Reviewerly/exHarmony huggingface dataset:
End-to-End Deep Attentive Personalized Item Retrieval for Online Content-sharing Platforms

Modern online content-sharing platforms host billions of items like music, videos, and products uploaded by various providers for users to discover items of their interests. To satisfy the information needs, the task of effective item retrieval (or item search ranking) given user search queries has become one of the most fundamental problems to online content-sharing platforms. Moreover, the same query can represent different search intents for different users, so personalization is also essential for providing more satisfactory search results. Different from other similar research tasks, such as ad-hoc retrieval and product retrieval with copious words and reviews, items in content-sharing platforms usually lack sufficient descriptive information and related meta-data as features. In this paper, we propose the end-to-end deep attentive model (EDAM) to deal with personalized item retrieval for online content-sharing platforms using only discrete personal item history and queries. Each discrete item in the personal item history of a user and its content provider are first mapped to embedding vectors as continuous representations. A query-aware attention mechanism is then applied to identify the relevant contexts in the user history and construct the overall personal representation for a given query. Finally, an extreme multi-class softmax classifier aggregates the representations of both query and personal item history to provide personalized search results. We conduct extensive experiments on a large-scale real-world dataset with hundreds of million users from a large video media platform at Google. The experimental results demonstrate that our proposed approach significantly outperforms several competitive baseline methods. It is also worth mentioning that this work utilizes a massive dataset from a real-world commercial content-sharing platform for personalized item retrieval to provide more insightful analysis from the industrial aspects.
Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph Embedding

The recent success of deep graph embedding innovates the graphical information characterization methodologies. However, in real-world applications, such a method still struggles with the challenges of heterogeneity, scalability, and multiplex. To address these challenges, in this study, we propose a novel solution, Genetic hEterogeneous gRaph eMbedding (GERM), which enables flexible and efficient task-driven vertex embedding in a complex heterogeneous graph. Unlike prior efforts for this track of studies, we employ a task-oriented genetic activation strategy to efficiently generate the “Edge Type Activated Vector” (ETAV) over the edge types in the graph. The generated ETAV can not only reduce the incompatible noise and navigate the heterogeneous graph random walk at the graph-schema level, but also activate an optimized subgraph for efficient representation learning. By revealing the correlation between the graph structure and task information, the model interpretability can be enhanced as well. Meanwhile, an activated heterogeneous skip-gram framework is proposed to encapsulate both topological and task-specific information of a given heterogeneous graph. Through extensive experiments on both scholarly and e-commerce datasets, we demonstrate the efficacy and scalability of the proposed methods via various search/recommendation tasks. GERM can significantly reduces the running time and remove expert-intervention without sacrificing the performance (or even modestly improve) by comparing with baselines.
SVM Hyper-Parameters Optimization using Multi-PSO for Intrusion Detection

Among all the classifiers available in recent time support vector machine (SVM) can be considered as one of the powerful classifiers. Support vector machine (SVM) modeled with properly tuned parameters can give significantly increasing accuracy rate for classification problem. Therefore, choosing the optimal values for the SVM hyper-parameters cannot be a mundane task. It is the most crucial and important task for SVM modeling. The basic emphasis of this paper is to optimize the values of C and γ which are two important kernel parameters of SVM which in turn can be used an intrusion detector in network. A set of properly optimized values of C and γ can increase the effectiveness and efficiency of SVM model. There are many state-of-the-art and metaheuristic techniques such as traditional grid search, gradient descent, genetic algorithm (GA), and particle swarm optimization (PSO). Among these, the most used optimization technique for SVM model selection is PSO. In our work, we have proposed a framework which uses a variant of PSO, multi-PSO for the selection of optimal values for the C and γ selection. The results show that it outperforms the other models for model selection in support vector machine.
Searching for Embeddings in a Haystack: Link Prediction on Knowledge Graphs with Subgraph Pruning

Embedding-based models of Knowledge Graphs (KGs) can be used to predict the existence of missing links by ranking the entities according to some likelihood scores. An exhaustive computation of all likelihood scores is very expensive if the KG is large. To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities. Our technique first creates embeddings of subgraphs using the embeddings from the model. Then, it ranks the subgraphs with some proposed ranking functions and considers only the entities in the top k subgraphs. Our experiments show that our technique is able to reduce the search space significantly while maintaining a good recall.
Adaptive Low-level Storage of Very Large Knowledge Graphs

The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 1011 edges using inexpensive hardware, delivering competitive performance on multiple workloads.
Efficient Implicit Unsupervised Text Hashing using Adversarial Autoencoder

Searching for documents with semantically similar content is a fundamental problem in the information retrieval domain with various challenges, primarily, in terms of efficiency and effectiveness. Despite the promise of modeling structured dependencies in documents, several existing text hashing methods lack an efficient mechanism to incorporate such vital information. Additionally, the desired characteristics of an ideal hash function, such as robustness to noise, low quantization error and bit balance/uncorrelation, are not effectively learned with existing methods. This is because of the requirement to either tune additional hyper-parameters or optimize these heuristically and explicitly constructed cost functions. In this paper, we propose a Denoising Adversarial Binary Autoencoder (DABA) model which presents a novel representation learning framework that captures structured representation of text documents in the learned hash function. Also, adversarial training provides an alternative direction to implicitly learn a hash function that captures all the desired characteristics of an ideal hash function. Essentially, DABA adopts a novel single-optimization adversarial training procedure that minimizes the Wasserstein distance in its primal domain to regularize the encoder’s output of either a recurrent neural network or a convolutional autoencoder. We empirically demonstrate the effectiveness of our proposed method in capturing the intrinsic semantic manifold of the related documents. The proposed method outperforms the current state-of-the-art shallow and deep unsupervised hashing methods for the document retrieval task on several prominent document collections.
Understanding the Performance Costs and Benefits of Privacy-focused Browser Extensions

Advertisements and behavioral tracking have become an invasive nuisance on the Internet in recent years. Indeed, privacy advocates and expert users consider the invasion significant enough to warrant the use of ad blockers and anti-tracking browser extensions. At the same time, one of the largest advertisement companies in the world, Google, is developing the most popular browser, Google Chrome. This conflict of interest, that is developing a browser (a user agent) and being financially motivated to track users’ online behavior, possibly violating their privacy expectations, while claiming to be a ”user agent,” did not remain unnoticed. As a matter of fact, Google recently sparked an outrage when proposing changes to Chrome how extensions can inspect and modify requests to ”improve extension performance and privacy,” which would render existing privacy-focused extensions inoperable.
Keeping out the Masses: Understanding the Popularity and Implications of Internet Paywalls

Funding the production of quality online content is a pressing problem for content producers. The most common funding method, online advertising, is rife with well-known performance and privacy harms, and an intractable subject-agent conflict: many users do not want to see advertisements, depriving the site of needed funding.
Image compression optimized for 3D reconstruction by utilizing deep neural networks

Computer vision tasks are often expected to be executed on compressed images. Classical image compression standards like JPEG 2000 are widely used. However, they do not account for the specific end-task at hand. Motivated by works on recurrent neural network (RNN)-based image compression and three-dimensional (3D) reconstruction, we propose unified network architectures to solve both tasks jointly. These joint models provide image compression tailored for the specific task of 3D reconstruction. Images compressed by our proposed models, yield 3D reconstruction performance superior as compared to using JPEG 2000 compression. Our models significantly extend the range of compression rates for which 3D reconstruction is possible. We also show that this can be done highly efficiently at almost no additional cost to obtain compression on top of the computation already required for performing the 3D reconstruction task.
Hallucinating Color Face Image by Learning Graph Representation in Quaternion Space

Recently, learning-based representation techniques have been well exploited for grayscale face image hallucination. For color images, the previous methods only handle the luminance component or each color channel individually, without considering the abundant correlations among different channels as well as the inherent geometrical structure of data manifold. In this article, we propose a learning-based model in quaternion space with graph representation for color face hallucination. Instead of the spatial domain, the color image is represented in the quaternion domain to preserve correlations among different color channels. Moreover, a quaternion graph is learned to smooth the quaternion feature space, which helps to not only stabilize the linear system but also enclose the inherent topology structure of quaternion patch manifold. Besides, considering that single low-resolution (LR) image patch can just provide limited informative information in representation, we propose to simultaneously encode the query smaller LR patch as well as a larger patch containing the surrounding pixels seated at the same position in the objective. The larger patch with rich patterns is used to compensate the lost information in the query LR patch, which further enhances the manifold consistency assumption between the LR and HR patch spaces. The experimental results demonstrated the efficiency of the proposed method in hallucinating color face images.
An Electronic Medical Record Management System Based on Smart Contracts

This study proposed a management system of the electronic medical records in the blockchain environment. In the proposed system, electronic medical records are first stored in the InterPlanetary File System (IPFS), and then the system generates the hash value, which will be sent to the smart contract to correlate with the patients' data. After that, if the medical staff's medical authority is confirmed by the system, the smart contract can send back the hash value that IPFS generated, and thus the system will present the complete electronic medical records. In the experimental simulation, the results showed that our proposed electronic medical record management and storage process not only can block forged or tampered electronic medical records via smart contracts, but also make it easy to integrate electronic medical records and share medical resources.
Anonymous and Coercion-Resistant Distributed Electronic Voting

Electronic voting (e-voting) presents a convenient and cost-effective alternative to current paper ballot-based voting. It provides many benefits such as increased voter turnout and accuracy in the decision-making process. While presenting many improvements, e-voting still faces serious security challenges that hinder its adoption. In this paper, we propose a novel remote e-voting scheme that can enhance the integrity, efficiency, and voter turnout of the election process. Our proposed work is secure and preserves the privacy of voters through secure multi-party computations performed by parties of differing allegiances. It also leverages a blockchain running smart contracts as a publicly accessible and tamper-resistant bulletin board to permanently store votes and prevent double-voting. In our security and privacy analysis, we show that our proposed scheme is secure against potential threats and provides voter anonymity. Our performance discussion shows that the proposed scheme is practical for large-scale elections.
Augmented reality direct current glow discharge experimentation

Abstract The glowing discharges of plasma obtained by introducing direct current high voltages between two electrodes in a chamber that contains a low pressure gas produces a spectacular effect that makes it attractive to be used as a laboratory experiment for students. Setting up this experiment is however costly and has crucial safety issues to be addressed. An augmented reality approach here allows students to conduct such an experiment effectively. It was designed to guide students on how to conduct the experiment safely and properly, as well as to allow them to note the voltage breakdown values according to Paschen’s rule. The tool has the strong advantage of allowing students to conduct experimentation without removing any of the real-life and tactile features that help to constitute cognition events in the natural world. It also yielded positive responses when tested on 39 high school students as part of an outreach program for science and technology.
Implementing innovative customer due diligence: proposal for universal model

Purpose The purpose of this paper is to present the identification-verification-confirmation of identity (IVCid) model that can be used to retroactively analyze the existing customer identification programs and devise new ones that can be used in face-to-face or non-face-to-face environment. Design/methodology/approach This paper outlines the main elements of the customer due diligence (CDD) process and identifies those which may present a barrier to the customers. It then outlines the IVCid model. The model is used to analyze existing CDD approaches in physical presence, using reliable databases, biometrics and electronic signatures. Findings The IVCid model suggests that any customer identification program contains three elements: identification (collection of information), verification (checking the veracity of information) and confirmation of identity (linking the information to the individual). The accuracy of this model is confirmed by the analysis of the existing CDD procedures in some countries. Research limitations/implications This paper looks at a limited number of practical cases of CDD implementation. Further research might be needed to assess the strengths and weaknesses of biometric-based or e-signature-based solutions. Research might be needed to establish links between the IVCid model and financial inclusion. Practical implications The IVCid model allows for “modular” approach for the CDD procedures. It also underlines some risks associated with current CDD models. Social implications The IVCid model can be used to devise the CDD procedures that more effectively contribute to financial inclusion. Originality/value This paper proposes the first universal model for the CDD procedures that works for both face-to-face and remote scenarios while also being technology- and business-neutral.
The Complex Pathway towards Farm-Level Sustainable Intensification: An Exploratory Network Analysis of Stakeholders’ Knowledge and Perception

Farm-level sustainable intensification of agriculture (SIA) has become an important concept to ensuring food security while minimising negative externalities. However, progress towards its achievement is often constrained by the different perceptions and goals of various stakeholders that affect farm management decisions. This study examines farm-level SIA as a dynamic system with interactive components that are determined by the interests of the stakeholders involved. A systems thinking approach was used to identify and describe the pathways towards farm-level SIA across the three main pillars of sustainability. An explanatory network analysis of fuzzy cognitive maps (FCMs) that were collectively created by representative groups of farmers, farm advisors and policy makers was performed. The study shows that SIA is a complex dynamic system, affected by cognitive beliefs and particular knowledge within stakeholder groups. The study concludes that, although farm-level SIA is a complex process, common goals can be identified in collective decision making.
SensIR: Towards privacy-sensitive image retrieval in the cloud

With the advance in content-based image retrieval and the popularity of Data-as-a-Service, enterprises can outsource their image retrieval systems on cloud platforms to reduce heavy storage, computation, and communication burdens. However, this brings many privacy problems. Although several privacy-preserving image retrieval schemes have been proposed to protect users’ privacy, they have two major drawbacks: i) the outsourced images are fully encrypted and thus cannot be used for other applications, which makes them impractical; ii) they mainly focus on traditional image retrieval systems and do not use new techniques such as convolutional neural network (CNN) to improve the accuracy. To address the above problems, we propose a novel privacy-sensitive image retrieval scheme, named SensIR, to search for similar images from an outsourced image database. In particular, we propose a privacy region detection, PRDet, to prevent private regions of images from exposing. We also propose a partial CNN (PCNN) to reduce the impact of the encrypted pseudorandom pixels. Further, we use similarity-preserving hash encoding and propose a systematic methodology to improve the accuracy of PCNN-based image retrieval when the privacy regions are large. Extensive experiments are conducted to illustrate the efficiency of privacy protection and the superior of the proposed scheme.
Mapping of ferric ( and ferrous ( iron oxides distribution using band ratio techniques with ASTER data and geochemistry of Kanjamalai and Godumalai, Tamil Nadu, south India

The iron ores found in Tamil Nadu State, South India, are major varieties that have been confined with banded magnetite quartzite. The occurrence, distribution, and grade of these deposits significantly vary according to their geological structure and geomorphologic control. In this article, presents a novel approach, based on spectral remote sensing and digital processing of ASTER data, to identify and characterize the iron ores of Kanjamalai and Godumalai areas located in Tamil Nadu, India. By analyzing the ASTER images, the abundance of iron oxides including ferric (Fe3+) and ferrous (Fe2+) components was determined. The band ratioing technique, a multiband analysis was used to generate the abundance of iron oxide content in various parts of the study area using different band combinations such as band 2/band 1 (for Fe3+) and band 5/band 3 + band 1/band 2 (for Fe2+). The geochemical analysis is an important part of this work to arrive with the outcome of band ratio techniques to decipher the relationship of the band ratio to the chemical composition of the ore samples. Accordingly, the correlation between the results of the geochemical analysis of the samples collected from the random locations was determined by Pearson's coefficient of correlation (ρ) and compared with the corresponding locations in the abundance image. In addition to ρ, various factors such as mean (μ), variance (σ2) and corresponding standard deviations (σ) were also analyzed for a comparative analysis. This comparative analysis indicated that most of the samples have considerably high iron oxide content in the locations. Thus, this study shows the possibility of detecting iron oxide content and its spatial distribution by using ASTER satellite images analysis. Hence, from the mapping results, it is evident that the band ratio technique of ASTER images can be used to map and characterize with limited fieldwork and geochemistry.
KB-Transformer: Incorporating Knowledge into End-to-End Task-Oriented Dialog Systems

Existing end-to-end task-oriented dialogue systems are lack of proper support of knowledge base, and training based on RNN seq2seq models is time-consuming. In this paper, we propose a novel parallel computing framework(KB-Transformer) to incorporate KB which can be trained faster than RNN seq2seq models. A key contribution is that we propose multi-head key-value memory network for the first time, comprehensively encoding semantic information of KB and incorporating KB into task-oriented dialog systems from multiple dimensions and sub-spaces. KB-Transformer tries to combine memory network with transformer, implementing a parallel dialogue framework based entirely on attention mechanism. As a result, we show that KB-Transformer can be trained faster and attain better performance than RNN seq2seq models on two different task-oriented dialog datasets.