Investigating the Cost of Anonymity on Dynamic Networks

In this paper we study the difficulty of counting nodes in a synchronous dynamic network where nodes share the same identifier, they communicate by using a broadcast with unlimited bandwidth and, at each synchronous round, network topology may change. To count in such setting, it has been shown that the presence of a leader is necessary. We focus on a particularly interesting subset of dynamic networks, namely \textit{Persistent Distance} - ${\cal G}($PD$)_{h}$, in which each node has a fixed distance from the leader across rounds and such distance is at most $h$. In these networks the dynamic diameter $D$ is at most $2h$. We prove the number of rounds for counting in ${\cal G}($PD$)_{2}$ is at least logarithmic with respect to the network size $|V|$. Thanks to this result, we show that counting on any dynamic anonymous network with $D$ constant w.r.t. $|V|$ takes at least $D+ \Omega(\text{log}\, |V| )$ rounds where $\Omega(\text{log}\, |V|)$ represents the additional cost to be payed for handling anonymity. At the best of our knowledge this is the fist non trivial, i.e. different from $\Omega(D)$, lower bounds on counting in anonymous interval connected networks with broadcast and unlimited bandwith

Determinants of innovativeness in SMEs. disentangling core innovation and technology adoption capabilities

This paper studies innovativeness in SMEs from a set of innovation indicators at the firm level, capturing various types of innovation (product, process, organisational, and marketing innovations) and the level of innovativeness in these firm’s developments. The article identifies two separate dimensions in the innovativeness of Spanish SMEs, using factor analysis techniques. One dimension is associated with the capabilities for core/internal innovation and the other with the capabilities for the adoption of technology. The paper shows that significant differences exist in the personal and organisational factors that favour these two dimensions. The entrepreneur’s motivation, business planning, and cooperation in R&D activities constitute significant factors when considering the core dimension of a firm’s innovativeness, but have no effect on the firm’s capabilities for technology adoption. However, the use of external consultancy services seems to have no significant effect on the core dimension of the innovativeness of anSME, whereas it is a relevant factor for its technology adoption. Furthermore, it is shown that the entrepreneur’s education plays a more significant role in the core dimension of a firm’s innovativeness than in its capabilities for technology adoption. Depending on the policy objectives, these differences should lead to the application of specific policy approaches when an attempt to stimulate innovation in SMEs is made

Determinants of technology adoption in the retail trade industry - the case of SMEs in Spain

This paper analyzes the determinants of small and medium-sized enterprises’ technology adoption in the retail trade industry. From the theoretical perspective, two types of influential factors are differentiated in this respect: the personal characteristics of the manager/business owner and the business’s organizational characteristics. The empirical analysis is based on a survey of 268 small and medium-sized enterprises in the Spanish retail trade sector. A logistic regression specification is used as an econometric method. The results indicate that both the acquisition of new technical and electronic equipment and the obtaining of new software are affected by the two types of determinants previously pointed out. The manager/business owner’s entrepreneurial motivation and educational background have significant influences on technology adoption in this type of companies. Furthermore, being part of a business group, carrying out training activities for the employees and inter-firm cooperation also positively influence technology adoption in the retail trade industry

Archaeal diversity in deep-sea sediments estimated by means of different Terminal-Restriction Fragment Length Polymorphisms (T-RFLP) protocols

Despite the increasing recognition of the quantitative importance of Archaea in all marine systems, the protocols for a rapid estimate of Archaeal diversity patterns in deep-sea sediments have been only poorly tested yet. We collected sediment samples from 11 deep-sea sites covering a wide latitudinal range (from 79°N to 36°N, at depths comprised from 469 to 5500 m) and compared the performance of two different primer sets (ARCH21f/ARCH958r and ARCH109f/ARCH 915r) and three restriction enzymes (AluI, Rsa I and HaeIII) for the fingerprinting analysis (T-RFLP) of Archaeal diversity. In silico and experimental analyses consistently indicated that different combinations of primer sets and restriction enzymes can result in different values of benthic Archaeal ribotype richness and different Archaeal assemblage compositions. The use of the ARCH109f/ARCH 915r primer set in combination with AluI provided the best results (a number ribotypes up to 4-folds higher than other combinations), suggesting that this primer set should be used in future studies dealing with the analysis of the patterns of Archaeal diversity in deep-sea sediments. Multivariate, multiple regression analysis revealed that, whatever the T-RFLP protocol utilized, latitude and temperature explained most of the variance in benthic Archaeal ribotype richness, while water depth had a negligible role

Ressenyes

Obra ressenyada: Michael A. COHEN; Blair A. RUBLE; Joseph S. TULCHIN; Allison M. GARLAND, Preparing for the Urban Futures: Global Pressures and Local Forces. Washington D.C.: The Woodrow Wilson Center Press, 1996

On deterministic counting in anonymous dynamic networks

Nella tesi di dottorato si analizza il problema del counting in reti anonime dinamiche ed interval connesse. Vengono dimostrati lower bound non triviali sul tempo di conteggio in reti a diametro costante. Inoltre vengono sviluppati nuovi algoritmi di conteggio.Counting is a fundamental problem of every distributed system as it represents a basic building block to implement high level abstractions [2,4,6]. We focus on deterministic counting algorithms, that is we assume that no source of randomness is available to processes. We consider a dynamic system where processes do not leave the compu- tation while there is an adversary that continuously changes the communication graph connecting such processes. The adversary is only constrained to maintain at each round a connected topology, i.e. 1-interval connectivity G(1-IC) [3]. In such environment, it has been shown, [5], that counting cannot be solved without a leader. Therefore, we assume that all processes are anonymous but the distinguished leader. In the thesis we will discuss bounds and algorithms for counting in the aforementioned framework. Our bounds are obtained investigating networks where the distance between the leader and an anonymous process is persistent across rounds and is at most h, we denote such networks as G(PD)h [1]. Interestingly we will show that counting in G(PD)2 requires Ω(log |V |) rounds even when the bandwidth is unlimited. This implies that counting in networks with constant dynamic diameter requires a number of rounds that is function of the network size. We will discuss other results concerning the accuracy of counting algorithms. For the possibility side we will show an optimal counting algorithm for G(PD)h networks and a counting algorithm for G(1-IC) networks

SAFE: Self-Attentive Function Embeddings for Binary Similarity

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations. However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem. In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures. We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions. Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).Comment: Published in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) 201