Research from April 2016
This area brings together all research published after April 2016.
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Mobile computing and IoT: radio spectrum requirement for timely and reliable message delivery over Internet of Vehicles (IoVs)This paper studied the required amount of radio spectral resource enough to support timely and reliable vehicular communication via vehicular ad-hoc networks (VANETs). The study focussed on both DSRC/WAVE and the European standard ITS-G5 that are based on recently approved IEEE 802.11p specification, which uses a simplified version of CSMA/CA as MAC protocol, and an STDMA MAC recently proposed by European Telecommunications Standards Institute (ETSI). The paper further carried out a feasibility analysis of radio spectrum requirement for timely and reliable vehicle-to-vehicle (V2V) communication. In the feasibility analysis, synchronized STDMA MAC is compared with the CSMA/CA MAC protocol, which 802.11p is based on. Message Reception Failure (MRF) probability is used as a performance metric to investigate and ascertain the minimum spectrum requirement for efficient, timely, and reliable V2V communication. Simulation results show that even at the same allocation of 10MHz channel bandwidth, STDMA MAC outperforms
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Instant messaging and social networks: the advantages in online research methodologyThe use of instant messaging and social networks in the world today is a concept with high esteem. The efforts of many researchers to merge it with various research methods is still a new trend of strong debate. This study will examine the advantages of instant messaging and social networks in online research methodology scenario. With the advent of web-based messaging applications, one now wonder the difficulties researchers face on a daily basis to schedule a one-on-one interview with the potential participants. Instant message apps are surging in popularity across the world as these apps encourage peer-to-peer communications and allow users to exchange instant messages online and through their mobile devices. This study is done using online focus group discussion consisting of five members expressing their views on this novel domain, the important of instant messaging and social networks on research methods as against the traditional one-on-one interview approach for data collection. The results obtained f
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Effective piecewise CNN with attention mechanism for distant supervision on relation extraction taskRelation Extraction is an important sub-task in the field of information extraction. Its goal is to identify entities from text and extract semantic relationships between entities. However, the current Relationship Extraction task based on deep learning methods generally have practical problems such as insufficient amount of manually labeled data, so training under weak supervision has become a big challenge. Distant Supervision is a novel idea that can automatically annotate a large number of unlabeled data based on a small amount of labeled data. Based on this idea, this paper proposes a method combining the Piecewise Convolutional Neural Networks and Attention mechanism for automatically annotating the data of Relation Extraction task. The experiments proved that the proposed method achieved the highest precision is 76.24% on NYT-FB (New York Times-Freebase) dataset (top 100 relation categories). The results show that the proposed method performed better than CNN-based models in most cases.
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Understanding negative sampling in knowledge graph embeddingKnowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a low-dimensional vector space, which has made steady progress in recent years. Conventional KGE methods, especially translational distance-based models, are trained through discriminating positive samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks, such as recommendation, link prediction and node classification. We summarize current negative sampling approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches and their characteristics. It is a hope that this review can provide some guidelines for new thought
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Hierarchical user intention-preference for sequential recommendation with relation-aware heterogeneous information network embeddingExisting recommender systems usually make recommendations by exploiting the binary relationship between users and items, and assume that users only have flat preferences for items. They ignore the users' intentions as an origin and driving force for users' performance. Cognitive science tells us that users' preference comes from an explicit intention. They first have an intention to possess a particular (type of) item(s) and then their preferences emerge when facing multiple available options. Most of the data used in recommender systems are composed of heterogeneous information contained in a complicated network's structure. Learning effective representations from these heterogeneous information networks (HINs) can help capture the user's intention and preferences, therefore, improving recommendation performance. We propose a hierarchical user's intention and preferences modeling for sequential recommendation based on relation-aware HIN embedding (HIP-RHINE). We first construct a multirelational semantic spa
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Digital forensics challenges in cyberspace: overcoming legitimacy and privacy issues through modularisationThe significance of the cloud environment is growing in the current digital world. It provides several advantages, such as reduced expenses, the ability to adjust to different needs, adaptability and enhanced cooperation. The field of digital forensic investigations has encountered substantial difficulties in reconciling the requirement for efficient data analysis with the increasing apprehensions regarding privacy in recent times. As investigators analyse digital evidence to unearth crucial information, they must also traverse an intricate network of privacy rules and regulations. Given the increasing prevalence of remote work and the necessity for businesses to be adaptable and quick to react to shifting market circumstances, the cloud infrastructure has become a crucial asset for organisations of various scales. Although the cloud offers benefits such as scalability, flexibility and enhanced collaboration, it presents difficulties in digital forensic investigations regarding data protection, ownership and
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Information-centric mobile networks: a survey, discussion, and future research directionsInformation-centric networking (ICN) and its fruition, the named data networking (NDN) is a paradigm shift from host-centric address-based communication architecture to the content-centric name-based one. ICN intends to resolve various major issues faced by today's internet architecture such as privacy, security, consistent routing, and mobility, to name a few. With the massive increase of mobile data traffic in today's era, mobility is one of the major concerns in networking. On the one hand, ICN realization i.e., the NDN follows a pull-based communication model and natively supports the consumer (end-user) mobility in wired networks by maintaining the forwarding states on intermediate nodes. Nevertheless, the mobile consumer nodes confront issues in wireless networking environments such as excessive energy consumption as a result of request flooding, content retrieval delays due to intermittent connectivity, and bandwidth consumption due to the broadcasting nature of the wireless medium, among others. The p
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Predictive modelling of Air Quality Index (AQI) across diverse cities and states of India using machine learning: investigating the influence of Punjab's stubble burning on AQI variabilityAir pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the atmosphere. The dataset used for this research has been taken from the official website of CPCB. The dataset has the air pollutant concentration from 22 different monitoring stations in different cities of Delhi, Haryana, and Punjab. This data is checked for null values and outliers. But, the most important thing to note is the correct understanding and imputation of such values rather than ignoring or doing wrong imputation. The time series data has been used in this research which is tested for stationarity using The Dickey-Fuller test. Further different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep
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Barriers facing e-service technology in developing countries: a structured literature review with Nigeria as a case studyE-Government services adoption rate is rapidly increasingly in the developing and lower-middle-income countries for promoting good governance capability and accountability of public organisations. E-Government services are helping to boost government revenue, very fast and secured transactions, reduce corruption through the use of modern technology and transparent operations. It is imperative to state that the e-Service in an e-Governance domain has been gaining more attention over past two decades. This paper will examine a structured literature review (SLR) of the barriers facing E-Service Technology in developing countries with Nigeria as a case study. In this study, the use of a structured literature review gives rise to reviewing papers from conferences on Google Scholar between 2009 and 2014. A total of 3100 papers were reviewed using content analysis and 68 papers from Google Scholar after careful filtering, classification and analysis met the inclusion criteria for barriers facing E-Service adoption a
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Digital Village Index (DVI) for Indonesia case studyDigitalization has been implemented in the city and village as part of development. Some existing indices include the Developing village index, Sustainable Development Goals (SDGs), Smart City Index, E-Government Index, and Digital Town Readiness Framework. However, there is limited research on how to measure the impact of village digitalization, especially in the Indonesian context. This paper aims to develop a novel Digital Village Index (DVI) based on a literature review. This is based on progress research about the village digital index in Indonesia. The novel index is an initial DVI based on desk research and contains 24 indicators. It will be validated by fieldwork in four villages supported by Telkom Smart Village Nusantara (Telkom SVN), Village of Krandegan at Purworejo, and five villages in Madura Island supported by Universitas Trunojoyo Madura (UTM), then improve the novel index. The novelty and contribution of the research is the new index to measure village digitalization in Indonesia.
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Adiabatic compressed air energy storage technologyAdiabatic compressed air energy storage (ACAES) is frequently suggested as a promising alternative for bulk electricity storage, alongside more established technologies such as pumped hydroelectric storage and, more recently, high-capacity batteries, but as yet no viable ACAES plant exists. At first sight, this appears surprising, given that technical literature consistently refers to its potential as a promising energy storage solution and the fact that two diabatic compressed air energy storage (DCAES) plants exist at utility scale (Huntorf, Germany and Macintosh Alabama, USA), with over 80 years of combined operation. In this article, we discuss aspects of the main components that constitute a compressed air energy storage (CAES) system, the fundamental differences between how they operate in diabatic and adiabatic contexts, and the design challenges that need to be overcome for ACAES to become a viable energy storage option in the future. These challenges are grounded in thermodynamics and are consistent
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Why is adiabatic compressed air energy storage yet to become a viable energy storage option?Recent theoretical studies have predicted that adiabatic compressed air energy storage (ACAES) can be an effective energy storage option in the future. However, major experimental projects and commercial ventures have so far failed to yield any viable prototypes. Here we explore the underlying reasons behind this failure. By developing an analytical idealized model of a typical ACAES design, we derive a design-dependent efficiency limit for a system with hypothetical, perfect components. This previously overlooked limit, equal to 93.6% under continuous cycling for a typical design, arises from irreversibility associated with the transient pressure in the system. Although the exact value is design dependent, the methodology we present for finding the limit is applicable for a wide range of designs. Turning to real systems, the limit alone does not fully explain the failure of practical ACAES research. However, reviewing the available evidence alongside our analytical model, we reason that underestimation of th
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An alternative sequence of operation for Pumped-Hydro Compressed Air Energy Storage (PH-CAES) systemsIn a previous publication, entitled “Experimental study of a PH-CAES system: Proof of concept”, we presented results of an innovative solution for energy storage that uses only air and water as working fluids, named PH-CAES (Pumped-Hydro Compressed Air Energy Storage). Differently from a conventional CAES that operates with air turbines and air compressors, the PH-CAES uses a pump to compress water against air, and a hydraulic turbine to generate power. In the time of the aforementioned publication, it was possible to see that with a suitable thermodynamic cycle the PH-CAES could reach a high round-trip efficiency. Since then, we have worked on this cycle, and in this article we share the progress we have made. We redefined the sequence of charging and discharging aiming to provide constant power output. We present here simulations based on the balance of energy and entropy for transient regime, also used datasheets to simulate the pump characteristics. The maximum round-trip efficiencies were approximately 42%. We show that this is a relatively high round-trip efficiency, when compared to other CAES systems, which usually depend on multiple heat exchangers, burning fuel or an external heat source, validating thus, the technical relevance of the proposed solution.
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On the use of micro-perforated panels for sound absorptionThis study deals with the sound absorption for Micro-Perforated Panels (MPP) as an effective solution for sound reduction. Single and multiple MPPs backed by an air cavity are presented, analysed, and both their behaviour and response are modelled and measured. The experimental setup relies on the use of an impedance tube. Three MPP samples were fabricated for this study: two MPP samples are made of Aluminium and one sample is polymer-made to analyse the contribution of the panel vibration to the overall sound absorption. To support the analysis, two models are presented: a model based on the acoustic propagation in short and narrow tubes and a model based on the equivalent fluid. Both models are compared to the experimental data and discussed. The theory considers no interactions between the holes. It is particularly showed that the sound absorption in the low-frequency ranges can be enhanced by using the combined effects of multiple MPPs and their vibrational effects. Relatively good agreement is also obser
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Filtration characterisation of leather-fiber wastewaterThe treatment of industrial sludge has been going on for a while now and there exists various treatment methods and techniques which differ in terms of financial (device, process, etc…) and practical (space and treatment volume) constraints. The discharge of industrial waste often results into harmful agents that negatively affect our environment and lifestyle. Leather treatment finds many applications to our daily life and its manufacturing process makes it one of the most important sources of pollution to the environment. The present work deals with the filtration characterisation and dewatering techniques applied to an industrial sludge made of leather-fibre particles. The raw sample was collected from leather factory and was tested. The study focuses on characterising particles’ physical and geometry properties obtained from the sedimentation rate, centrifuge machine, particle size and spectrophotometry measurements. Particle size analysis of the raw sample showed that it contained large size and nano-par
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Synthetic brain images: bridging the gap in brain mapping with generative adversarial modelMagnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.
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Workplace productivity, health and wellbeing: findings from a cluster randomised controlled trial of a workplace intervention to reduce sitting in office workersOBJECTIVE: The aim of the study was to evaluate the feasibility and potential effects of a workplace intervention to reduce and break up sitting. METHODS: Office workers were randomized in clusters to intervention ( n = 22) or control ( n = 22). The intervention included a height-adjustable workstation, education, computer prompt software, and line manager support. Outcomes included device-measured workplace sitting and ecological momentary assessed workplace productivity. Recruitment, retention, and data completion rates were assessed. RESULTS: Recruitment ( N = 44), retention (91%), and workplace sitting measurement rates demonstrated study feasibility. At 8 weeks, workplace sitting was 11% lower (95% CI: -20.71, -1.30) in the intervention group compared with control participants. Intervention participants were also more engaged, motivated, and productive while sitting ( P ≤ 0.016). CONCLUSIONS: It was feasible to implement and evaluate this office workplace intervention, with potential benefits on workplac