1,720,983 research outputs found
Ricostruzione di traiettorie dinamiche dal potenziale di differenziamento di singole cellule
No full textFin dall’introduzione del sequenziamento dell’RNA a singola cellula (scRNA-seq), numerosi metodi computazionali sono stati sviluppati allo scopo di ricostruire la progressione delle singole cellule lungo percorsi di sviluppo a partire dai cambiamenti nel tempo dei rispettivi programmi trascrizionali, e di definire i processi di differenziamento delle cellule adulte a partire dai rispettivi antenati pluripotenti. La maggior parte di tali metodi sono progettati per ricostruire i processi dinamici cellulari a partire da profili trascrizionali statici tramite l’ordinamento delle cellule in traiettorie continue sulla base della loro similarità sul piano dell’espressione genica. Questa strategia si basa su assunzioni che potrebbero non essere soddisfatte a priori da tutti i sistemi cellulari, e richiede un certo livello di conoscenza pregressa sulla topologia e sulla direzione della genealogia attesa. Poiché la tecnologia del scRNA-seq è sempre più utilizzata per lo studio di sistemi complessi e inesplorati, caratterizzati da schemi di sviluppo ancora poco chiari, è necessaria l’implementazione di nuovi approcci per l’inferenza di genealogie cellulari direttamente sulla base dell’evoluzione dinamica del potenziale di differenziamento delle singole cellule, senza la necessità di assunzioni pregresse. Benché diversi metodi siano già stati sviluppati per la stima del potenziale delle singole cellule tramite la misura del livello di entropia dei rispettivi trascrittomi, non è ancora stato presentato un chiaro modello matematico per l’inferenza delle dinamiche di tale potenziale nel tempo. Per sopperire a tale mancanza, abbiamo sviluppato FIERCE (Framework for InfERence of veloCity of the Entropy), una nuova pipeline computazionale composita che utilizza l’implementazione matematica del metodo RNA velocity per predire l’evoluzione temporale dell’entropia trascrizionale delle singole cellule durante i processi dinamici. In tal modo, FIERCE è in grado di inferire le genealogie cellulari attraverso un approccio non supervisionato e completamente centrato sulle singole cellule, il quale non necessita della specificazione di alcun parametro evolutivo. A seguito dell’applicazione su dati di scRNA-seq provenienti da due sistemi di differenziamento murini ben conosciuti, il nostro metodo è riuscito a ricostruire il corretto percorso di sviluppo delle sottopopolazioni di cellule adulte altamente specializzate a partire dai rispettivi progenitori pluripotenti. Lo scopo del nostro algoritmo è quello di costituire una valida risorsa computazionale per l’inferenza di traiettorie cellulari nell’assenza di una solida conoscenza biologica pregressa.Since the introduction of single cell RNA sequencing (scRNA-seq), numerous computational methods have been developed to infer the progression of single cells along “developmental” paths from changes in their transcriptional programs and to model the differentiation processes of adult cells from their pluripotent ancestors. Most of these methods reconstruct dynamic cellular processes from static transcriptional profiles by ordering cells on continuous trajectories based on their similarity in the gene expression space. This strategy relies on assumptions that may not be a-priori guaranteed in every cellular system and requires some prior knowledge on the topology and direction of the expected genealogy. Since scRNA-seq technology is being increasingly used to study complex and unexplored systems characterized by still unclear developmental patterns, new approaches are required that are able to infer cell lineages directly from the dynamic evolution of the differentiation potency of single cells without the need of prior assumptions. Although several methods have been devised to estimate the potency of single cells by measuring the entropy level of their transcriptomes, a clear mathematical model for the inference of the dynamics of such potency across time is still lacking. To tackle this issue, we developed FIERCE (Framework for InfERence of veloCity of the Entropy), a novel composite computational pipeline that employs the mathematical framework of RNA velocity to predict the temporal evolution of the signalling entropy of single cells during dynamic processes. As such, FIERCE allows inferring cell lineages through a fully unsupervised and cell-centered approach that does not need the prior specification of evolutionary parameters. When tested on scRNA-seq data from two well-known mouse differentiation systems, our method correctly reconstructed the developmental genealogy of the adult specialized cell subpopulations from their respective pluripotent progenitors. We envisage that our tool will be a valuable computational resource for the inference of cell trajectories in the absence of solid prior biological knowledge
Direct product primality testing of graphs is GI-hard
Full text linkWe investigate the computational complexity of the graph primality testing problem with respect to the direct product (also known as Kronecker, cardinal or tensor product). In [1] Imrich proves that both primality testing and a unique prime factorization can be determined in polynomial time for (finite) connected and nonbipartite graphs. The author states as an open problem how results on the direct product of nonbipartite, connected graphs extend to bipartite connected graphs and to disconnected ones. In this paper we partially answer this question by proving that the graph isomorphism problem is polynomial-time many-one reducible to the graph compositeness testing problem (the complement of the graph primality testing problem). As a consequence of this result, we prove that the graph isomorphism problem is polynomial-time Turing reducible to the primality testing problem. Our results show that connectedness plays a crucial role in determining the computational complexity of the graph primality testing problem
Bloom filter variants for multiple sets: a comparative assessment
Full text linkIn this paper we compare two probabilistic data structures for association queries derived from the well-known Bloom filter: the shifting Bloom filter (ShBF), and the spatial Bloom filter (SBF). With respect to the original data structure, both variants add the ability to store multiple subsets in the same filter, using different strategies. We analyse the performance of the two data structures with respect to false positive probability, and the inter-set error probability (the probability for an element in the set of being recognised as belonging to the wrong subset). As part of our analysis, we extended the functionality of the shifting Bloom filter, optimising the filter for any non-trivial number of subsets. We propose a new generalised ShBF definition with applications outside of our specific domain, and present new probability formulas. Results of the comparison show that the ShBF provides better space efficiency, but at a significantly higher computational cost than the SBF
Privacy preservation in outsourced mobility traces through compact data structures
No full textIndoor localization is widely used as enabling technology for location-based services, such as advertising, indoor routing, and behavioral analysis. To keep these features available, service providers passively collect a large amount of data that may reveal strictly personal information about an individual. As an example, a timestamped mobility trace acquired in a mall may help the business owner to rearrange the user surroundings relying on a punctual analysis of the user behavior. In this paper we discuss some information processing techniques relying on probabilistic data structures designed to mitigate the user’s privacy leakage. The work is also accompanied by a case study. Our experiments were carried out using well-known networking equipment, Cisco Meraki, which is provided in combination with several primitives designed to passively infer and collect the user position in an indoor environment
Benchmarking Cloud Providers on Serverless IoT Back-End Infrastructures
No full textIoT is one of the trending topics in the technological revolution of the last decade. The huge amount of sensors composing IoT systems implies the need for powerful back-end infrastructures that find a perfect habitat in cloud services. Nowadays many players offer cloud services and it is thus essential for the user to consciously learn which one mostly fits his needs. Among cloud providers, three conquered a leader position in the sector: Amazon Web Sevices, Google Cloud Platform, and Microsoft Azure. In this paper, we thoroughly test these providers to highlight their strengths and weaknesses. To produce relevant results, we stress a back-end infrastructure designed to handle a national-sized network of IoT nodes. Our analysis is not limited to the cloud provider performance as a whole, while it also investigates and compares several cloud components separately. As part of the contribution, we also test different time series databases and we discuss the advantages of such kind of technologies. Finally, an in-depth pricing analysis is conducted to better understand the differences between each platform from an economic perspective
Introduction to the special issue on privacy and security for location-based services and devices
Full text linkThe evolution of mobile phones into smartphones, and the diffusion of location-based services (LBS), are cornerstones of the digital era, but at the same time introduced a number of challenges to the privacy of individuals. Traditional information (as names, addresses and phone numbers) shared across the Internet with an increased number of services is now frequently coupled with positional data.
With such detailed information, service providers are able to infer with alarming precision a number of sensitive information about their users, including religious, sexual and political preferences, as well as details of their social relationships and private life in general
A privacy-aware zero interaction smart mobility system
No full textSmart cities often rely on technological innovations to improve citizens’ safety and quality of life. This paper presents a novel smart mobility system that facilitates people’s access to public mobility while preserving their privacy. In contrast to several well-known smart mobility systems discussed in this paper, the one we propose combines privacy guarantees with user friendliness. Specifically, the system is based on a zero-interaction approach whereby a person can use public transport services without any need to perform explicit actions. Operations related to ticket purchases and validation were fully automated. The system is also designed with the privacy-by-design paradigm to preserve user privacy as much as possible. Throughout the paper, several technical details are discussed as well to describe a prototype version of the system that was implemented. The prototype was successfully tested in the city of Imola (Emilia Romagna, Italy) to prove the validity of the system in the field
Lightweight Security Settings in RFID Technology for Smart Agri-Food Certification
No full textIn this paper we propose a novel technique to implement secure and efficient applications for the agri-food certification chain. Our proposal relies on RFID technologies which is probably the most relevant enabling solution for ubiquitous IoT systems. We analyze a recently introduced and promising RFID tag and we prove it is possible to certify its genuineness without installing any third party application upon the consumer smartphone. The proposed solution is based on a lightweight security technique which provides the mirroring of the tag identifier combined with the encryption of a NDEF formatted file
An anonymous inter-network routing protocol for the Internet of Things
Full text linkWith the diffusion of the Internet of Things (IoT), computing is becoming increasingly pervasive, and different heterogeneous networks are integrated into larger systems. However, as different networks managed by different parties and with different security requirements are interconnected, security becomes a primary concern. IoT nodes, in particular, are often deployed “in the open”, where an attacker can gain physical access to the device. As nodes can be deployed in unsurveilled or even hostile settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore necessary to secure the communication within IoT networks, and in particular, maintain context information private, including the network topology and the location and identity of the nodes. In this paper, we propose a protocol achieving anonymous routing between different interconnected networks, designed for the Internet of Things and based on the spatial Bloom filter (SBF) data structure. The protocol enables private communication between the nodes through the use of anonymous identifiers, which hide their location and identity within the network. As routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain, the proposed routing strategy preserves context privacy, preventing adversaries from learning the network structure and topology. This, in turn, significantly reduces their ability to gain valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation
Private inter-network routing for wireless sensor networks and the Internet of Things
Full text linkAs computing becomes increasingly pervasive, different heterogeneous networks are connected and integrated. This is especially true in the Internet of Things (IoT) and Wireless Sensor Networks (WSN) settings. However, as different networks managed by different parties and with different security requirements are integrated, security becomes a primary concern. WSN nodes, in particular, are often deployed "in the open", where a potential attacker can gain physical access to the device. As nodes can be deployed in hostile or difficult scenarios, such as military battlefields or disaster recovery settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore crucial to secure the communication within the WSN, and in particular, maintain context information, such as the network topology and the location and identity of base stations (which collect data gathered by the sensors) private.
In this paper, we propose a protocol achieving anonymous routing between different interconnected IoT or WSN networks, based on the Spatial Bloom Filter (SBF) data structure. The protocol enables communications between the nodes through the use of anonymous identifiers, thus hiding the location and identity of the nodes within the network. The proposed routing strategy preserves context privacy, and prevents adversaries from learning the network structure and topology, as routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain. Preserving context privacy is crucial in preventing adversaries from gaining valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation
- …
