Search Results (23)

IoT (Internet-of-Things)-powered devices can be exploited to connect vehicles to smart city infrastructure, allowing vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. In this paper, we place ourselves in a transient scenario in which next-generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT capabilities. We focus on intersection management, in particular on reusing existing traffic lights empowered by a new management system. We propose an auction-based system in which traffic lights are able to exchange contextual information with vehicles and other nearby traffic lights with the aim of reducing average waiting times at intersections and consequently overall trip times. We use bid propagation to improve standard vehicle trip times while allowing emergency vehicles to free up the way ahead without needing ad hoc system for such vehicle, only an increase in their budget. The proposed system is then tested against two baselines: the classical Fixed Time Control system currently adopted for traffic lights, and an auction strategy that does not exploit traffic light coordination. We performed a large set of experiments using the well known MATSim transport simulator on both a synthetic Manhattan map and on a map we built of an urban area located in Modena, Northern Italy. Our results show that the proposed approach performs better than the classical fixed time control system and the auction strategy that does not exploit coordination among traffic lights. Full article

Lots of Insurance companies have constructed databases for ad hoc query software in Taiwan that combines customer relationship management and marketing campaign management. An ad hoc query is a non-routine and specific query performed in real time to filter specific customer information from big data. Ad hoc query has the strength to retrieve customer information more quickly and conveniently than by filtering target customer lists using a mainframe or OLAP. In this study, the strengths and weaknesses of ad hoc query, online analytical processing (OLAP), and general query using a mainframe are analyzed. The results indicate that ad hoc query has the advantage of flexibility for users’ specific needs. Ad hoc query has obstacles and challenges for users regarding how to learn its system fields and writing programs. It is concluded that the design between individual assured suggestions and a convenient operation process is critical for raising the response rate. Additionally, precisely filtering technology for target customers is the key success factor for an accident insurance campaign. Full article

Over the past few years, word embeddings and bidirectional encoder representations from transformers (BERT) models have brought better solutions to learning text representations for natural language processing (NLP) and other tasks. Many NLP applications rely on pre-trained text representations, leading to the development of a number of neural network language models for various languages. However, this is not the case for Amharic, which is known to be a morphologically complex and under-resourced language. Usable pre-trained models for automatic Amharic text processing are not available. This paper presents an investigation on the essence of learned text representation for information retrieval and NLP tasks using word embeddings and BERT language models. We explored the most commonly used methods for word embeddings, including word2vec, GloVe, and fastText, as well as the BERT model. We investigated the performance of query expansion using word embeddings. We also analyzed the use of a pre-trained Amharic BERT model for masked language modeling, next sentence prediction, and text classification tasks. Amharic ad hoc information retrieval test collections that contain word-based, stem-based, and root-based text representations were used for evaluation. We conducted a detailed empirical analysis on the usability of word embeddings and BERT models on word-based, stem-based, and root-based corpora. Experimental results show that word-based query expansion and language modeling perform better than stem-based and root-based text representations, and fastText outperforms other word embeddings on word-based corpus. Full article

Sequences of multi-temporal synthetic aperture radar (SAR) images are routinely used for land-use land-change (LULC) applications, allowing the retrieval of accurate and up-to-date information on the state of the Earth’s surface and its temporal variations. Change detection (CD) methods that rely on the exploitation of SAR data are, generally, made of three distinctive steps: (1) pre-processing of the SAR images; (2) comparison of the pairs of SAR images; and (3) the automatic extraction of the “changed areas”, employing proper thresholding algorithms. Within this general framework, the reduction in speckle noise effects, which can be obtained by applying spatial multi-looking operations and ad hoc noise filters, is fundamental for the better detecting and classifying of changed regions. Usually, speckle noise filters are singularly and independently applied to every SAR image without the consideration of their inherent temporal relationships. In particular, most use local (spatial) approaches based on determining and averaging SAR backscattered signals related to neighboring SAR pixels. In this work, conversely, we explore the potential of a joint 3D space-time non-local mean filter (NLMF), which relies on the discrimination of similar features in a block of non-local SAR pixels extracted from the same or different SAR images. The theory behind non-local-mean filters is, first, shortly revised. Then, the developed space-time NLMF is applied to a real test case for the purposes of identifying flooded zones due to the massive inundations that hit the Kerala region, India, during the summer of 2018. To this aim, a set of 18 descending SAR images collected from the European (EU) Copernicus Sentinel-1 (S-1) sensor was exploited. The performance of the developed NLMF has also been assessed. It is worth remarking that the proposed method can be applied for the purposes of analyzing a heterogenous set of natural and/or artificial disastrous conditions. Further, it can also be helpful during the pre-processing stages of the sequences of SAR images for the purposes of CD applications. Full article

A vehicular ad hoc network (VANET) is essential in building an intelligent transportation system that optimizes traffic conditions and makes traffic information conveniently accessible. However, malicious vehicles may disrupt the traffic order via propagating forged traffic/road information. Therefore, using digital certificates based on cryptography, some existing authentication schemes were proposed to manage vehicles’ identities. At first glance, these schemes can effectively identify malicious vehicles. However, these schemes require more computation and storage resources to maintain certificates. This is because the data storage of the database increases in a near-linear trend as the number of certificates grows. In this paper, we propose an efficient blockchain-based authentication scheme for secure communication in VANET (EBAS) to address the aforementioned issues. In EBAS, the regional trusted authority (RTA) receives traffic messages uploaded by the vehicle, together with transactions constructed via the unspent transaction output (UTXO) model. The verifier checks the legitimacy of the single input contained in the uploaded transaction to verify the legitimacy of the message sender’s identity. In terms of privacy preservation, a asymmetric key encryption technique, elliptic curve cryptography (ECC), is applied for constructing the transaction pseudonym, and users participate in the authentication process anonymously. In addition, our scheme guarantees the scalability of EBAS by proposing a transaction update mechanism, which can keep data storage at a stable level rather than near-linear growth. Under the simulation, the retrieving overhead remains at approximately 0.32 ms while the storage cost is stable at around 32.7 M for the blockchain state database. In terms of authentication efficiency, the average overhead of the proposed scheme is around 0.942 ms, which outperforms the existing schemes. Full article

Water is a vital resource for citizens’ economic and social development. However, the uses to which it can be put often conflict. Possible solutions to mitigate disputes involve political options, scarce economic resources, and the search for mechanisms to ensure its adequate allocation. For over half a century, countries such as Australia, Spain, Chile, and the western states of the United States have been considering the possibility of using markets for rights of use. They are defined as formal or informal trading exchanges of rights, whose aim is to improve efficiency, ensure security of supply, and make allocations more flexible. In this context, the aim of this article is to show a current picture of the scientific production related to Water Markets using the comparative bibliometric study of the documents indexed in the Web of Science (WoS) and Scopus databases as a tool. The advanced search of relevant terms resulted in the retrieval of 261 papers from WoS and 305 from Scopus, with a time limit of 2020, which make up the ad hoc basis of the analysis. From this basis, it can be deduced that the subject of the Water Market has been present in the scientific literature on a more or less regular basis since the beginning of the 1990s. However, it has emerged as a topical issue in recent years, being in a phase of exponential growth, which means that interest in the area is likely to continue in the coming years. Full article

Nowadays, pre-trained language models such as Bidirectional Encoder Representations from Transformer (BERT) are becoming a basic building block in Information Retrieval tasks. Nevertheless, there are several limitations when applying BERT to the query-document matching task: (1) relevance assessments are applicable at the document-level, and the tokens of documents often exceed the maximum input length of BERT; (2) applying BERT to long documents leads to a great consumption of memory usage and run time, owing to the computational cost of the interactions between tokens. This paper explores a novel multi-layer contextual passage architecture that leverage text summarization extraction to generate passage-level evidence for the pre-selected document passage thus brought new possibilities for the long document relevance task. Experiments were conducted on two standard ad-hoc retrieval collections from the Text Retrieval Conference (TREC) 2004 Robust Track (Robust04) and ClueWeb09 with two different characteristics individually. Experimental results show that our approach can significantly outperform the strong baselines and even compared with the same BERT-based models, the precision of our methods as well as state-of-the-art neural ranking models. Full article

With many advances in sensor technology and the Internet of Things, Vehicle Ad Hoc Network (VANET) is becoming a new generation. VANET’s current technical challenges are deploying decentralized architecture and protecting privacy. Because Blockchain features are decentralized, distributed, mass storage, and non-manipulation features, this paper designs a new decentralized architecture using Blockchain technology called Blockchain-based VANET. Blockchain-based VANET can effectively resolve centralized problems and mutual distrust between VANET units. To achieve this, it is needed to provide scalability on the blockchain to run for VANET. In this system, our focus is on the reliability of incoming messages on the network. Vehicles check the validity of the received messages using the proposed Bayesian formula for trust management system and some information saved in the Blockchain. Then, based on the validation result, the vehicle computes a rate for each message type and message source vehicle. Vehicles upload the computed rates to Roadside Units (RSUs) in order to calculate the net reliability value. Finally, RSUs using a sharding consensus mechanism generate blocks, including the net reliability value as a transaction. In this system, all RSUs collaboratively maintain the latest updated Blockchain. Our experimental results show that the proposed system is effective, scalable and dependable in data gathering, computing, organization, and retrieval of trust values in VANET. Full article

Digitalisation offers great potential to improve vaccine uptake, supporting the need for effective life-course immunisation services. We conducted semi-structured in-depth interviews with public health experts from 10 Western European countries (Germany, Greece, Italy, Luxembourg, Malta, the Netherlands, Norway, Poland, Portugal, and the United Kingdom) to assess the current level of digitalisation in immunisation programmes and retrieve data on interventions and best practices. Interviews were performed using an ad hoc questionnaire, piloted on a sample of national experts. We report a mixed level of digital technologies deployment within vaccination services across Europe: Some countries are currently developing eHealth strategies, while others have already put in place robust programmes. Institutional websites, educational videos, and electronic immunisation records are the most frequently adopted digital tools. Webinars and dashboards represent valuable resources to train and support healthcare professionals in immunisation services organisation. Text messages, email-based communication, and smartphone apps use is scattered across Europe. The main reported barrier to the implementation of digital-based programmes is the lack of resources and shared standards. Our study offers a comprehensive picture of the European context and shows the need for robust collaboration between states and international institutions to share best practices and inform the planning of digital intervention models with the aim of countering vaccine hesitancy and increasing vaccine uptake. Full article

Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents. Some of these methods’ limitations consist of their inability to work with polysemic concepts. In addition, these methods introduce fake orthogonalities between semantically related words. To address these limitations, model-based IR approaches based on topics have been explored. Specifically, topic models based on Latent Dirichlet Allocation (LDA) allow building representations of text documents in the latent space of topics, the better modeling of polysemy and avoiding the generation of orthogonal representations between related terms. We extend LDA-based IR strategies using different ensemble strategies. Model selection obeys the ensemble learning paradigm, for which we test two successful approaches widely used in supervised learning. We study Boosting and Bagging techniques for topic models, using each model as a weak IR expert. Then, we merge the ranking lists obtained from each model using a simple but effective top-k list fusion approach. We show that our proposal strengthens the results in precision and recall, outperforming classic IR models and strong baselines based on topic models. Full article

Vehicular ad-hoc network (VANET) is a technology that allows ubiquitous mobility to mobile users. Inter-vehicle communication is an integral component of intelligent transportation systems that enables a wide variety of applications where vehicles interact and cooperate with each other, from safety applications to non-safety applications. VANETs applications have different needs (e.g., latency, reliability, delivery priorities, etc.) in terms of delivery effectiveness. In the last decade, named data networking (NDN) gained the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. In NDN, the content’s name has a vital role in storing and retrieving the content effectively and efficiently. In NDN-based VANETs, adaptive content dissemination solutions must be introduced that can make decisions related to forwarding, cache management, etc., based on context information represented by a content name. In this context, our main contributions are two-fold: (i) we present the hierarchical context-aware content-naming (CACN) scheme for NDN-based VANETs that enables naming the safety and non-safety applications, and (ii) we present a decentralized context-aware notification (DCN) protocol that broadcasts event notification information for awareness within the application-based geographical area. Simulation results show that the proposed DCN protocol succeeds in achieving reduced transmissions, bandwidth, and energy compared to existing critical contents dissemination protocols. Full article

This paper discusses the exploitation of a cooperative navigation strategy for improved in-flight estimation of inertial sensors biases on board unmanned aerial vehicles. The proposed multi-vehicle technique is conceived for a “chief” Unmanned Aerial Vehicle (UAV) and relies on one or more deputy aircrafts equipped with Global Navigation Satellite System (GNSS) antennas for differential positioning which also act as features for visual tracking. Combining carrier-phase differential GNSS and visual estimates, it is possible to retrieve accurate inertial-independent attitude information, thus potentially enabling improved bias estimation. Camera and carrier-phase differential GNSS measurements are integrated within a 15 states extended Kalman filter. Exploiting an ad hoc developed numerical environment, the paper analyzes the performance of the cooperative approach for inertial biases estimation as a function of number of deputies, formation geometry and distances, and absolute and relative dynamics. It is shown that exploiting two deputies it is possible to improve biases estimation, while a single deputy can be effective if changes of relative geometry and dynamics are also considered. Experimental proofs of concept based on two multi-rotors flying in formation are presented and discussed. The proposed framework is applicable beyond the domain of small UAVs. Full article

I-CARE is a hand-held activation system that allows professional and informal caregivers to cognitively and socially activate people with dementia in joint activation sessions without special training or expertise. I-CARE consists of an easy-to-use tablet application that presents activation content and a server-based backend system that securely manages the contents and events of activation sessions. It tracks various sources of explicit and implicit feedback from user interactions and different sensors to estimate which content is successful in activating individual users. Over the course of use, I-CARE’s recommendation system learns about the individual needs and resources of its users and automatically personalizes the activation content. In addition, information about past sessions can be retrieved such that activations seamlessly build on previous sessions while eligible stakeholders are informed about the current state of care and daily form of their protegees. In addition, caregivers can connect with supervisors and professionals through the I-CARE remote calling feature, to get activation sessions tracked in real time via audio and video support. In this way, I-CARE provides technical support for a decentralized and spontaneous formation of ad hoc activation groups and fosters tight engagement of the social network and caring community. By these means, I-CARE promotes new care infrastructures in the community and the neighborhood as well as relieves professional and informal caregivers. Full article

This paper describes in detail VISIONE, a video search system that allows users to search for videos using textual keywords, the occurrence of objects and their spatial relationships, the occurrence of colors and their spatial relationships, and image similarity. These modalities can be combined together to express complex queries and meet users’ needs. The peculiarity of our approach is that we encode all information extracted from the keyframes, such as visual deep features, tags, color and object locations, using a convenient textual encoding that is indexed in a single text retrieval engine. This offers great flexibility when results corresponding to various parts of the query (visual, text and locations) need to be merged. In addition, we report an extensive analysis of the retrieval performance of the system, using the query logs generated during the Video Browser Showdown (VBS) 2019 competition. This allowed us to fine-tune the system by choosing the optimal parameters and strategies from those we tested. Full article

On 25 May 2018, the General Data Protection Regulation (GDPR) Article 17, the Right to Erasure (“Right to be Forgotten”) came into force, making it vital for organisations to identify, locate and delete all Personally Identifiable Information (PII) where a valid request is received from a data subject to erase their PII and the contractual period has expired. This must be done without undue delay and the organisation must be able to demonstrate that reasonable measures were taken. Failure to comply may incur significant fines, not to mention impact to reputation. Many organisations do not understand their data, and the complexity of a hybrid cloud infrastructure means they do not have the resources to undertake this task. The variety of available tools are quite often unsuitable as they involve restructuring so there is one centralised data repository. This research aims to demonstrate that compliance with GDPR’s Article 17 Right to Erasure (“Right to be Forgotten”) is achievable in a hybrid cloud environment by following a list of recommendations. However, full retrieval, all of the time will not be possible, but we show that small organisations running an ad-hoc hybrid cloud environment can demonstrate that reasonable measures were taken to be Right to Erasure (“Right to be Forgotten”) compliant. Full article