Finding maximum-length repeating patterns in music databases

This paper introduces the problem of discovering maximum-length repeating patterns in music objects. A novel algorithm is presented for the extraction of this kind of patterns from a melody music object. The proposed algorithm discovers all maximum-length repeating patterns using an “aggressive” accession during searching, by avoiding costly repetition frequency calculation and by examining as few as possible repeating patterns in order to reach the maximum-length repeating pattern(s). Detailed experimental results illustrate the significant performance gains due to the proposed algorithm, compared to an existing baseline algorithm.

Maximal software execution time: a regression-based approach

This work aims at facilitating the schedulability analysis of non-critical systems, in particular those that have soft real-time constraints, where worst-case execution times (WCETs) can be replaced by less stringent probabilistic bounds, which we call maximal execution times (METs). To this end, it is possible to obtain adequate probabilistic execution time models by separating the non-random dependency on input data from a modeling error that is purely random. The proposed approach first utilizes execution time multivariate measurements for building a multiple regression model and then uses the theory related to confidence bounds of coefficients, in order to estimate the upper bound of execution time. Although certainly our method cannot directly achieve extreme probability levels that are usually expected for WCETs, it is an attractive alternative for MET analysis, since it can arguably guarantee safe probabilistic bounds. The method’s effectiveness is demonstrated on a JPEG decoder running on an industrial SPARC V8 processor.     

XFlavor: providing XML features in media representation

We present XFlavor, a framework for providing XML representation of multimedia data. XFlavor can be used to convert multimedia data back and forth between binary and XML representations. Compared to bitstreams, XML documents are easier to access and manipulate, and consequently, the development of multimedia processing software is greatly facilitated, as one generic XML parser can be used to read and write different types of data in XML form.

WebOWL: A Semantic Web search engine development experiment

This paper presents WebOWL, an experiment in using the latest technologies to develop a Semantic Web search engine. WebOWL consists of a community of intelligent agents, acting as crawlers, that are able to discover and learn the locations of Semantic Web neighborhoods on the Web, a semantic database to store data from different ontologies, a query mechanism that supports semantic queries in OWL, and a ranking algorithm that determines the order of the returned results based on the semantic relationships of classes and individuals. The system has been implemented using Jade, Jena and the db4o object database engine and has successfully stored over one million OWL classes, individuals and properties.     

A lightweight framework for secure life-logging in smart environments

As the world becomes an interconnected network where objects and humans interact with each other, new challenges and threats appear in the ecosystem. In this interconnected world, smart objects have an important role in giving users the chance for life-logging in smart environments. However, smart devices have several limitations with regards to memory, resources and computation power, hindering the opportunity to apply well-established security algorithms and techniques for secure life-logging on the Internet of Things (IoT) domain. The need for secure and trustworthy life-logging in smart environments is vital, thus, a lightweight approach has to be considered to overcome the constraints of Smart Objects. The purpose of this paper is to present in details the current topics of life-logging in smart environments, while describing interconnection issues, security threats and suggesting a lightweight framework for ensuring security, privacy and trustworthy life-logging. In order to investigate the efficiency of the lightweight framework and the impact of the security attacks on energy consumption, an experimental test-bed was developed including two interconnected users and one smart attacker, who attempts to intercept transmitted messages or interfere with the communication link. Several mitigation factors, such as power control, channel assignment and AES-128 encryption were applied for secure life-logging. Finally, research into the degradation of the consumed energy regarding the described intrusions is presented.     

A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.     

Phone duration modeling: overview of techniques and performance optimization via feature selection in the context of emotional speech

Accurate modeling of prosody is prerequisite for the production of synthetic speech of high quality. Phone duration, as one of the key prosodic parameters, plays an important role for the generation of emotional synthetic speech with natural sounding. In the present work we offer an overview of various phone duration modeling techniques, and consequently evaluate ten models, based on decision trees, linear regression, lazy-learning algorithms and meta-learning algorithms, which over the past decades have been successfully used in various modeling tasks. Furthermore, we study the opportunity for performance optimization by applying two feature selection techniques, the RReliefF and the Correlation-based Feature Selection, on a large set of numerical and nominal linguistic features extracted from text, such as: phonetic, phonologic and morphosyntactic ones, which have been reported successful on the phone and syllable duration modeling task. We investigate the practical usefulness of these phone duration modeling techniques on a Modern Greek emotional speech database, which consists of five categories of emotional speech: anger, fear, joy, neutral, sadness. The experimental results demonstrated that feature selection significantly improves the accuracy of phone duration prediction regardless of the type of machine learning algorithm used for phone duration modeling. Specifically, in four out of the five categories of emotional speech, feature selection contributed to the improvement of the phone duration modeling, when compared to the case without feature selection. The M5p trees based phone duration model was observed to achieve the best phone duration prediction accuracy in terms of RMSE and MAE.     

Infrared Solution‐Processed Quantum Dot Solar Cells Reaching External Quantum Efficiency of 80% at 1.35 µm and Jsc in Excess of 34 mA cm−2

Developing low‐cost photovoltaic absorbers that can harvest the short‐wave infrared (SWIR) part of the solar spectrum, which remains unharnessed by current Si‐based and perovskite photovoltaic technologies, is a prerequisite for making high‐efficiency, low‐cost tandem solar cells. Here, infrared PbS colloidal quantum dot (CQD) solar cells employing a hybrid inorganic–organic ligand exchange process that results in an external quantum efficiency of 80% at 1.35 µm are reported, leading to a short‐circuit current density of 34 mA cm^?2 and a power conversion efficiency (PCE) up to 7.9%, which is a current record for SWIR CQD solar cells. When this cell is placed at the back of an MAPbI₃ perovskite film, it delivers an extra 3.3% PCE by harnessing light beyond 750 nm.     

Preparation and Characterization of Dentin Phosphophoryn‐Derived Peptide‐Functionalized Lignin Nanoparticles for Enhanced Cellular Uptake

The surface modification of nanoparticles (NPs) using different ligands is a common strategy to increase NP?cell interactions. Here, dentin phosphophoryn‐derived peptide (DSS) lignin nanoparticles (LNPs) are prepared and characterized, the cellular internalization of the DSS‐functionalized LNPs (LNPs‐DSS) into three different cancer cell lines is evaluated, and their efficacy with the widely used iRGD peptide is compared. It is shown that controlled extent of carboxylation of lignin improves the stability at physiological conditions of LNPs formed upon solvent exchange. Functionalization with DSS and iRGD peptides maintains the spherical morphology and moderate polydispersity of LNPs. The LNPs exhibit good cytocompatibility when cultured with PC3‐MM2, MDA‐MB‐231, and A549 in the conventional 2D model and in the 3D cell spheroid morphology. Importantly, the 3D cell models reveal augmented internalization of peptide‐functionalized LNPs and improve antiproliferative effects when the LNPs are loaded with a cytotoxic compound. Overall, LNPs‐DSS show equal or even superior cellular internalization than the LNPs‐iRGD, suggesting that DSS can also be used to enhance the cellular uptake of NPs into different types of cells, and release different cargos intracellularly.