A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

An Efficient Reconfigurable Architecture for Fingerprint Recognition

The fingerprint identification is an efficient biometric technique to authenticate human beings in real-time Big Data Analytics. In this paper, we propose an efficient Finite State Machine (FSM) based reconfigurable architecture for fingerprint recognition. The fingerprint image is resized, and Compound Linear Binary Pattern (CLBP) is applied on fingerprint, followed by histogram to obtain histogram CLBP features. Discrete Wavelet Transform (DWT) Level 2 features are obtained by the same methodology. The novel matching score of CLBP is computed using histogram CLBP features of test image and fingerprint images in the database. Similarly, the DWT matching score is computed using DWT features of test image and fingerprint images in the database. Further, the matching scores of CLBP and DWT are fused with arithmetic equation using improvement factor. The performance parameters such as TSR (Total Success Rate), FAR (False Acceptance Rate), and FRR (False Rejection Rate) are computed using fusion scores with correlation matching technique for FVC2004 DB3 Database. The proposed fusion based VLSI architecture is synthesized on Virtex xc5vlx30T-3 FPGA board using Finite State Machine resulting in optimized parameters

Low Space External Memory Construction of the Succinct Permuted Longest Common Prefix Array

The longest common prefix (LCP) array is a versatile auxiliary data structure in indexed string matching. It can be used to speed up searching using the suffix array (SA) and provides an implicit representation of the topology of an underlying suffix tree. The LCP array of a string of length $n$ can be represented as an array of length $n$ words, or, in the presence of the SA, as a bit vector of $2n$ bits plus asymptotically negligible support data structures. External memory construction algorithms for the LCP array have been proposed, but those proposed so far have a space requirement of $O(n)$ words (i.e. $O(n \log n)$ bits) in external memory. This space requirement is in some practical cases prohibitively expensive. We present an external memory algorithm for constructing the $2n$ bit version of the LCP array which uses $O(n \log \sigma)$ bits of additional space in external memory when given a (compressed) BWT with alphabet size $\sigma$ and a sampled inverse suffix array at sampling rate $O(\log n)$. This is often a significant space gain in practice where $\sigma$ is usually much smaller than $n$ or even constant. We also consider the case of computing succinct LCP arrays for circular strings

An empirical examination of the three elements (actions, means and purpose) of the Palermo Protocol to establish an offence of human trafficking

The internationally agreed definition of human trafficking, contained in the Protocol to Prevent, Suppress and Punish Trafficking in Persons Especially Women and Children (Palermo Protocol), supplementing the United Nations Convention against Transnational Organized Crime is comprised of three elements: action, means and purpose. Empirical exploratory research considers the extent to which the definitional construct of three elements reflects convicted offender method to commit human trafficking. Empirical research was conducted on 972 offenders convicted of human trafficking and the actions and means they used to fulfil different purposes to commit human trafficking. Data was collected and disaggregated from 486 conviction case summaries contained in SHERLOC, the United Nations Office on Drugs & Crime database, related to prosecutions brought by 40 Member States to the Palermo Protocol. Analysis explores academic discord on the extent to which human trafficking is the process of moving a victim to the point of exploitation, but not including exploitation of the victim (Chuang, 2014) (Stoyanova, 2015a) or includes both the process of moving the victim and the static action of end exploitation (Gallagher, 2010). Furthermore, empirical analysis is made of the actual actions and means performed by offenders to further an understanding of problematic terms in the definition and explore other insights from an analysis of the three elements. Finally, empirical analysis through structural equation modelling explores an order and structure to human trafficking and results are presented through a series of visuals to facilitate the practical translation of findings for investigators

Overlay virtualized wireless sensor networks for application in industrial internet of things : a review

Abstract: In recent times, Wireless Sensor Networks (WSNs) are broadly applied in the Industrial Internet of Things (IIoT) in order to enhance the productivity and efficiency of existing and prospective manufacturing industries. In particular, an area of interest that concerns the use of WSNs in IIoT is the concept of sensor network virtualization and overlay networks. Both network virtualization and overlay networks are considered contemporary because they provide the capacity to create services and applications at the edge of existing virtual networks without changing the underlying infrastructure. This capability makes both network virtualization and overlay network services highly beneficial, particularly for the dynamic needs of IIoT based applications such as in smart industry applications, smart city, and smart home applications. Consequently, the study of both WSN virtualization and overlay networks has become highly patronized in the literature, leading to the growth and maturity of the research area. In line with this growth, this paper provides a review of the development made thus far concerning virtualized sensor networks, with emphasis on the application of overlay networks in IIoT. Principally, the process of virtualization in WSN is discussed along with its importance in IIoT applications. Different challenges in WSN are also presented along with possible solutions given by the use of virtualized WSNs. Further details are also presented concerning the use of overlay networks as the next step to supporting virtualization in shared sensor networks. Our discussion closes with an exposition of the existing challenges in the use of virtualized WSN for IIoT applications. In general, because overlay networks will be contributory to the future development and advancement of smart industrial and smart city applications, this review may be considered by researchers as a reference point for those particularly interested in the study of this growing field

Economy of grid-connected photovoltaic systems and comparison of irradiance/electric power predictions vs. experimental results

This thesis is focused on various aspects concerning the Distributed Generation (DG) from Renewable Energy Sources (RES) and in particular from PhotoVoltaics (PV). The PV generation strongly depends on weather conditions (irradiance and temperature), therefore the solar irradiance forecast is very important for grid-connected PV systems. The PV power injected into the grid is concentrated during sunlight hours, in which the maximum peak load demand occurs and, as a consequence, an impact on the electrical system occurs. The task of the Transmission System Operator (TSO) is to ensure a constant balance between supply and consumption within the grid. Therefore, the presence of strong fluctuations of the solar radiation requires additional regulatory actions and compensation, through the use of short-term power backup, causing an increase in network costs. Thus, the solar irradiance forecast is necessary for an accurate evaluation of the PV power from PV systems, for the management of electrical grids in order to minimize the costs of energy imbalance and for the decisions concerning the energy market. This thesis essentially consists of two parts. In the first part, the profitability of investments in the rooftop grid-connected PV systems subjected to incentive and the grid-parity analysis in the two main European PV markets (Italy and Germany) are presented. In the second part, in order to minimize the costs of energy imbalance in the Italian electricity market, the comparison of irradiance and electric power predictions with respect to the experimental results of grid-connected PV systems is presented

Universities in the fight against mafias : research, teaching and training

[Italiano]: L’impegno delle università italiane nella formazione alla legalità e nella ricerca sulle mafie è al centro di questa indagine curata da Gaetano Manfredi e Stefano D’Alfonso. Il lavoro, nato dal confronto tra diversi docenti e ricercatori universitari, la Commissione parlamentare antimafia, la Conferenza dei rettori delle università italiane e lo stesso Gaetano Manfredi, a suo tempo Ministro dell’Università e della Ricerca, con il coordinamento del Laboratorio interdisciplinare di ricerca su mafie e corruzione del Dipartimento di Scienze sociali dell’Università Federico II, ha portato alla costruzione di un prezioso database, che consente di individuare le attività formative e la produzione scientifica degli studiosi dei singoli atenei, in circa cento settori disciplinari diversi. Ventinove studiosi di diverse aree scientifiche e differenti atenei ragionano sullo stato dell’arte dell’impegno dell’università nella lotta alle mafie, mettendo in luce alcuni punti di forza e debolezza. Nelle riflessioni avanzate emerge l’effetto combinato di alcuni fattori principali. In particolare, da un lato la dinamicità del contesto territoriale e culturale su cui insistono gli atenei e le modalità di interazione con la sfera locale e nazionale. Allo stesso modo appare importante il ruolo delle aspettative che il contesto — a vari livelli — matura nei confronti dell’università in termini di domanda della conoscenza esprimibile come strumento di contrasto alla illegalità. Soprattutto, emerge la presenza di un notevole capitale di conoscenze negli atenei italiani, da valorizzare per trovare una più consapevole posizione nel sistema antimafia./ [English]: The commitment of Italian universities to education in the field of legality and to research on the subject of mafias is the main focus of this investigation edited by Stefano D’Alfonso and Gaetano Manfredi. For the first time, data is shown about the overall framework of teaching and research activities dedicated to this topic, with twenty-nine scholars from different academic disciplines and several universities who come together to reflect on the current situation and on the commitment of universities in the fight against mafias, highlighting both strengths and weaknesses of the system. The reflections illustrated here bring to light the dynamic nature of the local and cultural context where universities operate as well as the type of interaction that these institutions maintain with the national and local context. At the same time, it seems clear that great expectations are held at various levels in terms of what universities could do, especially with regards to the demand for knowledge about the best ways to fight mafias. This research reveals that Italian universities possess a considerable capital of knowledge which represents a ‘hidden treasure’ to be valued and used with the aim of promoting widespread awareness of their role in the anti-mafia field. This book is the result of cooperation between many university professors and researchers, the Parliamentary anti-mafia Committee, the Conference of Italian University Rectors and former minister of University and Research Gaetano Manfredi, under the supervision and coordination of the interdisciplinary research laboratory on mafias and corruption of the Department of Social Sciences of the University of Naples Federico II. This network of professionals and institutions enabled the construction of a significant database where teaching and research activities carried out by scholars from different universities can be identified, across about one hundred academic disciplines