Reviewerly/exHarmony huggingface dataset:
Secure Service Provisioning Scheme for Lightweight IoT Devices With a Fair Payment System and an Incentive Mechanism Based on Blockchain

The Internet of Things (IoT) industry is growing very fast to transform factories, homes, farms and practically everything else to make them efficient and intelligent.IoT is applied in different resilient scenarios and applications.IoT faces lots of challenges due to lack of computational power, battery and storage resources.Fortunately, the rise of blockchain technology facilitates IoT in many security solutions.Using blockchain, communication between IoT and emerging computing technologies is made efficient.In this work, we propose a secure service provisioning scheme with a fair payment system for Lightweight Clients (LCs) based on blockchain.Furthermore, an incentive mechanism based on reputation is proposed.We use consortium blockchain with the Proof of Authority (PoA) consensus mechanism.Furthermore, we use Smart Contracts (SCs) to validate the services provided by the Service Providers (SPs) to the LCs, transfer cryptocurrency to the SPs and maintain the reputation of the SPs.Moreover, the Keccak256 hashing algorithm is used for converting the data of arbitrary size to the hash of fixed size.AES128 encryption technique is used to encrypt service codes before sending to the LCs.The simulation results show that the LCs receive validated services from the SPs at an affordable cost.The results also depict that the participation rate of SPs is increased because of the incentive mechanism.
Wrapped phase denoising using convolutional neural networks

We propose a wrapped phase denoising method based on convolutional neural networks (CNN), which can effectively denoise a noisy wrapped phase. The noisy numerator and denominator of the arctangent function are firstly denoised by CNN, and then the filtered numerator and denominator use the arctangent function to obtain the clean wrapped phase. We experimentally verify the denoising performance using various wrapped phase that contains different noise conditions, where the denoised wrapped phase can achieve a satisfactory unwrapping performance using the existing simple unwrapping method. In addition, the proposed method is further demonstrated through the comparison of the existing methods, and shows an accurate denoising result without adjusting any parameters.
Moment Retrieval via Cross-Modal Interaction Networks With Query Reconstruction

Moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a comprehensive system for further performance improvement. In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including the syntactic dependencies of natural language queries, long-range semantic dependencies in video context and the sufficient cross-modal interaction. Specifically, we devise a syntactic GCN to leverage the syntactic structure of queries for fine-grained representation learning and propose a multi-head self-attention to capture long-range semantic dependencies from video context. Next, we employ a multi-stage cross-modal interaction to explore the potential relations of video and query contents, and we also consider query reconstruction from the cross-modal representations of target moment as an auxiliary task to strengthen the cross-modal representations. The extensive experiments on ActivityNet Captions and TACoS demonstrate the effectiveness of our proposed method.
Laplace Landmark Localization

Landmark localization in images and videos is a classic problem solved in various ways. Nowadays, with deep networks prevailing throughout machine learning, there are revamped interests in pushing facial landmark detectors to handle more challenging data. Most efforts use network objectives based on L1 or L2 norms, which have several disadvantages. First of all, the generated heatmaps translate to the locations of landmarks (i.e. confidence maps) from which predicted landmark locations (i.e. the means) get penalized without accounting for the spread: a high- scatter corresponds to low confidence and vice-versa. For this, we introduce a LaplaceKL objective that penalizes for low confidence. Another issue is a dependency on labeled data, which are expensive to obtain and susceptible to error. To address both issues, we propose an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims state-of-the-art on all of the 300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in the Wild (AFLW) dataset. Furthermore, our model is robust with a reduced size: 1/8 the number of channels (i.e. 0.0398 MB) is comparable to the state-of-the-art in real-time on CPU. Thus, this work is of high practical value to real-life application.
Analysing construction student experiences of mobile mixed reality enhanced learning in virtual and augmented reality environments

Mixed reality (MR) and mobile visualisation methods have been identified as important technologies that could reimagine spatial information delivery and enhance higher education practice. However, there is limited research on the impact of mobile MR (MMR) within construction education and improvement of the learners’ experience. With new building information modelling (BIM) workflows being adopted within the architecture, engineering and construction industry, innovative MMR pedagogical delivery methods should be explored to enhance this information-rich spatial technology workflow. This paper outlines qualitative results derived through thematic analysis of learner reflections from two technology-enhanced lessons involving a lecture and a hands-on workshop focussed on MMR-BIM delivered within postgraduate construction education. Seventy participants across the two lessons recruited from an Australian university participated to answer the research question: ‘Does applied mobile mixed reality create an enhanced learning environment for students?’ The results of the analysis suggest that using MMR-BIM can result in an enhanced learning environment that facilitates unique learning experiences, engagement and motivation. However, the study outcome suggests that to understand the processes leading to these learning aspects, further empirical research on the topic is required. This paper is part of the special collection Mobile Mixed Reality Enhanced Learning edited by Thom Cochrane, James Birt, Helen Farley, Vickel Narayan and Fiona Smart. More papers from this collection can be found here.
Segment Blockchain: A Size Reduced Storage Mechanism for Blockchain

The exponential growth of the blockchain size has become a major contributing factor that hinders the decentralisation of blockchain and its potential implementations in data-heavy applications. In this paper, we propose segment blockchain, an approach that segmentises blockchain and enables nodes to only store a copy of one blockchain segment. We use \emph{PoW} as a membership threshold to limit the number of nodes taken by an Adversary---the Adversary can only gain at most $n/2$ of nodes in a network of $n$ nodes when it has $50\%$ of the calculation power in the system (the Nakamoto blockchain security threshold). A segment blockchain system fails when an Adversary stores all copies of a segment, because the Adversary can then leave the system, causing a permanent loss of the segment. We theoretically prove that segment blockchain can sustain a $(AD/n)^m$ failure probability when the Adversary has no more than $AD$ number of nodes and every segment is stored by $m$ number of nodes. The storage requirement is mostly shrunken compared to the traditional design and therefore making the blockchain more suitable for data-heavy applications.
CLCDSA: Cross Language Code Clone Detection using Syntactical Features and API Documentation

Software clones are detrimental to software maintenance and evolution and as a result many clone detectors have been proposed. These tools target clone detection in software applications written in a single programming language. However, a software application may be written in different languages for different platforms to improve the application's platform compatibility and adoption by users of different platforms. Cross language clones (CLCs) introduce additional challenges when maintaining multi-platform applications and would likely go undetected using existing tools. In this paper, we propose CLCDSA, a cross language clone detector which can detect CLCs without extensive processing of the source code and without the need to generate an intermediate representation. The proposed CLCDSA model analyzes different syntactic features of source code across different programming languages to detect CLCs. To support large scale clone detection, the CLCDSA model uses an action filter based on cross language API call similarity to discard non-potential clones. The design methodology of CLCDSA is two-fold: (a) it detects CLCs on the fly by comparing the similarity of features, and (b) it uses a deep neural network based feature vector learning model to learn the features and detect CLCs. Early evaluation of the model observed an average precision, recall and F-measure score of 0.55, 0.86, and 0.64 respectively for the first phase and 0.61, 0.93, and 0.71 respectively for the second phase which indicates that CLCDSA outperforms all available models in detecting cross language clones.
DIRE: A Neural Approach to Decompiled Identifier Naming

The decompiler is one of the most common tools for examining binaries without corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Decompilers can reconstruct much of the information that is lost during the compilation process (e.g., structure and type information). Unfortunately, they do not reconstruct semantically meaningful variable names, which are known to increase code understandability. We propose the Decompiled Identifier Renaming Engine (DIRE), a novel probabilistic technique for variable name recovery that uses both lexical and structural information recovered by the decompiler. We also present a technique for generating corpora suitable for training and evaluating models of decompiled code renaming, which we use to create a corpus of 164,632 unique x86-64 binaries generated from C projects mined from GitHub. Our results show that on this corpus DIRE can predict variable names identical to the names in the original source code up to 74.3% of the time.
Visual Object Search by Learning Spatial Context

We present a visual navigation approach that uses context information to navigate an agent to find and reach a target object. To learn context from the objects present in the scene, we transform visual information into an intermediate representation called context grid which essentially represents how much the object at the location is semantically similar to the target object. As this representation can encode the target object and other objects together, it allows us to navigate an agent in a human-inspired way: the agent will go to the likely place by seeing surrounding context objects in the beginning when the target is not visible and, once the target object comes into sight, it will reach the target quickly. Since context grid does not directly contain visual or semantic feature values that change according to introductions of new objects, such as new instances of the same object with different appearance or an object from a slightly different class, our navigation model generalizes well to unseen scenes/objects. Experimental results show that our approach outperforms previous approaches in navigating in unseen scenes, especially for broad scenes. We also evaluated human performances in the target-driven navigation task and compared with machine learning based navigation approaches including this work.
Fair Coresets and Streaming Algorithms for Fair k-means

We study fair clustering problems as proposed by Chierichetti et al. [CKLV17]. Here, points have a sensitive attribute and all clusters in the solution are required to be balanced with respect to it (to counteract any form of data-inherent bias). Previous algorithms for fair clustering do not scale well. We show how to model and compute so-called coresets for fair clustering problems, which can be used to significantly reduce the input data size. We prove that the coresets are composable [IMMM14] and show how to compute them in a streaming setting. This yields a streaming PTAS for fair k-means in the case of two colors (and exact balances). Furthermore, we extend techniques due to Chierichetti et al. [CKLV17] to obtain an approximation algorithm for k-means, which leads to a constant factor algorithm in the streaming model when combined with the coreset.
Living media interfaces: a multi-perspective analysis of biological materials for interaction

Living media interfaces (LMIs) have emerged as a new way to interact with digital systems by incorporating actual living organisms in user interfaces. There is increasing interest in utilizing living media in the context of interaction design influenced by recent advancements in biological sciences that make new forms of responsive living media possible. We establish a definition of LMIs appropriate for HCI’s interdisciplinary domain. We provide an overview of the current design space and identify open research questions from four different perspectives: Biological, Ethical, Artistic, and HCI. For each perspective, we use a series of LMI exemplars to illustrate and ground themes and arguments. We conclude with a series of implications for design, including a discussion of LMIs’ ability to engage human users through being alive, their potential to symbolize and embody dynamic information, and the practical and ethical questions that designers need to consider when working with them.
Representing Robot Task Plans as Robust Logical-Dynamical Systems

It is difficult to create robust, reusable, and reactive behaviors for robots that can be easily extended and combined. Frameworks such as Behavior Trees are flexible but difficult to characterize, especially when designing reactions and recovery behaviors to consistently converge to a desired goal condition. We propose a framework which we call Robust Logical-Dynamical Systems (RLDS), which combines the advantages of task representations like behavior trees with theoretical guarantees on performance. RLDS can also be constructed automatically from simple sequential task plans and will still achieve robust, reactive behavior in dynamic real-world environments. In this work, we describe both our proposed framework and a case study on a simple household manipulation task, with examples for how specific pieces can be implemented to achieve robust behavior. Finally, we show how in the context of these manipulation tasks, a combination of an RLDS with planning can achieve better results under adversarial conditions.
Towards Designing Bodily Integrated Play

There is an increasing trend in utilizing interactive technology for bodily integrations, such as additional limbs and ingestibles. Prior work on bodily integrated systems mostly examined them from a productivity perspective. In this article, we suggest examining this trend also from an experiential, playful perspective, as we believe that these systems offer novel opportunities to engage the human body through play. Hence, we propose that there is an opportunity to design "bodily integrated play". By relating to our own and other's work, we present an initial set of design strategies for bodily integrated play, aiming to inform designers on how they can engage with such systems to facilitate playful experiences, so that ultimately, people will profit from bodily play's many physical and mental wellbeing benefits even in a future where machine and human converge.
Metalinguistic density as an indicator of sharedness: Economic and financial terms in online interaction

Metalanguage, language about language, is one of language's most creative and intriguing properties. Yet it has rarely been crystallised as an analytical focus, nor has its occurrence been systematically described or scrutinised. This paper employs a focus on (explicit) metalanguage as a means to detect the extent to which a term and its denotational norms/indexical links are shared across a subset of language users. It argues that the relative amount of metalanguage focusing on a term, the term's ‘metalinguistic density’, offers insights about the term's status in a particular community. Specifically, it proposes the categories of Contentious, Common, and Unstable Terms to describe the contextually influenced behaviour of terms.
Topic Modelling Twitter Data with Latent Dirichlet Allocation Method

Twitter is a popular social media for every user to issue thoughts and emotional forms which are tweets, tweets that only have 140 characters with limitations to write in text. Twitter is one of the social media places to get information that is always up to date, tweets are categorized into big data because tweets are information that can be used as a source of data for research. Latent Dirichlet Allocation (LDA) as an algorithm that can process large text data (big data). In this study using the LDA method as an algorithm to produce topic modeling, each topic similarity, and visualization of topic clusters from the tweet data generated as many as 4 topics (Economic, Military, Sports, Technology) in Indonesian, where each topic has a number different tweets. The LDA method used in the processing of tweet data is successfully carried out and works optimally, in each topic extraction, topic modeling, generating index words that are in each topic cluster and computer visualization in the topic.LDA output shows optimal performance in the process of word indexing in Sport topics with 1260 tweets with an accuracy of 98% better than the LSI method in Topic Modeling.
Classification of Shopify App User Reviews Using Novel Multi Text Features

App stores usually allow users to give reviews and ratings that are used by developers to resolve issues and make plans for their apps. In this way, these app stores collect large amounts of data for analysis. However, there are several challenges that must first be addressed, related to redundancy and the volume of data, by using machine learning. This study performs experiments on a dataset that contains reviews for Shopify apps. To overcome the aforementioned limitations, we first categorize user reviews into two groups, i.e., happy and unhappy, and then perform preprocessing on the reviews to clean the data. At a later stage, several feature engineering techniques, such as bag-of-words, term frequency-inverse document frequency (TF-IDF), and chi-square (Chi2), are used singly and in combination to preserve meaningful information. Finally, the random forest, AdaBoost classifier, and logistic regression models are used to classify the reviews as happy or unhappy. The performance of our proposed pipeline was evaluated using average accuracy, precision, recall, and f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score. The experiments reveal that a combination of features can improve machine learning models performance and in this study, logistic regression outperforms the others and achieves an 83% true acceptance rate when combined with TF-IDF and Chi2.
Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital

In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge.
Recognition Model for Solar Radiation Time Series based on Random Forest with Feature Selection Approach

Solar photovoltaic (PV) energy, one of fast growing energy technologies in the world, provides cheap opportunities for future energy generation. Solar radiation estimation models play an important role in efficient utilization of solar PV energy. In this paper, we propose a high performance recognition model for solar radiation based on random forest (RF) with feature selection approach to cope with nonlinear dynamics in time series. Forty-five features have been extracted from the temperature, pressure, wind speed and humidity data as well as solar radiation data using the moving average indicator. In addition, the total of 50 features has been extracted together with the five features obtained based on the previous values of the five parameters specified. The features that will improve model performance have been determined by the forward selection approach and the model, which has the highest performance, has been proposed for solar radiation estimation.
Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications

Deep Learning is considered to leverage smart cities through social media sentiment analysis. The digital content in social media can be used for many smart city applications (SCAs)1 . Classical convolutional neural networks (CNNs) are challenging to parallelize and insufficient to capture long term contextual semantic features for sentiment analysis. In this perspective, this paper initially proposes a domain-specific distributed word representation (DS-DWR)2 with a considerably small corpus size induced from textual resources in social media. In DS-DWR, different Distributed Word Representations are concatenated to builds rich representations over the input sequence, which is worthwhile for infrequent and unseen terms. Second, a dilated convolutional neural network (D-CNN)3 , which is composed of three parallel dilated convolutional neural network (PD-CNN)4 layers and a global average pooling (GAP)5 layer. Our considered parallel dilated convolution reduces dimension and incorporates an extension in the size of receptive fields without the loss of local information. Further, the long-term contextual semantic information is achieved by the use of different dilation rates. Experiments demonstrate that our architecture accomplishes comparable results with multiple hyperparameters tuning for better parallelism which leads to the minimized computational cost.