain Technology: A Smart and Efficient Way for Securing IoT Communication

Blockchain is the trending topic now-a-days due to its decentralized and security feature. Internet usage on the other hand is increasing rapidly along with the increase in threats and attacks. The Internet of Things, or IoT, is the arrangement of billions of physical devices around the world that are connected to the internet for the purpose of collecting and sharing data, worldwide. IoT appliances and devices are the boom in the technology but the biggest concern in IoT devices is its security. A lot of attacks are discussed on IoT. Existing technologies are not enough to secure the IoT devices completely. BIoT or Blockchain IoT is the mechanism of using security feature of Blockchain in IoT to make it more secure and vulnerable to communication attacks. In this paper we have discussed the basics of Blockchain, IoT and How various work have been varied out on BIoT.
An Unsupervised Feature Selection for Web Phishing Data using an Evolutionary Approach

Phishing is one of the most serious cybercrimes used by fraudsters to steal individuals and organizations' identities and financial information. The most common form of phishing is phishing through fake websites. In recent years, phishing detection methods based on machine learning have gained attention due to their high accuracy. Feature selection is a preprocessing step in data mining and machine learning that is used to reduce the size of the feature space and find significant features while achieving comparable or higher accuracy. In this paper, an unsupervised feature selection method, called LAPPSO, is proposed for web phishing data. To find the most informative features, LAPPSO applies an improved version of PSO with a greater exploration for improving the global search and also uses the Laplacian score for local search. Based on experimental results obtained from applying LAPPSO on two well-known phishing datasets, our algorithm achieves the average F-measure of 96% while reducing the number of the features significantly. Moreover, the training time of the learning model is reduced to almost half using the selected features.
Trust Model for Efficient Honest Broker based Healthcare Data Access and Processing

With the increased push to promote data-driven methods in modern healthcare, there is a tremendous need for fast access to clinical datasets in order to pursue medical breakthroughs in the areas of personalized medicine and big data knowledge discovery. However, the inherent lack of trust between the data custodians and data consumers/users has resulted in a fully manual honest broker approach to access and process protected healthcare data. Such a manual approach leads to slow data handling, and adds to overheads needed to address data auditability and assurance needed for compliance with healthcare data security standards. In this paper, we address these challenges by proposing a trust model to enable semi-automation of the honest broker process to increase its efficiency. The trust model is based on multi-dimensional risk management principles and considers risk associated with data identifiers, as well as requestor profile and reputation. We implement and evaluate a semi-automated honest broker that uses our trust model in a community cloud testbed using the SynPUF synthetic dataset. Our experiment results show that our multidimensional risk management approach consistently identifies the lower confidentiality risk configuration in the semi-automation in comparison with a one-dimensional strategy. Thus, our semiautomated honest brokering approach improves efficiency for data custodians and data consumers by facilitation of fast and secure data access, while also ensuring compliance in the processing of the protected datasets.
Detecting Malicious Domain Names with Abnormal WHOIS Records Using Feature-Based Rules

Abstract Millions of new domain names are registered every day, but a large proportion of them are malicious and usually discovered and blacklisted after the crime has been committed. In order to improve the security of domain name registration, this paper proposes a lightweight detection method based on the AdaBoost to identify malicious domain names, which focuses on proactively detecting malicious domain names by exploring the abnormal WHOIS records. The domain name registries and registrars can adopt the proposed method as the first layer of defense to identify malicious domains on the domain registration stage. Extensive experiments on a large-scale database demonstrate that the proposed approach achieves satisfactory results on various malicious domain names.
Generalized AdaGrad (G-AdaGrad) and Adam: A State-Space Perspective

Accelerated gradient-based methods are being extensively used for solving non-convex machine learning problems, especially when the data points are abundant or the available data is distributed across several agents. Two of the prominent accelerated gradient algorithms are AdaGrad and Adam. AdaGrad is the simplest accelerated gradient method, which is particularly effective for sparse data. Adam has been shown to perform favorably in deep learning problems compared to other methods. In this paper, we propose a new fast optimizer, Generalized AdaGrad (G-AdaGrad), for accelerating the solution of potentially non-convex machine learning problems. Specifically, we adopt a state-space perspective for analyzing the convergence of gradient acceleration algorithms, namely G-AdaGrad and Adam, in machine learning. Our proposed state-space models are governed by ordinary differential equations. We present simple convergence proofs of these two algorithms in the deterministic settings with minimal assumptions. Our analysis also provides intuition behind improving upon AdaGrad’s convergence rate. We provide empirical results on MNIST dataset to reinforce our claims on the convergence and performance of G-AdaGrad and Adam.
Application of Blockchain Technology in the Agri-Food System: A Systematic Bibliometric Analysis and Policy Imperatives

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Predicting Paper Acceptance via Interpretable Decision Sets

Measuring the quality of research work is an essential component of the scientific process. With the ever-growing rates of articles being submitted to top-tier conferences, and the potential consistency and bias issues in the peer review process identified by scientific community, it is thus of great necessary and challenge to automatically evaluate submissions. Existing works mainly focus on exploring relevant factors and applying machine learning models to simply be accurate at predicting the acceptance of a given academic paper, while ignoring the interpretability power which is required by a wide range of applications. In this paper, we propose a framework to construct decision sets that consist of unordered if-then rules for predicting paper acceptance. We formalize decision set learning problem via a joint objective function that simultaneously optimize accuracy and interpretability of the rules, rather than organizing them in a hierarchy. We evaluate the effectiveness of the proposed framework by applying it on a public scientific peer reviews dataset. Experimental results demonstrate that the learned interpretable decision sets by our framework performs on par with state-of-the-art classification algorithms which optimize exclusively for predictive accuracy and much more interpretable than rule-based methods.
Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement

Diet planning is a basic and regular human activity. Previous studies have considered diet planning a combinatorial optimization problem to generate solutions that satisfy a diet's nutritional requirements. However, this approach does not consider the composition of diets, which is critical for diet recipients' to accept and enjoy menus with high nutritional quality. Without this consideration, feasible solutions for diet planning could not be provided in practice. This suggests the necessity of diet planning with machine learning, which extracts implicit composition patterns from real diet data and applies these patterns when generating diets. This work is original research that defines diet planning as a machine learning problem; we describe diets as sequence data and solve a controllable sequence generation problem. Specifically, we develop the Teacher-forced REINFORCE algorithm to connect neural machine translation and reinforcement learning for composition compliance with nutrition enhancement in diet generation. Through a real-world application to diet planning for children, we validated the superiority of our work over the traditional combinatorial optimization and modern machine learning approaches, as well as human (i.e., professional dietitians) performance. In addition, we construct and open the databases of menus and diets to motivate and promote further research and development of diet planning with machine learning. We believe this work with data science will contribute to solving economic and social problems associated with diet planning.
TaoHighlight: Commodity-Aware Multi-Modal Video Highlight Detection in E-Commerce

In e-commerce, product related video is important content to introduce product characteristics and attract consumers. Especially in the recommendation system of e-commerce platform, video highlight detection methods are usually adopted to capture the most attractive clips for showing to consumers, so as to improve the click through rate of products. However, the effect of the current research methods applied to the actual scene is not satisfactory. Compared with other video understanding tasks, video highlight detection is relatively abstract and subjective, and it is difficult to make accurate judgment only by using visual information. Consequently, we put forward multi-modal video highlight detection task, which introduces video related linguistic information as supervised information. And we propose a graph-based commodity-aware model to solve multi-modal video highlight detection in e-commerce scene. Our model consists of multi-modal highlight detection stage and graph-based fine-tuning stage, in which we adopt graph aggregation method to fuse multi-source natural language information and introduce effective visual feature composition method for graph convolution network based highlight detection. Besides, we release the largest e-commerce video highlight detection dataset, TaoHighlight, in which the videos and related data are collected from Taobao e-commerce platform. Our model achieves state-of-art in all separate categories and overall dataset of TaoHighlight, which shows the superiority of our model.
Improved geometric modeling using the method of fundamental solutions

In this paper, we propose a new geometric model that includes a fourth-order partial differential equation (PDE) for reconstructing 2D curves. For instance, we use this model to reproduce letters in Time Roman font. The method of fundamental solutions (MFS), which is a simple and easily implemented meshless method, is employed for solving the proposed PDEs. In addition, no fictitious boundary is required for the proposed MFS formulation, which further simplifies the implementation of the numerical method. Three examples of 2D curve reproduction are presented to demonstrate the effectiveness of the proposed model.
Performance Models of Data Parallel DAG Workflows for Large Scale Data Analytics

Directed Acyclic Graph (DAG) workflows are widely used for large-scale data analytics in cluster-based distributed computing systems. Building an accurate performance model for a DAG on data-parallel frameworks (e.g., MapReduce) is critical to implement autonomic self-management big data systems. An accurate performance model is challenging because the allocation of preemptable system resources among parallel jobs may dynamically vary during execution. This resource allocation variation during execution makes it difficult to accurately estimate the execution time. In this paper, we tackle this challenge by proposing a new cost model, called Bottleneck Oriented Estimation (BOE), to estimate the allocation of preemptable resources by identifying the bottleneck to accurately predict task execution time. For a DAG workflow, we propose a state-based approach to iteratively use the resource allocation property among stages to estimate the overall execution plan. Extensive experiments were performed to validate these cost models with HiBench and TPC-H workloads. The BOE model outperforms the state-of-the-art models by a factor of five for task execution time estimation.
Multi-task analysis discriminative dictionary learning for one-class learning

One-class classification is a generalization of supervised learning based on one class of examples. It attracts growing attention in machine learning and data mining. In this paper, we propose a novel approach called multi-task dictionary learning for one-class learning (MTD-OC), which incorporates analysis discriminative dictionary learning into one-class learning. The analysis discriminative dictionary learning makes sure that dictionaries responding to different tasks are independent and discriminating as much as possible. The analysis discriminative dictionary learning simultaneously minimize l 2 , 1 -norm constraint, analysis incoherence term and sparse code extraction term, which aim to promote analysis incoherence and improve coding efficiency and accuracy for classification. The one-class classifier on the target task is then constructed by learning transfer knowledge from multiple source tasks. Here, one-class classification improves the performance of analysis discriminative dictionary, while analysis discriminative dictionary improves the performance of one-class classification term. In MTD-OC, the optimization function is formulated to deal with one-class classifier and analysis discriminative dictionary learning based on one class of examples. Then, we propose an iterative framework to solve the optimization function, and obtain the predictive classifier for the target class. Extensive experiments have shown that MTD-OC can improve the accuracy of one-class classifier by learning analysis discriminative dictionary from each task to construct a transfer classifier.
DNA-Net: Age and Gender Aware Kin Face Synthesizer

Visual kinship verification aims to detect blood relatives in facial images. Its practical application have motivated many researchers to focus on the topic as of recent. In this paper, we focus on a new view of visual kinship technology: kin-based face generation. Specifically, we propose a two-stage kin-face generation model to predict the appearance of a child given a pair of parents. The first stage includes a deep generative adversarial auto-encoder conditioned on ages and genders to map between facial appearance and high-level features. The second stage is our proposed DNA-Net, which serves as a transformation between the deep and genetic features based on a random selection process to fuse genes of a parent pair to form the genes of a child. We demonstrate the effectiveness of the proposed method quantitatively and qualitatively. Experiments validate that the proposed model synthesizes convincing kin-faces using both subjective and objective standards.
Parsing and Summarizing Infographics with Synthetically Trained Icon Detection

Widely used in news, business, and educational media, infographics are handcrafted to effectively communicate messages about complex and often abstract topics including `ways to conserve the environment' and `coronavirus prevention'. The computational understanding of infographics required for future applications like automatic captioning, summarization, search, and question-answering, will depend on being able to parse the visual and textual elements contained within. However, being composed of stylistically and semantically diverse visual and textual elements, infographics pose challenges for current A.I. systems. While automatic text extraction works reasonably well on infographics, standard object detection algorithms fail to identify the stand-alone visual elements in infographics that we refer to as `icons'. In this paper, we propose a novel approach to train an object detector using synthetically-generated data, and show that it succeeds at generalizing to detecting icons within in-the-wild infographics. We further pair our icon detection approach with an icon classifier and a state-of-the-art text detector to demonstrate three demo applications: topic prediction, multi-modal summarization, and multi-modal search. Parsing the visual and textual elements within infographics provides us with the first steps towards automatic infographic understanding.
A Machine Learning Approach to Classify Hypersonic Vehicle Trajectories

Compared to conventional ballistic vehicles and cruise vehicles, hypersonic vehicles exhibit unprecedented and clearly superior abilities. Hypersonic glide vehicles (HGVs) travel at speeds faster than Mach 5, enabling them to fly at least one mile per second. Furthermore, they possess maneuvering capabilities that assist them in evading defense systems, increasing precision of their impact points, and hindering prediction of their final destinations. In this paper, we examine machine learning methods to automatically identify different hypersonic glide vehicles and a ballistic reentry vehicle (RV) based on trajectory segments. Trained on aerodynamic state estimates, our methods analyze key vehicle maneuvers to classify vehicles with high accuracy. We also identify vehicles with higher accuracy as time after liftoff (TALO) increases and more data becomes available for analysis.
Predictive Modeling With an Adaptive Unsupervised Broad Transfer Algorithm

Deep-learning algorithms have produced promising results, however, domain adaptation remains a challenge. In addition, excessive training time and computing resource requirements need to be addressed. Deep-learning algorithms face a domain adaptation issue when the data distribution of a target domain differs from that of the source domain. The emerging concept of broad learning shows potential in addressing the domain adaptation and training time issues. An adaptive unsupervised broad transfer learning (AUBTL) algorithm is proposed to tackle the cross-domain problems. The proposed algorithm utilizes a sparse autoencoder and random orthogonal mapping to extract and augment the feature space. Then, it initializes the weights of a classifier by solving a ridge regression problem. The logit ranking strategy is applied to develop a transfer estimator to evaluate and sample data in the target domain for an adaptive transfer. Based on the sampled data, AUBTL optimizes the hyperparameter space. The performance of the AUBTL algorithm is validated with three benchmark datasets, including 20 transfer tasks. The computational results demonstrated the efficiency and accuracy of the proposed algorithm over other deep-learning algorithms considered in this research.
The Subfield Codes and Subfield Subcodes of a Family of MDS Codes

Maximum distance separable (MDS) codes are very important in both theory and practice. There is a classical construction of a family of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[{2^{m}+1, 2u-1, 2^{m}-2u+3}]$ </tex-math></inline-formula> MDS codes for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \leq u \leq 2^{m-1}$ </tex-math></inline-formula> , which are cyclic, reversible and BCH codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GF}}(2^{m})$ </tex-math></inline-formula> . The objective of this paper is to study the quaternary subfield subcodes and quaternary subfield codes of a subfamily of the MDS codes for even <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> . A family of quaternary cyclic codes is obtained. These quaternary codes are distance-optimal in some cases and very good in general. Furthermore, two infinite families of 3-designs from these quaternary codes and their duals are presented.
Cross-View Equivariant Auto-Encoder

Unsupervised representation learning on multi-view data (multiple types of features or modalities) becomes a compelling topic in machine learning. Most existing methods focus on directly projecting different views into a common space to explore the consistency across different views. Al-though simple, the underlying relationships among different views are not guaranteed during the learning process. In this paper, we propose a novel unsupervised multi-view representation learning model termed as Cross-View Equivariant Auto-Encoder (CVE-AE), which jointly conducts data re-construction with view-specific autoencoder for information preservation within each view, and transformation reconstruction with transformation decoder for correlations preservation across different views. Accordingly, the generalization ability of our model is promoted due to the preserved intra-view intrinsic information and underlying inter-view relationships. We conduct extensive experiments on real-world datasets, and the proposed model achieves superior performance over state-of-the-art unsupervised representation learning methods.
Normalization and Back-Transliteration for Code-Switched Data

Codeswitching is an omnipresent phe nomenon in multilingual communities all around the world but remains a challenge for NLP systems due to the lack of proper data and processing techniques.HindiEnglish codeswitched text on social media is often transliterated to the Roman script which prevents from utilizing monolingual resources available in the native Devanagari script.In this paper, we propose a method to nor malize and backtransliterate codeswitched HindiEnglish text.In addition, we present a graphemetophoneme (G2P) conversion technique for romanized Hindi data.We also release a dataset of scriptcorrected HindiEnglish codeswitched sentences labeled for the named entity recognition and partofspeech tagging tasks to facilitate further research in this area.