MOTIVATION: Since the introduction of single-cell RNA sequencing (scRNA-seq), numerous computational approaches have been developed to reconstruct dynamic cellular processes from static transcriptional profiles. These methods order cells along continuous trajectories by assessing their similarity in the gene-expression space. However, they rely on several assumptions, such as prior knowledge of the structure and directionality of the expected genealogy. These assumptions can limit their application to complex cellular systems with poorly understood developmental paths. RESULTS: To address this challenge, we introduce FIERCE (Framework for InfERence of the veloCity of Entropy), a novel computational pipeline designed to predict the changes in the differentiation potency of single cells during dynamic processes. Through a fully unsupervised approach, FIERCE enables the inference of cell lineages directly on the differentiation landscape of the biological system, thus eliminating the need for prior specification of developmental parameters. We demonstrate the efficacy of FIERCE by reconstructing three well-known mouse differentiation systems and by quantifying its accuracy on simulated data. AVAILABILITY AND IMPLEMENTATION: The FIERCE R package is available on GitHub at https://github.com/bicciatolab/FIERCE.

Internet of Things (IoT) 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: 1) Amazon Web services; 2) Google cloud platform; and 3) Microsoft azure. In this article, 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.

As evident from an in-depth analysis of the state of the art concerning device tracking through Wi-Fi probes and MAC addresses, these techniques represent an increasingly relevant privacy threat. In this paper we provide design and implementation details of a low-cost and low-power people counter based on the Espressif ESP8266 board, and we explicitly analyze the overall cost of the introduced solution. The proposed device can gather MAC addresses from Wi-Fi packets and is designed to circumvent MAC address randomization, as we demonstrate through practical experiments. Our study also shows that, as IoT devices and components are less and less expensive, even a single person could set up a personal people counting systems to be maliciously installed in urban areas or indoor environments.

In 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.

Cloud computing platforms are widely used as enabling frameworks for the Internet of Things. Within this context, a number of peripheral devices are connected to the middleware and constantly communicate raw data in order for them to be processed. Although several countermeasures were designed (and often adopted) in order to secure the communication channel and to ensure device and back-end authentication, several deployed IoT solution are subject to context security threats, where authenticated devices act in an allowed yet unexpected way and trigger undesired processing tasks in the back-end. As AWS IoT Core is currently the most adopted middleware in this context, this paper proposes a practical solution to achieve context security in such a scenario.

The classical Bloom filter data structure is a crucial component of hundreds of cryptographic protocols. It has been used in privacy preservation and secure computation settings, often in conjunction with the (somewhat) homomorphic properties of ciphers such as Paillier's. In 2014, a new data structure extending and surpassing the capabilities of the classical Bloom filter has been proposed. The new primitive, called spatial Bloom filter (SBF) retains the hash-based membership-query design of the Bloom filter, but applies it to elements from multiple sets. Since its introduction, the SBF has been used in the design of cryptographic protocols for a number of domains, including location privacy and network security. However, due to the complex nature of this probabilistic data structure, its properties had not been fully understood. In this paper, we address this gap in knowledge and we fully explore the probabilistic properties of the SBF. In doing so, we define a number of metrics (such as emersion and safeness) useful in determining the parameters needed to achieve certain characteristics in a filter, including the false positive probability and inter-set error rate. This will in turn enable the design of more efficient cryptographic protocols based on the SBF, opening the way to their practical application in a number of security and privacy settings.