Reviewerly/exHarmony huggingface dataset:
Unsupervised multi-view non-negative for law data feature learning with dual graph-regularization in smart Internet of Things

In the real world, the law data in the smart Internet of Things usually consists of heterogeneous information with some noises. Non-negative matrix factorization is a popular tool for multi-view learning, which can be employed to represent and learn heterogeneous law features comprehensively. However, current NMF-based techniques generally use clean multi-view datasets to generate common subspace, while in practice, they often contain some noises or unrelated items so that the performance of the algorithms may be severely degraded. In this paper, we propose to develop a novel subspace learning model, called Adaptive Dual Graph-regularized Multi-View Non-Negative Feature Learning (ADMFL), for multi-view data representation. We utilize the geometric structures of both data and feature manifold to model the distribution of data points in the common subspace. Meanwhile, we lift the effect of unrelated features down through separating the view-specific features for each view. Moreover, we introduce a weight factor for all views and maintain the sparsity of the latent common representation. An effective objective function is thus designed and iteratively updated until convergence. Experiments on standard datasets demonstrate that the proposed ADMFL method outperforms other compared methods in the paper.
Emoji Prediction for Hebrew Political Domain

In this study, we aim to predict the most likely emoji given only a short text as an input. We extract a Hebrew political dataset of user comments for emoji prediction. Then, we investigate highly sparse n-grams representations as well as denser character n-grams representations for emoji classification. Since the comments in social media are usually short, we also investigate four dimension reduction methods, which associates similar words to similar vectorial representation. We demonstrate that the common Word Embedding dimension reduction method is not optimal. We also show that the character n-grams representations outperform all the other representation for the task of emoji prediction for Hebrew political domain.
Advice reification, learning, and emergent collective intelligence in online health support communities

Online health support forums utilize straightforward online discussion designs to create a sociotechnical space where people can seek social support from others. The advice generated in these forums exists as an archival resource for future health information seekers. The present study uses mixed methods to investigate how invisible social processes lead advice to be adapted over time by forum members. Drawing on the construct of 'reification' from the communities of practice (COP) literature, we operationalize the reification of advice (RoA) as a process by which advice is developed across multiple discussion threads, and construct an algorithmic procedure to extract posts that trace this process. We evaluate our algorithm with crowd-workers, and perform an inductive, qualitative analysis to identify different modes of advice reification. We suggest that RoA could be used as the basis of a mid-level theory that treats online support communities and bundles of advice trajectories embedded in a shifting sociotechnical context. In our closing analysis, we propose that our approach might be a first step in an algorithmic procedure for assessing advice quality, drawing on the idea that reified advice may be considered a product of the collective intelligence of an online health support community.
R2SIGTP: a Novel Real-Time Recommendation System with Integration of Geography and Temporal Preference for Next Point-of-Interest

With the rapid development of location-based social networks (LBSNs), point of interest (POI) recommendation has become an important way to meet users' personalized demands. The aim of POI recommendation is to provide personalized recommendation of POIs for mobile users. However, traditional POI recommendation systems cannot satisfy users' personalized demands. The reason is that the traditional POI recommendation system cannot recommend the next POI to a user based on the user's context information. Also, the traditional POI recommendation system provides no real-time guarantee on performance. In this demo, we propose a novel real-time next POI recommendation system named R2SIGTP which provides more personalized real-time recommendation compared with existing ones. Our system has the following advantages: 1) it has real-time performance; 2) it uses a unified approach to integrate geographic and preference information; 3) it considers the feedback of each single user to provide more personalized recommendation. We have implemented our system. R2SIGTP is easy to use and can be used by the mobile terminal's browser to recommend the next POI to the user in real-time based on the automatically identified user location and current time. The experimental results on real-world LBSNs show that R2SIGTP's performance is satisfactory.
Approaches to address the data skew problem in federated learning

A Federated Learning approach consists of creating an AI model from multiple data sources, without moving large amounts of data across to a central environment. Federated learning can be very useful in a tactical coalition environment, where data can be collected individually by each of the coalition partners, but network connectivity is inadequate to move the data to a central environment. However, such data collected is often dirty and imperfect. The data can be imbalanced, and in some cases, some classes can be completely missing from some coalition partners. Under these conditions, traditional approaches for federated learning can result in models that are highly inaccurate. In this paper, we propose approaches that can result in good machine learning models even in the environments where the data may be highly skewed, and study their performance under different environments.
UniWiG: Unified Winograd-GEMM Architecture for Accelerating CNN on FPGAs

Convolutional Neural Networks (CNN) have emerged as the most efficient technique for solving a host of machine learning tasks, especially in image and video processing domains. However deploying CNNs on computing systems with smaller form factors have found to be extremely challenging due to the complex nature of CNNs. Hardware acceleration of CNNs using FPGAs have emerged as a promising approach due to high performance, energy efficiency and reconfigurability of FPGAs. Winograd filtering based convolution is the most efficient algorithm for calculating convolution for smaller filter sizes. In this paper, we propose a unified architecture named UniWiG, where both Winograd based convolution and general matrix multiplication (GEMM) can be accelerated using the same set of processing elements. This enables efficient utilization of FPGA hardware resources for accelerating all the layers in the CNNs. The proposed architecture has been used to accelerate AlexNet CNN, which shows performance improvement in the range of 1.4 to 4.02 with only 13% additional FPGA resources than state-of-art GEMM accelerator. We have also analyzed the performance with varying Winograd tile sizes and found out the most appropriate tile sizes for maximizing the performance while reducing on-chip memory resources.
Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-Based Big Data

Wireless Multimedia Sensor Networks (WMSN), for object tracking, have been used as an emerging technology in different application areas, such as health care, surveillance, and traffic control. In surveillance applications, sensor nodes produce data almost in real-time while tracking the objects in a critical area or monitoring border activities. The generated data is generally treated as big data and stored in NoSQL databases. In this paper, we present a new object tracking approach for surveillance applications developed using a big data model based on graphs and a multilevel fusion. Our approach consists of three main steps: intra-node fusion, inter-node fusion, and object trajectory construction. Intra-node fusion exploits the detection and tracking of objects in each sensor, while inter-node fusion uses spatio-temporal data and neighboring sensors. Then, the fused data of all sensor nodes are combined to construct global trajectories of the detected objects in the monitored area on the WMSN. We implemented a prototype system and evaluated the performance of the proposed approach with both a real dataset and a synthetic dataset. The results of our experiments on the two datasets show that the use of third-level fusion in addition to inter-node and intra-node fusions provides significantly better performance for object tracking in the WMSN applications.
A Framework for the Adoption of Blockchain Technology in Healthcare Information Management Systems

The role of record keeping and information sharing in the health sector cannot be overemphasized. More recently, there has been renewed interest in finding a solution to the issues pertaining to privacy of health records and how these records can be easily accessed by different healthcare providers in healthcare-service delivery. Thus, such records comprise an individual's health history and other information that facilitates healthcare decisions. Furthermore, easy access to a patient's health information is an important aspect of health-service delivery that must be regulated and monitored because of the sensitivity of the information. Current approaches adopted in many hospitals face challenges of missing files, or records, lack of information sharing between healthcare providers, insecure records and also inaccessibility of patients' health information for healthcare providers that are needed to make informed health decisions. Building from a case study grounded in interviews and document review, we propose a scalable framework that supports the adoption of blockchain technology in addressing the issues of privacy, information sharing, and record keeping in the health sector.
A compiler and virtual machine for constraint-logic object-oriented programming with Muli

The development of enterprise software typically relies on object-oriented (OO) programming languages. However, OO languages are not particularly suited for the implementation of software which involves solving complicated search problems with dynamically appearing constraints, e. g. as found in logistics. Aiming to improve this situation, we propose the Münster Logic-imperative Language (Muli). As a constraint-logic OO language, it facilitates an integrated implementation of applications that use aspects from both constraint-logic and object-oriented programming, thus eliminating the need to resort to JNI for the integration of search applications that are written in a (constraint) logic language. Muli extends Java by logic variables and encapsulated search. Its runtime is based on a symbolic Java virtual machine and leverages constraint solvers. Outside of search regions, Muli behaves just like Java. We motivate the benefits of integrating object-oriented programming and constraint-logic programming into a single language and introduce novel concepts that are required for a seamless integration. Furthermore, we present an operational semantics and transfer concepts and semantics into implementations of a compiler and a virtual machine.
Source magnification of cyberhate: affective and cognitive effects of multiple-source hate messages on target group members

Drawing from the literature on the psychological harm of hate speech and the source magnification framework, we investigated the effects of hate messages from multiple sources on target group members in the context of Twitter. An online experiment conducted with Black Americans living in the United States provided evidence for source magnification of anti-Black messages. In the experiment, participants were exposed to one of three sets of tweets: hate tweets from multiple sources, hate tweets (of identical content) from a single source, and non-hate (control) tweets. The results showed that hate tweets from multiple sources, when compared to the identical hate messages from a single source and to the non-hate tweets, led to greater emotional distress, which, in turn, resulted in greater likelihood of attributing ambiguous social situations to racial prejudice. The findings provide initial evidence for source magnification of hate messages in the context of communication based on social media where simultaneous exposure to asynchronously posted hate messages from multiple sources is likely to happen. The findings also suggest that multiple-source hate messages, when compared to single-source hate messages, are more likely to indirectly result in potential relational harm.
A State Validation System for Augmented Reality Based Maintenance Procedures

Maintenance has been one of the most important domains for augmented reality (AR) since its inception. AR applications enable technicians to receive visual and audio computer-generated aids while performing different activities, such as assembling, repairing, or maintenance procedures. These procedures are usually organized as a sequence of steps, each one involving an elementary action to be performed by the user. However, since it is not possible to automatically validate the users actions, they might incorrectly execute or miss some steps. Thus, a relevant open problem is to provide users with some sort of automated verification tool. This paper presents a system, used to support maintenance procedures through AR, which tries to address the validation problem. The novel technology consists of a computer vision algorithm able to evaluate, at each step of a maintenance procedure, if the user correctly completed the assigned task or not. The validation occurs by comparing an image of the final status of the machinery, after the user has performed the task, and a virtual 3D representation of the expected final status. Moreover, in order to avoid false positives, the system can identify both motions in the scene and changes in the camera’s zoom and/or position, thus enhancing the robustness of the validation phase. Tests demonstrate that the proposed system can effectively help the user in detecting and avoiding errors during the maintenance process.
Adoption Issues in DevOps from the Perspective of Continuous Delivery Pipeline

DevOps and Continuous Delivery (CD) are the terms that are always related to each other in Software Delivery and Operation Process area. DevOps introduces a significant agile perspective to deliver the software product in short cycle time that will reduce technical debt that is caused by delay. Continuous Delivery is one of the DevOps' practices that enables software organization to release new features and new products rapidly. However, the correct practices are still in ambiguity to the current CD process. This paper investigates the advantages and limitation of DevOps adoption to improve the CD process. A qualitative web survey has been conducted to identify the DevOps and Continuous Delivery advantages and adoption problems. 13 respondents' feedbacks have been collected and analyzed. Based on the survey, there are four significant DevOps' practices that need to be considered and developed as a proper guideline to introduce to practitioners.
Standardisation of enterprise architecture development for smart cities

When managing the information and communications technology (ICT) services in smart cities, we commonly encounter complexities. To solve the complexity issues, enterprise architecture frameworks are typically utilised. However, majority of the existing enterprise architecture frameworks have been developed to address the concerns of the stakeholders in their associated world. To address diverse challenges including complexity, multi-stakeholders, and the service-oriented nature of smart cities, this paper presents an enterprise architecture framework that can be used as a methodology to manage enterprise architectures in smart cities. This framework focuses on establishing contextual requirements and definitions for smart city systems and services. In contrast to other approaches, in this paper we focus on two important layers, i.e. the context layer and the service layer, as well as their relationships with other conventional layers. The framework is not only invaluable in developing smart services but also contributes to our understanding of smart city enterprise architectures.
Local adaptive learning for semi-supervised feature selection with group sparsity

Feature selection is often an important tool for many machine learning and data mining tasks. By largely removing the irrelevant features and reducing the complexity of the data processing, feature selection can significantly improve the performance of subsequent classification or clustering tasks. As a result of the rapid development of social networking, large amounts of high-dimensional data have been generated. Due to the high cost of collecting sufficient labels, graph-based semi-supervised feature selection algorithms have attracted the most research interest; however, these approaches neglect the local sparsity of data. Accordingly, motivated by the merits of adaptive learning and sparse learning, we propose a novel feature selection method with a local adaptive loss function and a global sparsity constraint in this paper. Our method can operate more flexibly to model data with different distributions. Moreover, when both the local and global sparsity of data is considered, our method is more capable of selecting the most discriminating features. Experimental results on various real-world applications demonstrate the effectiveness of the proposed feature selection method compared to several state-of-the-art methods.
The Effects of Working Memory, Perceptual Speed, and Inhibition in Aggregated Search

Prior work has studied how different characteristics of individual users (e.g., personality traits and cognitive abilities) can impact search behaviors and outcomes. We report on a laboratory study ( N = 32) that investigated the effects of three different cognitive abilities (perceptual speed, working memory, and inhibition) in the context of aggregated search. Aggregated search systems combine results from multiple heterogeneous sources (or verticals ) in a unified presentation. Participants in our study interacted with two different aggregated search interfaces (a within-subjects design) that differed based on the extent to which the layout distinguished between results originating from different verticals. The interleaved interface merged results from different verticals in a fairly unconstrained fashion. Conversely, the blocked interface displayed results from the same vertical as a group, displayed each group of vertical results in the same region on the SERP for every query, and used a border around each group of vertical results to help distinguish among results from different sources. We investigated three research questions (RQ1--RQ3). Specifically, we investigated the effects of the interface condition and each cognitive ability on three types of outcomes: (RQ1) participants’ levels of workload, (RQ2) participants’ levels of user engagement, and (RQ3) participants’ search behaviors. Our results found different main and interaction effects. Perceptual speed and inhibition did not significantly affect participants’ workload and user engagement but significantly affected their search behaviors. Specifically, with the interleaved interface, participants with lower perceptual speed had more difficulty finding relevant results on the SERP, and participants with lower inhibitory attention control searched at a slower pace. Working memory did not have a strong effect on participants’ behaviors but had several significant effects on the levels of workload and user engagement reported by participants. Specifically, participants with lower working memory reported higher levels of workload and lower levels of user engagement. We discuss implications of our results for designing aggregated search interfaces that are well suited for users with different cognitive abilities.
OBSERVING THE EMERGENCE OF CONSTRUCTIONAL KNOWLEDGE

Abstract Based on writing produced by second language learners at different proficiency levels (CEFR A1 to C1), we adopted a usage-based approach (Ellis, Römer, &amp; O’Donnell, 2016; Tyler &amp; Ortega, 2018) to investigate how German and Spanish learner knowledge of 19 English verb-argument constructions (VACs; e.g., “V with n,” illustrated by he always agrees with her ) develops. We extracted VACs from subsets of the Education First-Cambridge Open Language Database, altogether comprising more than 68,000 texts and 6 million words. For each VAC, L1 learner group, and proficiency level, we determined type and token frequencies, as well as the most dominant verb-VAC associations. To study effects of proficiency and L1 on VAC production, we carried out correlation analyses to compare verb-VAC associations of learners at different levels and different L1 backgrounds. We also correlated each learner dataset with comparable data from a large reference corpus of native English usage. Results indicate that with increasing proficiency, learners expand their VAC repertoire and productivity, and verb-VAC associations move closer to native usage.
Votereum: An Ethereum-Based E-Voting System

Blockchain, which is the underlying technology of the first cryptocurrency Bitcoin, has drawn a lot of global attention in recent years. Its notable characteristics of the distributed ledger, trustless system, and immutability not only makes it a disruptive innovation in the electronic payment industry but also potential solutions for other areas that require trust establishment. Electronic voting (E-voting) scheme is a use-case where all attributes of blockchain can offer a mechanism for an open, fair and universally verifiable electoral process. In this paper, we review the requirements and then propose Votereum, an E-voting system that utilizes the blockchain technology. The proposed system is empowered by Ethereum platform, including one server manages the entire system and the other handles all blockchain-related requests. The implementation is also deployed to Rinkeby testing network for evaluation on the feasibility and discussion on some security concerns, which are mentioned in the conclusion of this paper.
Detection of HEVC Double Compression With the Same Coding Parameters Based on Analysis of Intra Coding Quality Degradation Process

The emergence of the high-efficiency video coding (HEVC) standard enables people to enjoy high definition (HD) video content; meanwhile, HD videos, tamper detection has become a crucial issue and gradually aroused people's attention. The detection of double HEVC compressed videos with the same coding parameters is challenging since the recompression traces are inconspicuous. To deal with this issue, a novel method based on the intra prediction mode is proposed in this paper. First, the quality degradation mechanism is analyzed to facilitate the selection of classification features and the source of error in intra coding is fully considered to establish the equivalent error model. Second, the feature model of double HEVC compression detection, which is mainly based on the statistical feature of intra prediction mode, is proposed. Finally, the experiment is carried out in 720p and 1080p HEVC videos instead of low-resolution (CIF or QCIF) videos. Experimental results have demonstrated better efficiency of the proposed method in comparison to the state-of-the-art methods. Besides, the proposed method is more robust to various encoding configurations.